DP2014-28

Decoding the Growth-Nutrition
Nexu s in China : Inequ ality,
Uncertainty and Food Insecurity*
Jing YOU
Katsushi S. IMAI
Raghav GA IHA
Revised December 2, 2014

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Decoding the Growth-Nutrition Nexus in China: Inequality,
Uncertainty and Food Insecurity
Jing You,1 Katsushi S. Imai,2 and Raghav Gaiha3
1. School of Agricultural Economics and Rural Development, Renmin University of China
2. Economics, School of Social Sciences, University of Manchester & RIEB, Kobe University
3. Faculty of Management Studies, University of Delhi, India & Statistics and Studies forDevelopment (SSD), Strategy and Knowledge Management Department, International Fundfor Agricultural Development (IFAD), Italy
This Draft: 2nd December 2014
Abstract
Chinese households have experienced significant income growth, while their nutrition
intake has not increased pari passu. This paper uses household data in both rural andurban China over the period 1989-2009 to explain the paradox of higher income but
lower nutrition. In addition to traditional inputs into nutrition intake, we emphasise
different sources of income, the heterogeneous income effects across households, and theprice effects under rising and volatile food prices. The instrumental variable estimationshows that, although nutrition is not responsive to aggregate income, pro-agriculture
income growth in terms of proportionally more crop income raises rural households’
nutrient intake, while business and wage incomes improve urban households’ nutrition.
The estimation of a quantile instrumental variable fixed-effects panel model further
documents a nutrition-improving effect of income for the least nourished and only thebetter-nourished are able to benefit from widely believed contributors of nutrition intake
such as dietary knowledge, local off-farm employment and out-migration. Uncertainties
attached to prices of meat, eggs and oil and fat accentuate nutrition poverty and could
offset the positive income effect, raising the risk of food insecurity despite growing
income.

Key words: food security, nutrition, inequality, poverty, IV regression, quantile regression,
China
JEL classification: C23, I10, I31, O53, Q18* Corresponding author:
Jing You (Dr)
Address: School of Agricultural Economics and Rural Development, Renmin University of
China, 59 Zhongguancun Street, Beijing 100872, China
E-mail: jing.you@ruc.edu.cn
Tel.: +86-(0)10-6251-1061
Fax: +86-(0)10-6251-1064
Acknowledgements:
This study is sponsored by IFAD (International Fund for Agricultural Development).
Additional financial support from the University of Manchester (the Faculty SIRF grant) is
also greatly appreciated. We are grateful to Thomas Elhaut and Constanza di Nucci for theirsupport and guidance throughout this study and for their insistence on highest standards ofanalytical rigour. We are grateful to David Heady and an anonymous referee at BWPI for
valuable comments. The views expressed are personal and not necessarily of the organisations
to which we are affiliated or of IFAD.

1

Decoding the Growth-Nutrition Nexus in China: Inequality,
Uncertainty and Food Insecurity
1. Introduction
The Chinese households have experienced continuous income growth for more than
three decades. Per capita disposable income increased at an annual rate of 3.4% from1980 to 2012 for urban households, and 2.8% from 1985 to 2012 for rural
households.1 High income growth helped the income poverty headcount ratio (at the
US$1.25 poverty line) plummet from 85.3% in 1981 to 11.8% in 2009.2 Nevertheless,nutritional situation has not improved in parallel with income growth. Based on thenationally representative China Health and Nutrition Survey (CHNS), 2000-2006,
Shimokawa (2010) finds little correlation between household wealth and nutrient
intake. In both urban and rural areas, the income elasticities for food (e.g., grain andvegetables) and nutrients (e.g., calories and protein) with a low unit value are nearlyzero (Gale and Huang, 2007; Bishop et al., 2010) and even negative as income keeps
rising (Huang and Gale, 2009), implying a satiation for food consumption for wealthy
households (Zheng and Henneberry, 2010). Ma et al. (2004) and Hovhannisyan and
Gould (2011) also find that expenditure elasticities became smaller and stabilised overthe period 1995-2003.
As a result, malnutrition and dramatic income poverty reduction have co-existed
in China’s economic development. This is in sharp contrast to common wisdom andcross-country studies documenting positive impact of income growth on nutrition (e.g.
Headey, 2013; Ogundari and Abdulai, 2013). However, FAO (2013) argues that incountries where food insecurity is more pervasive, nutrition is weakly associated with
1

The authors’ calculations are based on data from China Statistical Yearbook published annually by
the National Bureau of Statistics (NBS) of China.
2
Based on Chen and Ravallion (2010) and World Bank (2014).

2

income. Ample food availability and the shifts in consumption and lifestyle benefitingfrom income increases do not necessarily enable improvement in nutrition in terms ofcalorie intake, e.g., India in 2004-2009 (Gaiha et al., 2014a), Nicaragua in 2005-2007
and Nigeria (Ogundari and Abdulai, 2013). Why has not rapid and substantial income
growth translated into nutritional improvement, but rather accentuated malnutrition?
This is a puzzle that the present study seeks to resolve.
One explanation for this paradox pertains to the Chinese dietary transition along
income growth. Household per capita consumption of fat and oil grew annually at an
average of 2.7% in rural areas and 11.6% in urban areas between 2003 and 2011 (You,2014), while lower amounts of vegetables and fruits were consumed over time. Fine
grains and rice became increasingly popular instead of coarse grains (Carter andZhong, 1999). Chinese diet has been shifting away from traditional foods with coarse
grains as the staple to the westernised ones full of high carbohydrates (Du et al.,2004). The CHNS data suggest that income effects on low-fat and high-fibre food,such as wheat-flour products and coarse grains, fell from 1989 to 1993, proportionallymore among richer households, while income elasticities of pork, edible oil and eggs
increased significantly (Guo et al., 2000). Despite a number of studies documenting
preferences towards a western-diet, their micro-level datasets are for either urban orrural areas, and the time period is too short to capture usually long and gradual socioeconomic transitions. More importantly, there has not been direct analysis of whether
the above changes in Chinese diet are associated with the decline in nutrient intake. It
is important in this context to investigate the relative importance of dietary transitions
to declining nutrition, compared to other possible contributing factors, for example,

3

less energy demand in production activities and daily life as a natural consequence ofsocioeconomic development and the increasing food prices along with food crisis.3
An alternative explanation for the income-nutrition paradox is the intensified
uncertainties in households’ livelihood. Meng et al. (2009) find that income
elasticities of caloric intake are not close to zero based on the 1986-2000 waves of the
Urban Household Survey conducted annually by the National Bureau of Statistics(NBS) in all provinces. The found that the poorer the individuals were, the higher theincome elasticities of calorie consumption became. It is the soaring food prices in theearly 1990s and income uncertainties - proxied by the proportion of household labour
force working in the state sector and the household savings rate - that supressedcalorie consumption, especially for those in the lower end of the income distribution.
In fact, uncertainties attached to household income and consumption streams haverisen markedly not only in urban (Chamon et al., 2013) but also in rural Chinesehouseholds (Whalley and Yue, 2009), leading to various negative impacts on
livelihood. 4 To the best of our knowledge, Meng et al. (2009) is the only study
estimating the nutritional effects of income uncertainties during major social reforms,
but their measure of uncertainties only reflects the job instability and heavy burden of
education, medical and housing expenses and their study is merely for urban areas.
This paper aims to demystify the paradox of rising income but declining
nutrient intake during the period of China’s remarkable economic development. It
provides the first comprehensive investigation of the determinants of household intake
of various micro nutrients in both urban and rural China based on the repeated nationwide surveys for two decades (1989-2009). Not only “traditional” inputs of nutrient3

A similar income-nutrition puzzle can also be observed in rural India (e.g. Deaton and Dreze, 2009).
These negative impacts, are, for example, considered to be manifestations of transient poverty in
Jalan and Ravallion (1998), or direct welfare loss caused by volatilities and inequality of consumption
in You and Ozanne (2014).
4

4

intake such as income, it also takes into account the changing lifestyles, dietary
preferences and, in particular, direct proxies for uncertainties in household livelihoodin terms of rising and volatile food prices that face households in their decision
making. In particular, these income uncertainties from levels and volatilities ofdomestic food prices could be more of unanticipated than anticipated (seasonal) given
the property of our dataset.5 Furthermore, a point of analytical departure of the present
study in light of its unique contribution to the previous literature on the determinantsof nutrition in China or in other developing countries is to model (i) the effects onnutrient intake of various determinants conditional on the household’s position in
distribution of nutrient intake, indicated by heterogeneity in nutritional responses toincome in the aforementioned literature and to address (ii) the endogenoity ofhousehold income when estimating these distributional effects. This methodologicaladvancement will provide consistent estimates for the effect of income on nutrition
conditional on different levels of nutritional intake. Our analysis will add to theunderstanding of China’s experience of rising income but declining nutrition in recent
years. It will thus inform government interventions in nutrition improvement at the
micro-level and food security for not only China but also for other developing

5

Our data are based on surveys in different years, and the price data are calculated on annual basis,
which does not allow us to reflect seasonal variations. It is considered that anticipated ‘seasonal’
uncertainties may be dominated by unanticipated ones in food prices due to the following reasons. The
annual crop yield in China has kept increasing since 2003 and the government has long implemented
price regulation and stabilisation in domestic market, limited exports of major grains, as well as importprotections. These may help attenuate anticipated or seasonal components in food price volatilities. At
the same time, government interventions (e.g. aiming at increasing farmers’ income in facing nationwide increases in agricultural products in the mid-1990s or responding to external shocks such as theworld food crisis in 2007 and 2008) could incur substantial unanticipated price volatilities. In particular,
the first two sorts of unanticipated volatilities (i.e., domestic price volatilities) may be more detrimental
to household wellbeing than the external or international ones. Jalan and Ravallion (1999) and You(2014) reject the hypothesis of full insurance at the household level regardless of its position in wealthdistribution, and find that 40% of income shocks can be passed into consumption. By contrast, thosestemming from the international shocks exhibit limited influence on domestic prices (You, 2014) andthus, have little negative unanticipated nutritional consequence for households (Jensen and Miller,2008).

5

countries facing the similar dual challenge of reducing income poverty andmalnutrition at the same time.
The remainder of this paper is organised as follows. The next section introduces
the dataset and describes the trend in Chinese households’ dietary transition and thechanges in distribution of their nutritional status. Section 3 spells out the estimation
strategy. Section 4 discusses econometric results and the final section provides
concluding observations and policy implications.

2. Data
We use the China Health and Nutrition Survey (CHNS) conducted by the Carolina
Population Center at the University of North Carolina in 1989, 1991, 1993, 1997,
2000, 2004, 2006 and 2009. The multi-stage random sampling method and broadly
same questionnaires have been applied to each wave. 6 The sample provinces are
spread across northeast (Liaoning and Heilongjiang), coastal (Jiangsu and Shandong),
central (Henan, Hubei and Hunan), and southwest regions (Guangxi and Guizhou).
There are about 4,400 households including 26,000 individuals in each wave, with ahigher proportion for rural than urban households defined by their actual places ofresidence. We have chosen the household as the main unit of the analysis and haveconstructed the pooled cross-sectional data as well as the (unbalanced) panel dataset
based on 8 rounds of CHNS for our empirical analyses. After excluding householdswith missing values on key nutrition and economic indicators, we extract 29,402
households in the pooled cross-section. The sample size for each round varies from2,174 in 1989 to 4,444 in 2009. The share of urban households ranges between 29.8%(in 2000) and 39.7% (in 1989). As scrutinised by Zhang et al. (2014), the sampling in
6

Detailed information on sampling procedures can be found on the CHNS webpage:
http://www.cpc.unc.edu/projects/china [accessed on 30 November, 2014].

6

the CHNS assures its provincial representativeness and the data are particularly usefulfor research on health and nutrition as food consumption and the associated nutrition
data have been recorded in a more precise diary approach than retrospective surveys.
However, in 1989, the dietary data were only obtained from children younger than 6
years and adults aged 20-45 years within sample households due to constraints offunding (Popkin et al., 2010), which might push upward the household adultequivalent nutrient intake in the first wave and thus overstate the magnitude of total
reduction in household adult equivalent nutrient intake over time.
Households’ consumption of various food items (in quantity) was recorded by a
24-hour recall diary in 3 consecutive days which were randomly chosen within aweek. The initial amount of each food item (including both self-produced andpurchased items), the remaining and discarded food, those being purchased and ate
away from home during the 3-day recording period were all scaled and recorded at theindividual level. As such, food consumption per person per day is highly precise.
According to the 1989, 1991 and 1992 Food Composition Tables issued by theInstitute of Nutrition and Food Hygiene at the Chinese Academy of Preventative
Medicine, the CHNS team translated the quantity of each food item into theindividuals’ 3-day average total energy intake (in kcal) and different micro nutrientssuch as fat, carbohydrates and protein (in grams). We used these translated individual
nutrition data to further calculate our variables of interests. A list of all variables used
for those households, including the definitions and descriptive statistics, can be foundin Appendix 1.
The macro data suggest a decreasing trend in Chinese food consumption. As
shown by Figure 1, the household per capita total food consumed has decreased by aquarter in rural areas and by 4.6% in urban areas since 1978. However, the timing of7

these changes is different between rural and urban areas: they first took place in urban
areas prior to 2000, and then in rural areas. Figure 1 also demonstrates changes in
dietary composition over time. There are observed significant decreases in the
proportions of grain and stable consumption of vegetables except a drop in the early
1990s, while the proportions of oil, meat (including poultry and seafood), eggs, dairy,
fruit and liquor have increased. Rural households mainly consume grain andvegetables, while other foods constitute little in their diet. The proportion of grain
consumption has declined only since 2000, and has decreased by 8 percentage points
over the period 2000-2012. On the other hand, urban households consumed
vegetables most, and then grain. The proportion of the former has been relatively
stable, while the proportion of the latter has been halved since 1978, and 95% of thisdecrease has happened since 1992 when the second wave of economic reform incoastal (urban) areas was initiated by Mr. Xiaoping Deng. Comparing rural and urbanhouseholds, the former consumed more quantity of food which came from grain,
while the latter consumed more vegetables, meat, and oil.
[Figure 1 to be inserted]
It is interesting to see in Figure 2 that the total amount of household per capita
food consumption converged to the same level for urban and rural areas at the end of
2012. Figure 1 suggests that this was caused by proportionately greater reduction in
grain consumption in rural households than in urban ones, while the differences inother foods have remained similar between the two groups. This converging patterncorresponds to the narrowing gap between rural and urban households’ per capita totalenergy intake which has shrunk by 46.6% over the period 1989-2009, although theformer’s total energy intake remained consistently higher than the latter.
[Figure 2 to be inserted]8

