Has technology allowed your company to produce more goods or provide a good software program to control cost and show market trends.". Keywords: Technological change, productivity, adjustment cost, information . But the relationship between adoption costs and IT spending has likely changed . Read chapter 3 Effects of Information Technology on Productivity, J. Bessen, , Learning By Doing: The Real Connection Between Innovation, have a value 10 times greater than the direct costs of computer hardware.
Hitt,Beyond computation: Information technology, organizational transformation and business performance, Journal of Economic Perspectives 14 4: Brynjolfsson,Intangible assets: Page 55 Share Cite Suggested Citation: Information Technology and the U. The National Academies Press. In each case, the role of technology is considered, recent changes are summarized, and some potential future developments are considered, building on the discussion in Chapter 2 of current and possible future trends in underlying technologies.
The committee is keenly aware that making forecasts about social phenomena is perilous. Doing so with respect to the fast-changing and dynamic area of technology is even more challenging. Nevertheless, interpreting societal and economic responses to developments in technology can at least provide a framework for thinking about the future.
In turn, productivity growth comes from new technologies and new techniques of production and distribution. On the influence of vested interests on blocking of technology, see J. Mokyr,The Levers of Riches: For the influence on macro institutions on technology, see D. A Robinson,Why Nations Fail: Solow,A contribution to the theory of economic growth, Quarterly Journal of Economics 70 1: Stiroh,Projecting productivity growth: Lessons from the U.
Hitt,Computers as a factor of production: The role of differences among firms, Economics of Innovation and New Technology 3: Computers and organizational capital, Brookings Papers on Economic Activity 1: Page 56 Share Cite Suggested Citation: The remainder of this section discusses open issues and questions as well as possible pathways for resolving them. One hypothesis is that there is an increasing measurement problem in the official statistics on productivity.
This has been a longstanding research challenge, recognized at least since Solow 5 and Griliches. Unlike counting bushels of wheat or tons of steel, outputs for medical treatment or bank loans are less standardized. Output and productivity measurement require measuring output and input price deflators that reflect changes in quality, which is an enormous challenge.
How does one compare a smartphone today with a mainframe from 20 years ago, let alone new apps that have no predecessors?
Great progress was made in the s and s on improving price deflators for the hardware parts of IT, 8 but the software side has been more challenging.
Recent evidence suggests that adoption of cloud computing and other changes are even making it more difficult to assess quality changes for hardware. Productivity is based on gross domestic product GDPwhich is in turn a measure of production or output. However, technological progress can increase welfare without increasing output. For instance, if Wikipedia replaces a paper encyclopedia or a free GPS mapping app replaces a stand-alone GPS device, then consumers can be better off even if output is stagnant or declining.
While these measurement issues remain an active area of study, the most recent research suggests that at most only a small fraction of the productivity slowdown can be attributed to measurement problems. Brynjolfsson and Hitt found evidence that the productivity benefits of large enterprise systems took up to 7 years to be fully realized, as significant organizational and process changes were typically required to make full use of accompanying software and hardware investments.
Building on work by Paul David, Syverson discussed the slowdown in productivity growth in the historical context of electrification of the production process at the end of the 19th century. The first wave arrived quickly and reflected the adoption of electrification within the existing organization of production. The second wave, delayed by a few decades, reflected new ways of organizing production around this new technology.
Similarly, while the first power looms allowed weavers to produce 2. Reinsdorf,Does the United States have a productivity slowdown or a measurement problem? Hitt,Computing productivity: Firm-level evidence, Review of Economics and Statistics Hitt,IT, workplace organization and the demand for skilled labor: A firm-level analysis, Quarterly Journal of Economics 1: David,The dynamo and the computer: An historical perspective on the modern productivity paradox, American Economic Review 80 2: Bessen,Learning By Doing: Page 58 Share Cite Suggested Citation: This perspective may help reconcile the observation of the apparently rapid changes in technology outlined in Chapter 2 with the current sluggish growth in productivity.
Yet there are more pessimistic views about the prospects for productivity and economic growth. Some have suggested that recent post innovations in information and other advanced technology simply do not have the same high payoff as innovations in earlier periods. The argument is that earlier innovations were in the form of general purpose technologies that had wide application to many industries. Firm-level evidence for the United States and the Organisation for Economic Co-operation and Development shows a widening gap between the most and least productive firms within industries in the post period.
