Cost Of Technology Adoption Research Articles

Determinants of the adoption of Information and Communication Technologies (ICT): An empirical analysis based on firm-level data for the Swiss business sector

The paper aims at explaining empirically timing and intensity of a firm’s adoption of Information and Communication Technologies (ICT) using a large sample of firms. The analysis is based on the rank and the epidemic model of technology adoption. The explanatory variables include many dimensions of anticipated benefits from and costs of technology adoption, what allows to capture the impact of uncertainty and adjustment costs. The analysis yields results pertaining to the timing and the intensity of ICT adoption (inter- and intra-firm diffusion). Notwithstanding some interesting differences, we find quite a robust pattern of explanation across the adoption variables used. An extended version of the approach explores the role of “New Workplace Organisation” (NWO) as a determinant of the adoption of ICT, as well as the reverse relationship, i.e. the impact of ICT on the adoption of NWO.

Replacement, adoption and economic dynamics: lessons from a canonical creative destruction model

In this paper, we provide a first inspection into how structural technology adoption costs affect economic fluctuations. To this end, we choose a simple extension of a canonical creative destruction model. We analytically characterize the optimal replacement–adoption policies, and study numerically the induced dynamics. Our model predicts that the countries supporting the highest adoption costs are those which display the longest and sharpest business and employment fluctuations, and the lowest convergence speeds to their steady state equilibria. Moreover, as the position of the workers in the labor market weakens, the fluctuations are shown to get even sharper.

IT adoption costs and productivity:: A reply to Diego Comin

Diego Comin (2002, Review of Economic Dynamics 5 (2)), critiques my paper “Technology adoption costs and productivity growth: The transition to information technology” (2002, Review of Economic Dynamics 5 (2)), concluding that all of my major results are unfounded. I contend, to the contrary, that my results hold up against his criticism. My paper analyzes the effect of investment adjustment costs on subsequent productivity growth for manufacturing industries. I find that adjustment costs increased substantially during the late 70's and early 80's and that this increase is associated with increased investment in information technology. I also estimate the effect of these adjustment costs on BLS productivity growth estimates. This analysis uses two datasets, the NBER–CES Manufacturing Industry Database (Bartelsman and Gray, 1996. NBER Working Paper 205) and the BLS Manufacturing Productivity release of April 2001. For the former I calculate productivity growth using a standard Solow residual; for the latter, I use BLS productivity growth estimates. Comin's critique, however, uses a different measure entirely. He introduces a “corrected TFP” measure that I believe is flawed. Most of his critique uses this measure, rather than the standard productivity growth measures I use. As a consequence, most of his criticism is not actually about my paper, but is, instead, about a paper Comin might have written using his measure. And because this measure is flawed, so is this alternative. In addition, he raises criticism of my evidence of a link between information technology (IT) and adjustment costs and of my corrections to BLS productivity estimates. I present additional evidence to address these concerns and to examine the robustness of my results.

Technology Adoption Costs and Productivity Growth: The Transition to Information Technology

Using two panels of U.S. manufacturing industries, this paper estimates capital adjustment costs from 1961 to 1996. I find that from 1974–1983 adjustment costs rose sharply—they more than doubled from about 3% of output to around 7%. Moreover, this increase is specifically associated with a shift to investment in information technology. But such large adoption costs imply that the Solow residual mismeasures productivity growth: Adoption costs are resource costs representing an unmeasured investment. I find that when this investment is included, productivity grew about 0.4% per annum faster than official measures during the 1970's and early 1980's, reducing the size of the productivity “slowdown.” Indeed, estimated productivity growth rates were roughly the same from 1974–1988 as from 1949–1973. Thus technology transitions critically affect productivity growth measurement. Journal of Economic Literature Classification Numbers: O30, O47, E22.

Comments on James Bessen's “Technology Adoption Costs and Productivity Growth: The 70's as a Technological Transition”

Comments on James Bessen's “Technology Adoption Costs and Productivity Growth: The 70's as a Technological Transition”

Technology Choice and Saving in the Presence of a Fixed Adoption Cost

This paper explores the relationship between technology choice and saving in the presence of fixed costs of technology adoption. While richer agents adopt the more productive technology immediately it is available, poorer agents optimally choose to wait before switching to the better technology. In the interim, they save more than others and more than in the absence of the prospect of switching to the new technology. The paper thus provides an explanation for the phenomenon that the saving rate and the growth rate of output increase over time in the transition.

The Determinants Of The Adoption Of Advanced Manufacturing Technology

The paper investigates empirically the decision of firms to adopt ‘Advanced Manufacturing Technologies’ (AMT) based on a comprehensive specification of a ‘rank model’ of technology adoption using firm-level data for Swiss manufacturing. The explanatory variables include numerous dimensions of (anticipated) benefits from and costs of technology adoption allowing for uncertainty as well as for information and adjustment costs. Moreover, the effect of complementarities between various functional groups of AMT (design, fabrication, communication, etc.) as well as of learning from the use of previous technology vintages within such functional groups is analyzed, Finally, the size-dependence of the adoption decision is studied in detail. The model yields a quite robust pattern of explanation across estimates with different adoption variables (time period of introduction of AMT, intensity of use of AMT, etc.) with plausible differences of the results based on the alternative adoption measures used.

