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China Activity Proxy – Methodology

The China Activity Proxy (CAP) is our attempt to track the pace of economic growth in China without relying on the official GDP figures. We began compiling the CAP in 2009 and expanded its scope in 2020, though the framework remains the same. A detailed analysis of how the CAP improves on the GDP data, and the advantages it offers in tracking China’s economy can be found in our Focus, “Introducing our new China Activity Proxy“.

The CAP is based on a set of carefully selected indicators that capture activity across a broad spectrum of the economy. While much of the data is collected by government entities, we have avoided using high-profile indicators such as industrial production and retail sales, focusing instead on low-level indicators that are less politically-sensitive and so less likely to be manipulated. The CAP is based solely on monthly indicators, giving a more regular and timely view of economic conditions than is possible using quarterly GDP.


The CAP is made up of fourteen indicators that are combined from the bottom up into three sector proxies:

Industry Proxy

  1. CE Industrial Output Index (units vary). Our in-house proxy for industrial production that aggregates data on the output volumes of key products (metals, chemicals, electronics, etc).
  2. Domestic Freight Traffic (tons-km). A broad measure that captures the movement of goods across China.
  3. Seaport Container Traffic (TEU). A proxy for the volume of foreign and intercoastal trade in manufactured goods.
  4. Seaport Freight Traffic (tons). A measure of wider seaborne trade that captures shipments of bulk commodities.

Construction Proxy

  1. CE Construction Materials Output Index (units vary). An aggregate measure for the production of materials used in construction (cement, certain steel products, etc). This is a subset of the CE Industrial Output Index.  
  2. CE Construction Machinery Sales Index (units). A proxy for construction activity covering both property and infrastructure that aggregates data on the sale of machinery such as excavators, cranes, road rollers and forklifts.
  3. Domestic Freight Traffic (tons-km). A broad measure that captures the movement of goods across China.

Services Proxy

  1. Intercity Passenger Traffic (persons-km). Captures leisure and business travel between cities (excluding by private car).
  2. Intracity Passenger Traffic (persons). Captures use of urban transportation networks within cities (excludes personal vehicle usage). 
  3. Property Sales (sqm). A proxy for real estate services activity.
  4. Car Sales (units). A proxy for discretionary consumer spending.
  5. Domestic Mobile Phone Shipments (units). A proxy for discretionary consumer spending. 
  6. Express Deliveries (units). A proxy for postal services and online consumer spending. 

  7. Telecom Business Volume (RMB, constant prices). A measure of telecommunication usage and a proxy for wider business activity. 

  8. Service Sector Electricity Consumption (kwh). A proxy for overall service sector activity.

All the CAP components are measured in volume terms, allowing us to avoid problems translating nominal into real values. Most extend back at least a decade and many go back much further. We have made some adjustments to the series to accommodate year-end revisions and shifts in the timing of Lunar New Year.


Estimation of annual (% y/y) growth for each sector proxy follows a three-stage process.

  1. First, we calculate the annual growth rates for each of the component series.
  2. Next, we calculate weights for the component series to ensure that changes in less volatile series have the same impact on the sector proxy as proportionally-equal changes in more volatile series. We do this by de-trending and normalising their growth rates.
  3. We then average the original growth rates using these weights. This allows us to adjust for volatility in the component series but still preserve the trends in the underlying data.

We combine the three sector proxies into the headline CAP using the five-year rolling weights of industry, construction and services in nominal GDP, which we believe is subject to less manipulation than the real GDP figures. This is to ensure that the CAP takes into account broad structural shifts in the economy over time. The official GDP data are not used at any other point in the calculation of the CAP, whether as inputs or to weight or scale the indicators within each sector proxy.

We also construct a seasonally-adjusted version of the CAP and its sector proxies. We use thee monthly industrial production data for 1999 as a base and then extrapolate forward using the CAP growth rates. We then seasonally adjust the resulting series using X-12-ARIMA with custom holiday regressors for Chinese New Year.


Our approach of averaging normalized growth rates of component series is simple but has an important advantage over more sophisticated econometric techniques such as principal components analysis (PCA) or dynamic factor models (DFM).

PCA and DFM using the same underlying series result in an output with broadly the same trajectory of growth as our approach. But their output needs to be scaled in some way to generate an estimate for the pace of economic growth, as these approaches discard the information contained in the level of growth of the component series.

This scaling requirement is not a drawback if the goal is to “nowcast” official GDP. But our goal is to estimate actual growth, which by assumption we cannot observe. Any scaling would be arbitrary and introduce biases.