Productivity

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This article is about the economic and business concept. For other uses, see Productivity (disambiguation).

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Productivity describes various measures of the efficiency of production. A productivity measure is expressed as the ratio of output to inputs used in a production process, i.e. output per unit of input. Productivity is a crucial factor in production performance of firms and nations. Increasing national productivity can raise living standards because more real income improves people's ability to purchase goods and services, enjoy leisure, improve housing and education and contribute to social and environmental programs. Productivity growth also helps businesses to be more profitable.^[1] There are many different definitions of productivity and the choice among them depends on the purpose of the productivity measurement and/or data availability.

Contents

 [hide] 

• 1 Partial productivity
  • 1.1 Labor productivity
• 2 Multi-factor productivity
  • 2.1 Accounting procedure
• 3 Total productivity
• 4 Benefits of productivity growth
• 5 Drivers of productivity growth
• 6 Individual and team productivity
  • 6.1 Detrimental impact of bullying, incivility, toxicity and psychopathy
• 7 Business productivity
• 8 Productivity paradox
• 9 National productivity
• 10 Validity
• 11 See also
• 12 Footnotes
• 13 References
• 14 Further reading
• 15 External links

Partial productivity[edit]

Main article: Partial productivity

Productivity measures that use one class of inputs or factors, but not multiple factors, are called partial productivities.^[2] In practice, measurement in production means measures of partial productivity. Interpreted correctly, these components are indicative of productivity development, and approximate the efficiency with which inputs are used in an economy to produce goods and services. However, productivity is only measured partially – or approximately. In a way, the measurements are defective because they do not measure everything, but it is possible to interpret correctly the results of partial productivity and to benefit from them in practical situations. At the company level, typical partial productivity measures are such things as worker hours, materials or energy used per unit of production.^[2]
Before widespread use of computer networks, partial productivity was tracked in tabular form and with hand-drawn graphs. Tabulating machines for data processing began being widely used in the 1920s and 1930s and remained in use until mainframe computers became widespread in the late 1960s through the 1970s. By the late 1970s inexpensive computers allowed industrial operations to perform process control and track productivity. Today data collection is largely computerized and almost any variable can be viewed graphically in real time or retrieved for selected time periods.

Labor productivity[edit]

Labour productivity levels in 2012 in Europe. OECD

Comparison of average labor productivity levels between the OECD member states. Productivity is measured as GDP per hour worked. Blue bars = higher than OECD-average productivity. Yellow bars = lower than average.

In macroeconomics, a common partial productivity measure is labour productivity. Labour productivity is a revealing indicator of several economic indicators as it offers a dynamic measure of economic growth, competitiveness, and living standards within an economy. It is the measure of labour productivity (and all that this measure takes into account) which helps explain the principal economic foundations that are necessary for both economic growth and social development. In general labour productivity is equal to the ratio between a measure of output volume (gross domestic product or gross value added) and a measure of input use (the total number of hours worked or total employment).

${\displaystyle {\text{labour productivity}}={\frac {\text{output volume}}{\text{labor input use}}}}$

The output measure is typically net output, more specifically the value added by the process under consideration, i.e. the value of outputs minus the value of intermediate inputs. This is done in order to avoid double-counting when an output of one firm is used as an input by another in the same measurement.^[3] In macroeconomics the most well-known and used measure of value-added is the Gross Domestic Product or GDP. It is widely used as a measure of the economic growth of nations and industries. GDP is the income available for paying capital costs, labor compensation, taxes and profits.(OECD 2008,11) Some economists instead use gross value added (GVA); there is normally a strong correlation between GDP and GVA. (Freeman 2008,5)
The measure of input use reflects the time, effort and skills of the workforce. Denominator of the ratio of labour productivity, the input measure is the most important factor that influences the measure of labour productivity. Labour input is measured either by the total number of hours worked of all persons employed or total employment (head count). (Freeman 2008,5) There are both advantages and disadvantages associated with the different input measures that are used in the calculation of labour productivity. It is generally accepted that the total number of hours worked is the most appropriate measure of labour input because a simple headcount of employed persons can hide changes in average hours worked and has difficulties accounting for variations in work such as a part-time contract, leave of absence, overtime, or shifts in normal hours. However, the quality of hours-worked estimates is not always clear. In particular, statistical establishment and household surveys are difficult to use because of their varying quality of hours-worked estimates and their varying degree of international comparability.
GDP per capita is a rough measure of average living standards or economic well-being and is one of the core indicators of economic performance. (OECD 2008, 14) GDP is, for this purpose, only a very rough measure. Maximizing GDP, in principle, also allows maximizing capital usage. For this reason GDP is systematically biased in favour of capital intensive production at the expense of knowledge and labour-intensive production. The use of capital in the GDP-measure is considered to be as valuable as the production’s ability to pay taxes, profits and labor compensation. The bias of the GDP is actually the difference between the GDP and the producer income. (Saari 2011,10,16)
Another labour productivity measure, output per worker, is often seen as a proper measure of labour productivity, as here: “Productivity isn't everything, but in the long run it is almost everything. A country's ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.“ ^[4] This measure (output per worker) is, however, more problematic than the GDP or even invalid because this measure allows maximizing all supplied inputs, i.e. materials, services, energy and capital at the expense of producer income.^{[citation needed]}

