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How Badly Flawed is
Chinese Economic Data?The Opening Bid is $1 Trillion
By:
Christopher Balding
Associate Professor
HSBC Business School
Peking University
Short Abstract:
Baseline Chinese economic data is unreliable. Conservatively, correcting for housing price inflation in
the Chinese CPI data adds approximately 1% to annual consumer price inflation in China, reducing real
GDP by more than $1 trillion.
Long Abstract:
Baseline Chinese economic data is unreliable. Taking published National Bureau of Statistics China dataon the components of consumer price inflation, I attempt to reconcile the official data to third party
data. Three problems are apparent in official NBSC data on inflation. First, the base data on housing
price inflation is manipulated. According to the NBSC, urban private housing occupants enjoyed a total
price increase of only 6% between 2000 and 2011. Second, while renters faced cumulative price
increases in excess of 50% during the same period, the NBSC classifies most Chinese households has
private housing occupants making them subject to the significantly lower inflation rate. Third, despite
beginning in the year 2000 with nearly two-thirds of Chinese households in rural areas, the NSBC applies
a straight 80/20 urban/rural private housing weighting throughout our time sample. This further skews
the accuracy of the final data. To correct for these manipulative practices, I use third party and related
NBSC data to better estimate the change in consumer prices in China between 2000 and 2011. I find
that using conservative assumptions about price increases the annual CPI in China by approximately 1%.This reduces real Chinese GDP by 8-12% or more than $1 trillion in PPP terms.
Keyword: China, inflation, real GDP, GDP deflator
JEL Codes: E31, E4, E5
mailto:[email protected]:[email protected]:[email protected]7/27/2019 How Badly Flawed is Chinese Economic Data - $1 Trillion and Over Hit to GDP
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Key Facts
According to the National Bureau of Statistics China (NBSC), the price of private housing in Chinarose by a total of 8.14% between 2000 and 2011 and only 5.99% in urban areas, where Chinese
were moving to in large numbers.
According to the NBSC, the annual price of private housing in rural areas grew at 1.67%, morethan three times faster than prices in urban areas at 0.53%.
According to the NBSC, the price increase of renting outpaced the change in the price of privatehousing by nearly 50%, making it significantly more advantageous to purchase an apartment
between 2000 and 2011 in China.
According to the NBSC, only 12% of Chinese households are renters, skewing inflation data. To calculate the total private housing price change, the NBSC utilizes a straight 80% weighting of
the urban population and a 20% weighting of the rural population despite the fact that in 2000
nearly two-thirds of Chinese households were rural and only rose to 51% urban in 2011.
According to the NBSC, rural home values rose by 249% between 2000 and 2011 for acompounded annual growth rate of 8.65%.
According to the third party data, from the first quarter of 2000 to the first quarter of 2010, thenominal value of apartments in urban areas in China rose nearly threefold.
When using third party data and reconciling NBSC data in place of official housing inflation data,annual Chinese CPI increases by approximately 1% annually.
Incorporating this change in CPI into real GDP calculations reduces total real GDP by a mid-rangeestimate of 8-12% or $1 trillion in PPP.
Other significant discrepancies exist that will likely increase the size of the needed restatementof total real GDP.
According to NBSC data, the food component of the CPI in China was responsible for 99% ofinflation between 2003 and 2011. This implies that the NBSC is claiming that the onlyprices to
rise in China between 2003 and 2011 were food prices.
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Introduction
Since China opened up to the world in 1979, it has been an economic juggernaut, officially
expanding by a compounded growth rate of 10% between 1980 and 2012. This rate actually accelerated
after 1999, averaging 10.2% from 2000 to 2012. China has pulled hundreds of millions of its citizens out
ofextreme poverty becoming one of the worlds largest economies.
However, there is strong evidence indicating that the rate of real Chinese GDP growth and
ultimately total real GDP may be significantly over stated. This discrepancy stems primarily from
significant and systematic irregularities in the inflation data maintained by China and the pass through
impact on a wide variety of other data such as real GDP and disposable income. If inflation data is not
accurate, or is willfully fraudulent as appears to be the case, it will impact many other areas of economic
and financial data leading to large disparities over time.
Though there are undoubtedly other irregularities within Chinese inflation data, the focus of this
paper is the discrepancy around price changes in the cost of housing. The cost of housing is normally
one of the biggest single line items in many national price baskets. Consequently, changes in its
composition will have a disproportionate impact on the calculation of real GDP relative to other items.