Our micro data also lend support to the ‘income growth-nutrition decline’
paradox. The household per capita equivalent net income had quadrupled over theperiod 1989-2009. 7 By contrast, Figure 3 illustrates clearly a decreasing trend ofhousehold total nutrient intake. 8 The household per adult equivalent total nutrient
intake per day decreased from 3,629 kcal in 1989 to 2,883 kcal in 2009, registering a
proportional decrease of 20.6%.9 Figure 3 also shows that this decline was mainly
driven by reduction in carbohydrate intake: its proportion in household nutrition
consumption declined from 64% to 54%. The proportion of fat in total nutrient intake
suggests an increasing trend, from 24.5% in 1989 to 32.8% in 2009 (with increasing
daily consumption of fat as well) which has surpassed the threshold of 30% for ahealthy diet according to the World Health Organisation (WHO). The proportion ofprotein also showed a slight increase, from 11.0% to 12.1% at the same time, but thequantity decreased from 100g to 87g per equivalent adult per day, which is fortunately
still above the WHO’s threshold at 66g per equivalent adult per day. The above
pattern reveals that the diet pattern of Chinese households over time is consistent withthe changing composition of household food consumption – less grain and vegetables
but more oil and fat – as illustrated in Figure 1 and supported by recent studies
reviewed in Section 1.
[Figure 3 to be inserted]
7

The household equivalence is defined by using the OECD criteria. Refer to Appendix 1 for detailed
definitions.
8
The total nutrient intake is calculated as the sum of substrates including fat, carbohydrate and protein.
The FAO conversion rates are adopted to calculate total energy measured in kilocalorie from substrates
measured in grams. Refer to Appendix 1 for detailed definition.
9
Yu and Abler (2014) have reported consistently low caloric intake in Chinese rural household survey(the Rural Household Survey (RHS) conducted annually in every province by the National Bureau of
Statistics) over the period 1990-2008 than in the statistics based on the Food Balance Sheet from FAO
for China. They find that this gap can be explained by measurement errors in the household total foodconsumption (i.e., the unrecorded food away from home and guest consumption) and the use of simple
count of household members rather than adult equivalence. The 3-day diary approach in the CHNS
avoids the first bias as the respondents were required to record all food consumption away from home
and we do not include guests and their food consumption when calculating the per capita nutrient
intake. The per capita energy intake in our analysis is higher than that in the existing literature becauseof different scaling factors (see Appendix 1 for details).

9

Rural households on average consumed more total energy than their urban
counterparts in all waves, which is predictable given energy-demanding farming work.
The rural-urban gaps of the quantity of food consumption and total energy intake havenarrowed first and then widened: the household equivalence per capita daily energy
intake was 10.4% higher in rural than in urban areas in 1989 and dropped to 3.2% in
2004, but returned to 7% thereafter. Despite a general declining rural-urban nutrition
gap over the period 1989-2009, inequality in nutrition distribution among all
households in China has widened over time. The coefficient of variation of household
per capita total energy intake decreased from 0.33 in 1989 to 0.29 in 1997, albeit withfluctuations, but rose to 0.55 in 2009, which may well be driven by the within area
nutrition inequality rather than the rural-urban one. We further calculate the Gini
coefficients for household equivalent per capita calorie intake for rural and urban
areas separately in Table 1. As income inequality grew in China, household nutrition
inequality also accentuated, although by a smaller magnitude. The Gini coefficients of
total calorie intake increased by 12.2% and 21.0% in rural and urban areas,
respectively. Bishop et al. (2010) also identified the same trend. In both rural andurban areas, this increasing nutrition inequality stemmed from unequal carbohydrate
consumption (from, for example, cereals) and protein. On the contrary, the Gini
coefficient of fat intake decreased in rural areas. This is understandable as households
shift their diet preferences towards fat and oil when income rises all over China, andespecially so among the relatively poor, as recently reviewed in You (2014). Anotherimportant message from the inequality statistics is that FAO’s measurement ofmalnutrition relying on constant coefficient of variation of nutrient intake is likely to
be imprecise given significant and complex changes in the nutrition distribution overtime. In particular, the number of malnourished people may have been underestimated10

for China, since the FAO has not taken into account the higher dispersion of nutrient
intake within urban and rural areas. 10 This requires more disaggregated and subnational investigations based on household food consumption surveys such as the
CHNS in the present study.11[Table 1 to be inserted]

3. Methodology
The analysis is at the disaggregated level for households’ intake of different macro
nutrients. Let household i consume nutrient j with j 1, 2,3 at t . At the household
level for each nutrient j , we regress the logarithmic household equivalent intake ofthe j th nutrient ( K ijt ) on a number of correlates of nutrition and health. Theoretically,
this specification is related to the model of health production function (Thomas, 1994)
where health or nutritional outcomes as an output is a function of a number of inputs(e.g. nutrient intakes and the quantity and quality of health care, and individual andhousehold characteristics) with a standard utility function of household members
under a budget constraint for the household.

ln Kijt  0  β1Xit  β2 X pt  β3 Xct  β4 Xmt    s Ds   tWt  ij   ijt
s

(1)

t

where the disturbances  ijt follow an i.i.d. normal distribution for each nutrient j ; thehouseholdfixedeffect ij denotes time-invariant and household-specificunobservables determining i ’s intake of nutrient j. The nutrient ( ln Kijt ) we estimate

10

FAO’s SOFI 2013 reports a declining trend in undernourishment in China. The proportion of theundernourished in total population has declined from 22.9% in 1990-1992 to 11.4% in 2011-2013(FAO, 2013). Apart from using calorie availability as an approximation for calorie intake, their
methodology assumes a constant probability distribution of individual daily dietary energy
consumption. See, for instance, de Haen et al. (2011) and de Weerdt et al. (2014) for detailed critiquesof the FAO methodology.
11
Further data analyses are found in Appendix 2.

11

is either fat, carbohydrate, protein, or the total calorie intake which is derived as thesum of the above three substrates using FAO’s (2003) conversion rate.12 We consider
several inputs in household nutrient intake as the explanatory variables. First, Xit
includes the household per capita equivalent net income, demographic transition,
education, gender, ethnicity, household wealth indicators that might be relevant tohealth and nutrition, such as water quality, the toilet type and the main cooking fuels,
labour supply, the occupation of household head which would proxy calorie demandcaused by certain occupational physical activities,13 and health status. In particular,
we specify five age groups in a 10-year interval and the proportion of adults within ahousehold in each age cohort to account for demographic transition and householdcomposition, respectively. They are posited to capture calorie and nutrition demand.
This is particularly important for East Asia where the bulge is moving from the young
toward the older end of the working-age years (Bloom, 2011). Together with theeconomic miracle and wide-ranging economic and social reforms, the demographictransition would have profound influences on nutrition demand, especially when there
is a sharp age divide in food preference or a taste for variety.14 Second, the level andvolatility of price indices of various foods are included in X pt to represent theincreasing costs as well as uncertainties of livelihood. The food categories covercereals, oil and fat, meat, eggs and vegetables. Comparison of the magnitudes of theestimated coefficients ( β̂ 2 ) across different food items also indicates their relative

12

See Appendix 1 for definitions of these variables.
We consider occupation as exogenous given that it is often inherited- especially in rural areas.
Moreover, it appears to be difficult to adjust occupations for their endogeneity (e.g., Kijima, 2006 forIndia) and the OLS-type estimator of occupational choice is moderate given the direction of selection
bias (e.g., Matsumoto et al., 2006 for Ethiopia, Kenya and Uganda).
14
For example, our data suggest that the average age for those favouring fast food most is 20 years as
opposed to 52 years for those showing the least interest. A similar phenomenon is also observed insalty snack food (19.3 vs 52.3 years) and soft/sugared drinks (23 vs. 52.5 years). By contrast, thosefavouring fruits (vegetables) most are on average 54.6 (52) years old, while those disliking fruits are on
average 46 (44.7) years old.
13

12

importance in affecting household nutrition. 15 Third, we explicitly control forhouseholds’ knowledge of Chinese balanced diet and eating and living patterns in
X ct . The latter is proxied by household preferences for (i) fast food and soft but

sweetened drinks, and (ii) physical exercises. Fourth, X mt incorporates communitylevel correlates listed in Appendix 1. These include population density, transport,
health and social services, development of traditional and modern markets, and an
overall economic development index. The provincial and wave dummies are denotedby Ds and Wt , respectively, and also enter into our regressions.
We estimate Eq. (1) using unbalanced panel data with household fixed effects
for urban and rural areas, respectively. 16 In this model, there is likely to beendogeneity due to a two-way causality between income and nutrition. We refer to thestandard instrumental variable estimation (IV) and introduce two instruments: theproportion of farmland that suffered various natural disasters within the province, andthe 5-year average provincial annual growth rate of average wage (per worker).
Natural events are believed to be strictly exogenous and have been widely used as
instruments for rural households’ wellbeing. In this study, the proportion of farmland
suffering from natural disasters is a proxy for the proneness to natural disasters which
are found unrelated to economic development in terms of GDP per capita, geography,
population density and political regimes for both developed and developing countries
15

Note that the price indices of different food items are based on time series data on level of real priceof each food item separately for each food item in each sample province. We do not have data on theunit value of each food item, so, as an alternative, merged the real price indices of different food items
and their volatilities over years into the CHNS at the provincial level. But Eq. (1) cannot identify thespatial or cross-price (substitution) effects, as pointed out by Deaton (1997, pp. 283-314). Given our
measurement of price-related indicators, the estimated price effects may be dominated by the ownprice effects, while the existence of spatial and cross-price effects may cloud identification. Although it
is impossible to rectify this identification problem due to our data limitations, we will seek supporting
evidence from other data or studies to help interpret the estimated price effects in Section 4.
16
We have also used the pooled cross-sectional data without household fixed effects as a robustness
check. The results of pooled cross-sections are consistent with those with fixed effects, but are notreported due to limited space. We also tested for the equality of coefficients between the urban andrural equations. The F-test rejects the null hypothesis of equal coefficients at the 1% of significancelevel (F-statistic=6.4, p-value=0.00).

13

over the period 1980-2004 (Gaiha et al., 2014b) and thus, would affect nutritional
intakes. The choice of the second instrument is inspired by the strong link between
wage rates and nutrition as indicated by the well-known nutrition-based efficiency
wage hypothesis: higher wages allow households to invest more income in health andnutrition (Leibenstein, 1957; Bliss and Stern, 1978a, b; Dasgupta and Ray, 1986, 1987;
Dasgupta, 1997; Strauss and Thomas, 1998; Jha et al., 2009). Moreover, theprovincial growth rate of wage is presumably correlated with personal wages andincome, especially for urban households, but uncorrelated with personal decisions on
nutrition consumption. To further prevent the simultaneous determination of wage
and nutrient intake, we calculated the growth rate of provincial mean wage as theaverage figure over the past 5 years. The performance of these instruments will be
discussed in Section 4.1.
To reflect heterogeneous nutritional outcomes within urban and rural areas as
discussed in Section 2, we combine Canay (2011) and Lee’s (2007) approaches toestimate Eq. (1) for urban and rural areas, respectively, by a quantile regression withhousehold fixed effects and endogenous income. Specifically, Eq. (1) is re-written as
the following two joint equations:

Qln Kijt |X,Z  | X,ln yit   0    1   ln yit  Xβ2    uit(2)

Qln yit |X,Z   | X, Z    0     Xπ1     Zπ2     vit(3)

where  and  denote the quantiles of household per capita equivalent nutrient intake
and net income, respectively. ln yit denotes household i ’s natural logarithm of percapita equivalent net income at t . The vector X includes all correlates in Eq. (1),except income and household fixed effects. Z contains two instruments as stated
before. The disturbances follow U | X, Z ~ U  0,1 and V | X, Z ~ U  0,1 where U ,14containing household unobservable characteristics influencing nutrient intake, iscorrelated with V, including unobservables determining the income level (e.g.,capability for income generation) and income ( ln yit ) is uncorrelated with V but
correlated with U under the two-way causality between income and nutrition.
In the first step, we estimate Eq. (1) by a household fixed-effect panel
specification to obtain standard within estimators ˆ ij . They are used to get rid of fixed
effects in ln Kijt by calculating ln Kijt  ln Kijt  ˆij . In the second step, we adopt a
linear-in-parameter specification and estimate the  th quantile function conditional
on X and Z for nutrition by using Eq. (3) to obtain the residual vˆit . As the third step,in Eq. (2), ln Kijt is replaced by ln Kijt and vˆit is plugged into the  th quantile
function conditional on X and Z to correct for the endogeneity. Thus, Eq. (2)becomes:Qln Kijt |X,Z  | X   0    1   ln yit  Xβ2    3   vˆit  uit(4)

where  and  range from 0.1 to 0.9 with an increment of 0.01. 17 Consistentestimators are defined byβˆ    arg min nT   ln Kijt  ˆ1 ln yit  Xβˆ 2  ˆ3vˆit 


β

(5)

̂1   picks up the heterogeneous nutritional impact of income at each quantile .
Other distribution-sensitive nutritional effects are in the vector βˆ 2   . A significant

̂3   indicates the existence of endogenous income. The assumption underpinning
this three-step estimation strategy is that the impact of household unobserved andtime-invariant heterogeneity on nutritional outcomes is same across quantiles.

17

The values of λ and  can be different, but we equated them for simplicity.

154. Results and discussion
4.1. Identifying the determinants of urban and rural households’ nutrient intake
Tables 2-3 summarise the results based on the unbalanced panel with household fixed
effects for urban and rural areas, respectively. Two instruments perform well in allcolumns: they are jointly significantly different from zero in explaining householdincome according to F-statistics, indicating that the instruments are not weak. In thefirst stage, as expected, the ‘proportion of farmland suffering from natural disasters’ isnegative and significant and the ‘growth rate of provincial average wage’ is positiveand significant, as shown at the bottom of Table 2 or Table 3. The matrix of reduced
form coefficients has full rank according to the Anderson LM test, indicating Eq. (1)is not underidentified. The instruments are uncorrelated with the error term according
to Sargan-Hansen test, indicating that there is no overidentification problem, or
exclusion restrictions are deemed satisfied.
[Tables 2-3 to be inserted]
Columns 1-4 of Table 2 show positive and significant impact of income on total
calorie intake and its substrates except fat for which the income is positive but
statistically insignificant. The income elasticity of protein (0.14) is slightly higher
than that of carbohydrate (0.12). Household nutrition measured in either total calorie
consumption per equivalent adult or three substrates is insensitive to the demographictransition, household composition, education, 18 ethnicity, indicators for wealth andliving conditions, and health status. This seemingly implausible finding is aconsequence of substantial heterogeneity in nutritional responses to various
explanatory variables across households. Section 4.2 provides further analysis by
estimating the instrumental quantile model, Eq. (4).
18

We re-estimated Table 2 by replacing household average education by males’ and females’ average
education levels. Only males’ education significantly reduces total energy intake (-0.05) through lessfat consumption (-0.08), both at the 5% significance levels.

