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Components of the Gross Domestic Product -
http://www.moneychimp.com/articles/econ/gdp_diagram.htm


 * BCC video on GDP**

http://news.bbc.co.uk/2/hi/business/8637712.stm

=US GDP compared to other countries= http://tutor2u.net/blog/index.php/economics/comments/us-gdp-compared-to-other-countries/


 * Limitations of GDP**

http://tutor2u.net/economics/content/topics/livingstandards/limitations_of_gdp.htm

The myth of soviet strength []

Soviet Union 1945 to present (economic analysis) []

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 =Gross domestic product= From Wikipedia, the free encyclopedia

Jump to: [|navigation], [|search] Not to be confused with [|Gross national product] or [|Gross domestic income]. [|CIA World Factbook] 2008 figures of total [|nominal] GDP (bottom) compared to [|PPP]-adjusted GDP (top) Countries by 2008 GDP (nominal) per capita (IMF, October 2008 estimate) [|GDP (PPP) per capita] Gross domestic product is related to [|national accounts], a subject in [|macroeconomics]. [[|hide]]
 * Gross domestic product** (**GDP**) refers to the market value of all final goods and services produced within a country in a given period. It is often considered an indicator of a country's [|standard of living].[|[][|1][|]][|[][|2][|]]
 * ==Contents==
 * [|1] [|History]
 * [|2] [|Determining GDP]
 * [|2.1] [|Income approach]
 * [|2.2] [|Expenditure approach]
 * [|2.2.1] [|Components of GDP by expenditure]
 * [|2.2.2] [|Examples of GDP component variables]
 * [|2.3] [|Income approach]
 * [|3] [|GDP vs GNP]
 * [|3.1] [|International standards]
 * [|3.2] [|National measurement]
 * [|3.3] [|Interest rates]
 * [|4] [|Adjustments to GDP]
 * [|5] [|Cross-border comparison]
 * [|6] [|Per unit GDP]
 * [|7] [|Standard of living and GDP]
 * [|8] [|Limitations of GDP to judge the health of an economy]
 * [|8.1] [|Other Metrics]
 * [|9] [|Defense of GDP]
 * [|10] [|Lists of countries by their GDP]
 * [|11] [|See also]
 * [|12] [|Bibliography]
 * [|13] [|References]
 * [|14] [|External links]
 * [|14.1] [|Global]
 * [|14.2] [|Data]
 * [|14.3] [|Articles and books] ||

[[|edit]] History
GDP was first developed by [|Simon Kuznets] for a [|US Congress] report in 1934,[|[][|3][|]] who immediately said not to use it as a measure for welfare (see below under //limitations//).
 * [[image:http://upload.wikimedia.org/wikipedia/commons/thumb/1/1c/Wiki_letter_w_cropped.svg/20px-Wiki_letter_w_cropped.svg.png width="20" height="14" caption="Wiki letter w cropped.svg" link="http://en.wikipedia.org/wiki/File:Wiki_letter_w_cropped.svg"]] || This section requires [|expansion]. ||

[[|edit]] Determining GDP
[|South America] **·** [|Asia] [|Europe] **·** [|Oceania] || [|History of economic thought] [|Methodology] **·** [|Heterodox approaches] || [|Cross section] & [|Panel] [|Data] [|Simultaneous equations] **·** [|Time series] [|Computational] **·** [|Experimental] [|National accounts] || [|Growth] **·** [|Development] **·** [|History] [|International] **·** [|Economic systems] [|Monetary] and [|Financial economics] [|Public] and [|Welfare economics] [|Health] **·** [|Education] **·** [|Welfare] [|Population] **·** [|Labour] **·** [|Managerial] [|Business] **·** [|Information] **·** [|Game theory] [|Industrial organization] **·** [|Law] [|Agricultural] **·** [|Natural resource] [|Environmental] **·** [|Ecological] [|Urban] **·** [|Rural] **·** [|Regional] **·** [|Geography] || [|Categories] · [|Topics] · [|Economists] || [|Communism] **·** [|Corporatism] [|Fascism] **·** [|Feudalism] [|Georgism] **·** [|Imperialism] [|Islamic] **·** [|Laissez-faire] [|Leninism] **·** [|Maoism] [|Market socialism] **·** [|Marxism] [|Monarchy] **·** [|Monopolism] [|Mercantilism] **·** [|Neocolonialism] [|Oligarchy] **·** [|Oligopoly] [|Plutocracy] **·** [|Primitivism] [|Protectionism] **·** [|Socialism] [|Syndicalism] **·** [|Third Way] || GDP can be determined in three ways, all of which should, in principle, give the same result. They are the product (or output) approach, the income approach, and the expenditure approach. The most direct of the three is the product approach, which sums the outputs of every class of enterprise to arrive at the total. The expenditure approach works on the principle that all of the product must be bought by somebody, therefore the value of the total product must be equal to people's total expenditures in buying things. The income approach works on the principle that the incomes of the productive factors ("producers," colloquially) must be equal to the value of their product, and determines GDP by finding the sum of all producers' incomes.[|[][|4][|]] Example: the expenditure method: //GDP = [|private consumption] + [|gross investment] + [|government spending] + ([|exports] − [|imports])//, or > > **Note:** "Gross" means that GDP measures production regardless of the various uses to which that production can be put. Production can be used for immediate consumption, for investment in new fixed assets or inventories, or for replacing depreciated fixed assets. "Domestic" means that GDP measures production that takes place within the country's borders. In the expenditure-method equation given above, the exports-minus-imports term is necessary in order to null out expenditures on things not produced in the country (imports) and add in things produced but not sold in the country (exports). Economists (since [|Keynes]) have preferred to split the general consumption term into two parts; private consumption, and [|public sector] (or government) spending. Two advantages of dividing total consumption this way in theoretical [|macroeconomics] are:
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 * **Private consumption** is a central concern of [|welfare economics]. The private investment and trade portions of the economy are ultimately directed (in mainstream economic models) to increases in long-term private consumption.
 * If separated from [|endogenous] private consumption, **government consumption** can be treated as [|exogenous], [//[|citation needed]//] so that different government spending levels can be considered within a meaningful macroeconomic framework.

[[|edit]] Income approach
This method measures GDP by adding incomes that firms pay households for the factors of production they hire- wages for labour, interest for capital, rent for land and profits for entrepreneurship. The US "National Income and Expenditure Accounts" divide incomes into five categories: These five income components sum to net domestic income at factor cost. Two adjustments must be made to get GDP:
 * 1) Wages, salaries, and supplementary labour income
 * 2) Corporate profits
 * 3) Interest and miscellaneous investment income
 * 4) Farmers’ income
 * 5) Income from non-farm unincorporated businesses
 * 1) Indirect taxes minus subsidies are added to get from factor cost to market prices.
 * 2) Depreciation (or capital consumption) is added to get from net domestic product to gross domestic product.

[[|edit]] Expenditure approach
In economies, most things produced are produced for sale, and sold. Therefore, measuring the total expenditure of money used to buy things is a way of measuring production. This is known as the expenditure method of calculating GDP. Note that if you knit yourself a sweater, it is production but does not get counted as GDP because it is never sold. Sweater-knitting is a small part of the economy, but if one counts some major activities such as child-rearing (generally unpaid) as production, GDP ceases to be an accurate indicator of production. Similarly, if there is a long term shift from non-market provision of services (for example cooking, cleaning, child rearing, do-it yourself repairs) to market provision of services, then this trend toward increased market provision of services may mask a dramatic decrease in actual domestic production, resulting in overly optimistic and inflated reported GDP. This is particularly a problem for economies which have shifted from production economies to [|service economies].

[[|edit]] Components of GDP by expenditure
Components of U.S. GDP A fully equivalent definition is that **GDP (Y)** is the sum of **[|final consumption expenditure] (FCE)**, **[|gross capital formation] (GCF)**, and **net exports (X - M)**. Note that **C**, **G**, and **I** are expenditures on [|final goods] and services; expenditures on intermediate goods and services do not count. (Intermediate goods and services are those used by businesses to produce other goods and services within the accounting year.[|[][|5][|]] ) According to the U.S. Bureau of Economic Analysis, which is responsible for calculating the national accounts in the United States, "In general, the source data for the expenditures components are considered more reliable than those for the income components [see income method, below]."[|[][|6][|]]
 * GDP (Y)** is a sum of **Consumption (C)**, **Investment (I)**, **Government Spending (G)** and **Net Exports (X - M)**.
 * Y** = **C** + **I** + **G** + **(X − M)**Here is a description of each GDP component:
 * **C (consumption)** is normally the largest GDP component in the economy, consisting of private ([|household final consumption expenditure]) in the economy. These personal expenditures fall under one of the following categories: [|durable goods], non-durable goods, and services. Examples include food, rent, jewelry, gasoline, and medical expenses but does not include the purchase of new housing.
 * **I (investment)** includes business investment in equipments for example and does not include exchanges of existing assets. Examples include construction of a new [|mine], purchase of software, or purchase of machinery and equipment for a factory. Spending by households (not government) on new houses is also included in Investment. In contrast to its colloquial meaning, 'Investment' in GDP does not mean purchases of [|financial products]. Buying financial products is classed as '[|saving]', as opposed to **investment**. This avoids double-counting: if one buys shares in a company, and the company uses the money received to buy plant, equipment, etc., the amount will be counted toward GDP when the company spends the money on those things; to also count it when one gives it to the company would be to count two times an amount that only corresponds to one group of products. Buying [|bonds] or [|stocks] is a swapping of [|deeds], a transfer of claims on future production, not directly an expenditure on products.
 * **G (government spending)** is the sum of [|government expenditures] on [|final goods] and services. It includes salaries of [|public servants], purchase of weapons for the military, and any investment expenditure by a government. It does not include any [|transfer payments], such as [|social security] or [|unemployment benefits].
 * **X (exports)** represents gross exports. GDP captures the amount a country produces, including goods and services produced for other nations' consumption, therefore exports are added.
 * **M (imports)** represents gross imports. Imports are subtracted since imported goods will be included in the terms **G**, **I**, or **C**, and must be deducted to avoid counting foreign [|supply] as domestic.
 * Y** = **FCE** + **GCF**+ **(X − M)**FCE can then be further broken down by three sectors (households, governments and non-profit institutions serving households) and GCF by five sectors (non-financial corporations, financial corporations, households, governments and [|non-profit institutions serving households] [//[|dead link]//] ). The advantage of this second definition is that expenditure is systematically broken down, firstly, by type of final use (final consumption or capital formation) and, secondly, by sectors making the expenditure, whereas the first definition partly follows a mixed delimitation concept by type of final use and sector.

[[|edit]] Examples of GDP component variables
If a hotel is a private home, spending for renovation would be measured as **c**onsumption, but if a government agency converts the hotel into an office for civil servants, the spending would be included in the public sector spending, or **G**. If the renovation involves the purchase of a [|chandelier] from abroad, that spending would be counted as **C**, **G**, or **I** (depending on whether a private individual, the government, or a business is doing the renovation), but then counted again as an import and subtracted from the GDP so that GDP counts only goods produced within the country. If a domestic producer is paid to make the chandelier for a foreign hotel, the payment would not be counted as **C**, **G**, or **I**, but would be counted as an export. [|GDP real growth rates] for 2007
 * C**, **I**, **G**, and **NX**(net exports): If a person spends money to renovate a hotel to increase occupancy rates, the spending represents private investment, but if he buys shares in a consortium to execute the renovation, it is [|saving]. The former is included when [|measuring GDP] (in **I**), the latter is not. However, when the consortium conducted its own expenditure on renovation, that expenditure would be included in GDP.