There is strong evidence that China has systematically manipulated its housing price data in order to
lower official inflation which, using conservative estimates, has lowered annual inflation by
approximately 1%. Over this time frame, this would conservatively reduce real Chinese GDP by
approximately 10%.
Furthermore, this irregularity in the calculation of inflation may help explain discrepancies in
other macroeconomic variables such as growth in money supply, nominal, and real GDP growth in China.
Taken over a longer time horizon, there are discrepancies in other aspects of Chinese data that may be
explained by the inflation data.
This paper is broken into three sections. First, I begin with an examination of Chinese price data
focusing on the housing market. Second, I study broader macroeconomic variables including crosscountry data from other emerging markets and developed countries for comparison. Third, using this
data and plausible assumptions, I produce an estimate of real Chinese GDP.
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Chinese Housing Price Data
The National Bureau of Statistics of China (NBSC) houses the primary national economic,
financial, and social data. It is publicly available in Chinese and English with monthly, quarterly, annual,
and census data, including a separate portal for data searches. Within their annual data collection, in
most years Section 9, they maintain a lot of data on prices for producers, consumers, and other areas of
interest. In Table 3 under Section 9, they produce the consumer price index change with a lengthy list of
major cost line items and their individual annual price changes.1
At the bottom of Table 3 under Section 9, the NBSC presents the price change on Renting and
Private Housing with numbers giving the price change from the previous year multiplied by 100.
Consequently, the number 105 implies a 5% increase from the previous year, 95 means a drop of 5%
from the previous year, and 100 is no change. The raw data for year 2000 to 2011 is available in Table 1.
Table 1
Source: National Bureau of Statistics of China
Even before moving to more detailed analysis, there a two items that cannot be overlooked even to
casual observation. First, the price of private housing appears to grow quite slowly. According to raw
NBSC data, from 2000 to 2011, private housing price growth was negative or flat in 4 out of 12 years. To
anyone with even a rudimentary knowledge of China, this seems questionable at best. Second, the price
of renting grows faster than the price of private housing. In 8 of the 12 years, the price of renting
exceeds the price increase to private housing. Furthermore, renting is a much bigger winner than loser.For instance, in 2009, the price of renting increased 1.6% while private housing dropped 14.7% for a
total differential of 16.3%. In 2002, renting increased in price 4.4% while private housing dropped 4.6%
1I am including this detail so that anyone that wants to verify or download the same data may do so. Though all
data used to calculate the figures and tables in this paper are available upon request and will be available shortly
on my website.
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for a total differential of 9%. The obvious difference between rental and private housing prices implies
that it is becoming more affordable to purchase a home in China than rent.
If we transform the raw data into indexed data with the base year 2000, the discrepancies
between Chinese housing price data and common sense become increasingly stark. The indexed datawith 2000 as the base year equal to 100 is presented below in Table 2.
Table 2
Source: National Bureau of Statistics of China and the authors calculation
There are numerous results that need mentioning. First, according to the NBSC, the price of private
housing in China rose by a total of 8.14% between 2000 and 2011. More implausibly, the price of
private housing rose by only 5.99% in urban areas, where Chinese were moving to in large numbers.
Second, according to the NBSC, the annual price of private housing in rural areas grew at 1.67%, more
than three times faster than prices in urban areas at 0.53%. In other words, it became more expensive
in China to live in rural areas than in urban centers. Third and finally, according to the NBSC, the price
increase of renting outpaced the change in the price of private housing by nearly 50%. Put another way,
the price to rent ratio should have made it significantly more advantageous to purchase an apartment
between 2000 and 2011 in China.
There are three further ways in which the NBSC manipulates inflation data which are more
subtle. First, according to the NBSC, only 12% of Chinese households are renters. As noted in Appendix
1, the NBSC counts only 11.95% of households as renters. When including all categories of private
housing as defined by the NBSC, and excluding the undefined Other category, a total of 85.4% of
Chinese households are counted as private housing occupants. This has a very big implication: even
though the consumer price of renting increased a total of 53% from 2000 to 2011, this price applied to
less than 15% of Chinese households according to the NBSC. The remaining 85% of households are
counted as only seeing an 8% increase in their price of housing. In other words, even beyond the
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manipulation of basic price data, the NBSC is further slanting it by claiming an 85% weighting on the
lowest price.
Second, the NBSC is skewing the distribution of population between rural and urban when
calculating the total change to the private housing price index. The past decade in China has witnessedone of the largest migrations in human history from rural farms to urban apartment dwellings in China.