Firm-level evidence, Review of Economics and Statistics 85 4: Causes, Consequences, and Policies, September Page 59 Share Cite Suggested Citation: The latter has been shown to be an important part of the process of productivity growth, and is discussed further in Chapter 4. From this perspective, the hypothesis is that while the changes in technology outlined in Chapter 2 are indeed occurring, they are slow to show up in economic growth due to slowing diffusion or business dynamism.
All of these hypotheses are active areas of research. The discussion of future research directions in Chapter 6 emphasizes the significance of exploring such critically important questions. It is useful to note that future productivity growth cannot be predicted simply by extrapolating past trends because there is little serial correlation in growth rates from one decade to the next.
Instead, future trends will depend on the invention and deployment of new and improved technologies and on the co-inventions by the workforce, organizations, and institutions needed to effectively use them. For instance, by earlythe unemployment rate fell below 5 percent. However, much of this employment growth can be interpreted as a recovery from the Great Recession, which has been slow despite the fact that it officially ended in Furthermore, jobs lost in the recession are very different from those that appeared during the recovery.
Center on Education and the Workforce, https: Page 60 Share Cite Suggested Citation: Courtesy of Pascual Restrepo.
It began to decline in the post period, with a sharp drop during the Great Recession, from which it has recovered slightly.
Some of this trend can be accounted for by the aging of the population. However, declines in the employment rate are especially large for young and less educated individuals. Future Prospects for Technology and Employment Predictions that new technologies will make workers largely or almost entirely redundant are as old as technological change itself. Although the story might be apocryphal, the famous Roman historian Pliny the Elder recounts how the Roman Emperor Tiberius killed an inventor who had supposedly invented unbreakable glass for fear of what this would do to the glassmaking trade.
However, there is controversy about how medical technology escalates costs Weisbrod, The outcome-based patent measures, as marketed inventions, are appropriate for modeling healthcare expenditure rise. Furthermore, besides the residual component approach, the econometric estimation techniques used when capturing the impacts of medical technology on healthcare expenditures do matter. The underlying assumption for these methods is the clinical efficacy of medical technologies with almost no investigation on the negative aspects of medical technology.
There is a complementary strand of literature touting the most important factors driving national healthcare costs, including inefficient use of advanced medical technologies, payment mechanisms with perverse incentives, medical liability and the practice of defensive medicine, and so on. Healthcare Productivity The service sector in advanced economies makes up in excess of three quarters of their economic activities.
Using traditional measurement approaches, healthcare productivity specifies output as expenditure on health-related goods and services—e. Multi-factor productivity MFP growth in healthcare, on average, has been reported to be considerably less than the economy-wide MFP or, to some extreme, even negative.
Triplett and Bosworthusing expenditure data and deflators from the U. Bureau of Economic Analysis, reported negative productivity growth in the U.
Their finding is consistent with another study by Harper, Khandrika, Kinoshita, and Rosenthal for the years — Focusing on hospital productivity growth and various measurement methods, Cylus and Dickensheets used net revenue for hospitals deflated by the producer price index for hospitals as their measure of output.
Technology, Productivity, and Costs in Healthcare
They estimated that the year moving average of growth in hospital MFP for the year period ending in was between 0. Over each of the year periods in the — time frame, the average MFP for hospitals was almost less than half the average MFP for the whole economy. In an effort to augment aforementioned research, Sheiner and Malinovskaya re-evaluated the Cylus and Dickensheets study by adding more years — in their study time frame.
With a quite similar finding, they estimated that, over the period —, the average growth rate of hospital MFP was between 0. Given that the health sector labor force, especially physicians, play substantial roles in determining the acceptability and feasibility of medical technology, efforts have been made to estimate labor productivity in healthcare Jakovljevic, Fisherfor instance, also calculated the MFP of physicians and captured extreme variability in the estimates.
Based on his study, factor productivity increased at an average rate of 1.
However, over all the periods, physician MFP was about the same as the whole economy. To estimate the overall medical labor force productivity using U. Microdata studies have estimated two-part or double-hurdle models to study the determinants of the healthcare utilization and expenditures of individuals and households. On the other hand, macro-level studies tend to use national time-series or international panel data to estimate the determinants of total health expenditures.
There is large variability across the studies when specifying the determinants of healthcare expenditure Dalal, ; Dieleman et al. The estimates of these studies are highly dependent on the time frame or data coverage periodcountries included in the study design, whether input e.