[[|edit]] Income approach
Another way of measuring GDP is to measure total income. If GDP is calculated this way it is sometimes called Gross Domestic Income (GDI), or GDP(I). GDI should provide the same amount as the expenditure method described above. (By definition, GDI = GDP. In practice, however, measurement errors will make the two figures slightly off when reported by national statistical agencies.) Total income can be subdivided according to various schemes, leading to various formulae for GDP measured by the income approach. A common one is: //GDP = [|compensation of employees] + [|gross operating surplus] + [|gross mixed income] + taxes less subsidies on production and imports//**GDP** = **COE** + **GOS** + **GMI** + **TP & M** - **SP & M** The sum of **COE**, **GOS** and **GMI** is called total factor income; it is the income of all of the factors of production in society. It measures the value of GDP at factor (basic) prices. The difference between basic prices and final prices (those used in the expenditure calculation) is the total taxes and subsidies that the government has levied or paid on that production. So adding taxes less subsidies on production and imports converts GDP at [|factor cost] to GDP(I). Total factor income is also sometimes expressed as: //Total factor income = Employee compensation + Corporate profits + Proprieter's income + Rental income + Net interest//[|[][|7][|]]Yet another formula for GDP by the income method is: [//[|citation needed]//] //G////D////P// = //R// + //I// + //P// + //S////A// + //W// where R : rents I : interests P : profits SA : statistical adjustments (corporate income taxes, dividends, undistributed corporate profits) W : wages Note the mnemonic, "ripsaw". A "production boundary" that delimits what will be counted as GDP. > "One of the fundamental questions that must be addressed in preparing the national economic accounts is how to define the production boundary–that is, what parts of the myriad human activities are to be included in or excluded from the measure of the economic production."[|[][|8][|]] All output for market is at least in theory included within the boundary. Market output is defined as that which is sold for "economically significant" prices; economically significant prices are "prices which have a significant influence on the amounts producers are willing to supply and purchasers wish to buy."[|[][|9][|]] An exception is that illegal goods and services are often excluded even if they are sold at economically significant prices (Australia and the United States exclude them). This leaves non-market output. It is partly excluded and partly included. First, "natural processes without human involvement or direction" are excluded.[|[][|10][|]] Also, there must be a person or institution that owns or is entitled to compensation for the product. An example of what is included and excluded by these criteria is given by the United States' national accounts agency: "the growth of trees in an uncultivated forest is not included in production, but the harvesting of the trees from that forest is included."[|[][|11][|]] Within the limits so far described, the boundary is further constricted by "functional considerations."[|[][|12][|]] The Australian Bureau for Statistics explains this: "The national accounts are primarily constructed to assist governments and others to make market-based macroeconomic policy decisions, including analysis of markets and factors affecting market performance, such as inflation and unemployment." Consequently, production that is, according to them, "relatively independent and isolated from markets," or "difficult to value in an economically meaningful way" [i.e., difficult to put a price on] is excluded.[|[][|13][|]] Thus excluded are services provided by people to members of their own families free of charge, such as child rearing, meal preparation, cleaning, transportation, entertainment of family members, emotional support, care of the elderly.[|[][|14][|]] Most other production for own (or one's family's) use is also excluded, with two notable exceptions which are given in the list later in this section. Nonmarket outputs that //are// included within the boundary are listed below. Since, by definition, they do not have a market price, the compilers of GDP must //impute// a value to them, usually either the cost of the goods and services used to produce them, or the value of a similar item that is sold on the market.
 * **Compensation of employees** (COE) measures the total remuneration to employees for work done. It includes wages and salaries, as well as employer contributions to [|social security] and other such programs.
 * **Gross operating surplus** (GOS) is the surplus due to owners of incorporated businesses. Often called [|profits], although only a subset of total costs are subtracted from [|gross output] to calculate GOS.
 * **Gross mixed income** (GMI) is the same measure as GOS, but for unincorporated businesses. This often includes most small businesses.
 * Goods and services provided by governments and non-profit organisations free of charge or for economically insignificant prices are included. The value of these goods and services is estimated as equal to their cost of production. This ignores the consumer surplus generated by an efficient and effective government supplied infrastructure. For example, government-provided clean water confers substantial benefits above its cost. Ironically, lack of such infrastructure which would result in higher water prices (and probably higher hospital and medication expenditures) would be reflected as a higher GDP. This may also cause a bias that mistakenly favors inefficient privatizations since some of the consumer surplus from privatized entities' sale of goods and services are indeed reflected in GDP.[|[][|15][|]]
 * Goods and services produced for own-use by businesses are attempted to be included. An example of this kind of production would be a machine constructed by an engineering firm for use in its own plant.
 * Renovations and upkeep by an individual to a home that she owns and occupies are included. The value of the upkeep is estimated as the rent that she could charge for the home if she did not occupy it herself. This is the largest item of production for own use by an individual (as opposed to a business) that the compilers include in GDP.[|[][|15][|]] If the measure uses historical or book prices for real estate, this will grossly underestimate the value of the rent in real estate markets which have experienced significant price increases (or economies with general inflation). Furthermore, depreciation schedules for houses often accelerate the accounted depreciation relative to actual depreciation (a well built house can be lived in for several hundred years - a very long time after it has been fully depreciated). In summary, this is likely to grossly underestimate the value of existing housing stock on consumers' actual consumption or income.
 * Agricultural production for consumption by oneself or one's household is included.
 * Services (such as chequeing-account maintenance and services to borrowers) provided by banks and other financial institutions without charge or for a fee that does not reflect their full value have a value imputed to them by the compilers and are included. The financial institutions provide these services by giving the customer a less advantageous interest rate than they would if the services were absent; the value imputed to these services by the compilers is the difference between the interest rate of the account with the services and the interest rate of a similar account that does not have the services. According to the United States Bureau for Economic Analysis, this is one of the largest imputed items in the GDP.[|[][|16][|]]

[[|edit]] GDP vs GNP
GDP can be contrasted with [|gross national product] (GNP) or [|gross national income] (GNI). The difference is that GDP defines its scope according to location, while GNP defines its scope according to ownership. In a global context, [|world GDP and world GNP] are therefore equivalent terms. GDP is product produced within a country's borders; GNP is product produced by enterprises owned by a country's citizens. The two would be the same if all of the productive enterprises in a country were owned by its own citizens, and those citizens did not own productive enterprises in any other countries. In practices, however, foreign ownership makes GDP and GNP non-identical. Production within a country's borders, but by an enterprise owned by somebody outside the country, counts as part of its GDP but not its GNP; on the other hand, production by an enterprise located outside the country, but owned by one of its citizens, counts as part of its GNP but not its GDP. To take the United States as an example, the U.S.'s GNP is the value of output produced by American-owned firms, regardless of where the firms are located. Similarly, if a country becomes increasingly in debt, and spends large amounts of income servicing this debt this will be reflected in a decreased GNI but not a decreased GDP. Similarly, if a country sells off its resources to entities outside their country this will also be reflected over time in decreased GNI, but not decreased GDP. This would make the use of GDP more attractive for politicians in countries with increasing national debt and decreasing assets. Gross national income (GNI) equals GDP plus income receipts from the rest of the world minus income payments to the rest of the world.[|[][|17][|]] In 1991, the United States switched from using GNP to using GDP as its primary measure of production.[|[][|18][|]] The relationship between United States GDP and GNP is shown in table 1.7.5 of the //[|National Income and Product Accounts]//.[|[][|19][|]]

[[|edit]] International standards
The international standard for measuring GDP is contained in the book //[|System of National Accounts]// (1993), which was prepared by representatives of the [|International Monetary Fund], [|European Union], [|Organization for Economic Co-operation and Development], [|United Nations] and [|World Bank]. The publication is normally referred to as SNA93 to distinguish it from the previous edition published in 1968 (called SNA68) [//[|citation needed]//][//[|why?]//]. SNA93 provides a set of rules and procedures for the measurement of national accounts. The standards are designed to be flexible, to allow for differences in local statistical needs and conditions.
 * [[image:http://upload.wikimedia.org/wikipedia/commons/thumb/1/1c/Wiki_letter_w_cropped.svg/20px-Wiki_letter_w_cropped.svg.png width="20" height="14" caption="Wiki letter w cropped.svg" link="http://en.wikipedia.org/wiki/File:Wiki_letter_w_cropped.svg"]] || This section requires [|expansion]. ||

[[|edit]] National measurement
Within each country GDP is normally measured by a national government statistical agency, as private sector organizations normally do not have access to the information required (especially information on expenditure and production by governments). Main article: [|National agencies responsible for GDP measurement]

[[|edit]] Interest rates
Net interest expense is a [|transfer payment] in all sectors except the financial sector. Net interest expenses in the financial sector are seen as [|production] and [|value added] and are added to GDP.

[[|edit]] Adjustments to GDP
When comparing GDP figures from one year to another, it is desirable to compensate for changes in the value of money – i.e., for the effects of inflation or deflation. The raw GDP figure as given by the equations above is called the **nominal, or historical, or current, GDP**. To make it more meaningful for year-to-year comparisons, it may be multiplied by the ratio between the value of money in the year the GDP was measured and the value of money in some base year. For example, suppose a country's GDP in 1990 was $100 million and its GDP in 2000 was $300 million; but suppose that inflation had halved the value of its currency over that period. To meaningfully compare its 2000 GDP to its 1990 GDP we could multiply the 2000 GDP by one-half, to make it relative to 1990 as a base year. The result would be that the 2000 GDP equals $300 million × one-half = $150 million, //in 1990 monetary terms.// We would see that the country's GDP had, realistically, increased 1.5 times over that period, not three times, as it might appear from the raw GDP data. The GDP adjusted for changes in money-value in this way is called the real, or constant, GDP. The factor used to convert GDP from current to constant values in this way is called the //[|GDP deflator]//. Unlike the [|Consumer price index], which measures inflation or deflation in the price of household consumer goods, the GDP deflator measures changes in the prices all domestically produced goods and services in an economy–including investment goods and government services, as well as household consumption goods.[|[][|20][|]] Constant-GDP figures allow us to calculate a GDP growth rate, which tells us how much a country's production has increased (or decreased, if the growth rate is negative) compared to the previous year. Real GDP growth rate for year //n// = [(Real GDP in year //n//) − (Real GDP in year //n// − 1)] / (Real GDP in year //n// − 1)Another thing that it may be desirable to compensate for is population growth. If a country's GDP doubled over some period but its population tripled, the increase in GDP may not be deemed such a great accomplishment: the average person in the country is producing less than they were before. //Per-capita GDP// is the measure compensated for population growth.

[[|edit]] Cross-border comparison
The level of GDP in different countries may be compared by converting their value in national currency according to //either// the current currency exchange rate, or the purchase power parity exchange rate. The ranking of countries may differ significantly based on which method is used. There is a clear pattern of the //purchasing power parity method// decreasing the disparity in GDP between high and low income (GDP) countries, as compared to the //current exchange rate method//. This finding is called the [|Penn effect]. For more information, see [|Measures of national income and output].
 * **Current currency exchange rate** is the exchange rate in the international [|currency market].
 * **Purchasing power parity exchange rate** is the exchange rate based on the [|purchasing power parity] (PPP) of a currency relative to a selected standard (usually the [|United States dollar]). This is a comparative (and theoretical) exchange rate, the only way to directly realize this rate is to sell an entire [|CPI] basket in one country, convert the cash at the currency market rate & then rebuy that same basket of goods in the other country (with the converted cash). Going from country to country, the distribution of prices within the basket will vary; typically, non-tradable purchases will consume a greater proportion of the basket's total cost in the higher GDP country, per the [|Balassa-Samuelson effect].
 * The //current exchange rate method// converts the value of goods and services using global currency [|exchange rates]. The method can offer better indications of a country's international purchasing power and relative economic strength. For instance, if 10% of GDP is being spent on buying hi-tech foreign [|arms], the number of weapons purchased is entirely governed by //current exchange rates//, since arms are a traded product bought on the international market. There is no meaningful 'local' price distinct from the international price for high technology goods.
 * The //purchasing power parity method// accounts for the relative effective domestic purchasing power of the average producer or consumer within an economy. The method can provide a better indicator of the living standards of less developed countries, because it compensates for the weakness of local currencies in the international markets. For example, India ranks 11th by nominal GDP, but fourth by PPP. The PPP method of GDP conversion is more relevant to non-traded goods and services.

[[|edit]] Per unit GDP
GDP is an aggregate figure which does not account for differing sizes of nations. Therefore, GDP can be stated as //GDP per capita// (per person) in which total GDP is divided by the resident population on a given date, //GDP per citizen// where total GDP is divided by the numbers of citizens residing in the country on a given date, and less commonly GDP per unit of a resource input, such as //GDP per GJ of energy// or [|Gross domestic product per barrel]. //GDP per citizen// in the above case is pretty similar to //GDP per capita// in most nations, however, in nations with very high proportions of temporary foreign workers like in Persian Gulf nations, the two figures can be vastly different.

[[|edit]] Standard of living and GDP
GDP per capita is not a measurement of the [|standard of living] in an [|economy]. However, it is often used as such an indicator, on the rationale that all citizens would benefit from their country's increased economic production. Similarly, GDP per capita is not a measure of personal income. GDP may increase while [|real incomes] for the majority decline. The major advantage of GDP per capita as an indicator of standard of living is that it is measured frequently, widely, and consistently. It is measured frequently in that most countries provide information on GDP on a quarterly basis, allowing trends to be seen quickly. It is measured widely in that some measure of GDP is available for almost every [|country] in the [|world], allowing inter-country comparisons. It is measured consistently in that the technical definition of GDP is relatively consistent among countries. The major disadvantage is that it is not a measure of standard of living. GDP is intended to be a measure of total national economic activity— a separate concept. The argument for using GDP as a standard-of-living [|proxy] is not that it is a good indicator of the [|absolute] level of standard of living, but that living standards tend to move with per-capita GDP, so that //changes// in living standards are readily detected through changes in GDP.