16

Rural households show broadly similar results of various household
demographic characteristics in Table 3 as their urban counterparts, 19 except forincome. Negative income-nutrition nexus surfaces due to the negative impact ofincome on protein (Columns 1 and 4 of Table 3).
We further specify three sources of income - agriculture, household business,
and wage income - and re-estimate Tables 2-3, in order to decode the above mixed
results between rural and urban households. The negative impact of income on protein
comes from agricultural and wage incomes, while a 1% increase in householdbusiness income can raise protein intake by 0.33% at the 10% significance level. This
seems to be inconsistent with the traditional view on agriculture-nutrition link indeveloping countries or the poverty reducing effect of agricultural income indeveloping countries (Imai and Gaiha, 2014) as well as in rural China (Imai and You,2014). Based on a number of empirical findings in the developing world, Fan andBrzeska (2012) argue that the growth pattern also matters in addition to income
growth per se. They summarise factors affecting the link between growth andnutrition: it is the growth of (staple) crop income that raises rural households’ calorie
intake rather than that of livestock income. We investigate this intriguing result byfurther inserting the proportions of crop and livestock incomes in total agricultural
income in the above regressions (with three specified income sources) for rural
households’ total energy intake and three substrates separately. An additional
percentage point of the crop income share in rural households’ total income canincrease their total energy intake per adult equivalent by 0.79% at the 10%
significance level. The results for three substrates show that these increases in totalenergy intake stem from 7% and 1.17% increases in carbohydrates and protein at the19

We also re-estimated Table 3 by replacing household average education with male and female adults’
average education levels. They are statistically insignificant in all cases.

17

10% significance levels when there is one more percentage point of the crop income
share. By contrast, the estimator of the proportion of livestock income is statisticallyinsignificant in all the cases.20
For urban households in Table 2, we replaced the household’s overall net
income indicator by logarithmic household per capita equivalent net business andwage incomes and re-estimated all regressions. Both promote urban households’ total
nutrition intake at the 10% significance level. The magnitude of the former (0.14) is
statistically larger than that of the latter (0.01) at the 10% significance level. The
channel underlying this better nutritional outcome is their positive impacts on
carbohydrates (0.18 and 0.02 at the 10% significance level for business and wage
incomes, respectively) and protein (0.21 and 0.02 at the 10% significance level forbusiness wage incomes, respectively) compared with fat (0.05 and 0.01 without
statistical significance for business and wage incomes, respectively). 21
As expected, rural households headed by farmers consume 14.3% more fat(Column 2 of Table 3) and 14.9% more protein (Column 4 of Table 3), leading tooverall 10.7% more total energy (Column 1 of Table 3), compared with non-farmer
headed ones. Both out-migration and local off-farm employment help increasecarbohydrates and protein and total energy (Columns 1, 3 and 4 of Table 3). The
magnitude of the impact of local off-farm employment is only significantly larger
20

Fan and Brzeska (2012) did not provide a clear reason for why crop income raises rural households’
calorie intake more than livestock income. It is conjectured that the former may increase the crops set
aside for their own consumption or may require more physically intense activity. Our data lend someindirect support to the latter. Specifically, for each adult family member (≥ 18 years old), we calculated
his/her proportion of time doing heavy physical activities (e.g., farming) in total time doing all physicalactivities in a typical week and then, took the average among all adult members within the household.
It correlates closely to household per adult equivalent net crop income with the correlation coefficientof 0.38, but not with livestock income with the correlation coefficient of 0.04. Both coefficients are
statistically significant at the 1% level. We also regressed respectively the natural logarithm ofhousehold per adult equivalent net crop and livestock incomes on the proportion of time doing heavy
physical activities, various household and community-level characteristics as in Table 2, and thehousehold fixed effects. A 1 percentage point increase in the time proportion of heavy physicalactivities per week is associated with 0.65% increases in crop income at the 5% significance level, but
unrelated to livestock income.
21
Econometric results for these disaggregated cases will be furnished on request.

18

than that of out-migration in the case of protein: for each column of 1, 3 and 4, theWald test only rejects the null hypothesis of equal magnitude of their estimates at the
10% significance level in Column 4 of Table 3. Fat intake also rises by 0.1% if there
is a one more percentage point of household members obtaining local off-farm
employment, but is unresponsive to out-migration (Column 2 of Table 3).
Food prices appear to be the main sources of differences in both urban and rural
households’ nutrition status. In Table 2, urban households’ intake of total nutritionand substrates are not affected by the level of prices of cereals, but would be loweredby higher prices of meat and eggs. The magnitude of these negative effects is large: aone percentage point increase in the meat (egg) price index could reduce thehousehold’s total energy intake by 3.14% (1.59%) in Column 1 (2) of Table 2. The
reduction caused by the rising meat price originates from all three substrates,
especially fat (-3.5% in Column 2 of Table 2), while that caused by a higher egg pricecomes from carbohydrates (-2.32% in Column 3 of Table 2) and protein (-1.79% inColumn 4 of Table 2). Higher vegetable prices are associated with more energy intake(1.08 in Column 1 of Table 2) through their positive impact on fat and carbohydrates
intake (Columns 2-3 of Table 2). A reason underlying the above changes in demand
for nutrition could be the relative price changes as mentioned by Deaton (1997). The
community survey of the CHNS shows that both the level and proportionate increases
in the prices in meat and eggs are higher than in rice, flour and vegetables (Zhai et al.,2014), households would therefore substitute vegetables, which have dominated urbanhouseholds’ dietary structure as indicated by the top line in Figure 1(a), for meat andeggs as vegetables are less expensive than meat and eggs. An example supporting this
explanation can be found during the food crisis (2007-2008). The data from China
Urban Life and Price Yearbook published annually by the National Bureau of19

Statistics show that an average urban household kept consuming more vegetables on a
per capita basis at the rate of 4.5% per annum during the food crisis, while less beef (14.3%), lamb (-9%), eggs (-0.8%), and dairy products (-14.4%). By using a monthly
panel in Henan and Gansu provinces in 2006, Jensen and Miller (2008) have also
found substitution behaviour for relatively cheaper food under food crisis. This
indicates that vegetables are likely to be inferior goods for urban households, while
meat and eggs are normal goods. Uncertainties in terms of volatilities of prices of
cereals and ‘fat and oil’ do not appear to hurt urban households’ total energy intake,but decrease fat consumption. Volatilities in vegetable prices decrease total energy
intake through its negative impact on carbohydrates (Columns 1 and 3 of Table 2). It
is worth noting the large positive effect of volatility of egg price on fat (6.86 inColumn 3 of Table 2). There might be considerable substitution of starchy foods for
eggs under the price shock of eggs.
On the contrary, Table 3 shows that rural households would consume more fat,carbohydrates and protein under higher egg prices and thus, more total energy. Eggs
are inferior goods as found in Bishop et al. (2010) and Du et al. (2004) by using the
CHNS as well.22 Considering our result for urban areas, their finding may have been
driven by rural rather than urban households. Higher fat and oil prices are associatedwith more consumption of fat and carbohydrates, which indicates that fat or oil is also
an inferior good for rural households. A higher price of vegetables suppresses
carbohydrates and protein but encourages fat consumption. As a result, total energy is
not responsive to it. It is worth noting consistently positive coefficient estimates ofvolatility of meat price on total energy and all three substrates with considerably large

22

Again, due to data limitation, we cannot purge the impact of relative price changes between eggs andother food items. This positive response of household nutrient intake to the price of eggs could be
attributed to inferior property of eggs (the absolute price effect) and/or less expensive eggs comparedwith other foods (the relative price effect).

20

magnitudes (varying from 3.82 for carbohydrate in Column 3 of Table 3 to 13.86 forfat in Column 2 of Table 3). This may be driven by rural households’ strong
preferences of meat.23 As shown in Column 2 of Table 3, households tend to consume
more fat proportionately as well under volatile meat prices and even after knowing
what a balanced diet should be (0.08 in Column 2 Table 3). Other supporting evidence
can be found from Figure 1: although the proportion of different kinds of meat in diethas been much lower in rural than in urban households, the average annual increase inthis proportion is similar between two populations over the sample period 1989-2009(0.2 percentage points).
Overall, households’ demand for nutrition appears not to respond to the pricechanges of staple food, but rather to more expensive items such as meat, eggs andvegetables. The reason from the demand side, as shown in Figure 1, is that grains still
dominate rural households’ diet and are the second food source for urban households.
From the supply side, the Chinese government has long smoothed domestic prices ofgrains by various administrative means such as price regulation, import protection,
limiting export (Jansen and Miller, 2008), and offering increasing and substantial

23

We suspect there might be a lexicographic preference for meat in general, but this preference isrealised through substitution between different varieties of meat. For example, during the food crisis(2007-2008), the China Agricultural Development Report 2012 published by the Ministry ofAgriculture suggests that although the average rural household per capita consumption of meat(including pork, beef and lamb) measured in kilograms decreased by 7.1% in 2007 compared with theprevious year and 6.7% in 2008, while that of poultry and seafood increased by 12% and 4%,
respectively, per annum over the period 2007-2008. This is because both the levels and volatilities ofthe prices of meat (pork, beef and lamb) are higher than those of poultry and seafood. Rural households
preferred to use other relatively cheap meats in the food crisis rather than cereals or vegetables tosupplement their diets – they would rather sacrifice consumption of these two items to satisfy theirlexicographic preference for meat. The above report shows that the rural household per capita
consumption of cereals and vegetables measured in kilograms actually declined by 3% and 1%,
respectively, in 2007 and stayed stable in 2008. The CHNS community survey also shows that an
average rural household’s fat intake per equivalent adult per day kept increasing at an annual rate of 4.2%
over the period 2006-2009, even though the real average community price of fatty and lean pork
increased dramatically by 10.4% per annum, from 6.4 in 2006 to 8.4 yuan per kg in 2009 (Zhai et al.,2014).

21

agricultural subsidies (Huang et al., 2013).24 These policies also promote grain yields
which have increased continuously for more than a decade since 2003 and help
stabilise their prices during the international food price spike in 2008 (World Bank,
2012). By contrast, the price of meat escalated by, for example, 24% during the foodcrisis between 2007 and 2008 due to increasingly costly inputs such as raw materials
and agricultural wages (You, 2014).
As found by Shimokawa (2013), acquiring more knowledge of healthy dietdoes not necessarily raise nutrient intake, except fat for rural households (Column 2 ofTable 3), as indicated by insignificant estimated coefficients of ‘dietary knowledge’ inTables 2-3. Switch in preferences towards energy - and sugar - rich diet or doing more
physical exercises appears to be irrelevant for higher nutrition for both urban and rural
households. Similar to the case of the insignificant nutritional effect of income, thisseemingly implausible finding is again subject to considerable heterogeneity acrosshouseholds in different nutrition strata. The estimates of our IV quantile model inTables 4-5 in Section 4.2 shed new light on these relationships.
Among various community indicators, only improvement in ‘social services’
including provision of preschool for children under 3 years old, availability ofcommercial medical insurance in the community, free medical insurance, and/or
insurance for women and children (following Jones-Smith and Popkin’s (2010)
definition of this variable) tend to increase total energy intake through carbohydrates
for urban households (Columns 1 and 3 of Table 2) and through carbohydrates andprotein for rural households (Columns 1, 3 and 4 of Table 3). Traditional markets arelikely to deter the growth of total energy intake through carbohydrates for urban
24

A nationally representative dataset in Huang et al. (2013) suggests that about 87% of farmhouseholds in rural China received subsidies in 2008. China is arguably the major subsidiser in theworld on per acre cultivated land basis. The authors’ calculations also show that the sum of subsidies
actually received by farmers was US$34.4 per acre in 2008, which is equivalent to agricultural
subsidies received by a typical farmer in Illinois (US$30-50 per acre).

22

households (Columns 1 and 3 of Table 2) and fat for rural households without
reducing the total caloric consumption (Column 2 of Table 3) – it alternatively is
lowered through less carbohydrates under development of modern markets (Column 1
and 3 of Table 3).

