Price Index Numbers Research Articles

Nankai school: The experience of adapting economics to Chinese conditions in the 1920s–1930s

Using the example of the activities of the Nankai Institute of Economics in the second quarter of the twentieth century, the article analyses the problem of adapting Western economic theories to the study of the Chinese economy. At the heart of the program of sinicization of economic research and education proposed by the Nankai school was the work of collecting and systematising reliable information on the Chinese economy. In the second half of the 1920s, Nankai University became a leader in China in conducting socio-economic surveys, compiling index numbers of prices, studying selected industries and rural regions. The founders of the Nankai school. He Lian and Fang Xianting were educated in economics in the United States; up until the late 1940s, the Nankai Institute of Economics was highly dependent on American grant support. This did not prevent them from setting the objectives of “knowing China” and “serving China” by combining foreign theories and methods with an understanding of the real economic situation based on reliable quantitative data. The task of “localization” of economics stimulated writing of pioneering university textbooks that explained general theoretical concepts through Chinese examples. Focus on solving China’s problems led the economists to abandon copying ready-made foreign prescriptions. During the two decades of activity in the Republican period the Nankai school made major achievements in collecting factual material on Chinese economy and adapting courses, its legacy has become an important starting point of the contemporary policy of sinicization of economics in the PRC.

New Characteristics and Hedonic Price Index Numbers

Abstract Changes in product characteristics on the extensive margin (the addition of new features and the removal of old ones) are an important and hitherto neglected dimension of quality change. Standard techniques for adjusting price indices for new goods cannot handle such changes satisfactorily, and this leads to an economically and statistically significant bias in the measurement of prices and real output. We combine insights from the theories of exact index numbers and demand for characteristics to develop a new method for incorporating changes on the extensive characteristic margin. Applied to U.K. data on new car sales, our method leads to revisions in estimated inflation rates for this commodity group that are both plausible and quantitatively important.

Updated BG Prasad's classification for the year 2022.

Sir, Socioeconomic status (SES) is a proxy indicator for the health of an individual and his/her family.[1] The modified BG Prasad scale is one of the commonly used SES scales to measure the SES in urban and rural areas. BG Prasad’s classification was introduced in 1961.[2] BG Prasad’s classification is based on the Consumer Price Index for Industrial Workers (CPI-IW). CPI-IW is designed to measure a change over time in prices of a given basket of goods and services consumed by a defined population (i.e., Industrial Workers). This index is prepared using the Industrial Workers residing in 70 centers of industrial importance, distributed among the states in proportion to industrial employment. This index is revised every month by the Labour Bureau, Government of India. This reflects the previous month’s change in the Indian Economy and Indian Rupee. CPI-IW and linking factors between the base years are the essential components for calculating the SES. The base year of price index numbers is revised at regular intervals, reflecting the changes in the population’s consumption pattern.[1] Linking factors are used to convert the CPI from the new base to the old base. The first base year for CPI is 1960. Taking the base of CPI for 1960 as 100[2] and it got modified in 1982, 2001, and 2016. The linking factors for 1982, 2001, and 2016 were 4.93, 4.63, and 2.88, respectively.[3,4] The linking factors from the year 1960 to 2016 are summarized in Table 1.Table 1: Linking factors for the calculation of per-capita income from the year 1960 to 2016Updated and modified value for BG Prasad’s socioeconomic classification for July 2022 can be calculated as follows: Calculating multiplying factor for July 2022 from the latest available CPI-IW for May 2022[5] Multiplying factor = Current index value for May 2022 (=129.0)/Base index value in 2016 (=100) =1.29. The formula for calculating new income value for July 2022: New value = Multiplying factor (1.29) × [Old value × Linking factor between 1960 and 1982 series (4.93) × Linking factor between 1982 and 2001 series (4.63) × Linking factor between 2001 and 2016 series (2.88)]. The multiplication factor came out after the above calculation came out to be 84.80. That means 100 INR in 1961 is equal to 8480 INR in 2022. This shows the amount of recession that happened to the value of the Indian Rupee in just 40 years time span. The updated and modified value for BG Prasad’s socioeconomic classification was calculated as per the new income value for July 2022. is given in Table 2.Table 2: Modified BG Prasad classification for May 2022Usually many do not feel comfortable revealing their income details. So, BG Prasad’s socioeconomic classification can include other indicators of income such as the preferred mode of travel: self-funded air travel to domestic and international destinations, travel by train in an air-conditioned (AC) coach, non-AC coach. Other SES scales also need to be updated periodically which portray the change happening in the social strata. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Hedonic Price Index Number for Blocks of Flats and Terraced Houses in Finland