Multi-factor productivity[edit]

Main article: Total factor productivity

Trends in U.S. productivity from labor, capital and multi-factor sources over the 1987-2014 period.

When multiple inputs are considered, the measure is called Multi Factor Productivity or MFP.^[3] Multifactor productivity is typically estimated using growth accounting. If the inputs specifically are labor and capital, and the outputs are value added intermediate outputs, the measure is called total factor productivity or TFP. TFP measures the residual growth that cannot be explained by the rate of change in the services of labour, capital and intermediate outputs. MFP replaced the term TFP used in the earlier literature, and both terms continue in use (usually interchangeably) (Hulten 2009,7).
TFP is often interpreted as a rough average measure of productivity, more specifically the contribution to economic growth made by factors such as technical and organisational innovation. (OECD 2008,11). The most famous description is that of Solow’s (1957): ”I am using the phrase ’technical change’ as a shorthand expression for any kind of shift in the production function. Thus slowdowns, speed ups, improvements in the education of the labor force and all sorts of things will appear as ’technical change’ ”. The original MFP model (Solow 1957) involves several assumptions: that there is a stable functional relation between inputs and output at the economy-wide level of aggregation, that this function has neoclassical smoothness and curvature properties, that inputs are paid the value of their marginal product, that the function exhibits constant returns to scale, and that technical change has the Hicks’n neutral form (Hulten, 2009,5). In practice, TFP is "a measure of our ignorance", as Abramovitz (1956) put it, precisely because it is a residual. This ignorance covers many components, some wanted (like the effects of technical and organizational innovation), others unwanted (measurement error, omitted variables, aggregation bias, model misspecification) (Hulten 2000,11). Hence the relationship between TFP and productivity remains unclear.

Accounting procedure[edit]

Accounting procedure of MFP (Saari 2012)

The MFP measure can be compactly introduced with an accounting procedure in the following calculation. We can use the fixed price values of the real process in the production model to show the accounting procedure. Fixed price values of the real process depict commensurate volumes of the outputs and inputs. When we subtract from the output the intermediate inputs we obtain the value-added. Value-added is used as an output in MFP measure. The principle is to compare the growth of the value-added to the growth of labour and capital input. The formula of the MFP growth is as follows (Schreyer 2005,7):

• change of MFP = change of output (1.119)
• minus change of labour input x cost share of labour (1.150 x 0.475 = 0.546)
• minus change of capital input x cost share of capital (1.030 x 0.525 = 0.541)

As an accounting result the MFP growth is 1.119-0.546-0.541=0.032 or 3.2%.

Total productivity[edit]

When all outputs and inputs are included in the productivity measure it is called total productivity. Total productivity is a measure of total efficiency of a production process and as such is often presented as the objective to be maximized in a production process such as a business.^[2] Compared to total productivity, the MFP model replaces real income with the capital usage by assuming they are equal. The reason of this modification is not known nor argued but it weakens the validity of the measure.
Davis has deliberated^[5] the phenomenon of productivity, measurement of productivity, distribution of productivity gains, and how to measure such gains. He refers to an article^[6] suggesting that the measurement of productivity shall be developed so that it ”will indicate increases or decreases in the productivity of the company and also the distribution of the ’fruits of production’ among all parties at interest”. According to Davis, the price system is a mechanism through which productivity gains are distributed, and besides the business enterprise, receiving parties may consist of its customers, staff and the suppliers of production inputs.

Benefits of productivity growth[edit]

Labour productivity growth in Australia since 1978, measured by GDP per hour worked (indexed)

Productivity growth is a crucial source of growth in living standards. Productivity growth means more value is added in production and this means more income is available to be distributed.
At a firm or industry level, the benefits of productivity growth can be distributed in a number of different ways:

• to the workforce through better wages and conditions;
• to shareholders and superannuation funds through increased profits and dividend distributions;
• to customers through lower prices;
• to the environment through more stringent environmental protection; and
• to governments through increases in tax payments (which can be used to fund social and environmental programs).