[[|edit]] Limitations of GDP to judge the health of an economy
GDP is widely used by economists to gauge the health of an economy, as its variations are relatively quickly identified. However, its value as an indicator for the [|standard of living] is considered to be limited. Not only that, but if the aim of economic activity is to produce ecologically sustainable increases in the overall human standard of living, GDP is a perverse measurement; it treats loss of ecosystem services as a benefit instead of a cost.[|[][|21][|]] Other criticisms of how the GDP is used include: [|Simon Kuznets] in his very first report to the US Congress in 1934 said:[|[][|3][|]] > ...the welfare of a nation can, therefore, scarcely be inferred from a measure of national income... In 1962, Kuznets stated:[|[][|22][|]] > Distinctions must be kept in mind between quantity and quality of growth, between costs and returns, and between the short and long run. Goals for more growth should specify more growth of what and for what.
 * **Wealth distribution**–GDP does not take disparity in incomes between the rich and poor into account. See [|income inequality metrics] for discussion of a variety of inequality-based economic measures.
 * **Non-market transactions**–GDP excludes activities that are not provided through the market, such as household production and volunteer or unpaid services. As a result, GDP is understated. Unpaid work conducted on [|Free and Open Source Software] (such as [|Linux]) contribute nothing to GDP, but it was [|estimated] that it would have cost more than a billion US dollars for a commercial company to develop. Also, if Free and Open Source Software became identical to its [|proprietary software] counterparts, and the nation producing the propriety software stops buying proprietary software and switches to Free and Open Source Software, then the GDP of this nation would reduce, however there would be no reduction in economic production or standard of living. The work of New Zealand economist [|Marilyn Waring] has highlighted that if a concerted attempt to factor in unpaid work were made, then it would in part undo the injustices of unpaid (and in some cases, slave) labour, and also provide the political transparency and accountability necessary for democracy. Shedding some doubt on this claim, however, is the theory that won economist Douglass North the Nobel Prize in 1993. North argued that the creation and strengthening of the patent system, by encouraging private invention and enterprise, became the fundamental catalyst behind the Industrial Revolution in England.
 * **Underground economy**–Official GDP estimates may not take into account the [|underground economy], in which transactions contributing to production, such as illegal trade and tax-avoiding activities, are unreported, causing GDP to be underestimated.
 * **Non-monetary economy**–GDP omits economies where no money comes into play at all, resulting in inaccurate or abnormally low GDP figures. For example, in countries with major business transactions occurring informally, portions of local economy are not easily registered. [|Bartering] may be more prominent than the use of money, even extending to services (I helped you build your house ten years ago, so now you help me).
 * GDP also ignores [|subsistence production].
 * **Quality improvements and inclusion of new products**–By not adjusting for quality improvements and new products, GDP understates true [|economic growth]. For instance, although computers today are less expensive and more powerful than computers from the past, GDP treats them as the same products by only accounting for the monetary value. The introduction of new products is also difficult to measure accurately and is not reflected in GDP despite the fact that it may increase the standard of living. For example, even the richest person from 1900 could not purchase standard products, such as antibiotics and cell phones, that an average consumer can buy today, since such modern conveniences did not exist back then.
 * **What is being produced**–GDP counts work that produces no net change or that results from repairing harm. For example, rebuilding after a natural disaster or war may produce a considerable amount of economic activity and thus boost GDP. The economic value of [|health care] is another classic example—it may raise GDP if many people are sick and they are receiving expensive treatment, but it is not a desirable situation. Alternative economic estimates, such as the [|standard of living] or [|discretionary income] per capita try to measure the human [|utility] of economic activity. See [|uneconomic growth].
 * **Externalities**–GDP ignores [|externalities] or economic [|bads] such as damage to the environment. By counting goods which increase utility but not deducting bads or accounting for the negative effects of higher production, such as more pollution, GDP is overstating economic welfare. The [|Genuine Progress Indicator] is thus proposed by ecological economists and green economists as a substitute for GDP, supposing a consensus on relevant data to measure "progress". In countries highly dependent on resource extraction or with high ecological footprints the disparities between GDP and GPI can be very large, indicating ecological overshoot. Some environmental costs, such as cleaning up oil spills are included in GDP.
 * **Sustainability of growth**–GDP is not a tool of economic projections, which would make it subjective, it is just a measurement of economic activity. That is why it does not measure what is considered the [|sustainability of growth]. A country may achieve a temporarily high GDP by over-exploiting natural resources or by misallocating investment. For example, the large deposits of [|phosphates] gave the people of [|Nauru] one of the highest per capita incomes on earth, but since 1989 their standard of living has declined sharply as the supply has run out. Oil-rich states can sustain high GDPs without industrializing, but this high level would no longer be sustainable if the oil runs out. Economies experiencing an [|economic bubble], such as a [|housing bubble] or stock bubble, or a low private-saving rate tend to appear to grow faster owing to higher consumption, mortgaging their futures for present growth. Economic growth at the expense of environmental degradation can end up costing dearly to clean up.
 * One main problem in estimating GDP growth over time is that the purchasing power of money varies in different proportion for different goods, so when the GDP figure is deflated over time, GDP growth can vary greatly depending on the basket of goods used and the relative proportions used to deflate the GDP figure. For example, in the past 80 years the GDP per capita of the United States if measured by purchasing power of potatoes, did not grow significantly. But if it is measured by the purchasing power of eggs, it grew several times. For this reason, economists comparing multiple countries usually use a varied basket of goods.
 * Cross-border comparisons of GDP can be inaccurate as they do not take into account local differences in the quality of goods, even when adjusted for [|purchasing power parity]. This type of adjustment to an exchange rate is controversial because of the difficulties of finding comparable baskets of goods to compare purchasing power across countries. For instance, people in country A may consume the same number of locally produced apples as in country B, but apples in country A are of a more tasty variety. This difference in material well being will not show up in GDP statistics. This is especially true for goods that are not traded globally, such as housing.
 * [|Transfer pricing] on cross-border trades between associated companies may distort import and export measures [//[|citation needed]//].
 * As a measure of actual sale prices, GDP does not capture the [|economic surplus] between the price paid and subjective value received, and can therefore underestimate [|aggregate utility].

[[|edit]] Other Metrics
Some people have looked beyond standard of living at a broader sense of [|quality of life] or well-being:
 * [|Human development index] (HDI) - HDI uses GDP as a part of its calculation and then factors in indicators of life expectancy and education levels.
 * [|Genuine progress indicator] (GPI) or [|Index of Sustainable Economic Welfare] (ISEW) - The GPI and the ISEW attempt to address many of the above criticisms by taking the same raw information supplied for GDP and then adjust for income distribution, add for the value of household and volunteer work, and subtract for crime and pollution.
 * [|Gross national happiness] (GNH) - GNH measures quality of life or social progress in more holistic and psychological terms than GDP.
 * [|Gini coefficient] - The Gini coefficient measures the disparity of income within a nation.
 * Wealth estimates - The [|World Bank] has developed a system for combining monetary wealth with intangible wealth (institutions and human capital) and environmental capital.[|[][|23][|]]
 * [|Private Product Remaining] - [|Murray Newton Rothbard] and other Austrian economists argue as if government spending is taken from productive sectors and produces goods that consumers do not want, it is a burden on the economy and thus should be deducted. In his book, **[|America's Great Depression]**, Rothbard argues that even government surpluses from [|taxation] could be deducted to create an estimate of PPR.
 * European Quality of Life Survey - The survey, first published in 2005, assessed quality of life across European countries through a series of questions on overall [|subjective life satisfaction], satisfaction with different aspects of life, and sets of questions used to calculate deficits of time, loving, being and having.[|[][|24][|]]
 * [|Gross national happiness] - The Centre for Bhutanese Studies in [|Bhutan] is working on a complex set of subjective and objective indicators to measure 'national happiness' in various domains (living standards, health, education, eco-system diversity and resilience, cultural vitality and diversity, time use and balance, good governance, community vitality and psychological well-being). This set of indicators would be used to assess progress towards gross national happiness, which they have already identified as being the nation's priority, above GDP.
 * [|Happy Planet Index] - The happy planet index (HPI) is an index of human well-being and environmental impact, introduced by the [|New Economics Foundation] (NEF) in 2006. It measures the environmental efficiency with which human well-being is achieved within a given country or group. Human well-being is defined in terms of [|subjective life satisfaction] and [|life expectancy] while environmental impact is defined by the [|Ecological Footprint].

[[|edit]] Defense of GDP
GDP is a value neutral measure and expresses, what we can do, not what we should do. This is compatible with the fact that different people have different preferences and different opinions on what is well-being. Competing measures like GPI define well-being to mean things that the definers ideologically support. Therefore, they cannot function as the goals of a plural society. Moreover, they are more vulnerable to political manipulation.[|[][|25][|]] According to Index of Sustainable Economic Welfare, Finland should return to year 1980, or according to [|GPI], to early 1970s. People would hardly accept this, so these alternative measures are worse than GDP.[|[][|26][|]][|[][|27][|]]

[[|edit]] Lists of countries by their GDP

 * [|Lists of countries by GDP]
 * [|List of countries by GDP (nominal)], ([|per capita])
 * [|List of countries by GDP (PPP)], ([|per capita]), ([|per hour])
 * [|List of countries by GDP growth]
 * [|List of countries by GDP (real) growth rate], ([|per capita])
 * [|List of countries by GDP sector composition]
 * [|List of countries by future GDP estimates (PPP)], ([|per capita]), ([|nominal])

[[|edit]] See also

 * * [|Annual average GDP growth]
 * [|Chained volume series]
 * [|Eco-sufficiency]
 * [|Green gross domestic product]
 * [|Gross domestic product per barrel]
 * [|Gross output]
 * [|Gross regional domestic product] || * [|Gross state product]
 * [|Gross value added]
 * [|Gross world product]
 * [|Intermediate consumption]
 * [|Inventory investment]
 * [|List of countries by average wage]
 * [|List of countries by household income] || * [|List of economic reports by U.S. government agencies]
 * [|Misery index (economics)]
 * [|National average salary]
 * [|Potential output] Natural gross domestic product
 * [|Real gross domestic product]
 * [|China GDP] ||

[[|edit]] Bibliography
Australian Bureau for Statistics, [|//Australian National Accounts: Concepts, Sources and Mathods//], 2000. Retrieved November 2009. In depth explanations of how GDP and other national accounts items are determined. United States Department of Commerce, Bureau of Economic Analysis, [|//Concepts and Methods of the United States National Income and Product Accounts//] PDF. Retrieved November 2009. In depth explanations of how GDP and other national accounts items are determined.

[[|edit]] References
> [|European Parliament, Policy Department Economic and Scientific Policy: Beyond GDP Study] PDF (1.47 MB) > [|"User's guide: Background information on GDP and GDP deflator"]. HM Treasury. []. > [|"Measuring the Economy: A Primer on GDP and the National Income and Product Accounts"] (PDF). Bureau of Economic Analysis. []. > Some of the complications involved in comparing national accounts from different years are suggested in this World Bank [|document].
 * 1) **[|^]** O'Sullivan, Arthur.
 * 2) **[|^]**[|French President seeks alternatives to GDP], The Guardian 14-09-2009.
 * 1) ^ [|//**a**//][|//**b**//] Simon Kuznets, 1934. "National Income, 1929-1932". 73rd US Congress, 2d session, Senate document no. 124, page 7. []
 * 2) **[|^]** World Bank, Statistical Manual >> National Accounts >> [|GDP–final output], retrieved October 2009.
 * 1) **[|^]** Thayer Watkins, San José State University Department of Economics, [|"Gross Domestic Product from the Transactions Table for an Economy"], commentary to first table, " Transactions Table for an Economy". (Page retrieved November 2009.)
 * 2) **[|^]**//Concepts and Methods of the United States National Income and Product Accounts//, chap. 2.
 * 3) **[|^]** United States Bureau of Economic Analysis, [|//A guide to the National Income and Product Accounts of the United States//] PDF, page 5; retrieved November 2009. Another term, "business current transfer payments," may be added. Also, the document indicates that Capital Consumption Adjustment (CCAdj) and Inventory Valuation Adjustment (IVA) are applied to the proprieter's income and corporate profits terms; and CCAdj is applied to rental income.
 * 4) **[|^]** BEA, //Concepts and Methods of the United States National Income and Product Accounts//, p 12.
 * 5) **[|^]**//Australian National Accounts: Concepts, Sources and Methods//, 2000, sections 3.5 and 4.15.
 * 6) **[|^]** This and the following statement on entitlement to compensation are from //Australian National Accounts: Concepts, Sources and Methods//, 2000, section 4.6.
 * 7) **[|^]**//Concepts and Methods of the United States National Income and Product Accounts//, page 2-2.
 * 8) **[|^]**//Concepts and Methods of the United States National Income and Product Accounts, page 2-2.//
 * 9) **[|^]**//Australian National Accounts: Concepts, Sources and Methods//, 2000, section 4.4.
 * 10) **[|^]**//Concepts and Methods of the United States National Income and Product Accounts//, page 2-2; and //Australian National Accounts: Concepts, Sources and Methods//, 2000, section 4.4.
 * 11) ^ [|//**a**//][|//**b**//]//Concepts and Methods of the United States National Income and Product Accounts//, page 2-4.
 * 12) **[|^]**//Concepts and Methods of the United States National Income and Product Accounts//, page 2-5.
 * 13) **[|^]** Lequiller, François; Derek Blades (2006). [|//Understanding National Accounts//]. OECD. p. 18. [|ISBN] [|978-92-64-02566-0] . [] . "To convert GDP into GNI, it is necessary to add the income received by resident units from abroad and deduct the income created by production in the country but transferred to units residing abroad."
 * 14) **[|^]** United States, Bureau of Economic Analysis, Glossary, [|"GDP"]. Retrieved November 2009.
 * 15) **[|^]** [|"U.S. Department of Commerce. Bureau of Economic Analysis"]. Bea.gov. 2009-10-21 . [] . Retrieved 2010-07-31.
 * 16) **[|^]** HM Treasury, //Background information on GDP and GDP deflator//
 * 1) **[|^]** [|"Eric Zencey-G.D.P. R.I.P."]. Nytimes.com. August 2009 . [] . Retrieved 2011-01-31.
 * 2) **[|^]** Simon Kuznets. "How To Judge Quality". The New Republic, October 20, 1962
 * 3) **[|^]** [|"World Bank wealth estimates"] . [].
 * 4) **[|^]** [|"First European Quality of Life Survey"] . [].
 * 5) **[|^]**[|GDP and its Enemies], Centre for European Studies, September 2010
 * 6) **[|^]**[|Talouden mittarit ja tavoitteet], professor [|Matti Liski], 2009-10-4
 * 7) **[|^]** "Politiikanteon ohjaamiseen ei tarvita 'onnellisuusmittareita'", professor Mika Maliranta and research manager Niku Määttänen, [|Helsingin Sanomat] 2011-02-06, page C6

