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China–US trade flow behavior: the implications of alternative exchange rate measures and trade classifications

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Abstract

The authors examine Chinese-US trade flows over the 1994–2012 period, and find that, in line with the conventional wisdom, the value of China’s exports to the US responds negatively to real renminbi (RMB) appreciation, while imports respond positively. Further, the combined price effects on exports and imports imply an increase in the real value of the RMB will reduce China’s trade balance. The use of alternative exchange rate measures and data on different trade classifications yields additional insights. Firms more subject to market forces exhibit greater price sensitivity. The price elasticity is larger for ordinary exports than for processing exports. Finally, accounting for endogeneity and measurement error matters. Hence, purging the real exchange rate of the portion responding to policy, or using the deviation of the real exchange rate from the equilibrium level yields a stronger measured effect than when using the unadjusted bilateral exchange rate.

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Notes

  1. In addition, we tried three other measures for the RMB real exchange rate—the value added REER (Bems and Johnson 2012), the REER incorporated Chinese Agriculture productivity (Dekle and Ungor 2013), and the integrated REER (Thorbecke 2013). The results are not reported in the paper, but available from authors upon request.

  2. Processing exports are exports that comprise imported raw and intermediate components. These components are imported into China by authorized enterprises with preferential tax treatments and for producing products for exports.

  3. In this study, the once popular township and village enterprises, also known as collective or alliance enterprises are grouped under the heading of private enterprises. Strictly, the term “township and village enterprises” does not mean that these enterprises are owned by towns and villages. They are typically located in towns and villages, sponsored by townships and villages, and owned by private entities. Exports by these enterprises are quite small and declining over time.

  4. Eichengreen et al. (2007), Gaulier et al. (2006) and Thorbecke (2006), for example, report evidence on China is competing with ASEAN economies in exports markets; especially in labor-intensive products.

  5. Pesaran et al. (2001) derive critical value bounds based on two sets of distribution functions to cover cases in which the variables have different orders of integration. Thus, the price for the robustness is the possibility of an inconclusive inference if the test statistic falls within the bounds. The exact critical value can be derived with information about the stationarity of the explanatory variables.

  6. In estimating the model, a vector of time dummy variables capturing effects of seasonal factors, 1997 financial crisis, China’s WTO accession in 2001, the RMB exchange rate reform in 2005, and 2008 global financial crisis, as well as a time trend and its interaction terms with those time dummies are also included. For brevity, coefficient estimates of these dummy variables are not reported below.

  7. The asymptotic distribution of θ can be derived using the delta lemma.

  8. The BLS reports a price index for Chinese imports into the United States, but the series only begins in 2004. Hence, we do not calculate a volume series based on this deflator.

  9. The lag parameters p and q are selected based on the Bayesian Information Criterion and the Jarque–Bera test.

  10. The same null hypothesis was rejected in all the subsequent exports equation specifications. That is, there is empirical evidence of the presence of long-term relationship between the selected Chinese exports series and the associated factors. These test results again are not reported to conserve space but are available upon request.

  11. Given previous research (e.g. Eichengreen et al. 2007; Thorbecke 2006), our prior is that Chinese and ASEAN goods are substitutes in the US market. Thus, a depreciation of ASEAN real exchange rate makes ASEAN exports more competitive in the US market.

  12. Studies using different exchange rate models and different methods of calculation, along with the issue of China data uncertainty, generate a wide dispersion of RMB misalignment estimates. A sample of these studies includes Cheung et al. (2007, 2009, 2010b), Coudert and Couharde (2007), Frankel (2006), and Wang (2004).

  13. The importance of differentiating the ordinary and processing exports is emphasized in, for example, Ahmed (2009), Cheung et al. (2012), Garcia-Herrero and Koivu (2007), and Marquez and Schindler (2007).

  14. See, for example Fratzscher and Mehl (2014), McKinnon and Schnabl (2003), Subramanian and Kessler (2013).

  15. For discussions on including relative productivity in China’s imports equation, see, for example, Aziz and Li (2008), Cheung et al. (2012), and Chinn (2006).

