This year's edition of EuroHealthConsumerIndex covers 48 healthcare performance indicators for 35 countries.
Though still a somewhat controversial standpoint, HCP advocates that quality comparisons within the field of healthcare is a true win-win situation. To the consumer, who will have a better platform for informed choice and action. To governments, authorities and providers, the sharpened focus on consumer satisfaction and quality outcomes will support change. To media, the ranking offers clear-cut facts for consumer journalism with some drama into it. This goes not only for evidence of shortcomings and method flaws but also illustrates the potential for improvement. With such a view the EHCI is designed to become an important benchmark system supporting interactive assessment and improvement.
The Scottish NHS deserves recognition for providing excellent Internet access to healthcare data ( www.isdscotland.org/ ), going to such lengths as producing a special version of the WHO Health for All database (2012) with Scotland as a separate country. The only problem with Scottish data is that in true British tradition, parameters are not necessarily measured in a way which is compatible with WHO or other measurements. One example is Alcohol intake, where the common measure is “litres of pure alcohol per year”. The Scottish data are “units of alcohol per day/week”. Fortunately, on this and other parameters, the same method of measuring can be found for other parts of the UK. As the scoring in the EHCI is a relative measurement, the Scottish scores on some indicators have been obtained by comparing with England. One such is Depression, where Scotland does not appear in the main source used (a Eurobarometer survey). The Scottish Red score stems from a BBC news item stating that 15 % of Scots seek medical attention for depression every year 4 , which is almost twice the number for England.
Second, it is important to see what criteria should be met by the Index. For this purpose, I introduce the theory for assessing of league tables. Gormley & David (1999) describe this theory as criteria for organizational cards. However, I argue, this theory can be applied on a higher level of aggregation. Cards are aimed at assessing not only specific organizations, but also “certain policy domains” in terms of Gormley & David (1999). The authors also suggest that “the states (in the USA) are very often themselves the subject of report cards in policy domains. For example, in the USA the Corporation for Enterprise Development, Washington, D.C., public policy advocacy and research group publishes an economic development card for the states every year. Working Mother Magazine publishes an annual report card on child quality, safety, availability, and commitment for all 50 states. And several organizations publish state-by-state summaries of environment protection efforts” (Gormley & David, 1999, p. 2). Thus, I argue, health care policy can also be viewed as a certain policy domain. Moreover, the criteria of Gormley & David (1999) proved to be useful for cross-national analysis of ranking, or league tables (Dill & Soo, 2005). Therefore, these criteria will also be used for the Index evaluation.
Minimum outstanding par value of at least EUR 250mn.
Quality Must be rated between Caa3/CCC-/CCC- and Ba1/BB+/BB+ using the middle rating of Moody’s, S&P, and Fitch after dropping the highest and lowest available ratings. No security can be rated below Caa3/CCC-/CCC- by any rating agency. When a rating from only two agencies is available, the lower (“more conservative”) is used. When a rating from only one agency is available, that is used to determine index eligibility.
It is in general infeasible to measure the substitution bias from its proposed deﬁnition since this requires knowledge of the true cost of living index. Section 4 therefore develops a second order approximation to the true substitution bias, which can be used in practice to determine the magnitude of the eﬀect. This approximate measure also decomposes the bias into a price substitution eﬀect, the magnitude of which is the size, or norm, of the price change in the Laspeyres plane, and a curvature or substitution eﬀect, which is measured by the directional shadow elasticity of substitution (DSES), a decomposition which quantiﬁes the relationship suggested by Braithwait (1980). The approximate bias allows us to compute an approximate index which may be considered an alternative to the Lloyd-Moulton-Shapiro-Wilcox approach mentioned above. The paper concludes, in section 5, with some comments and ideas for further work.
Results are presented in the form of relative RMSE of each equation against a convenient benchmark, for different forecasting horizons. The chosen benchmark is a simple version of (3.1) in which no indicator is used. Alternative specifications include as indicators the unemployment rate, GDP growth, the output gap and growth of M3. Dynamic factors comprise from 1 to 3 factors of the balanced and unbalanced panels. Each time, forecasting equations are estimated for a conveniently chosen sub-sample, out-of-sample forecasts done for the necessary steps ahead or until the end of the full sample was reached, and corresponding RMSEs collected. The same operation is repeated for longer sub-samples (extended recursively), each time collecting RMSEs. Finally, all RMSEs are averaged separately for each specific horizon. The RMSE for each combination of equation and horizon is divided by the corresponding one for the benchmark, and the resulting ratio shown on the table. A ratio of less than one means that for that horizon, the corresponding equation can beat the benchmark, the opposite being true otherwise. This procedure provides estimates of the true underlying forecasting performance of the equation by simple averaging of forecast errors. These forecasts take place within sample, but in periods not used to estimate the equation. At each step it is necessary to split the observed sample between a part dedicated to the estimation and a part dedicated to the calculation of forecast errors. If care is not exercised, a too early split date may lead to inaccuracies in the first estimations, and may bias the resulting RMSE test. Even worse, structural breaks in the data may lead to seemingly large RMSE numbers because of shifts in the forecasts done before any structural break. These problems dictate prudence in setting the initial date at which recursions are started, compounded in our case by the potentially unstable nature of euro area data. Accordingly, a relatively late first date for the out-of-sample exercises was chosen, i.e. 1995Q1. Results for earlier starting dates were performed and are reported, although a structural break before 1995 cannot, in our view, be dismissed so that stronger weight should be put on the findings for 1995Q1.