[[|edit]] Global

 * [|World GDP Chart (since 1960)]
 * [|Australian Bureau of Statistics Manual on GDP measurement]
 * [|GDP-indexed bonds]
 * [|GDP scaled maps]
 * [|Euro area GDP growth rate (since 1996) as compared to the Bank Rate (since 2000)]
 * [|World Development Indicators (WDI)]
 * [|Economist Country Briefings]
 * [|UN Statistical Databases]
 * [|Is Life Getting Better : What is GDP?] Pamphlet describing the basic idea of GDP, from OECD's Measuring Progress project.

[[|edit]] Data

 * [|Thermal Maps of the World Nominal GDP in US$ purchasing power parity from the EIU 2007-2010]
 * [|Bureau of Economic Analysis: Official United States GDP data]
 * [|Graphs of Historical Real U.S. GDP]
 * [|Historicalstatistics.org: Links to historical statistics on GDP for different countries and regions]
 * [|Historical US GDP (yearly data)], 1790–present
 * [|Historical US GDP (quarterly data)], 1947–present
 * [|OECD Statistics]
 * [|Google - public data]: GDP and Personal Income of the U.S. (annual): Nominal Gross Domestic Product

[[|edit]] Articles and books
<span style="background: none transparent scroll repeat 0% 0%; font-size: 100%; white-space: nowrap; word-spacing: -0.12em;">[|v] **·** [|d] **·** [|e]  [|Lists of countries] by GDP ([|Nominal] / [|PPP]) [|rankings] || || || || || ||
 * [|What's wrong with the GDP?]
 * [|Limitations of GDP Statistics by Schenk, Robert.]
 * [|whether output and CPI inflation are mismeasured, by Nouriel Roubini and David Backus, in Lectures in Macroeconomics]
 * [|Fengbo Zhang] - the founder of [|China GDP]
 * Chapter 22 of Dr. Roger A. McCain's [|Essential Principles of Economics: A Hypermedia Text]
 * Rodney Edvinsson, [|Growth, Accumulation, Crisis: With New Macroeconomic Data for Sweden 1800-2000] PDF
 * Clifford Cobb, Ted Halstead and Jonathan Rowe. "If the GDP is up, why is America down?" The Atlantic Monthly, vol. 276, no. 4, October 1995, pages 59–78.
 * Jerorn C.J.M. van den Bergh, "[|Abolishing GDP]"
 * ||||~ [[|show]]
 * [|Nominal] ||< [|Per capita] **·** [|Past] ([|per capita]) **·** [|Future] ([|per capita]) **·** [|Sector composition] **·** [|Ten largest historically]
 * [|Nominal] ||< [|Per capita] **·** [|Past] ([|per capita]) **·** [|Future] ([|per capita]) **·** [|Sector composition] **·** [|Ten largest historically]
 * [|Purchasing power parity (PPP)] ||< [|Per capita] **·** [|Past] ([|per capita]) **·** [|Future] ([|per capita]) **·** [|Per hour] **·** [|Per person employed]
 * [|Purchasing power parity (PPP)] ||< [|Per capita] **·** [|Past] ([|per capita]) **·** [|Future] ([|per capita]) **·** [|Per hour] **·** [|Per person employed]
 * [|Growth] ||< [|Real] **·** [|Per capita] **·** [|1990–2007 growth] **·** [|Industrial growth]
 * [|Growth] ||< [|Real] **·** [|Per capita] **·** [|1990–2007 growth] **·** [|Industrial growth]
 * [|GNI] ||< [|Nominal] **·** [|PPP]
 * [|GNI] ||< [|Nominal] **·** [|PPP]
 * Countries by region ||< Africa ([|nominal] **·** [|PPP]) **·** Latin America & Caribbean ([|nominal] **·** [|PPP]) **·** North America ([|nominal] **·** [|PPP]) **·** South America ([|nominal] **·** [|PPP]) **·** [|Arab League] **·** [|Asia] **·** Asia & Pacific ([|nominal] **·** [|nominal per capita] **·** [|PPP]) **·** [|Former Soviet Republics] **·** Europe ([|nominal] **·** [|nominal per capita] **·** [|PPP] **·** [|PPP per capita]) **·** [|Oceania]
 * Countries by region ||< Africa ([|nominal] **·** [|PPP]) **·** Latin America & Caribbean ([|nominal] **·** [|PPP]) **·** North America ([|nominal] **·** [|PPP]) **·** South America ([|nominal] **·** [|PPP]) **·** [|Arab League] **·** [|Asia] **·** Asia & Pacific ([|nominal] **·** [|nominal per capita] **·** [|PPP]) **·** [|Former Soviet Republics] **·** Europe ([|nominal] **·** [|nominal per capita] **·** [|PPP] **·** [|PPP per capita]) **·** [|Oceania]
 * [|Subnational divisions] ||< [|Argentine provinces] **·** [|Australian states & territories] **·** [|Brazilian states] **·** [|Canadian provinces and territories] **·** [|Chilean regions] ([|per capita]) **·** [|Chinese provinces] ([|per capita]) **·** [|French regions] **·** [|German states] **·** [|Indian states] **·** [|Indonesian provinces] **·** [|Japanese prefectures] **·** [|Mexican states] **·** [|Russian federal subjects] **·** [|South Korean regions per capita] **·** [|U.S. states] ([|per capita] **·** [|comparison with countries]) ||
 * [|Lists by country] **·** [|Lists of countries and territories] **·** [|Lists of countries by financial rankings] **·** [|List of international rankings] **·** [|List of top international rankings by country] ||  ||
 * [|Lists by country] **·** [|Lists of countries and territories] **·** [|Lists of countries by financial rankings] **·** [|List of international rankings] **·** [|List of top international rankings by country] ||  ||
 * [|Lists by country] **·** [|Lists of countries and territories] **·** [|Lists of countries by financial rankings] **·** [|List of international rankings] **·** [|List of top international rankings by country] ||  ||

<span style="background: none transparent scroll repeat 0% 0%; font-size: 100%; white-space: nowrap; word-spacing: -0.12em;">[|v] **·** [|d] **·** [|e]  Economic classification of countries || || || || || ||  ||
 * ||||~ [[|show]]
 * [|Developed country] **·** [|Developing country] **·** [|Least developed country] **·** [|High income economy] **·** [|Newly industrialized country] **·** [|Heavily Indebted Poor Countries] ||
 * [|Worlds Theory] ||< [|First World] **·** [|Second World] **·** [|Third World] **·** [|Fourth World]
 * [|Worlds Theory] ||< [|First World] **·** [|Second World] **·** [|Third World] **·** [|Fourth World]
 * [|Worlds Theory] ||< [|First World] **·** [|Second World] **·** [|Third World] **·** [|Fourth World]
 * GDP ||<  || **Nominal** ||< [|By country] ([|future estimates] **·** [|growth] **·** [|per capita] [[|future estimates]]) ||
 * [|Purchasing power parity] (PPP) ||< [|By country] ([|future estimates] **·** [|per capita] [[|future estimates]] **·** [|per hour worked], [|per person employed]) ||  ||
 * [|GNI per capita] ||< [|List of countries by GNI (nominal) per capita] **·** [|List of countries by GNI (PPP) per capita]
 * [|Purchasing power parity] (PPP) ||< [|By country] ([|future estimates] **·** [|per capita] [[|future estimates]] **·** [|per hour worked], [|per person employed]) ||  ||
 * [|GNI per capita] ||< [|List of countries by GNI (nominal) per capita] **·** [|List of countries by GNI (PPP) per capita]
 * [|GNI per capita] ||< [|List of countries by GNI (nominal) per capita] **·** [|List of countries by GNI (PPP) per capita]
 * [|Wages] ||< [|per hour] **·** [|monthly] ([|Europe]) **·** [|per year] **·** [|Minimum wage] ([|Europe] **·** [|USA] **·** [|Canada]) ||
 * Other [|national accounts] ||< [|Net material product] **·** [|Gross/Net national wealth] **·** [|Expenditures on R&D]
 * Other [|national accounts] ||< [|Net material product] **·** [|Gross/Net national wealth] **·** [|Expenditures on R&D]
 * Other [|national accounts] ||< [|Net material product] **·** [|Gross/Net national wealth] **·** [|Expenditures on R&D]
 * [|Human development] ||< [|List of countries by Human Development Index] **·** [|Human Poverty Index] **·** [|List of countries by percentage of population living in poverty]
 * [|Human development] ||< [|List of countries by Human Development Index] **·** [|Human Poverty Index] **·** [|List of countries by percentage of population living in poverty]
 * [|Digital divide] ||< [|Digital Opportunity Index] **·** [|List of countries by number of Internet users] **·** [|List of countries by number of broadband Internet users]
 * [|Digital divide] ||< [|Digital Opportunity Index] **·** [|List of countries by number of Internet users] **·** [|List of countries by number of broadband Internet users]


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June 2002

Reassessing the Standard of Living in the Soviet Union: An Analysis Using Archival and Anthropometric Data

Elizabeth Brainerd Williams College CEPR, IZA and WDI Elizabeth.Brainerd@williams.edu

Abstract: Both Western and Soviet estimates of GNP growth in the USSR indicate that GNP per capita grew in every decade – sometimes rapidly – in the postwar era, suggesting a rising standard of living throughout the period. This paper uses archival data and anthropometric studies conducted across the Soviet Union to reassess the standard of living in the USSR using alternative measures of well-being. Previously unpublished archival data on adult and infant mortality, as well as numerous studies of the heights of children, adolescents and adults are used to analyze health and living conditions across the Soviet republics and Russia’s regions from 1955 to 1989. Overall, these data paint a picture of a society far behind other developed countries in the health status of its population in the early postwar era. For example, children born in the mid-1950s reached only the 10 th to 20 th  percentile by height on U.S. growth charts, suggesting widespread stunting of children during that period; infant and adult mortality rates similarly indicate a large and growing mortality gap with western countries. In other words, the high GNP growth rates achieved in the Soviet Union in this period failed to translate into improved well-being for the population as a whole, and in fact by these measures the standard of living worsened significantly in the later decades of this period. 1 I. Introduction Despite the obvious and ultimately fatal shortcomings of the Soviet system of central planning, the Soviet growth model nevertheless achieved impressive rates of economic growth and promoted the rapid industrialization of the USSR, particularly in the decades from the 1930s to the 1960s. Both Western and Soviet estimates of GNP growth in the Soviet Union indicate that GNP per capita grew in every decade in the postwar era, at times far surpassing the growth rates of the developed western economies. By this measure – and according to the propaganda spread by Soviet promoters – the standard of living in the country rose concurrently with rising GNP per capita. Yet due to the highly restricted publication of data and the questionable quality of the data that were published, little is known about the standard of living in the Soviet Union in the postwar era. Some trends, such as the decline in male life expectancy that began in 1964, suggest a deterioration of living standards; however this decline itself remains a puzzle, and little additional evidence has been available to assess other aspects of living standards in the USSR since the second World War. The question of whether the standard of living rose or fell in the Soviet Union in the postwar period is an important one, as our judgment of the Soviet growth model must rest not only on the rates of economic growth it  achieved, but also on whether this growth translated into improved well-being for the population as a whole. This paper reassesses the standard of living in the Soviet Union using a number of previously unexploited data sources. The focus is on alternative measures of well-being, in particular child and adult heights and infant and adult mortality, all of which directly measure the well-being of a population in terms of health status, nutrition and longevity. These biological indicators are a useful supplement to traditional measures of living standards, such 2 as real income or wages, because the latter may be misleading if measured incorrectly and in  any case can only measure the means by which the good health and nutrition of a population can be achieved. In addition, it is important to examine alternative measures of well-being in the Soviet Union because GNP and other economic data were of unusually poor quality and reliability in that country. The data used in this paper comprise previously unpublished data on adult and infant mortality in the USSR and across Russia’s regions from 1959 to 1975, collected from the Soviet archives, as well as the results of anthropometric studies of children and adolescents conducted across the Soviet Union from 1955 to the early 1990s. These data are supplemented by a study of trends in adult heights by year of birth which provides a window on living conditions in the early childhood years of each cohort. Overall, these data paint a picture of a society far behind other developed countries in the health status of its population in the early postwar era. For example, children born in the mid-1950s reached only the 10 th to 20 th

percentile by height on U.S. growth charts, suggesting widespread stunting of children during that period; infant and adult mortality rates similarly indicate a large and growing mortality gap with western countries. In other words, the high GNP growth rates achieved in the Soviet Union in this period failed to translate into improved well-being for the population as a whole, and in fact by many measures the standard of living worsened significantly in the later decades of this period. In light of the growing body of evidence that serious adult morbidities such as stroke and heart disease develop in infancy and early childhood (Barker 1989, 1995, 1997,  1998), it is likely that the deteriorating living conditions of the USSR of the 1970s sowed the seeds for the extraordinarily high mortality rates experienced in Russia and other countries of the former Soviet Union in the 1990s.