Productivity growth is important to the firm because it means that it can meet its (perhaps growing) obligations to workers, shareholders, and governments (taxes and regulation), and still remain competitive or even improve its competitiveness in the market place. Adding more inputs will not increase the income earned per unit of input (unless there are increasing returns to scale). In fact, it is likely to mean lower average wages and lower rates of profit. But, when there is productivity growth, even the existing commitment of resources generates more output and income. Income generated per unit of input increases. Additional resources are also attracted into production and can be profitably employed.

Drivers of productivity growth[edit]

See also: Productivity improving technologies

In the most immediate sense, productivity is determined by the available technology or know-how for converting resources into outputs, and the way in which resources are organized to produce goods and services. Historically, productivity has improved through evolution as processes with poor productivity performance are abandoned and newer forms are exploited. Process improvements may include organizational structures (e.g. core functions and supplier relationships), management systems, work arrangements, manufacturing techniques, and changing market structure. A famous example is the assembly line and the process of mass production that appeared in the decade following commercial introduction of the automobile.^[7]
Mass production dramatically reduced the labor in producing parts for and assembling the automobile, but after its widespread adoption productivity gains in automobile production were much lower. A similar pattern was observed with electrification, which saw the highest productivity gains in the early decades after introduction. Many other industries show similar patterns. The pattern was again followed by the computer, information and communications industries in the late 1990s when much of the national productivity gains occurred in these industries.^[8]
There is a general understanding of the main determinants or drivers of productivity growth. Certain factors are critical for determining productivity growth. The Office for National Statistics (UK) identifies five drivers that interact to underlie long-term productivity performance: investment, innovation, skills, enterprise and competition. (ONS 3, 20)

• Investment is in physical capital — machinery, equipment and buildings. The more capital workers have at their disposal, generally the better they are able to do their jobs, producing more and better quality output.
• Innovation is the successful exploitation of new ideas. New ideas can take the form of new technologies, new products or new corporate structures and ways of working. Speeding up the diffusion of innovations can boost productivity.
• Skills are defined as the quantity and quality of labour of different types available in an economy. Skills complement physical capital, and are needed to take advantage of investment in new technologies and organisational structures.
• Enterprise is defined as the seizing of new business opportunities by both start-ups and existing firms. New enterprises compete with existing firms by new ideas and technologies increasing competition. Entrepreneurs are able to combine factors of production and new technologies forcing existing firms to adapt or exit the market.
• Competition improves productivity by creating incentives to innovate and ensures that resources are allocated to the most efficient firms. It also forces existing firms to organise work more effectively through imitations of organisational structures and technology.

Individual and team productivity[edit]

See also: Programming productivity

Technology has enabled massive personal productivity gains — computers, spreadsheets, email, and other advances have made it possible for a knowledge worker to seemingly produce more in a day then was previously possible in a year.^[9] Environmental factors such as sleep and leisure play a significant role in work productivity and received wage.^[10] Drivers of productivity growth for creative and knowledge workers include improved or intensified exchange with peers or co-workers, as more productive peers have a stimulating effect on one's own productivity.^{[11] [12]} Productivity is influenced by effective supervision and job satisfaction. Having an effective or knowledgeable supervisor (for example a supervisor who uses the Management by objectives method) has an easier time motivating their employees to produce more in quantity and quality. An employee who has an effective supervisor, motivating them to be more productive is likely to experience a new level of job satisfaction thereby becoming a driver of productivity itself.^[13]

Detrimental impact of bullying, incivility, toxicity and psychopathy[edit]

Workplace bullying results in a loss of productivity, as meaured by self-rated job performance.^[14] Over time, targets of bullying will spend more time protecting themselves against harassment by bullies and less time fulfilling their duties.^[15] Workplace incivility has also been associated with diminished productivity in terms of quality and quantity of work.^[16]
A toxic workplace is a workplace that is marked by significant drama and infighting, where personal battles often harm productivity.^[17] While employees are distracted by this, they cannot devote time and attention to the achievement of business goals.^[18] When toxic employees leave the workplace, it can improve the culture overall because the remaining staff become more engaged and productive.^[19] The presence of a workplace psychopath may have a serious detrimental impact on productivity in an organisation.^[20]
In companies in where the traditional hierarchy has been removed in favor of an egalitarian, team-based setup, the employees are often happier, and individual productivity is improved (as they themselves are better placed to increase the efficiency of the workfloor). Companies that have these hierarchy removed and have their employees work more in teams are called Liberated companies or "Freedom inc.'s".^{[21][22][23][24][25]} See also: The Toyota Way

Business productivity[edit]

Productivity is one of the main concerns of business management and engineering. Many companies have formal programs for continuously improving productivity, such as a production assurance program. Whether they have a formal program or not, companies are constantly looking for ways to improve quality, reduce downtime and inputs of labor, materials, energy and purchased services. Often simple changes to operating methods or processes increase productivity, but the biggest gains are normally from adopting new technologies, which may require capital expenditures for new equipment, computers or software. Modern productivity science owes much to formal investigations that are associated with scientific management.^[26] Although from an individual management perspective, employees may be doing their jobs well and with high levels of individual productivity, from an organizational perspective their productivity may in fact be zero or effectively negative if they are dedicated to redundant or value destroying activities.^[9]

