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This is to certify that the
dissertation entitled

Three Essays on Household Saving and Wealth

presented by

Kyeongwon Yoo

has been accepted towards fulfillment
of the requirements for the

Ph.D. degree in Economics

 

 

 

   

 

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THREE ESSAYS ON HOUSEHOLD SAVING AND WEALTH
By

Kyeongwon Yoo

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Economics

2003

ABSTRACT

THREE ESSAYS ON HOUSEHOLD SAVING AND WEALTH
By

Kyeongwon Yoo

This dissertation explores the impact of risk on household saving, portfolio
decisions and evolution of non-land wealth with a panel of household data from rural
China. It consists of three chapters.

In the ﬁrst chapter, we develop a test of precautionary behavior in the
consumption and saving decisions of rural agricultural households. We ﬁrst present a
constant relative risk aversion model of household consumption decisions in which
consumption risk is explicitly related to yield risk. Next we discuss ways of using
rainfall variance as a proxy for yield risk, and consider the possibility of using a GARCH
model to estimate conditional rainfall variance. Finally, we test the empirical model
using household panel data from rural China and ﬁnd evidence of precautionary motives
behind consumption and saving decisions. We estimate that roughly 11-12 percent of
household savings can be attributed to a precautionary motive for households facing a
median level of consumption risk.

Based on relatively recent panel data (1995 - 2000) in rural China the second
chapter presents the empirical evidence that rainfall variability as a proxy for yield risk
inﬂuences the portfolio composition of a household. We ﬁnd support for an allocation
toward liquid assets in response to more volatile rainfall variability. We observe that cash

and deposits are important instruments for their precautionary wealth allocation, while it

is difﬁcult to ﬁnd the similar evidence for grain stocks. It may imply that ﬁnancial
instruments have played an important role in precautionary portfolio allocation while
dramatic institutional changes of the post-reform period have been proceeding in
contemporary rural China.

The composition and inequality of wealth in nual China is examined in the third
chapter. We ﬁrst document the evolution of rural households’ non-land wealth during the
period from 1986 to 2000. Our results Show that ﬁnancial and ﬁxed productive assets
have become more important in their weight in wealth composition, while housing assets
have played a dominant role during this period. Based on various inequality measures we
observe that the inequality of rural non-land wealth has worsened but there was

improvement in these asset holdings across the period in rural China.

ACKNOWLEDGEMENTS

I would like to express my sincere appreciation to my advisor, John Strauss, for
his guidance and advice. Without his encouragement and insightful comments this work
would not have appeared in its present form. I have learnt lots of things from him during
my stay at Michigan State. I owe special thanks to John Giles, who is in effect my second
advisor and provided helpful comments and the data for my dissertation. I would also like
to thank the other members of my committee, Professors Peter Schmidt and Jack Meyer,
who provided valuable comments and suggestions.

For the completion of my dissertation, I am indebted to many people. I especially
want to thank my friends at Michigan State for their invaluable support and friendship.
Finally, I give special thanks to my family. I am too much indebted to my wife,
Eunryeong, and my son, Hyunsol. Without their sacriﬁces and patience I was not able to

complete this long journey. Thank for their love and support.

iv

TABLE OF CONTENTS

LIST OF TABLES ..................................................................... ' ........... vii
LIST OF FIGURES ................................................................................ ix
CHAPTER 1

PRECUATIONARY BEHAVIOR AND HOUSEHOLD CONSUMPTION DECISIONS:
AN EMPIRICAL ANALYSIS USING HOUSEHOLD PANEL DATA FROM RURAL
CHINA

1. Introduction ....................................................................................... l
2. Survey Data and Rainfall Variability in China ............................................... 5
2.1 The RCRE Village and Household Surveys ....................................... 5
2.2 Historical Monthly Rainfalls and Rainfall Variability in China .................. 8
Rainfall Variability in China ...................................................... 9
Rainfall and the Crop Cycle in the Survey Provinces ........................ 10
3. Theory .............................................................................................. 12
4. Empirical Strategy and Results ................................................................. 14
4.1 Do We Observe GARCH Effects in Village Rainfall Time Series? ........... .14
4.2 Empirical Speciﬁcation ................................................................ 16
4.3 Results and Robustness Checks ...................................................... l9
Robustness Checks ................................................................. 20
4.4 Potential Problems with Our Approach ............................................. 23
5. Conclusion ........................................................................................ 25
Appendix A
Crop Production and Rainfall Variability ........................................................ 34
Appendix B
Consumption Growth and Yield Risk with CRRA Preferences .............................. 41
Appendix C
Estimation of Conditional Variance of Rainfall Using GARCH Model ..................... 44
References ............................................................................................ 47
CHAPTER 2
AN EMPIRICAL ANLAYSIS OF PRECAUTIONARY PORTFOLIO ALLOCATION
1. Introduction ....................................................................................... 50
2. Literature Review ................................................................................. 52
3. Theoretical Background ......................................................................... 54
4. Data and Empirical Speciﬁcation .............................................................. 59
5. Empirical Results ................................................................................. 64
6. Conclusions ....................................................................................... 68
Appendix D ........................................................................................... 86
References ............................................................................................ 92

CHAPTER 3
HOUSEHOLD NON-LAND WEALTH IN RURAL CHINA: COMPOSITION AND
DISTRIBUTION FROM 1986 TO 2000

1. Introduction ....................................................................................... 94
2. Historical Background and Studies on Rural China ......................................... 96
2.1 Historical Background ................................................................ 96

2.2 Studies on Income and Wealth Inequality in Rural China ........................ 97
Income Inequality in Rural China ............................................... 97

Wealth Inequality in Rural China ............................................... 99

3. Valuation, Measurement, and Data ........................................................... 100
3.1 Valuing Wealth ....................................................................... 101

3.2 Inequality Statistics .................................................................. 102

3.3 The RCRE Data ...................................................................... 105

4. Evolution of Wealth and its Components .................................................... 106
5. The Distribution of Wealth in Rural China .................................................. 108
5.1 Housing Assets ....................................................................... 108

5.2 Fixed Productive Assets ............................................................ 109

5.3 Financial Assets ...................................................................... 110

5.4 Comparison of Wealth Inequality .................................................. 111

5.5 Dynamic Analysis of Non-land Wealth ........................................... 112

6. Conclusion ....................................................................................... 1 14
References .......................................................................................... 140

vi

LIST OF TABLES

CHAPTER 1
Table 1. Descriptive Statistics ...................................................................... 27
Table 2. Summary of Estimation Results Using Village Variables. . . . . . . . . . . . . . . . ....30

Table 3. Summary of Estimation Results Using Village-Year

Dummy Variables .................................................................................... 33
Table 4. The Predicted Effect of Precautionary Behavior in Saving .......................... 33
Appendix A

Table A1. Total Sown Areas of Farm Crops in 4 Provinces .................................. 34
Table A2. Crop Calendar in China (Wheat/Rice) ............................................... 35
Table A3. Summary of Rainfall Data ............................................................ 37
CHAPTER 2

Table 1. Regression Results for Share of Liquid Wealth-holdings with
Province-year Dummies ............................................................................ 72

Table 2. Regression Results for Share of Liquid Wealth-holding with
Village-year Dummies .............................................................................. 84

Table 3. Regression Results for Share of Liquid Wealth-holdings with

Rainfall Shock and Village-year Dummies ...................................................... 85
Appendix D

Table D1. Follow-up Rates of RCRE Survey ................................................... 86
Table D2. Grain Balance ........................................................................... 86
Table D3. Grain Yield per Household ............................................................ 87
Table D4. Value of Production and Non-production Assets .................................. 87

vii

Table D5. Household Income ..................................................................... 88

Table D6. Cash/Deposits Balance ................................................................. 89
Table D7. Cash/deposits Balance ................................................................. 89
Table D8. Composition of Non-land Wealth .................................................... 90
Table D9. Share of Non-land Wealth ............................................................ 90
Table D10. Descriptive Statistics of Variables .................................................. 91
CHAPTER 3

Table 1. Rural per capita Income and Growth Rate ............................................ 116
Table 2. Major Economic Indicators (1985 — 2000) ............................................ 117
Table 3. Mean, Composition, and Growth Rate of Household Assets

(1986 —— 2000) ........................................................................................ 119
Table 4. Distribution of Household Assets (1986 - 2000) ..................................... 120
Table 5. Test for First-order Lorenz dominance (1986 and 2000) .......................... 121
Table 6. Decomposition of Wealth Inequality by Sources ................................... 121
Table 7. Decomposition of Wealth Inequality Across Provinces ........................... 123
Table 8. Fixed Productive Assets (Closer look) ............................................... 124
Table 9. Percentiles of Fixed Productive Assets (1986 — 2000) ............................. 124
Table 10. Financial Asset Holdings (Closer look) ............................................. 125
Table 11. Percentiles of Financial Assets (1986 - 2000) ..................................... 125

Table 12. Comparison of Inequality in Household Wealth Distribution in Various
Countries ............................................................................................ 126

Table 13. Test for F irst-order Stochastic Dominance between 1986 and 2000. . . . . . ...127

Table 14. Dynamic Analysis of Household’s Non-land Wealth: Two Stage Least Squares
First Differenced Estimation (F DIV) ............................................................ 128

viii

LIST OF FIGURES

CHAPTER 1

Figure A1. Crop Calendar (Rice/Wheat) ........................................................ 36
Figure A2. Rainfall Pattern in Each Province (1978-1997) .................................... 39
Figure C1. Evolution of (Fitted) Conditional Variance of Rainfall ........................... 46
CHAPTER 2

Figure 1. The Convexity of Marginal Value of the Liquid Asset ............................. 71
CHAPTER 3

Figure l. Lorenz Curves of Non-Land Wealth and its Components

(1986 — 2000) ........................................................................................ 131
Figure 2. Percentiles of Non-land Wealth and its Components

(1986 -2000) ........................................................................................ 134
Figure 3. F irst-order Poverty Dominance ....................................................... 136

Figure 4. Smoothed Relationship between Wealth Per Capita
in 1986 and 2000 ................................................................................... 137

Figure 5. Smoothed Relationship between Wealth Per Capita
in 1986 and 1991 ................................................................................... 138

Figure 6. Smoothed Relationship between Wealth Per Capita
in 1995 and 2000 ................................................................................... 139

ix

Chapter 1

PRECUATIONARY BEHAVIOR AND HOUSEHOLD CONSUMPTION
DECISIONS: AN EMPIRICAL ANALYSIS USING HOUSEHOLD PANEL DATA
FROM RURAL CHINA

1. Introduction

In recent years, considerable effort has gone into understanding the nature of
precautionary responses to risk and uncertainty when households make consumption and
savings decisions.1 A micro-econometric literature attempting to identify the strength of
precautionary motives generally conﬁrms the prediction that income risk plays a role in
determining the timing of consumption decisions, though this literature has also produced
some confusion and anomalous results due to differences in empirical strategies.2 In this
paper we ﬁrst extend an analytic framework developed by Blundell and Stoker (1999) to
consider risks to income earned by rural households in the developing world, and then we
develop an empirical test that avoids three common weaknesses found in the literature:
(1) lack of an exogenous proxy for consumption risk; (2) lack of a mechanism for
updating perceived risk as uncertainty is resolved; and (3) failure to control for the
possibility that responses to risk may depend on household wealth.

If proxies for risk are endogenous with other household decisions or confounded
with differences across households in the noise from income reports, then they may

introduce serious bias into analyses of precautionary behavior. Jalan and Ravallion

 

' See Browning and Lusardi (1996), and Carrol (2001) for useful reviews of the literature. Deaton (1992)
provides an important early exposition of the precautionary motive.

Lusardi (1997) and Carroll (1994) suggest that the precautionary motive may explain a signiﬁcant fraction
of wealth accumulation. Carroll and Samwick (1997) estimate a wealth equation with direct measures of
the variance of shocks to permanent and transitory income and ﬁnd some evidence of a precautionary
motive, but Jappelli and Terlizzese (1992) and Dynan (1993) both produce results suggesting that
precautionary motives for saving are weak or non—existent. Ludvigson and Paxson (2001) suggest that
approximation error is likely to be one important factor driving anomalous results.

(2001), for example, test whether households hold higher Shares of their wealth in liquid
form when they face higher risk, and ﬁnd that only a small share of unproductive liquid
wealth is held as a precaution against income risk. While Jalan and Ravallion (2001)
employ a technically sophisticated approach to calculate income risk from a ﬁve-year
panel, their measure of risk is likely to be endogenous for two reasons: time-invariant
sources of uncertainty will be correlated with their measure of income variance, and they
make no distinction between transitory income and measurement error. Furthermore,
from what has become known as Chamberlain ’s Critique, we know calculating a
consistent measure of risk from a Six-year panel is particularly problematic.3 Our
approach to dealing with this problem in a short panel of six years is to use rainfall
variability as an exogenous and observable proxy for yield risk rather than estimating it
from the panel itself.4

Recent work in the consumption literature suggests that analyses of responses to
risk should reﬂect the plausibility that perceptions of risk change as new information is
revealed.5 For this reason, use of conditional income variance with respect to time is
preferable to the unconditional variance as a measure of income risk. One important

characteristic of agricultural production is that it occurs over an extended period, and that

 

3 Consistency of empirical estimates of variance in panel data analyses relies on a long time series of
observations for each individual. See Chamberlain (1984) for the original exposition. Deaton (1992) and
Browning and Lusardi (1996) also emphasize that if the panel used is short, then the usual orthogonality
conditions used in regression analyses will not hold if there are common shocks and these impact
households in different ways.

‘ We use twenty years of monthy rainfall data collected at 44 different local weather stations. Rose (2001)
also uses rainfall variance as a proxy for yield risk when looking at how risk inﬂuences off-farm labor
supply decisions and Chaudhuri (1999) shows that rainfall patterns provide good proxies for news and
uncertainty and exploits these characteristics to test for forward-looking behavior in the ICRISAT villages.
It should be noted that even in developed countries, farmers continue to be concerned with factors
inﬂuencing production risk. A 1996 USDA survey, for example, indicates that producers are most
concerned about decreases in crop yields or livestock output (production risk), and uncertainty in
commodity prices. See Harwood et a1. (1999).

5 See Blundell and Stoker (1999), Chaudhuri (1999), and Behrman, Foster, & Rosenzweig ( 1997).

farmers are likely to adjust consumption as new information about yield risk is revealed
over the crop cycle. New information revealed through rainfall allows farmers to update
assessments of risk, and when combined with historical rainfall data, rainfall can be used
to construct exogenous proxies for yield risk. In order to consider the possibility that
farmers update expectations about future rainfall variability and respond to these changes,
we also considered the possibility of using a GARCH model to estimate conditional
rainfall variances for each of the surveyed villages.6

In addition to failing to allow for updates in risk perceptions with information
revelation, important early research in the area either neglected to control for the impact
of wealth and changes in expected wealth on perceptions of risk, or introduced the impact
of changes to expected wealth in an ad hoc manner. Models using quadratic preferences
(e.g., Campbell, 1987) or constant absolute risk aversion (e.g., Caballero, 1990) were
tractable, but have unrealistic behavioral implications.7 Blundell and Stoker (1999)
provide an approach to working with constant relative risk aversion preferences in
analysis of consumption decisions and the timing of income risk Banks, Blundell and
Brugiavini (2001) Show how to implement Blundell and Stoker (1999) empirically using
quasi-panels of British household data, and ﬁnd that cohort speciﬁc risk terms indeed
have an impact on consumption growth. This paper extends the general approach
outlined in Blundell and Stoker (1999) to an environment in which income risk is driven

by yield risk in agricultural production.

 

6 Use of historical rainfall information as an exogenous shock to agricultural production has a long history
in the literature. Wolpin (1982) instruments income using information on historical regional rainfall in
India and Paxson (1992) uses time-series information on regional rainfall to construct estimates of
transitory income caused by rainfall shocks. Rosenzweig and Binswanger (1993) and Jacoby and Skouﬁas
(1997) also use rainfall to proxy for risk and shock, respectively.

The quadratic preferences model provides a solution in which households save in anticipation of negative
shocks, but without any accommodation for risk (Campbell, 1987). Constant absolute risk aversion
neglects the possibility that household expected wealth matters when considering responses to income risk.

Below, in Section 2, we first review the household panel data set and discuss the
construction of variables important in our analyses. Next we discuss the characteristics
of rainfall and the crop cycle in the villages of the panel dataset. In Section 3 we
introduce a theoretical model based on Blundell and Stoker (1999), which we extend to
consider risk of changes in household “full income,” which includes yield risks faced in
an agricultural environment. This model suggests that it is important to consider ways in
which households update their assessments of consumption risk. Irnportantly, this
updating may occur through changing estimates of the conditional variance of income,
through updated household expectations of life-time wealth, and through current income
realizations.

In the empirical analyses of Section 4, we ﬁrst test whether the predicted
conditional variance of rainfall innovations can be used as a proxy for conditional income
variance and conclude that it does not matter whether we use unconditional or conditional
variance of rainfall as a proxy for variance of full income. Our analyses still allows for
updating of consumption exposure to income risk though changes in expected life-time
wealth, changes in the value of grain yield, and changes in estimates of the household’s
full-income. We ﬁnd that, during this period, 11 percent of saving for a household facing
a median level of consumption risk can be attributed to precautionary motives. Further,
we ﬁnd that when households face an increase in scaled consumption risk equal to the
average interquartile range, current period consumption is depressed by 6.3 to 8.2 percent
when we use the 1986 -1991 panel, and by 8.0 to 9.1 percent when we use the 1995-2000
panel. In light of Banks, Blundell and Brugiavani’s (2001) ﬁnding that a comparable

change in consumption risk in British households would depress current period

consmnption by 0.6 percent, this result demonstrates the much stronger effect of
precautionary motives in regions of the world where formal credit markets and other

smoothing mechanisms are not well-developed.

2. Survey Data and Rainfall Variability in China

2.1 The RCRE Village and Household Surveys

The analyses of household consumption and saving decisions in the paper use
village and household survey data provided by the Survey Department of the Research
Center on the Rural Economy (RCRE) at the Ministry of Agriculture in Beijing. Annual
village surveys from 44 villages of Shanxi, Jiangsu, Anhui and Henan provinces are used
in conjunction with two panels of data from the same villages, spanning the periods 1986
to 1991 and 1995 to 2000, from roughly 3400 households per year.8 Households are
asked a range of questions regarding income from on-farm activities and household
consumption, land use, asset ownership, savings, formal and informal access to and
provision of credit, and transfers from both village members and friends and family
outside the village. The household surveys are monitored by county agricultural research
ofﬁces charged with collecting expenditure, income and labor allocation information on a
monthly basis. A staff person from each ofﬁce works with households to clear up
inconsistencies in the survey.

In several of our empirical speciﬁcations we make use of village survey

information to control for proximity to off-farm markets, local topographical

 

8 RCRE has collected data from a panel of households from 1986 to 2002. Survey years are missing for
1992 and 1994, and we only have rainfall information through 1997.

characteristics, village irrigation infrastructure, and ownership structure of local
enterprises. Location variables include distance to the nearest public road, and a dummy
variable indicating whether the village is near a city. Indicator variables denoting village
location on a plain or in mountainous or hilly areas provide information about local
topography. Share of land in the village with irrigation allows us to control for the extent
to which a village exposed to risk in dry seasons. We use share of village assets owned
and controlled by private sector and share of gross revenue from collective and private
enterprises, to control for the extent of village involvement in the local economy. Finally,
share of gross revenue from non—agricultural activities and numbers of village laborers
employed in local and distant labor markets may be used to control access to non-farm
employment.

Household Consumption. Our measure of per capita consumption is the sum of current
non-durable consumption and the calculated value of a stream of services from durable
goods and housing. The ﬁrst is non-durable consumption, and includes the value of
household food consumption (both purchased and out of home-production), cooking fuel,
clothing, education and health expenses, and sundry household items. In the calculation
of both consumption and farm proﬁts we must have estimated the value of grain
produced and consumed by the household at market prices. RCREs survey values home
production of grain at state quota prices, which were far below the market price for grain
during the earlier panel. We followed the procedure outlined in Chen and Ravallion

(1996) to adjust the values of both farm proﬁts and household consumption so that non-

marketed grain, whether consumed or stored by the household, is valued at market
prices.9
The second measure of consumption includes all current non-durable
consumption, plus the consumption stream of services from durable goods and housing.
In order to convert the stock of durables into a consumption ﬂow, we assume that current
and past investments in housing are consumed over a twenty-year period, and that
investments in durable goods are consumed over a seven-year period.10
Household “Full Income.” Our analyses use the estimated “full income” of the
household in order to have a measure of income earning potential that is not biased by
endogenous labor supply decisions. Full income is calculated as farm proﬁts less the cost
of all inputs including an estimate of household labor input, plus the annual value of
household labor in the off-farm labor market.ll Since we lack individual data, but know

numbers of adult laborers with different levels of education, we predict local wage rates

for households based on their education proﬁles. To calculate this value for a household,

 

9 In their surveys RCRE has followed the deﬁnitions of the National Statistical Bureau (N SB) throughout
the 19805 and 19905 (and in many areas they have hired the same county agricultural research ofﬁcers
charged with administering the NSB rural household survey). The NSB and RCRE data for the 19805
valued self-supplied consumption and the revenue from these activities at prices that are well below
market-clearing levels. In 1992 (and in some areas beginning with the calendar year 1990), the NSB began
using a new set of “imputed” prices that were signiﬁcantly higher than the old set. However, the average
imputed price for households’ own consumption remained well below the market price, and, in fact, was
only roughly equal to the quota price. By the late 19905 the market price did not difference much from the
quota price. For this reason, measurement error in valuation of farm proﬁts and consumption is still likely
to be more of a problem in the early panel than during the later panel.