1 See Ofer (1987) and Fischer (1994) for more detailed discussions of these problems.

3 The outline of the paper is as follows. Section II presents a brief overview of what is currently known about economic growth and consumption in the Soviet Union. Section III describes the new data sources used in the paper; Section IV discusses the use of anthropometric data as an alternative measure of living standards and analyzes the data on  child and adult heights. Section V examines the trends in infant and adult mortality in this period, and Section VI concludes. II. Previous assessments of economic growth and well-being in the USSR Economic growth in the Soviet Union was the subject of intense scrutiny for many years by the CIA and western Sovietologists, in part due to the importance of the issue for U.S. national security interests, but also due to the extraordinary effort required to make Soviet economic statistics comparable to U.S. measures and to correct for the deficiencies in the data published by TsSU, the Soviet statistical agency. This section of the paper briefly reviews the estimates of national income growth and consumption in the Soviet Union calculated by various investigators, to provide a background against which to assess the alternative measures of well-being presented in the following sections of the paper. Before turning to the estimates of national income growth in the USSR, it is worthwhile to note some of the shortcomings of Soviet economic data; indeed, as Easterly and Fischer state, “the fundamental problem in evaluating Soviet growth is data quality” (1994, p. 3). The problems fall into three main categories: incentives for misreporting; methodological differences between Soviet and Western national income accounting practices; and selective publication of data. 1 Regarding incentives for misreporting, the work of all economic units, 4 from factory floor to central ministries, was judged based on the fulfillment of plan targets established annually at all levels. Given this, it is clear that the incentive to over-report pervaded the entire system, raising doubts about the credibility of most reported economic magnitudes. Equally problematic were the differences in methodological approaches to national income accounting between the USSR and developed market economies. For ideological reasons, for example, important components of national income – such as services and interest on capital – were excluded from the national accounts of the Soviet Union (services were excluded because they were considered “nonproductive”; interest on capital because it was considered a non-legitimate factor payment). The lack of market prices in the Soviet Union also complicated the task of researchers assessing Soviet growth, and particularly those wishing to compare Soviet growth with growth in developed countries in which prices more closely reflected shadow prices. The third major problem with Soviet economic data was selective publication, in which data considered to be embarrassing were simply suppressed, or definitions changed to suit the purposes of propaganda. The poor quality and questionable reliability of Soviet economic data means that a high degree of uncertainty surrounds the estimates of GNP growth in the country, and underscores the importance of examining alternative measures of well-being. Anthropometric indicators such as height are also advantageous because they take into account that some economic activity is non-monetized and therefore unmeasured by conventional indicators of living standards. This is particularly beneficial for the Soviet Union, because, as is well-known, increasing shares of economic activity took place in the “second economy” of the USSR as macroeconomic imbalances intensified in the 1970s and 1980s. Keeping these data-quality caveats in mind, the range of estimates of national income

2 See the discussion of this issue in Ofer (1987) and Easterly and Fischer (1994).

5 growth for the Soviet Union is shown in Table 1. By any measure this growth record is impressive, particularly in the early postwar years when Soviet economic growth exceeded U.S. growth by a substantial margin, even using the more conservative Western estimates of Soviet growth. In later years growth began to slow, declining from an average annual rate of 6.0 percent in the 1950s to 2.0 percent in 1980-1985 (using the Bergson/CIA estimates). Comparing the Soviet growth record with that of the OECD and the United States, the growth rate of GNP per capita in the Soviet Union equaled that of the OECD for the 1950-1980 period (3.3 percent annual average) and exceeded that of the U.S. by a significant amount, at 3.3 versus 1.9 percent, respectively, from 1950 through 1980 (Table 2). In the last decade of the period, 1970 - 1980, GNP growth per capita was roughly similar in all three regions, averaging about 2 percent annually over those years. The sources of the slowdown in economic growth in the Soviet Union remain a topic of debate among scholars, with deteriorating productivity growth and low elasticity of substitution in industry likely the most important contributing factors. 2 While it is clear that Soviet growth rates declined after the 1950s, the Soviet growth record in the postwar period nevertheless compares reasonably well with that of the developed market economies. Based on this measure, at least, there was little reason to suspect that living standards may have been declining during this long period of positive economic growth. Household consumption data also support the picture of rising living standards throughout this period; the growth in per capita household consumption met or exceeded the growth rates of household consumption in the OECD and the United States over the entire 1950 - 1980 period (Table 2), as Soviet leaders allowed consumption to grow relatively rapidly until the early 1980s. According to Gur Ofer, this created a “radical change in the quality of

3 Chernichovsky // et al // (1996) and Tulchinsky and Varavikova (1996) provide useful overviews of the development of the public health service in the USSR.

6 life in the Soviet Union” (Ofer 1987, p. 1790), with an increased variety and quality of goods leading to significant improvements in the standard of living. This progress was further enhanced by the massive expansion of the public health care system and educational facilities across the country, with the vast majority of these services provided for free by the government. 3

While the consumption growth record seems clear, it should be kept in mind that this growth took place in the context of a relatively low initial level of consumption, particularly in comparison with the U.S. and the OECD. As a result, even with rapid growth the absolute level of household consumption remained well below that of the United States throughout the postwar period. Estimates vary widely, but per capita consumption in the USSR likely reached no more than one-third that of the United States in the mid-1970s, and probably declined in subsequent years. Schroeder and Edwards (1981) estimate Soviet consumption per capita at 34.4 percent that of the United States in 1976, while Bergson (1991) calculates a proportion of  28.6 percent by 1985; even the Soviet statistical agency itself estimated that consumption per capita reached only 30 - 33 percent that of the U.S. in 1980 and fell to 22 - 26 percent by 1985 (Bergson 1991). Most investigators made herculean efforts to correct Soviet consumption measures for the important sources of bias – the persistent shortages of consumer goods, the cost of time spent in search, the poor quality of goods, and the lower level of retail services – but it remains likely that the actual level of consumption was even lower than the estimates given here, and the figures remain controversial. For example, Birman (1983) argues that actual Soviet consumption per capita reached only 22 percent of the U.S. level in 1976 when 7 the data are properly adjusted for measurement problems. Given the degree of controversy over these estimates it is difficult to draw clear conclusions regarding household consumption in the Soviet Union. Most analysts would likely agree that the level of per capita consumption in the USSR never exceeded one-third that of the United States, and that the level of consumption fell relative to that of the United States between the mid-1970s and mid-1980s. The lack of reliable information on Soviet consumption again underscores the benefits of examining alternative indicators of well-being in the USSR, such as anthropometric evidence and mortality, both of which are more objective measures of well-being than economic growth or consumption, and which are not subject to the data problems inherent in the more conventional measures of living standards. Because the Soviet statistical agency ceased publication of infant mortality and other demographic data in 1974, these indicators of living standards were unavailable to researchers until the mid-1980s when publication of a limited amount of mortality data resumed. These data revealed that male life expectancy had begun to decline in 1965 and that infant mortality rates started to rise in 1971, both nearly unprecedented developments in industrialized countries and both signals that, despite the apparent continuous improvements in economic growth and consumption in the USSR in the postwar period, a serious deterioration in the health of some groups in the population was well underway. III. New data sources The opening of the Soviet archives has provided researchers with new opportunities for investigating all aspects of life in the Soviet Union, including changes in health status, mortality, and the standard of living more broadly across the country. The two archives in

4 The specific location of each data series by // fond, opis // and // delo // is given in Appendix 1.

8 which the mortality data are housed are the GARF archive (  // Gosurdarstvennyi arkhiv Rossiiskoi //  Federatsii  (State Archive of the Russian Federation)) and the RGAE archive (  // Rossiiskii //  gosudarstvennyi arkhiv ekonomiki  (Russian State Archive of the Economy)), both in Moscow. The infant and adult mortality data in the archives are tabulated on standardized reporting forms by region and USSR republic; most of the data are hand-written onto the forms and are enumerated simply as the total number of deaths in each category, e.g. by age group, cause, region and so on. Almost without exception the number of deaths by age group and region add up to the RSFSR and USSR totals in the archives and also agree with the published aggregates, indicating that systematic misreporting of deaths did not occur at this level. Data on deaths by sex, five-year age group and oblast or USSR republic in 1959 and 1970 were collected from the RGAE archive; these data were combined with data on the distribution of the population by age, sex and oblast or republic from the 1959 and 1970 censuses contained in the GARF archive to calculate age-specific death rates by region in those years. 4 Births and infant deaths by oblast and republic were collected for 1956 through 1975 from the GARF archive and were used to calculate infant mortality rates by region in those years. Additional data collected from the archives include average monthly wages by region for 1959 and 1970 and alcohol purchases in 1959 (from family budget surveys); these data are supplemented by published data on food consumption, divorce rates and urbanization in the relevant years. The anthropometric data used in the paper are the average heights of children and adolescents collected primarily by researchers at the Semashko Institute of Public Hygiene in studies initiated in the 1950s and continuing through the present day. Much of the data are published in the Semashko Institute volumes (1962, 1965, 1977, 1988, 1998). Most of the

5 See U.S. Department of Health, Education and Welfare (1976) for a description of the surveys and methodology for constructing the growth charts.

6 A detailed description of the sampling design and implementation of the RLMS, as well as data access, is available at the RLMS website at http://www.cpc.unc.edu/rlms.

9 studies were conducted in schools by trained researchers according to uniform standards; on  average in every region surveyed about 100 children of each sex at each age were measured for height and weight. The Semashko data are supplemented by other anthropometric surveys of children in the USSR conducted by researchers and published in Soviet medical journals such as // Sovietskoye zdravookhraneniye // ( // Soviet Public Health // ) and // Zdravookhraneniye Rossiiskoi // Federatsii ( // Public Health in the Russian Federation // ); these sources are listed in Appendix 1. The data in these studies appear to be comparable to the Semashko data in terms of methodology, particularly in the standards used for measurement of children as well as for presentation of the data. These data are presented below both in raw form (i.e. height attained at each age in centimeters) and converted into percentiles of U.S. growth standards. These percentiles were calculated by Richard Steckel (1996) and are derived from the standard U.S. growth charts which are based on nationally representative surveys of well-nourished children in the United States taken in the 1960s and early 1970s; these growth charts are widely used and have been adopted by the World Health Organization (WHO) as the standard for evaluating child growth in developing countries. 5 The child height data are supplemented with data on adult heights in Russia from second panel of the Russian Longitudinal Monitoring Survey (RLMS); heights were measured by trained interviewers in these surveys and are not self-reported. 6 For comparison, data on the heights of (native) adults in the United States are also included in the analysis; these data are from the National Health and Nutrition Examination Surveys (NHANES II) conducted in 1976 - 1980.

