国民经济核算体系

国民经济核算体系 ( SNA) 是国民经济核算的国际标准体系，第一版国际标准于1953年发布。 并随后发布了1968年修订版、1993年修订版和2008年修订版。 国民经济核算体系以其各种发布的版本，经常进行重大的本地调整，已被许多国家采用。它不断发展，并由联合国、国际货币基金组织、世界银行、经济合作与发展组织和欧盟统计局维护。
SNA 的目标是提供一个综合、完整的账户系统，以便对所有重要经济活动进行国际比较。建议各国以SNA为指导构建本国的国民核算体系，以促进国际可比性。然而，遵守国际标准完全是自愿的，不能严格执行。一些国家（例如法国、美国和中国）使用的系统与国民账户体系有很大不同。就其本身而言，这并不是一个主要问题，只要每个系统都提供足够的数据，这些数据可以根据国民账户体系标准进行修改以编制国民账户。
SNA为市场经济国家使用的核算体系。中国大陆在改革开放后，自1992年完整引入该核算体系，取代之前的《国民经济平衡表体系》（MPS），并与联合国国民经济核算体系（UNSNA）接轨。
数据发布.
成员国的经济和金融数据用于编制有关生产总值、投资、资本交易、政府支出和对外贸易的年度（有时是季度）数据。结果发表在联合国年鉴《国民账户统计：主要总量和详细表格》中，该年鉴目前（直至 2008 年修订版生效）遵循 1993 年的建议。 提供的值以本国货币表示。
此外，国家统计局也可能发布 SNA 类型的数据。更详细的数据通常可以按需提供。由于国民账户数据极易被修改（因为涉及大量不同数据源、条目和估算过程会影响总量结果），因此不同年份不同出版物针对同一会计期间的总量数据也经常存在差异。由于新的数据来源、统计方法或概念的变化，“最终数字”可能会被多次追溯修改。每年的修订程度可能很小，但累积例如十年的修订后，数据差异可能会显着改变趋势。这是研究人员在寻求连贯一致的数据集时应该牢记的事情。
质量和覆盖范围.
各国国民账户数据的质量和全面性各不相同。原因包括：
SNA主要账户.
SNA 包括以下主要账户.
这些账户包括各种附件和子账户，还为显示生产部门之间交易的投入产出表提供了标准。
几乎所有联合国成员国都提供收入和产品账户，但不一定提供全套标准账户或全套数据，以提供标准会计信息。例如，家庭的标准化资产和负债账户几乎不存在，有待开发。
最近的一项进展是尝试建立自然资源战略库存的标准账户。 
发展.
SNA继续得到进一步发展，并定期召开国际会议来讨论各种概念和计量问题。
一些例子包括环境资源账户的构建、服务贸易和资本存量的计量、保险付款的处理、灰色经济、股票期权或其他非工资收入形式的员工补偿、无形资本
Discussions and updates are reported in "SNA News &amp; Notes" .
SNA Revisions are documented at the UN Statistics Division site 
The 2008 SNA Revision.
For the 2008 SNA Revision, the full text is available online: . The OECD provides some overview commentary .
The revision of the 1993 system was coordinated by the Intersecretariat Working Group on National Accounts (ISWGNA) comprising the United Nations Statistics Division (UNSD), International Monetary Fund (IMF), World Bank (WB), Organisation for Economic Co-operation and Development (OECD), Statistical Office of the European Communities (Eurostat) and the United Nations regional commissions.
The ISWGNA working group has its own website under the UN Statistics Division.
对国民账户体系的批评.
一般批评.
对国民账户体系最普遍的批评一直是它的概念没有充分反映现实世界的相互作用、关系和活动——原因有多种，但主要是因为：
Criticism of GDP.
The most popular criticism of national accounts is made against the concept of gross domestic product (GDP).
In part, this criticism of GDP is misplaced, because the fault is not so much with the concept itself. It is useful to have a measure of a country's total net output, and its changes over time – that's better than having no measure at all.
The fault is with the actual "use" that is made of the concept by governments, intellectuals, and businessmen in public discourse. GDP is used for all kinds of comparisons, but some of those comparisons are conceptually not very appropriate.
GDP measures are frequently abused by writers who neither understand what they mean, how they were produced, nor what they can be validly used for.
Economists like Joseph Stiglitz argue that a measure of "well-being" is needed to balance a measure of output growth.
Feminist criticism.
SNA has been criticised as biased by feminist economists such as Marilyn Waring and Maria Mies because no imputation for the monetary value of unpaid housework, or for unpaid voluntary labor is made in the accounts, even though the accounts do include the "imputed rental value of owner-occupied dwellings" (the market-rents which owner-occupiers would receive if they rented out the housing they occupy). This obscures the reality that market production depends to a large extent on non-market labour being performed.
However, such criticism raises several questions for the statisticians who would have to produce the data:
The intention of those who would like to produce this kind of standard data might be perfectly honorable, but the production of the data has to be practically justifiable in terms of technical feasibility and utility. Attaching an imaginary price to housework might not be the best data to have about housework.