4.2. Distributional nutritional effects within urban and rural areas
We proceed to examine the distributional nutritional effects for urban and rural
households in Tables 4 and 5, respectively, by using IV quantile regressions withhousehold fixed effects.
[Tables 4 and 5 to be inserted]
Columns 4 and 7 of Table 4 show that while the income elasticities ofcarbohydrates and protein for urban households in Table 2 are positive in the
population deficient in these two nutrients (at the 10% point, or  =0.1 of theirconditional distributions), these elasticities cease to be statistically significant among
better-nourished households (at median or 90%, or  =0.5 or 0.9 of their conditional
distributions). As such, income growth would help alleviate inequality ofcarbohydrates and protein for urban households. Younger households with averageage of household members between 30 and 40 years and with moderate nutrient
intake consume more carbohydrate and protein (Columns 5-6 and 7-8 of Table 4),
while among the older the intake decreases at a faster speed. In contrast, the least
nourished households only experience the negative impact of age, especially thecohorts aged above 40 (Columns 4 and 7 of Table 4). Ethnic differences matter.
Belonging to the ethnic minority group is associated with lower intake of fat,carbohydrate, or protein, at the 90% point of its conditional distribution, that is,among the better-nourished, ethnic minorities tend to take a smaller amount of23

nutrient than the ethnic majority, ceterus paribus. However, this pattern is reversed
for fat or protein at the 10% point of its conditional distribution. Among themalnourished, ethnic minorities tend to take a larger amount of nutrient than theethnic majority, ceterus paribus.
Higher prices of meat are detrimental for all urban households’ nutrition, except
for those who have consumed many carbohydrates (Column 6 of Table 4). Comparedwith the broadly insignificant impact in the standard IV estimation, food knowledge
becomes a statistically positive correlate of all three substrates for well-nourishedgroups (Columns 3, 5, 6 and 9 of Table 4). However, it is those having the least fat or
at least moderate carbohydrates and protein rather than the well-nourished who tend
to consume more nutrients (Columns 1, 2, 5, 8 and 9 of Table 4). Doing exercises
suggests mixed effects: it increases fat and protein for those having at least moderateintake of the two substrates, but decreases carbohydrates and protein for those at thebottom 50% of the distribution of these two substrates, respectively. This
heterogeneity across distributions of household intake of various nutrients explainsinsignificant impact of shifts in diet and activity preferences in the standard IV
estimation. Transport and health services also benefit those with high carbohydrate
intake (Column 6 of Table 4), while social services and overall economic
development are associated with nutrition improvement for those with the lowest
carbohydrate and protein intake (Columns 4-8 of Table 4).
For rural households, income growth would help alleviate inequality of fat andcarbohydrates as a positive income-nutrition link is found for carbohydrate at the 10%
point in its conditional distribution (Column 4 of Table 5), while a negative link isfound between income and fat among those who are already consuming much oil andfat (at the 90% point) (Column 3 of Table 5). Ethnic minorities tend to consume less24fat regardless of their position in the nutritional distribution (Columns 1-3 of Table 5),
but no ethnic differences exist in other nutrients. Out-migration can increase rural
households’ fat intake for those with lowest fat consumption (Columns 1-2 of Table5), and this impact is proportionately large for the least nourished. However, only
well-nourished households benefit from out-migration and raise their carbohydrate
intake (Column 6 of Table 5). Similarly, local off-farm employment benefit fat andprotein intake for at least moderately nourished households (Columns 2-3 and 9 ofTable 5), but are ineffective means for improving nutritional status of those withlowest and highest nutrient intakes. Different from their urban counterparts, acquiringknowledge on healthy diet will only raise rural households’ fat consumption and thiseffect is only significant for those at the upper 50% of distribution of fat intake, which
is predicted given rural households’ strong preferences for meat. However, it seems
counterintuitive that preferences for fast food and the like negatively relate to fat
intake, as well as carbohydrate and protein. This could be because those voting for‘like’ for fast food were less able to afford it. For example, the average household net
income per adult equivalent is 14.5%-18% lower among those answering ‘like fast
food’ than ‘neutral’ in different waves. As result, it is those at the bottom half of the
distribution of nutrient intake who experience lower consumption of three substrates
even their diet preferences shift to fat- and energy-rich food (Columns 1, 2, 4, 5 and 8of Table 5).

4.3. Implications for nutrition poverty
Decreasing nutrient intake has given rise to nutrition poverty. Based on the cut-off at
2,100 kcal per person per day as suggested by Park and Wang (2001), Figure 425

depicts the profile of nutrition poverty in China.25 The nutrition poverty rate (FGT(0)),
nutrition poverty gap (FGT(1)) and its square (FGT(2)) all increased in both urban
and rural areas, with the urban households seeing slightly higher magnitude due to its
lower level of total energy intake. This implies that those just escaped from income
poverty might still be deficient in nutrition. Meanwhile, the remaining income poor
have to confront the dual burden of both income poverty and nutritional deficiency,
which could result in nutrition-poverty traps in the long term.
[Figure 4 to be inserted]
Using the specification of Eq. (1), we replaced the nutrient intake with an array of
FGT-class indicators: the dummy variable which takes the value one if the household
per capita equivalent total energy intake per day is less than 2,100 kcal and 0
otherwise (FGT(0)); the gap between the household per capita equivalent total energy
intake per day and 2,100 kcal for those below the 2,100 kcal and 0 otherwise(FGT(1)); and the squared nutrition gap (FGT(2)). Table 6 reports the standard errors
for each estimator based on the unbalanced panel for urban and rural households,
respectively.
[Table 6 to be inserted]
Despite positive impact of income on nutrient intake for urban households inTable 2, income growth does not exhibit a statistically significant impact on reducing
the incidence of nutrition poverty or narrow the nutrition poverty gap (Columns 1 and
2 of Table 6). It only alleviates the severity of nutrition deprivation (Column 3 ofTable 6) because it reduces the inequality of nutrient intake among households byincreasing consumption of carbohydrates and protein for the least nourished (Columns
4 and 7 of Table 4). A 1% increase in income growth would even add 0.08% to the25

The pattern in Figure 4 remains same for a higher nutrition poverty line of 2,400 kcal which is alsosuggested by Park and Wang (2001) in the Chinese context.

26

probability of falling below the nutrition poverty line for rural households (Column 4of Table 6). This is consistent with the negative impact of income on total calorie
intake which is likely to be caused by lower protein consumption when the household
becomes rich (Column 1 and 4 of Table 3). Neither depth nor severity of ruralnutrition poverty could be addressed by income growth, which is broadly consistent
with the limited income effects for the least nourished, as found in Table 5.
In order to check the robustness of the above findings, we also re-estimated
Table 6 by using a higher nutritional poverty line at 2,400 kcal per person per day asrecommended by Park and Wang (2001). The estimated coefficients of income
become statistically significant at the 10% level: 1% of income growth can reduce thelikelihood of nutrition poverty by 0.103 percentage points and alleviate the nutrition
poverty gap and its square by 0.03 and 0.02, respectively, in urban areas. This raises a
concern about the income-nutrition traps for the least nourished in urban households,
as the ‘nutritional bonus’ of income growth has not been shared by the poorest but
only approached as far as ‘the not-so-poor’. Although income growth increases
consumption of carbohydrates and protein for the least nourished households (Table4), this effect per se is not large enough to allow households to escape nutrition
poverty. For rural households, the nutrition poverty-increasing effect of income isenhanced to 0.15 at the 5% significance level for FGT(0) under this higher cut-off,and further extends to FGT(1): its estimated coefficient becomes doubled (0.02) andsignificant at the 10% level. This is not unexpected (Figure 1), as richer rural
households record lower nutrient intake due to lower carbohydrate consumption.
Age of urban household head is associated positively with the probability for
nutritional deficiency, as can be observed by the statistically significant and positivecoefficient estimates for those above 50 years and those in retirement age or above 60

27

years with the coefficient for the latter much higher (Columns 1 and 2 of Table 6).26
For instance, those in retirement age are 16.4% more likely to be in nutritional
poverty. Again, education does not contribute to alleviating nutrition poverty in either
urban or rural areas. Ethnic difference surfaces in rural areas: among the nutritionpoor, ethnic minorities tend to sink further into the mire than the rest.
Urban households’ nutrition poverty responds positively to rising meat prices(Columns 1 and 3 of Table 6), which is expected as meat is a normal good (Table 2).
This poverty-increasing effect is also observed for rural households. However, in thiscase, nutritional poverty is more negatively related with higher prices of ‘fat and oil’and eggs (Columns 4-6 of Table 6), which is consistent with Table 3.
The community development in terms of better transport, social services andeconomic prosperity helps combat rural nutrition poverty (Columns 4-6 of Table 6),
while urban households seem to only benefit from social services in reducing theincidence of nutritional deprivation. Neither traditional nor modern markets’
development exhibits poverty-reducing effects, but rather deteriorates the nutritional
status.

5. Conclusion
This study has examined a number of factors in determining Chinese households’
nutrient intake over two decades (1989-2009). A point of departure of the present
study is to model (i) heterogeneity in the effect of household income and nutritional
intake, and (ii) the endogeneity of household income to provide a robust estimate forthe effect of income on nutrition at different levels of nutritional intake. To do this,26

This still holds under the nutrition poverty line of 2,400 kcal per person per day.

28

we have combined recent seminal works by Canay (2011) and Lee (2007) to estimate
the quantile instrumental variable (IV) panel model.
Income growth, especially stemming from household business and wages, isassociated with nutritional improvement of urban rather than rural households. For thelatter population, only crop income appears to encourage total energy consumption. 27
As income keeps rising, those with moderate or higher nutrient intake tend to
restrain their further nutrition consumption, especially fat for rural households, andthe least nourished tend to consume more carbohydrates and protein. Thus, income
growth is likely to reduce nutrition inequality among households. Nevertheless,somewhat surprisingly, education, dietary knowledge or habits of eating and physicalexercise seem unimportant for household nutrients’ intake. Only the better-nourishedin urban areas are able to benefit from widely believed contributors of nutrition intake
such as dietary knowledge, local off-farm employment and out-migration.
Furthermore, uncertainties in terms of soaring and volatile food prices that haverecently been observed all over the world suggest substantial but different effects onhousehold nutrient demand, varying with specific food commodities. It is conjectured
that in urban areas, the positive aggregate household income effects on nutrition, orthe nutrition-poverty reducing effects of income, are weak and other factors, such as,increases in food price and its volatilities (e.g. meat) and aging, can easily offset the
weak income effects, which have resulted in the increase in the proportion ofundernourished despite the high income growth of urban households.28 In rural areas,

27

Weak effects of household income or expenditure in improving nutrients have been found in India.
Gaiha et al. (2014a) have applied the estimation embedded in a standard demand theory framework to
the National Sample Survey data in India whereby nutrient demand is a function of assets, prices anddemographics. They found that diet diversification has slowed down faster- especially in rural areas and this appears to be linked to the weak link between household expenditure and nutrient intake.
28
This reflects the weak (though statistically insignificant) elasticity of nutrition poverty headcountwith respect to household income whereby even doubling the average household income reduces
nutrition poverty only by -5.1%, ceterus paribus (Column 1 of Table 6).

29the aggregate household income effect on nutrition is negative. As the average
household income goes up, the proportion of undernourished tends to increase unlessthey have access to out-migration or local off-farm employment, or alternatively,
significantly increases the share of crop income in the total income. Overall, thepattern of income growth, the rising food prices and uncertainties attached to themappear to jointly explain the paradoxical rising income and declining nutrient intake in
China.
Turning to policy implications, agriculture still plays a key role in improving
rural households’ nutrition if the policy maker takes advantage of positive loops
between income and nutrition. However, policies promoting income growth alone
may not be sufficient to raise household nutrient intakes, as the poor, especially inurban areas where people also face nutrition deficiency because of aging, seem not toenjoy the benefit of income growth. Certain traditional recipes alone, such as
promoting education, may not be effective in the Chinese context. Interventions haveto be tailored to serve better urban and rural population, respectively. For the former,
more pro-(nutrition) poor income growth would enhance nutrient intake and narrownutrition inequality between households to some extent. For the latter, growth of crop
income is of paramount importance. Moreover, community development is also apolicy instrument to promote rural household nutrients’ intakes. For both urban andrural populations, the policies stabilising income streams and helping cushion against
the risks of soaring and volatile prices of food commodities would generate
substantial positive impact on household nutrition status.

30

References
Behrman, J. R., & Deolalikar, A. B. (1987). Will Developing Country Nutrition
Improve with Income? A Case Study for Rural South India. Journal of Political
Economy, 95(3), 492-507.
Bishop, J. A., Haiyoung, L., & Buhong, Z. (2010). Rising incomes and nutritional
inequality in China. In J. A. Bishop (Ed.). Research on Economic Inequality,(Vol. 18). Emerald Group Publishing Limited, pp.257-266.
Bliss, C. J., & Stern, N. H. (1978a). Productivity, wages and nutrition: Part I: The
Theory. Journal of Development Economics, 5, 331-362.
Bliss, C. J., & Stern, N. H. (1978b). Productivity, wages and nutrition: Part II: Someobservations. Journal of Development Economics, 5, 363-398.
Bloom, D. E. (2011). 7 Billion and counting. Science, 333, 562-569.
Canay, I. A. (2011). A simple approach to quantile regression for panel data.
Econometric Journal, 14(3), 368-386.
Carter, C. A., & Zhong, F. (1999). Rural wheat consumption in China. American
Journal of Agricultural Economics, 81, 582-592.
Chamon, M., Liu, K., & Prasad, E. (2013). Income uncertainty and household savingsin China. Journal of Development Economics, 105, 164-177.
Chen, S., & Ravallion, M. (2010). China is poorer than we thought, but no lesssuccessful in the fight against poverty. In S. Anand, P. Segal, & J. E. Stiglitz,(Eds.), Debates on the Measurement of Global Poverty, Oxford: Oxford
University Press.
Dasgupta, P. (1997). Nutritional status, the capacity for work, and poverty traps.
Journal of Econometrics, 77, 5-37.

31

Dasgupta, P., & Ray, D. (1986). Inequality as a determinant of malnutrition andunemployment: Theory. Economic Journal, 96, 1011-1034.
Dasgupta, P., & Ray, D. (1987). Inequality as a determinant of malnutrition andunemployment: Policy. Economic Journal, 97, 177-188.
de Haen, H., Klasen, S., & Qaim, M. (2011). What Do We Really Know? Metrics for
Food Insecurity and Undernutrition. Food Policy, 36, 760-769.
de Weerdt, J., Beegle, K., Friedman, J., & Gibson, J. (2014). The Challenge of
Measuring Hunger (Policy Research Working Paper 6736). Washington DC:
World Bank.
Deaton, A. (1997). The analysis of household surveys: A microeconometric approachto development policy. Baltimore and London: The Johns Hopkins University
Press.
Deaton, A., & Dreze, J. (2009). Food and nutrition in India: Facts and interpretations.
Economic and Political Weekly, XLIV(7), 42-65.
Du, S., Mroz, T. A., Zhai, F., Popkin, B. M. (2004). Rapid income growth adversely
affects diet quality in China – particularly for the poor! Social Science &Medicine, 59, 1505-1515.
Fan, S., & Brzeska, J. (2012). The nexus between agriculture and nutrition: Do
growth patterns and conditional factors matter? In: S. Fan & R. Pandya-Lorch(Eds.). Reshaping Agriculture for Nutrition and Health, Washington DC: IFPRI.
FAO (2003). Food energy – methods of analysis and conversion factors (FAO Foodand Nutrition Paper 77). Rome: FAO.
FAO (2013). The state of food insecurity in the world: The multiple dimensions offood security. Rome: FAO.

32

Gaiha, R., Kaicker, N., Imai, K. S., Kulkarni, V. S., & Thapa, G. (2014a). Dietaryshift and diet quality in India: an analysis based on the 50th, 61st and 66th
rounds of the NSS. In R. Jha, R. Gaiha, & A. Deolalikar (Eds.). Handbook on
Food: Demand, Supply, Sustainability and Security, Gloucestershire, UK:
Edward Elgar Publishing.
Gaiha, R., Jha, R., & Kulkarni, V. S. (2014b). Diets, Malnutrition and Disease: The
Indian Experience. New Delhi: Oxford University Press.
Gale, F., & Huang, K. (2007). Demand for food quantity and quality in China(Economic Research Report No. 32). Washington DC: United States
Department of Agriculture.
Guo, X., Mroz, T. A., Popkin, B. M., & Zhai, F. (2000). Structural change in theimpact of income on food consumption in China, 1989-1993. Economic
Development and Cultural Change, 48, 737-760.
Headey, D. D. (2013). Development drivers of nutritional change: A cross-country
analysis. World Development, 42, 76-88.
Hovhannisyan, V., & Gould, B. W. (2011). Quantifying the structure of food demandin China: An econometric approach. Agricultural Economics, 42, 1-17.
Huang, K. S., & Gale, F. (2009). Food demand in China: Income, quality, and nutrient
effects. China Agricultural Economics Review, 1, 395-409.
Huang, J., Wang, X., & Rozelle, S. (2013). The subsidization of farming households
in China’s agriculture. Food Policy, 41, 124-132.
Imai, K. S., & Gaiha, R. (2014). Dynamic and Long-term Linkages among Growth,Inequality and Poverty in Developing Countries (BWPI working papers, WP
198/2014), Manchester: The University of Manchester.