Schooling and Distortions in a Vintage Capital Model

This paper integrates the analysis of choices on education and on technology adoption to study international economic disparities. Two candidate explanations are considered: differences in distortions that affect the cost of technology adoption and differences in the effectiveness of schools. The implications of these two factors for differences in output per capita, educational attainment, and the age of technologies across countries are assessed in a vintage capital model with technology-specific learning-by-doing. Predictions are obtained for a parameterized economy that matches U.S. aggregate observations and evidence on learning. Differences in investment distortions produce plausible correlations only if the major role of education is to improve the ability to learn technologies. On the other hand, differences in school effectiveness produce plausible results only if the role of education is to provide a productive ability that is independent of learning. Journal of Economic Literature Classification Numbers: I2, O1, O3, O4.

An efficient cost-sharing program to reduce nonpoint-source contamination: theory and an application to groundwater contamination

This research evaluates the economics of cost-sharing improved irrigation technologies to reduce agricultural, nonpoint-source contamination. Irrigation and fertilization inefficiencies are modeled within a nonjoint production process to evaluate both private and public costs of technology adoption and its effect on groundwater nitrate-contamination levels. A central Nebraska application indicates that even without a current government subsidy, a farmer is economically better off switching from gravity-flow to surge-flow irrigation rather than a center-pivot system. An annual government subsidy of $22.50 (US$) per hectare per year is required over the life of a center-pivot system to make the farmer financially indifferent. However, cost-sharing center-pivot adoption improves the groundwater contamination level, while other irrigation systems result in continued deterioration of groundwater quality.

Firm Size And Capabilities, Regional Agglomeration, And The Adoption Of New Technology

The literature on agglomeration economies suggests that, in addition to firm-specific attributes, the local geographic context conditions the expected profitability of technology adoption. All rheories of technology diffusion assumc that inter-firm learning is the outcome of contact with prior adopters. Yet, with few exceptions, thc attributes of location that maximize the opportunities for learning (and hence, reduce thc costs of technology adoption for all firms in the same locale) have been given only cursory treatment. In this paper, we develop and test a model in which both firm-specific capabilities and placc-specific cxtcrnal economies affect the firm's decision to adopt a new technology. Our data colile from two national surveys conducted in 1987 and 1991. Because we have information on two different time periods, we are able to specify firm and place-specific conditions that precede the technology adoption decision. We find that localization (as measured by regional clustering of enterprises in related industries) and urbanizalion (as mcasured by the diversity of industries, and by the concentration of degree granting engineering institutions) provide knowledge spillovers that facilitate the adoption of new tcchnology by local establishments. Moreover, the impact of urbanization econonlies is size-related: The impact of a diverse region on adoption is evcn greater for small enterprises than for large ones.

Differential human capital and structural evolution in agriculture

The growth of market capitalism and the technological advances of the last two centuries underlie the relentless process of structural change in agriculture. Substantial occupational migration out of farming and geographical migration from rural to urban areas is a characteristic of most, if not all, economies in the 20th century. The process of rural-urban migration, and the resulting urban problems have received considerable attention. The fate of the residual farm population has received less scrutiny and is less well understood. In the United States we observe considerable farm poverty, increasing farm-size inequality, and a disturbing decay of rural communities. For the last half century, the problem of low farm incomes has been perceived as a farm problem amenable to solutio by appropriate farm policy. In this paper, a model is proposed which suggests that farm poverty and inequality are inevitable consequences of a rural-urban migration process in which two types of human capital play a fundamental role. General human capital determines a person's expected nonfarm income, while farming-specific human capital determines his/her productivity in farming. It is argued that inadequate human capital combined with positive rural-urban migration cost, positive farm technology adoption costs, and inelastic demand for farm output, can explain persistently high levels of farm poverty, and increasing farm size inequality. The model suggests that traditional farm policies are inappropriate. Programs that directly address the problems of inadequate human capital are advocated.