Hedonic Price Index Number for Blocks of Flats and Terraced Houses in Finland

Retrospective Computations of Price Index Numbers: Theory and Application

Due to outdated weighting information, a Laspeyres‐based consumer price index (CPI) is prone to accumulating upward bias. Therefore, the present study introduces and examines simple and transparent revision approaches that retrospectively address the source of the bias. They provide a consistent long‐run time series of the CPI and require no additional information. Furthermore, a coherent decomposition of the bias into the contributions of individual product groups is developed. In a case study, the approaches are applied to a Laspeyres‐based CPI. The empirical results confirm the theoretical predictions. The proposed revision approaches are adoptable not only to most national CPIs but also to other price‐level measures such as the producer price index or the import and export price indices.

EFFECT OF INFLATION, ER, SBI AND NET INCOME ON INDUSTRIAL AND CHEMICAL SECTOR COMPANIES

IHSG is a stock price index number that has been compiled and calculated by generating a trend, where the index number is a number that is processed in such a way that it can be used to compare events that can be changes in stock prices from time to time. The purpose of this study was to determine whether there is a significant effect between inflation, exchange rates, SBI interest rates, and net income on the composite stock price index. There are 80 company populations during 2017-2019 with a total sample of 34. This type of research uses explanatory research, the research location is carried out on the IDX. Data collection was obtained from BEI, BPS and BI. The data analysis technique used statistical analysis techniques with multiple linear regression analysis methods. Based on the tests that have been carried out, the results show that inflation, interest rates, and net income have no effect on the IHSG, but the exchange rate has a significant effect on the IHSG

Price Index Numbers under Large-Scale Demand Shocks–The Japanese Experience of the COVID-19 Pandemic

Abstract We investigate the prices and quantities of face masks when the 2020 COVID-19 pandemic was particularly serious to understand the impact of demand shocks on the cost of living index (COLI). Using a recently developed index number formula that is exact for the constant elasticity of substitution utility function with variable preferences, we quantified the degree of demand shock caused by the pandemic. Our empirical analysis revealed that shifts in preferences during the pandemic were so large that the COLI with variable tastes became very different from the standard superlative indexes. While the prices of face masks decreased in the Fisher index in May 2020 by 0.76% per week, the COLI increased by 1.92% per week.

Comparison of Production Costs in the Financial Statements PT Japfa Comfeed Indonesia Tbk With PT Charoen Pokphand Indonesia Tbk Using Number Index

This study used a relatively simple price and an unweighted aggregate price index number used to calculate the level of production cost annually in the form of a percent. The use of this index number is to compare the current year period divided by the based year and multiplied by 100%. The purpose of this study is to analyze the comparison of production costs from animal feed companies in Indonesia, namely PT Japfa Comfeed Indonesia. Tbk and PT Charoen Pokphand Indonesia Tbk in 2014-2018 with 2014 as the base year. Based on the calculation of production costs using the index number method at PT Japfa Comfeed Indonesia Tbk and PT Charoen Pokphand Indonesia Tbk there is an increase and decrease in production costs. PT Charoen Pokphand Indonesia Tbk has better production costs than PT Japfa Comfeed Indonesia Tbk because the profits of PT Charoen Pokphand Indonesia Tbk company have increased more than PT Japfa Comfeed Indonesia Tbk every year.

