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This paper argues that long-run trends in R&D and TFP are more supportive of fully endogenous \"Schumpeterian\" growth theory than they are of endogenous growth theory. The distinctive prediction of semi-endogenous theory that sustained TFP growth requires sustained growth of R&D input is not supported by co-integration tests and forecasting exercises, as TFP growth has been stationary even though the growth rate of R&D input has fallen three-fold since the early 1950s. In contrast, the prediction of Schum- peterian theory that sustained TFP growth requires a sustained fraction of GDP to be spent on R&D is not contradicted by similar tests.

U.S. data reveal three facts: (1) the share of goods in total expenditure declines at a constant rate over time, (2) the price of goods relative to services declines at a constant rate over time, and (3) poor households spend a larger fraction of their budget on goods than do rich households. I provide a macroeconomic model with non-Gorman preferences that rationalizes these facts, along with the aggregate Kaldor facts. The model is parsimonious and admits an analytical solution. Its functional form allows a decomposition of U.S. structural change into an income and substitution effect. Estimates from micro data show each of these effects to be of roughly equal importance.

How does globalization affect economic growth? We discuss mechanisms that link international integration to the incentives for knowledge accumulation and the efficacy of that process. First, integration facilitates the flow of knowledge across national borders. Second, integration affords innovators a larger potential market even as it subjects them to additional competition from foreign rivals. Third, integration encourages specialization according to comparative advantage. Finally, integration affects the incentives for technological diffusion. Taken together, the literature offers many theoretical insights. Some progress has also been made on the empirical side, although data and methodological impediments have left assessment and measurement lagging behind.

The study aims to investigate and report on the growth of Open-Access (OA) social science scholarly publications in India from 2013 to 2022 by applying various growth models. During the study period, 13,378 records were extracted from the scopus database, and a dataset was created to enhance the effectiveness of statistical analysis using the “R” Open-Source software. It is found that the Exponential Growth Model, with a peak R-squared value of 0.9749, highlights a substantial Annual Growth Rate (AGR), reaching its highest in 2020 (94.43 %) and lowest in 2021 (7.14 %). The Mean Relative Growth Rate (0.42) signifies the average growth pace, and doubling time (average 1.81) indicates a notably rapid doubling rate in OA social science publications. India ranks 18th in open-access social science publications with 13,378 papers (1.31 %), reflecting a modest contribution compared to China (5.12 %) and Germany (3.78 %). A cross-disciplinary analysis highlights significant disparities, with medicine leading at 115,228 publications (17.67 %), while social sciences (2.05 %) and humanities contribute comparatively less (0.39 %). This emphasises the urgent need for substantial support from Indian social scientists to augment the OA movement in the field. Notably, 2021 witnessed a rise to 2.92 citations annually, contrasting sharply with 2022’s decline to 1.65, revealing dynamic trends in influence within Indian open-access social science papers.

Technological improvement is the most important cause of long-term economic growth. In standard growth models, technology is treated in the aggregate, but an economy can also be viewed as a network in which producers buy goods, convert them to new goods, and sell the production to households or other producers. We develop predictions for how this network amplifies the effects of technological improvements as they propagate along chains of production, showing that longer production chains for an industry bias it toward faster price reduction and that longer production chains for a country bias it toward faster growth. These predictions are in good agreement with data from the World Input Output Database and improve with the passage of time. The results show that production chains play a major role in shaping the long-term evolution of prices, output growth, and structural change.

The evidence for the United States points to balanced growth despite falling investment-good prices and a less-than-unitary elasticity of substitution between capital and labor. This is inconsistent with the Uzawa Growth Theorem. We extend Uzawïs theorem to show that the introduction of human capital accumulation in the standard way does not resolve the puzzle. However, balanced growth is possible if education is endogenous and capital is more complementary with schooling than with raw labor. We present a class of aggregate production functions for which a neoclassical growth model with capital-augmenting technological progress and endogenous schooling converges to a balanced growth path.

This article investigates the strength of empirical evidence for various growth theories when there is model uncertainty with respect to the correct growth model. Using model averaging methods, we find little evidence that so-called fundamental growth theories play an important role in explaining aggregate growth. In contrast, we find strong evidence for macroeconomic policy effects and a role for unexplained regional heterogeneity, as well as some evidence of parameter heterogeneity in the aggregate production function. We conclude that the ability of cross-country growth regressions to adjudicate the relative importance of alternative growth theories is limited.

By operationalizing the notion of creative destruction, Schumpeterian growth theory generates distinctive predictions on important microeconomic aspects of the growth process (competition, firm dynamics, firm size distribution, cross-firm and cross-sector reallocation) which can be confronted using rich micro data. In this process the theory helps reconcile growth with industrial organization and development economics.

This paper describes a growth model with the property that human capital accumulation can account for all observed growth. The model is shown to be consistent with evidence on individual productivities as measured by census earnings data. The central hypothesis is that we learn more when we interact with more productive people.

This paper proposed a new computing method to quantitatively and non-destructively determine the corrosion of steel strands by analyzing the self-magnetic flux leakage (SMFL) signals from them. The magnetic dipole model and three growth models (Logistic model, Exponential model, and Linear model) were proposed to theoretically analyze the characteristic value of SMFL. Then, the experimental study on the corrosion detection by the magnetic sensor was carried out. The setup of the magnetic scanning device and signal collection method were also introduced. The results show that the Logistic Growth model is verified as the optimal model for calculating the magnetic field with good fitting effects. Combined with the experimental data analysis, the amplitudes of the calculated values (BxL(x,z) curves) agree with the measured values in general. This method provides significant application prospects for the evaluation of the corrosion and the residual bearing capacity of steel strand.