Scientists of Western Europe actively research how the rise of living standard changes consumer behaviour. It is becoming more and more difficult to understand Euro- pean consumer behaviour. According to J. M. Gillies (2003), “a weekend of a German couple may be like this: a 19 Euro flight to France with „German Wings” on Sat- urday, a luxury dinner for 160 Euro at “L’Ape Piera” restaurant, purchase of “Prada” handbag for 590 Euro and a flight back for 19 Euro“. According to the author, “a frequent consumer applies a formula before purchasing goods: “Aldi“and “Armani“, “Lidl“and “Louis Vuitton“, “Plus“and “Prada“. This example proves that a modern European consumer may be at the same time attributed to the high end as well as low end consumer segment. Here we may point out 2 noticeable tendencies. First, a middle social class or a high social class European tends to save when purchasing food and he usually shops at widespread shopping centres throughout all Europe. A typical Euro- pean is in the constant hunt for daily cheaper goods. On the second hand, the same consumers, in order to become exceptional and to emphasize their social status in the society, seek after famous trademarks. In other words, they purchase clothes or other status symbols with “fa- mous labels”; they budget for leisure, entertainment and pleasure.
36 “The treatment of mandated pollution control measures in the CPI,”
Consumer Price Index (U.S. Bureau of Labor statistics, October 16, 2001),
In addition to quality adjustments for physical changes to cars and trucks, adjustments are made for changes in the war- ranty coverage provided by auto manufacturers when suffi- cient data are available to derive estimates of their values. Vehicle leasing. The vehicle leasing index was first published by BLS in January 2002. The prices used in the index are monthly lease payments. As with new vehicles, the agreed- upon purchase price of the vehicle must be estimated. BLS economic assistants collect the base price and the prices for options, dealer preparation, transportation, and so forth. Also, any rebates available are included, along with the larg- est estimated concession or discount the dealer would allow for the leased vehicle on the day of pricing. Then, the lease terms are applied to obtain the residual value, depreciation amount, rent charge, and the total monthly lease payment. During the annual model changeover, the quality adjustments developed for the CPI new car index are also used in the CPI vehicle leasing index.
3 Investigating Different Rounding Policies with Real Data To construct a measure of inflation which is free from rounding error, this section uses the CPI’s Research Database (RDB) index data files. This database includes all of the major indexes from January 1986 to July 2005 at the full level of precision used internally at the BLS. For this paper, the CPI all-items index and its top-level components are consid- ered. Additionally, the information technology and personal computers indexes are included because they have seen rapid declines in price, and are probably the worst case scenario for rounding error in the post-1986 period.
Today, anyone can go on the internet and seek a diagnosis. There are options available such as question and answer chat boards, email services, and phone‑based call‑back services. At the most robust level, there are visits that engage both audio and video streams. What model do consumers percieve as highest quality for health care needs?
The calculation of the monthly All-Jamaica index starts with the measurement of price indices for a particular commodity. Price indices for the commodities are then combined following the hierarchy, with the appropriate weight being applied along the way. For example, the indices for fresh (or frozen) beef, canned beef, fresh (or frozen) fish, and other meat products are combined to form an index for “Meat Poultry & Fish”. Similarly, the various items that make up “Dairy Products Oils & Fats” are combined to obtain that index. These sub-group indices are then further combined to arrive at the group: “Food & Drink”.
the decline in the relative price of missed new goods prior to their introduction into the CPI basket, and i the measured rate of total inflation. Given the examples in the previous paragraphs, the missed price decline of a new good prior to its introduction into the CPI appears to be roughly 10 per cent annually. 40 The introduction of new products into the CPI in recent years helps provide an estimate of the share that had — until that point — been missed in the basket. In December 2002, for example, Statistics Canada introduced new basic classes into the CPI that accounted for just less than 1 per cent of the basket. 41 Admittedly, it is unlikely that prices were decreasing in the years just prior to their introduction for all products, since a few of these services had already existed for a number of years. Accounting also for new goods and services that are introduced below the level of basic class, 42 it is unlikely that more than 1 per cent of consumer spending in any given year is spent on goods or services not yet included in the CPI basket. 43 This would result in an annual upward bias of 0.05 p.p., with up to 0.10 p.p. of bias in periods of high product introduction (where s equals 2 per cent).