'0 Consumer durables, housing and production assets held by the household at the beginning of the year are
reported in there original value, and only assets purchased during the current year are valued at current
prices. In an attempt to correct for both likely depreciation, due to wear and tear, and appreciation, due to
inﬂation, we assume that durable goods and housing were accumulated in an even stream between 1978, an
early date used for the beginning of reform, and 1986 (or whenever the household entered the panel). We
assume straight line depreciation of housing and durable goods over a 20 and 7 year periods, respectively,
to depreciate the used portion of housing and durables, and then allow the remaining value to appreciate
using a rural durable goods price index. Additional purchases of assets, additions to houses and purchases
of durables are valued at current prices and can be measured more accurately.

” Our calculation follows in spirit the calculation used by Rosenzweig (1988b) and Jacoby and Skouﬁas
(1997), but with adjustments due to the characteristics of the RCRE dataset. Farm proﬁts are household
revenue minus costs which include household labor.

we ﬁrst exclude a household ﬁom the village, and then, using all other households in the
village, we regress earnings divided by labor days in the off-farm labor market on share
of household members in each of three education categories (elementary, lower middle
school, upper middle school), adult sex ratio, share with a special skill, and year dummies
to control for macroeconomic ﬂuctuations. The coefﬁcients from this regression are then
used to predict the off-farm wage of the excluded household. This predicted off-farm
wage is then used to value family labor time.12

Household Managed Land. Area of land managed by the household and number of
household managed plots are the two measures of land available for the early years of the
RCRE survey. From 1986-1991, households typically held long-term leases to land and
were proscribed from trading or selling land use rights. During the 19905, rental markets
and informal exchange of land among related households started to appear as leases were
ofﬁcially extended from 15 to 30 years, but in these four provinces less than ﬁve percent
of arable land was rented out by 2000. For this reason, we do not believe that year-to-year
changes in household land area will be correlated with shocks experienced by the village

or household.13

2.2 Historical Monthly Rainfalls and Rainfall Variability in China

 

’2 Estimates of full income may also be sensitive to estimates of available household labor time. We use
the village average labor days per capita in each village and year as a local measure of each adult laborers
annual labor time. We also make alternative assumptions of 250, 275, 300 and 325 labor days per year.
Results of our analyses are robust to choice of this parameter.

‘3 While it is well known in rural China (Benjamin and Brandt, 2002; Jacoby, Li and Rozelle, 2002) and
speciﬁcally in the RCRE villages (Giles, 2002), that village leaders have engaged in land reallocations
before the expiration of lease contracts, there were few adjustments before the 19905 and most later
adjustments can be attributed to changing demographic characteristics of households within villages
(Burgess, 2001).

In addition to the RCRE survey data, enumerators working with the authors
collected twenty years of monthly rainfall data (January 1978 - December 1997) from
county weather stations near each village. These historical rainfall data Show
considerable variation across the four contiguous provinces and even across counties
within provinces (summary tables and ﬁgures of village rainfall characteristics are
provided in Appendix A). We next provide more information about rainfall in China and

discuss ways of using moments of its distribution in our analyses.

Rainfall Variability in China

Most annual precipitation in China comes during a summer rainy season, but the
timing of this season in each location is not ﬁxed. The duration of the rainy season varies
from year to year, and hence variations in annual and seasonal rainfall can be quite large.
High concentrations of torrential rainfall may not only cause insufﬁcient utilization of
rainwater during the rest of the year, but also result in soil erosion, ﬂoods, and water
logging.

In North China (including Henan and Shanxi provinces), where annual
precipitation is lower, torrential rains make up a considerable fraction of annual rainfall,
and in some years, a few storms during the summer may amount to 80 percent of total
annual precipitation. While annual rainfall is lower than southern and eastern coastal
areas, ﬂoods, serious soil erosion, and water logging are frequent occurrences in North
China. Precipitation from summer rainstonns, however, cannot be efﬁciently utilized in
agriculture unless it falls in areas equipped with water conservation facilities. A

considerable proportion of the annual rainfall is, therefore, not necessarily beneﬁcial for

agricultural production in China’s semi-arid and semi-humid regions. Furthermore, scant
precipitation in winter often develops into drought conditions. China’s long history of

drought and ﬂood is related, in part, to a non-uniform seasonal distribution of the rainfall.

Rainfall and the Crop Cycle in the Survey Provinces

Our empirical test uses variability of rainfall as a proxy for yield risk and requires
that we ﬁrst determine which months of rainfall will be most important for agricultural
producers. Since we have monthly precipitation for each village, alternative
speciﬁcations will employ total precipitation during important months, as well as annual
precipitation. 14

The four provinces where our survey villages are located produce 41 percent of
the wheat in China (Henan 20.4 percent, Jiangsu 9.9 percent, Anhui 7.9 percent and
Shanxi 3.1 percent). Most wheat is grown in eastern China, and just 5 provinces (Henan,
Anhui, Jiangsu, Hebei, Shandong) account for more than 70 percent of China’s total
wheat output, and winter wheat accounts for 90 percent of China’s total wheat crOp.
Table A1, provided in Appendix A, shows that wheat is grown throughout the four
provinces. While rice is major crop in southern Jiangsu and Anhui provinces, wheat is
also one of the crOps used in the two-crop-per-year and three-crop-per-year rotations for
rice paddy. For the 1986-1991 period we do not have detailed information on crops
cultivated by households, but we are comfortable with the assumption that wheat was a

major crop in these areas.15

 

" We also performed our analyses using annual rainfall precipitation (January-December), but this did not
affect our results much.

'5 The fact that these are the major grain crops in these regions is conﬁrmed by exarrrination of more
detailed information in 1995-2000 surveys.

10

Winter wheat typically comes out of dormancy in March, at which time moisture
requirements increase signiﬁcantly. '6 Rainfall and supplemental irrigation are most
important in the spring when most of the crop is in a drought-sensitive heading/ﬂowering
stage. Spring droughts are one of the more serious threats for the crop. Further, summer
rainfall proves to be important for the following spring’s crop development because
rainfall during this season determines soil moisture, which is important during wheat
planting in the following winter. For rice crops, the moisture and temperature sensitive
heading stages occur in July and September.

From the crop cycle we infer that rainfall in March, July, and September will be
important for crop (wheat/rice) cultivation, though this will depend somewhat on
differences across varieties. We also performed regressions of household grain
production on each month of rainfall to gauge the importance of the speciﬁc months of
rainfall. These regression results conﬁrm that more rain in March, July and September is
in fact beneﬁcial for grain production in the surveyed villages. We also ﬁnd that when we
add rainfall from July to November of the previous year to the regression, reﬂecting
wheat crop cycle, spring rainfall becomes relatively less signiﬁcant (particularly in
Shanxi, Henan provinces) and more rain during this period of the previous year is also
helpful for the current year’s production. This is not surprising as precipitation during
the latter half of the previous year is important for determining the moisture level during
the winter period and affects the likelihood of a spring drought.l7 In consideration of the

wheat and rice crop calendars, our analyses use the sum of rainfall for the months July-

 

” See the crop calendar in the Appendix A.
'7 Dr. Scott Swinton in the department of agricultural economics provides valuable suggestions on the crop
cultivation and rainfall.

11

November as rainfall in these months is most closely related to crOp production and yield

risk.

3. Theory
The consumption growth equation behind our empirical model can be derived from a

standard Euler equation for optimal consumption allocation across periods t and t+1:

(1) uc(Cz)=ﬂ(1+r)E{uc(Ct+1)|Qz}
where uC is the marginal utility of consumption, r is the real interest rate, and B is a

discount factor less than unity.

We assume a constant relative risk aversion (CRRA) iso-elastic utility function:

(2) u(C) = 1—11 C‘"1

where 21 is the coefﬁcient of relative risk aversion and independent of lifetime wealth
levels. From this we can derive the speciﬁc Euler equation associated with utility

maximization:

—/l
(3) ﬂ(l+r){C—g—1} =l+et+1 where E(e,+1 |Q,) =0

1

Taking logs and using a Taylor approximation for logs yields a linearized Euler equation:

1 l 1 l 2
(4) AlnCHl=Ilnﬂ+11n(l+r)+-2-1—at+l+ut+l
1 1 2 2
where “t+1 =_I{9t+l _§(et+l '0't+1)} 50 that E(“H1 th)=0

12

The conditional consumption shock variance, 0,2“ , is the variance of e, +1 conditional on

information available at time t. From this we can separately identify three determinants
of consumer behavior: an intertemporal substitution effect, a precautionary saving effect,
and a life cycle effect reﬂected in the consumption path. The precautionary saving
motive, captured in the third term of (4), predicts that increases in the expected variance
of future consumption shocks will lead to higher observed consumption growth as
households save more in period t.

Blundell and Stoker (1999) point out that the variance term in the equation (4)
should not be replaced by the conditional variance of income because variance of the
consumption shock subsequent to unexpected income changes depends on the amount of
ﬁnancial wealth held by a household and on the magnitude of current income relative to
expected future income. Starting from their insight, we derive a modiﬁed version of
Blundell and Stoker’s model that explicitly introduces yield risk from agricultural
production.

Since their insight is used in empirical study of precautionary saving by Banks,
Blundell and Brugiavini (2001) we follow their derived speciﬁcation with CRRA

preference:

1 1
(5) AlnCHl=Elnﬂ+11n(l+r)+km,+1+ut+1,

_ 2 18
Where mr+1 -7?z+10r+1

 

’8 Their speciﬁcation is based on the below derived relation between consumption growth and risk with

CRRA preference. A In Ct+l z kﬂt+lat2+l + “t+1 , where the scaling term 71',“ = Y} /(c W)? See
Banks et a1. (2001) for further details.

13

Notice that the variance of income innovations conditional on the previous period t
should be also scaled by the expected wealth in (5) following the insight of Blundell and
Stoker (1999). '9

An empirical question arises at this point. How should we estimate the conditional
variance of income innovation or yield shocks? Banks, Blundell, and Brugiavini (2001)
estimate the conditional variance of income innovations using an ARCH regression and
exploiting synthetic panel data. Since we have a long time-series of rainfall data for each
village, we use a similar approach to predict the conditional variance of rainfall and use
this as a proxy for yield risk. We ﬁrst test whether rainfall shocks show
heteroskedasticity in most villages of the survey. If we cannot reject heteroskedasticity,
then we could apply the GARCH model to predict values of conditional rainfall variance.
Alternatively, if we reject heteroskedasticity of rainfall shocks, then predicting the
conditional rainfall variance with a GARCH model will not yield any improvements over
the unconditional rainfall variance.

In our empirical discussion below, we ﬁrst review our tests of the plausibility of
estimating conditional rainfall variances using GARCH, and determine that we should
use unconditional rainfall variance. Next we discuss the empirical consumption growth
model used to test for presence of a precautionary savings motive, and ﬁnally we review

results of various speciﬁcations of model.

4. Empirical Strategy and Results

4.1 Do We Observe GARCH Effects in Village Rainfall Time Series?

 

'9 We also derive a modiﬁed version of Blundell and Stoker’s model that explicitly introduces yield risk
from agricultural production. See Appendix B for further details.

14

Since theory suggests using a proxy for the conditional variance of income, we
ﬁrst test the possibility of using a GARCH model to estimate the conditional variance of
rainfall in each year. We thus perform autocorrelation, trend and heteroskedasticity tests
with respect to rainfall data of 44 villages. The autocorrelation test conﬁrms that neither
annual (12-month) nor the July-November rainfall series show signiﬁcant autocorrelation.
Further, we conﬁrm that there is no time trend to either rainfall series in each of the 44
villages. Finally and most important for using the GARCH model, we show that rainfall
in most villages is not heteroskedastic, thus implying that variance of rainfall might not
vary across the periods.20 Even when performing GARCH estimation for each village,
these tests are conﬁrmed. Rainfall shocks are not persistent and tend to die out rather fast,
meaning that forecasted rainfall variance converges and would not vary much over our
sample period. 21 Predictions of conditional variance would not provide additional
information across time for identifying a precautionary motive in the consumption
growth equation, and thus we use the unconditional rainfall variance of rainfall for each
village as our proxy for yield risk.22

Changes in consumption exposure to income risk are captured exclusively by a
scaling term that controls for exposure to rainfall variation. Since the numerator of the
scaling term is the value of household full income in period t, it also contains information
about expectations of future grain yield and a possible source of information about

changes in expected future income. Factors other than rainfall (e.g., expectations about

 

2° We can reject the null of homoskedasticity at the 10 percent level in only 9 of 44 villages.
See Appendix C.

2' These results are summarized in Figure C1 of Appendix C.

22 We calculate the sample variance of rainfall for each village, j, as

a":- Liar-Rf
1” T4,:l ,

J

15

future prices, or changes in quota policy) are likely to have a more persistent effect on
both future grain yields and future off-farm activities, and they are likely to be captured

by this term.

4.2 Empirical Specification

The base speciﬁcation for consumption grth is derived from our previous
model and similar to those derived from the standard Euler equation models (Banks,
Blundell and Burgavani (2001), Ludvigson and Paxson (2001), Chaudhuri (1999),

Browning and Lusardi (1996)):
(6) A1“ Cit+1 = a + 7221+ ¢mir+1+ 5RSit + 4 Vi! + ”Dir + “n+1
where A ln Ci,“ : Growth in non-durable consumption from period t to t+1.

Z,: Area of land managed by the household
I; 2
mml: Scaled unconditional variance of rainfall (= and} where nit = [—] &

If}, is full income in period t.23

RS”: Rainfall shock (= |R,-, —I_ti |)
Vi, : Vector of village variables such as village population, location, industry

structure.

D": Province-year interaction dummies.

 

23 In our empirical implementation we replace the expected wealth term (W) in the scaling factor by
consumption in period t as in Banks, Blundell and Brugiavini (2001). Since this most likely introduces
measurement error problems in estimation, particularly as a result of the measurement of home produced-
home consumed goods, we will employ IV methods to control for measurement error. Use of wealth might
be problematic because it is quite noisy and poorly measured. When we perform a kernel density
estimation, we can ﬁnd that the distribution of wealth is skewed and kurtotic.

l6

The coefﬁcient on scaled rainfall variance, ¢, in (6) will be the focus of our estimation
efforts, as a positive value indicates that household consumption is lower (and saving
higher) in period I when households expect that future yield shocks will have a greater
potential impact on the variability of consumption. Much effort in presenting alternative
speciﬁcations will center on demonstrating the robustness of this coefﬁcient to different
potential sources of bias.

Other coefﬁcients, however, are also of potential interest. Our model predicts that
households will update their expectations of earnings after realization of a period t
rainfall shock, and that the impact of this shock on local agriculture will depend on levels
of moisture in the soil and the previous year’s rainfall shock. The rainfall shock term is
speciﬁed as the absolute value of the difference between rainfall in period t and the
sample mean of rainfall across the period since we expect that both positive shocks (e.g.
ﬂood) and negative shocks (e. g. drought) will have unfavorable impact on the agricultural
production and consumption.

In the ﬁrst set of speciﬁcations, we estimate (6) without separately distinguishing
heavy rainfall or drought conditions and we observe signiﬁcant positive signs on the
rainfall shock term (Table 2). In our ﬁrst extension to the base speciﬁcation, we interact
the scaled rainfall variance term with the share of land that is irrigated in each village,
and with dummy variables for provinces other than Shanxi. Since yield risk varies
regionally, and depends on soil type, climate, and the use of irrigation, we would expect
that rainfall variability will be more important for consumption and saving decisions in
dry regions and where less of the land is irrigated. Thus we will expect that the

interaction between share of village land with irrigation and the scaled rainfall variance

17

term to carry a negative Sign. When looking province by province, we also note from
Appendix A that rainfall variability appears to be more pronounced in Shanxi than in
most villages of other provinces. Given that Shanxi and Henan are more arid than J iangsu
or Anhui, we also expect to ﬁnd that rainfall variance has a greater impact on savings and
consumption decisions in these provinces.

We next explicitly introduce additional village level variables to control for
omitted village speciﬁc factors that may be correlated with consumption risk related to
yield variability. These variables include village population, the dummy indicating
whether the village is in a mountainous or hilly area, a dummy variable for proximity to
an urban area, share of irrigated land in the village, distance to the nearest public road,
share of village assets owned and controlled by private sector in the village, cadre share
of village population, total land area in a village, share of gross revenue from livestock
production, share of gross revenue from non-agricultural activities, and share of gross
revenue from collective and private enterprises.

Finally we introduce speciﬁcations that control for access to local and migrant
employment opportunities. Under the assumption that off-farm employment can be used
as an alternative means for smoothing yield shocks, we assume that the precautionary
motive for saving may be mitigated if households expect that they might be able to ﬁnd

or expand off-farm employment subsequent to experiencing a serious yield shock. 2"

 

2’ These “measures of access to off-farm markets” are constructed as shares of the village with off-farm
employment in either local or migrant labor markets in period t-l. These measures may be endogenous
with expected growth of the local economy, and this fact is not considered when we introduce these terms.
In addition, our rainfall shock term is speciﬁed as the difference in rainfall between period t and H. It is
quite plausible that off-farm labor market participation in period H is related to last years shock and our
rainfall shock term. It might be better to use interactions of the scaled rainfall variance and the dummy
variable for proximity to a city, or distance between the village and a major metropolitan area (e.g., the
provincial capital, Beijing or Shanghai). Other village level variables may also suffer similar endogeneity
problems. .

18

We have not explicitly included household demographic information because the
structure of the household may itself be determined by consumption smoothing
considerations (Rosenzweig, 1988a; and Rosenzweig and Stark, 1989).25 Measures of
human capital, which could be constructed at the level of the household from information
about numbers of individuals with different amounts of education, would also re-
introduce demographic structure and potential biases in our statistical test. While not
considered in this paper, it may be of use to consider speciﬁcations in which these

variables are included and treated as predetermined but not strictly exogenous regressors.

4.3 Results and Robustness Checks

Table 2 summarizes results for different ﬂavors of the base speciﬁcation.
Coefﬁcients on the scaled rainfall variance term appear to provide strong evidence of
precautionary behavior in the farm household’s consumption decision. AS exposure to
weather risk increases, households depress current consumption in favor of future
consumption. The sign on the irrigation interaction term is in the direction that we would
expect, though not signiﬁcant in some Speciﬁcations 26 Interactions between scaled
rainfall variance and dummy variables for Anhui, Jiangsu, and Henan provinces carry
negative coefﬁcients, suggesting that precautionary motives are stronger in Shanxi
province. Given that Shanxi is more arid but also experiences greater rainfall variability,

this result is consistent with our expectations.

 

2’ Kin based inter-household income transfers, ‘exogamous’ marriage migration and inter-household
contractual arrangements are manifestations of income smoothing in an environment of spatially covariant
risks.

2° A large number of reservoirs and water diversion structures have been built and many tube wells have
been installed, in order to supply water for the irrigation and ﬂood control depends on the land drainage
system or water pumping station in China. Refer to Xu and Peel (1991).

19

Robustness Checks

The regression models shown in Table 2 suffer from three potential problems.
First, the village variables used are likely to be endogenous and may be biasing the
coefﬁcients on scaled rainfall variance terms. Second, use of household land and a
household speciﬁc scaling term introduces the possibility that our results are biased by
some source of unobserved heterogeneity. Third, the presence of period t consumption in
the scaling term makes it more likely that errors in the measurement of this term will be
correlated with errors in the measurement of the dependent variable.

Our approach to the ﬁrst problem is to drop all village variables and estimate the

model:
(7) A1“ Cit+1 = a +7211 +¢mit “LII/it +“it+1
where Vi, are now a vector of village time dummies. This speciﬁcation will controls for
aggregate shocks to villages and all ﬁxed village effects. Due to gaps in the time series
we perform analyses separately on early and late six-year panels. Columns (1) in Table 3
show results of this regression and we observe coefﬁcients on scaled rainfall variance
similar to those in our base regressions.

Next, in order to control for possibility that measurement error of the scaling term
may be correlated with the dependent variable, we estimate versions of growth model in
(7) in which the scaling term is ﬁrst instrumented with the period t-l and t-2 values of the

scaled term. To implement this we use Wooldridge’s approach to ﬁrst predicting 72,-,

from the regression:

”it = C + Igl’rit—l + ﬂzﬂn-z + err

20

and then use iii” = “itajz- as the instrumental variable in the IV estimation.27 Columns

(2) in Table 3 Show results that are signiﬁcant and carry a positive Sign associated with a
precautionary motive.