In 2000, nearly two-thirds of the Chinese population were classified as rural. By 2011, the number
classified as urban residents topped 51% according to the NBSC. However, when calculating the implied
population distribution of the total private housing price index between rural and urban residents, it
becomes obvious the NBSC has not accounted for actual population dynamics in China. These results
can be seen in Table 3.
Table 3
Source: National Bureau of Statistics of China and Authors Calculations
To calculate the total private housing price change, the NBSC utilized essentially an automatic 80%
weighting of the urban population and a 20% weighting of the rural population. In every year except
2000 and 2005, utilizing a strict 80% urban weighting yields a private housing price change within one-
tenth of one percent of the actual NBSC number. In 2005, the difference is less than twenty-five basis
points. Given the fact that nearly two-thirds of Chinese residents began 2000 as rural residents the
NBSC decision to give an 80% weight of the total price index to urban residents strikes one as
particularly egregious.
Third, the NBSC significantly under weights the cost of housing in the total consumer price
inflation basket. According to one report, from 2000 through 2010, the NBSC gave only a 13% weighting
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to housing in the CPI basket.2 To put this weighting in perspective, it gave a higher weighting to
education and cultural articles and only a slightly lower weighting to clothing. In 2011, the NBSC
reweighted the housing portion to a 17.2% percent weighting to housing despite the fact that it grew
significantly slower than all other components of the CPI. In other words, even though housing fell
significantly relative to other items in the NBSC basket between 2000 and 2010, it was magically
reweighted upward in 2011. The NBSC is implicitly saying its own statistics are unreliable.
This fraudulent compilation of price index data has an important implication: it drastically
lowers the price impact of housing even more than the manipulated price data indicates. With 85% of
residents classified as occupying private housing and an 80% weighting given to urban residents in the
price index, this implies that according to the NBSC nearly 70% of Chinese residents enjoyed a
cumulative increase in the price of housing of 6% between 2000 and 2011.3 Put another way, the NBSC
is claiming that 68% of Chinese households faced annual price increases of 0.53%. If we utilize a simple
and more realistic assumption that the price of housing should represent 20-30% of a sample price
basket, this would imply a contribution to the Chinese CPI of one-tenth of one percent annually. Any
increase in the urban price of private housing would have a significant pass through effect on other
economic data.
Lets turn from price basket data to comparisons against other changes of price in housing and
real estate beginning with changes in the value of rural home values. The NBSC rural home value price
with index base year 2000 is presented below in Table 4.
Table 4: Rural Home Value Raw Price and Index
Source: National Bureau of Statistics of China and authors calculation
2Please seehttp://www.china.org.cn/business/2011-02/16/content_21933515.htmfor more detail.
3This number is obtained by taking the 85% of private housing dwellers, multiplying by the 80% weighting given to
urban dwellers, then utilizing the accumulated total price change for urban private housing dwellers in Table 2.
http://www.china.org.cn/business/2011-02/16/content_21933515.htmhttp://www.china.org.cn/business/2011-02/16/content_21933515.htmhttp://www.china.org.cn/business/2011-02/16/content_21933515.htmhttp://www.china.org.cn/business/2011-02/16/content_21933515.htm7/27/2019 How Badly Flawed is Chinese Economic Data - $1 Trillion and Over Hit to GDP
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According to the NBSC, rural home values rose by 249% between 2000 and 2011 for a compounded
annual growth rate of 8.65%. As there is no known third party research on the pricing of rural real
estate and housing price data in China, the veracity of these statistics is virtually impossible to
independently corroborate. However, even taking the NBSC rural house value as a baseline, it stretches
the boundaries of plausibility to absurd lengths that rural home values would increase 9% annually while
rural private housing prices increased only 1.7% as noted in Table 2. It is important to briefly note that
according to the NBSC, 96% of rural households are classified as private housing occupants, hence this
analysis will focus on the price of private housing ignoring the renting price change.4
There are a couple of additional points about this. First, the growth in rural private home values
rose close to the official real GDP and well below nominal GDP numbers. It is at best implausible in a
statistical series when nominal GDP grows more than 500% and rural home values increase 350% that
the price of rural housing would grow just 19%.5 Second, while a significant migration from rural to
urban areas took place during this time sample, the rising rural home value indicates rising demand that
would impact the price index. In other words, despite the migration from rural to urban areas, rising
rural incomes prompted increases in home values. Third, while consumer price index measures will not
move in perfect correlation with asset prices, the enormity of the disparity deserves skepticism. Fourth,
while it is difficult to independently verify the rural home value data, it is extremely problematic that the
NBSC presents two such closely related numbers that vary so enormously. Given the reduction in the
consumer price measure, this appears to be more strategic data manipulation.