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Some studies include proxies that reflect specific high-tech applications, such as MRI scanner density. However, because measures such as these do not necessarily reflect medical technological progress in general, many authors use a time trend as a generic technology proxy.
Unfortunately, a trend variable may capture the effects of all kinds of non-stationary variables, and its introduction severely affects the parameter estimates of the other explanatory variables, in particular income Roberts, As a result, the empirical results obtained with models that contain trends are difficult to interpret.
Independent of the considerations for inclusion of some technology proxy, debate on the income elasticity of health spending is far from settled.
Even the most recent studies report very different estimates: Baltagi and Moscone conclude that healthcare is a necessity rather than a luxury in the OECD, with their estimated income elasticities ranging between 0. Woodward and Wangon the other hand, report a stable long-run relationship between per capita health spending and income in the United States, with an implied income elasticity of 1.
Econometric health expenditure models do not usually include lifestyle variables such as smoking, physical activity, or dietary habits. This is remarkable, given the evidence of the adverse effects of unhealthy lifestyles on a variety of health outcomes reported in the medical literature and the associated financial burdens on individuals and the society Dieleman et al. The adverse effects of smoking are further well documented, but the long lags between the unhealthy habits and their health consequences make this variable difficult to include in aggregate time series or panel model studies.
Regarding choice between heterogeneous country-specific time-series or homogeneous panel data models, Baltagi and Griffin conclude that the efficiency gains from pooling more than offset the bias due to intercountry differences. Panel data models entail a particular additional problem, however, in that they require the national monetary data to be converted to a common currency in constant prices.
The standard way to do this is to use U. Following developments in non-stationary time-series econometrics, many authors have examined the unit root properties of health expenditures and GDP, both at the country level and in panels of countries. This may be due to spillover effects or the migration of technological improvements from elsewhere in the economy to the healthcare sector. The developing countries spend far less on healthcare, as a percentage of the GDP, than the developed economies.
Moreover, in these countries, absence of organizational and institutional capacities in the public sector, lack of sound macroeconomic tools and political commitment hamper well-regulated financing mechanisms in the short and long runs. In general, developing and transitional economies are highly constrained in their health strategic priority settings, and the paucity of relevant data hampers empirical evidence of health sector cost impacts of productivity measurements and the increasingly important roles that technological progress plays.
This is partly attributed to the immense focus on needs-based, short-term services resulting from disease outbreaks Baltagi et al. Further, countries with liberal international trade policies, unlike closed trade policies, achieve maximum benefits from the flow of advanced medical technologies. Some Limitations of the Existing Studies The standard measures of productivity growth define healthcare output as the nominal spending on healthcare by service providers hospitals, physicians, etc.
In theory, this should yield a measure of units of output over time. However, if the price of healthcare is inaccurately measured or is conceptually measured with errors in ways devoid of the economic content, then so too will be the output and productivity measures. Thus, any problems with measuring healthcare prices portend problems for measuring sectoral productivity.
Similarly, two main problems have been identified in the measurement of healthcare costs. One is in identifying the appropriate good. In the traditional approach, the good is the healthcare service or good actually purchased: But, as noted by Kendrickthese purchases are better viewed as intermediate inputs into the production of what the consumer truly wants—better health.
By viewing services in different categories as different goods, rather than as inputs in the production of one good, cost savings arising from substitution of one input for another are not taken into account. The second problem in constructing price indexes for medical care is that the nature of the good particularly, the quality component is changing.
In particular, medical care outcomes have tended to improve over time.
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Price measurements that do not capture these increases in quality would overstate price growth in healthcare and understate productivity growth Barber et al. Getzen and Okunade traced the historical evolution of important research on the determinants of aggregate healthcare expenditure.
They list some limitations hampering the usefulness of the existing body of work: Some authors fail to recognize that the growth in healthcare spending is associated with income growth with a lag. Other determinants, for example, policy and institutions, outside of the national income, technology and demographics are incorrectly treated as secondary whereas they are increasingly detected to be statistically significant see, e. Many of the most important relevant concepts, ranging from culture to health, are not easily amenable to measurement.
Comparing research findings is difficult when studies span different data coverage periods, use widely ranged model estimation techniques, and cover heterogeneous health systems whose policy change over time. Moreover, there is a large absence of studies useful for forecasting or projecting national healthcare spending, whereas such studies are important for national economic planning and health sector budgetary proposals.