Unobserved differences in household discount factors due to unobserved life-
cycle effects may introduce a source of unobserved heterogeneity that biases the
coefﬁcient on risk, and so we implement a ﬁrst-differenced growth model:

AA ln Cit+1 = C + YAZit + ¢Amit + [Viz 'l’ Auit+1
and Show results in columns (3). The ﬁrst differenced scaled rainfall variance term picks
up changes in risk associated with changes in the inverse of expected lifetime wealth, and
suggests that once we control for individual heterogeneity, households appear more
sensitive to risk.
Finally, we also control for measurement error biases as well using a ﬁrst-differenced
instrumental variables estimation. As in the IV level speciﬁcation, the change in sealed

unconditional rainfall variance is instrumented by ﬁrst predicting A7?” from

A701 = C + 7171.11—1+ 727Fir-2 + er:

2 2
Y- Y- _
i it—l

and then using Aﬁzi, = Ara-,0}- as the instrumental variable in FDIV estimation.28

where

 

27 See Wooldridge (2001) and Wooldridge (2002) for discussion.
28 Anderson and Hsiao (1982) ﬁrst suggested that instruments of this type will be valid if m,_1 is

correlated with Am, but not the error term.

21

Results from the FDIV estimation are shown in columns (4) of Table 3. Again,
we see that households respond to changes in relative consumption risk by reducing
current growth in their consumption.

One may at ﬁrst be puzzled as to why coefﬁcients of interest are signiﬁcant, but

differ dramatically between the early and late panels shown in Table 3. First, recall that

the scaling term is (yi, /ci,)2. The downward trend in grain prices during the 19905

implies that the value of stored grain will be much lower in later periods, and that the
scaling term will trend downwards. Further, over the 19905 households have invested
considerably in housing, and given the 20 year amortization of new housing expenses, we
expect the denominator of the scaling term to be increasing.

In order to interpret coefﬁcients on the scaled rainfall variance term we predict
the effect of scaled rainfall variance on consumption growth in Table 4 for different
values of the scaled rainfall variance term. Overall, we see consistent evidence of
precautionary saving over both the early and late periods. We ﬁnd that, during this
period, roughly 11 percent of saving for a household facing a median level of
consumption risk can be attributed to precautionary motives.29 Further, when households
face an increase in sealed consumption risk equal to the average interquartile range,
current period consumption is depressed by 6.3 to 8.2 percent in 1986 — 1991 and by 8.0
to 9.1 percent in 1995 -2000. In light of Banks, Blundell and Brugiavini’s (2001) ﬁnding
that a comparable change in consumption risk in British households would depress

current period consumption by 0.6 percent, this result demonstrates the much stronger

 

2" This calculation is based on strong restricted assumptions on the household’s precautionary savings.

22

effect of precautionary motives in regions of the world where formal credit markets and
other smoothing mechanisms are not well-developed.

While the identiﬁed precautionary motive appears stronger in rural China, as one
might expect, one might still ask whether the magnitudes involved are economically
meaningful. Estimated precautionary saving for a household facing median consumption
risk during the 1995-2000 period was 19 Yuan RMB (in 1986 Yuan), and the 75‘”
percentile of consumption risk, this value rose to 38.3 Yuan RMB (in 1986 Yuan).30 On
the face of it these magnitudes do not seem that large, yet we may place them in context
by comparing them to average household expenses per capita on education and health
care, which we also show in Table 4. Reducing current consumption by 4 percent at
median levels of consumption risk leads to precautionary saving equal to nearly half the
education-related expenditure of households Spending resources on education during the
1995 to 2000 period, and for households above the 75 percentile of consumption risk
precautionary saving per capita is equal to or greater than average education related
expenses. Further, health care expenses, typically the cost of vaccines and medicines for
households not facing a major crisis, are on average below the level of precautionary
saving for households facing median levels of consumption risk. While we by no means
make any claim that these expenditures are necessarily reduced in light of consumption
risk, this simple comparison of magnitudes suggests that precautionary motives for
reducing current expenditures may have real long term impact in some areas of rural

China.

4.4 Potential Problems with Our Approach

 

30At 1986 exchange rates, this would be roughly $5 and $10, respectively.

23

Our model fails to consider some of the constraints faced by rural households in
the developing world. By introducing yield risk in our model, we add one aspect of
agricultural production, but other than this the model motivating our test is based on an
exogenous income process and not endogenous agricultural income. Such standard
intertemporal consumption models with exogenous income and credit constraints, though
dynamic and perhaps suitable for the case where wage labor is the primary source of
income, may not be relevant for analyzing rural households where income from farm
production contributes signiﬁcantly to total household income—although off-farm
income is important source of household in contemporary China. As a next step, we will
add risk to a dynamic model that explicitly considers these features in the spirit of
dynamic household models presented in Behnnan, Foster, and Rosenzweig (1997) and
Saha(1994)3‘

How would a dynamic model help to inform our empirical analysis? And what
would be the implications for our current empirical strategy? Chaudhuri (1999) suggests
that the income process may be conditionally heteroskedastic when we introduce yield or
price risk in an agricultural household production model, but that this added complication
will not pose serious problems because even with a conditionally heteroskedastic income
process, we would expect households to have the same behavioral response to risk. Still,
we believe that our analyses would be stronger with formal derivation of a model
incorporating both production and consumption behavior under uncertainty.

Finally, although off-farm earnings are a major source of income for many

farmers in China, and may be used to stabilize farm household income, our model does

 

3' Saha (1994) analyses a two-season agricultural household model of output and price uncertainty. Roe
and Graham-Tomasi (1986) also introduce the risk into their dynamic household model. Chaudhuri (1999)
exploit the inter-seasonal dimensions of household decisions to analyze the precautionary saving behavior.

24

not explicitly include this possibility. Risk mitigation arrangements such as off-farm
labor supply and on-farm storage based on inter-seasonal framework could be
appropriately analyzed by introducing price and yield uncertainty in the agricultural

household’s Optimization problem (Saba, 1994; Rose, 2001).

5. Conclusion

Traditionally farming is a risky occupation in that the consequences of decisions
or events are often not known with certainty until long after they occur. While there are
many sources of risk in agriculture, ranging from price and yield risk to the personal risks
associated with injury or poor health, our analysis focuses on farm household
precautionary response to identiﬁable yield risk related to rainfall variance. While other
forms of risk may indeed have a more important inﬂuence on farm household decisions,
we use rainfall variance because it makes an ideal exogenous proxy for yield risk. One
might argue that using conditional heteroskedasticity in income processes for
identiﬁcation of precautionary motives is appealing in that it allows us to control for risk
associated with all income earning activities, but due to a lack of long panels of data,
most analyses of the effects of income risk using this approach will be biased and/or
suffer serious endogeneity problems. In rural agricultural environments, a relatively long
time series of rainfall data can be an adequate proxy for yield risk. Since historical
rainfall data is much less time-consuming to acquire, use of rainfall data is much less
costly than execution of long panel household surveys with the explicit aim of studying

precautionary behavior.

25

Given that we ﬁnd evidence of economically signiﬁcant evidence of
precautionary behavior in household consumption decisions using rainfall as a proxy for
risk, a risk that China’s farmers are arguable better equipped to cope with than price risk
or labor market risk, we take this as signiﬁcant support for the existence of a
precautionary motive in household consumption decisions in rural China. Precautionary
behavior may have long term costs. Households with a strong precautionary motive may
reduce education-related expenses for children or important preventive healthcare
expenditures on vaccines. Further, given the growing inequality that we witness within
and across regions in rural China, existence of a precautionary motive among households
with lower permanent wealth may be part of the explanation for a rise in inequality if less
afﬂuent households more exposed to risk systematically depress consumption and
spending for precautionary reasons rather making the relatively low investments
necessary to diversify into such lucrative activities as raising livestock. Finally, our
model suggests an important way in which a precautionary motive might interact with
negative shocks to permanent wealth to produce persistently lower levels of consumption
in future periods. A negative shock to permanent wealth will lead to a rise in the relative
consumption risk faced by a household, which will then lead to a stronger precautionary

motive.

26

Table 1. Descriptive Statistics

1. 1986 — 1991

 

Variables

1986 1987 1988 1989 1990 1991

 

Consumption per capita (yuan)

Non-durable good consumption per capita (yuan)

Full income per capita (yuan)

Area of land per capita

Village population

1 if in mountains

I if in hills

1 if in near city

Distance to nearest public roads (Km)

Irrigated share of land in village

Share of assets in village owned or controlled

by private sector in village

Cadre share of village population

Village share Of gross revenue from livestock

Village share of gross revenue from non-agricultural act.

27

376.7 384.3 417.9 373.7 399.7 392.4
(321.8) (318.5) (397.8) (313.4) (369.8) (399.4)

277.8 284.5 290.9 284.3 297.9 292.1
(134.8)(134.1)(150.4)(14l.2)(157.1)(157.1)

510.0 563.3 550.8 505.4 535.5 530.6
(264.9) (303.5) (301.2) (245.0) (258.6) (317.5)

0.07 0.07 0.07 0.07 0.06 0.06
(0.05) (0.06) (0.05) (0.05) (0.05) (0.05)

1662.1 1524.8 1545.6 1558.0 1603.6 1629.7
(912.2) (847.5) (851.1) (875.5) (897.3) (924.6)

0.23 0.24 0.23 0.24 0.23 0.23
(0.41) (0.42) (0.42) (0.42) (0.42) (0.42)

0.30 0.31 0.33 0.28 0.32 0.30
(0.46) (0.46) (0.47) (0.45) (0.46) (0.46)

0.11 0.10 0.10 0.12 0.10 0.10
(0.31) (0.30) (0.30) (0.32) (0.30) (0.30)

2.36 2.71 2.68 2.72 2.68 2.68
(3.41) (3.63) (3.61) (3.64) (3.61) (3.60)

0.42 0.49 0.48 0.48 0.50 0.51
(0.34) (0.39) (0.39) (0.39) (0.39) (0.38)

0.75 0.76 0.78 0.79 0.77 0.78
(0.26) (0.23) (0.24) (0.23) (0.22) (0.23)

0.006 0.006 0.007 0.006 0.007 0.006
(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

0.14 0.15 0.16 0.18 0.18 0.17
(0.09) (0.10) (0.11) (0.12) (0.13) (0.14)

0.36 0.35 0.35 0.34 0.34 0.35
(0.29) (0.26) (0.27) (0.27) (0.27) (0.28)

Table l (cont’d).

Rainfall (July - November)

Variance of rainfall (July - November, *1000000)

Scaled variance of rainfall (*1000000)

Notes: 1. Standard deviations in parentheses.

2. Sealed variance of rainfall =(full income/consumption)*rainfall variance

2. 1995 — 2000

349 502
(129) (294)

409
(83)

491

402

454

(219) (146) (292)

0.030 0.030 0.030 0.030 0.030 0.030
(0.027) (0.027) (0.027) (0.027) (0.027) (0.027)

0.083 0.118 0.114 0.102 0.101 0.106
(0.136) (0.213) (0.208) (0.165) (0.168)(0.333)

 

 

Variables 1995 1996 1997 1998 1999 2000

Total expenditure per capita (yuan) 551.4 532.4 523.8 514.3 526.0 530.6
(285.5) (290.3) (275.3) (284.5) (302.7) (314.7)

Non-durable good expenditure per capita (yuan) 458.8 436.6 425.0 420.8 423.8 427.6
(234.7) (236.0) (219.6) (234.5) (247.6) (256.6)

Full income per capita (yuan) 394.1 413.4 442.8 410.5 417.3 431.9
(221.6) (232.1) (243.8) (236.0) (268.0) (271.9)

The area of land (mu) 0.05 0.05 0.05 0.05 0.05 0.05
(0.04) (0.04) (0.04) (0.04) (0.04) (0.04)

Village population 1583.0 1587.3 1586.8 1597.6 1543.2 1550.0
(916.5) (929.8) (930.8) (954.9) (899.6) (908.0)

1 if a village is located in mountains 0.24 0.24 0.23 0.24 0.23 0.23
(0.43) (0.43) (0.42) (0.43) (0.42) (0.42)

l ifa village is located in hills 0.33 0.33 0.35 0.34 0.34 0.34
(0.47) (0.47) (0.48) (0.47) (0.47) (0.47)

1 ifa village is located in near city 0.11 0.11 0.13 0.14 0.17 0.13
(0.31) (0.32) (0.33) (0.34) (0.38) (0.33)

Average distance to nearest public road (Km) 3.15 2.57 2.73 2.82 2.54 2.65
(5.26) (3.40) (5.06) (5.29) (4.24) (4.26)

Village cadre Share of population 0.007 0.007 0.006 0.006 0.006 0.006
(0.005) (0.004) (0.004) (0.003) (0.004) (0.004)

28

Table 1 (cont’d).

Total land area of village 4845.2 4899.6 4842.4 4876.1 4561.0 4600.8
(5268.6) (5280.7) (5274.2) (5449.2) (4984.4) (5017.1)

Scaled rainfall variance (*1000000) 0.020 0.027 0.030 0.027 0.028 0.031
(0.033) (0.043) (0.052) (0.044) (0.044) (0.053)

Notes: 1. Standard deviations in parentheses.
2. Sealed variance of rainfall =(full income/consumption)’rainfall variance
3. Some village variables are not available in the later survey.

29

3.888 :888 :288 2.28.8 2.28.8 2.88.8 2.28.8 2.888

82.22 828 82.22 2222.22 22.22 2222.22 82.22 :28 8882:2222 2
128.8 .288 2.288 228.8 ...28.8 28.8 3.288 28.8
88.8 A88 88.8 2.8.8 2.8.8 2.2228 822.22 2.8.22 ”22222 222.22 2

2.8.8 888 28.8 88.8 38.8 88.8 28.8 88.8
28.8 222.22 222.22 822.22- 28.8 822.8- 822.8 888- 22822228282222

2288 £2.88 3.28.8 2.2.8.8 2.28.8 2.28.8 3.28.8 2.288
822.22 888 $88 $228 8228 3.88 8228 2.88 2828222223 682252222

2828 2222.8 282.8 282.8 282.8 282.8 282.8 282.8
28.22 288 $8.22 288 2288 .2288 8228 2288 2628.2

128.8 2,222.8
E2. 2- 328. 28282225 2222222252 266286m8.22«2.2622”668562.28

2.2.8.8 5.228
822. 2- 83. 28528> 2222222252 2.6288222222188588

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28.68 282 .8 28.8 2 2.8 28225 a 26282 .26
228.8 888. 8.8 88.22. 8am 2328225 .2889 228222232 28288

128.8 :8228 2.8228 2.28.8 .5888 3.28.8 2.288 :888
82.2 .822 222.222 88.22 $22.22 2 2 2.8.22 222 8.822; 2288822 Baum
28 E 3% 5 28 28 S 28 82222222;

 

 

832E222» owe—=2» wan: 3.2—mom eaten—cum .«e baﬁaam .N 935.

30

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x0983..— Eoc 0250>0m 8.80 we 020nm own—22>

2.2 82.222 26
2888285 222882 22223 82.223 26 68.2w

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228222822225 283 22223 68225 2c seam

own—25 E Cumo>v 00:058— 5:20: 8 $000.22.

88223.28 .82 2028.2 286.8

noun—25m 0w£=> mo 023m 0.600

0w£=> E 2o200m 0202.5 .3 00:82:00

20 00:30 0mg; 5 80mm< mo 023m

.8225 222 208.2 26 282m 2.888222

25¢ 0223— 02325 80.802 9 002385

31

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838 20a 222292225250 :38 5.13.?va 20.2 .3an 20¢ noun—E2288 032222012292 ._+2 9 2 totem Sow 5380 2229529200 20382229 2:00:0Q0Q .— 80202
no.0 cod no.0 mod cod mod cod 3.2 20032220..-.»—

mnwn numb mums numb mums numb numb mums EowatomnO

 

1.258 ..62228 .2828 ..2828 3.888 ..2828 ..888 2.2.2.8
88.8- 82.8- 88.8- 828. 8a.? 828- 82- 828- 2228.80

288 282.8 28.8 28.8 228.8 82.8

~36. mi; mwmw ocmd mhvs 32.2 a «I 2% _v x09? Based
28.8 238.8 28.8 .288 88.8 .288 68.8 .288 8.229828 822.2 8 6.228228
888 888 888 228.8 8228 822.22 2288 88.22 882 628.32 .85 2o 298 .8225
.288 68.8 .288 88.8 .288 28.8 .288 28.8 8222.28... 2.522.628.4282

8.2.58 2 02.22:.

32

Table 3. Summary of Estimation Results Using Village-Year Dummy Variables

 

1986 -1991 1995 -2000
Regressors OLS IV FD FDIV . OLS IV FD FDIV

 

Scaled rainfall variance 0.45 0.15 1.54 0.91 l 1.31 0.92 5.63 3.00

(0.06)" (0.07)* (0.15)" (0.15)" (0.10)“ (0.21)M (0.30)" (0.45)"

Land 0.25 -0.16 0.01 0.01 0.25 0.08 0.01 0.01
(0.07)M (0.10) (0.004)* (0.004) (0.08)" (0.12) (0.004) (0.005)‘I

Obs. 10,646 5,584 7,956 5,594 14,779 8,618 11,571 8,616
R squared 0.18 0.24 0.09 0.17

 

 

Notes: 1. Robust standard errors in parentheses 2. * signiﬁcant at 5%, ** signiﬁcant at 1% 3. All
speciﬁcations include village‘year dummy variables to control for aggregate shocks to the village economy
and coefﬁcients on village*year dummy variables are jointly signiﬁcant. 4. Instrumental variables in [W
FDIV estimation are based on the t-l and t-2 periods of the scaling term.

Table 4. The Predicted Effect of Precautionary Behavior in Saving

(In 1986 RMB Yuan)
1986-1991 1995-2000

 

Per capita earned income of rural household (A) 460 637
Per capita expenditure of rural household (B) 338 461
Per capita saving (C=A-B) 122 176
The amount of predicted ‘precautionary saving’ based on the different scaled variance of rainfall
75‘” percentile of scaled variance of rainfall (D) 0.08 0.08
Value of predicted ‘precautionary saving’ (E=D*B) 27.4 38.3
50‘h percentile scaled variance of rainfall (D) 0.04 0.04
Value of predicted ‘precautionary saving’ (E=D*B) 14.5 18.9
25‘h percentile of scaled variance of rainfall (D) 0.02 0.02
Value of predicted ‘precautionary saving’ (E=D*B) 7.8 9.2
Proportion of precautionary motive in saving (= predicted saving (E)/saving (C))
At 75“I percentile of variance of rainfall 0.22 0.22
At 50“I percentile of variance of rainfall 0.12 0.11
At 25‘h percentile of variance of rainfall 0.06 0.05
Average education expenses per capita (for households with ed expense>0) 18.0 43.9
Average health care expense per capita 10.7 21.9

 

Notes: 1. The calculation is based on the estimates of FDIV method in Table 3.
2. The scaling term is deﬁned as the squared value of (full income/consumption).
3. Consumption is total consumption.

33

Appendix A

Crop Production and Rainfall Variability

Table A1. Total Sown Areas of Farm Crops in 4 Provinces

(Units: 1000 hectares, %)

 

Sown area of

grain crops Rice Wheat Corn
Shanxi 3128.1 6.1 951.2 822.8
Jiangsu 5994.4 2377.6 2341.4 439
Auhui 6030.6 2212.1 2137.6 512.2
Henan 8879.9 489.5 4927.3 1952.4

 

Shanxi 100.0 0.2 30.4 26.3

Jiangsu 100.0 39.7 39.1 7.3
Auhui 100.0 36.7 35.4 8.5
Henan 100.0 5.5 55.5 22.0

Source: China Statistical Yearbook 1998.

 

34

Table A2. Crop Calendar in China (Wheat/Rice)

January

0 Wheat: Dormant
February

0 Wheat: Dormant
March

0 Early rice: Planting; Wheat: Vegetative
April

0 Early & single rice: Planting; Wheat: Heading*
My

0 Early rice: Heading*; Wheat: Filling
June

0 Early rice: Maturing; Single rice: Vegetative; Wheat: Harvesting
DAY

0 Early rice: Harvesting; Single rice: Heading*; Late rice: Planting
August

0 Single rice: Maturing; Late rice: Vegetative
September

0 Single rice: Harvesting; Late rice: Heading*; Wheat: Planting
October

0 Single rice: Harvesting; Late rice: Maturing; Wheat: Planting
November

0 Late rice: Harvesting; Wheat: Vegetative
December

0 Late rice: Harvesting; Wheat: Dormant

Note: * Moisture/Temperature sensitive stage of development
Source: Production estimates and crop assessment division, F AS, USDA.

35

Figure Al. Crop Calendar (Rice/Wheat)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Single rice: P H Hr
. P H

Late rrce:
Early rice: P H Hr
Wheat:

_ H P

Hr
.’

 

 

 

 

1 2 3 4 5 6 7 8 9 10

P: Planting, H: Heading, Hr: Harvesting

36

Table A3. Summary of Rainfall Data

GJnﬁ=nun)

Number Village ID Average(village) Average(province)

 

1

1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
3205
3206
3207
3208
3209
3210
3401
3402
3403
3404
3406
3407
3408
3409
3410
3412

369
424
394
407
518
477
529
491
545
547
1013
1013
1024
1024
1102
1102
805
890
837
918
950
703
977
995
1285
1077

37

Shanxi

Shanxi = 470

J iangsu

Jiangsu = 1046
Auﬂuﬁ

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

3413
3415
3417
3418
4101
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114

Table A3 (cont’d).

1097
1632
1673
1867
590
656
598
279
1312
805
727
643
858
914
783
514
560
627

Anhui= 1122

lienan

Henan = 705

 

Source: Monthly village rainfall data from 1978.1 — 1997.12

38

Figure A2. Rainfall Pattern in Each Province (1978-1997)

350

Rainfall(mm)

700

Rainfall(mm)

Shanxi province (10 villages)

 

 

 

 

 

 

 

 

Month/Year

J iangsu province (6 villages)

 

dr—~*

 
   

 

- 17'! I” r "I‘l‘l 1le1.