In most OECD countries, statisticians have in recent years estimated the value of housework using data from time use surveys. The valuation principle often applied is that of how much a service would cost, if it was purchased at market rates, instead of being voluntarily supplied. Sometimes an "opportunity cost" method is also used: in this case, statisticians estimate how much women could earn in a paid job if they were not doing unpaid housework. Typically, the results suggest that the value of unpaid housework is close to about half the value of GDP.
Christine Lagarde, the head of the International Monetary Fund, claimed at the IMF World Bank annual meetings in Tokyo in October 2012 that women could rescue Japan's stagnating economy, if more of them took paid jobs instead of doing unpaid care work. A 2010 Goldman Sachs report had calculated that Japan's GDP would rise by 15 percent, if the participation of Japanese women in the paid labour force was increased from 60 percent to 80 percent, matching that of men. The difficulty with this kind of argument is, that domestic and care work would still need to be done by someone, meaning women and men would need to share household responsibilities more equally, or rely on public- or private-sector provided child and eldercare. According to the ILO, there are over 52 million domestic workers in the world, who mostly work for little pay and with little legal protection. They are mainly servants of the wealthy and the middle class.
Marxist criticism.
Marxian economists have criticized SNA concepts also from a different theoretical perspective on the new value added or value product. On this view, the distinctions drawn in SNA to define "income from production" and "property income" are rather capricious or eclectic, obscuring thereby the different components and sources of realised surplus value; the categories are said to be based on an inconsistent view of newly created value, conserved value, and transferred value (see also double counting). The result is that the true profit volume is underestimated in the accounts – since true profit income is larger than operating surplus – and workers' earnings are overestimated since the account shows the total labour costs to the employer rather than the "factor income" which workers actually get. If one is interested in what incomes people actually get, how much they own, or how much they borrow, national accounts often do not provide the required information.
Additionally, it is argued by Marxists that the SNA aggregate "compensation of employees" does not distinguish adequately between pre-tax and post-tax wage income, the income of higher corporate officers, and deferred income (employee and employer contributions to social insurance schemes of various kinds). "Compensation of employees" may also include the value of stock options received as income by corporate officers. Thus, it is argued, the accounts have to be substantially re-aggregated, to obtain a true picture of income generated and distributed in the economy. The problem there is that the detailed information to do it is often not made available, or is available only at a prohibitive cost.
US government statisticians admit frankly that "Unfortunately, the finance sector is one of the more poorly measured sectors in national accounts". The oddity of this is, that the finance sector nowadays dominates international transactions, and strongly influences the developmental path of the world economy. So, it is precisely the "leading" sector in the world economy for which systematic, comprehensive, and comparable data are not available.
Statisticians' criticisms.
Statisticians have also criticized the validity of international statistical comparisons using national accounts data, on the ground that in the real world, the estimates are rarely compiled in a uniform way – despite appearances to the contrary.
For example, Jochen Hartwig provides evidence to show that "the divergence in growth rates [of real GDP] between the U.S. and the EU since 1997 can be explained almost entirely in terms of changes to deflation methods that have been introduced in the U.S. after 1997, but not – or only to a very limited extent – in Europe".
The "magic" of national accounts is that they provide an instant source of detailed international comparisons, but, critics argue, on closer inspection, the numbers are not really so comparable as they are made out to be. The effect is that all sorts of easy comparisons are tossed around by policy scientists which, if the technical story behind the numbers was told, would never be attempted because the comparisons are scientifically untenable (or at the very least rather dubious).
Both the strength and the weaknesses of national accounts are that they are based on an enormous variety of data sources. The strength consists in the fact that a lot of cross-checking between data sources and data sets can occur, to assess the credibility of the estimates. The weakness is that the sheer number of inferences made from different data sets used increases the possibility of data errors, and makes it more difficult to assess error margins.
The data quality has also often been criticized on the ground that what pretends to be "data" in reality often consists only of estimates extrapolated from mathematical models, not direct observations. These models are designed to predict what particular data values ought to be, based on sample data for "indicative trends". One can, for example, observe that if variables X, Y, and Z go up, then variable P will go up as well, in a specific proportionality. In that case, one may not need to survey P or its components directly, it is sufficient to get trend data for X, Y, and Z and feed them into a mathematical model which then predicts what the values for P will be at each interval of time.
Because statistical surveys are very costly or may be difficult to organize, or because the data has to be produced rapidly to meet a deadline, statisticians often try to find cheaper, quicker, and more efficient methods to produce the data, by means of inferences from data that they already have, or from selected data which they can get more easily.
But the objection to this approach - although it can sometimes be proved to provide accurate data successfully - is that there is a loss in data accuracy and data quality. 
A typical reply of statisticians to this kind of objection is that although it is preferable to have comprehensive survey data available as a basis for estimation, and although data errors and inaccuracies do occur, it is possible to find techniques that keep the margins of error within acceptable bounds.