Productivity paradox[edit]

Main article: Productivity paradox

Overall productivity growth was relatively slow from the 1970s through the early 1990s.^[27] Although several possible causes for the slowdown have been proposed there is no consensus. The matter is subject to a continuing debate that has grown beyond questioning whether just computers can significantly increase productivity to whether the potential to increase productivity is becoming exhausted.^[28]

National productivity[edit]

In order to measure productivity of a nation or an industry, it is necessary to operationalize the same concept of productivity as in a production unit or a company, yet, the object of modelling is substantially wider and the information more aggregate. The calculations of productivity of a nation or an industry are based on the time series of the SNA, System of National Accounts. National accounting is a system based on the recommendations of the UN (SNA 93) to measure total production and total income of a nation and how they are used. (Saari 2006, 9)
International or national productivity growth stems from a complex interaction of factors. Some of the most important immediate factors include technological change, organizational change, industry restructuring and resource reallocation, as well as economies of scale and scope. A nation's average productivity level can also be affected by the movement of resources from low-productivity to high-productivity industries and activities. Over time, other factors such as research and development and innovative effort, the development of human capital through education, and incentives from stronger competition promote the search for productivity improvements and the ability to achieve them. Ultimately, many policy, institutional and cultural factors determine a nation's success in improving productivity.
At the national level, productivity growth raises living standards because more real income improves people's ability to purchase goods and services (whether they are necessities or luxuries), enjoy leisure, improve housing and education and contribute to social and environmental programs. Some have suggested that the UK's 'productivity puzzle' is an urgent issue for policy makers and businesses to address in order to sustain growth.^{[citation needed]} Over long periods of time, small differences in rates of productivity growth compound, like interest in a bank account, and can make an enormous difference to a society's prosperity. Nothing contributes more to reduction of poverty, to increases in leisure, and to the country's ability to finance education, public health, environment and the arts’.^[29]
Productivity is considered basic statistical information for many international comparisons and country performance assessments and there is strong interest in comparing them internationally. The OECD^[30] publishes an annual Compendium of Productivity Indicators^[31] that includes both labor and multi-factor measures of productivity. Several statistical offices publish productivity accounting handbooks and manuals with detailed accounting instructions and definitions. For example, the following:

• Measuring Productivity – OECD Manual
• Office for National Statistics (UK) Productivity handbook
• Bureau of Labor Statistics, Productivity Statistics (U.S.)

Validity[edit]

Validity is a characteristic of the measure which is used in measuring. Validity implies how exact information the used measure can generate from the phenomenon. We need to understand the phenomenon, the measure and the possible difference between them. Often when we aim at simplicity and understandability in measuring, we have to lower the requirements for validity. For this reason it is important to evaluate the validity of the measurements used, case by case. Good measuring presupposes that those responsible for measuring are familiar with the validity of the measurements and also keep users informed of the validity. In order to evaluate validity of any measure we need to understand the phenomenon, the measure and the possible difference between them. In the case of productivity we cannot make this evaluation in a traditional way because the phenomenon intended to be measured is somewhat unclear. Instead we can identify the differences between models.^{[citation needed]}

See also[edit]

• Agile construction
• Assembly line
• Computer-aided manufacturing
• Counterproductive work behavior
• Division of labour
• Industrial Revolution
• Mass production
• Productive and unproductive labour
• Production (economics)
• Production, costs, and pricing
• Productive forces
• Production function
• Productivity model
• Production possibility frontier
• Production theory basics
• Productivity improving technologies (historical)
• Second Industrial Revolution

Footnotes[edit]

