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In an economic sense, urbanization is a process of transformation of rural economy to modern economy. It is measured by the increase in urban population to total population. In India, urbanization is increasing over the last 100 years. In 1911, urbanization in India was 10.29% which reached to 31.16% in 2011. In 2018, the urban population of India was 460.78 million or 34% of the total population. In the present world, economic growth of an economy is highly dependent on the growth of Information and Communication technology (ICT). The Indian Information Technology (IT) industry also has created an important place in the global IT market. The objective of this chapter is to search for a relationship between urbanization and development of the ICT sector in India. Secondary time series data of urbanization of India have been analyzed for census years from 1951 to 2011. The data on ICT have been taken for the period 2014–2015. The data have been collected from Internet and Mobile Association of India, Telecom Regulatory Authority of India, Cellular Operations Association of India, and District Information System of Education. For analyzing the development of ICT sector in India the variables taken are e-infrastructure, telephone density per 100 persons, mobile subscribers per 100 persons, mobile subscribers with Internet, schools with computers, and e-participation. Hypothetically, growth of urbanization is expected to develop the ICT sector. From the analysis it comes out that apart from some exceptions, the relatively economically developed and urbanized states of India are found to have a developed ICT sector. Whereas in relatively less urbanized states the development of ICT sectors are not up to the mark.

This paper presents the second-generation estimates for the Italian engineering industry in 1911, a year documented both by the customary demographic census, and the first industrial census. The first part of this paper uses the census data to estimate the industry’s value added, sector by sector; the second further disaggregates each sector by activity, and estimates the value added, employment, physical product, and metal consumption of each one. A third, concluding section dwells on the dependence of cross-section estimates on time-series evidence. Three appendices detail the specific algorithms that generate the present estimates; a fourth, a useful sample of firm-specific data.

In this paper, we use data from the US census to document the history of the relationship between fertility choice and key economic indicators at the individual level for women born between 1826 and 1960. We find that this data suggests several new facts that should be useful for researchers trying to model fertility. (1) The reduction in fertility known as the Demographic Transition (or the Fertility Transition) seems to be much sharper based on cohort fertility measures compared to usual measures like Total Fertility Rate; (2) The baby boom was not quite as large as is suggested by some previous work; (3) We find a strong negative relationship between income and fertility for all cohorts and estimate an overall income elasticity of about −0.38 for the period; (4) We also find systematic deviations from a time invariant, iso-elastic, relationship between income and fertility. The most interesting of these is an increase in the income elasticity of demand for children for the 1876–1880 to 1906–1910 birth cohorts. This implies an increased spread in fertility by income which was followed by a dramatic compression.

This paper seeks to provide researchers and librarians with an overview of the US Census Bureau's American Community Survey (ACS), with a specific focus on practical issues that users must face when choosing and using ACS datasets.

Each of the following issues are explored subsequent to a general overview of the ACS: choosing among census datasets from different census programs, interpreting and choosing between the different ACS period estimates, selecting census geography, understanding and recalculating margins of error, and accessing the data. Samples of ACS tables and formulas for creating derived estimates are used to illustrate how to interpret and work with the data.

The ACS datasets are fundamentally different from the decennial census as they are period estimates created from rolling sample surveys. The ACS has a steeper learning curve; this complexity is due in part to the number of choices users must make between datasets, but the primary challenge is learning how to understand and work with estimates as opposed to population counts.

While other papers have discussed the benefits and challenges of the ACS, this paper is structured around the practical issues that researchers must face when using it. Special consideration is given to calculating derived estimates using spreadsheet formulas, as this is a key task that many users will need to perform and spreadsheets are the most likely tool users will employ to manipulate the data.

The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems.

This study employs NHPP to model the failure data. Initially, Nelson's graph method is employed to estimate the mean number of repairs and the MCRF value for the repairable system. Second, the time series decomposition approach is employed to predict the mean number of repairs and MCRF values.

The proposed method can analyze and predict the reliability for repairable systems. It can analyze the combined effect of trend‐cycle components and the seasonal component of the failure data.

This study only adopts simulated data to verify the proposed method. Future research may use other real products' failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems.

Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.

The time series decomposition approach was used to model and analyze software aging and software failure in 2007. However, the time series decomposition approach was rarely used for modeling and analyzing the failure data for repairable systems. This study proposes the time series decomposition approach to analyze and predict the failure data of the repairable systems and the proposed method is better than the ARIMA model and neural networks in predictive accuracy.

Soon after beginning operations, the Federal Reserve established a nationwide network for collecting information about the economy. In 1919, the Fed began tabulating data by about retail sales, which it viewed as a fundamental measure of consumption. From 1920 until 1929, the Federal Reserve published data about retail sales each month by Federal Reserve district, but ceased to do so after 1929. It continued to compile monthly data on retail sales by reserve district, but this data remained in house. We collected these in-house reports from the archives of the Board of Governors and constructed a consistent series on retail trade at the district level. The new series enhances our understanding of economic trends during the Roaring ‘20s and Great Depression.