33

Imai, K. S., & You, J. (2014). Poverty dynamics of households in rural China. Oxford
Bulletin of Economics and Statistics, 76(6), 898-923.
Jalan, J., & Ravallion, M. (1998). Transient poverty in rural China. Journal ofComparative Economics, 26, 338-357.
Jalan, J., & Ravallion, M. (1999). Are the poor less well insured? Evidence on
vulnerability to income risk in rural China. Journal of Development Economics,
58(1), 61-81.
Jensen, R. T., & Miller, N. H. (2008). The impact of food price increases on caloric
intake in China. Agricultural Economics, 39(s1), 465-476.
Jha, R. Gaiha, R., & Sharma, A. (2009). Calorie and Micronutrient Deprivation andPoverty Nutrition Traps in Rural India. World Development, 37(5), 982-991.
Jones-Smith, J. C., & Popkin, B. M. (2010). Understanding community context andadult health changes in China: Development of an urbanicity scale. Social
Science & Medicine, 71, 1436-1446.
Kijima, Y. (2006). Caste and tribe inequality: Evidence from India, 1983-1999.
Economic Development and Cultural Change, 54(2), 369-404.
Lee, S. (2007). Endogeneity in quantile regression models: A control function
approach. Journal of Econometrics, 141, 1131-1158.
Leibenstein, H. (1957). Economic Backwardness of Economic Growth, New York:
Wiley.
Ma, H., Rae, A., Huang, J., & Rozelle, S. (2004). Chinese animal productconsumption in 1990s. Australian Journal of Agricultural and Resources
Economics, 48, 569-590.

34

Matsumoto, T., Kijima, Y., & Yamano, T. (2006). The role of local nonfarm activities
and migration in reducing poverty: Evidence from Ethiopia, Kenya, and Uganda.
Agricultural Economics, 35(s3), 449-458.
Meng, X., Gong, X., & Wang, Y. (2009). Impact of income growth and economic
reform on nutrition availability in urban China: 1986-2000. Economic
Development and Cultural Change, 57, 261-295.
Ogundari, K., & Abdulai, A. (2013). Examining the heterogeneity in calorie-income
elasticities: A meta-analysis. Food Policy, 40, 119-128.
Park, A. & Wang, S. (2001). China’s poverty statistics. China Economic Review, 12,384–398.
Popkin, B. M., Adair, L. S., & Ng, S. W. (2012). Global nutrition transition and the
pandemic of obesity in developing countries. Nutrition Reviews, 70(1), 3-21.
Popkin, B. M., Du, S., Zhai, F., & Zhang, B. (2010). Cohort profile: The China Healthand Nutrition Survey – monitoring and understanding socio-economic andhealth change in China, 1989-2011. International Journal of Epidemiology, 39,
1435-1440.
Ravallion, M., & Sen, B. (1996). When method matters: Monitoring poverty in
Bangladesh. Economic Development and Cultural Change, 44(4), 761-792.
Reyes, J. A. D., & Himatay, M., S., G. (2010). Food consumption patterns andhousehold food security in Calabarzon. Journal of ISSASS, 16(2), 53-66.
Shimokawa, S. (2013). When does dietary knowledge matter to obesity andoverweight prevention? Food Policy 38, 35-46.
Shimokawa, S. (2010). Nutrient intake of the poor and its implications of thenutritional effect of cereal price subsidies: Evidence from China. World
Development, 38, 1001-1011.

35

Strauss, J., & Duncan, T. (1998). Health, nutrition, and economic development.
Journal of Economic Literature, 36, 766-817.
Subramanian, S., & Deaton, A. (1996). The Demand for Food and Calories. Journalof Political Economy, 104(1), 133-162.
Duncan, T. (1994). Like Father, like Son; Like Mother, like Daughter: Parental
Resources and Child Height. Journal of Human Resources, 29(4), 950-988.
Whally, J., & Yue, X. (2009). Rural income volatility and inequality in China. CESifo
Economics Studies, 35(3-4), 648-668.
World Bank (2012). Global monitoring report 2012: Food prices, nutrition and the
MDG. Washington, DC: The World Bank.
You, J. (2014). Dietary change, nutrient transition and food security in fast growing
China. In R. Jha, R. Gaiha, A. B. Deolalikar (Eds.) Handbook on Food:
Demand, Supply, Sustainability and Security. Cheltenham, UK: Edward Elgar
Publishing.
You, J., & Ozanne, A. (2014). Volatility and inequality: Household vulnerability as
uncertain welfare in rural China. European Journal of Development Research,advance online publication 18 September 2014 (doi: 10.1057/ejdr.2014.55).
Yu, X., & Abler, D. (2014). Matching food with mouths: A statistical explanation to
the abnormal decline of per capita food consumption in rural China. Paper
presented at the International Conference on Food Security and Rural
Development, Nanjing Agricultural University, 20 October 2014.
Zhai, F. Y., Du, S. F., Wang, Z. H., Zhang, J. G., Du, W. W., & Popkin, B. M. (2014).
Dynamics of the Chinese diet and the role of urbanicity. Obesity Reviews,
15(S1), 16-26.

36

Zhai, F., Wang, H., Du, S., He, Y., Wang, Z., Ge, K., & Popkin, B. M. (2009).
Prospective study on nutrition transition in China. Nutrition Reviews, 67(Suppl.
1), S56-S61.
Zhang, B., Zhai, F. Y., Du, S. F., & Popkin, B. M. (2014). The China Health andNutrition Survey, 1989-2011. Obesity Reviews, 15(S1), 2-7.
Zheng, Z., & Henneberry, S. R. (2010). The impact of changes in income distribution
on current and future food demand in urban China. Journal of Agricultural andResource Economics, 35, 51-71.

37

Figure 1 Household per capita annual consumption of foods (e.g. grain, vegetable,
edible oil, egg and dairy products) (1978-2012)

Source: Authors’ calculation based data from China Statistical Yearbooks and China Urban Life and Price Yearbooks from 2007
to 2013, and the Great Changes of China’s Economy in 60 Years (1947-2007). All were published by the NBS.
Note: Because data on fruit purchase have only been available since 1992, there was a significant increase in the urban household
per capita total purchase in 1992. This results in significant drops in the share of grain and vegetables in the same year. Similarly,
the rural household per capita total purchase has included fruit since 2003.

38

Figure 2 Trends of food consumption of urban and rural households in China

Note: The food consumption indicator is the household per capita annual total consumption of food measured in kg.
Source: See Figure 1.

Figure 3 Nutrient transition

Source: Authors’ calculation based on the CHNS.

Figure 4 Nutrition poverty profile

39

Source: Authors’ calculation based on the CHNS.

40

Table 1 Household nutritional inequality measured by Gini coefficients
Rural
Total calories
Fat
Carbohydrate
1989
0.172
0.348
0.184
1991
0.161
0.300
0.182
1993
0.170
0.314
0.194
1997
0.166
0.291
0.191
2000
0.169
0.288
0.189
2004
0.180
0.296
0.195
2006
0.185
0.331
0.198
2009
0.193
0.335
0.187
% Change compared to 1989
12.21%-3.74%
1.63%
Note: Total and direct calories are measured in kcal. Other nutrients are measured in grams.
Source: Authors’ calculation based on the CHNS.
Wave

Protein
0.196
0.184
0.192
0.193
0.199
0.201
0.210
0.203
3.57%

41

Urban
Total calories
0.162
0.142
0.154
0.153
0.169
0.178
0.179
0.196
20.99%

Fat
0.299
0.250
0.246
0.243
0.264
0.263
0.260
0.300
0.33%

Carbohydrate
0.168
0.157
0.172
0.182
0.182
0.190
0.199
0.200
19.05%

Protein
0.172
0.160
0.164
0.179
0.186
0.212
0.206
0.206
19.77%

Table 2 Determinants of household nutrition (urban)
Independent variable
IncomeLn(Household per capita net income equivalent
adult)
Household demographics
Age cohort (30≤average age<40)
Age cohort (40≤average age<50)
Age cohort (50≤average age<60)
Age cohort (average age≥60)
Proportion of adults
GenderEthnicity
Education
Household wealth/living conditions
Water source
Toilet type
Cooking fuelOccupation physical activity
Labourer
HealthIllness
Insurance
Level of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables
Volatility of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables
Food knowledge
Chinese diet knowledge
Culture and preference
Diet preference
Activity preference
Community
Population density
Transport
Health services
Social services
Traditional market
Modern market
EconomyProvincial dummy
Wave dummy
No. of obs.
LM statistic for the underidentification test (p-value)

Total calorie
(1)

Fat
(2)

Carbohydrate(3)0.095 (0.047)**0.041 (0.057)

0.123 (0.055)**

0.138 (0.059)**

0.046 (0.049)-0.004 (0.059)-0.017 (0.065)-0.056 (0.082)-0.018 (0.073)
0.003 (0.063)
0.036 (0.116)-0.029 (0.031)

0.012 (0.067)-0.004 (0.077)-0.026 (0.083)
0.041 (0.106)
0.026 (0.088)
0.042 (0.074)
0.095 (0.160)-0.037 (0.038)

0.053 (0.059)-0.023 (0.071)-0.047 (0.080)-0.133 (0.097)-0.047 (0.088)-0.022 (0.076)-0.024 (0.164)-0.027 (0.039)

0.040 (0.056)
0.0003 (0.069)
0.029 (0.079)-0.098 (0.097)-0.041 (0.094)-0.047 (0.080)
0.061 (0.158)-0.031 (0.039)-0.016 (0.021)
0.004 (0.017)
0.012 (0.018)-0.003 (0.031)
0.009 (0.022)
0.003 (0.023)-0.023 (0.024)
0.001 (0.022)
0.013 (0.022)

0.015 (0.025)
0.014 (0.022)
0.024 (0.023)-0.002 (0.057)

0.023 (0.068)-0.019 (0.069)-0.017 (0.069)-0.029 (0.024)-0.033 (0.043)-0.014 (0.030)-0.002 (0.055)-0.028 (0.028)-0.066 (0.050)-0.034 (0.031)-0.034 (0.053)-0.770 (0.111)
0.093 (0.367)-3.140 (1.087)***-1.591 (0.874)*
1.079 (0.552)*

1.548 (1.475)
0.464 (0.500)-3.498 (1.407)**-0.651 (1.131)
1.273 (0.681)*

0.876 (1.322)
0.191 (0.432)-3.035 (1.305)**-2.322 (1.086)**
1.061 (0.654)*

0.343 (1.349)-0.014 (0.450)-2.582 (1.290)**-1.786 (1.081)*
0.621 (0.677)-1.373 (1.658)-0.786 (0.963)
2.110 (1.744)
4.676 (3.113)-1.436 (0.770)*-3.612 (2.219)*-3.583 (1.313)***3.097 (2.225)-0.194 (4.046)-0.736 (0.947)-0.941 (1.928)
0.179 (1.169)
1.502 (1.993)
6.862 (3.664)*-2.095 (0.914)**0.617 (2.132)-1.717 (1.172)
1.731 (2.113)
4.880 (3.775)-0.913 (0.957)

0.027 (0.024)

0.020 (0.030)

0.044 (0.028)

0.029 (0.030)

0.023 (0.019)-0.008 (0.017)

0.029 (0.024)
0.015 (0.022)

0.007 (0.023)-0.019 (0.020)

0.031 (0.024)-0.014 (0.021)-0.006 (0.016)
0.001 (0.007)
0.007 (0.007)
0.009 (0.005)*-0.011 (0.005)**-0.0001 (0.007)
0.003 (0.007)
Yes
Yes
2,469
8.48 (0.014)-0.007 (0.021)
0.001 (0.009)
0.003 (0.009)
0.008 (0.006)-0.011 (0.007)-0.005 (0.009)
0.001 (0.009)
Yes
Yes
2,469
8.48 (0.014)

0.002 (0.019)
0.001 (0.008)
0.011 (0.009)
0.010 (0.006)*-0.013 (0.006)**
0.0005 (0.008)
0.002 (0.008)
Yes
Yes
2,469
8.48 (0.014)-0.007 (0.020)-0.002 (0.009)-0.001 (0.009)
0.009 (0.006)-0.010 (0.007)
0.002 (0.009)
0.004 (0.009)
Yes
Yes
2,469
8.48 (0.014)

42

Protein(4)

F-statistic for the weak identification (p-value)
4.27 (0.014)
4.27 (0.014)
4.27 (0.014)
4.27 (0.014)
Estimation of instruments in the first stage
Proportion of farmland suffering from natural-10.621 (4.170)**-10.621 (4.170)**-10.621 (4.170)**-10.621 (4.170)**
disasters
Provincial growth rate of average wage
15.820 (6.057)***15.820 (6.057)***15.820 (6.057)***15.820 (6.057)***
Sargan Chi-/Hansen J statistic for the
0.234 (0.628)
0.547 (0.460)
0.01 (0.909)
0.98 (0.323)
overidentification test (p-value)
Note: The dummy variable indicating the age cohort (age<30) is dropped as the reference group. Estimations are based on data from 2000 to 2009 because variables of foodknowledge and two cultural and preference indicators have only been collected since 2000. ***, **, and * denote 1%, 5%, and 10% significance levels in turn.
Heteroscedasticity-robust errors are in parentheses.

