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Certain discrepancies in datasources for estimating the household consumption expenditure to derive Gross Domestic Product (GDP) of India are discussed in this paper. Simple implementable strategies are suggested toimprove the estimation of GDP.

Industry was the most important economic sector in the GDR. Of all the countries within the Eastern Bloc (Comecon), only the USSR achieved higher added value per capita than the GDR's industrial sector. The quantitative description of industrial output in the GDR nevertheless continues to be characterized by significant data gaps and a lack of comparable, long-term time series for important performance and expenditure values calculated in accordance with contemporary statistical standards. This HSR Focus presents new calculations for added value, employment and capital expenditures which close these gaps at both an overall industrial level and branch level between 1950 and 1989. The calculations take the form of backward projections carried out in accordance with the current conceptual and methodological principles of national accounting (ESA95). The incorporation of current data for the new German federal states from 1991 onwards into the data base facilitates the extension of the time horizon for the time series. This yields a comparable reflection of the development of economic indicators for the industrial sector in Eastern Germany over a 50-year period (1950-2000). The time series determined pave the way for a new, fact-based assessment of the real results achieved by the GDR economy. The presentation of the data assessed is accompanied by a thorough description of the methods and sources used. This paper serves as an outline for the entire HSR Focus.

This book analyzes the objectives and content of the World Bank's assistance program during the period 1994-2003, the economic and social development outcomes in Pakistan, and the contributions of the Bank to development outcomes.

This work was supported by The Nature Conservancy (http://www.nature.org/ourinitiatives/regions/northamerica/unitedstates/california/) to MAM, and a Marie Curie International Incoming Fellowship to EB.

Forecast reconciliation is the post-forecasting process aimed to revise a set of incoherent base forecasts into coherent forecasts in line with given data structures. Most of the point and probabilistic regression-based forecast reconciliation results ground on the so called “structural representation” and on the related unconstrained generalized least squares reconciliation formula. However, the structural representation naturally applies to genuine hierarchical/grouped time series, where the top- and bottom-level variables are uniquely identified. When a general linearly constrained multiple time series is considered, the forecast reconciliation is naturally expressed according to a projection approach. While it is well known that the classic structural reconciliation formula is equivalent to its projection approach counterpart, so far it is not completely understood if and how a structural-like reconciliation formula may be derived for a general linearly constrained multiple time series. Such an expression would permit to extend reconciliation definitions, theorems and results in a straightforward manner. In this paper, we show that for general linearly constrained multiple time series it is possible to express the reconciliation formula according to a “structural-like” approach that keeps distinct free and constrained, instead of bottom and upper (aggregated), variables, establish the probabilistic forecast reconciliation framework, and apply these findings to obtain fully reconciled point and probabilistic forecasts for the aggregates of the Australian GDP from income and expenditure sides, and for the European Area GDP disaggregated by income, expenditure and output sides and by 19 countries.

We study the behavior of the U.S. labor share over the past 90 years. We find that the observed decline of the labor share is entirely explained by the capitalization of intellectual property products in the national income and product accounts.

The International Comparison Program (ICP) is a worldwide statistical initiative led by the World Bank under the auspices of the United Nations Statistical Commission. It produces comparable price and volume measures of gross domestic product (GDP) and its expenditure aggregates across economies. Through a partnership with international, regional, sub-regional and national agencies, the ICP collects price data and GDP expenditures to estimate purchasing power parities (PPPs) for the world's economies. The report provides ICP results for the benchmark year 2017 and revised results for earlier years. ICP data are used for socio-economic analyses by researchers, academics, policy makers at the national and international levels, and by organizations such as the European Union, the International Monetary Fund, the Organization for Economic Co-operation and Development, the United Nations, and the World Bank. Notably, PPPs and ICP data are used in indicators monitoring progress towards eight goals of the United Nations' 2030 Agenda for Sustainable Development, the World Bank's international poverty lines, and the construction of the Human Development Index by the United Nations, among others. The use of PPPs continues to grow and the ICP website (icp.worldbank.org) lists many applications of the data by the development community, academia, media and others.

This paper proposes an accounting framework that breaks up a country's gross exports into various value-added components by source and additional double-counted terms. Our parsimonious framework bridges a gap between official trade statistics (in gross value terms) and national accounts (in value-added terms), and integrates all previous measures of vertical specialization and value-added trade in the literature into a unified framework. To illustrate the potential of such a method, we present a number of applications including re-computing revealed comparative advantages and the magnifying impact of multi-stage production on trade costs.

The paper analyses the sources of income measurement error in surveys with a unique data set. We use the Austrian 2008–2011 waves of the European Union ‘Statistics on income and living conditions’ survey which provide individual information on wages, pensions and unemployment benefits from survey interviews and officially linked administrative records. Thus, we do not have to fall back on complex two-sample matching procedures like related studies. We empirically investigate four sources of measurement error, namely social desirability, sociodemographic characteristics of the respondent, the survey design and the presence of learning effects. We find strong evidence for a social desirability bias in income reporting, whereas the presence of learning effects is mixed and depends on the type of income under consideration. An Owen value decomposition reveals that social desirability is a major explanation of misreporting in wages and pensions, whereas sociodemographic characteristics are most relevant for mismatches in unemployment benefits.