Prior to 1984, the measurement errors in the FAI are essentially reproduced in the sectoral data. The only information available for a sectoral breakdown comes from the FAI data. Consequently, there is no possibility to reduce the differences between the data for fixed asset investment and that for GFCF.

Between 1985 and 2002, residential property development became increasingly important. So, using an estimate of residential GFCF rather than residential FAI results in the sectoral estimates of GFCF moving slightly closer to the aggregate GFCF data.

Between 2003 and 2015, the aggregate FAI data become essentially useless as a measure of gross fixed capital formation, but the estimated sector GFCF tracks aggregate GFCF quite well. On average, the yearly estimates of sectoral GFCF are less than 2% different from the aggregate GFCF data. Over the whole period, the cumulated sectoral GFCF is practically equal to the cumulated aggregate GFCF series – the difference being just 0.02%. However, in the two most recent years (2014 and 2015), the difference has become much larger: rising to 10% for reasons that cannot be determined. However, this difference will have little impact on the estimates of the capital stock.

Overall, between 1985 (when the reliability of investment data improved) and 2015, the annual sectoral estimates of GFCF differ from the aggregate estimate of GFCF by just 1.7%, though the standard deviation of the difference is 4.7%. Thus, the methodology for estimating sectoral GFCF gives a good basis for evaluating the development of the sectoral capital stock over the medium term but does not accurately reflect the annual year-to-year changes in GFCF and the capital stock.

In order to remove both the year-to-year differences, a uniform scaling factor has been applied to the sectoral estimates to ensure that they sum to the aggregate GFCF data.

# **_Measuring GDP by sector_**

The above estimates of investment and capital stock have to be put into the context of the valueadded that is generated by the capital stock. The sectoral classification used here does not correspond to an official breakdown published by the NBS. There are two problems: there are no estimates of the value-added of the housing sector and the breakdown of the service sector in constant price terms is inadequate to measure the value-added of the infrastructure and government sectors.

For housing, the NBS defines the gross value-added of the housing sector to be equal to the depreciation of the housing stock. The depreciation rates used are 2% per year for urban housing and 4% per year for rural housing. However, neither the stock of housing nor the value of depreciation are published by the NBS. This paper follows Herd (2013) in making an estimate of nominal and real housing depreciation, which are set equal to the value added of the housing sector.

In the Chinese national accounts, the infrastructure and government sectors are subsumed into the sector “Other” in the breakdown of the GDP in the tertiary sector, both in real and nominal terms. From 2004 onwards, more detail is available for the sub-sectors of the “Other” grouping. In particular, the data show nominal values for the following sectors:

- Information Transmission, Software and Information Technology

- Leasing and Business Services

- Service to Households, Repair and Other Services.

Once these sectors have been removed from the “Other” grouping, the remaining sectors correspond to the definition of infrastructure and government used in this paper. The coverage of the value-added in this sector extends beyond the value added created by infrastructure itself and includes the value added generated by services using this infrastructure. In particular for transport infrastructure both 33