At the elementary aggregate level of the index it is usually impractical to assign a specific weight to each individual price observation. The three formulas described above implicitly apply equal weights to each observation, although the bases of the weights differ. The geometric mean applies weights such that the expenditure shares of each observation are the same in each period. In other words the geometric mean formula implicitly assumes households buy less (more) of items that become more (less) expensive relative to the other items in the sample. The geometric mean therefore appears to provide a better
Expressed at end of the period, not annual average data, consumer price index (CPI) measures changes in the prices of goods and services that households consume. Such changes affect the real purchasing power of consumers’ incomes and their welfare. As the prices of different goods and services do not all change at the same rate, a price index can only reflect their average movement. A price index is typically assigned a value of unity, or 100, in some reference period and the values of the index for other periods of time are intended to indicate the average proportionate, or Consumer Price Index (CPI). In February 2014, consumer price indices by COICOP increased on average when related to
establishes the short-term relationship between the exponential autocorrelation model of the first-order single covariance vector. In the short term, the change in the purchase price index for raw materials, fuels and power is the driving force behind changes in other price indices. In addition, Chen (2008) analyzed the influence of macroeconomic variables on price level and the relationship between indicators by using Granger causality test and K2L information and time difference correlation analysis. and do a further study on China's price transmission. Analyzes the transmission mechanism of the upstream price to the consumer price and the transmission price of the means of production to the classified consumer price. The analysis shows that in the market-oriented industry, the price transmission works; and some government-controlled or monopoly industries, the price transmission does not work. These conclusions provide an important basis for the government to formulate macro-control policies. He (2008) using the CPI and PPI data from January 2001 to July 2008, and
It should be noted that the expansion of new and low-priced outlets such as discounters and road-side shops, sometimes represented by the development expressed as “price busting,” does not progress at a constant pace. In particular, recent price development and consumer behavior suggest that the shift from department stores and specialty shops to discount outlets has largely subsided, and price differences between these outlets has settled down to a level consistent with the difference in retail service quality provided by them. This phenomenon implies that measurement errors induced by structural changes in the retail market have been diminishing in recent years.
The consumer price index (CPI) mainly includes the year-on-year index, which is calculated by using the same month of previous year as the base period (pre- vious year = 100), and the chain index by taking the previous month as the base period (previous month = 100). Although the year-on-year index could weaken the influence of seasonal factors to a certain extent, the year-on-year index in- cludes the carryover effects and the new price-rising factor, which cannot reflect the turning point of the macro-economy timely, thus affecting the accuracy of the fluctuation calculation and the forecast of the level of consumer price and bringing difficulty for the formulating of macro-control policy. Research shows that the inflection point of the economic cycle, which is reflected by the non- seasonally adjusted year-on-year CPI lags 6 months on average . Moreover, the chain index mainly reflects the short-term trend of price changes, but it does not exclude seasonal factors, holiday, working days, trading day and other non- How to cite this paper: Zhang, T.Y. (2017)
Apart from the sheer effectiveness of the approach, the basic reason for the concentration on CUTS, when available, is that data collection primarily based on information obtained from 30 national sources, even if those sources are official Ministry of Health or National Health/Statistics agencies, generally yields a high noise level. It is notoriously difficult to obtain precise answers from many sources even when these sources are all answering the same, well-defined question. For example, in an earlier Index project, it was difficult to ask questions about a well-defined indicator such as “SDR of respiratory disease for males >45 years of age”. For one country protesting violently against their score, it took three repeats of asking the question in writing before the (very well-educated) national representative observed that the indicator was for “males 45+” only, not the SDR for the entire population. It has to be emphasized that also when a CUTS for an indicator has been identified, the data are still reviewed through cross-check procedures, as there have frequently been occasions where national sources or scientific papers have been able to supply more recent and/or higher precision data.
It is well known that the Consumer Price Index (CPI), as a Laspeyres-type index, attempts to measure the average change in the prices paid by urban consumers for a fixed market of goods and services, and new samples for most item categories are routinely introduced over time to keep the CPI sample representative of consumer spending patterns. The CPI normally overstates the true rate of increase of the cost of living. In this paper, our main objective is to propose a new measurement in the CPI which combines with the Gross Domestic Product (GDP). This new method will make the bias effectively decreased.