Quarterly estimates of productivity in the total economy and in the industries are derived from a Fisher chained index of gross domestic product (GDP). The approach to measure the GDP in the total economy differs from the one that used in the estimates by industry. For the total economy, GDP is measured using the expenditure approach at market prices published by the Quarterly Income and Expenditure Accounts. For the estimates by industry, GDP is measured using the value added approach at basic prices published by the Industry Accounts Division. This was the Fisher chained index in the case of years for which final input-output tables are available. For the most current years or annual post-benchmarks, the real GDP is based on a fixed-weight Laspeyres chained index formula. GDP estimates in the productivity measures for the businesses producing services and for real estate, and rental and leasing exclude the rental value of owner occupied dwellings. The estimate of the total number of jobs covers four main categories: employee jobs, work owner of an unincorporated business, own account self-employment, and unpaid family jobs. The last category is found mainly in sectors where family firms are important (agriculture and retail trade, in particular). Jobs data are consistent with the System of National Accounts. This is the quarterly average of hours worked for jobs in all categories. The number of hours worked in all jobs is the quarterly average for all jobs times the annual average hours worked in all jobs. Hours worked data are consistent with the System of National Accounts. According to the retained definition, hours worked means the total number of hours that a person spends working, whether paid or not. In general, this includes regular and overtime hours, breaks, travel time, training in the workplace and time lost in brief work stoppages where workers remain at their posts. On the other hand, time lost due to strikes, lockouts, annual vacation, public holidays, sick leave, maternity leave or leave for personal needs are not included in total hours worked. Labour productivity is the ratio between real GDP and hours worked. For the estimates of productivity in the total economy, a Fisher chain index of GDP at market prices is used as measure of the output. On the other hand, in the quarterly productivity estimates for the industries, a Fisher chain index of GDP at basic prices for each industry is used as measure of the output up to the last year benchmark for which final input-output tables are available, after that by a fixed-weight volume Laspeyres chained index formula for the most recent years. The ratio between total compensation for all jobs, and the number of hours worked. The term hourly compensation" is often used to refer to the total compensation per hour worked." This measures the cost of labour input required to produce one unit of output, and equals labour compensation in current dollars divided by the real output. It is often calculated as the ratio of labour compensation per hour worked and labour productivity. Unit labour cost increases when labour compensation per hour worked increases more rapidly than labour productivity. It is widely used to measure inflation pressures arising from wage growth. The measure of real value added used in the labour unit cost estimation is based on a Fisher chain index excluding the rental value of owner occupied dwellings. The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply side or production oriented principles, to ensure that industrial data, classified to NAICS, is suitable for the analysis of production related issues such as industrial performance. Since 1997, the System of National Accounts' (SNA) input-output industry classification system is based on NAICS. In the National Accounts industries, the levels of the different classification systems were chosen so as to provide the most detail possible in order to maximise continuity with the previous classification systems used in Statistics Canada since 1961. Therefore, the greatest level of detail that is available over time occurs at the L level of aggregation, which corresponds, to 105 industries. This L level can also be aggregated to the M level (medium - 56 industries) and to the S level (small - 21 industries). This combines the business establishments of the North American Industry Classification System (NAICS) codes 11, 21, 22, 23, 31-33. This combines the business establishments of the North American Industry Classification System (NAICS) codes 41, 44-45, 48-49, 51, 52, 53, 54, 55, 56, 61, 62, 71, 72, 81. The Gross Domestic Product (GDP) used to measure productivity excludes rent value for owner occupied dwellings from the business service producing industries. This combines the business establishments of the North American Industry Classification System (NAICS) code 53. The gross domestic product (GDP) used to measure productivity excludes rent value for owner occupied dwellings from this industry code. This combines the business establishments of the North American Industry Classification System (NAICS) codes 61, 62, 81. This combines the part of non-business establishments of the North American Industry Classification System (NAICS) codes 11-91, but also including the owner occupied dwellings industry and the private households. Total economic activities that have been realized within the country. That covers both business and non-business sectors. Unit labour cost in United States dollars is the equivalent of the ratio of Canadian unit labour cost to the exchange rate. This latter corresponds to the United States dollar value expressed in Canadian dollars. This combines the business establishments of the North American Industry Classification System (NAICS) codes 52 and 55.