7 See Steckel (1995) for a survey of research in this area.

10 IV. Trends in child and adult heights in the Soviet Union These anthropometric data are used here to evaluate the health and nutritional status of the Soviet population in the postwar period, and, more broadly, to assess the standard of living across regions and in the country as a whole. This use of anthropometric data draws on the pioneering work of researchers such as Robert Fogel and Richard Steckel, which has demonstrated that anthropometric data can provide a wealth of information on the living standards of the past and present, and can be particularly useful when data on traditional measures such as GNP are absent or of questionable quality (Fogel 1986, 1991, 1994; Steckel 1979a, 1979b). 7 More specifically, the influences of past and current nutritional status are reflected in adult heights and body mass indices (a measure of weight for height): adult height is a cumulative measure of nutritional status in infancy, childhood and early adulthood, while the body mass index is an indicator of current nutritional status. Both adult height and the body mass index have been found to be strong predictors of the probability of dying, and the ideal measures of these appear to be constant over time and across countries. Stature as a measure of living standards has several advantages over more conventional measures. It is a measure of net nutrition in the sense that it takes into account not only the inputs to health – nutrition, health care – but the demands placed on an individual’s biological system as well, such as through disease and work intensity in the growing years. Even a mild illness during the growing years will tend to slow growth, and although catch-up growth is possible it will depend on the availability of sufficient caloric and nutrient intake to enable such growth. It has now also been established that psychosocial stress can slow a child’s growth, because stress affects the secretion of the growth hormone (Eveleth and Tanner 1990). In

8 The one exception to this rule appears to be individuals of Far Eastern origin; height differences between Far Eastern populations and other populations do appear to have a genetic basis.

11 addition, stature and family income are usually highly correlated, which one might expect given that family income is closely linked with the ability to purchase health inputs and with the demands on these inputs; height is also especially sensitive to income at low income levels (Steckel 1995). Even within developed countries, however, height still rises with socioeconomic class (Eveleth and Tanner 1990). Child height has an advantage as an indicator of welfare over adult height because for adults the causality between income and stature may run in both directions, with healthier (taller) individuals able to be more productive and earn higher wages (see Strauss and Thomas 1998). For children this direction of causality is unlikely to hold; in addition children are more sensitive to environmental insults, especially in the years of  rapid growth (infancy and the adolescent years, i.e. age 10 to 14). Indeed it appears that adult height is largely determined by age 3 to 4, and is affected even by nutritional inputs during the fetal growth period (Thomas 2001). While genetic influences in part determine individual height, at the population level nearly all differences in average height are the result of environmental influences, enabling one to compare stature across countries and over time. In other words, well-nourished populations tend to follow the same growth curves, whether the population is European, African, or North American in origin (Martorell and Habicht 1986). 8

Because of the comparability of heights across populations and over time, and due to the clear link between height and nutritional status, stature is viewed as a useful index of the standard of living. Table 3 illustrates the data on child stature for several representative regions of the USSR; to conserve space only selected ages, regions and years are shown. The data used in

9 Stunting is defined as height below two standard deviations of the median of the reference population.

12 the analysis are for urban areas only, due to limited data availability for rural areas. Panel (a) presents the trends in stature of children in Moscow, likely the most well-nourished children in the Soviet Union and with access to the best health care. On the eve of the second World War, Muscovite children were remarkably short in stature: boys age 16, for example, reached only the 5 th percentile on U.S. growth charts; girls reached the 18 th  percentile. Because the heights in Table 3 are the average heights of a sample of children and heights are usually normally distributed (Steckel 1996), this implies that a substantial share of children in Moscow were stunted in growth. 9 Conditions improved significantly by the late 1960s, with boys age 16 reaching the 43 rd percentile of growth and girls the 48 th  in 1969. However, in the early 1970s growth appeared to halt or even regress: 16 year-old boys met only the 34 th percentile of U.S.  height standards on average, girls the 37 th . As will become evident, this pattern persists across many regions of the Soviet Union during this period. By the early 1990s conditions had improved – at least in Moscow – and 16 year-old boys achieved the 50 th percentile of growth. Note that the percentiles of growth for girls exceed the percentiles of growth for boys across most regions and time periods; this is consistent with research indicating that males are more sensitive to environmental insults than are females (Eveleth and Tanner 1990). Panel (b) of Table 3 shows similar data for other regions of the USSR in order to shed light on health and living conditions outside the capital city. This panel indicates that children in Moscow were taller on average than children living in other regions of the country, as would be expected given the greater and more diverse supply of food in Moscow as well as the better health facilities. Again in all urban areas of the country, boys and girls reached no higher than 13 the 20 th - 25 th  percentiles of growth on average in the late 1950s and early 1960s; even in  relatively well-supplied cities such as St. Petersburg, Nizhny Novgorod and Kharkov (Ukraine) 16 year old boys failed to reach the 10 th percentile on average on U.S. growth charts (not  shown). Moreover, children in rural areas almost uniformly achieved lower stature than did children in urban areas. In Ryazanskaya oblast in the Central region, for example, urban children age 8 grew on average to 124.9 cm in 1960, or the 20 th percentile on U.S. growth charts; the average height of rural children in Ryazanskaya oblast reached only 122.7 cm or the 11 th  growth percentile in the same year. By 1970 these differences had widened – although children in both urban and rural areas achieved higher stature – with the average height of children age 7 in urban areas at 125.6 cm (50   th percentile) and in rural areas at 120.0 cm (21  st

percentile). Because the data for rural areas are limited, this analysis focuses on child growth in urban areas and therefore clearly overstates the average child growth rates in the country as a  whole.  Figure 3 illustrates the anthropometric data for all regions graphically, showing the  percentiles of growth achieved by boys and girls age 7 and 14 by year of birth. Overall these  graphs paint a picture of widespread stunting of children born in the 1950s, rapid growth in  child and adolescent stature in the 1960s, and a marked slowdown or halting of growth for  children born in the 1970s. As late as the mid-1980s child stature on average remained well  below U.S. height standards in most regions. The substantial and rapid increases in height  across most regions and birth cohorts in the USSR in the 1955-1969 period indicate that  significant improvements likely occurred in the nutrition, sanitary practices, and public health infrastructure in the country in that period. This evidence also suggests, however, that these conditions began to deteriorate soon thereafter.

10 The samples used are for prime-age adults (age 23 - 55) and contain 3,851 observations for men and 4,099 observations for women. The graph illustrates locally weighted smoothing (or lowess) estimates of the relationship between stature and exact date of birth. Lowess is a nonparametric estimator that uses a small amount of data near the point in order to generate smoothed values of height. The procedure is described in Cleveland (1979).

14 These trends are corroborated by a study of current adult heights in Russia taken from the Russian Longitudinal Monitoring Survey. As noted above, adult height is largely determined in early childhood (i.e., age 3 to 4) including the fetal period; like child stature adult stature also reflects the cumulative effects of nutrition and exposure to disease in early childhood. Figure 2 illustrates the trend in adult heights by date of birth and by sex over the 1935 - 1975 period, and includes a similar graph for the United States for comparison. 10 Again it is clear that final attained height increased rapidly for children born from 1940 until the late 1960s, with a relatively brief period of stagnant growth around 1950 - 55. The increase in stature averaged about 2 centimeters per decade between 1940-1950 and 1960-1970, which is  comparable to or exceeds the average rates of increase in stature in developing countries in the twentieth century (see Strauss and Thomas 1998). The average height of adult men born in the early part of the century in Russia was well below that of U.S. men (e.g., about 171 cm in Russia versus 175 cm in the U.S. in 1940; men in the U.S. had reached a height of 171 cm by  1750 (Costa and Steckel 1995)), but by the late 1960s men in both countries attained an  average height of about 177 cm. The striking feature of Figure 2, however, is the extraordinarily rapid and steep decline in the average heights of men and women that began around 1970 (for men) and 1973 (for women): both men and women appear to have lost 2 to 3 cm of height in less than a decade. This appears to be an almost unprecedented decline in height in modern times; even in Vietnam during the turbulent years of 1965 - 1975, average heights stagnated rather than declined (Strauss and Thomas 1998). The only equivalent decline 15 in height appears to be that of the U.S. between 1830 and 1880, when adult male heights fell by  3 to 4 centimeters; however this decline occurred over a much longer time period than that in  Russia. Because the Russian data are so dramatic and are based on an increasingly small share of observations as one approaches 1975, however, it is probably best to await further data before drawing any firm conclusions regarding the trends in adult heights in Russia. However, the timing of the decline in adult heights is the same as the timing of the stagnation in child heights discussed above, and is nearly identical to the timing of the increase in infant mortality rates in the Soviet Union, to which we now turn. V. Trends in infant and adult mortality in the USSR Infant mortality rates supplement the anthropometric data because they are a reasonably good proxy for low birth weight (an indicator of health and nutrition during the fetal period), have been widely used as measure of the quality of life across countries, and are available across all of Russia’s regions in the 1955 - 1975 period. Infant mortality rates in the Soviet Union have attracted the attention of demographers and social scientists for years, particularly after 1986 when the Soviet statistical agency resumed publication of mortality data (publication of detailed mortality data ceased in 1974; see Anderson and Silver 1990), which revealed a  dramatic increase in infant mortality rates in the Soviet Union beginning in the early 1970s. The trends in urban and rural infant mortality rates in Russia from 1960 to 1989 are shown in Figure 3; this figure reveals that, while infant mortality rates fell sharply from 1960 to 1970, the trend reversed in Russia in 1971 and particularly affected the rural areas of the country. Rising infant mortality rates in the Soviet Union have been quite controversial among demographers, with a heated debate ensuing between those who argue that the increase was an

11 See the debate between Jones and Grupp (1983), Anderson and Silver (1986), and Velkoff and Miller (1995). Note that Soviet infant mortality rates are not directly comparable to Western infant mortality rates, because the Soviet data exclude live-born infants of less than 28 weeks gestation, less than 1000 grams in weight, and less than 35 centimeters in length who die within 7 days of birth (which are included in the WHO-recommended definition of infant mortality). Anderson and Silver (1986) estimate that Soviet infant mortality rates would be 22 to 25 percent higher if the data were adjusted to include these deaths.

16 artefact of improved birth and death registration in the less developed regions of the USSR (such as the Central Asian republics and the North Caucuses) and those who argue that the increase in infant mortality rates was real and reflected deteriorating conditions in the public health infrastructure due possibly to budgetary cutbacks. 11

While the archival data on infant mortality cannot resolve this issue completely, they can shed light on the controversy because they show the trends in infant mortality rates across all regions of Russia (as well as all regions of the USSR in general). If the increase in infant mortality rates was due only to improved registration of births and infant deaths, one would not expect infant mortality rates to have increased in the more developed regions of Russia, such as Moscow, which had achieved essentially complete vital event reporting decades earlier. However, as is evident in Table 4, which shows the percentage change in infant mortality rates across Russia’s regions between 1971 and 1975, infant mortality rates rose dramatically even in Moscow, with a 14 percent increase over that period. The largest increase in infant mortality was registered in Khabarovskii Krai (on the far eastern coast of Russia), at nearly 60 percent, followed by Altaiskii Krai in Western Siberia at almost 50 percent. However there is no obvious regional pattern in the increases in infant mortality rates, with large increases registered in such diverse regions as Moscow, Novgorod and Saratov, and improvements recorded in other areas such as Leningradskaya oblast and Tyumenskaya oblast (the latter in Western Siberia). There is a strong negative correlation between the percentiles of child height 17 and infant mortality across Russia’s regions in 1970 (correlation = -0.83), but this only confirms the evidence provided by child heights and infant mortality rates separately: in the late 1960s or early 1970s, the health status of infants and children in the Soviet Union began to deteriorate markedly. A similar deterioration occurred among adult several years earlier. The unfavorable trends in mortality and life expectancy in the Soviet Union in the postwar period have long been known and, some argue (e.g., Eberstadt 1993), should have been taken as the first signal that the impressive rates of economic growth in the USSR either were exaggerated or failed to translate into an improved standard of living for the population. The trends in male and female life expectancy at birth are shown in Figures 4 and 5 for Russia, Ukraine and Lithuania, along with the United States for comparison. In Russia – and in the Soviet Union as a whole – life expectancy improved rapidly from the mid-1950s to the early 1960s (at least in part due to the large declines in infant mortality in this period) and in most republics life expectancy reached or exceeded that of the United States. Around 1964, however, male life expectancy in the Soviet Union began to decline and female life expectancy failed to improve, resulting in a gap of nearly 8.5 years in life expectancy between Russian and U.S. men by 1980, and a gap of 4.3 years for women in that same year. As is evident from Figures 4 and 5, the decline in male life expectancy was largest in the Russian republic, but a similar pattern of deterioration occurred in the other republics as well. Little is known regarding the underlying reasons for this startling and unprecedented decline in male life expectancy. Dutton (1979) was among the first to note increasing adult mortality in the Soviet Union in the mid-1960s and hypothesized a link with Soviet drinking habits. Others argued that declining life expectancy was due to excess mortality experienced

12 See these studies for an overview of the system of death registration and classification in the Soviet Union, as well as for a discussion of data quality issues surrounding the mortality data. In general, the quality of the mortality data is considered to be high, at least in the European republics of the Soviet Union and for broad categories of death.