1. Jump up ^ Courbois & Temple 1975, Gollop 1979, Kurosawa 1975, Pineda 1990, Saari 2006
2. ^ Jump up to: ^{a b c} Sumanth, David J. Total Productivity Management (TPmgt): A Systemic and Quantitative Approach to Compete in Quality, Price and Time. CRC Press. p. 5. ISBN 9781574440577. Retrieved 11 March 2017. 
3. ^ Jump up to: ^{a b} "Multifactor Productivity - Overview". Bureau of Labor Statistics. U.S. Department of Labor. Retrieved 11 March 2017. 
4. Jump up ^ Paul Krugman (1994). The Age of Diminishing Expectations. MIT Press. 
5. Jump up ^ Davis, 1955
6. Jump up ^ 1947, Journal of Accountancy, Feb. p. 94
7. Jump up ^ Field, Alexander (2004). "Technological Change and Economic Growth the Interwar Years and the 1990s" (PDF). 
8. Jump up ^ Ayres, Robert U.; Warr, Benjamin (2006). "Economic growth, technological progress and energy use in the U.S. over the last century: Identifying common trends and structural change in macroeconomic time series, INSEAD" (PDF). 
9. ^ Jump up to: ^{a b} Fuller, Ryan (19 April 2016). "The Paradox of Workplace Productivity". 
10. Jump up ^ Gibson, Matthew; Shrader, Jeffrey (14 July 2014). "Time Use and Productivity: The Wage Returns to Sleep". eScholarship. Retrieved 11 March 2017. 
11. Jump up ^ Borowiecki, Karol J. "Geographic clustering and productivity: An instrumental variable approach for classical composers" "Journal of Urban Economics", 2013, 73: 94-110
12. Jump up ^ Borowiecki, Karol J. "Agglomeration Economies in Classical Music" "Papers in Regional Science", 2015, 94(3): 443-68
13. Jump up ^ Thompson, K.R., & And., 0. "The Effects of MBO on Performance and Satisfaction in a Public Sector Organization" "Journal of Management", 1981
14. Jump up ^ Helge H, Sheehan MJ, Cooper CL, Einarsen S "Organisational Effects of Workplace Bullying" in Bullying and Harassment in the Workplace: Developments in Theory, Research, and Practice (2010)
15. Jump up ^ Fisher-Blando, J. L. (2008). Workplace bullying: Aggressive behavior and its effect on job satisfaction and productivity (Unpublished doctoral dissertation). University of Phoenix
16. Jump up ^ "9 signs your work place needs civility, 6 steps to achieve it - TechJournal". Retrieved 25 September 2014. 
17. Jump up ^ Benoit, Suzanne (2011) "Toxic Employees: great companies resolve this problem, you can too!" Falmouth, Maine: BCSPublishing
18. Jump up ^ Bitting, Robert. "Using Effective Leadership Strategies in the Workplace". http://www.robertbitting.com/files/articles/Toxic-Employees-in-the-Work-Place.pdf. Retrieved May 13, 2011.
19. Jump up ^ Mayhew, Ruth "Is employee turnover always bad?" accessed at: http://smallbusiness.chron.com/employee-turnover-always-bad-11089.html
20. Jump up ^ Boddy, C. R. (2010) ‘Corporate Psychopaths and Productivity', Management Services Spring, 26–30.
21. Jump up ^ Free Inc. terminology and list of companies
22. Jump up ^ L'entreprise liberée
23. Jump up ^ Holacracy
24. Jump up ^ Poult system
25. Jump up ^ SEMCO
26. Jump up ^ Nelson, Daniel (1980). Frederick W. Taylor and the Rise of Scientific Management. University of Wisconsin Press. ISBN 978-0299081607. 
27. Jump up ^ Brynjolfsson, Erik (1993). "The productivity paradox of information technology". Communications of the ACM. 36 (12): 66–77. ISSN 0001-0782. doi:10.1145/163298.163309. 
28. Jump up ^ Zelenyuk (2014) "Testing Significance of Contributions in Growth Accounting, with Application to Testing ICT Impact on Labor Productivity of Developed Countries" International Journal of Business and Economics 13:2, pp. 115-126.
29. Jump up ^ Alan Blinder and William Baumol 1993, Economics: Principles and Policy, Harcourt Brace Jovanovich, San Diego, p. 778.
30. Jump up ^ http://stats.oecd.org/Index.aspx?DataSetCode=LEVEL
31. Jump up ^ Compendium of Productivity Indicators, OECD

References[edit]