Turning to urban housing prices, the overall picture is strikingly similar though, with different
data limitations. The NBSC did not publish a continual urban value throughout this time period as they
did with the rural value. When they did publish value data, it gained such wide spread Chinese
skepticism, that the NBSC stopped publishing it in 2011. The official 70 Cities Index showed an annual
urban value increase of just over 4% annually from 2006 through 2010 (Wu et al. 2012). Even more
unbelievably, the 70 Cities Index claimed that the average annual real growth in Chinese boom towns
like Shenzhen was a miniscule 0.04% while in Shanghai it was only 1% (Deng et al. 2012). As official
estimates of private housing prices are implausible and widely discredited within China, we turn to third
party data on the change in value of private housing.
4This data is available in the back in Appendix 2.
5We will review in a later section the relationship to Chinese and cross country macroeconomic aggregates.
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To compile improved pricing estimates outside the official data, I borrow heavily from the
research carried out by the Department of Construction Management at Tsinghua University in Beijing
and the Institute of Real Estate Studies at the National University of Singapore (Wu et al. 2010). In
three papers, researchers lay out in great detail third party pricing values, compare them to official
indicators, and improve price index methodologies. The figures, tables, and data are borrowed from
their research. There are numerous pricing and value patterns which are worth noting. First, the asset
value of private housing has risen much faster than official data claims. Despite the NBSC data claiming
that the price of private housing has risen by 8% since 2000, third party data indicates that underlying
asset values have risen rapidly and especially in recent years. According to the Tsinghua University
researchers, from the first quarter of 2000 to the first quarter of 2010, the nominal value of apartments
in urban areas in China rose nearly threefold as presented below in Figure 1.
Figure 1: Constant Quality Price Index for Newly Built Private Housing in 35 Major Chinese
Cities, 2000(q1)-2010(q1)
Source: Wu et al. 2010 Figure 1 p. 28
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There are a couple of points worth noting about Figure 1. First, the urban home price value has risen
much faster than official estimates. Second, the nominal value increase closely approximates the
change in the rural housing price as given by the NBSC. Third, the real value is close to the nominal
value because of the manipulated consumer price inflation data. If inflation data accurately reflects
consumer prices, specifically in housing prices, the differences will become much larger. This furthers
the argument that official Chinese data grossly understates the change since 2000 in housing prices and,
for the purposes of this paper, the specific pass through impact on price indexes and real GDP.6
Next, I turn to the price of private urban housing relative to the price of renting. According to
official NBSC CPI statistics presented in Table 2, the accumulated price change since 2000 between
renting and private housing in urban areas was nearly ten times greater in 2011. In other words, it
became significantly more advantageous to rent in urban areas than purchasing. However, third party
data indicates that this is inaccurate as presented in Figure 2 (Wu 2010).
Figure 2: Price to Rent Ratio in Eight Major Chinese Cities, 2007(q1)-2010(q1)
6Please see Appendix 4 for additional information on the change in Chinese urban home values.
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Source: Wu et al. 2010 Figure 11 p. 38
While this data figure does not cover the entire time frame under question and only covers 8 major
cities, it demonstrates quite clearly that private housing prices are significantly outpacing rental price
increases as claimed by the NBSC. It is noteworthy that most cities entered the sample at elevated to
significantly elevated levels and end the sample at highly elevated price to rent ratios. In other words,
third party data simply does not support the NBSC private housing to rental market price data.
There are numerous problems revealed with the NBSC compilation of residential pricing data.
First, the base price data is obviously manipulated. Second, the smallest price increases are seen in
urban private housing owners which given the weights skews the results. Third, given the division
between urban and rural residents it significantly overweight urban residents. Fourth, NBSC data
significantly underweights in the total CPI basket the impact of housing. Fifth, the official data does not
reflect independent third party data with regards to changes in the price of housing. While we do not
expect the price change in consumer prices to be perfectly correlated with asset values, the sheer
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enormity of the discrepancies presented are real cause for concern. Furthermore, while we do not
expect third party data to perfectly mirror official data, the enormity of the discrepancy is noteworthy.
Even the patterns revealed, such as the difference between private housing and renting, are so starkly
different as to warrant investigation. Using this data as a baseline for estimating better prices, I will use
the primary findings of this research to better estimate the impact on Chinese inflation and real GDP
growth.
Chinese Home Prices and Real Estate Values in Macroeconomic Context
Despite the evidence that the NBSC has manipulated the change in housing price data to systematically
lower inflation data, it is important to place this data within the larger picture of Chinese
macroeconomic data since 2000. We begin with a comparison to official Chinese CPI data as presented
below in Table 5.
