Human SciencesWithin the human sciences quantitative data has its limits, this is because of the nature of humans being so unpredictable and therefore very difficult to place exact theories upon. The exception of this is of course Economics, in Economics quantitative data is recorded because it is commonly believed that within this field we act rationally. Therefore we are able to place expectations using the quantitative data recorded from experiments. If these assumptions that have been made in Economics become proved to be incorrect or not entirely correct to all situations limits and conditions are placed upon these rules and theories such as; that the consumer has full and complete knowledge of the market.
Economically developed societies apply considerable resources to collecting economic and social indicators to help policy makers in their applications about how to best increase quality of life. These measures have had notable successes, however they suffer from substantial limitations. The reasons that economic and social indicators cannot reflect the full range of factors that affect quality of life are described. For example, no complete list of factors affecting quality of life can be created, and the way people consider these factors differs. Also, it is often not clear which set of measures best reflects desirable states in various areas such as the economy. In the context of the economy, there is disagreement about which forms of goods and services need to be counted, for example whether housework should be part of the gross domestic product. Because of the shortcomings of economic and social indicators, additional information is required for wide policy making.
The two main explanations that can explain these gaps are (1) the fact that they do not address the full range of factors that affect satisfaction and (2) the difficulties of estimating the long-term effects of measurement costs. However, in order for more precise measures to be able to guide policy makers, they must become a factor in selecting appropriate quality measures. As in any field, policy makers need information on how measures and indicators reflect the long-term benefits and cost of policy making. In the case of purchasing power, a number of measures and indicators on individual or local levels do not fit well with the information given above. The best available way to assess the effect of some such measure and indicator is to conduct a real-time survey, such as the World Bank’s Real World Index or the International Monetary Fund’s World Bank Economic Survey, on local population. By comparing the effects of various measures and indicators on the long-term value of the purchasing power of different countries, the data can be taken into account. Moreover, it is also possible to determine the long-term effect of other measures and metrics such as the quality of education and health and health benefits or government spending, which are not measured in any particular way, on local real economic productivity. A similar problem exists with expenditure, and the results must then have the full context of their effects on local real output. Both of these problems are discussed in Chapter 3 and the results are outlined in Part II when the final conclusions are drawn on them. However, since the short term and long term affect all aspects of life and are often referred to as long-term causality, they represent a separate work.
Finally, as in any field, the data can be used to help policy makers design effective measures and policies. Since the goal of economic and social programs is to maximize the quality of life of each population, this is not always the case with quality measures, for which there seems to be a lack of information. In some countries, such as the USA, the cost of living remains high, which is problematic. Thus there is a need to gather as much data as possible on the impact of various economic measures and indicators, and it can be done by purchasing power. It is also not possible today to use some measures of long-term cost estimates on local production (both in terms of quality assurance and price of labor) to assess the benefits and cost-effectiveness of measures or indicators. Since economic or social measures must not be used to estimate long-term quality gains or long-term cost reductions,[4] they must be evaluated by those measures and measures that have been identified in the results of the measurement and the results of the analysis. Measures that take the form of an indicator of living standards, the cost of basic needs of households[5], the cost of basic living training, and spending as a ratio or percentage of population growth are not suitable to measure the effect of measured quality on local productivity. For example, measures of the social investment of individuals as a percentage of GDP and productivity may not be justified for the longer term—or as a useful indicator of the long-term economic and social benefits of living. It is therefore useful the measurement of quality of life or labor quality that may be used in decisions involving purchasing power. Measures that reflect the economic and social costs that are being paid by individuals to perform public services like education and health are not appropriate because they distort the cost of services and they do not help the people of the affected country. While quality measures can be used as measures of other issues, such as health and education, they do not reflect the full range of economic and social quality of life or are not compatible with measures that have more specific aims or measures that include more specific needs of each population over time. Measures that represent quality of life are not compatible with some measures of quality of life because they often do not have a consistent quality measure to measure a specific set of benefits or cost-off-allocative outputs. The cost of health and health care is seen to