• Abramovitz, M (May 1956). "Resource and Output Trends in the United States since 1870" (PDF). American Economic Review. NATIONAL BUREAU OF ECONOMIC RESEARCH. 46 (2): 5–23. 
• Bechler, J.G (1984). "The Productivity Management Process". American Productivity Center. 
• Courbois, R.; Temple, P. (1975). La methode des "Comptes de surplus" et ses applications macroeconomiques. 160 des Collect,INSEE,Serie C (35). p. 100. 
• Craig, C.; Harris, R. (1973). "Total Productivity Measurement at the Firm Level". Sloan Management Review (Spring 1973): 13–28. 
• Davis, H.S. (1955). Productivity Accounting. University of Pennsylvania. 
• Freeman, R. (2008). Labour productivity indicators (PDF). OECD. 
• Genesca, G.E.; Grifell, T. E. (1992). "Profits and Total Factor Productivity: A Comparative Analysis". Omega. the International Journal of Management Science. 20 (5/6): 553–568. doi:10.1016/0305-0483(92)90002-O. 
• Gollop, F.M. (1979). "Accounting for Intermediate Input: The Link Between Sectoral and Aggregate Measures of Productivity Growth". Measurement and Interpretation of Productivity. National Academy of Sciences. 
• Hulten, C.R. (January 2000). "TOTAL FACTOR PRODUCTIVITY: A SHORT BIOGRAPHY" (PDF). NATIONAL BUREAU OF ECONOMIC RESEARCH. doi:10.3386/w7471. 
• Hulten, C.R. (September 2009). "GROWTH ACCOUNTING" (PDF). NATIONAL BUREAU OF ECONOMIC RESEARCH. doi:10.3386/w15341. 
• Jorgenson, D.W.; Griliches, Z. (1967). "The Explanation of Productivity Change". Review of Economic Studies. 34 (99): 249–283. JSTOR 2296675. doi:10.2307/2296675. 
• Jorgenson, D.W.; Ho, M.S.; Samuels, J.D. (2014). Long-term Estimates of U.S. Productivity and Growth (PDF). Tokyo: Third World KLEMS Conference. 
• Kendrick, J.; Creamer, D. (1965). "Measuring Company Productivity: A handbook with Case Studies" (89). The National Industry Productivity Board. 
• Kendrick, J.W. (1984). Improving Company Productivity. The Johns Hopkins University Press. 
• Kohli, U (2012). Productivity: National vs. Domestic (PDF). Sydney, Australia: EMG Workshop, University of New South Wales, November 21–23, 2012. 
• Kurosawa, K (1975). "An aggregate index for the analysis of productivity". Omega. 3 (2): 157–168. doi:10.1016/0305-0483(75)90115-2. 
• Loggerenberg van, B.; Cucchiaro, S. (1982). "Productivity Measurement and the Bottom Line". National Productivity Review. 1 (1): 87–99. doi:10.1002/npr.4040010111. 
• Mundel, M.E. (1983). Improving Productivity and Effectiveness. Prentice-Hall, Inc. 
• OECD (2008). Compendium of Productivity Indicators 2008. OECD. 
• ONS 3 (Office for National Statistics, UK). Productivity Theory and Drivers.
• Pineda, A. (1990). A Multiple Case Study Research to Determine and respond to Management Information Need Using Total-Factor Productivity Measurement (TFPM). Virginia Polytechnic Institute and State University. 
• Riistama, K.; Jyrkkiö E. (1971). Operatiivinen laskentatoimi (Operative accounting). Weilin + Göös. p. 335. 
• Saari, S. (2006a). Productivity. Theory and Measurement in Business. Productivity Handbook (In Finnish). MIDO OY. p. 272. 
• Saari, S. (2011). Production and Productivity as Sources of Well-being. MIDO OY. p. 25. 
• Saari, S. (2006). Productivity. Theory and Measurement in Business (PDF). Espoo, Finland: European Productivity Conference. 
• Schreyer, P. (2005). Measuring Productivity (PDF). Tokyo: OECD. 
• Solow, R.N. (August 1957). "Technical Change and the Aggregate Production Function" (PDF). The Review of Economics and Statistics. NATIONAL BUREAU OF ECONOMIC RESEARCH. 39 (3): 312–320. doi:10.2307/1926047. 
• Vygotsky, L. (1962). Thought and Language. MIT Press (original work 1934). 

Further reading[edit]

• Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers," Economic Journal, Royal Economic Society, vol. 92(365), pages 73–86, March.
• Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, vol. 50(6), pages 1393-1414, November.
• Alexandra Daskovska & Léopold Simar & Sébastien Bellegem, 2010. "Forecasting the Malmquist productivity index," Journal of Productivity Analysis, Springer, vol. 33(2), pages 97–107, April.
• Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. 1994. Productivity growth, technical progress, and efficiency change in industrialized countries. The American Economic Review 84, pages 66–83.
• Simar, Leopold & Wilson, Paul W., 1999. "Estimating and bootstrapping Malmquist indices," European Journal of Operational Research, Elsevier, vol. 115(3), pages 459-471, June. ]
• Mayer, A. and Zelenyuk, V. 2014. "Aggregation of Malmquist productivity indexes allowing for reallocation of resources," European Journal of Operational Research, Elsevier, vol. 238(3), pages 774-785.
• Zelenyuk, V. 2006. "Aggregation of Malmquist productivity indexes," European Journal of Operational Research, vol. 174(2), pages 1076-1086.