18 by cohorts who were born during or fought in World War II (Dinkel 1985; Anderson and  Silver 1989b). The latter authors (1989a) also argue that declining life expectancy was partly the result of improved accuracy of death registration at the older ages, particularly in the 1960s and 1970s. However, even when the mortality data are adjusted for improvements in the accuracy of death registration, the negative trends in life expectancy from the 1960s to the 1980s persist (Blum and Monnier 1989). While the earlier literature is largely agreed on the general trends in mortality and immediate causes of death in the former USSR, few researchers systematically investigated the underlying reasons for the trends and thus little consensus on these reasons emerged. Additional information on adult mortality has come to light recently with the opening of the Soviet archives to researchers. A team of Russian and French demographers has painstakingly reconstructed mortality by detailed cause of death and by five-year age group for Russia, Ukraine, and the Baltic republics, using the original records from the Soviet statistical agency stored in the archives. The results of this reconstruction for Russia are presented in Shkolnikov // et al // (1996a, 1996b) for the years 1970 - 1993. 12  To briefly summarize their findings, deteriorating male life expectancy in the 1970 to 1980 period was due to increased deaths from cardiovascular disease; rising deaths due to trauma also contributed significantly to higher male mortality rates. Of the violent deaths, the most important increases in mortality rates were those from homicide, accidental poisoning and alcoholism. Note that in developed countries, the period from 1970 to the present has been a period of long-term decline in 19 mortality from cardiovascular disease as well as from violent deaths. Little can be added to the extensive analysis of the mortality data by cause provided by Shkolnikov and co-authors. However, some additional insight into the mortality trends can be gained by examining age-specific death rates at the regional and republican levels. Figure 6 illustrates changes in age-specific death rates for men and women between 1959 and 1970, calculated from data on deaths and age structure of the population collected from the RGAE and GARF archives. The largest increase in death rates in this period occurred for men age 40 to 44, a nearly 40 percent increase in the death rate in this age group; dramatic increases in death rates also occurred among men age 45-49 and 25-39. In contrast, death rates improved for women in most age groups in this period. As Shkolnikov // et al // (1996a) show, adult male mortality rates in the same age groups also increased significantly in the 1970 - 1980 period. In other words, declining male life expectancy in Russia in the 1964 - 1980 period reflects a significant worsening of mortality among middle-aged men, rather than an increase in death rates of infants or the elderly. Needless to say this is a highly unusual pattern of mortality. This pattern was repeated across all European republics of the Soviet Union; Figure 7 provides an example by illustrating the change in male age-specific death rates in Ukraine over the same period. The pattern is similar to that of Russia, with men age 40-49 experiencing the largest increase in death rates; graphs for Belarussia and the three Baltic republics follow the same pattern (not shown). Whatever caused rising adult male mortality in Russia, it seems clear it was not unique to that republic alone. Table 5 lists the changes in death rates for men age 40-44 by Russian region, sorted in order from the region with the smallest increase (or decrease) in this death rate to the region

13 There are only 54 regions in this table (instead of the usual 72 regions) because some of the data on deaths by region in 1970 were not available in the archives at the time the data were collected.

20 with the largest increase. 13 As with the infant mortality rates, it is not simply the most rural regions with the least well-developed vital registration system experiencing large increases in mortality rates, which one might have attributed to improvements in the vital registration system. Quite surprisingly, some of the most industrially advanced regions in the country registered the highest increases in death rates in this age group: Orlovskaya, Bryanskaya, Vladimirskaya, Ivanovskaya, Moskovskaya and Tulskaya oblasts, all in the Central region surrounding Moscow; in Moscow itself the male death rate in this age group increased by over 37 percent between 1959 and 1970. The regions that experienced an improvement in the mortality rate in these years (Pskov in the Northwest, Astrakhan in the Povolzhsky region) or a slight worsening of the death rate (Magadan in the Far East) appear to have little in common geographically or economically, so no obvious explanation presents itself to explain the large regional differences in changing mortality rates. Note also that there is little correlation between the change in adult male mortality rates and the change in infant mortality rates across Russia’s regions (correlation = -0.07). For example, Astrakhan was one of the few regions with improved adult male mortality in this period, yet it also experienced one of the largest increases in infant mortality rates in the early 1970s (Table 4). This suggests that the factors affecting adult health in this period differed from those that caused the increase in infant mortality rates. What caused the decline in male life expectancy in Russia between 1959 and 1970? While data are extremely limited, it is possible to test the correlation between changes in adult mortality rates by age group and changes in several economic and social variables between 21 1959 and 1970. Ideally one would like to test the hypotheses, suggested by the previous literature on adult mortality in the Soviet Union, that rising male deaths in the working ages were due to a combination of rising alcohol consumption and a poor diet increasingly comprised of fatty meat and other high-cholesterol foods, as well as limited fruit and vegetables. Smoking is likely a contributing factor as well. Unfortunately, however, much of these data appear to be unavailable, even unpublished data in the Soviet archives. The data that are available across Russia’s regions in this period include the following variables: average monthly wages, divorce rates, share of the population living in urban areas, per capita consumption of broad categories of foods, and a very limited number of observations on average purchases of alcohol (in liters) per capita. Table 6 shows the results of regressions that regress the percentage change in the death rate by selected age group for 1959 - 1970 on percentage changes in other variables across Russia’s regions, beginning with average monthly wages in the first column of each panel. For younger men (age 30-34) there appears to be a positive, although statistically insignificant, relationship between death rates and wages; the relationship turns negative for older men and is negative and highly significant for women of all age groups. This finding is consistent with research on the U.S. which shows that higher income is generally protective of health, except for young men, for whom higher incomes appear to lead to higher mortality rates (Deaton and Paxson 1999; Ruhm 2000). Adding other variables, urbanization has no relationship with death rates for both men and women (columns 3, 6, 9, 12); changes in the divorce rate – included as a possible proxy for social and family structure changes occurring over the period – has a surprising negative relationship with changes in the death rate for men age 40-44. Columns (2), (5), (8) and (11) add a variable for the change in per capita sales of alcohol, for which there are observations on only 28 regions. 22 These limited data indicate a positive (but statistically insignificant) relationship between alcohol consumption and mortality for adult men, but a strong and significant negative relationship for women. One interpretation of these results is that alcohol consumption leads to more violent deaths for men, but is protective of the health of women who drink more moderately in Russia. The last column in each panel adds a variable for the change in per capita consumption of sugar. This variable was intended to capture changes occurring in the Russian diet over this period; the country underwent a significant change in its eating patterns when Krushchev implemented policies designed to increase the consumption of meat and dairy products among the population. These policies succeeded in shifting the composition of the Russian diet away from grains and starches toward meat, dairy products and sugars, while consumption of fruits and vegetables remained extremely low by western standards (Popkin // et al // 1997). However, since sugar is a key ingredient in home-brewed alcohol, it may proxy for alcohol consumption as well. The results indicate that a greater increase in sugar consumption was associated with a higher death rate for men age 40-44 (but not men age 30-34); this could be due to the negative health consequences of increasing sugar in the diet, or could reflect greater alcohol consumption. The relationship is the same for women age 40-44; however it has the opposite sign and is statistically significant for women age 30-34. It is difficult to interpret this change in sign, but perhaps multicollinearity between changes in sugar consumption and wages is affecting the results. A number of other measures of food consumption were included in these regressions, for example consumption of meat, milk and eggs; however in nearly all cases the results were highly statistically insignificant. Although the data are limited and undoubtedly affected by measurement error, this first pass at correlations shows little support for the idea 23 that changes in diet (other than possibly sugar consumption) accounted for the increase in adult male mortality rates in Russia between 1959 and 1970. The evidence is suggestive that declining wages and increased alcohol consumption may have played some role, but this is a tenuous conclusion given the data limitations. V. Conclusion Did the standard of living rise or fall in the Soviet Union in the postwar period? The conventional measures of GNP growth and household consumption indicate a long, uninterrupted upward climb in the Soviet standard of living from 1950 to 1989; even Western estimates of these measures support this view, albeit at a slower rate of growth than the Soviet measures. This growth record compares favorably with that of the United States, yet a stark contradiction emerges when one compares longevity in the Soviet Union and the United States: life expectancy continued its long upward trend in the latter country over these years, but for men in Russia the year 1964 marked the beginning of a period of decline in life expectancy that lasted until 1980. This evidence of deteriorating living conditions is corroborated by changes in three biological measures of the standard of living that also began to decline around this time: an apparently real increase in infant mortality rates; a stagnation or decrease in child heights; and a significant decline in the heights of men and women born in the early 1970s. In other words, what is currently known about living standards in the Soviet Union in the postwar period tells a conflicting story: positive economic growth and growing household consumption occurred simultaneously with rising infant and adult mortality rates and falling child and adult heights, a rare and incongruous combination. Which story should we believe? In light of the data quality 24 problems inherent in much of the Soviet economic data and even in Western estimates that seek to correct for the deficiencies in these data, and given the relative objectiveness with which height and mortality are measured, this would seem to argue in favor of believing the trends portrayed by stature and death rates. These trends suggest a substantial improvement in the standard of living in the 1950s and early 1960s, then a worsening of the standard of living from the late-1960s or early 1970s until at least the early 1980s. Unfortunately the evidence remains inconclusive regarding the underlying causes of this deterioration in well-being, but changing diet, declining incomes and higher alcohol consumption may have played a role. 25

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Appendix 1: Data sources

Archival data: Distribution of the population by age, sex, and RSFSR oblast or USSR republic, 1959 Census tabulations: GARF, F. A-374, op. 4, d. 1, 2, 3, 4 Distribution of the population by age, sex, and RSFSR oblast or USSR republic, 1970 Census tabulations: RGAE, F. 1562, d. 336, op. 4435, 4436, 4437, 4438 Deaths by age, sex, and RSFSR oblast or USSR republic, 1959: RGAE F. 1562, op. 27, d. 826 Deaths by age, sex, and RSFSR oblast or USSR republic, 1970: RGAE F. 1562, op. 47, d. 1421 Births and infant deaths by RSFSR oblast, 1971: GARF, F. 1562, op. 48, d. 1266, 1267; 1975: RGAE, F. 1562, op. 56, d. 1928 Average monthly wages by RSFSR oblast, 1959: GARF, F. A-374, op. 31, d. 2779 Average alcohol purchases, liters per person, by RSFSR oblast (family budget survey results): GARF F. 374, op. 31, d. 5299 Anthropometric studies published in Soviet public health journals: (to be added) 30

Table 1. Estimates of national income (GNP) growth in the Soviet Union, 1928 - 1985 (annual rates of growth)

Khanin Bergson/CIA TsSU 1928-1985 3.3 4.3 8.8 1928-1941 2.9 5.8 13.9  1950s 6.9 6.0 10.1 1960s 4.2 5.2 7.1 1970s 2.0 3.7 5.3 1980-85 0.6 2.0 3.2 Source: Fischer (1994), Table 7.4.

Table 2. Comparisons of Soviet and Western economic performance, 1950 - 1980 (annual rates of growth)

Soviet Union E-OECD United States 1950-80 1960-80 1970-80 1950-80 1970-80 1950-80 1970-80 GNP per capita 3.3 3.1 2.1 3.3 2.3 1.9 2.0 Household consumptio n per capita 3.7 3.2 2.6 3.2 2.6 2.1 2.3 Notes: Soviet data are Western estimates. Data for E-OECD and the U.S. are GDP rather than GNP. Household consumption is at established prices for the Soviet Union, at factor cost for E-OECD and the United States. Source: Ofer (1987), Table 2. 31

Table 3. Child anthropometric indicators, USSR regions in the postwar years (urban areas) Panel a: Moscow

1939-40 1959 1969 1976 1980 1993 1939-40 1959 1969 1976 1980 1993 Age Boys, height in cm Boys, height in percentiles of US growth standards 2 na na 88.3 na 87.9 na na na 31 na 28 na 3 na 93.5 95.7 97.0 na na na 12 23 30 na na  7 na 119.6 123.9 124.0 na 126.3 na 18 46 47 na 50 10 132.2 135.1 140.3 139.9 na 139.1 11 21 50 48 na 43 14 152.6 157.7 162.6 162.3 na 163.0 6 16 34 33 na 35 16 163.2 167.9 174.0 172.4 na 175.2 5 14 43 34 na 50 Age Girls, height in cm Girls, height in percentiles of US growth standards 2 na na 86.1 na 86.5 na na na 18 na 21 na 3 na 922.6 95.3 96.5 na na na 11 28 37 na na  7 na 119.2 123.6 123.3 na 136.5 na 24 49 49 na 49 10 131.6 134.7 140.3 138.4 na 139.8 9 18 43 33 na 41 14 152.0 156.6 160.9 159.2 na 159.7 9 25 48 38 na 41 16 156.8 158.4 162.3 160.6 na na 18 25 48 37 na na

32

Table 3, Panel b: Orlovskaya oblast (Central region) Ulyanovsk (Povolzhsky region) Novosibirisk (West Siberia)