(”@0980'10’590‘308809099988958099”
888888-8888888.88.88.88

Month/Year

39

Rainfall(mm)

Rainfall(mm)

1000

‘1 4_1A 2197.1_.A*1

ﬁlir 42__171

24—42.

Figure A2 (cont’d).

Anhui province (14 villages)

Month/Year

Henan province (13 villages)

ea c stirs 6
0065009999890?
9388198843943»?

Month/Year

40

Appendix B

Consumption Growth and Yield Risk with CRRA Preferences

Starting from their insight, we derive a modiﬁed version of Blundell and Stoker’s
model that explicitly introduces yield risk from agricultural production.

The model analyzes the three-period consumption choice c0,c1,c2 of a consumer
over time periods t=0,l,2. For the purposes of tractability, we assume a constant relative

risk aversion felicity function U, with logarithmic preferences, U,(c,)=a, Inc,. The

consumer’s problem is:

Max 110 111(60) + 111 111(61) + 112 111(62)

a a
where we normalize +2 + =1 and =0: ,2 =—1, =__2_
40 1 42 40 0 1 1+6] 42 1+62

subject to the budget constraint:

c0+ cl + CZ =W+ 8‘ + 82
1+4 (1+0)(1+r2) 1+4 (1+a)(1+r2)

 

 

 

 

E E
Wheregl =y1-E0y1, 82 =Y2-Eoy2,W=/10+)’0+ 0311+ 0Y2.
1+r1 1+r2

W is expected wealth at period 0, which contains initial assets and the present value of
expected income to be received over the three periods. 81,82 are innovations in income
that are unknown as of period of 0, 81 is revealed in period 1, and 82 is revealed in period

2. Thus it is natural to assume that information about expected innovations in income is
updated in period 1.
The Euler equation for optimal allocation between period 1 and 2 is
a2 _ a1

— ——(I+£2)
CZ cl

From which we can derive consumption grth

41

a l a 8
Alnc2=—ln—‘-+— 022114-442-
a2 02 02 01

t
Var 8 8 4:
whereozzll =-———(—22—“—), 82 = 82 -E(82 I81)
W

Expected grth increases with the variance of updated income innovations conditional
on the previous period, 03'“ , and will be linear in the updated income innovation

normalized by the previous wealth.

To add agricultural production to this model, assume that the households manage
agricultural production like a competitive ﬁrm by hiring the needed labor inputs (from
the market or the family), use their land, and selling their commodities as price-takers.
Household income, in this case, would simply be agricultural proﬁt. We introduce this
assumption to rule out the endogeneity of labor supply decisions and income (In our
empirical analyses, we will relax the assumption on household activity but still control
for endogeneity of labor supply by using estimated measures of full income). ‘ Thus, we
deﬁne household proﬁts as:

y, = mg —w,_1L,_1 = p, f (L,_1)77, —w,_1L,_1 where 77, is i.i.d with mean 1.

We assume that period t production depends on inputs in period H, and that there are no
changes in price or wage during the period. Taking income as proﬁt less the value of
labor input and applying these to the income innovations of the previous model, we can

Show that income innovation terms based on crop production will be:
81 = yr - Eoyr = PQr = pf (Limit
82 = Y2 — Eon = Pf (L1 )772 - WL1 - E0 (Pf (L1 )772 '- W14)
«‘32 = 52 " E182 =Y2 — Eoy2 - Er {yz - Eoyz}
Since production is realized after input decisions are determined, the conditional variance

of income subsequent to shock realizations in period 1 will be: Var(8; Isl) = ”6'22“ with

pf (L1)

azzll- — Var(772 1771) and the scaling term 7r: (—

 

‘ Although this assumption may be unrealistic in the real world. Chaudhuri (1999) suggests that it is not
likely to change the relationship between the consumption and saving decisions, and yield risk under a
more ‘realistic’ model speciﬁcation.

42

If 77, the yield shock is proxied by a rainfall shocks, it is plausible that the

conditional variance of the rainfall shock will be an adequate proxy for the conditional
variance of the shock, 6’22“ = Var(772 | 771) .

Based on this derivation our analyses make use of two different measures of
income: gross value of annual grain yield, and the estimated “ﬁrll income” of the
household. When we use the value of grain yields to reﬂect this model derivation we still

have similar positive results.

43

Appendix C
Estimation of conditional variance of rainfall using GARCH model

A standard GARCH(1,1) model with no regressors in the mean and variance

equations:
Mean equation: R, = c + 8,

Variance equation: 0,2 = a) + ae,2_1 + ,60,2_1

We ﬁrst should study the basic statistical features of the monthly rainfall data, in order to
know if it is sensible to use the GARCH model with the rainfall data. For the proper
speciﬁcation of the mean equation in the model we have to test autocorrelation of the
coefﬁcients for the rainfall series as well as trend. If the rainfall has strong persistence
then we can use the ﬁrst difference of rainfall in the mean equation.

We did the below tests for each village using annual rainfall/selected months of rainfall

(sum of July through November).

1. Autocorrelation test: test for mean equation speciﬁcation
R, = a + pR,_1+ a,
Test H 0 : p = 0
All villages could not reject the null. Thus it implies that there is no serial correlation in

the rainfall in these villages.

2. Trend test: test for whether time trend exist in the rainfall
R, = a + pT + 6‘,
Test H 0 : p = 0
All villages could not reject the null. Thus it implies that there is no trend in the rainfall

in these villages.

3. Heteroskedasticity test: (R,, — R,)2 = a + p(R,-,_1 - 1702 + u,-,

44

Based on our mean equation speciﬁcation (6‘, = R, - c) this is the same as the

ARCH(I) speciﬁcation test.

Test H 0 : p = 0
Regression results show that 5 villages could reject the null at 10% level under ARCH(l)

Speciﬁcation and 4 villages could additionally reject the null under ARCH(2)

speciﬁcation at 10% level.

4. GARCH speciﬁcation test: test for whether GARCH (1,1) speciﬁcation is appropriate

for the data. Based on 1 and 2 results mean equation is speciﬁed like the below.
Mean equation: R, = c + 6‘,

Variance equation: 0,2 = a) + ae,z_1 + 60,24

Test HO :a=0,,B=0
16 villages among 44 villages could not reject the null. Most estimation shows that

d + ,3 < 1 so it implies that the rainfall shock cannot persist long time.

45

Figure C1. Evolution of (Fitted) Conditional Variance of Rainfall

 

100000

90000

80000

70000

60000

 

 

 

50000

40000

 

30000

 

20000

 

 

 

10000

 

 

 

 

 

46

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Uncertainty,” Journal of Development Economics, Vol.45, pp.245-269.

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Average Treatment Effects in the Correlated Random Coefﬁcient Model,” mimeo,
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Weather on the Income and Consumption of Farm Households in India,” International
Economic Review, Vol.23, pp.583 —— 594.

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1091 - 1135.

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49

Chapter 2

AN EMPIRICAL ANLAYSIS OF PRECAUTIONARY PORTFOLIO
ALLOCATION

1. Introduction

Economic theory predicts that households facing substantial risks increase their
savings, which are channeled into safe liquid assets in their portfolios. Although the issue
of household portfolio composition has attracted considerable attention in the literature,
most empirical work appears to focus on the presence of a precautionary saving motive
on total wealth rather than the allocation of wealth. Moreover, the literature explaining
the effect of risk on portfolios has generally studied ﬁnancial risks that could be
diversiﬁed against or are insurable in well-developed ﬁnancial markets, or the literature
has examined earnings risks, focusing on the implication of ‘temperance’.I

This paper aims to provide empirical evidence of the effect of rainfall variability
on farming household’s portfolio allocation based on Chinese household panel data.
Given the observation of substantial precautionary saving behavior in Yoo and Giles
(2003) it may be a natural question; which assets are favored by the rainy-day motive?
We test whether households facing more variable rainfall tend to hold more liquid assets
and consider the importance of the precautionary motive on holdings of each liquid asset:
including cash-in-hand, bank deposits, and grain stocks. Especially, we explore whether

grain stock is still an important instrument for a precautionary portfolio allocation of

 

' Kimball (1993) introduced the concept of ‘temperance’, which is a theoretical explanation of
precautionary portfolio allocation behavior. It implies that any risk that leads to an increase in
precautionary saving reduces the demand for risky assets. It will be explained further in section 3.

50

households in a rapidly growing transitional economy where ﬁnancial expansion is
occurring.2

We present empirical evidence based on relatively recent panel data (1995 —
2000) of households and villages in rural China that rainfall variability, as a proxy for
yield risk, inﬂuences the portfolio composition of households. We ﬁnd strong support for
an allocation toward liquid assets in response to greater rainfall variability. The result is
in accord with the model that both the desire to moderate total exposure to risk and
precautionary demand for liquidity contribute to portfolio choice.

We also observe that cash and deposits are important instruments for
precautionary wealth allocation in rural households, while it is difficult to ﬁnd similar
evidence for grain stocks. This ﬁnding agrees with the noticeable increase in ﬁnancial
assets in rural areas, in both absolute terms and in the percentage of total wealth holdings
in the recent years.3 It may imply that ﬁnancial instruments have been substituted for
conventional physical assets in precautionary portfolio allocation, at the same time as
dramatic institutional changes have occurred in contemporary rural China.4

The next section of this paper reviews the existing works literature on
precautionary portfolio allocation. Section 3 introduces models that theoretically explain
precautionary portfolio allocation behavior. We focus on the implications of demand for

liquidity in this section. It is followed by a description of our main data source, Research

 

2 Mckinley (1996) reports that farming households in rural China in a 1988 survey often did not hold any
ﬁnancial assets and implies that much of wealth is likely to take more concrete forms, such as grain stocks
and other commodities.

3 We ﬁnd from our data that ﬁnancial assets demonstrated a highest 9.3 percent rate of growth in the period
(1995 — 2000) among wealth components and the proportion of ﬁnancial assets in the total wealth has
increased from 11.2 percent in 1995 to 15.1 percent in 2000. Refer to Chapter 3 of the dissertation for more
detailed information.

4 We also witness that the proportion of the ﬁxed productive assets has decreased from 17.1 percent in 1995
to 15.1 percent in 2000 although the absolute amount of ﬁxed productive asset holdings slightly increased.
Chapter 3 of the dissertation documents in detail the evolution of composition in wealth in rural China.

51

Center on Rural Economy (RCRE) panel data and the empirical speciﬁcations. Our main
ﬁndings are presented in Section 5, which highlights the role of risk-induced portfolio

allocation. Finally, we summarize our results and their implications in Section 6.

2. Literature Review

Dreze and Modigliani (1972) ﬁrst studied the effect of earning risk on
consumption and portfolio choice and demonstrated using a two-period model that if
income risk is perfectly uninsurable and markets are not complete, uninsurable risks
could not only affect the level of wealth but also the composition of the portfolio.
Kimball (1993) provides a more general framework to study the interaction between
background risk and other undesirable risks. He shows that ‘temperance’, which is the
desire to reduce overall risk exposure even if both risks are statistically independent,
explains people’s response to unavoidable risk by adjusting their portfolio to choose safer
assets. Using simulations, Bertaut and Haliassos (1996) study the implications of a
consumption capital asset pricing model with income and stock-holding risk, and they
conclude that income risk lowers the demand for stocks and raises that for the secure
asset at any level of wealth. Paxson (1990) shows that when a limit on borrowing is
exogenous, this cost can be avoided by holding safer and more liquid assets, and it
implies that the effect of borrowing constraints reinforces that of income risk.

The theoretical prediction that unavoidable income risk and demand for liquidity
produced by the anticipation of liquidity constraints reduces the optimal investment in
risky securities and increases the liquid assets lend themselves to empirical scrutiny.

Although the effect of uncertainty on wealth allocation is clearly important, most

52

empirical work on the precautionary saving motive focuses on the presence rather than
the allocation of precautionary wealth and mostly on households in developed economies.
Only a few concentrate on the impact of uninsurable income risk on portfolio
composition and so far, these studies are primarily theoretical. Little empirical evidence
has been produced to back these theoretical claims. Dicks-Mireaux and King (1982) ﬁnd
a small effect of pension wealth on household portfolio composition in Canada. Guiso et
a1. (1996) ﬁnd evidence of a precautionary portfolio allocation in Italy, and Chakraborty
and Kazarosian (1999), using the National Longitudinal Survey, ﬁnd evidence of the
presence and allocation of precautionary assets in the United States.

Although most empirical studies on precautionary wealth allocation have been
based on households in developed economies, an important issue in development
economics is whether rural households keep speciﬁc liquid assets (grain stocks and
livestock) as buffer stocks to insulate their consumption from income ﬂuctuations.
Rosenzweig and Wolpin (1993) provide evidence that livestock sales and purchases are
used as part of farm households’ consumption-smoothing strategies. Udry (1995)
provides evidence that consumption smoothing is effected through the sale or purchase of
only those assets (mostly grain) not used in production. Fafchamps et al. (1998) indicate
that livestock transactions play less of a consumption-smoothing role than is often
assumed.

Compared to the previous studies that focused on liquid asset transactions in
response to the risk, Jalan and Ravallion (2001) focus on precautionary portfolio
decisions and ﬁnd evidence using the surveys of rural China in 1986 — 1991 that liquid

wealth, especially grain stocks, are held as a precaution against risk by farming

53

households in rural areas. Rosenzweig and Binswanger (1993) also showed that Indian
households hold a large amount of grain and other liquid assets based on the portfolio
theory. Dercon (1998) also provides empirical evidence that in Tanzania, cattle are used
as a liquid buffer which provides insurance against income shortfalls. In sum, although
economic theory emphasizes the role of risk in household portfolio decisions, little

empirical investigation has been done in the fast-growing transitional economies.

3. Theoretical Backgrounds

The theory of portfolio choice relates asset shares to the expected value and
degree of risk of asset returns. However, portfolio decisions may also be affected by
types of uncertainty other than interest rates. In particular, uninsurable, non-diversiﬁed
risks may induce prudent investors to reduce holdings of risky assets and also increase
holdings of liquid assets in order to cut their overall exposure to risk.

Here we present existing theories to explain why rural farmers hold liquid assets
among their wealth. First we introduce a prototype portfolio model.
Portfolio model.6 We think about a simple two-period portfolio problem involving two
assets, one with a risky return and one with a certain return. Since the rate of return on the
risky asset is uncertain, we denote it by a random variable 7r. Let w be initial wealth, and
let i 2 0 be the dollar amount invested in the risky asset. For convenience we assume that
the sure asset has a zero rate of return. In this case the second period wealth can be
written as

W=i(1+7r)+w-i=7r-i+w

 

5 I thank Dr. Jack Meyer for helpful comments on this section.
6 Refer to Varian (1992).

54

The expected utility from investing i in the risky asset can be written as
v(i) = Eu(w+ 7r-i),
and the ﬁrst two derivatives of expected utility with respect to i are
v'(i) = Eu'(w + 7: - i)7r
v"(i) = Eu"(w+ 7r - i)7r2.
Note that risk aversion implies that v"(i) is everywhere negative, so the second-order

condition will be automatically satisﬁed.

When we solve the maximization problem of the expected utility function, v(i)

with respect to i and assume that E7: > 0, the optimal investment will satisfy the ﬁrst-
order condition
Eu'(w+ 7r - i)7r = 0
We investigate how the demand for a risky asset changes as the probability distribution of
its return changes.
An interesting shift in the random variable is a mean-preserving spread that
increases the variance of It but leaves the mean constant. We can pararneterize such a

change by writing n+l(7r—7r') which is a special case. The expected value of this

random variable is 77 , but the variance is (l+/l)20'2 , so an increase in 21 leaves the

mean ﬁxed but causes an increase in the variance. A mean preserving spread of this sort
reduces investments in the risky assets. Thus the model implies that the demand for risky
assets will be reduced as the rate of the return becomes more uncertain.

Temperance. Pratt and Zeckhauser (1987) suggest that an unavoidable risk might lead an
agent to reduce exposure to another risk even if the two risks are statistically independent.

This tendency is called temperance. Although temperance cannot be measured as neatly

55

as risk aversion or prudence, Kimball (1990) shows that just as decreasing absolute risk
aversion implies that prudence is greater than risk aversion, decreasing absolute prudence
implies that temperance is greater than prudence.7 It means that any risk that leads to an
increase in precautionary saving reduces an agent’s demand for risky assets, both in
absolute terms and as a share of total savings.
Precautionary Demand for Liquidity. Prudence and temperance lead to increased
savings in terms of safe assets and, in the presence of unavoidable risk, even to an
absolute shiﬁ out of risky assets. The precautionary demand for liquidity leads to
increased holding of liquid assets in response to risk.8 It is less restrictive compared to
prudence and temperance in the sense that the precautionary demand for liquidity does
not depend critically on the shape of the underlying utility function. It arises instead from
convexity induced in the marginal value of a liquid asset (money) by liquidity constraints.

We consider a simple cash-in-advance model in which the level of cash balances
must be chosen before the size of a random cash infusion 6 is known.9 Let the direct
utility function be

u(c)+W =u(c)+w+6—c—z'-l,

where W is ﬁnal wealth, c is immediate consumption, u(c) is concave utility function, w
is initial wealth holding, z' is the cost of liquid asset holding and I is the initial liquid
asset the household chooses to hold. It implies that the precautionary demand for liquidity
can arise in response to uncertainty about temporary ﬂuctuations in cash infusions and

cash requirements.

 

7 The corresponding measure of the convexity of marginal utility is absolute prudence deﬁned as

-(um(-) / u"(-)) while the Pratt-Arrow measure of absolute risk aversion is deﬁned as -(u"(-) / u'(-)).

8 Here liquid asset is implicitly assumed as the riskless asset.
9 Refer to Svensson (1985) and Kimball (1992).

56

Given the direct utility function, the indirect utility function of a liquid asset is
V(l,6) =M€ax u(c)+w+6—c—rl s.t. c Sl+6
The ﬁrst-order condition for the optimal choice of c is
u'(c) -l = ,1 .
The Kuhn-Tucker condition for the Lagrange multiplier 11 is
c<1+6 and i=0 or c=l+6 and A>0.
The envelope theorem indicates the marginal value of liquid asset is
V,(l,6) = A — r = max(u'(l+6) —1—r,—r).

Since the problem of choosing the optimal initial amount of the liquid asset,

Mlax E[V(1,Ei)]
has the ﬁrst and second order conditions

Emma] = 0.

E[V,, (1,5)] < 0

We use these conditions later to show that the demand for liquidity increases as cash

infusions become more uncertain.

Figure 1 illustrates the marginal value of liquid asset graphically. Even if u(c) is

quadratic, so that the underlying marginal utility of consumption is linear, the marginal

value of liquid asset is a convex function of 1+ 6 because of the kink induced where the

cash-in—advance constraint stops binding. If u'(c) is itself convex, the cash-in-advance

constraint interacting with the convexity of u'(c) leads to a precautionary demand for

liquidity even if the constraint is certain to bind, making the kink irrelevant.

57

Now let on be a parameter that represents a mean-preserving spread to the
distribution of 5. The ﬁrst order condition deﬁnes a choice function I = l‘(a). A change
in or will affect the value of 1° and the value of E[V,(l,g)] directly as well.

Differentiating the ﬁrst order condition with respect to or, we get

a:
a

6

—E[V,(l,5)]eo
6a

Em (1,5)1+

Since V, is a convex function in 5 , E[V,(l,g)] does not decrease as g

undergoes a mean-preserving spread. Thus the second term is not negative. Since

E [V,, (1,5 )] is negative by the second order condition, ?— > 0. Thus the convexity of the
a

marginal value of a liquid asset as a ftmction of l + 6 means that a household will react
to uncertainty relative to the size of the liquid asset infusion (6 ) by increasing his or her
initial liquid wealth holding (I ). For instance, when a farming household expects its
yields to become more uncertain due to the volatile rainfall distribution, their demand for
liquid assets will increase.

Combined with temperance, the demand for liquid assets will magnify the effect
of risk and uncertainty on household wealth allocation and leads households to increase
their liquid assets away from risky ones. In this paper we focus on the precautionary
demand for liquidity rather than temperance, which emphasizes the safeness or volatility
of the asset. Estimating the share of liquid asset holding equation, our empirical
speciﬁcation presents evidence that households increase liquid assets in the face of

exogenous rainfall variability.

58

4. Data and Empirical Speciﬁcation

The analyses of household precautionary wealth portfolios in this paper use
village and household survey data provided by the Survey Department of the Research
Center on the Rural Economy (RCRE) at the Ministry of Agriculture in Beijing. Annual
village surveys from 44 villages of Shanxi, J iangsu, Anhui, and Henan provinces are used
in conjunction with panel data spanning the period 1995 to 2000 from roughly 3,400

10” Households are asked a range of questions regarding various

households per year.
asset ownership, savings, income from on-farm activities and household consumption,
land use, grain stocks, formal and informal access to and provision of credit, and transfers
from both village members and ﬁiends and family outside the village.12 The household
surveys are monitored by county agricultural research ofﬁces charged with collecting
expenditure, income, and labor allocation information on a monthly basis. A staff person
from each ofﬁce works with households to clear up inconsistencies in the survey.