External links[edit]

Wikiquote has quotations related to: Productivity

• Field, Alexander J. (2008). "Productivity". In David R. Henderson. Concise Encyclopedia of Economics (2nd ed.). Indianapolis: Library of Economics and Liberty. ISBN 978-0865976658. OCLC 237794267. 
• Productivity and Costs – Bureau of Labor Statistics United States Department of Labor: contains international comparisons of productivity rates, historical and present
• Productivity Statistics – Organisation for Economic Co-operation and Development
• Greenspan Speech
• OECD estimates of labour productivity levels

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Africa: Why the World Bank's Efforts to Marshal Private Capital Won't Reduce Poverty

The Conversation (Johannesburg) 

With the model of Transfinancial Economics the possible problems which could result with the "new" Marshal type Plan could be eradicated
By Misheck Mutize

Cape Town — The planned reinvention of the World Bank may be a mea culpa of sort from the multilateral funding institution. But it is still a bearer of bad news for poor African countries.

The World Bank is looking to migrate from the model that largely relies on member states providing loans for development projects, to one in which it becomes more of a broker of private capital to be invested in development projects.

The World Bank Group President Jim Yong Kim believes that a sizeable portion of private capital lies idle. With proper steps to eliminate unacceptable risks, this capital can be channelled into funding development in poor countries.

Private investors are generally risk-averse. This means that mountains of idle cash remain largely untapped at the expense of real investments. These could generate jobs and green energy as well as reduce poverty, improve health care and extinguish debts that are haunting countries the world over.

Kim argues that development finance needs fundamentally to change in speed and scale, growing from billions of dollars in development aid to trillions in investment.

He believes that there are significant financial resources readily available and sitting on the sidelines of capital markets. They generate little in the way of returns, particularly compared to what they could make if invested in developing countries. Private investors lack knowledge about these countries, and their tendency to remain generally risk-averse means that the funds remain largely untapped.

Kim's argument amounts to an admission that the Bretton Woods system has failed to address gaps in the global capital markets. And that its institutions - the International Monetary Fund and the World Bank - which were established after the second world war to foster international economic cooperation - have failed to support the world's developmental needs.

But private sector funding won't help the situation because the much needed developmental investments in Africa are social in nature. Private investments will also be costly, and as a consequence, exploitative.

Weaknesses in Bretton Woods institutions

The Bretton Woods multilateral institutions have been strongly criticised for their corporate led model, which tends to underminesocial justice. Over the years they have been focusing on profit oriented investments. Many have impoverished people in emerging economies, particularly in Asia and Africa through displacements, large scale privatisation, natural resource looting and environmental degradation.

Aid and loans from the World Bank and the International Monetary Fund usually come with strict conditions and restrictive policy recommendations. These take away the economic freedom of aid recipients and borrowing countries. They include strict inflation controls, high taxation, large scale privatisations, rapid trade liberalisation and cutting government expenditure on social services.

Conditions on aid and loans usually forfeit states' authority in governing their own economy, as national economic policies are predetermined under the loan packages. This ultimately shifts regulation of national economies from state governments to the Washington institution in which African developing countries hold little voting power.

The number of emerging countries depending on the World Bank funding has drastically declined over the past ten years. This has been mainly due to increasingly attractive alternative sources of financing. The bank has been rendered irrelevant as private capital flows to the developing world have grown on the back of governments issuing sovereign bonds. Its role has gradually become a mere aid agency dealing with a smaller group of low-income fragile states,

The new generation of institutions spearheaded by emerging market governments led by China is further threatening the traditional multilateral institutions.

This is what lies behind the World Bank's efforts to reposition itself from being a lender for major development projects relying on funding states, into a broker for private sector investment. This would shift it from being a body that disburses development aid to one that mobilises investment.

But the World Bank's proposed repositioning will have a number of negative implications on countries in Africa.

Negative consequences

First, it will further disadvantage developing nations as most investments in Africa are classified as risky. This means that most investors are unwilling to commit funds for longer time frames. And, given the high risk assessment, borrowing will be expensive. This in turn will push countries further in debt and expose them to exploitation by private lenders.

Second, the repositioning from public to private funding will further cement the World Bank's business model at the expense of social benefit. This will undermine the role of the state as the primary provider of essential goods and services, such as health care and education.

Last, it will be almost impossible for the bank successfully to mediate between the interests of a global markets system, developing country governments, and people in poverty. This is because projects attractive for private investment are out of the reach of poor people.

There's no reason to believe that the bank's envisaged new role will lead to a reduction in poverty. The more likely outcome will be that it once again fails to address international capital market shortcomings.

Disclosure statement

Misheck Mutize does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond the academic appointment above.

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Regulating the internet giants...The world’s most valuable resource is no longer oil, but data

The data economy demands a new approach to antitrust rules



Blogger Ref  http://www.p2pfoundation.net/Transfinancial_Economics

May 6th 2017

A NEW commodity spawns a lucrative, fast-growing industry, prompting antitrust regulators to step in to restrain those who control its flow. A century ago, the resource in question was oil. Now similar concerns are being raised by the giants that deal in data, the oil of the digital era. These titans—Alphabet (Google’s parent company), Amazon, Apple, Facebook and Microsoft—look unstoppable. They are the five most valuable listed firms in the world. Their profits are surging: they collectively racked up over $25bn in net profit in the first quarter of 2017. Amazon captures half of all dollars spent online in America. Google and Facebook accounted for almost all the revenue growth in digital advertising in America last year.