1959 1977 1992 1965-66 1976 1993 1959 1970 1976 Age Boys, height in percentiles Boys, height in percentiles Boys, height in percentiles 3 na na na 16 na na na na na 7 11 30 32 38 50 50 20 30 49 10 12 32 32 15 36 38 11 30 30 14 10 28 12 12 29 35 8 23 27  16 15 38 na 17 33 45 7 27 32 Age Girls, height in percentiles Girls, height in percentiles Girls, height in percentiles 2 na na na 11 na na na na na 3 na na na 19 na na na na na  7 12 37 39 35 49 49 25 38 49 10 10 29 27 17 38 32 12 23 28 14 12 38 17 26 42 45 16 33 40  16 18 43 na 34 49 48 18 43 40

Perm (Urals region) Kemerovo (West Siberia) Kiev, Ukraine

1962 1970 1976 1980 1994 1962 1969 1979 1992 1955 1960 1967 1972 1979 Age Boys, height in percentiles Boys, height in percentiles Boys, height in percentiles 3 na na 14 na na na na na na na na na na na 7 na 24 24 30 30 na 22 43 27 na na na na na  10 8 33 na 28 34 11 25 36 27 17 24 36 41 47 14 6 27 na 27 32 6 18 26 33 8 17 32 30 41 16 na 9 na 37 31 8 19 41 35 na na na na 44 Age Girls, height in percentiles Girls, height in percentiles Girls, height in percentiles 2 na na 14 na na na na na na na na na na na 3 na na 14 na na na na na na na na na na na  7 na 33 25 23 39 na 27 49 28 na na na na na  10 8 23 na 23 22 8 23 35 37 10 27 32 37 43 14 10 29 na 30 45 15 27 45 37 24 22 31 36 49 16 na 34 na 38 49 20 38 49 49 na na na na 49

33

Boys age 7 Percentile Year of birth 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 0  10  20  30  40  50  60  Boys age 14 Percentile Year of birth 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 0  10  20  30  40  50  60  Girls age 7 Percentile Year of birth 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 0  10  20  30  40  50  60  Girls age 14 Percentile Year of birth 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 0  10  20  30  40  50  60

Figure 1. Percentiles of child growth across urban regions of the Soviet Union by year of birth

34

Men Date of birth Women Men Women 35 40 45 50 55 60 65 70 75 169  170  171  172  173  174  175  176  177  158  159  160  161  162  163  164  165  166

Men Year of birth Women Men Women 30 40 50 60 70 169  170  171  172  173  174  175  176  177  158  159  160  161  162  163  164  165  166

Figure 2. Male and female adult heights, Russia and United States

Russia, 1935 - 1975 United States, 1935 - 1970

35

Urban Rural 1960 1965 1970 1975 1980 1985 1989 10  20  30  40

Figure 3. Infant mortality rates in Russia, urban and rural

36

Table 4. Percentage change in infant mortality rates by Russian region, 1971 - 1975

Infant mort. rate % change Infant mort. rate % change 1971 1975 1971 1975 Leningradskaya obl. 19.9 18.0 -9.6 Rep. of Bashkortostan 20.1 22.4 11.5 North Ossetia 20.7 18.8 -9.2 Voronezhskaya obl. 17.7 19.8 11.8 Kabardino-Balk. rep. 29.2 26.8 -8.3 Vladimirskaya obl. 16.5 18.5 12.0 Chuvashskaya rep. 26.3 24.3 -7.7 Volgogradskaya obl. 21.6 24.3 12.6 Astrakhanskaya obl. 24.4 23.1 -5.4 City of Moscow 20.8 23.7 14.0 Marii el rep. 23.2 22.3 -3.5 Bryanskaya obl. 18.7 21.4 14.6 Nizhegorodskaya obl. 18.5 17.9 -3.0 Kemerovskaya obl. 21.2 24.3 14.7 Samarskaya obl. 22.8 22.3 -2.4 Orlovskaya obl. 16.3 19.1 17.4 Moskovskaya obl. 20.2 19.8 -1.9 Permskaya obl. 23.6 28.1 19.4 Tyumenskaya obl. 25.0 24.6 -1.8 Amurskaya obl. 24.9 29.8 19.8 Omskaya obl. 20.6 20.3 -1.6 Yaroslavskaya obl. 18.9 22.7 19.9 Pskovskaya obl. 24.0 24.0 -0.1 Chechenskaya rep. 19.3 23.4 21.1 Sakha rep. 27.9 28.2 1.0 Ryazanskaya obl. 16.3 20.0 22.5 Komi rep. 27.2 27.5 1.2 Primorskii krai 21.7 26.8 23.2 Kamchatskaya obl. 26.6 26.9 1.3 Tomskaya obl. 19.7 24.3 23.2 St. Petersburg 18.8 19.1 1.4 Rep. of Kalmykiya 26.0 33.1 27.3 Kostromskaya obl. 21.9 22.3 1.9 Sakhalinskaya obl. 18.5 23.6 27.4 Kurganskaya obl. 24.4 24.9 1.9 Tambovskaya obl. 18.1 23.0 27.6 Udmurtskaya rep. 21.4 22.2 3.4 Rep. of Buryatia 21.3 27.4 28.8 Murmanskaya obl. 17.5 18.2 4.0 Orenburgskaya obl. 17.7 22.7 28.8 Ivanovskaya obl. 21.1 22.0 4.3 Chelyabinskaya obl. 18.9 24.7 30.5 Tverskaya obl. 19.5 20.5 4.8 Novgorodskaya obl. 23.2 30.3 30.7 Ulyanovskaya obl. 20.0 21.0 4.9 Chitinskaya obl. 25.0 32.9 31.5 Tuva rep. 33.1 34.8 5.1 Saratovskaya obl. 16.5 21.9 32.8 Belgorodskaya obl. 15.2 16.0 5.3 Krasnodarskii krai 16.0 21.2 32.9 Vologodskaya obl. 21.5 22.7 5.6 Krasnoyarskii krai 23.8 32.0 34.2 Kirovskaya obl. 18.9 20.0 5.7 Rostovskaya obl. 20.6 28.0 36.2 Arkhangelskaya obl. 24.5 26.0 5.9 Stavropolskii krai 15.3 21.6 41.5 Rep. of Tatarstan 19.0 20.1 6.3 Magadanskaya obl. 24.1 34.2 42.2 Kaluzhskaya obl. 19.9 21.2 6.5 Novosibirskaya obl. 19.5 27.9 42.6 Sverdlovskaya obl. 21.5 23.1 7.4 Altaiskii krai 18.8 28.2 49.6 Irkutskaya obl. 25.2 27.4 8.4 Khabarovskii krai 21.2 33.7 58.9 Smolenskaya obl. 20.3 22.0 8.4 Rep. of Mordovia 17.1 18.6 8.5 Kurskaya obl. 20.4 22.2 8.7 Lipetskaya obl. 19.8 21.6 9.3 Penzenskaya obl. 19.1 21.0 9.6 Rep. of Dagestan 41.4 45.5 9.9 Karelia rep. 23.5 25.8 10.0 Tulskaya obl. 21.1 23.2 10.2 Kaliningradskaya obl. 20.4 22.7 11.4

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Years Ukraine Russia Lithuania U.S. 58 60 65 70 75 80 85 89 55 60  65  70  75  Years Ukraine Russia Lithuania U.S. 58 60 65 70 75 80 85 89 65 70  75  80

Figure 4. Male life expectancy at birth, 1958 - 1989 Figure 5. Female life expectancy at birth, 1958 - 1989

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-50 -25  0  25  50  Men Women 0-4 05-09  10-14  15-19  20-24  25-29  30-34  34-39  40-44  45-49  50-54  55-59  60-64  65-69  -50  -25  0  25  50  Russia Ukraine 0-4 05-09  10-14  15-19  20-24  25-29  30-34  34-39  40-44  45-49  50-54  55-59  60-64  65-69

Figure 6. Percentage change in age-specific death rates, Russia, 1959 - 1970 Figure 7. Percentage change in male age-specific death rates, 1959 - 1970, Russia and Ukraine

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Table 5. Percentage change in male death rates age 40 - 44 by Russian region, 1971 - 1975

Death rate per 1000 % change Death rate per 1000 % change 1959 1970 1959 1970 Pskovskaya obl. 8.0 5.3 -34.0 Belgorodskaya obl. 5.3 7.6 42.7 Astrakhanskaya obl. 6.0 5.9 -3.1 Rep. of Dagestan 5.1 7.3 44.1 Magadanskaya obl. 11.2 11.5 3.0 Chechenskaya rep. 5.3 7.7 45.1 Penzenskaya obl. 7.6 8.0 4.6 Voronezhskaya obl. 5.2 7.6 45.4 Tuva rep. 9.8 10.8 10.9 Nizhegorodskaya obl. 5.7 8.4 47.4 Lipetskaya obl. 6.7 7.5 12.6 Ivanovskaya obl. 6.1 9.0 47.6 Murmanskaya obl. 7.9 8.9 12.8 Tulskaya obl. 6.0 8.9 48.2 St. Petersburg 5.7 6.5 12.8 Kirovskaya obl. 7.1 10.6 48.7 Rep. of Mordovia 7.5 8.6 14.3 Stavropolskii krai 4.6 6.9 48.8 Karelia rep. 6.8 8.0 16.9 Rep. of Buryatia 6.4 9.7 52.1 Saratovskaya obl. 6.1 7.2 17.4 Moskovskaya obl. 5.3 8.2 54.0 Novosibirskaya obl. 6.9 8.2 18.5 North Ossetia 4.1 6.4 55.1 Rep. of Tatarstan 6.6 8.0 21.5 Arkhangelskaya obl. 6.6 10.3 55.2 Novgorodskaya obl. 7.7 9.5 23.1 Ulyanovskaya obl. 5.2 8.1 55.4 Sakhalinskaya obl. 8.9 10.9 23.1 Vologodskaya obl. 6.7 10.8 60.6 Khabarovskii krai 6.9 8.5 23.2 Vladimirskaya obl. 5.4 8.8 64.0 Kurganskaya obl. 7.4 9.4 26.4 Bryanskaya obl. 4.5 7.5 68.4 Primorskii krai 6.4 8.4 31.2 Komi rep. 5.6 9.7 75.2 Orenburgskaya obl. 6.0 7.8 31.3 Orlovskaya obl. 4.8 9.4 94.1 Kemerovskaya obl. 6.7 8.8 31.7 Leningradskaya obl. 6.0 8.0 32.0 Tverskaya obl. 7.1 9.4 32.9 Kostromskaya obl. 7.3 9.7 33.0 Rostovskaya obl. 4.7 6.3 33.0 Rep. of Bashkortostan 6.3 8.4 33.9 Rep. of Kalmykiya 4.8 6.5 34.8 City of Moscow 4.9 6.7 37.4 Ryazanskaya obl. 6.0 8.3 38.3 Kaluzhskaya obl. 5.8 8.1 38.6 Permskaya obl. 7.4 10.3 39.1 Krasnoyarskii krai 7.3 10.2 39.5 Udmurtskaya rep. 8.6 12.0 40.4 Samarskaya obl. 5.8 8.2 41.3 Sverdlovskaya obl. 5.9 8.4 42.1 Yaroslavskaya obl. 5.8 8.3 42.7

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Table 6. Correlation between changes in age-specific death rates and other variables, 1959 - 1970

DV: % change in death rate by age group, men DV: % change in death rate by age group, women Age 30-34 Age 40-44 Age 30-34 Age 40-44 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) % Change in  average monthly wage .380 (.271)  [0.167]  -.125  (.305)  [0.686]  .333  (.286)  [0.250]  -.306  (.238)  [0.203]  -.467  (.323)  [0.162]  -.351  (.240)  [0.150]  -.441  (.187)  [0.022]  -.154  (.207)  [0.464]  -.391  (.190)  [0.045]  -.404  (.196)  [0.044]  .203  (.235)  [0.397]  -.420  (.176)  [0.021]  % Change in  urban pop. – .020 (.291)  [0.945]  .001  (.015)  [0.945]  – .159  (.309)  [0.611]  .018  (.012)  [0.150]  – -.250  (.197)  [0.218]  -.007  (.010)  [0.485]  – -.396  (.224)  [0.091]  .004  (.009)  [0.624]  % Change in  divorce rate – -.117 (.084)  [0.176]  .021  (.088)  [0.816]  – -.150  (.089)  [0.103]  -.130  (.074)  [0.086]  – -.012  (.057)  [0.836]  .086  (.059)  [0.150]  – .040  (.064)  [0.797]  -.075  (.054)  [0.176]  % Change in  per capita alcohol sales – .046 (.034)  [0.185]  – – .037  (.036)  [0.311]  – – -.040  (.023)  [0.095]  – – -.076  (.026)  [0.008]  –  % Change in  consumption of sugar per capita – – .167 (.363)  [.648]  – .500  (.305)  [0.108]  – – -.498  (.241)  [.044]  – .388  (.224)  [0.089]  Adj. R2 0.02 0.01 0.01 0.01 0.02 0.06 0.08 0.11 0.12 0.06 0.20 0.09 N 54 28 53 54 28 53 54 28 53 54 28 53 Note: Standard errors in parentheses; p-values in brackets.