We provide more detailed information about the household asset holdings in the
RCRE data through Tables D2 — D9 in the Appendix. Tables D2 and D3 provide
information about the grain stock and production of the sample households at each year-
end. They show that grain stocks were stable from 1986 to 2000 but grain production

declines after it achieved the peak point in 1996. The summary statistics of the total value

of production and non-production assets are presented in Table D4. Non-production

 

'0 RCRE has collected data from a panel of households from 1986 to 2002. Survey years are missing for
1992 and 1994, and we only have rainfall information through 1997. We use the ﬁrst half of the panel in
the analyses of this paper. We recently learned that the data for 1992 and 1994 were actually collected in
these provinces but that the forms were archived because shortages in staff and funds made it impractical to
enter the data.

” Table D1 in the Appendix presents the follow-up rates of the data set. It started with 4,152 households in
1995 and RCRE had followed 3,724 households for 6 years, so that its follow-up rate is 89.7%.

'2 The data are annual so we cannot identify intra-year risk. In a rural economy, one naturally expects there
to be seasonality.

59

assets consist of housing and durable goods, but mostly (80%) they are housing assets. As
the decline in ﬁxed investments in agriculture have been widely discussed both in the
media in China as well as in the academic literature (Brenner, 2001), our data also
conﬁrms both the fears of Chinese ofﬁcials and predictions of researchers that present
incentives are insufﬁcient to promote ﬁxed investment in agriculture. The growth rate of
the production asset has drastically declined from 15.5% in 1996 to 2.9% in 2000. This
phenomenon may partly relate to the return of the farming, and Table D5 provides the
partial conﬁrmation on this.

In Table D5 we show the household income and its composition from 1995 to
2000. The negative growth rate of farming income (on average —1 % across the period)
may affect a household’s time allocation and investment decision on production assets.
Traditionally, household agricultural production contributes most of income to farm
households, but the share of this income has decreased continuously from 73.4 % in 1995
to 64.3 % in 2000. Instead, the share of wage income has increased from 8.6% in 1995 to
14.9% in 2000.

Table D6 and D7 present a summary of ﬁnancial asset holdings of the sample
households in the period. Household bank deposits have increased a lot during the period
and the average growth rate is 18%, but more than 50% of households in the sample
report that they do not have any bank deposits. The households in the sample who have
cash-in-hand hold 9 — 11 % of their annual income as cash. Households borrow a similar
amount of money but their primary institutions for loans are not formal ones such as
banks and rural credit cooperatives, but informal ones such as individuals who do not

charge interest (refer to Table D7). It indicates an on-going process of ﬁnancial

60

deepening in the rural area or perhaps the uneven development of the Chinese rural
ﬁnancial system.

In this paper we deﬁne wealth as the sum of cash in hand, grain stock, bank
deposits, value of productive farm assets, housing materials, and consumer durables, but
we are not able to include land since it is difﬁcult to evaluate appropriately value of land
that rural Chinese households are allowed to use.13 Liquid wealth is deﬁned as cash-in-
hand, grain holdings, and, bank deposits of the household. 14 Table D8 presents the
composition of the household non-land wealth holdings. On average, 29% of (non-land)
wealth is held in liquid forms such as cash, deposits and grain stock. Housing is the most
important element in a household wealth (46%). Productive assets accounted for 12% of
wealth, consumer durables 13%. In comparison with the similar survey (1985 — 1990)
that Jalan and Ravallion (2001) used, the most conspicuous change in our data (1995 —
2000) is the rapid increase in bank deposits, which reﬂects the ﬁnancial deepening in
rural areas (refer to Table D9).

Next we introduce the baseline speciﬁcation for empirical analysis. We propose
the empirical speciﬁcations below to test whether a household reacts to rainfall variability
by increasing their share of liquid wealth holdings. The empirical speciﬁcations take the

reduced form approach following the previous works.

 

'3 Since land is not owned privately by households and a rental market for land in rural China has not been
well developed, most of studies that tried to include land in household’s total wealth had imputed the value
of user right to land. Brenner (2001) reports that land is one of the largest components of household wealth
in rural China, but changes in average imputed values of user rights to land were smallest (1.8 % per annum
from 1988 to 1995), although all components of wealth had increased. He points out that land exerts an
equalizing effect on overall wealth distribution in rural China since user rights to land are fairly evenly
distributed.

1’ Jewelry (especially gold) might also be an important liquid asset. Frankenberg, Smith, and Thomas
(2003) report that many Indonesian households store some of their wealth in the form of gold and rural
households used liquid wealth, particularly gold to smooth consumption. Since we do not have any
information about jewelry in our data, we are not able to include this asset.

61

LS,-, = a +Z£,y +214, +,ij + Vjp+Djn+uit ,
where LS”: The share of liquid asset holding by the household i at time t

Z i, : Household demographics

Li, : Area of land managed by the household

m : Rainfall risk of village j (the coefﬁcient of rainfall variation)

Vj : Village variables

D, : Province-year interaction dummies.

Here we estimate the share of total precautionary liquidity wealth holdings as well as
every component of conventionally assumed liquid assets such as cash-in-hand, deposits,
and grain stocks.

If households hold more wealth in liquid form when they face a higher risk of

rainfall, then ,6 will be positive. The coefﬁcient of rainfall variation, which is the

variance of rainfall divided by the average rainfall in village j, is used for the proxy for
exogenous aggregate risk measure. In estimating the above equation, the vector Z
includes age and age squared of the household head, household composition, and
education level of adult members in a household. L is the land area per capita that a
household manages and V are geographic variables including features of topography of
the communities in which the household resides, as well as socio-economic
characteristics of the country in which the village of the household is located in. A time
trend and province level ﬁxed effect can be controlled by the interaction terms between

the year and province dummies

62

Since the rainfall variability that we use does not vary across households within a
village or over time, there may be other village-speciﬁc factors that affect the liquid
wealth holdings. Hence, in our baseline empirical speciﬁcations we make use of many
village variables to control for proximity to off-farm markets, local topographical
characteristics, and village socio-economic infrastructure which might be correlated with
liquid asset holdings and thus the estimates that we are interested in could be biased.
Location variables include distance to the nearest public road, and a dummy variable
indicating whether the village is near a city. Indicator variables denoting village location
in city, suburb, or in mountainous or hilly areas provide information about local
topography. We use annual net income per capita, a permanent village population,
average education of a village, and various village infrastructures such as safe, sanitary
water, TV, phones, and roads to control for the extent of village involvement and
capability in the local economy. Finally, the share of full-time farming households in a
village and the number of village laborers employed in local and distant labor markets
may be used to control access to non-fann employment. Table D10 provides summary
statistics of the variables we use in the regressions. We can ﬁnd some characteristics of
the typical household; they are mostly farmers (82%) and the size of households is 4 on
average. The head of the household is approximately 45 years old and typically a
secondary school graduate.

Next, we estimate the equation based on the above speciﬁcation and as an
extension of the baseline model we introduce an interaction term between rainfall

variability and household characteristics (land holdings per capita). We also introduce the

63

village-year dummies to control the village-speciﬁc factors across the period that may be

correlated with rainfall variability.”

5. Empirical Results

In this section, we ﬁrst introduce the results from our basic speciﬁcations. The
baseline results are presented in Table 1. It shows that the estimated ,8 is negative but

insigniﬁcant in the share of total liquid wealth regression. When we do the same

regressions in each liquid asset the results are somewhat different; especially, cash-in-
hand and bank deposit regressions show ,3 is positive as we expected, but it is still

negative and signiﬁcant for grain stock. When we introduce the interaction term between
land and the rainfall variability as a proxy for idiosyncratic risk measure, the net effect of
the rainfall risk is positive and signiﬁcant in most regressions except grain stocks when it
is evaluated at the mean of arable land per capita.16

It is most noticeable in the baseline regression results that the grain stock
regressions provide counter-intuitive results. We suspect that this is mainly due to
omitted variable problems. Although the geographic effects that we introduced in the
regressions might pick up precautionary wealth effects of covariate risk, we cannot
control every possible aggregate variable under the baseline speciﬁcation. We introduce
the village-time dummies to control these village-level covariate effects in order to better
identify the precautionary portfolio effect of village rainfall variability although we can’t

directly estimate level effects but only through interactions.

 

‘5 When we check the empirical densities of liquid wealth and its, liquid wealth holdings appear to have
skewed and kurtosic distributions. When we can ﬁnd there are some extreme values least absolute
deviation (LAD) procedures might be preferable and could be applied. Although it is not a big issue
because we get rid of gross outliers we also use the quantile estimation and the results are similar to OLS.

‘6 Mean of arable land per capita is 1.369 in Table A10. Please refer to Table l for the detailed result

64

Although we introduce some important village variables with province-time
dummies in the baseline regressions, there might still be important missing variables in
the regression. One of the possible important covariate risk variables may be the risk
associated with grain prices. As Park (2001) argued, households are likely to store grains
as an ex-ante hedge against consumption price risk.17 In isolated markets, grain is likely
to be expensive when the harvest is poor, and the household’s added incentive to store
grain is an ex-ante hedge against consumption price risk. If the grain price risk is strongly
positively correlated with the rainfall variation, households tend to store more grain stock
as they face more volatile rainfall. Since we do not have information on grain prices in
our data, we introduced province time dummies to at least control for province-level price
variation.

Next we extend our baseline estimation in order to introduce the village-time
dummies instead of province-time dummies. When we introduce idiosyncratic risk as an
interaction term between land and the coefﬁcient of rainfall variation, we are able to
introduce village-time dummies to control the aggregate village level variables such as
consumption price variation. We expect that households who manage more land are
likely to increase liquid wealth holdings in response to greater rainfall variation, since
more land may be affected to a greater degree by rainfall variability and rainfall shocks.18

Table 2 presents the new results with village-time dummies.

 

'7 There are several candidate explanations for the reason farmers in China hold grain stocks despite the
relatively low production return of grain investment. According to Kc (2000), securing grain consumption
is the most important motivation for high levels of grain storage in rural China. This attitude is partly
attributed to changes in government’s grain policies, market risks, and vulnerability to agroclirnatic shocks.
But Yang (1999) has argued that with the near realization of grain self-sufﬁciency after 1984, security
motives for grain storage should be weaker.

'8 We here implicitly assumed that households with more land have greater exposure to agroclimatic
variables for the identiﬁcation. However, there is a possibility that transfers of land are correlated with
these variables experienced by the households and it can contribute to bias. Severe weather shocks affect

65

Each liquid asset, except grain stocks, provides a positive coefﬁcient of the
interaction of an idiosyncratic risk with land but the regression result for total liquid
assets still shows a negative sign of the coefﬁcient. Since rainfall shocks could affect the
liquid wealth decision especially grain stocks and the rainfall variability, we also
introduce another interaction term between land and the lagged rainfall shock.19 Share of
total liquid wealth estimation results prove to be signiﬁcant and provide the expected
positive sign of the coefﬁcients. Grain stock estimation results still show negative but
insigniﬁcant ones when we introduce the rainfall shock variable. Results are reported in
Table 3.

Aside from rainfall risk, we ﬁnd a number of other factors that inﬂuence a
household’s composition of wealth. There are strong demographic effects on portfolio
behavior. On the other hand, it is difﬁcult to ﬁnd from the results that education has a
consistently strong effect. Households with more dependents tend to hold less liquid
wealth; more arable land would not increase cash-in-hand or bank deposits, but would
increase the grain stocks of the household. Liquid wealth holdings increase as household
size increases and liquid wealth proﬁles show the inverted U shape with respect to age
like the typical life cycle pattern of wealth accumulation. Based on our estimation the

peak point of the cash holdings of a household is when the head of a household reaches

 

agriculture and contribute the decisions to transfer land to other households. But if we are able to assume
that farmers know the distribution of rainfalls in their villages, not speciﬁc rainfall shocks it is likely that
their current land holdings would not be varied much by the distribution of rainfall which is assumed here
to be constant across the period.

'9 Since we only have the village rainfall data until 1997 number of observations is reduced when we
introduced the rainfall shock. Thus we introduced the rainfall shock variables only in some of our
regressions. Another issue on this interaction term is that the coefﬁcient might be biased when it is likely
that household land is endogenous with the rainfall shock if households respond to negative shocks by
reducing land. Thus endogeneity of household land and the rainfall shock will mean that the coefﬁcient of
the newly introduced interaction term will be biased toward zero if land is adjusted in response to negative
shocks.

66

an approximate age of 43 years. Conceivably they are more disposed toward engaging in
emerging opportunities for money-based market transactions.

There are some caveats for interpretation of these results. First there is an
inconsistency between the model and the empirical results that we provided. Although
our major conclusions mostly depend on the empirical results of interaction terms
between land and rainfall variability our model does not provide any speciﬁc implications
on this. The positive coefﬁcient of the interaction term would not necessarily imply the
precautionary portfolio behavior of a household. Another issue related to estimation is
potential measurement error. It may be a general issue in this type of empirical work
since the wealth variables are notorious in measurement error problems in most
household surveys.20 Thus the interpretation of our empirical results should be very
cautious and limited due to these major issues.

Since we have doubts about some of the explanatory variables being strictly
exogenous, it is useful to test and correct errors for serial correlation.21 We ﬁnd fairly
strong evidence of positive serial correlation. First- differencing the dependent variable
might be a possible econometric solution for this. When we do ﬁrst difference the
dependent variables, nothing of interest is signiﬁcant. We can calculate serial correlation-
robust standard errors, and retain signiﬁcant results (Refer to Table 3).

Our results appear to provide limited evidence on the precautionary portfolio
behavior of a rural Chinese household, and especially cash-in-hand and bank deposits

seem to be important assets for this purpose while grain stock regression would not

 

2° Deaton (1989) notes that savings or wealth is even more difﬁcult to measure in developing countries and
the resulting data inadequacies are pervasive and have seriously hampered progress in answering the basic
questions. Gersovitz (1988) points out that cash-in-hand is usually under-reported in most household
surveys and these systematic reporting errors might cause the skewed distribution of their liquid wealth.

2' Serial correlation in the error term happens when there is an unobserved time invariant factor.

67

provide the consistent and signiﬁcant results through our estimations. Our empirical
results on household portfolio behavior might provide some implications on current

macroeconomic issues in China. We will brieﬂy mention these as concluding remarks.

6. Conclusions

Accompanying recent ﬁnancial deepening in rural China, ﬁnancial assets that
farming households keep have increased dramatically, both in absolute terms and as a
percentage of total wealth holdings. With relatively recent panel data (1995 — 2000) we
analyzed the issue of precautionary portfolio allocation and tried to answer the question:
why the farming households in China hold substantial amounts of liquid assets in their
wealth portfolio. 22

Although economic theory emphasizes the role of risk in household portfolio
decisions, which induces them to maintain high levels of relatively liquid wealth, little
has been done to empirically investigate it under the fast-growing transitional economy.
We provide limited empirical evidence of precautionary portfolio allocation in rural
Chinese households and document that ﬁnancial assets are one of their important
instruments to cope with the uninsured aggregate risk in contemporary rural China. We
imply that as the new available ﬁnancial instrument emerges in rural areas the role of

conventional instruments such as grain stocks may become smaller.23 It is in accord with

 

22 Although we could not speciﬁcally explain the increase in holdings of ﬁnancial assets, one possible
explanation will be macroeconomic phenomenon such as ﬁnancial deepening rural areas.

23 It has been argued that under a thin and particularly fragmented ﬁnancial markets in many rural
developing areas, physical asset accumulation may substitute for the store of wealth function of ﬁnancial
assets as well as a buffer stock in the face of income variability.

68

the recent argument that grain storage may be held as other risk concerns such as a price
hedge, not in lieu of credit or other ex-post consumption smoothing mechanisms.24

Our empirical ﬁndings also have some implications on macroeconomic issues in
China. While this paper provides one possible explanation of the rapid growth in
ﬁnancial asset holdings in rural China, it also sheds light on the question of how China
has been able to combine a rapid expansion in monetary supply with only a moderate
degree of inﬂation. Illustration of precautionary wealth portfolio behavior in this paper
could provide microeconomic evidence for possible explanations of this macroeconomic
puzzle in China. It implies that an increase in precautionary demands for liquidity of the
rural households appears to be helpﬁll for reducing inﬂationary pressure in a rapidly
growing Chinese economy.

Our empirical ﬁndings may also provide some implications on the issue of
poverty in developing economies.25 As many development economists and politicians
have pointed out, this rational portfolio behavior in the presence of uninsured risk can
help perpetuate poverty in rural areas. Although the plausibility of the claim that
precautionary portfolio behavior can cause poverty is not self-evident, it is problematic
that the poor household might hold excessively large amounts of unproductive liquid
assets. The precautionary desire to hold grain or liquid ﬁnancial assets may prevent
households from undertaking productive investments. Opportunities for investment in
nonagricultural activity or housing, coupled with better functioning ﬁnancial markets,

have served to divert resources away from ﬁxed investments. Understanding this aspect

 

2’ Refer to Park (2001) and Jalan and Ravallion (2001).
25 Refer to Jalan and Ravallion (2001).

69

of household portfolio behavior may offer some insights into the issue of the decline in

ﬁxed investment of agriculture in China.

70

Figure 1. The Convexity of Marginal Value of the Liquid Asset

‘1
VI

u'(0)—1—z'

u"'(1)

 

=l+6

 

 

 

71

Table 1. Regression Results for Share of Liquid Wealth-holdings with Province-year

Dummies

1. Share of Total Liquid Wealth

 

Variables
Coefﬁcient of rainfall variation

Land*Coefﬁcient of variation

Land“ Rainfall shock

(=lRainfall-Average of Rainfall!)

Household size

Age of household head

Age squared of household head

Number of dependents

Proportion of illiterates in household

Proportion of elementary school

educated in household

Proportion of secondary school
educated in household

Arable land per capita

Village located in hilly area (dummy)

Village located in mountain area (dummy)

Village located in a city suburb (dummy)

Share of Total Liquid Wealth

-0.0026 0.0023 -0.0469 0.0573 0.0518
(0.0085) (0.0145) (00122)" (00214)" (0.0228)*
0.0738 0.0899 0.0868

(0.0151)M (0.0280)" (0.0282)“

0.0039

(0.0023)

0.0098 0.0081 0.0095 0.0079 0.0079

(00013)" (00022)" (00013)" (00022)" (00022)"

0.0006 0.0008 0.0005 0.0008 0.0008
(0.0007) (0.0012) (0.0007) (0.0012) (0.0012)
0.00001 0.00001 0.00001 0.00001 0.0000
(0.00001) (0.00001) (0.00001) (0.00001) (0.0000)

0.0041 0.0023 0.0044 0.0028 0.0027
(0.0016)* (0.0028) (0.0016)" (0.0028) (0.0028)
0.0206 0.0251 0.0212 -0.0260 0.0260
(0.0086)* (0.0149) (0.0086)* (0.0148) (0.0148)

0.0080 0.0027 0.0066 0.0008 0.0009
(0.0081) (0.0141) (0.0081) (0.0141) (0.0141)
0.0122 0.0300 0.0132 0.0309 0.0308
(0.0080) (0.0141)* (0.0080) (0.0141)* (0.0140)*

0.0033 0.0117 0.0193 0.0150 0.0150
(0.0017) (00030)" (00049)" (0.0088) (0.0088)
0.0125 0.0576 0.0133 0.0570 0.0557

(0.0045)W (00092)" (00045)" (00092)" (00093)"

0.1051 0.1359 0.0958 0.1240 0.1232
(0.0056)" (0.0117)M (0.0060)M (00124)" (00125)"

-0.0873 -0.0700 -0.0861 -0.0698 -0.0699

72

Annual net income per capita in village

Number of people who migrated out

among total residents

Permanent residents in a village

Full time farming households ratio in village

Share of village cadre in village

Share of rice among total sown area in village

Share of village wheat among
total sown area in village

Share of households with TVs

Share of households with phones

Share of illiterate laborers

among total laborers

Share of people graduate
elementary school in village

Share of people who graduate
secondary school in village

Share of households using safe
and sanitary water

Distance to the main
concreted road (Km)

Table l (cont’d)

(00053)“ (0.0093)" (0.0053)"

0.0001 -0.0001 0.0001
(0.0000)" (0.0001) (0.0000)"

0.5726 2.0103 0.5692
(0.1638)" (0.3233)" (0.1644)"

0.0011 -0.0020 0.0011

(00093)" (00093)"

0.0001 0.0001
(0.0001) (0.0001)

2.0859 2.0710
(03241)" (03249)"

-0.0001 -0.0001

(0.0005)* (0.0003)" (0.0009) (000005)" (00000)"

-0.0008 0.0304 0.0006
(0.0058) (0.0115)" (0.0058)

-2.4294 -7. 1025 -2.8005
(0.5339)" (1.0960)" (0.5429)"

0.0870 0.0179 0.0902
(0.0071)" (0.0135) (0.0071)**

-0.l749 -0. 1918 -0. 1662
(0.0104)" (0.0181)" (0.0107)“

0.0685 -0.0753 0.0717
(00107)“ (00162)" (0.0107)"“'I

-0.0405 0.3702 -0.0317
(0.0097)" (0.0923)" (0.0098)"

-0.0546 0.0398 0.0409
(0.0349) (0.0707) (0.0349)

0.1455 0.0144 0.1474
(0.0300)M (0.0526) (0.0300)M

0.0753 0.1845 0.0911
(0.0357)* (0.0790)* (0.0356)*

0.0056 0.0336 0.0112
(0.0038) (0.0074)** (0.0039)M

0.0031 0.0051 0.0029
(0.0004)** (0.0008)“ (0.0004)M

73

0.0365 0.0380
(0.0116)" (0.0120)"

-7.5288 -7.4251
(1.1053)" (1.1177)"

0.0305 0.0308
(0.0140)* (0.0140)*

0.1897 0.1914
(0.0181)" (0.0181)“

0.0673 0.0664
(0.0164)" (0.0164)"

0.3356 0.3287
(00936)" (00939)"

0.0328 0.0390
(0.0707) (0.0720)

0.0230 0.0258
(0.0529) (0.0536)

0.2087 0.1975
(0.0787)** (0.0822)*

0.0266 0.0273
(0.0077)"“I (0.0078)"

0.0050 0.0050
(0.0008)" (0.0008)"

Table 1 (cont’d)

Rainfall shock of M 00038 -0.0049 -0.0023
(0.0019)* (0.0019)* (0.0029)

Constant 0.3338 0.3646 0.3407 0.3659 0.3696
(00358)" (0.0721)" (0.0359)" (0.0722)" (0.0727)"

Observations 1661 1 5437 1661 1 5437 5437
R-squared 0.13 0.19 0.13 0.19 0.19

 

I Notes: 1. Robust standard errors in parentheses
2. * signiﬁcant at 5%, ** signiﬁcant at 1%
3. All speciﬁcations include province*year dummies. Their coefﬁcients are jointly signiﬁcant.