  16. For all the imports equations reported below, the null hypothesis of \( \beta_{1} = \beta_{2} = \beta_{3} = \beta_{4} = \beta_{5} = 0 \) is rejected at the 1 % level. That is, there is an empirical long-term relationship between Chinese imports and other variables. The lag parameters p and q are selected based on the Bayesian Information Criterion and the Jarque–Bera test. For brevity, the results of testing the null hypothesis are not reported but available upon request.

  17. The price effects based on the other two measures are, in general, qualitatively similar to those in Table 5.

  18. There might be a concern that China’s export to the US contained in China’s total trade balance in the first-stage regression (6) explains itself in the second stage regression. While it is possible in theory, the data show the correlation between China’s overall exports/imports and China-US bilateral exports/imports are relatively low, about 14.9 and 5.5 %, respectively. Moreover, China’s overall trade balance only explains a small fraction of real exchange variability.

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Acknowledgments

We thank Willem Thorbecke, and participants of the 2014 CEANA/CEA session at ASSA, the 5th annual G2 at GWU conference, and seminar participants at the Shanghai University of Finance and Economics for their comments and suggestions. We also thank Rob Johnson and Robert Dekle for sharing data with us. Previous versions of the paper were circulated under the title “The structural behavior of China–US trade flows.”

Author information

Authors and Affiliations

  1. Hung Hing Ying Chair Professor of International Economics, Department of Economics and Finance, City University of Hong Kong, Kowloon, Hong Kong

    Yin-Wong Cheung

  2. Robert M. La Follette School of Public Affairs, University of Wisconsin, 1180 Observatory Drive, Madison, WI, 53706-1393, USA

    Menzie D. Chinn

  3. Department of Economics, University of Wisconsin, 1180 Observatory Drive, Madison, WI, 53706-1393, USA

    Menzie D. Chinn

  4. Economics and Finance Department, SUNY Buffalo State, 1300 Elmwood Ave, Buffalo, NY, 14222, USA

    Xingwang Qian

Authors
  1. Yin-Wong Cheung
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  2. Menzie D. Chinn
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  3. Xingwang Qian
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Corresponding authors

Correspondence to Yin-Wong Cheung or Menzie D. Chinn.

Appendices

Appendix 1: Definitions of variables

Dependent variables: China’s exports to, imports from the US in aggregate, trade type (ordinary and processing trade), products type (primary and manufactured products), and firm type (SOE, FIE, and Private firms) data. Nominal exports data are deflated by the Hong Kong unit value index of re-exports to US; and nominal imports are deflated by the Hong Kong unit value index of re-exports to China.

GDP_US:

The US real GDP in the 2005 price, in logarithmic form

GDP_CN:

China’s real GDP in the 1995 price, in RMB and logarithmic form

RER:

The bilateral real exchange rate between RMB and USD, calculated as the nominal exchange rate deflated by both CPIs of China and the US, in logarithmic form. A large RER indicates a high RMB value

REER_US/ASEAN:

The real effective exchange rate of USD against ASEAN-5, in logarithmic form. A large REER_US/ASEAN means a high USD value

REER_CN/EU:

The real effective exchange rate of the RMB against the Europe Union currencies, in logarithmic form. A large REER_CNY/EU means a high RMB value

Proc_Imports:

China’s total procession imports deflated by the Hong Kong unit value index of re-exports to China, in logarithmic form

Prod:

China’s relative productivity, measured by the Chinese GDP per employment in the secondary industry relative to the US manufacture output per job

Afc97:

A time dummy variable of the Asian financial crisis (=1 if t ≥ 1998Q1; = 0, otherwise)

WTO:

A time dummy variable of China’s accession to WTO at Dec, 2001 (=1 if t ≥ 2002Q1; =0, otherwise)

Reform:

A time dummy variable of China’s exchange rate reform in July 2005 (=1 if t ≥ 2005Q3; =0, otherwise)

Gfc08a:

A time dummy variable of the global financial crisis in 2008 (=1 if t ≥ 2008Q4; =0, otherwise)

Gfc08b:

A time dummy variable of the plummet time periods of the global financial crisis in 2008 (=1 if t ≥ 2008Q4, 2009Q1, 2009Q2; =0, otherwise)

SED:

A time dummy variable of the Strategic and Economic Dialogue between China and US

Q1, Q2, Q3:

The quarterly dummy variables

Trend:

A time trend variable

Appendix 2: Results of unit root tests

 