Such dominance has prompted calls for the tech giants to be broken up, as Standard Oil was in the early 20th century. This newspaper has argued against such drastic action in the past. Size alone is not a crime. The giants’ success has benefited consumers. Few want to live without Google’s search engine, Amazon’s one-day delivery or Facebook’s newsfeed. Nor do these firms raise the alarm when standard antitrust tests are applied. Far from gouging consumers, many of their services are free (users pay, in effect, by handing over yet more data). Take account of offline rivals, and their market shares look less worrying. And the emergence of upstarts like Snapchat suggests that new entrants can still make waves.

But there is cause for concern. Internet companies’ control of data gives them enormous power. Old ways of thinking about competition, devised in the era of oil, look outdated in what has come to be called the “data economy” (see Briefing). A new approach is needed.

Quantity has a quality all its own

 

What has changed? Smartphones and the internet have made data abundant, ubiquitous and far more valuable. Whether you are going for a run, watching TV or even just sitting in traffic, virtually every activity creates a digital trace—more raw material for the data distilleries. As devices from watches to cars connect to the internet, the volume is increasing: some estimate that a self-driving car will generate 100 gigabytes per second. Meanwhile, artificial-intelligence (AI) techniques such as machine learning extract more value from data. Algorithms can predict when a customer is ready to buy, a jet-engine needs servicing or a person is at risk of a disease. Industrial giants such as GE and Siemens now sell themselves as data firms.

This abundance of data changes the nature of competition. Technology giants have always benefited from network effects: the more users Facebook signs up, the more attractive signing up becomes for others. With data there are extra network effects. By collecting more data, a firm has more scope to improve its products, which attracts more users, generating even more data, and so on. The more data Tesla gathers from its self-driving cars, the better it can make them at driving themselves—part of the reason the firm, which sold only 25,000 cars in the first quarter, is now worth more than GM, which sold 2.3m. Vast pools of data can thus act as protective moats.

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Access to data also protects companies from rivals in another way. The case for being sanguine about competition in the tech industry rests on the potential for incumbents to be blindsided by a startup in a garage or an unexpected technological shift. But both are less likely in the data age. The giants’ surveillance systems span the entire economy: Google can see what people search for, Facebook what they share, Amazon what they buy. They own app stores and operating systems, and rent out computing power to startups. They have a “God’s eye view” of activities in their own markets and beyond. They can see when a new product or service gains traction, allowing them to copy it or simply buy the upstart before it becomes too great a threat. Many think Facebook’s $22bn purchase in 2014 of WhatsApp, a messaging app with fewer than 60 employees, falls into this category of “shoot-out acquisitions” that eliminate potential rivals. By providing barriers to entry and early-warning systems, data can stifle competition.

Who ya gonna call, trustbusters?

 

The nature of data makes the antitrust remedies of the past less useful. Breaking up a firm like Google into five Googlets would not stop network effects from reasserting themselves: in time, one of them would become dominant again. A radical rethink is required—and as the outlines of a new approach start to become apparent, two ideas stand out.

The first is that antitrust authorities need to move from the industrial era into the 21st century. When considering a merger, for example, they have traditionally used size to determine when to intervene. They now need to take into account the extent of firms’ data assets when assessing the impact of deals. The purchase price could also be a signal that an incumbent is buying a nascent threat. On these measures, Facebook’s willingness to pay so much for WhatsApp, which had no revenue to speak of, would have raised red flags. Trustbusters must also become more data-savvy in their analysis of market dynamics, for example by using simulations to hunt for algorithms colluding over prices or to determine how best to promote competition (see Free exchange).

The second principle is to loosen the grip that providers of online services have over data and give more control to those who supply them. More transparency would help: companies could be forced to reveal to consumers what information they hold and how much money they make from it. Governments could encourage the emergence of new services by opening up more of their own data vaults or managing crucial parts of the data economy as public infrastructure, as India does with its digital-identity system, Aadhaar. They could also mandate the sharing of certain kinds of data, with users’ consent—an approach Europe is taking in financial services by requiring banks to make customers’ data accessible to third parties.

Rebooting antitrust for the information age will not be easy. It will entail new risks: more data sharing, for instance, could threaten privacy. But if governments don’t want a data economy dominated by a few giants, they will need to act soon.

This article appeared in the Leaders section of the print edition under the headline "The world’s most valuable resource"