Differential human capital and structural evolution in agriculture

AbstractThe growth of market capitalism and the technological advances of the last two centuries underlie the relentless process of structural change in agriculture. Substantial occupational migration out of farming and geographical migration from rural to urban areas is a characteristic of most, if not all, economies in the 20th century. The process of rural‐urban migration, and the resulting urban problems have received considerable attention. The fate of the residual farm population has received less scrutiny and is less well understood. In the United States we observe considerable farm poverty, increasing farm‐size inequality, and a disturbing decay of rural communities. For the last half century, the problem of low farm incomes has been perceived as a farm problem amenable to solution by appropriate farm policy. In this paper, a model is proposed which suggests that farm poverty and inequality are inevitable consequences of a rural‐urban migration process in which two types of human capital play a fundamental role. General human capital determines a person's expected nonfarm income, while farming‐specific human capital determines his/her productivity in farming. It is argued that inadequate human capital combined with positive rural‐urban migration cost, positive farm technology adoption costs, and inelastic demand for farm output, can explain persistently high levels of farm poverty, and increasing farm size inequality. The model suggests that traditional farm policies are inappropriate. Programs that directly address the problems of inadequate human capital are advocated.

Bringing advanced technology to rural America: The cost of technology adoption

This study examines the factors that are working for and against the development of an advanced telecommunications infrastructure to serve rural areas of the USA. There is a notable difference between the costs of local network upgrades for existing rural telephone subscribers and of bringing service to new and physically remote subscribers. The latter group should be treated separately for policy purposes. The infrastructure serving the existing subscriber base could be upgraded to a digital network without necessitating large rate increases, provided there is sharing of network facilities between the energy, transportation and telecommunication sectors. The preferred medium is likely to be digital fibre.

On the Joint Decisions of R&D and Technology Adoption

This paper attempts to bridge the gap between R&D models that do not consider technology adoption decisions and technology adoption models that do not consider R&D decisions. It develops and analyzes a decision-theoretic model on the joint decisions of R&D and technology adoption of a firm. Two interesting comparative statics results are obtained. First, an increase in the cost of R&D reduces the incentive for a firm to do R&D. Secondly, an increase in the cost of technology adoption or an increase in the market interest rate reduces the incentive for a firm to adopt new technology and also reduces the incentive for a firm to do R&D. Under certain conditions, an increase in the cost of R&D increases the incentive for a firm to adopt new technology.

Total Productivity and the Quality of Capital

Previous articleNext article No AccessTotal Productivity and the Quality of CapitalEvsey D. DomarEvsey D. Domar Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Journal of Political Economy Volume 71, Number 6Dec., 1963 Article DOIhttps://doi.org/10.1086/258818 Views: 4Total views on this site Citations: 12Citations are reported from Crossref Copyright 1963 The University of ChicagoPDF download Crossref reports the following articles citing this article:Dulani Jayasuriya Daluwathumullagamge A Tale of Two Returns: Stock and Investment Returns with Investment Specific Technology Shocks, SSRN Electronic Journal (Jan 2019).https://doi.org/10.2139/ssrn.3352916Amit Sharma, Victor Eduardo Da Motta, Jeong-Gil Choi, Naomi S. Altman Economic production in hospitality and tourism industry, International Journal of Contemporary Hospitality Management 28, no.55 (May 2016): 1026–1050.https://doi.org/10.1108/IJCHM-07-2014-0316Neal Solomon Policy Solutions to the Productivity Growth Crisis, SSRN Electronic Journal (Jan 2016).https://doi.org/10.2139/ssrn.2867027Jeff Luckstead, Seung Mo Choi, Stephen Devadoss, Ron C. Mittelhammer China’s catch-up to the US economy: decomposing TFP through investment-specific technology and human capital, Applied Economics 46, no.3232 (Aug 2014): 3995–4007.https://doi.org/10.1080/00036846.2014.948677Boyan Jovanovic, Yuri Yatsenko Investment in vintage capital, Journal of Economic Theory 147, no.22 (Mar 2012): 551–569.https://doi.org/10.1016/j.jet.2010.10.017Boyan Jovanovic When should firms invest in old capital?, International Journal of Economic Theory 5, no.11 (Mar 2009): 107–123.https://doi.org/10.1111/j.1742-7363.2008.00096.xNicholas Oulton Investment-specific technological change and growth accounting, Journal of Monetary Economics 54, no.44 (May 2007): 1290–1299.https://doi.org/10.1016/j.jmoneco.2006.02.004Jeremy Greenwood, Per Krusell Growth accounting with investment-specific technological progress: A discussion of two approaches, Journal of Monetary Economics 54, no.44 (May 2007): 1300–1310.https://doi.org/10.1016/j.jmoneco.2006.02.008Nicholas Oulton Investment-Specific Technological Change and Growth Accounting, SSRN Electronic Journal (Jan 2004).https://doi.org/10.2139/ssrn.598885James Bessen Technology Adoption Costs and Productivity Growth: The Transition to Information Technology, Review of Economic Dynamics 5, no.22 (Apr 2002): 443–469.https://doi.org/10.1006/redy.2001.0152Sumit K. Majumdar Modularity and productivity, Technological Forecasting and Social Change 56, no.11 (Sep 1997): 61–75.https://doi.org/10.1016/S0040-1625(97)00023-1Charles Kennedy, A. P. Thirlwall Technical Progress, (Jan 1973): 115–176.https://doi.org/10.1007/978-1-349-01860-4_3