DF-GLS with a trend

ADF test with one structural break in both mean and trend

tau-statistics

lags

t statistics

Break point

Aggregated exports

−1.127

5

−2.950

2001q4

Primary goods exports

−1.610

8

−3.037

2001q4

Manufactured goods exports

−1.770

4

−2.880

2001q4

Ordinary exports

−0.753

5

−2.596

2003q4

Processing exports

−1.442

4

−2.848

2002q4

SOE exports

−1.561

4

−3.915*

2008q2

FIE exports

−0.986

5

−4.083*

2001q4

Private firms exports

−1.046

4

−2.583

2001q4

Aggregated imports

−2.836*

4

−1.978

2002q1

Primary goods imports

−3.251**

4

−2.197

2002q3

Manufactured goods imports

−1.783

8

−1.648

2002q1

Ordinary imports

−4.552***

1

−1.451

2006q3

Processing imports

−2.188

1

−4.295**

2011q3

SOE imports

−6.009***

1

−0.291

2004q4

FIE imports

−1.236

1

−3.029

2001q4

Private firms imports

−1.155

2

−2.382

2002q1

China’s real GDP

−1.715

4

−0.935

2004q2

US real GDP

−0.997

2

−3.089

1995q4

RMB-USD real exchange rate

−2.740

8

−3.787

2007q1

USD-ASEAN real effective exchange rate (REER)

−1.371

1

−2.530

1997q1

RMB-Euro REER

−1.518

1

−2.362

2001q4

China’s total processing imports

−1.044

5

−2.936

2001q4

The change of China–US relative productivity

−1.162

3

−3.740*

2008q3

Output gap

−1.319

10

−0.656

2011q1

China’s inflation

−1.228

5

−4.140*

2007q2

China’s deposit rate

−0.479

1

−6.039***

1995q4

China’s trade surplus

−2.198

4

−3.169

2003q4

  1. All exports data are deflated by the Hong Kong re-export to the US unit value index and all imports data are deflated by the Hong Kong re-export to China unit value index, in logarithm. The finite sample critical value at 5 % significant level for DF-GLS test and ADF test with one structural break are from Cheung and Lai (1995) and Perron and Vogelsang (1992), respectively. The lag structure is decided by SBIC. The break points are endogenously identified from a grid search method. “***, **, *” indicate the 1, 5, and 10 % level of significance, respectively

Appendix 3: The two-stage process

The RMB exchange rate effect on Chinese exports to the US allowing for possible feedback of China’s trade surplus to the RMB exchange rate is studied as follows.

The feedback effect of trade surplus to RMB exchange rate is accounted for in the following equation in the 1st stage,

$$ r_{t} = \alpha + \gamma_{1} Z_{t} + \gamma_{2} X_{t} + \varepsilon_{t} $$
(6)

where r t is the RMB real exchange rate; Z t is a vector of variables that r t responds to, including China’s total trade balanceFootnote 18 and three Taylor rule factors—China’s output gap, inflation rate, and interest rate differential (Chinn 2008). Other variables in Eq. (1) in the text are included in X t as the exogenous variables to meet the necessary condition for identification in a two-equation system (see Baltagi 2002, Chapter 11 for details). A Bounds test for (6) is performed in an ARDL setting with a time trend and time dummy variables including 1997 and 2008 financial crises, 2005 exchange rate reform, SED, and their interaction terms with time trend, been included. The fitted value for r t , notated as \( \hat{r}_{t} \), is generated from the long-run level relation.

In the second stage, we run a Bounds test as specified in Eq. (2) and estimate the long-term level relation with r t in Eq. (1) replaced by \( \hat{r}_{t} \).

This two-step process might entail the issue of generated regressors, in that \( \hat{r}_{t} \) is an estimated variable. In essence, we use an estimated proxy to measure the actual variable. In doing so, we are measuring it with an error. Thus, regressions using generated regressors yield biased standard errors that can lead to improper inferences. In our exercise, the inferences are based on the corrected variance–covariance matrix estimate and the associated standard errors recommended by Baltagi (2002) and Pagan (1984).

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Cheung, YW., Chinn, M.D. & Qian, X. China–US trade flow behavior: the implications of alternative exchange rate measures and trade classifications. Rev World Econ 152, 43–67 (2016). https://doi.org/10.1007/s10290-015-0232-y

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