Table 3 Determinants of household nutrition (rural)
Independent variable
IncomeLn(Household per capita net income equivalent
adult)
Household demographics
Age cohort (30≤average age<40)
Age cohort (40≤average age<50)
Age cohort (50≤average age<60)
Age cohort (average age≥60)
Proportion of adults
GenderEthnicity
Education
Household wealth/living conditions
Water source
Toilet type
Cooking fuelHousehold labour
Out-migration
Local off-farm
Occupation physical activity
Farmer
HealthIllness
Insurance
Level of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables
Volatility of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables
Food knowledge
Chinese diet knowledge
Culture and preference

Total calorie
(1)

Fat
(2)

Carbohydrate(3)

Protein(4)-0.077 (0.040)*-0.053 (0.060)-0.048 (0.035)-0.132 (0.055)**

0.039 (0.032)
0.018 (0.038)
0.035 (0.048)-0.013 (0.057)
0.045 (0.054)
0.083 (0.058)-0.152 (0.121)
0.047 (0.029)

0.025 (0.050)
0.007 (0.059)
0.049 (0.073)
0.049 (0.089)
0.142 (0.079)*
0.053 (0.084)-0.366 (0.202)*
0.004 (0.043)

0.053 (0.029)*
0.030 (0.035)
0.021 (0.043)-0.042 (0.051)-0.011 (0.049)
0.076 (0.050)-0.042 (0.103)
0.059 (0.025)**

0.039 (0.044)
0.018 (0.052)
0.038 (0.065)-0.037 (0.079)
0.027 (0.076)
0.153 (0.080)*-0.141 (0.166)
0.071 (0.041)*

0.003 (0.017)
0.005 (0.010)-0.007 (0.008)-0.003 (0.027)
0.002 (0.016)-0.002 (0.013)

0.009 (0.015)
0.009 (0.009)-0.011 (0.008)

0.011 (0.022)
0.003 (0.013)-0.005 (0.012)

0.088 (0.031)***0.105 (0.038)***0.071 (0.046)
0.102 (0.057)*

0.074 (0.028)***0.072 (0.033)**

0.104 (0.041)**0.161 (0.051)***0.107 (0.052)**

0.143 (0.079)*

0.052 (0.045)

0.149 (0.071)**

0.015 (0.018)
0.020 (0.023)

0.027 (0.029)
0.035 (0.036)

0.005 (0.016)
0.008 (0.020)

0.042 (0.024)*
0.045 (0.031)-1.373 (0.994)
0.445 (0.291)-0.175 (0.999)
3.278 (0.788)***-0.184 (0.505)

2.187 (1.575)
1.034 (0.465)**-3.911 (1.565)**4.275 (1.219)***1.622 (0.784)**-1.527 (0.891)*
0.423 (0.254)*
0.089 (0.844)
2.283 (0.671)***-0.668 (0.403)*-2.062 (1.280)
0.164 (0.379)-0.065 (1.239)
1.927 (1.028)*-1.284 (0.654)**2.862 (1.349)**-1.476 (0.704)
6.299 (1.567)***-0.573 (1.900)-0.528 (0.609)

2.267 (2.022)-3.939 (1.209)***13.857 (2.495)***3.347 (3.071)-2.687 (0.954)***2.979 (1.194)**-1.132 (0.614)*
3.822 (1.384)***-0.847 (1.743)-0.169 (0.467)

3.908 (1.812)**-1.628 (0.906)*
4.691 (2.118)**
1.562 (2.467)
0.912 (0.778)

0.023 (0.023)

0.079 (0.037)**-0.003 (0.020)

0.033 (0.030)

43

Diet preference
Activity preference
Community
Population density
Transport
Health services
Social services
Traditional market
Modern market
EconomyProvincial dummy
Wave dummy
No. of obs.
LM statistic for the underidentification test (p-value)
F-statistic for the weak identification (p-value)
Estimation of instruments in the first stage
Proportion of farmland suffering from natural
disasters
Provincial growth rate of average wage
Sargan Chi-/Hansen J statistic for theoveridentification test (p-value)
Note: See Footnote of Table 2.

0.010 (0.013)
0.011 (0.013)-0.034 (0.023)
0.004 (0.021)

0.001 (0.013)
0.003 (0.011)-0.008 (0.020)
0.025 (0.017)-0.002 (0.014)
0.005 (0.004)
0.001 (0.004)
0.009 (0.004)**-0.004 (0.003)-0.011 (0.005)**
0.003 (0.005)
Yes
Yes
4,066
10.76 (0.005)
5.41 (0.005)-0.014 (0.020)
0.013 (0.007)**0.009 (0.007)
0.009 (0.006)-0.014 (0.005)***-0.007 (0.008)
0.013 (0.007)*
Yes
Yes
4,066
10.76 (0.005)
5.41 (0.005)

0.008 (0.012)
0.001 (0.004)-0.003 (0.004)
0.012 (0.004)***0.0003 (0.003)-0.009 (0.004)**
0.001 (0.004)
Yes
Yes
4,066
10.76 (0.005)
5.41 (0.005)

0.014 (0.020)
0.005 (0.006)-0.003 (0.006)
0.011 (0.005)**-0.006 (0.004)-0.006 (0.007)-0.0005 (0.007)
Yes
Yes
4,066
10.76 (0.005)
5.41 (0.005)-7.985 (4.053)**-7.985 (4.053)**-7.985 (4.053)**-7.985 (4.053)**

15.780 (4.814)***0.70 (0.500)

15.780 (4.814)***4.44 (0.035)

15.780 (4.814)***
1.39 (0.180)

15.780 (4.814)***3.80 (0.051)

44

Table 4 Distributional impact on household nutrient intake (urban)

Independent variable
IncomeLn(Household per capita net income
equivalent adult)
Household demographics
Age cohort (30≤average age<40)
Age cohort (40≤average age<50)
Age cohort (50≤average age<60)
Age cohort (average age≥60)
Proportion of adults
GenderEthnicity
Education
Household wealth/living conditions
Water source
Toilet type
Cooking fuelOccupation physical activity
Labourer
HealthIllness
Insurance
Level of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables
Volatility of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables
Food knowledge
Chinese diet knowledge
Culture and preference
Diet preference
Activity preference
Community
Population density
Transport
Health services
Social services
Traditional market
Modern market
EconomyProvincial dummy

τ=0.1
(1)

Fat
τ=0.5
(2)

τ=0.9
(3)

τ=0.1
(4)

Carbohydrate
τ=0.5
(5)

τ=0.9
(6)

τ=0.1
(7)

Protein
τ=0.5
(8)

τ=0.9
(9)

0.006 (0.043)

0.041 (0.195)-0.224 (0.298)

0.149 (0.047)***0.127 (0.154)

0.273 (0.245)

0.131 (0.036)***0.138 (0.161)

0.095 (0.264)-0.023 (0.042)
0.001 (0.041)-0.067 (0.049)-0.003 (0.084)-0.011 (0.128)
0.110 (0.051)**0.252 (0.059)***0.020 (0.096)

0.012 (0.011)-0.004 (0.009)-0.026 (0.012)**0.042 (0.050)
0.026 (0.075)
0.042 (0.022)**0.095 (0.022)***-0.037 (0.065)

0.047 (0.036)
0.016 (0.045)-0.010 (0.054)-0.018 (0.104)-0.067 (0.071)
0.063 (0.030)**-0.137 (0.036)***
0.043 (0.076)

0.064 (0.041)-0.060 (0.033)*-0.086 (0.035)**-0.118 (0.082)-0.014 (0.122)-0.010 (0.053)
0.061 (0.043)-0.095 (0.103)

0.053 (0.008)***-0.023 (0.007)***-0.047 (0.009)***-0.132 (0.040)***-0.046 (0.060)-0.022 (0.017)-0.024 (0.009)***-0.028 (0.051)

0.051 (0.021)**0.028 (0.033)
0.009 (0.036)-0.037 (0.084)-0.027 (0.058)-0.037 (0.023)-0.164 (0.027)***-0.040 (0.062)

0.021 (0.030)-0.056 (0.030)*-0.034 (0.036)-0.133 (0.058)**-0.039 (0.100)-0.038 (0.042)
0.150 (0.036)***-0.021 (0.079)

0.040 (0.009)***0.0003 (0.008)
0.029 (0.010)***-0.098 (0.042)**-0.041 (0.062)-0.047 (0.018)***0.061 (0.009)***-0.031 (0.054)

0.078 (0.022)***0.069 (0.032)**
0.102 (0.042)**-0.064 (0.093)-0.082 (0.070)-0.030 (0.023)-0.095 (0.030)***0.002 (0.068)-0.026 (0.023)
0.031 (0.032)
0.006 (0.023)-0.003 (0.006)
0.009 (0.017)
0.003 (0.017)

0.003 (0.028)
0.042 (0.040)
0.009 (0.016)-0.060 (0.015)***-0.017 (0.030)-0.007 (0.016)-0.023 (0.005)***0.001 (0.014)
0.012 (0.014)-0.015 (0.016)-0.024 (0.033)
0.016 (0.012)-0.003 (0.022)
0.009 (0.024)
0.021 (0.017)

0.015 (0.005)***0.014 (0.014)
0.024 (0.014)*

0.035 (0.013)***0.022 (0.034)
0.028 (0.015)*

0.175 (0.150)

0.023 (0.010)**-0.013 (0.034)-0.056 (0.153)-0.019 (0.008)**0.009 (0.024)

0.052 (0.123)-0.017 (0.009)*-0.013 (0.023)

0.0005 (0.030)
0.153 (0.180)**-0.014 (0.006)-0.002 (0.104)-0.024 (0.026)
0.066 (0.053)-0.022 (0.023)-0.138 (0.189)***-0.028 (0.005)-0.068 (0.082)**-0.044 (0.021)-0.072 (0.041)*-0.022 (0.022)
0.037 (0.159)***-0.034 (0.006)-0.034 (0.087)-0.044 (0.020)**-0.035 (0.043)-1.020 (1.843)-0.194 (0.537)-3.504 (1.538)**-1.460 (0.923)-0.005 (1.100)

1.548 (1.133)
0.464 (0.191)**-3.497 (0.808)***-0.651 (0.668)
1.273 (0.205)***

1.894 (1.307)
0.571 (0.474)-4.582 (1.803)**-1.860 (1.251)
0.769 (0.696)

0.299 (1.703)-0.321 (0.453)-2.211 (1.116)**-3.185 (0.696)***2.279 (1.123)**

0.899 (0.899)
0.196 (0.164)-3.049 (0.644)***-2.333 (0.525)***
1.061 (0.169)***-0.461 (1.438)-0.101 (0.383)-1.505 (1.426)-0.684 (0.964)
0.375 (0.508)-0.907 (1.536)-0.623 (0.424)-2.698 (1.019)***-1.979 (0.804)**
1.000 (0.968)

0.343 (0.933)-0.014 (0.169)-2.582 (0.675)***-1.786 (0.553)***0.621 (0.182)***-0.121 (1.194)-0.042 (0.378)-2.555 (1.264)**-1.595 (0.994)
0.850 (0.526)-2.741 (2.557)-3.524 (1.711)**5.139 (2.850)*-0.892 (3.913)
0.600 (1.296)-3.612 (0.711)***-3.583 (0.515)***
3.097 (0.766)***-0.194 (2.580)-0.736 (0.368)**-2.730 (2.062)-3.106 (0.944)***
1.938 (2.472)-1.274 (3.260)
0.353 (0.779)-1.954 (2.280)
0.638 (1.275)-0.989 (2.424)
5.757 (2.857)**-3.133 (1.394)**-0.944 (0.517)*
0.169 (0.408)
1.516 (0.627)**
6.907 (2.051)***-2.100 (0.293)***1.552 (1.460)-0.455 (0.864)
1.999 (1.969)
3.819 (2.636)-1.242 (0.592)**

2.676 (2.129)-1.538 (1.063)
2.708 (2.343)
6.419 (2.654)**-0.880 (1.100)

0.617 (0.608)-1.717 (0.402)***
1.731 (0.657)***4.880 (2.170)**-0.913 (0.314)***0.659 (1.717)-1.790 (0.728)**0.752 (1.983)
1.687 (2.416)-0.680 (0.620)

0.025 (0.054)

0.020 (0.024)

0.041 (0.022)*-0.003 (0.060)

0.044 (0.019)**0.055 (0.016)***0.028 (0.040)

0.029 (0.020)

0.028 (0.017)*

0.041 (0.023)*
0.040 (0.027)

0.029 (0.005)***0.015 (0.008)*

0.027 (0.019)
0.015 (0.017)

0.023 (0.019)-0.048 (0.023)**0.007 (0.004)*-0.019 (0.006)***0.022 (0.013)
0.013 (0.013)

0.022 (0.017)-0.018 (0.020)

0.031 (0.004)***-0.014 (0.007)**

0.031 (0.014)**0.022 (0.012)*

0.002 (0.012)-0.004 (0.010)-0.002 (0.007)
0.013 (0.006)**-0.017 (0.006)***-0.002 (0.006)
0.009 (0.007)
Yes-0.007 (0.002)***0.001 (0.002)
0.003 (0.002)*
0.008 (0.001)***-0.011 (0.001)***-0.005 (0.003)*
0.001 (0.008)
Yes-0.011 (0.009)-0.002 (0.005)
0.002 (0.007)
0.001 (0.005)-0.011 (0.004)***0.009 (0.007)
0.007 (0.009)
Yes-0.010 (0.009)
0.007 (0.009)
0.005 (0.005)
0.015 (0.004)***-0.009 (0.006)*-0.002 (0.005)
0.017 (0.009)*
Yes

0.002 (0.002)
0.001 (0.002)
0.011 (0.002)***0.010 (0.001)***-0.013 (0.001)***0.0004 (0.002)
0.001 (0.006)
Yes

0.013 (0.005)***0.007 (0.004)*
0.013 (0.006)**
0.006 (0.003)*-0.015 (0.003)***0.004 (0.004)
0.001 (0.007)
Yes-0.012 (0.008)-0.006 (0.009)-0.015 (0.004)***0.015 (0.004)***-0.006 (0.004)-0.004 (0.005)
0.012 (0.006)**
Yes-0.007 (0.002)***-0.002 (0.002)-0.001 (0.002)
0.009 (0.001)***-0.010 (0.001)***0.002 (0.002)
0.004 (0.007)
Yes-0.003 (0.006)-0.002 (0.004)
0.001 (0.006)
0.004 (0.004)-0.010 (0.003)***0.009 (0.005)*
0.008 (0.008)
Yes

45

Wave dummy
Yes
Yes
Yes
Yes
No. of obs.
3,442
3,442
3,442
3,442
R2
0.267
0.309
0.264
0.702
Machado-Santos Silva test for
48.90 (0.000)
24.32 (0.000)
6.30 (0.043)
20.62 (0.000)
heteroscedasticity, Chi-square (pvalue)
Note: ***, **, and * denote 1%, 5%, and 10% significance levels in turn. Heteroscedasticity-robust errors are in parentheses.

