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Wikipedia
Productivity is the efficiency of production of goods or services expressed by some measure. Measurements of productivity are often expressed as a ratio of an aggregate output to a single input or an aggregate input used in a production process, i.e. output per unit of input, typically over a specific period of time. The most common example is the (aggregate) labour productivity measure, one example of which is GDP per worker. There are many different definitions of productivity (including those that are not defined as ratios of output to input) and the choice among them depends on the purpose of the productivity measurement and/or data availability. The key source of difference between various productivity measures is also usually related (directly or indirectly) to how the outputs and the inputs are aggregated to obtain such a ratio-type measure of productivity.
Productivity is a crucial factor in the 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 can also help businesses to be more profitable.
Productivity measures that use one class of inputs or factors, but not multiple factors, are called partial productivities. 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.
Before the 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.
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).
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. In macroeconomics the most well-known and used measure of value-added is the gross domestic product or GDP. Increases in it are 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. Some economists instead use gross value added (GVA); there is normally a strong correlation between GDP and GVA.
The measure of input use reflects the time, effort and skills of the workforce. The 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). 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, paid leave, 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. 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.
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.” 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.
When multiple inputs are considered, the measure is called multi-factor productivity or MFP. Multi-factor 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 (TFP]. TFP measures the residual growth that cannot be explained by the rate of change in the services of labour and capital. MFP replaced the term TFP used in the earlier literature, and both terms continue in use (usually interchangeably).
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. The most famous description is that of Robert 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 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. 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 miss pecification) Hence the relationship between TFP and productivity remains unclear.
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.
A tool is an object that can extend an individual’s ability to modify features of the surrounding environment or help them accomplish a particular task. Although many animals use simple tools, only human beings, whose use of stone tools dates back hundreds of millennia, have been observed using tools to make other tools.
Early human tools, made of such materials as stone, bone, and wood, were used for preparation of food, hunting, manufacture of weapons, and working of materials to produce clothing and useful artifacts. The development of metalworking made additional types of tools possible. Harnessing energy sources, such as animal power, wind, or steam, allowed increasingly complex tools to produce an even larger range of items, with the Industrial Revolution marking an inflection point in the use of tools. The introduction of widespread automation in the 19th and 20th centuries allowed tools to operate with minimal human supervision, further increasing the productivity of human labor.
While a common-sense understanding of the meaning of tool is widespread, several formal definitions have been proposed.
In 1981, Benjamin Beck published a widely used definition of tool use. This has been modified to:
The external employment of an unattached or manipulable attached environmental object to alter more efficiently the form, position, or condition of another object, another organism, or the user itself, when the user holds and directly manipulates the tool during or prior to use and is responsible for the proper and effective orientation of the tool.
Other, briefer definitions have been proposed:
An object carried or maintained for future use.
— Finn, Tregenza, and Norman, 2009.
The use of physical objects other than the animal’s own body or appendages as a means to extend the physical influence realized by the animal.
— Jones and Kamil, 1973
An object that has been modified to fit a purpose … [or] An inanimate object that one uses or modifies in some way to cause a change in the environment, thereby facilitating one’s achievement of a target goal.
— Hauser, 2000
Anthropologists believe that the use of tools was an important step in the evolution of mankind. Because tools are used extensively by both humans and wild chimpanzees, it is widely assumed that the first routine use of tools took place prior to the divergence between the two species. These early tools, however, were likely made of perishable materials such as sticks, or consisted of unmodified stones that cannot be distinguished from other stones as tools.
Stone artifacts date back to about 2.5 million years ago. However, a 2010 study suggests the hominin species Australopithecus afarensis ate meat by carving animal carcasses with stone implements. This finding pushes back the earliest known use of stone tools among hominins to about 3.4 million years ago. Finds of actual tools date back at least 2.6 million years in Ethiopia. One of the earliest distinguishable stone tool forms is the hand axe.
Up until recently, weapons found in digs were the only tools of “early man” that were studied and given importance. Now, more tools are recognized as culturally and historically relevant. As well as hunting, other activities required tools such as preparing food, “…nutting, leather working, grain harvesting and woodworking…” Included in this group are “flake stone tools”.
Tools are the most important items that the ancient humans used to climb to the top of the food chain; by inventing tools, they were able to accomplish tasks that human bodies could not, such as using a spear or bow to kill prey, since their teeth were not sharp enough to pierce many animals’ skins. “Man the hunter” as the catalyst for Hominin change has been questioned. Based on marks on the bones at archaeological sites, it is now more evident that pre-humans were scavenging off of other predators’ carcasses rather than killing their own food.