Information and communication technology (ICT) products are the core of the digital economy, and their classified price index plays an important role in the compilation of CPI index. This paper starts from the characteristics of ICT products that have a fast update rate and do not necessarily meet the unit substitution elasticity between products, and improves the traditional product price index model by considering the mismatch item processing and product substitution elasticity and chain drift factors to construct the Hedonic-SV-RYGEKS price index model in this paper. Using the weekly data of Jingdong mobile phone price on whale staff platform and the monthly data of notebook computer on magic mirror insight platform, after processing, a total of 1586 sets of mobile phone data and 136 sets of notebook computer data are obtained. By writing SPSS macro program and python program, the weekly price index of mobile phone and the monthly price index of notebook computer are calculated, and the ring price index and fixed base price index of mobile phone and notebook computer are compiled respectively. The chain ring price index based on model calculation is compared with the fixed base price index to investigate the rationality of the model. The results show that: Firstly, based on the principle of the quality adjustment model, the characteristic variables that can reflect the characteristics of the product are selected, and a Hedonic quality adjustment model is established between them and the product price. Through the actual data test, the model is suitable for fitting the price of mismatched products. Secondly, from the perspective of reflecting the elasticity of substitution of products, the evaluation criteria of the price index, and the adjustment of product quality, this paper constructs the Hedonic-SV-RYGEKS price index based on the Hedonic model and SV index, which avoids the incomparability of samples caused by the low matching degree of inter-temporal samples, and effectively inhibits the chain drift of chain price index caused by the rapid update of products. Finally, it is hoped that the research content of this paper can provide a reference for improving and innovating the processing method of mismatched projects in the compilation of price index.

description: The Chained Consumer Price Index for All Urban Consumers, was introduced with the release of July data in August 2002. Designated the C-CPI-U, the index supplements the existing Consumer Price Indexes already produced by the BLS: the CPI for All Urban Consumers (CPI-U) and the CPI for Urban Wage Earners and Clerical Workers (CPI-W). The C-CPI-U employs a Tornqvist formula and utilizes expenditure data in adjacent time periods in order to reflect the effect of any substitution that consumers make across item categories in response to changes in relative prices. The new measure is designed to be a closer approximation to a "cost-of-living" index than the present measures. The use of expenditure data for both a base period and the current period in order to average price change across item categories distinguishes the C-CPI-U from the existing CPI measures, which use only a single expenditure base period to compute the price change over time.; abstract: The Chained Consumer Price Index for All Urban Consumers, was introduced with the release of July data in August 2002. Designated the C-CPI-U, the index supplements the existing Consumer Price Indexes already produced by the BLS: the CPI for All Urban Consumers (CPI-U) and the CPI for Urban Wage Earners and Clerical Workers (CPI-W). The C-CPI-U employs a Tornqvist formula and utilizes expenditure data in adjacent time periods in order to reflect the effect of any substitution that consumers make across item categories in response to changes in relative prices. The new measure is designed to be a closer approximation to a "cost-of-living" index than the present measures. The use of expenditure data for both a base period and the current period in order to average price change across item categories distinguishes the C-CPI-U from the existing CPI measures, which use only a single expenditure base period to compute the price change over time.

United States GDP: VA: 2009p: Not Allocated by Industry data was reported at 164.900 USD bn in 2017. This records an increase from the previous number of 157.000 USD bn for 2016. United States GDP: VA: 2009p: Not Allocated by Industry data is updated yearly, averaging -85.400 USD bn from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 164.900 USD bn in 2017 and a record low of -616.500 USD bn in 1997. United States GDP: VA: 2009p: Not Allocated by Industry data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A143: NIPA 2013: GDP by Industry: Value Added: Chain Linked 2009 Price: Annual. Chained (2009) dollar series are calculated as the product of the chain-type quantity index and the 2009 current-dollar value of the corresponding series, divided by 100. Because the formula for the chain-type quantity indexes uses weights of more than one period, the corresponding chained-dollar estimates are usually not additive. The value of the 'Not allocated by industry' line reflects the difference between the first line and the sum of the most detailed lines, as well as the differences in source data used to estimate GDP by industry and the expenditures measure of real GDP.

Information and communication technology (ICT) products are the core of the digital economy, and their classified price index plays an important role in the compilation of CPI index. This paper starts from the characteristics of ICT products that have a fast update rate and do not necessarily meet the unit substitution elasticity between products, and improves the traditional product price index model by considering the mismatch item processing and product substitution elasticity and chain drift factors to construct the Hedonic-SV-RYGEKS price index model in this paper. Using the weekly data of Jingdong mobile phone price on whale staff platform and the monthly data of notebook computer on magic mirror insight platform, after processing, a total of 1586 sets of mobile phone data and 136 sets of notebook computer data are obtained. By writing SPSS macro program and python program, the weekly price index of mobile phone and the monthly price index of notebook computer are calculated, and the ring price index and fixed base price index of mobile phone and notebook computer are compiled respectively. The chain ring price index based on model calculation is compared with the fixed base price index to investigate the rationality of the model. The results show that: Firstly, based on the principle of the quality adjustment model, the characteristic variables that can reflect the characteristics of the product are selected, and a Hedonic quality adjustment model is established between them and the product price. Through the actual data test, the model is suitable for fitting the price of mismatched products. Secondly, from the perspective of reflecting the elasticity of substitution of products, the evaluation criteria of the price index, and the adjustment of product quality, this paper constructs the Hedonic-SV-RYGEKS price index based on the Hedonic model and SV index, which avoids the incomparability of samples caused by the low matching degree of inter-temporal samples, and effectively inhibits the chain drift of chain price index caused by the rapid update of products. Finally, it is hoped that the research content of this paper can provide a reference for improving and innovating the processing method of mismatched projects in the compilation of price index.