74

 

2. Share of Cash-in-hand

Table 1 (cont’d).

 

Variables
Coefﬁcient of rainfall variation

Land‘Coefﬁcient of variation

Land“ Rainfall shock

(=|Rainfall-Average of Rainfalll)

Household size

Age of household head

Age squared of household head

Number of dependents

Proportion of illiterates in household

Proportion of elementary school

educated in household

Proportion of secondary school

educated in household

Arable land per capita

Village located in hilly area (dummy)

Village located in mountain area (dummy)

Village located in a city suburb (dummy)

Share of Cash-in-hand
0.0137 0.0132 -0.0091 -0.0623 -0.0538
(0.0044)" (0.0067)* (0.0065) (0.0106)” (0.0113)"

0.0380 0.0803 0.0756
(00075)“ (00125)" (0.0126)"

0.0029
(0.0009)M

-0.0027 -0.0040 -0.0026 -0.0039 -0.0039
(0.0006)" (0.0009)" (0.0006)" (0.0009)"”‘I (0.0009)"

0.0002 0.0002 0.0001 0.0002 0.0002
(0.0003) (0.0005) (0.0003) (0.0005) (0.0005)

0.00001 0.00001 0.00001 0.00001 0.0000
(0.00001) (0.00001) (0.00001) (0.00001) (0.0000)

0.0007 0.0017 0.0009 0.0021 0.0020
(0.0007) (0.0012) (0.0007) (0.0012) (0.0012)

0.0170 0.0029 0.0167 0.0021 0.0021
(0.0036)" (0.0060) (0.0036)" (0.0059) (0.0059)

0.0164 0.0141 0.0157 0.0123 0.0125
(0.0032)** (0.0056)* (0.0032)** (0.0056)* (0.0056)*

0.0040 0.0024 0.0036 0.0016 0.0017
(0.0031) (0.0054) (0.0031) (0.0054) (0.0054)

0.0057 0.0022 0.0173 0.0260 0.0261
(0.0008)" (0.0013) (0.0026)" (0.0042)** (0.0041)M

0.0118 0.0105 0.0114 0.0110 0.0130
(0.0019)" (0.0039)" (0.0019)" (0.0039)" (0.0040)"

0.0115 0.0168 0.0162 0.0274 0.0288
(00022)" (0.0038)" (00023)" (00040)" (00041)"

-0.0190 -0.0231 -0.0184 -0.0229 -0.0231
(0.0020)" (0.0036)“ (00020)" (00036)” (0.0036)”

75

Annual net income per capita in village

Number of people who migrated out

among total residents

Permanent residents in a village

Table 1 (cont’d)
-0.0001 0.0001 -0.0001 0.0001 0.0001
(0.0000)** (0.0000) (00000)" (0.0000) (0.0001)

0.5153 0.0660 0.5170 0.0015 0.0215
(0.0621)" (0.1040) (0.0623)" (0.1061) (0.1065)

-0.0001 -0.0001 -0.0001 -0.0001 -0.0001
(0.0000)* (0.0000)" (0.0000)" (0.0000)" (0.0000)"”'I

Full time farming households ratio in village -0.0119 0.0127 -0.011 1 0.0181 0.0205

Share of village cadre in village

(0.0025)M (00045)" (00025)" (0.0046)" (0.0046)"

0.1262 .1.3730 0.3170 -l.7538 -1.5926
(0.2743) (0.4906)“ (0.2854) (05025)" (05104)"

Share of rice among total sown area in village -0.0407 -0.0475 -0.0423 -0.0587 —0.0593

Share of village wheat among
total sown area in village

Share of households with TVs

Share of households with phones

Share of illiterate laborers

among total laborers

Share of people graduate
elementary school in village

Share of people who graduate
secondary school in village

Share of households using safe
and sanitary water

Distance to the main
concreted road (Km)

Rainfall shock of t-l

(00034)" (0.0061)“ (0.0034)“ (0.0062)“ (0.0062)“

0.0567 -0.0764 0.0522 -0.0746 0.0772
(0.0038)" (00074)" (00039)" (00074)" (00075)"

0.0074 -0.0420 0.0091 -0.0348 -0.0335
(0.0054) (0.0084)‘MI (0.0053) (0.0083)" (0.0083)"

0.0252 0.0101 0.0207 0.0410 0.0518
(0.0042)** (0.0384) (0.0043)M (0.0381) (0.0387)

0.0906 0.0179 0.0977 0.0242 0.0146
(0.0173)M (0.0295) (0.0173)" (0.0294) (0.0296)

0.0353 -0.0716 0.0363 0.0793 -0.0837
(0.0144)* (0.0248)” (0.0144)* (0.0247)M (00247)"

0.0840 0.0957 0.0922 0.1174 0.1000
(0.0158)" (0.0318)" (0.0160)" (00322)" (00329)"

0.0094 0.0041 0.0123 0.0104 0.0093
(0.0016)" (0.0033) (0.0017)** (0.0034)M (0.0034)M

0.0011 0.0005 0.0012 0.0006 0.0006
(0.0001)** (0.0003) (0.0001)" (0.0003) (0.0003)*

-0.0035 -0.0045 -0.0035
(0.0007)" (0.0007)" (0.0013)"

76

Table 1 (cont’d)

Constant 0.0775 0.1530 0.0811 0.1541 0.1598
(00166)" (00309)" (0.0167)“ (0.0308)" (0.0308)"

Observations 1661 l 5437 1661 l 5437 5437
R-squared 0.09 0.09 0.09 0.09 0.09

Notes: 1. Robust standard errors in parentheses
2. * signiﬁcant at 5%, ** signiﬁcant at 1%
3. All speciﬁcations include province*year dummies. Their coefﬁcients are jointly signiﬁcant.

 

77

3. Share of Grain Stocks

Table 1 (cont’d).

 

 

Variables Share of Grain Stocks
Coefficient of rainfall variation -0.0168 -0.0321 0.0342 -0.0044 -0.0209
(0.0034)" (0.0067)" (0.0056)“ (0.0111) (0.0117)
Land‘Coefﬁcient of variation -0.0850 -0.0470 -0.0380
(0.0086)*"' (0.0174)" (0.0176)*
Land“ Rainfall shock -0.0075
(=|Rainfall-Average of Rainfalll) (0.0018)?
Household size -0.0033 -0.0038 —0.0035 -0.0039 -0.0039
(0.0006)" (0.0011)” (0.0006)" (0.0011)" (0.0011)"
Age of household head -0.0005 0.0001 -0.0005 0.0001 -0.0001
(0.0003) (0.0006) (0.0003) (0.0006) (0.0006)
Age squared of household head 0.00001 -0.00001 0.00001 -0.00001 -0.0000
(0.00001) (0.00001) (0.00001) (0.00001) (0.0000)
Number of dependents 0.0059 0.0074 0.0055 0.0071 0.0072
(0.0007)“ (0.0013)" (0.0007)" (0.0013)" (0.0013)"
Proportion of illiterates in household 0.0191 0.0327 0.0198 0.0331 0.0333
(0.0043)" (0.0073)" (0.0042)" (0.0073)" (0.0073)"
Proportion of elementary school 0.0089 0.0225 0.0106 0.0236 0.0232
educated in household (0.0037)* (0.0064)” (0.0036)" (0.0063)" (0.0063)"
Proportion of secondary school -0.0033 0.0056 -0.0022 0.0060 0.0059
educated in household (0.0037) (0.0063) (0.0036) (0.0063) (0.0063)
Arable land per capita 0.0205 0.0272 0.0464 0.0412 0.0413
(0.0010)” (0.0021)" (0.0028)” (0.0055)" (00055)"
Village located in hilly area (dummy) -0.0148 -0.0287 -0.0138 -0.0290 -0.0329
(O.0021)"“'I (0.0048)" (0.0021)“ (0.0048)" (0.0048)"
Village located in mountain area (dummy) -0.0598 -0.0864 -0.0705 -0.0926 -0.0953
(0.0031)" (0.0062)” (0.0033)“ (0.0066)" (00067)"
Village located in a city suburb (dummy) -0.0153 0.0019 -0.0167 0.0018 0.0021
(00022)" (0.0048) (0.0022)** (0.0047) (0.0048)

78

 

Table 1 (cont’d)

Annual net income per capita in village
Number of people who migrated out
among total residents

Permanent residents in a village

Full time farming households ratio in village

Share of village cadre in village

Share of rice among total sown area in village

Share of village wheat among

total sown area in village

Share of households with TVs

Share of households with phones

Share of illiterate laborers

among total laborers

Share of people graduate
elementary school in village

Share of people who graduate
secondary school in village

Share of households using safe
and sanitary water

Distance to the main
concreted road (Km)

Rainfall shock of H

0.0000
(0.0000)

0.2163
(0.0661 )**

0.0000
(0.0000)**

0.0504
(0.0027)M

-3.1079
(0.2530)M

0.0252
(0.0037)"

0.0627
(0.0058)"

0.0085
(0.0052)

0.0031
(0.0038)

0.1142
(0.0140)**

0.2310
(0.0135)**

0.3133
(0.0168)"

-0.0123
(0.0020)’Ml

0.0001
(0.0001)

79

-0.0000
(0.0000)*

0.9419
(0.1655)"

0.0000
(0.0000)**

0.0772
(0.0065)"

-4.7006
(0.5860)"

0.0409
(0.0071)M

0.1231
(0.0110)**

0.0187
(0.0071)"

0.0194
(0.0420)

0.0581
(0.0303)

0.2431
(0.0237)**

0.3407
(0.0361)"

0.0050
(0.0045)

0.0023
(0.0004)"

0.0051
(0.0014)M

0.0000
(0.0000)

0.2202
(0.0651)”

0.0000
(0.0000)**

0.0487
(0.0027)**

-2.6806
(0.2515)M

0.0215
(0.0036)"

0.0726
(0.0060)"

-0.0122
(0.0052)*

0.0133
(0.0038)"

0.0985
(0.0138)"

0.2332
(0.0134)**

0.2950
(0.0163)"

0.0059
(0.0021)M

0.0001
(0.0001)

0.0000
(0.0000)*

0.9024
(0.1654)"

-0.0000
(0.0000)"

0.0740
(0.0064)"

-4.4776
(0.5892)"

-0.0343
(0.0071)“

0.1242
(00110)"

0.0146
(0.0073)*

0.0375
(0.0424)

0.0545
(0.0300)

0.2476
(0.0236)"

0.3280
(0.0351)**

0.0014
(0.0046)

0.0024
(00004)"

0.0045
(0.0014)**

-0.0000
(0.0000)""'l

0.9467
(0.1658)"

0.0000
(00000)"

0.0695
(0.0065)“

-4.7878
(05958)“l

0.0333
(0.0071)**

-0.1191
(0.0110)“

0.01 19
(0.0074)

0.0581
(0.0431)

0.0729
(0.0307)*

0.2562
(0.0240)**

0.3615
(0.0373)"

0.0035
(0.0046)

0.0024
(0.0004)**

0.0068
(0.0015)"

 

Table 1 (cont’d)

Constant 0.0694 0.0451 0.0774 0.0458 0.0567
(00156)" (0.0313) (0.0155)M (0.0311) (0.0312)

Observations 1661 1 5437 1661 1 5437 5437
R-squared 0.27 0.33 0.28 0.33 0.33

Notes: 1. Robust standard errors in parentheses
2. * signiﬁcant at 5%, "”" signiﬁcant at 1%
3. All speciﬁcations include province*year dummies. Their coefﬁcients are jointly signiﬁcant.

80

Table 1 (cont’d).

4. Share of Bank Deposits

 

Variables Share of Bank Deposits

 

Coefﬁcient ofrainfall variation 0.0005 0.0166 0.0721 0.0094 0.0229
(0.0063) (0.0055)* (0.0093)** (0.0169) (0.0182)

Land*Coefﬁcient of variation 0.1209 0.0566 0.0492
(0.0126)" (0.0240)‘ (0.0243)‘

Land“ Rainfall shock 0.0066
(=|Rainfall-Average of Rainfalll) (0.0021)"
Household size 00038 -0.0002 -0.0034 -0.0001 -0.0001

(0.0010)M (0.0017) (0.0010)** (0.0017) (0.0017)

Age of household head 0.0009 0.0005 0.0009 0.0005 0.0006
(0.0006) (0.0009) (0.0006) (0.0009) (0.0009)

Age squared of household head -0.00001 -0.00001 -0.00001 -0.00001 -0.0000
(0.00001) (0.00001) (0.00001) (0.00001) (0.0000)

Number of dependents -0.0025 -0.0067 -0.0020 -0.0065 -0.0066
(0.0013) (0.0021)" (0.0013) (0.0022)" (0.0022)”

Proportion of illiterates in household -0.0568 -0.0607 -0.0577 -0.0612 -0.0613
(0.0069)” (0.0116)" (0.0069)" (0.0116)” (0.0116)"

Proportion of elementary school -0.0173 -0.0339 -0.0197 -0.0351 -0.0348
educated in household (0.0065)" (0.0112)" (0.0065)‘MI (0.0112)" (0.0112)"

Proportion of secondary school -0.0130 -0.0379 -0.0145 -0.0385 -0.0384
educated in household (0.0066)* (0.0114)‘Ml (0.0066)* (0.0114)""" (0.0113)"

Arable land per capita -0.0115 —0.0133 -0.0484 -0.0301 -0.0302

(0.0014)“ (0.0022)" (0.0037)" (0.0068)" (00068)"

Village located in hilly area (dummy) -0.0095 -0.0393 -0.0110 -0.0389 -0.0358
(0.0037)" (0.0071)" (0.0037)“ (0.0070)" (00070)"

Village located in mountain area (dummy) -0.05 67 -0.0663 -0.0415 -0.0588 -0.0567
(0.0043)" (0.0094)" (0.0046)" (0.0100)" (00101)"

Village located in a city suburb (dummy) -0.0530 -0.0488 -0.0510 -0.0487 -0.0489
(0.0045)" (0.0073)" (0.0045)" (0.0073)" (0.0072)"

81

Table 1 (cont’d)

Annual net income per capita in village 0.0001 0.0001 0.0001 0.0001 0.0001
(0.0000)" (0.0000) (0.0000)" (0.0000) (0.0000)

Number of people who migrated out 0.8716 1.1344 0.8660 1.1820 1.1459
among total residents (0.1300)" (0.2580)“”'l (0.1300)" (0.2588)“ (0.2593)“
Permanent residents in a village 0.0001 0.0001 0.0001 0.0001 0.0001

(0.0000)** (0.0000)M (00000)“ (0.0000)M (00000)"

Full time farming households ratio in village -0.0393 -0.0595 -0.0370 -0.0557 -0.0520
(0.0046)" (0.0083)‘Ml (0.0046)" (0.0084)" (0.0087)“

Share ofvillage cadre in village 0.8022 .1.0290 0.1944 .1.2973 -l.0446
(0.4316) (0.8786) (0.4374) (0.8885) (0.8958)

Share of rice among total sown area in village -0.0211 0.0705 -0.0264 0.0626 0.0617
(0.0050)" (0.0093)" (0.0050)“ (0.0097)" (000%)“

Share of village wheat among -0.0556 0.0077 -0.0414 0.0090 0.0049
total sown area in village (0.0081)" (0.0132) (0.0083)" (0.0133) (0.0132)
Share of households with TVs 0.0696 -0.0521 0.0748 -0.0470 -0.0449

(0.0082)** (0.0135)" (0.0082)" (0.0136)" (0.0137)"

Share of households with phones -0.0122 0.3610 0.0023 0.3392 0.3224
(0.0081) (0.0726)" (0.0081) (0.0739)" (0.0735)”

Share of illiterate laborers -0.2595 -0.1 159 -0.2372 -0.1 1 15 -0.1265
among total laborers (0.0285)" (0.0573)* (0.0284)" (0.0569) (0.0581)‘
Share of people graduate -0.3414 -0.1859 -0.3445 -0.1913 -0.l983
elementary school in village (00240)“ (0.0405)" (0.0240)" (0.0408)” (0.0413)"
Share of people who graduate -0.3222 -0.2519 -0.2962 -0.2366 -0.2639
secondary school in village (0.0292)" (0.0634)" (0.0290)” (0.0622)" (0.0648)"
Share of households using safe 0.0162 0.0428 0.0070 0.0384 0.0401
and sanitary water (0.0028)“ (0.0053)" (0.0029)“ (0.0057)" (0.0057)“”'I
Distance to the main 0.0043 0.0033 0.0040 0.0033 0.0032
concreted road (Km) (0.0003)" (0.0006)" (0.0003)" (0.0006)” (0.0006)“
Rainfall shock of t-l 0.0048 0.0041 -0.0055

82

Table 1 (cont’d)

(0.0015)""I (0.0015)" (0.0024)‘

Constant 0.3259 0.2568 0.3372 0.2576 0.2665
(0.0295)" (0.0586)" (0.0295)“I (0.0586)" (0.0590)“

Observations 1661 l 5437 1661 l 5437 5437
R-squared 0.16 0.20 0.16 0.20 0.20

Notes: 1. Robust standard errors in parentheses
2. * signiﬁcant at 5%, ** signiﬁcant at 1%
3. All speciﬁcations include province*year dummies. Their coefﬁcients are jointly signiﬁcant.