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Table 5: Private Housing Relative to Chinese CPI
Source: National Bureau of Statistics of China and authors calculation
According to the NBSC, consumer price inflation (CPI) increased at a rate of 2.43% between 2000 and
2011. This resulted in an official accumulated price level increase of 30.2%. When we divide the
consumer change in the price of private housing by the CPI, the data implies that the real price of
private housing fellin China by a total of 17% or at an average annual rate of 1.68%. In other words,
according to the NBSC the real price of private housing dropped by almost 20%.
When we compare the change in the CPI component of private housing against other
macroeconomic indicators, we again witness a similar divergence. Since 2000, real GDP, nominal GDP,
and the money supply have all officially expanded enormously. While China has undoubtedly grown
significantly in real terms, what has been little understood about this growth has been the
accompanying expansion of the money base. Real GDP increased just under three fold between 2000
and 2011, but money grew more than twice as fast, increasing more than six fold over the same time
frame. When we demonstrate the growth in these key macroeconomic variables, with the growth in the
official price of private housing in China below in Figure 1, the discrepancy becomes increasingly obvious.
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Figure 1: Chinese Money Growth, Nominal GDP, Real GDP, and Private Housing Prices
Source: National Bureau of Statistics of China, International Monetary Fund International Financial Statistics and
authors calculations where 2000=100.
From 2000 to 2011, according to official statistics, M2 money grew more than six fold, nominal GDP
nearly fivefold, and real GDP three fold, but the price of private housing by only 8%. If we focus on
nothing other than the growth of official real GDP, it seems implausible that the price index of private
housing would be falling so significantly relative to output. If we include the nearly five-fold increase in
nominal GDP and six fold growth in M2 money into the analysis, it again strains credibility that the price
of private housing would remain nearly unchanged for eleven years.
The Chinese Growth Story in Cross Country Perspective
The sources and discrepancies of the Chinese growth story gain much more focus in a crosscountry perspective. Comparing the major macroeconomic variables, there are some stark contrasts
that further demonstrate the argument that systematic under reporting of inflation has significantly
boosted Chinese real GDP growth. We begin with an examination of the increase in numerous
measures of the money supply. Despite the reputation of China as managing prudent monetary policy,
it has increased money by one of the largest amounts among emerging economies since 2000. The first
-
100
200
300
400
500
600
700
China
M2
China
NGDP
China
RGDP
Private
Housing
Price
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figure is the growth of M1 among major emerging markets and select developed countries, presented
below in Figure 2.
Figure 2: M1 Growth of Major Emerging Market Economies 2000-2011
Source: International Monetary Fund International Financial Statistics where 2000=100
According to Figure 1 for major emerging market economies and the Euro Area, China experienced the
third highest growth in M1 money.7 While M1 is a narrower form of money, given the difficulties in
cross country money growth comparisons, it helps provide a basis for comparing money growth in
countries. As M1 comprises only the narrowest definitions of money, such as currency in circulation and
demand deposits, it omits a significant volume of potential money expansion. Specifically in China,
given the fixed currency which funneled the growth in money through banks and ultimately to state
owned enterprises, the M1 definition of money may overlook significant growth in broader money. The
growth of M2 money is similar, as can be seen in Figure 3.
Figure 3: M2 Growth of Major Economies from 2000-2011
7Please see Appendix 3 for a figure which includes India for 2006 to 2012 data.
-
500
1,000
1,500
2,000
2,500
3,000
3,500
243 369 438 459 569580
1,191
3,257
M1 Growth from 2000 to 2011
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Source: International Monetary Fund International Financial Statistics and authors calculations where 2000=100
According to IMF data, Chinese M2 growth was again the third highest among major emerging market
economies. Chinese M2 growth outpaced most every major emerging market except countries with
rapid inflation. However, rapid growth in the money supply does not necessarily portend problems if
there is growth in the economy. In fact, despite the Milton Friedman declaration that inflation is
always and everywhere a monetary phenomenon, most central banks have adopted strategies
unrelated to explicit money supply growth targets to maintain stable prices.
One problem relying on other measures of monetary policy is their reliance of flawed or
manipulated data. Lets take a simple variation of the well known Taylor Rule which has been rewritten
as the Mankiw Rule. The Mankiw rule is written as the following:
While the discount is public and well known, both inflation and unemployment are subject to politicalmanipulation. I have presented below the Mankiw Rule for China plotted with the discount rate,
consumer price inflation, and estimated core inflation in Figure 4.8
8China does not breakout a specific core inflation number. However, given what we believe is the official CI
weighting, I have estimated core CPI based upon this baset.