Yes
3,442
0.715
124.26 (0.000)

Yes
3,442
0.687
158.07 (0.000)

Yes
3,442
0.717
14.76 (0.000)

Yes
3,442
0.727
158.49 (0.000)

Yes
3,442
0.704
188.50 (0.000)

τ=0.1
(7)

Protein
τ=0.5
(8)

τ=0.9
(9)

Table 5 Distributional impact on household nutrient intake (rural)
Independent variable
IncomeLn(Household per capita net income
equivalent adult)
Household demographics
Age cohort (30≤average age<40)
Age cohort (40≤average age<50)
Age cohort (50≤average age<60)
Age cohort (average age≥60)
Proportion of adults
GenderEthnicity
Education
Household wealth/living conditions
Water source
Toilet type
Cooking fuelHousehold labour
Out-migration
Local off-farm
Occupation physical activity
Farmer
HealthIllness
Insurance
Level of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables
Volatility of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables
Food knowledge
Chinese diet knowledge
Culture and preference
Diet preference

Fat
τ=0.5
(2)

τ=0.9
(3)

-0.021 (0.031)-0.053 (0.077)-0.593 (0.283)**

0.064 (0.036)*

0.211 (0.204)-0.249 (0.360)-0.011 (0.035)

0.053 (0.212)-0.172 (0.327)-0.011 (0.038)-0.013 (0.032)-0.045 (0.040)
0.027 (0.071)
0.172 (0.050)***0.023 (0.032)-0.321 (0.078)***-0.025 (0.026)

0.025 (0.013)*
0.007 (0.015)
0.049 (0.015)***0.049 (0.046)
0.142 (0.016)***
0.053 (0.014)***-0.366 (0.012)***0.004 (0.022)

0.002 (0.042)-0.035 (0.048)-0.001 (0.057)-0.015 (0.100)
0.130 (0.054)**0.080 (0.034)**-0.341 (0.137)**
0.102 (0.067)

0.041 (0.047)-0.047 (0.049)-0.012 (0.061)
0.036 (0.096)
0.047 (0.085)
0.007 (0.046)-0.011 (0.471)
0.092 (0.032)***0.055 (0.039)
0.051 (0.046)
0.066 (0.050)
0.219 (0.126)*
0.197 (0.054)***-0.046 (0.044)
0.089 (0.069)
0.007 (0.057)

0.044 (0.052)
0.020 (0.060)
0.061 (0.071)
0.079 (0.128)
0.071 (0.074)-0.047 (0.045)-0.037 (0.123)
0.115 (0.082)

0.064 (0.048)
0.035 (0.043)
0.110 (0.051)**
0.096 (0.087)
0.084 (0.062)
0.071 (0.038)*
0.064 (0.169)
0.044 (0.028)

0.060 (0.037)*
0.070 (0.043)*
0.107 (0.049)**0.152 (0.131)
0.138 (0.054)**-0.004 (0.043)
0.063 (0.062)
0.011 (0.060)

0.047 (0.049)
0.062 (0.058)
0.088 (0.067)
0.129 (0.113)
0.146 (0.066)**
0.025 (0.041)
0.031 (0.106)
0.064 (0.072)-0.009 (0.025)
0.009 (0.010)-0.010 (0.011)-0.003 (0.004)
0.002 (0.006)-0.002 (0.005)-0.0003 (0.017)
0.037 (0.022)*
0.014 (0.016)-0.043 (0.029)
0.055 (0.014)***0.027 (0.016)*-0.032 (0.016)**0.034 (0.016)**0.010 (0.014)-0.027 (0.021)-0.044 (0.030)
0.036 (0.018)-0.049 (0.027)*
0.033 (0.014)
0.016 (0.014)-0.024 (0.014)*
0.012 (0.016)-0.007 (0.014)-0.015 (0.023)
0.006 (0.028)
0.016 (0.018)

0.123 (0.036)***
0.057 (0.066)

0.071 (0.017)***0.102 (0.025)***0.010 (0.016)
0.184 (0.051)***-0.059 (0.045)-0.086 (0.076)-0.007 (0.046)-0.008 (0.067)

0.065 (0.030)**
0.067 (0.064)

0.014 (0.044)
0.011 (0.076)

0.031 (0.048)
0.072 (0.070)

0.032 (0.024)
0.098 (0.060)*

0.087 (0.138)

0.143 (0.013)***0.082 (0.024)***-0.364 (0.162)**-0.088 (0.038)**-0.099 (0.031)***-0.160 (0.160)

0.043 (0.038)-0.014 (0.028)

0.087 (0.022)***0.030 (0.052)

0.027 (0.005)***0.035 (0.027)

0.029 (0.023)
0.140 (0.067)**-0.031 (0.033)
0.009 (0.074)

0.005 (0.020)-0.027 (0.075)-0.005 (0.028)
0.023 (0.082)

0.042 (0.027)
0.012 (0.057)

0.039 (0.018)**-0.006 (0.076)

0.024 (0.024)-0.080 (0.079)

5.497 (1.683)**
1.280 (0.562)**-6.046 (1.511)***3.035 (1.132)***
1.216 (1.025)

2.187 (0.458)***1.034 (0.161)***-3.911 (0.380)***4.275 (0.249)
1.622 (0.210)***3.123 (1.472)**
1.090 (0.505)**-6.399 (1.656)***
3.349 (1.187)***
3.165 (0.968)***8.258 (2.201)***1.624 (0.832)*-8.840 (2.082)***2.203 (1.675)
2.925 (1.177)**-0.464 (1.350)
0.743 (0.465)-2.304 (1.202)*
2.561 (0.799)***0.587 (0.641)

1.147 (2.103)
0.527 (0.621)-0.462 (2.160)
4.045 (1.513)***0.803 (1.101)

4.912 (2.028)**
1.099 (0.756)-6.072 (1.840)***3.037 (1.412)**1.832 (1.356)-0.308 (1.338)
0.641 (0.480)-1.946 (1.108)*3.320 (0.777)***0.368 (0.594)-1.606 (1.600)
0.567 (0.531)
0.655 (1.877)
5.498 (1.276)***
1.987 (1.029)*

4.284 (2.331)*-5.677 (1.442)***14.337 (3.131)***5.471 (3.760)-2.608 (1.224)**

2.267 (0.554)***-3.939 (0.395)***13.857 (0.720)***3.347 (0.908)***-2.687 (0.258)***-1.999 (2.294)-3.109 (1.060)***14.240 (2.510)***3.136 (3.299)-3.784 (0.955)***4.892 (3.503)-9.979 (2.134)***16.315 (4.646)***9.458 (5.377)*-4.426 (1.486)***3.324 (1.875)*-3.008 (1.130)***8.854 (2.100)***0.727 (2.832)-1.878 (0.786)**

0.871 (2.624)-1.525 (1.598)
6.201 (3.036)**-5.085 (4.554)-1.188 (1.124)

5.307 (3.288)-8.385 (1.846)***15.873 (4.288)***5.248 (4.744)-2.708 (1.631)*

1.557 (1.706)-2.943 (1.217)**8.592 (2.036)***-2.420 (2.706)-1.203 (0.766)-2.981 (2.830)-1.595 (1.401)
3.040 (2.978)-10.611 (3.910)-2.574 (1.050)

0.056 (0.043)

0.079 (0.013)***0.157 (0.041)***-0.060 (0.056)-0.003 (0.039)

0.050 (0.043)-0.030 (0.055)-0.017 (0.038)

0.037 (0.043)-0.054 (0.020)***-0.034 (0.005)***0.045 (0.029)-0.057 (0.025)**-0.033 (0.017)*-0.014 (0.036)-0.032 (0.022)-0.038 (0.016)**0.0005 (0.034)

τ=0.1
(1)

τ=0.1
(4)

46

Carbohydrate
τ=0.5
(5)

τ=0.9
(6)

Activity preference-0.008 (0.020)
0.004 (0.007)
0.024 (0.022)
0.038 (0.029)
Community
Population density-0.005 (0.008)-0.014 (0.002)***-0.036 (0.008)***0.018 (0.012)
Transport
0.013 (0.005)***0.013 (0.002)***0.017 (0.006)***0.018 (0.008)**
*
***
***
Health services
0.009 (0.006)
0.009 (0.001)
0.012 (0.004)
0.008 (0.008)
Social services
0.010 (0.005)*
0.009 (0.001)***0.004 (0.004)
0.002 (0.007)
Traditional market-0.015 (0.003)***-0.014 (0.001)***-0.017 (0.004)***-0.002 (0.005)***
Modern market-0.004 (0.006)-0.007 (0.002)-0.006 (0.005)
0.014 (0.007)****
Economy0.014 (0.009)
0.013 (0.001)
0.010 (0.006)
0.027 (0.011)**
Provincial dummy
Yes
Yes
Yes
Yes
Wave dummy
Yes
Yes
Yes
Yes
No. of obs.
5,583
5,583
5,583
5,583
R2
0.352
0.411
0.370
0.172
Machado-Santos Silva test for
29.70 (0.000)
44.53 (0.000)
18.01 (0.000)
236.01 (0.000)
heteroskedasticity, Chi-square (p-value)
Note: ***, **, and * denote 1%, 5%, and 10% significance levels in turn. Heteroscedasticity-robust errors are in parentheses.

0.015 (0.020)

0.026 (0.029)

0.017 (0.027)

0.010 (0.021)

0.015 (0.025)

0.0005 (0.008)
0.013 (0.005)**
0.009 (0.004)**
0.011 (0.004)***-0.001 (0.003)
0.001 (0.006)
0.023 (0.005)***
Yes
Yes
5,583
0.202
141.12 (0.000)-0.014 (0.010)
0.015 (0.008)*
0.018 (0.005)***0.007 (0.006)-0.004 (0.006)-0.009 (0.006)
0.012 (0.007)*
Yes
Yes
5,583
0.141
0.918 (0.632)

0.009 (0.012)
0.024 (0.008)***-0.005 (0.008)-0.005 (0.007)-0.004 (0.004)
0.008 (0.007)
0.016 (0.010)
Yes
Yes
5,583
0.209
121.53 (0.000)-0.002 (0.007)
0.017 (0.005)***0.003 (0.004)
0.007 (0.004)*-0.004 (0.003)-0.002 (0.006)
0.006 (0.005)
Yes
Yes
5,583
0.247
98.94 (0.000)-0.013 (0.025)
0.012 (0.008)
0.009 (0.005)*
0.009 (0.006)-0.008 (0.005)*-0.006 (0.006)
0.013 (0.007)*
Yes
Yes
5,583
0.230
24.80 (0.000)

Table 6 Determinants of the FGT-class nutrition poverty

IncomeLn(Household per capita net income equivalent
adult)
Household demographics
Age cohort (30≤average age<40)
Age cohort (40≤average age<50)
Age cohort (50≤average age<60)
Age cohort (average age≥60)
Proportion of adults
GenderEthnicity
Education
Household wealth/living conditions
Water source
Toilet type
Cooking fuelHousehold labour
Out-migration
Local off-farm
Occupation physical activity
Labourer
Farmer
HealthIllness
Insurance
Level of food price
Cereals
Fat and oil
Meat
Eggs
Vegetables

Urban
FGT(0)(1)

FGT(1)(2)

FGT(2)(3)

Rural
FGT(0)(4)

FGT(1)(5)

FGT(2)(6)-0.051 (0.044)-0.019 (0.013)-0.013 (0.007)*

0.080 (0.043)*

0.010 (0.008)

0.001 (0.003)-0.029 (0.056)
0.033 (0.066)
0.087 (0.071)
0.164 (0.087)*
0.107 (0.076)-0.069 (0.060)-0.005 (0.116)
0.008 (0.033)

0.014 (0.013)
0.023 (0.015)
0.029 (0.016)*
0.053 (0.021)**0.013 (0.018)-0.012 (0.017)
0.002 (0.040)
0.005 (0.009)

0.010 (0.007)
0.012 (0.008)
0.010 (0.008)
0.018 (0.011)
0.005 (0.009)
0.001 (0.008)-0.002 (0.024)
0.004 (0.004)

0.012 (0.037)
0.049 (0.044)
0.037 (0.054)
0.059 (0.065)-0.028 (0.060)-0.087 (0.066)
0.218 (0.143)-0.043 (0.032)-0.004 (0.006)
0.005 (0.008)
0.001 (0.010)
0.008 (0.013)-0.013 (0.011)-0.011 (0.012)
0.044 (0.026)*-0.005 (0.006)-0.001 (0.002)
0.002 (0.003)
0.001 (0.003)
0.003 (0.005)-0.007 (0.004)*-0.0004 (0.004)
0.019 (0.015)
0.00003 (0.002)

0.021 (0.023)
0.005 (0.017)-0.009 (0.018)

0.007 (0.005)
0.004 (0.005)
0.0003 (0.005)

0.003 (0.002)
0.002 (0.002)-0.00002 (0.002)

0.030 (0.019)
0.003 (0.011)
0.013 (0.009)

0.004 (0.004)
0.001 (0.002)
0.002 (0.002)

0.002 (0.001)-0.0002 (0.001)
0.0001 (0.001)-0.073 (0.033)**-0.102 (0.041)**-0.011 (0.007)-0.013 (0.007)*-0.003 (0.003)-0.002 (0.003)-0.068 (0.057)-0.008 (0.011)

0.001(0.004)

0.013 (0.056)

0.010 (0.015)

0.007 (0.008)-0.002 (0.024)-0.012 (0.043)

0.004 (0.007)
0.007 (0.013)

0.004 (0.004)
0.008 (0.007)-0.010 (0.020)-0.007 (0.025)-0.003 (0.004)-0.001 (0.005)-0.001 (0.002)-0.001 (0.002)

0.619 (1.189)-0.240 (0.387)
2.112 (1.145)*
0.046 (0.908)-0.318 (0.572)-0.107 (0.296)
0.0002 (0.100)
0.464 (0.288)
0.162 (0.243)-0.271 (0.149)-0.146 (0.140)-0.022 (0.049)
0.238 (0.137)*
0.143 (0.123)-0.150 (0.075)**

0.087 (1.009)-0.896 (0.332)***
0.992 (0.966)-3.076 (0.828)***-0.023 (0.513)-0.265 (0.195)-0.216 (0.068)***0.511 (0.195)***-0.414 (0.163)**-0.082 (0.095)-0.117 (0.081)-0.066 (0.028)**0.221 (0.082)***-0.082 (0.066)-0.045 (0.036)

47

Volatility of food price
Cereals
0.979 (1.726)
0.682 (0.444)
0.310 (0.223)
Fat and oil
1.634 (1.070)
0.393 (0.274)
0.108 (0.132)
Meat-3.121 (1.641)*-0.305 (0.454)-0.073 (0.232)
Eggs-3.486 (3.267)-0.671 (0.841)-0.428 (0.410)
Vegetables-0.129 (0.742)
0.181 (0.215)
0.172 (0.114)
Food knowledge
Chinese diet knowledge-0.031 (0.025)-0.010 (0.006)-0.002 (0.003)
Culture and preference
Diet preference-0.029 (0.021)-0.007 (0.005)-0.003 (0.003)
Activity preference
0.004 (0.018)
0.001 (0.005)
0.001 (0.002)
Community
Population density
0.023 (0.019)-0.0003 (0.005)-0.002 (0.002)
Transport-0.005 (0.007)-0.0004 (0.002)-0.0003 (0.001)
Health services
0.005 (0.007)
0.00004 (0.002)-0.0002 (0.001)
Social services-0.009 (0.005)**-0.002 (0.001)-0.001 (0.001)
Traditional market
0.018 (0.005)***0.005 (0.001)***0.001 (0.0007)***
Modern market
0.013 (0.007)
0.002 (0.002)
0.0002 (0.001)
Economy-0.004 (0.006)
0.0003 (0.002)
0.0002 (0.001)
Provincial dummy
Yes
Yes
Yes
Wave dummy
Yes
Yes
Yes
No. of obs.
2,469
2,469
2,469
LM statistic for the underidentification test (p-value)
8.48 (0.014)
8.48 (0.014)
8.48 (0.014)
F-statistic for the weak identification (p-value)
4.27 (0.014)
4.27 (0.014)
4.27 (0.014)
Sargan Chi-/Hansen J statistic for the
0.002 (0.969)
0.38 (0.540)
0.69 (0.405)
overidentification test (p-value)
Note: ***, **, and * denote 1%, 5%, and 10% significance levels in turn. Heteroscedasticity-robust errors are in parentheses.