This record contains the data and code for the published journal article "Global value chains and the CPTPP".

TABLE 2: PARTICIPATION OF CPTPP MEMBERS IN GVC (2005 - 2015)

Run "GVCparticipation.do" For CPTPP countries: the code directly extracts the Foreign Content for VS index (Hummels, Ishii and Yi, 2001), and the GVC index (Borin and Mancini, 2017). For World: the code extracts gross export (GEXP), traditional trade component (DAVAX), and the foreign contents (FC) between each country and their importers. Then sum up the components and calculate the GVC share using equation (2).

TABLE 3: GVC INTENSITY OF CPTPP COMPARED TO OTHER TRADE BLOCS (2015)

Use the decomposition for individual country from the GVC_participation.do to calculate the member's GVC share using equation (2). Sum the GVC share across all the members of the trade bloc.

TABLE 4: KEY DOWNSTREAM TRADE PARTNERS OF CPTPP MEMBERS (2015)

Run "Table4_Downstream_partners.do" The decomposition for Domestic contents (DC) and Traditional trade (TT) of each country with respect to each trade block is saved in the corresponding sheet of "downstream_partners.xlsm" Calculate the percentage trade of each country with regards to its downstream partners using equation (3).

TABLE 5: KEY UPSTREAM TRADE PARTNERS OF CPTPP MEMBERS (2015)

Run "Table5_Upstream_partners.do" The decomposition for Foreign contents (FC) of each country with respect to each trade block is saved in the corresponding sheet of "upstream_partners.xlsm" Calculate the percentage trade of each country with regards to its upstream partners using equation (4).

TABLE 7: PARTICIPATION IN GVC AT SECTORAL LEVEL

Run "Table7_GVCsectors.do"

TABLE 8: KEY DOWNSTREAM PARTNER IN SELECTED SECTORS

Run "Table 8_Downstream_partners_sectors.do" The decomposition for Domestic contents (DC) and Traditional trade (TT) of each country at the sectoral level is saved in "DC_by_sect_manuf" (for manufacturing sectors) and "DC_by_sect_svc" (for service sectors) Calculate the percentage trade of each country to its downstream partners at sectoral level using equation (3).

TABLE 9: KEY UPSTREAM PARTNER IN SELECTED SECTORS

Run "Table 9_Upstream_partners_sectors.do" The decomposition for Foreign contents (FC) of each country at the sectoral level is saved in the corresponding sheet Calculate the percentage trade of each country to its downstream partners using equation (4).

TABLE A.4: Key Downstream Partners by Alternative Formula

To obtain the decomposition for Foreign contents (FC), importer content, and the difference between domestic contents and the traditional trade (DC - TT), run "Table A4_Downstream_partners_apd.do" Calculate the percentage trade of each country with regards to its downstream partners using equation (8)

United States GDP: VA: 2009p: saar: Not Allocated by Industry data was reported at 244.500 USD bn in Mar 2018. This records an increase from the previous number of 238.500 USD bn for Dec 2017. United States GDP: VA: 2009p: saar: Not Allocated by Industry data is updated quarterly, averaging 70.900 USD bn from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 244.500 USD bn in Mar 2018 and a record low of -120.500 USD bn in Jun 2005. United States GDP: VA: 2009p: saar: Not Allocated by Industry data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A186: NIPA 2013: GDP by Industry: Value Added: Chain Linked 2009 Price: saar. Chained (2009) dollar series are calculated as the product of the chain-type quantity index and the 2009 current-dollar value of the corresponding series, divided by 100. Because the formula for the chain-type quantity indexes uses weights of more than one period, the corresponding chained-dollar estimates are usually not additive. The value of the 'Not allocated by industry' line reflects the difference between the first line and the sum of the most detailed lines, as well as the differences in source data used to estimate GDP by industry and the expenditures measure of real GDP.

Calculation of goodness of fit index.

Calculation Results of Moran Index for Sustainability.