0
500
1,000
1,500
2,000
2,500
3,000
3,5004,000
4,500
5,000
128 213 253408 430 435 529
623696
4,285
4,996
M2 Growth from 2000 to 2011
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Figure 4: The Mankiw Rule in China with Official Unemployment and Inflation Data
Source: National Bureau of Statics China, Federal Reserve, and authors calculation
When using official data and the Mankiw Rule, China appears to have run a tight monetary policy
between 2003 and 2011. However, it would be mistaken to believe that China ran tight monetary policy
since 2003 based upon fraudulent data. As this paper is primarily about inflation data, here I will focus
on the problem with unemployment data. Since 2002, has fluctuated constantly between 4% and 4.3%.
Despite the enormous up economic changes, both to China and the global economy, it should induce
serious skepticism that a rapidly growing export dependent economy enduring a global financial crisis
never sees its unemployment rate move. The quarterly data is so rigid that years pass without the
quarterly unemployment rate fluctuating even one-tenth of one percentage point. Even believing the
general trend, it seems implausible that Chinese unemployment has been so rigid so long. In fact,
between 2000 and 2012, China had the lowest standard deviation of 28 major economies which is
presented as a figure in Appendix 5. To provide some perspective, the Chinese standard deviation of
unemployment is a third less than Japan over the same period. The absolute rigidity should provide real
concern about the quality of data being furnished by official Chinese agencies.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
MankiwCPI
Mankiw
Core CPI
Discount
Rate
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Next, I focus on the increase in nominal GDP. While not widely focused on, nominal GDP is an
important place to start when considering the Chinese growth story since 2000. As has already been
noted both in Chinese specific and cross country data, the money supply grew rapidly. Consequently,
we would expect nominal GDP to grow quite rapidly. We examine cross country nominal GDP growth
below in Figure 5.
Figure 5: Nominal GDP Growth of Major Emerging Market Economies 2000-2011
Source: International Monetary Fund International Financial Statistics where 2000=100
Despite having one of the highest increases in M2 growth in major emerging market economies, China
had lower nominal GDP growth than Indonesia and Argentina. This means that to achieve the claimed
Chinese level of real GDP growth, it would need to maintain throughout this period extremely low
inflation. China would only grow faster than Indonesia, Argentina, and even India in real terms if its
annual inflation was extremely low. We present inflation data below in Figure 6.
-
100
200
300
400500
600
700
800
205 227
316351 406
477 534
647
764 779
Nominal GDP Growth 2000 to 2011
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Figure 6: Inflation Index of Major Economies
Source: International Monetary Fund World Economic Outlook where 2000=100
According to official statistics, between 2000 and 2011, China had one of the lowest accumulated
inflation indexes of any major economy. Officially, total Chinese inflation was less than the United
States and only slightly more than the UK and Canada. Given the enormous discrepancies between key
macroeconomic variables, the Chinese claims to such extra ordinarily low inflation appear tenuous. The
low growth in inflation cascades directly into Chinese claims of other key variables, specifically real GDP
growth. As Chinese nominal GDP growth was average for major emerging market economies, their real
GDP growth depends enormously on low inflation. I present real GDP growth data for major emerging
market economies below in Figure 7.
-
100
200
300
400
500
600
97126 128 130 131 142
163 188 201 203240
273353
533
Inflation Index from 2000 to 2011
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Figure 7: Indexed Real GDP Growth from 2000-2011
Source: International Monetary Fund World Economic Outlook where 2000=100
Figure 7 shows official data claiming that real Chinese GDP growth has grown significantly faster than
other major emerging market economies. The growth in real Chinese GDP is nearly completely
dependent upon the claims of emerging market growth, but developed country inflation rates. If
Chinese inflation rates increase even marginally, then the Chinese growth story is substantially different
between 2000 and 2011.