83

Table 2. Regression Results for Share of Liquid Wealth-holding with Village-year

 

 

 

Dummies
Variables Liquid W Cash Grain Deposits

Land*Coefﬁcient of rainfall variation -0.0497 0.0212 -0.0821 0.0112
(0.0161)" (0.0067)IMI (0.0394)* (0.0125)
Household size 00102 -0.0025 -0.0051 -0.0026
(0.0012)” (0.0005)” (0.0006)” (0.0010)"
Age of household head 0.00001 0.0002 -0.0008 0.0007
(0.0007) (0.0003) (0.0003)‘ (0.0005)
Age squared of household head -0.00001 -0.00001 0.00001 -0.00001
(0.00001) (0.00001) (0.000003)* (0.00001)
Number of dependents 0.0041 -0.0002 0.0085 -0.0042
(0.0016)‘ (0.0007) (0.0007)" (0.0013)“I
Proportion of illiterates in household -0.0394 0.0070 0.0179 -0.0643
(0.0086)" (0.0032)‘ (0.0043)“ (0.0066)"
Proportion of elementary school educated in household -0.0124 0.0106 0.0118 -0.0348
(0.0080) (0.0030)M (0.0037)" (0.0063)"
Proportion of secondary school educated in household -0.0309 0.0018 0.0005 -0.0332
(0.0080)" (0.0029) (0.0037) (0.0063)“
Arable land per capita 0.0365 -0.0124 0.0559 -0.0070
(0.0054)“ (0.0022)" (0.0033)" (0.0036)
Constant 0.2663 0.0613 0.0823 0.1228
(0.0178)" (0.0077)" (0.0089)" (0.0137)"
Observations 16667 16667 16667 16667
R-squared 0.22 0.26 0.33 0.28

Notes: 1. Robust standard errors in parentheses

2. "' signiﬁcant at 5%, '”' signiﬁcant at 1%

3. All speciﬁcations include village*year dummies. Their coefﬁcients are jointly signiﬁcant.

84

Table 3. Regression Results for Share of Liquid Wealth-holdings with Rainfall

Shock and Village-year Dummies

 

 

Variables Liquid W Cash Grain Deposits
Land*Coefﬁcient of rainfall variation 0.0966 0.0579 -0.0014 0.0402
(0.0471)* (0.0170)" (0.0298) (0.0396)
Land“ Rainfall shock 0.0074 0.0014 0.0033 0.0028
(lrainfall-average of rainfalll) (0.0024)" (0.0008) (0.0016)* (0.0017)
Household size -0.0062 -0.0032 -0.0035 0.0005
(0.0026)* (0.0010)" (0.0013)" (0.0021)
Age of household head 0.0009 0.0002 -0.0005 0.0012
(0.0014) (0.0006) (0.0007) (0.001 1)
Age squared of household head -0.00001 -0.00001 0.00001 -0.00001
(0.00001) (0.00001) (0.00001) (0.00001)
Number of dependents ~0.0019 -0.0004 0.0072 -0.0087
(0.0033) (0.0013) (0.0016)" (0.0026)"
Proportion of illiterates in household -0.0359 -0.0028 0.0335 -0.0667
(0.0173)* (0.0063) (00087)" (00134)"
Proportion of elementary school educated in household -0.0250 0.0042 0.0159 -0.0451
(0.0162) (0.0060) (0.0074)* (0.0130)"
Proportion of secondary school educated in household -0.0565 -0.0042 -0.0004 -0.0520
(0.0160)" (0.0057) (0.0072) (0.0132)"
Arable land per capita -0.0146 -0.0245 0.0291 -0.0192
(0.0158) (0.0060)" (0.0106)" (0.0120)
Constant 0.5325 0.0901 0.2603 0.1821
(0.0487)" (0.0159)“ (0.0330)" (0.0312)”
Observations 5494 5494 5494 5494
R-scmared 0.33 0.25 0.46 0.31

 

Notes: 1. Serial correlation robust standard errors in parentheses
2. * signiﬁcant at 5%, ** signiﬁcant at 1%

3. All speciﬁcations include village’year dummies. Their coefﬁcients are jointly signiﬁcant.

85

Appendix D

Table D1. Follow-up Rates of RCRE Survey

 

F ollow-up rates

 

(%) of 1995

Year Number of Households households Changes
1995 4,152 100.0 -
1996 4,004 96.4 -3.6
1997 3,978 95.8 -0.6
1998 3,798 91.5 -4.3
1999 3,758 90.5 -l.0
2000 3,724 89.7 -0.8

 

Table D2. Grain Balance

 

1995 1996 1997 1998 1999 2000

 

Grain Stock in year end (Mean) 895 1,118 1,145 1,193 1,197 1,186
Standard deviation 793 1053 1199 1151 1267 1366
Grain Stock in year end (Median) 724 877 860 916.5 863 864.5

 

86

Table D3. Grain Yield per Household

 

 

 

 

 

 

 

 

1995 1996 1997 1998 1999 2000
Grain yield (Kg) 2,169 2,273 2,254 2,143 1,996 1,951
Of which,
wheat 691 750 805 614 687 622
Of which,
rice 874 882 901 906 743 800
Of which,
com 415 431 371 439 409 366
Growth rate (%)
1995 1996 1997 1998 1999 2000
Grain Yield (Kg) 4.8 -0.8 -4.9 -6.9 -2.3
Of which, wheat 8.6 7.2 -23.6 11.8 -9.5
Ofwhich, rice 1.0 2.1 0.6 -18.0 7.6
Of which, corn 3.9 -14.0 18.3 ~6.9 -10.4
Table D4. Value of Production and Non-production Assets
Mean Median
Total value of production assets 3,470 (5,172) 1,000
Total value of non-production assets 14,946 (18,386) 8,850
Of which, Housing 11,839 (16,539) 6,500
Of which, Durable goods 2,785 (4,644) 1,500

 

Note: Figures in the parenthesis are the standard errors.

87

Table D4 (cont’d).

By year: 1995 —— 2000

 

 

 

 

 

 

 

 

1995 1996 1997 1998 1999 2000
Total value of production assets 2,760 3,187 3,444 3,706 3,809 3,921
Total value of non-production assets 11,382 12,748 14,223 15,675 17,257 18,417
Of which, Housing 8,521 10,071 11,291 12,511 13,894 14,771
Of which, Durable goods 2,040 2,401 2,694 2,973 3,202 3,406
Note: All values were deﬂated.
Growth rates (%)
1995 1996 1997 1998 1999 2000
Total value of production assets NA 15.5 8.1 7.6 2.8 2.9
Total value of non-production assets NA 12.0 11.6 10.2 10.1 6.7
Of which, Housing NA 18.2 12.1 10.8 11.1 6.3
Of which, Durable goods NA 17.7 12.2 10.3 7.7 6.4
Table D5. Household Income
1995 1996 1997 1998 1999 2000
Total income (mean value) 11,631 12,778 13,393 12,691 12,304 12,329
Of which, household business income 8,532 9,363 9,628 8,730 8,230 7,927
Of which, wage income 999 1,272 1,540 1,573 1,659 1,837
Others 817 843 877 996 1,027 1,154
Total income (%) 100.0 100.0 100.0 100.0 100.0 100.0
Of which, household management income 73.4 73.3 71.9 68.8 66.9 64.3
Of which, wage income 8.6 10.0 11.5 12.4 13.5 14.9
Others 7.0 6.6 6.6 7.8 8.3 9.4

 

88

Table D6. Cash/Deposits Balance

 

 

 

Mean Median
Total deposits 3,267 (8,197) 0
Total cash in hand 1,222 (2,441) 600
Total amounts of lending 343 (2,035) 0
Total amounts of borrowing 1,463 (7,320) 0
Of which, loans from banks and rural credit cooperatives 230 (3,135) 0

 

Note: Figures in the parenthesis are the standard errors.

Table D7. Cash/deposits Balance

 

1995 1996 1997 1998 1999 2000

 

 

 

Total deposits 2,070 2,623 3,049 3,303 3,902 4,678
Total cash in hand 907 1,183 1,234 1,353 1,284 1,379
Total amounts of lending 284 354 356 317 376 369
Total amounts of borrowing 942 1,308 1,529 1,604 1,714 1,688
Loans from banks and rural credit cooperatives 219 278 269 259 180 175
Growth rates (%)
1995 1996 1997 1998 1999 2000
Total deposits 26.7 16.2 8.3 18.1 19.9
Total cash in hand 30.4 4.3 9.7 -5.1 7.4
Total amounts of lending 24.7 0.6 -10.9 18.7 -2.0
Total amounts of borrowing 38.9 16.9 4.9 6.9 -l.5
Loans from banks and rural credit cooperatives 26.8 -3.2 ~3.9 30.6 -2.7

 

89

Table D8. Composition of Non-land Wealth

 

 

 

 

 

Variables Mean STD
Liquid wealth share 0.29 0.28
Of which, cash-in-hand 0.07 0.10
Of which, grain stock 0.11 0.11
Of which, deposits 0.11 0.17
Housing share 0.46 0.23
Fixed assets share 0.12 0.16
Durable share 0.13 0.11
Table D9. Share of Non-land Wealth
1995 1996 1997 1998 1999 2000
Liquid wealth share 0.26 0.30 0.30 0.29 0.28 0.28
Of which, cash-in-hand 0.06 0.07 0.07 0.07 0.07 0.07
Of which, grain stock 0.11 0.13 0.12 0.11 0.09 0.08
Of which, deposits 0.09 0.10 0.11 0.11 0.12 0.13
Housing assets share 0.48 0.45 0.46 0.46 0.47 0.46
Fixed assets share 0.13 0.13 0.12 0.13 0.12 0.12
Durable share 0.13 0.13 0.13 0.13 0.13 0.14

 

90

Table D10. Descriptive Statistics of Variables

 

 

Variables Obs. Mean SD
Coefﬁcient of rainfall variation 19601 0.372 0.172
Household size 24734 3.981 1.523
Age of household head (/1000) 20120 0.045 0.012
Number of dependents 24734 1.492 1.189
Proportion of illiterates among adults in household 24058 0.184 0.278
Proportion of elementary school educated among adults in household 24059 0.354 0.323
Proportion of secondary school educated among adults in household 24059 0.366 0.330
Arable land per capita 24680 1.369 1.288
Village located in plains (dummy) 24539 0.431 0.495
Village located in hilly area (dummy) 24539 0.332 0.471
Village located in a city suburb (dummy) 24539 0.131 0.338
Village located near township (dummy) 24539 0.208 0.406
Village located in a mining area (dummy) 24539 0.045 0.207
Village located in mountain area (dummy) 24539 0.232 0.422
Annual net income per capita in village 24539 0.200 0.093
Permanent residents in a village 24539 1.571 0.915
Full time farming households ratio in village 24539 0.480 0.317
Share of cadre in village 24539 0.006 0.004
Share of wheat among total sown area in village 24539 0.325 0.237
Share of households with TVs 24539 0.853 0.169
Share of households with phones 24539 0.120 0.194
Share of illiterate people in village 24539 0.127 0.102
Share of people who graduated elementary school in village 24539 0.377 0.127
Share of people who graduated secondary school in village 24539 0.391 0.128
Share of households using safe and sanitary water 24539 0.663 0.450
Share of households using electricity 24539 0.985 0.103

 

91

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93

Chapter 3

HOUSEHOLD NON-LAND WEALTH IN RURAL CHINA: COMPOSITION AND
DISTRIBUTION FROM 1986 TO 2000

1. Introduction

China has been experiencing a great transition from a socialist economy to a
market economy since the decollectivisation of agriculture and the introduction of market
reforms. With the expansion of liberalizing reforms and the development of rural
industry, China has achieved impressive growth in a relatively short period, and it is
reported that a large number of people have been lifted out of poverty.1 2 However, as
ofﬁcial economic indicators in China show dramatic growth and improvement (Table 2),
disparities in income and wealth have also increased and have become a big concern to
policy makers.3

Although the role of wealth and its distribution ﬁgures centrally in understanding
the dynamics of economic development, relatively little is known about wealth of rural
households in transitional economies. Also, in matters of agricultural policy much
attention seems to be focused on income and little on the assets held by farmers, although
distributions of asset holdings as well as income are considered to be among the key

factors of China’s economic security.4 Hence, questions about the distribution of wealth

 

' As shown in Table 1, per capita rural income in 1978 was 134 yuan and became 2,253 yuan (17 times
greater) while urban income is 18 times greater since 1978.

China’s ofﬁcial poverty statistics show a dramatic reduction of China’s poor population from 250 million
in 1978 to 32 million in 2000, although they underestimate rural poverty (Park and Wang (2001).
3 Ravallion and Chen (1998) provide the different empirical results on this using household surveys for 67
developing countries over 1981 - 94. They suggest that changes in inequality have been uncorrelated with
changes in average living standards and distribution improved as often as it worsened in growing
economies.
‘ Refer to Draguhn and Ash (1999).

94

in rural China are relevant, and gaining adequate information on this issue is an urgent
and important task.

This study is concerned with non-land wealth distribution and the asset position
of farming households in contemporary rural China.5 We will attempt to answer the
following questions: 1. What are the levels and dynamic changes in composition of non-
land wealth in rural areas? 2. Are there any dramatic changes in the composition of non-
land wealth during the period from 1986 to 2000? 3. How much non-land wealth
inequality is there in rural China? 4. How has inequality in rural China changed during
this period? 5. How do components of wealth affect non-land wealth distribution?

As concern about the distribution of wealth in rural China is a relatively new
phenomenon that has accompanied the dramatic institutional changes of the post-reform
period, this study is particularly timely. Given the sharp rise in rural income inequality
discussed in Khan and Riskin (1998) with possible links between income and wealth
distribution posited, it is natural to wonder if growing inequality can be found in the
distribution of household asset holdings in contemporary rural China. In this paper, we
ﬁrst describe the overall household non-land wealth composition and changes of the form
in which wealth is held in rural China. Next, we document in detail the extent and
determinants of wealth inequality over the years, based on various inequality measures.
At the end of the chapter we brieﬂy discuss whether low wealth households can
accumulate non-land wealth over the period from 1986 to 2000 with the results of wealth

dynamics.

 

5 Growing income disparity between rural and urban residents is also an important issue, but we focus on
the rural sector.

95

The paper will be structured in the following way: Section 2 provides background
information on rural China and reviews past studies on income and wealth inequality in
China. Section 3 brieﬂy describes methods and data used in the study. In section 4, we
examine the data in detail for differences in composition of household wealth holdings
across the period. Section 5 presents main ﬁndings of wealth inequality and dynamic

analysis of household’s non-land wealth in rural China. Section 6 concludes the paper.

2. Historical Background and Studies on Rural China

2.1 Historical Background

It is worth noting that the issue of the distribution of wealth among households in
rural China did not arise until after 1978, when a series of major economic and
institutional reforms, beginning in the rural areas, were introduced. Prior to that time,
most wealth, such as land, machinery, and equipment, was publicly owned. The main
privately owned assets were housing assets, small tools, livestock, and individual use-
rights to small plots. Most differences in household wealth were attributable to the
number of each household’s working members and their skills. Although the distribution
was reported as being very equal, it didn’t bring much growth to the rural economy since
at that time, rewards were not linked to performance.6

From 1979 to 1984, the ﬁrst phase of reform focused on decollectivizing

agriculture by introducing the household responsibility system. This system distributed

use rights of land to the farmers, although land could not be sold at the market. Land was

 

6 Grifﬁn and Saith (1982) reported that there was little income inequality in production brigades and teams
during the period.

96

allocated to households on an equal basis, and farmers were allowed to keep the rest of
their agriculture output after paying the quotas and taxes to the state. The household
responsibility system allowed some farmers to become rich, as their efforts were directly
linked to production performance. Grain production and rural incomes were increased
rapidly in most regions of the country during this period.

The second phase of the reform started from 1985 with a changed focus away
from the agriculture to the rural industrial sector. The structure of state taxes and prices
was reformed, and an attempt was made to build a marketing and trading network. Efforts
were made to develop non-farm enterprises—township and village enterprises (TVEs).
TVEs are owned and operated by villages or townships and ﬁnanced initially from
surpluses generated within the community.

The new policy in this period, so called, “leave the land but not the countryside”
was responded to with enthusiasm by Chinese farmers. As many farmers were transferred
out of agriculture, farm production slowed down and the share of wage income increased

substantially in rural households.

2.2 Studies on Income and Wealth Inequality in Rural China

Income Inequality in Rural China

Grifﬁn and Saith (1982) measured rural income inequality and its determinants
prior to the decollectivization period in China. They found little inequality in incomes in
production brigades and teams, but discovered a greater disparity in distribution of assets
across communes due to the structural factors of the quantity and quality of land. Rozelle

(1994) analyzed the data from 1984 to 1989 and determined that evolving patterns of

97

inequality were closely related to changing economic structures in rural China, and that
interregional inequality was increasing in large part owing to the expansion of rural
industry.

Hussain et a1. (1994) showed that rural income inequality in 1986 was very low
by international standards and that non-farming income was more unevenly distributed
than farming income, which implies that income inequality rose with a shift in labor from
farming activities to non—farming activities. Hare (1994) also found that non-agricultural
income was less equally distributed than agricultural income.

Khan and Riskin (1998) analyzed the income distribution in China based on a
1995 survey and found that household production activities still contributed most to total
rural income, while its share of total income decreased; yet wages, the second largest
component of income, increased sharply. They reported that the main income sources for
the wealthy were wages, non-farm entrepreneurship, and transfers from the state and
collectives, while the main sources of income for the poor were farming and rental value
of housing.

Contrary to Hussain et a1. (1994), World Bank (1997) concluded that the Gini
coefﬁcient for overall inequality increased from 0.288 in 1981 to 0.388 in 1995 and
greater opportunities for off-farm employment not only boosted income growth but also
contributed to rising inequality. Yao and Zhu (1998) also supported this argument,
providing the evidence that overall rural income inequality increased signiﬁcantly from
1978 to 1996, although rural per capita incomes quadrupled.

More recently, Benjamin, Brandt, and Giles (2002) analyzed the evolution of

income inequality in rural China using the same survey data that we use for this study.

98

They found that the Gini index for income inequality increased from 0.376 in 1987 to
0.440 in 1999 and reported that this rise was consistent with results using other rural
surveys in China. They also suggested that the distribution of long-run living standards,
which was reﬂected in current consumption, was less unequal than the distribution of

income in any one-year.

Wealth Inequality in Rural China

There have been relatively few works on wealth distribution in rural areas
compared to studies on income inequality. Until recently, poverty in China was mostly
conﬁned to the countryside, thus poverty has been a more urgent issue in rural China.
Moreover, there have been some difﬁculties in obtaining reliable and extensive micro
data on the ownership of livestock, agricultural tools and equipment, non-agricultural
capital assets, housing assets, ﬁnancial assets, and especially land, which is the most
important asset in most rural economies. As a result, it is difﬁcult to get accurate
estimates of the distribution of total wealth. Here, we brieﬂy review two studies that have
been widely cited in the studies on distribution of wealth in rural China.

McKinley (1996) extensively researched rural wealth inequality in China in 1988.
He argued that there was a relatively lower wealth inequality in rural China based on Gini
index estimates, and land and ﬁxed productive assets reduced inequality while housing
and ﬁnancial assets increased inequality. He suggested that land in China had the effect
of reducing the total wealth inequality, while in other developing countries, a large
proportion of rural households are landless and have very few other assets, resulting in a

higher wealth inequality than income inequality.

99

More recently Brenner (2001) analyzed the wealth distribution in rural China
using 1988 and 1995 surveys. He found that wealth distribution had worsened in 1995
compared to 1988, but continued to be remarkably equal in comparison to both rural
income distribution in China and wealth distribution in other countries. He also argued
that the source of growing divergence between wealth and income distribution is to be
found in access to non-farm employment for household members, either through off-farm
migration or local non-agricultural employment.

Both studies note that this relatively egalitarian distribution of wealth in rural
China was primarily due to decades of land reform in rural China that, on the whole, have
resulted in a more equal distribution of assets.7 Although this might be the unique
characteristic of wealth distribution in rural China we want to know the real situation of
wealth distribution without land assets which might systematically play an equalizing
role in the wealth distribution in rural China. We observe that the issues of income
distribution and poverty in rural China have received relatively much attention in past
studies and until recently, questions about wealth distribution in rural areas were not
considered to be relevant and so urgent by the policy makers or researchers mostly due to

these much lower inequality estimates of wealth distribution in rural China.

3. Valuation, Measurement, and Data

 

7 Brenner (2001) reports that land in value temrs continues to exert an equalizing effect on overall wealth
distribution since 1988; land in physical units appears to have become more equally distributed since 1988,
with the Gini coefﬁcient for land distribution falls from 0.465 to 0.414, while the coefﬁcient of variation
falls from 2.96 to 0.83 in 1995.

100

3.1 Valuing Wealth

The valuation of wealth in money terms can take one of two broad approaches.
The ﬁrst values assets according to the price they would command if sold on the open
market, and this can be labeled “realization” value. In the case of machinery, this would
correspond to the salvage value that could be raised by sale; for land it would be its sale
price. The second is to value the assets as they appear to the person who currently
possesses and uses them if they remain in his control, termed “use” value or “shadow
price.” For machinery, use value could be the cost of acquiring a replacement machine.
For land it might be considered the net income ﬂow generated by the land and capitalized
into a single ﬁgure — the “worth” of land as an income-generator.

The principle of wealth evaluation adopted here is similar to that of “use” value.
Assets in our sample were valuated in terms of the original prices when purchased. Since
we are not able to apply current market prices for assets, we use depreciated values for
housing assets and durable goods based on speciﬁc assumptions. Current values of
housing assets and durable goods are imputed by applying 20 years and 7 years
depreciation rates, respectively.8

In the following analysis we use the Survey Department of the Research Center
on the Rural Economy (RCRE) household surveys in the period from 1986 to 2000 to
obtain estimates for wealth in rural areas. Wealth in this study is comprised of four
principal components: housing assets, ﬁxed productive assets, ﬁnancial assets, and
durable goods, all expressed in deﬂated value terms. Value of use rights to land is not

included in our deﬁnition of wealth since a rental market for land in rural China seems

 

3 When we use the original values or apply the different years of discounted rates for each asset the results
would not change much.

101

slow to emerge and thus we only have to depend on the generated value.9 Since the
distribution of land would not change much and has played an equalizing role in the
distribution of wealth in rural China during the period, we focus on ‘actually available’
asset holdings whose investments have not been constrained by the government after the
post-reform period. Additional technical issues merit discussion such that the calculations
of the inequality indices are performed on a per capita basis to take into account

household size. Next we explain the inequality measures that we use in this paper.

3.2 Inequality Statistics

There are many ways to measure inequality, because inequality is a multi-
dimensional concept. To investigate the inequality of wealth quantitatively, we focus on
four measures: Theil inequality indices; the Gini coefﬁcient; quantile ratio; and the
concentration coefﬁcient. Most of all, quantile ratios are straightforward indicators of
inequality that are easy to interpret. They are insensitive to outliers either in the very top
or very bottom tail of the distribution, but they do not reﬂect what happens in other parts
of the distribution. To address this shortcoming, we also depend on Gini and Theil
indices.

The Gini coefﬁcient is used in this paper as one of our primary measures of
inequality. This choice is based on its wide usage in other work, which facilitates
comparison, as well as the fact that the Gini coefﬁcient satisﬁes several desirable

theoretical properties and at the same time is subject to fewer drawbacks than many other

 

9 McKinley (1996) and Brenner (2001) both generated the value of use right to land based on the gross
value of agricultural output.

102

competing measures.10 The Gini coefﬁcient is bounded between 0 and 1, with 0
indicating absolute equality and 1 indicating absolute inequality. The Gini coefﬁcient is
especially sensitive to changes in inequality in the middle of the distribution.