48-1.803 (1.418)
1.131 (0.789)-7.286 (1.737)***0.231 (2.012)
0.496 (0.621)

0.105 (0.283)
0.364 (0.169)**-1.247 (0.340)***-0.015 (0.416)
0.250 (0.117)**0.170 (0.116)
0.140 (0.074)*-0.291 (0.132)**
0.061 (0.172)
0.100 (0.046)**-0.038 (0.024)-0.005 (0.005)-0.001 (0.002)

0.008 (0.016)-0.007 (0.014)

0.001 (0.003)-0.003 (0.003)

0.0002 (0.001)-0.002 (0.001)

0.003 (0.016)-0.008 (0.005)*
0.004 (0.005)-0.004 (0.005)
0.006 (0.003)*
0.001 (0.005)-0.002 (0.005)
Yes
Yes
4,066
10.76 (0.005)
5.41 (0.005)
0.98 (0.323)-0.003 (0.003)-0.002 (0.001)**-0.001 (0.001)-0.003 (0.001)***0.002 (0.001)***0.001 (0.001)-0.002 (0.001)**
Yes
Yes
4,066
10.76 (0.004)
5.41 (0.005)
0.54 (0.521)-0.001 (0.001)-0.001 (0.0004)**-0.0005 (0.0004)-0.001 (0.0004)***0.001 (0.0002)***0.001 (0.001)-0.001 (0.0004)***
Yes
Yes
4,066
10.76 (0.005)
5.41 (0.005)
0.95 (0.264)

Appendix 1: Definition and descriptive statistics of variables
Variable

Definition

No. of obs.

Mean

1989
S.D.

Mean

2009
S.D.

Modified OECD equivalent household
size

Weighted sum of household members. The first adult in the household has a weight of 1. Each additional adult aged 14
and over has a weight of 0.5. Each child aged under 14 has a weight of 0.3. This definition can be found at Eurostat:
http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:Equivalised_disposable_income[accessed
December 5, 2013]

29,402

1.371

0.367

1.328

0.503

Household equivalent per capita net
incomeHousehold net income divided by the equivalent household size. Household net income is total income (including
agricultural and family business income, wages, transfer income and asset income) minus related costs, taxes and fees.
All monetary values are transformed into real terms by dividing them by the spatial CPI calculated by the CHNS team.

29,402

4,967

6,090

20,49928,219Household equivalent per capita
agricultural income

Household net agricultural income divided by the equivalent household size. Household net agricultural income is thetotal agricultural income produced by the household net of relevant costs. All monetary values are transformed into realterms by dividing them by the spatial CPI calculated by the CHNS team.

14,042

1,602

2,990

5,206

8,320

Household equivalent per capita businessincomeHousehold net business income divided by the equivalent household size. Household net business income is total income
from household small business activities net of relevant costs. All monetary values are transformed into real terms by
dividing them by the spatial CPI calculated by the CHNS team.

14,042

468.7

2,042

2,870

19,492

Household equivalent per capita wage
incomeHousehold wage income divided by the equivalent household size. Household wage income is the sum of individual
household members’ wages. All monetary values are transformed into real terms by dividing them by the spatial CPI
calculated by the CHNS team.

14,042

3,088

5,012

10,064

20,106

Household equivalent per capita total
calorie intake

The sum of individual total calories intake within the household, which is calculated by the CHNS team, divided by theequivalent household size. The CHNS team calculated the individual total calories intake as the sum of direct calories
intake and the converted amount from carbohydrate, fat and protein. Their conversion rates are from FAO (2003): 1 gramof fat, carbohydrate and protein equals separately 9, 4 and 4 kcal.

29,402

7,200

2,498

5,661

3,085

Household equivalent per capita directcalorie intake

The sum of individual direct calories intake within the household divided by the equivalent household size.

29,402

3,608

1,253

2,844

1,544

Household equivalent per capita calorie
intake from fat

The sum of individual calories intake inverted from fat within the household divided by the equivalent household size.
According to FAO (2003), 1 gram of fat=9 kcal.

29,402

982.7

742.7

929.9

1,309

Household equivalent per capita calorie
intake from carbohydrate

The sum of individual calories intake inverted from carbohydrate within the household divided by the equivalenthousehold size. According to FAO (2003), 1 gram of carbohydrate=4 kcal.

29,402

2,221

856.6

1,544

556.7

Household equivalent per capita calorie
intake from protein

The sum of individual calories intake inverted from protein within the household divided by the equivalent household
size. According to FAO (2003), 1 gram of protein=4 kcal.

29,402

388.5

148.5

343.7

132.1

Age

Age of household head in years.

29,40222.05914.98741.57422.143

GenderGender of household head, 0=female, 1=male.

29,402

0.636

0.481

0.701

0.458

Ethnicity

Ethnicity of household head, 0=majority (Han), 1=minorities (non-Han).

29,402

0.113

0.317

0.014

0.119

Education

Educational level of household head, i.e., having completed or finished part of the following educational levels.
Categorical variable, 0=illiterate, 1=primary education, 2=junior high school, 3=senior high school, 4=higher education.

29,402

6.132

3.993

6.954

4.381

Water source

Categorical variable, 1=natural water (rainfall, ice, snow, creek, spring, lake, and river), 2=open well (depth≤5m),
3=ground water (depth>5m), 4=tap water or water plant.

29,402

3.372

0.851

3.679

0.590

Toilet type

Categorical variable, 0=no toilet, 1=cement or earth openpit, 2=no flush, 3=flush but outside house, public restroom,

29,402

1.640

1.111

2.678

1.420

49

4=in-house flush.
Cooking fuelCategorical variable, 1=wood, sticks, straw, charcoal, etc., 2=coal or kerosene, 3=liquified natural gas or natural gas,
4=electricity.

29,402

1.890

0.624

2.850

1.038

Out-migration

No. of household members not living in the household and in out-migration.

29,402

2.507

1.117

0.189

0.535

Local off-farm

No. of household members living in the household and have local off-farm jobs.

29,402

0.930

1.081

0.571

0.838

FarmerDummy variable, 1=the occupation of household head is ‘farmer’, 0=otherwise.

29,402

0.257

0.437

0.200

0.400

Illness

Dummy variable, 1=any household member was ill in the last 4 weeks, 0=otherwise.

29,402

0.253

0.435

0.299

0.458

Insurance

Dummy variable, 1=any household member has health insurance, 0=otherwise.

29,4020.430

Level of food price
Level of cereal price

Provincial index of real food price, 2009 prices=1.

23,847

Provincial index of real cereal price, 2009 prices=1.

23,847

0.495

0.959

0.199

0.023

a

1

0

0.394

a

0.373

a

0.018

a

1

0

a

0.062

a

Level of fat price

Provincial index of real fat & oil prices, 2009 prices=1.

23,847

0.494

1

0

Level of meat price

Provincial index of real meat price, 2009 prices=1.

23,8470.331a0.043a1

0

23,847

0.604

a

0.081

a

1

0

b

0.092

b

1

0

Level of egg price

Provincial index of real egg price, 2009 prices=1.

Level of price of education services

Provincial index of real prices of education, including both tuition fees and other costs relating to obtaining education,2009 prices=1.

20,5030.449

Level of price of health services &equipment

Provincial index of real prices of health services and health equipment, 2009 prices=1.

16,9780.948c0.060c10Volatility of food price

Coefficient of variation of the level of food price defined above in a two-year window.

23,8470.076a

0a

0.010

0

d

0.328

d

0.199

0.399

Chinese diet knowledge

Dummy variable, 1=any household member knows the Chinese diet guidelines (also known as the Chinese diet pagoda),
0=otherwise.

13,1400.123

Diet preference

Average categorical variable. For each household member, 1=‘dislike very much’; 2=’dislike’, 3=’neutral’, 4=’like’, and
5=’like very much’ for (1) fast food, (2) salty snack foods, and (3) soft drinks or sugared drinks. We first calculate eachhousehold member’s average preference across 3 sorts of high-energy foods and then, calculate the average preference
within the household.

11,6052.370d

0.627d

2.415

0.579

Activity preference

Average categorical variable. For each household member, 1=‘dislike very much’, 2=’dislike’, 3=’neutral’, 4=’like’, and
5=’like very much’ for participation in (1) walking, (2) Tai Chi, and (3) sports or body building. We first calculate eachhousehold member’s average preference across 3 sorts of high-energy foods and then, calculate the average preference
within the household.

10,3002.771d

0.694d

2.614

0.603

Population density

Total population of the community divided by community area, from local official records.

29,402

5.871

1.374

5.935

1.489

Transport

Community index reflecting infrastructure: most common type of road; distance to bus stop; and distance to train stop.
Distance is categorized as (1) within community, (2) ≤1 km from community, and (3) ≥ 1 km from community.

29,402

4.431

2.456

5.941

2.184

Health services

Community index reflecting the number and type of health facilities in or nearby (≤12 km) the community and number ofpharmacies in community.

29,402

6.061

1.884

5.932

2.559

Modern market

Community index reflecting the number of supermarkets, cafes, internet cafes, indoor restaurants, outdoor fixed andmobile eateries, bakeries, ice cream parlours, fast food restaurants, fruit and vegetable stands, bars within the community
boundaries.

29,402

4.080

3.007

4.328

2.893

Traditional market

Community index reflecting the distance to three market categories; (1) within the boundaries of the community, (2)within the city but not in this community, or (3) not within the city/village/town); the number of days of operation foreight different types of market (including food and fuel markets).

29,402

4.976

3.032

4.839

3.468

50

EconomyCommunity index reflecting typical daily wage for ordinary male worker (reported by community official) and per centof the population engaged in non-agricultural work.

29,402

3.075

1.767

6.611

3.236

Urban

Dummy variable, 0=rural areas, 1=urban areas.

29,402

0.397

0.489

0.321

0.467

Source: Authors’ calculation based on the CHNS. Provincial data on average wages, various prices and natural disasters are authors’ calculation and compilation based on data from China Statistical Yearbooks published annually by the NBS.
Urbanisation indices for the communities where sample households locate were constructed by Jones-Smith and Popkin (2010) and compiled into the CHNS dataset by the CHNS team. Relevant indices include the population
density, transport, health services, modern market, traditional market and economy.
Note: a. Data have been collected since 1993.
b. Data have been collected since 1997.
c. Data have been collected since 2000.
d. Data have been collected since 2004.

51

Appendix 2: Further Data Analyses
By plotting provincial intertemporal mean household nutrient intake in Figure A1, we
further document a spatial pattern of nutrition status in China. If we focus on urban
areas (Figures 4(a) and 4(b)), provinces in northeast and southwest regions in China
show consistently the lowest nutrition status over time, as opposed to economicallyadvanced regions near the coast, such as Jiangsu, Shandong and Henan, which used tobe the least nourished in 1989 but have experienced the highest nutrient intake by
2009. Similar pattern can also be observed in rural areas (Figures 4(c) and 4(d)),
except Guizhou, showing the overall positive correlation between income andnutrition at province levels. This appears to contradict with the aforementioned
negative nexus between income and nutrition at the household level, giving rise to ourconjecture of substantial heterogeneity in the impact of income on nutrition and thus,
making our quantile estimation necessary. One may also note that Guizhou, which isthe poorest province in terms of real GDP per capita among all provinces over the past
three decades, had high initial nutrition level, which ranked 3rd in 1989 among all
sample provinces (3,773 kcal per household equivalent adult per day), but recorded
the largest reduction in the following waves (1,280 kcal between 1989 and 2009) as aresult of the highest increases in living costs (measured by the spatial consumer priceindex) in all sample provinces.29

29

The CHNS team constructed a spatial consumer price index for sample provinces, which iscomparable across all sample provinces and over time. The prices in Guizhou saw the highest increases(three-fold) in all sample provinces.
52

Figure A1 The ‘hunger’ map of China (1989-2009)(a) Urban areas in 1989(b) Urban areas in 200953

(c) Rural areas in 1989(d) Rural areas in 2009

Note: The provincial average nutritional status is measured as the average household
per capita equivalent total energy intake (kcal) per day for each sample
province in each year.
Source: Authors’ calculation based on the CHNS.
FAO’s SOFI 2013 reports a declining trend in undernourishment in China. The
proportion of the undernourished in total population has declined from 22.9% in

541990-1992 to 11.4% in 2011-2013 (FAO, 2013). Their methodology assumes aconstant probability distribution of individual daily dietary energy consumption, while
Figure A2 demonstrates obviously changing distribution over time. See, for instance,de Haen et al. (2011) and de Weerdt et al. (2014) for the detailed critiques of the FAO
methodology.
Figure A2 Distribution of household energy intake

Source: Authors’ calculation based on the CHNS.

55