Estimating Chinese Inflation and Real GDP Growth Between 2000 and 2011
Chinese inflation data suffers from obvious manipulation. To correct this systematic bias, I begin to
estimate, using a variety of reasonable assumptions based upon independent verifiable data, improved
values of Chinese inflation. It is important to cover a couple of major general data and methodological
issues. First, the NBSC throughout most of the time period in question assigned only a 13% weighting to
private housing or rental prices, which by a variety of other measures seems extremely low. According
to the NBSC, the price basket spent more on education and cultural articles and only slightly less on
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50
100
150
200
250
300
110124
147 149 152 156163
174 176
215
294
Total Real GDP Growth from 2000 to 2011
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clothing.9 The weighting for housing was only increased to 17.2% in 2011. To correct for this, we
include a NBSC weighting, a 20% weighting, and a 30% weighting. Second, rather than just adding in the
new housing inflation figure, it is also necessary to subtract out the previous number so as to avoid
double counting. Third, rather than using straight line and obviously false population weighting data, I
weight the pricing data based upon the proportion of rural and urban population and their respective
categorization between private housing and renting. This provides us increased accuracy in the inflation
data. These are the general assumptions made, but they are also realistic and conservative.
To accomplish our objective of improved estimations of Chinese inflation data, we need to make
a number of assumptions in each of the three price scenarios. In Inflation Scenario 1, I make one
primary assumption that the price index value of private housing will rise by no less than the price
change to renting. If the official price of private housing rises faster that number is used; if the official
price of renting is higher, that number is used. As previously noted in third party research and data,private housing prices increased faster than rental prices. This assumption significantly raises the price
of private housing, but only to slightly more than the rental price. This happens because in most years,
the rental price grew faster than the price of private housing in official NBSC statistics. In Inflation
Scenario 2, I make the same assumption as in Inflation Scenario 1, but never allow the annual price
change of private housing to drop beneath 4%. This changes the pricing data for five years for urban
residents and eight for rural residents. It does not however, change the data enormously. In Inflation
Scenario 1, the accumulated price difference between private housing and renting was 91% to 75%. In
Scenario #2, the difference is only slightly larger at 99% to 75%. In other words, this is a small and
conservative assumption that does not deviate significantly from other data. In Inflation Scenario 3,
rather than basing the new numbers on existing NBSC inflation data, I use asset price value with
assumptions about the underlying pass through to prices. For the urban private housing price I use
Deng et al. (2012) and their annual urban price change from 2004 to 2011 dividing by two as asset prices
rise faster than the price index, with all other assumptions from Price Inflation Scenario 2 for years 2000
to 2003. Urban rental values are obtained by dividing the price change from urban private housing in
half. Rural private housing price is taken from NBSC data on rural home value with the same discounting
factor applied to the price index. Rural rental prices are obtained in the same manner as urban rental
prices, dividing the change in rural private housing prices by half. Using the outlined methodology and
the IMF local currency GDP deflator, we add in the new inflation data, given the different assumptions
with results shown below in Table 6.
9Please seehttp://www.china.org.cn/business/2011-02/16/content_21933515.htmfor more information.
http://www.china.org.cn/business/2011-02/16/content_21933515.htmhttp://www.china.org.cn/business/2011-02/16/content_21933515.htmhttp://www.china.org.cn/business/2011-02/16/content_21933515.htmhttp://www.china.org.cn/business/2011-02/16/content_21933515.htm7/27/2019 How Badly Flawed is Chinese Economic Data - $1 Trillion and Over Hit to GDP
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Table 6: Estimated Chinese IMF GDP Deflator Change From Improved Housing Price Data
Source: National Bureau of Statistics of China, International Monetary Fund, and authors calculation
There are a number of points to make about the GDP deflator adjustments. First, the range is not
insignificant. Beginning with the most conservative of assumptions, where private housing prices only
keep pace with official rental prices and a 13% weighting for housing, both assumptions we know to be
inaccurate, real GDP should be reduced by more than 4%. Given a more realistic assumption with
regards to the change in prices and the amount spent by Chinese residents on housing, this implies
closer to an 8-12% reduction in real GDP. Translating this new deflation into PPP international dollars,
this would reduce real GDP by more than $1 trillion, if we use a mid-range estimate.
There are a couple of concerning points when placing this analysis in context. First, it is
disturbing that a statistical body would so obviously manipulate and produce blatantly fraudulent data.
Though improved third party data exists and has been used, the claim that the housing component of
CPI grew by less than 10% between 2000 and 2011 is nothing less than comical. Second, given the
relative ease with which obvious statistical manipulation was found, it is quite likely that less obvious
fraud is present in the CPI data. In fact, even a simple perusal of NBSC data reveals even more
discrepancies. For instance, the NSBC from 2000 to 2010 assigned a 34% weighting to the food
component of price inflation. In 2011, food was reweighted and assigned a 31.8% weighting within the
CPI basket. Using solely NBSC data on the price increase of food in China, it becomes quickly apparent
that this would require unrivaled intellectual gymnastics. Using NSBC data on the consumer price
inflation and the food component, I present the estimated food share of the Chinese CPI below in Figure
9.