To compensate for one of the Gini’s drawbacks — the insensitivity of the measure
to redistribution to or from the tails as opposed to the center of the distribution —- we have
to include another common measure of dispersion, Theil indices, which are the
generalized entropy class developed by Theil. Within that class we use the Theil entropy

index,
N
1 w- w-
E 1 = _ __11n _1
( ) N Z: v ( v l
1—1
and the Theil mean log deviation index,
N
l v
E 0 = — 1n —
( ) N Z [W]
1—1 1
where there are N individuals indexed by i, their wealth is given by w, , mean wealth is

denoted by v.

Both measures are zero for perfect equality. For complete inequality (one person
holds everything), E(O) goes to inﬁnity while E(l) reaches N1n(N). The two Theil
inequality measures differ in their sensitivity to inequality in different parts of the
distribution. The entropy measure, E(1), is most sensitive to inequality in the top range of
the distribution, while E(0) is most sensitive to inequality in the bottom range of the

distribution.

 

'0 The Gini coefﬁcient satisﬁes the properties of (1) symmetry, (2) scale independence, and (3) the Pigou-
Dalton transfer principle, with its largest drawback being that it is not decomposable, for example to
within-and between-group inequality. For a more detailed treatment of these theoretical properties and their
implications, see Fields (2001).

103

To compensate for another shortcoming of the Gini, namely its non-
decomposability, we make much use of the concentration coefﬁcient and the Theil
inequality decomposition. Inequality can be decomposed along two dimensions. One can
decompose total inequality in equivalent wealth into the contribution of each component
of wealth. This composition is usually performed for wealth inequality by the sources of
wealth (housing, ﬁxed, ﬁnancial assets, durable goods, and so on). This decomposition
can be performed using the Gini coefﬁcient.

We measure inequality in total wealth by the Gini coefﬁcient, and this in turn can
be decomposed as the weighted sum of concentration coefﬁcients for the various wealth
components.ll The weights are relative shares of wealth components in the total wealth,
which sum up to 1. Therefore, the relationship between the two measures can be

expressed as follows:

where vj,v are the means of wealth component j and total wealth, c j is the

concentration coefﬁcient of wealth component j.

The concentration coefﬁcient reﬂects the association between the wealth
component j and the total wealth. It can assume values ranging from —1 to 1. Although
the concentration coefﬁcient does not provide a direct alternative to decomposition of the
Gini, it does allow us to determine both the equalizing or disequalizing nature of various

components of household worth as well as their contribution to overall inequality. If the

 

” Refer to Brenner (2001) and Kahn and Riskin (1998) for more detailed information on the concentration
coefﬁcient.

104

concentration coefﬁcient of a wealth component were greater than the Gini coefﬁcient of
total wealth, this wealth component would be regarded as being disequalizing.

The second way of decomposing inequality is to decompose it into inequality
within and between subgroups. Since the Gini coefﬁcient does not lend itself well to
decomposition by population groups, this decomposition can be performed using the
Theil indices. Decomposition by population group allows us to look more closely at the

causes of inequality.

3.3 The RCRE Data

Our analyses of the evolution of wealth composition and inequality use household
survey data provided by the Survey Department of the Research Center on the Rural
Economy (RCRE), at the Ministry of Agriculture in Beijing. A panel of household data
spanning the period 1986 to 2000 that includes around 4,400 households per year in 4
provinces (Anhui, Henan, Jiangsu, Shanxi) is used for this study.12

The original intention of the RCRE survey was to collect annual data from the
same households and villages from 1986 to 2000.13 Given the relatively large number of
households from each village and the lengthy span of the panel, the RCRE data allow us
to study the evolution of wealth inequality as well as the changes in wealth composition
during a period of changing access to markets in rural China. Households are asked a
range of questions regarding various asset ownerships, savings, income from on-farm

activities and off-farm employment, and household consumption, etc.

 

'2 The complete RCRE survey covers over 22,000 households in 300 villages of 31 provinces and
administrative regions. Refer to Benjamin et a1. (2002).

'3 RCRE reports that 80 percent of the households remain in the survey of the entire period. Much of the
attrition is considered to correlate with gap in the survey (The RCRE did not conduct the survey in 1992 or
1994 as a result of funding difﬁculties).

105

4. Evolution of Wealth and its Components

Table 3 contains the basic results for level, composition, and real growth rate of
overall household assets. Total non-land wealth per capita has increased by an 8 percent
annual growth rate between 1986 and 2000 lower than the overall growth rate of per
capita income, 12.6 percent, seen in Chen (2002). The overall growth rate of non-land
wealth showed 8.7 percent in early period (1986 -— 1991) compared to 7.3 percent in the
later period (1995 — 2000). ‘4

First, housing may in fact be a more accurate barometer of changes in wealth
distribution than other rural assets. Rural households have not been constrained from
investing in housing, and for many households, housing was one of their ﬁrst spending
priorities since 1980s. From Table 3, we are able to conﬁrm that housing assets are the
most important component in household non-land wealth, and it showed 8.1 percent of
real grth rate on average. Rural households in China hold more than 50 percent of non-
land wealth in forms of housing assets.

While investment in other assets such as land and ﬁxed productive assets has been
constrained, this has not been the case for housing. Houses were privately owned even
during the collective era and remain so, with little prospect of collective ownership. Thus
building a new house has been one of the ﬁrst priorities for many rural families as soon
as their income increases. It has represented a very conspicuous sign of a family’s new

wealth and prestige. Moreover, it has been an investment with little risk or uncertainty

 

” The RCRE did not conduct the survey in 1992 or 1994 as a result of funding difﬁculties. Matching
households from 1991 to 1993 and 1993 to 1995 is complicated by these missing years of data. Thus
Benjamin et al. (2002), used the same RCRE data set they suggested to construct sub-samples of balanced
panels for early (1986 — 1991) and later (1995 — 2000) periods.

106

and one affording a highly valued immediate stream of very concrete, material services.
This is in contrast to the situation in many other countries, where the richest rural
households have secured their position mainly by accumulating stocks of land and other
ﬁxed productive assets.

Second and third most important components in overall wealth are the ﬁnancial
assets and the ﬁxed productive assets. Rural households keep around 30 percent of non-
land wealth in forms of ﬁxed productive assets and ﬁnancial assets. Fixed productive
assets replaced ﬁnancial assets as the second most important component in overall
wealth, rising in importance from 13.7 percent in 1986 to 15.2 percent of the total in
2000. It has been frequently noted that rural China, like many other rural developing
areas, has a thin and particularly fragmented ﬁnancial market. It has been argued that in
such a context, ﬁxed asset accumulation may substitute for the store of wealth function of
ﬁnancial assets as well as a buffer stock in the face of income variability.

Although now in third position, ﬁnancial assets have slightly declined in
importance, falling from 17.2 to 15.1 percent of total non-land wealth during the period,
while demonstrating the highest 9.3 percent rate of growth in the later period (1995 —
2000) among the non-land wealth components. Compared to the ﬁnancial assets in the
same period, the real growth rate of ﬁxed assets recorded only 7.4 percent in the later
period. It implies that the fast growth in household wealth was led by investments on the
ﬁxed productive assets in the early period (1986 -— 1991) while the investments on
ﬁnancial assets became main instruments for households’ wealth portfolios in more
recent years. Compared to the other assets, durable goods have changed little, remaining

at 13 percent of total worth during the period.

107

5. The Distribution of Wealth in Rural China

Turning to the distribution of assets, Table 4 displays the various measures of
inequality calculated for household non-land wealth and its components. Considering
ﬁrst the overall distribution of non-land wealth, it is evident that it has worsened since
1986. The Theil index increased from 0.248 to 0.351, the Gini coefﬁcient increased ﬁ'om
0.385 to 0.452 and the coefﬁcient of variation increased from 0.79 to 1.12, with the
change registering strong statistical signiﬁcance.

Figure 1 plots Lorenz curves for the distributions of non-land wealth and its
components between 1986 and 2000. The Lorenz curves confirm these results of the
inequality measures.” Figure 2 also plots the percentiles of non-land wealth and its
components across the period from 1986 to 2000. It shows that the real grth of non-
land wealth in 90th percentile is faster than 10th percentile. Now turning to the various

components of wealth, several interesting ﬁndings emerge.

5.1 Housing Assets

Ever since the household responsibility system enabled the farmers in China to
increase their income from 1978 on, total consumption expenditures of farm families
have increased accordingly. Since the construction of farm housing was not controlled by

the government, farmers used their greater income to improve their existing housing and

 

'5 We see in the Figure 1 that the 1986 Lorenz curve dominates the 2000 one. Additionally, Lorenz
dominance test in Table 5 show ﬁrst order Lorenz dominance of the 1986 distribution in rural China.

108

to build new houses on the land assigned to them. The construction of new housing
increased according to the escalating demand.16

Thus housing assets are of key importance in understanding the distribution of
wealth in rural China. From the table we could ﬁnd a similar deteriorating trend in the
distribution of housing assets. The Theil index increased from 0.265 to 0.448, the Gini
coefﬁcient increased from 0.386 to 0.505, and the coefﬁcient of variation increased ﬁ'om
0.81 to 1.24. The concentration coefficients reported in Table 6 indicate that housing
assets had an equalizing effect on overall non-land wealth distribution, although relative

contribution decreased ﬁ'om 62 percent in 1986 to 51 percent in 2000.

5.2 Fixed Productive Assets

Turning to ﬁxed productive assets, we have somewhat mixed results in Table 4.
While the coefﬁcient of variation increased ﬁom 2.02 to 2.55, most measures showed a
decrease in inequality across the period; the Theil index decreased from 0.98 to 0.78, The
Gini coefﬁcient fell from 0.68 to 0.63.17 Table 9 also provides ﬁgures supporting this; the
ratio between the top 10th percentile and median value of ﬁxed productive assets was 4.5
in 2000 compared to 5.9 in 1986. It implies that the distribution of ﬁxed productive assets
as a whole still tends to reﬂect transitional or mixed ownership patterns. The
concentration coefﬁcients reported in Table 6 indicate that ﬁxed productive assets have

reduced inequality, except in 1986.

 

1" Chow (2002) observed the increase in quantity and the improvement of quality of the houses of farm
families in 19805. He reported that the per capita ﬂoor space of rural households was 8.1 square meters in
1978, 9.4 in 1980, 14.7 in 1985, 17.83 in 1990, 21.0 in 1995, and 22.45 in 1997.

'7 Lorenz curves of ﬁxed productive assets distribution in F igurc 1 show crossings at top 5 percent between
1986 and 2000 distribution. Thus if we measure the inequality by the top 5 % in the ﬁxed asset productive
assets we will judge 2000 distribution to be more unequal than 1986. In the most shares below the top 5
percent, the 2000 distribution appears to be more equal than 1986.

109

With regard to the effect of the various ﬁxed assets on the distribution of total
wealth, useful aspect of the division between traditional and modern ﬁxed productive
assets becomes readily apparent. Indeed, as the concentration coefﬁcients for traditional
assets demonstrate, these elements are the source, within total ﬁxed productive assets, of
the equalizing effect on overall wealth distribution, as their concentration coefﬁcients are
all lower than the Gini coefﬁcient for total wealth in each year. Traditional assets showed
a decreased inequality in Table 8. In sum, modern capital inputs continue to play a role

in the unequal status of ﬁxed productive assets and for overall wealth.

5.3 Financial Assets

Inequality in ﬁnancial assets has increased during the period. There were rises in
the Theil index, from 0.842 to 1.006 and Gini coefﬁcient, from 0.661 to 0.703, and in the
coefﬁcient of variation from 1.76 to 2.02. While the concentration coefﬁcients for
ﬁnancial assets have declined somewhat from 1986 to 1995 and increased in 2000, they
are still much higher than the Gini coefﬁcient for total wealth, indicating the continued
unequal distribution of ﬁnancial assets in rural China. Table 11 shows that the 90th
percentile in ﬁnancial wealth was 6.3 times compared to median ﬁnancial asset value in
1986 and it widened into 7 .7 times in 2000. Moreover, the contribution of ﬁnancial assets
to overall inequality increased from 20 to 24 percent between 1986 and 2000, due to the
grth in these assets as a proportion of all wealth.

In Table 10, we present the various components of gross ﬁnancial assets as a
percentage of their total value along with their concentration coefﬁcients. Turning to the

results for 2000, deposits are the most important single component of ﬁnancial assets,

110

comprising 76 percent of all holdings. Next in importance is cash-in-hand, comprising 23
percent of the total, followed by investment with 1 percent.

This trend can be explained by the distinguishing feature of ﬁnancial asset
holdings and reﬂects uneven process of ﬁnancial deepening in rural China. While the
overwhelming majority of households own land, ﬁxed productive assets, housing and
durable goods, only half the rural population, on average, owns any ﬁnancial assets. This
is one reason why ﬁnancial wealth is the most unequally distributed component of total
wealth. Since ﬁnancial assets account for about 15 percent of the total value of assets and
the grth rate is the fastest among the component in later period (1995 — 2000), their
signiﬁcance lies much in their present impact on unequal distribution of wealth as in their

indication of future directions of changes in the distribution.

5.4 Comparison of Wealth Inequality

Finally, even with this increase in inequality registered between 1986 and 2000,
we can ﬁnd that wealth distribution in rural China is still one of the most equal compared
to the industrial countries, with the coefﬁcient of variation and the Gini coefﬁcient,
relatively speaking, registering low values. How does inequality of wealth in rural China
compare to inequality of wealth in other countries?

In Table 12, we report measures of wealth inequality based on household surveys
for 8 countries as gathered from various studies by Davies and Shorrocks (2000) and add
to it our estimates for rural China, although we could not include land in the deﬁnition of
wealth in rural households. Although there are differences in methods and in deﬁnitions

among the surveys and it is not the distribution of wealth in China as a whole, non-land

111

wealth in rural China appears to be much less unequally distributed than that of other
industrialized countries in Table 12.18 It is also worth to note that the differences would
be greater if land wealth included since land asset has been reported to have an equalizing
effect on total wealth distribution in rural China.19

It will also be interesting to compare the inequality between the four provinces in
our sample. Table 7 shows that Shanxi and Henan provinces contribute to increasing the
inequality compared to the other two provinces, Jiangsu and Anhui, which have relatively
higher average income. It also conﬁrms regional disparities have increased since 1986.20

This completes our examination of the composition and the distribution of wealth
in rural China in the period of 1986 - 2000. We found that ﬁnancial and ﬁxed productive
assets have become more important in wealth composition, although the share of housing
is decreased during the period. Based on our various inequality measures we found that
the distribution of rural wealth appeared to remain relatively equal in comparison to
wealth distribution in other countries. However, the results also showed that equality of

non-land wealth has deteriorated during the period.2|

5.5 Dynamic Analysis of Non-land Wealth

 

‘8 It cannot be a rigorous comparison since our table only compares the results with the industrialized
countries without information on their rural areas.

‘9 Refer to Brenner (2001) and McKinley (1996)

2° According to Yang (2002), the high inequality is attributable primarily to a large rural-urban income gap
and growing inland-coastal disparity. He reports that the ratio of urban-rural income gap and consumption
hovered between 2 and 3.5 since the inception of reform, a level much higher than the majority of countries
in the world.

2' Relatively equal distribution in rural China is of course possible because wealth might have been
underreported. This is a problem common to almost all household surveys. Underreporting is usually most
serious among the rich, who have innumerable avenues open to them to disguise the true dimensions of
their wealth.

112

Although we appear to ﬁnd evidence that inequality has been worsened from
1986 to 2000 it is not an answer for whether low wealth households can accumulate
assets over the same time in rural China. In this sub section we try to answer this
question.

A growing literature asks whether low wealth agents are trapped in poverty and
recent ﬁnding suggests that optimal portfolio strategies are bifurcate so that wealthier
households acquire a higher-yielding portfolio while poorer households acquire less
remunerative one.22 But this argument depends on the numerical simulation results and
thus it needs an empirical analysis. In this context we brieﬂy analyze whether this
simulated results accord with the rapidly growing economy like China.

Table 13 and Figure 3 show the test result of ﬁrst-order poverty dominance and
cumulative density functions of non-land wealth distribution of 1986 and 2000. It
graphically and statistically conﬁrms that non-land wealth distribution of 2000 ﬁrst-order
stochastically dominates the distribution of 1986.

Since we have panel data of non-land wealth holdings of rural households we are
able to do dynamic analysis of household wealth to exploit the characteristics of the panel
data. Non-parametric regressions give a more detailed view of relationship between base
year economic position and current one. The plots in Figure 4 are obtained by using a
running line smoother, which locally estimates slopes between each point taking into
account the nearest neighboring points. Figure 4 shows the smoothed relationship
between non-land wealth per capita of 1986 and 2000. We also draw the 45 degree line
for comparison; If the estimated line lies above the 45 degree line it implies that the

current wealth position of these households improves compared to base year position of

 

22 Refer to Zimmerman and Carter (2003).

113

1986. In the ﬁgure we observe that the households below 98 percentiles of 1986 non-land
wealth distribution lie above the 45 degree line so it implies that most of the households
improve in their wealth position. When we compare two different years between 1986
and 1991 and between 1995 and 2000 households below 96 percentile of the 1995
distribution experienced the improvement in their wealth position of 2000 although
households below 63 percentile of the distribution enjoyed the improvement in 1991
(refer to Figure 5 and 6).

Moreover, Table 14 provides the evidence of empirical analysis using two stage
least squares ﬁrst difference estimation. Although the results may be contaminated by
measurement error the dynamic analysis of wealth support the idea of improvement of

non-land wealth position compared to the previous year.

6. Conclusion

Wealth has been considered a potent component among the factors that determine
the position of the agricultural household within society. It is pointed out that wealth is
important because it gives rise not only to income in a variety of forms but also because it
provides security, freedom of movement, and economic and political power. The
importance of wealth as a contributor to the economic welfare of farmers cannot be
denied, yet it has not received enough attention among many researchers in the context of
developing economics as well as developed ones.23

Here we are concerned with the economic attributes of non-land wealth in rural
China which have been largely unexamined. In this paper, we ﬁrst determined the

composition and distribution of rural non-land wealth as well as the distribution of each

 

’3 Please refer to Hill (2000).

114

of its component — housing assets, ﬁxed production assets, ﬁnancial assets and durable
goods during the relatively recent period from 1986 to 2000. We examined the effect that
the distribution of each component has on the distribution of the total, focusing on which
components of total worth have a disequalizing effect on the distribution of rural wealth.

Although our results show that the distribution remains relatively equal during the
period compared to the other countries, we suggest that the increasingly uneven
distribution of non-land wealth will be one of the most serious challenges, and the most
embarrassing one for the Chinese policy makers. The Chinese government admits based
on Chinese ofﬁcial statistics, which are more conservative, that by 2000 the Gini
coefﬁcient passed the 0.4 “warning light”, and was averaging 0.40 — 0.43.24 This warning
sign is conﬁrmed even with our rural households estimates.

In this paper, however, we also observe that there is an improvement in non-land
wealth holdings of Chinese farming households from 1986 to 2000 while there is an
argument for the poverty trap based on the simulated results. Our results show that 2000
of non-land wealth distribution ﬁrst-order stochastically dominates the 1986 wealth
distribution in rural China. This is supported by the smoothed relationship between the

selected years as well as our tentative empirical results of dynamic panel regressions.

 

2‘ China Times, June 20, 2001, p.3.

115

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Figure 2. Percentiles of Non-land Wealth and its Couponents
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Figure 4. Smoothed Relationship between Wealth Per Capita in 1986 and 2000

Lowess smoother

 

 

 

   

 

 

 

 

 

 

 

 

 

 

$3 - 1 i I 1
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Log of wealth per capita in 1986
bandwidth = .a

Note: 1. Data are truncated at lowest and highest 1 percentile of log of wealth in 1986. 2. Number of
observations as a balanced panel between 1986 and 2000 is 3122.

137

Figure 5. Smoothed Relationship between Wealth Per Capita in 1986 and 1991

Lowess smoother

 

       

 

 

 

 

 

 

 

I I
T r
: I 63 percentile '

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bandwidth = .8

Notes: 1.Data are truncated at lowest and highest 1 percentile of log of wealth in 1986. 2. Number of
observations as a balanced panel between 1986 and 1995 is 3829.

138

Figure 6. Smoothed Relationship between Wealth Per Capita in 1995 and 2000

Lowess smoother

12
l

_96 percentile

Log of wealth per capita in 2000

 

 

 

Log of wealth per capita in 1995

bandwidth = .8

Note: 1. Data are truncated at lowest and highest percentile of log of wealth in 1995. 2. Number of
observations as a balanced panel between 1995 and 2000 is 3670.

139

References

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States and Great Britain,” The Journal of Human Resources, Vol.38, No.2, pp.24l — 279.

Benjamin, D., L. Brandt, and J. Giles (2002), Income persistence and the evolution of
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Brenner, M. (2001), “Reexamining the Distribution of Wealth in Rural China,” in Riskin,
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Davies, J. B. and A. Shorrocks (2000), “The Distribution of Wealth,” Chapter 11 in
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Chow, G. (2002), China ’s economic transformation, Blackwell publishers Inc.

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Hussain et al. (1994), “Income inequalities in China: evidence from household survey
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Khan, A. R. and C. Riskin (1998), “Income and inequality in China: composition,
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Park, A. and S. Wang (2001), “China’s poverty statistics,” China Economic Review,
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Rozelle, S. (1994), “Rural industrialization and increasing inequality: emerging patterns
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World Bank (1997), China: strategies for reducing poverty in the 19905, Washington
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141

   

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