The purpose of this article is to contribute to our stock of knowledge about who uses networks, how they are used, and what contribution the networks make to advancing the scientific enterprise. Between 1985 and 1990, the Survey of Income and Program Participation (SIPP) ACCESS data facility at the University of Wisconsin‐Madison provided social scientists in the United States and elsewhere with access through the electronic networks to complex and dynamic statistical data; the 1984 SIPP is a longitudinal panel survey designed to examine economic well‐being in the United States. This article describes the conceptual framework and design of SIPP ACCESS; examines how network users communicated with the SIPP ACCESS project staff about the SIPP data; and evaluates one outcome derived from the communications, the improvement of the quality of the SIPP data. The direct and indirect benefits to social scientists of electronic networks are discussed. The author concludes with a series of policy recommendations that link the assessment of our inadequate knowledge base for evaluating how electronic networks advance the scientific enterprise and the SIPP ACCESS research network experience to the policy initiatives of the High Performance Computing Act of 1991 (P.L. 102–194) and the related extensive recommendations embodied in Grand Challenges 1993 High Performance Computing and Communications (The FY 1993 U.S. Research and Development Program).

The institution of an annual series devoted to current and ongoing research in economics and business should be considered one of the notable developments during the period under review. Long standing need for such a reference not withstanding, there has been until this year no systematic attempt to organize a continuing series which concentrated on selected areas of ongoing research, especially adapted to the Jahrbucher format. By facilitating the publication of research papers which are longer than the conventional journal‐length article yet shorter than a monograph, publishing outlets available to scholars in the field have been infinitely expanded. Two years ago, the Royal Economic Society and the Social Science Research Council of Great Britain, developed an experimental series, published by Macmillan, entitled Surveys of Applied Economics. The JAI Press, Greenwich, Conn., has now come out with an annual series, which is expected to fill the gaps in at least seventeen areas of economic theory and business. These are briefly listed below, with pertinent bibliographical citations: Research in Economic Anthropology: An Annual Compilation of Research. Series editor, George Dalton. vol. 1. Sept. 1977‐ $22.00 ISBN 0‐89232‐040‐9; Research in Economic History: An Annual Compilation of Research. Series editor, Paul Uselding. vol. 1. Sept. 1976‐ $22.50 ISBN 0‐89232‐001‐X; Research in Health Economics: An Annual Compilation of Research. Series editor, Richard M. Scheffler. vol. 1. Sept. 1977‐ $22.50 ISBN 0‐89232‐042‐7; Research in Human Capital and Development: An Annual Compilation of Research. Series editor, Ismail Sirageldin. vol. 1. June/July 1977‐ $22.50 ISBN 0‐89232‐019‐2; Research in International Business and Finance: An Annual Compilation of Research. Series editor, Robert G. Hawkins. vol. 1. May/June 1977‐ $23.50 ISBN 0‐89232‐031‐1; Research in Labor Economics: An Annual Compilation of Research. Series editor, Ronald G. Ehrenberg. vol. 1. March 1977‐ $22.50 ISBN 0‐89232‐017‐6; Research in Law and Economics: An Annual Compilation of Research. Series editor, Richard O. Zerbe. vol. 1. Sept. 1977‐ $22.50 ISBN 0‐89232‐028‐1; Research in Marketing: An Annual Compilation in Research. Series editor, Jagdish N. Sheth. vol. 1. June 1977‐ $22.50 ISBN 0‐89232‐041‐9; Research in Philosophy and Technology: An Annual Compilation of Research. Series editor, Paul T. Durbin. vol. 1. March 1977‐ $22.50 ISBN 0‐89232‐022‐2; Research in Political Economy: An Annual Compilation of Research. Series editor, Paul Zarembka. vol. 1. Sept. 1977‐ $22.50 ISBN 0‐89232‐020‐6; Research in Population Economics: An Annual Compilation of Research. Series editor, Julian L. Simon. vol. 1. April 1977‐ $22.50 ISBN 0‐89232‐018‐4; Applications of Management Science. Series editor, Matthew J. Sobel. vol. 1. 1977‐ $22.50. ISBN 0‐89232‐023‐0; Research in Econometrics. Series editor, Dennis J. Aigner. vol. 1. 1977‐ $22.50 ISBN 0‐89232‐039‐7; Research in Experimental Economics. Series editor, Vernon L. Smith. vol. 1. 1977‐ $22.50 ISBN 0‐89232‐030‐3; Research in Finance. Series editor, Haim Levy. vol. 1. 1977‐ $22.50 ISBN 0‐89232‐043‐5; Research in Organizational Behavior. Series editor, Barry Staw. vol. 1. 1977‐ $22.50 ISBN 0‐89232‐045‐1; Research in Public Policy and Management. Series editor, Colin Blaydon. vol. 1. 1977‐ $22.50 ISBN 0‐89232‐044‐3.

Wages have been spreading out across workers over time – or in other words, the 90th/50th wage ratio has risen over time. A key question is, has the productivity distribution also spread out across worker skill levels over time? Using our calculations of productivity by skill level for the United States, we show that the distributions of both wages and productivity have spread out over time, as the right tail lengthens for both. We add Organization for Economic Co-Operation and Development (OECD) countries, showing that the wage–productivity correlation exists, such that gains in aggregate productivity, or GDP per person, have resulted in higher wages for workers at the top and bottom of the wage distribution. However, across countries, those workers in the upper-income ranks have seen their wages rise the most over time. The most likely international factor explaining these wage increases is the skill-biased technological change of the digital revolution. The new artificial intelligence (AI) revolution that has just begun seems to be having similar skill-biased effects on wages. But this current AI, called “supervised learning,” is relatively similar to past technological change. The AI of the distant future will be “unsupervised learning,” and it could eventually have an effect on the jobs of the most highly skilled.

Means, medians and SD for available socio‐economic status (SES) black‐white differences are here substituted for those of IQ in a between‐groups model published by the author over a decade ago. The goodness of fit of the SES variables used is compared with that for the earlier IQ data. Even when SES variables are relatively successful this can be viewed as additional evidence of the importance of IQ differences to black‐white differences in delinquency.