Hedonic Price Index Number for New Blocks of Flats and Terraced Houses in Finland

Statistics Finland has relatively long experience in constructing indices of prices of old flats using the hedonic approach where regression analysis and classification of flats (i.e. stratification) are combined. Practically this means: First the price model is based on the fixed effect model applied for separate regions and second the quality adjustment is performed within each regional cell in a classification (i.e. inside stratums) using so called Oaxaca decomposition. The hedonic price index is computed by aggregating regional cell level (i.e. stratum level) quality adjusted prices using the logarithmic Laspeyres formula. In this study we develop hedonic price index for new flats. In outline, we use similar methods as in case of old flats, but we develop Oaxaca decomposition in the cell or stratum level for unweighted and weighted arithmetic and geometric means – these are our first new results for hedonic methods. For that we develop new theorems for price aggregation from observed prices into stratum aggregates by logarithmic mean. New theorems of price aggregation are performed to semilogarithmic price models. Our price aggregation form observation level into strata leads to three new decomposition average prices – decompositions for unweighted arithmetic average prices and weighted arithmetic and geometric average prices. The stratum level Oaxaca decomposition for different price aggregates, that is, quality adjusted price changes and quality corrections, are aggregated using several basic (i.e. Laspeyres, Paasche, log-Laspeyres, log-Paasche,…) and excellent index number formulas (i.e. Tornqvist, Fisher, Mongomery-Vartia,…). The price aggregation is performed separately for weighted and unweighted arithmetic and geometric average prices. The construction strategy of index series in this study is based on the base strategy. By this strategy index series are free of chain error (or drift). Our test data is a high-quality register data on all free-market transactions of dwellings in new blocks of flats and terraced houses. Data includes statistical unit specific information of unit prices, quantities, values and of some unit specific quality characteristics form 2010/I to 2018/IV being quarter data. The quarter data includes about 2000 – 3000 observation per a quarter.

Hedonic Index Numbers for Rents of Office and Shop Premises in Finland

The focus of the study is to join together regression analysis and index numbers to get quality adjusted price change for rents of office and shop premises. First, we apply simple semilogarithmic linear price models, the fixed effects model (FE), and estimate them by the OLS method. We weight these estimated equations from observations into stratums for unweighted and weighted arithmetic and geometric averages such that the basic algebraic properties of the OLS method are satisfied. We show that the aggregation leads to reparameterization of the FE’s (here stratum effects) of the OLS method for the unweighted and weighted arithmetic and the weighted geometric averages. The solution for the unweighted geometric average is trivial and shown in any statistical textbooks. Second, we apply the Oaxaca decomposition (1973) for the aggregated price models (i.e. stratum aggregates) for these four basic averages and divide the actual price change of these averages into two parts: into quality correction and quality adjusted price changes. For these Oaxaca decompositions of different averages, we apply index number theory to estimate price changes for crude aggregation levels. All our analysis, including the basic and excellent index number formulas (see Vartia and Suopera, 2018), are based on their logarithmic representations. Our focus for the use of the logarithmic representations culminates into the theorem of additive decomposition of the value change by which we can compare the price change estimates based on unit values (i.e. weighted arithmetic averages) and especially Montgomery-Vartia price index. We show explicitly that inadequate control of rented squares leads to serious bias – the actual price change includes not only price changes but also quantity changes, which may dominate causing serious bias for price index numbers. Our test data comes from the KTI Property Information Ltd covering large municipals in Finland. The data do not include certain ID-key for the construction of bilateral price-links and thus a hedonic method is our approach to measure price index for rents of office and shop premises. The weighting into the population level is at the moment impossible because lack of data, we make all inference only for the conditional information set of our data. All statistical and mathematical methods in this study are programmed by the SAS and the user interface for the official production of statistics by the SAS EG.