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Figure 9The Food Share of Chinese Consumer Price Inflation
Source: National Bureau of Statistics China and authors calculation. As a brief note, in 2009 China
reported a declining CPI even though food prices rose .7%. It is therefore credited as responsible for all
inflation in China in 2009.
According to NBSC data, the food component of the CPI in China was responsible for 99% of inflation
between 2003 and 2011. Thinking of this another way, this implies that the NBSC is claiming that the
onlyprices to rise in China between 2003 and 2011 were food prices. To provide one more example, the
NSBC claims that food received a 34% weighting throughout most of the decade, the data does not
support this. In Table 7, I present the data using the NSBC weighting and the declared annual CPI.
100% 87%
55% 54%
88% 83% 100%
74% 70%
99%
0%
20%
40%
60%
80%
100%
120%
Food Share of
Chinese CPI
Using NBSC
Weighting
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Table 7 Component Pricing Compared to the NSBC Claimed CPI
Source: National Bureau of Statistics and the authors calculation.
The component weighting of the CPI data reveals more inconsistent results. There is an annual
discrepancy of .35%. Even when weighting the data according to the NSBC using their data, the
numbers fail to reconcile. This simply furthers the discrepancies and problems underlying Chinese
statistical data.
Conclusion
China has grown rapidly since its opening in 1979 to become one of the worlds largest economies.
However, its economic data is flawed at best and it appears to be politically manipulated. The
problematic data stems from two primary problems. First, the base data on housing prices is grossly
manipulated. Second, it systematically weights it to the lower price categories despite those groups
being under represented in the population. If we adjust the CPI data based upon conservative,
reasonable, and independent data or assumptions about the changes in the price of housing in China, it
lowers real GDP by a mid-range estimate of 8-12% or more than $1 trillion PPP USD. Given what we
obviously locate, it seems likely that much larger revisions to Chinese real GDP and other economic data
is needed to produce more reliable statistics.
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Appendix 1
Source: National Bureau of Statistics of China
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Appendix 2
Source: National Bureau of Statistics of China
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Appendix 3
Source: International Monetary Fund International Financial Statistics
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50
100
150
200
250
300
350
140
187 194200
224 251254 263
332M1 Growth 2006 Through 2011
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Appendix 4
Source: Deng et al. 2012 Table 4 p. 42
Comparison Between the Hedonic Price Index and Two Official House Price Indices (2006Q1-2010Q4)
Source: Wu et al. 2012 Table 4 p. 43
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Appendix 4 cont.
Newly Built House Price Indeces in the Sample City by Various Methods
Source: Wu et al. 2012 Figure 4 p. 39
Note: Wu et al. methodologically prefer the Hedonic Model. The Hedonic Model indicates approximately
150% increase between 2004 and the end of 2009. Even more conservative and traditional measures
like the weighted or unweighted average indicate a doubling of prices during this time.
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Appendix 4 cont.
Comparison Between House Price Appreciation and Income Growth
(35 Major Cities, 2006-2010)
Source: Wu et al. 2012 Figure 5 p. 40
Note: While the above figure is not a specific index, it does indicate that the real average annual growth
in most cities was above 10% with only one city averaging less than 5%. Considering this is the real price
and not the nominal price, it implies that a very small number of major cities witnessed average annual
nominal price appreciation of less than 10% between 2006 and 2010.
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Appendix 5Figure of Major Economy Standard Deviation of the Unemployment Rate of Major
Economies
Source: International Monetary Fund World Economic Outlook and authors calculation
Note: The countries in the figure are Argentina, Brazil, China, Denmark, France, Germany, Hong Kong,
Iceland, India, Indonesia, Ireland, Italy, Japan, Korea, Mexico, Netherlands, New Zealand, Nigeria,
Norway, Pakistan, Russia, South Africa, Spain, Sweden, Switzerland, Turkey, the United Kingdom, and
the United States.
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Bibliography
Deng, Yongheng, Joseph Gyourko, and Jing Wu. 2012. Land and House Price Measurement in China.
National University of Singapore Institute of Real Estate Studies Working Paper IRES2012-024
Wu, Jing, Yongheng Deng, and Hongyu Liu. 2012. Housing Price Index Construction in the Nascent
Housing Market: The Case of China. National University of Singapore Institute of Real Estate Studies
Working Paper IRES2011-017
Wu, Jing, Joseph Gyourko, and Yongheng Deng. 2010. Evaluating Conditions in Major Chinese Housing
Markets. National University of Singapore Institute of Real Estate Studies Working Paper IRES2010-007