Circular Error in Price Index Numbers Based on Scanner Data

Differences between chain and base methods (or strategies) are analyzed. This is a preliminary paper that concentrates on numerical results and new ideas. Omitted proofs will appear in later papers. As in title, we use the term circular or chain error. We do not use milder terms as drift or deviation in the identity or circular test, because it is definitely an error of the chain index. Chain error is a norm of all proper index numbers formulas (except Lowe, Jevons and their derivatives which are not proper) and it vanishes only in special circumstances. Our main claim is that chain error is almost zero only if all its links are described by the same almost ideal demand theory. This is demonstrated for a comprehensible set of both contingently biased and excellent index numbers.

Comparing Basic Averages, Index Numbers and Hedonic Methods as Price Change Statistic

Measurement of price change is based on averages - also, hedonic price index numbers. As NSIs obtain scannerdata having varying content, question arise: what is correct average to use? We compare in this paper price change estimates based on most known averages and index numbers including quality adjusted methods. All analysis is done in logarithmic scale. The statistical analysis of our data is based on graphical presentations and mathematical analysis is presented mostly in our earlier papers.

The impact of Consumer Price Index Numbers indices on inflation in Sulaymaniyah Governorate for the period 2009-2019

The impact of Consumer Price Index Numbers indices on inflation in Sulaymaniyah Governorate for the period 2009-2019

Revised Socioeconomic Status Scales for the Year 2021

Context: Most of the public health and medical research include economic and social status as one of the pertinent predictors for health-related variables at the individual or family level. Modified B. G. Prasad scale (1961) and Modified Kuppuswamy scale (1976) are two of the most widely used socioeconomic status scales in India for health and social research. The income ranges in these scales need a frequent update with a rapidly growing economy and changing consumption patterns with time. Aim: This study revised the Modified Kuppuswamy and B. G. Prasad scales by updating income ranges as per the latest consumer price index (CPI) numbers of industrial workers (IW) and latest base year (2016) extracted from the Labour Bureau, Government of India. Methodology of the Study: Multiplication and conversion factors were calculated using CPI-IW (base year 2016 = 100) and linking factor. Updated income ranges for the Modified Kuppuswamy scale were estimated after calculating the multiplication factor (MF) between the year 1976 and the present year, whereas linking factor 2.88 and MF 1.20 have been used to calculate the income limits for the Modified B. G. Prasad scale. Results: The updated income range of the Modified B. G. Prasad and Kuppuswamy scale was estimated to be 78.89 and 26.46 times the income range values of the original scales (1961 and 1976), respectively. Conclusion: This update is relevant and highly needed after the introduction of the latest series of the base year in 2020, without which the income ranges would have been overestimated if calculated with the previous series (Base year 2001 = 100).

Hedonic Price Index Number for New One-Family Detached Houses in Finland

Hedonic Price Index Number for New One-Family Detached Houses in Finland

Extended stochastic Laspeyres price index model with autocorrelated errors

This paper extends the stochastic approach to Laspeyres price index number by augmenting the simple model with a systematic change in relative prices when the errors are serially correlated of order one. The estimators and their standard errors are derived using weighted least square (WLS) approach where two round estimation process is considered. The first round of estimation gives the estimators of extended Laspeyres price model. While the standard errors of estimators are obtained in second round of estimation. The performance of proposed method is presented by an illustrative application.

Measuring Spatial Price Level Differences within a Country: Current Status and Future Developments

The importance of constructing sub-national purchasing power parities (PPPs) has been acknowledged in the literature for over two decades. In countries characterised by large territorial differences in prices and quality of products and household characteristics, calculation of sub-national PPPs can improve and serve as inputs for regular compilation of major indicators as spatial price index numbers, real per capita expenditure adjusted for price level differences across regions and estimates of inequality and poverty produced. This paper provides an overview of the data requirements and index number methods specifically designed for producing consistent set of spatial price comparisons; and a review of the attempts to compile sub-national PPPs around the world. Recent efforts by researchers in Italy in cooperation with the Italian Statistical Office (ISTAT) in the compilation of PPPs for regions in Italy are reviewed and preliminary results from these studies are presented. Finally, the paper scopes future efforts in this important area by discussing the opportunities and challenges offered by the availability of high-frequence point-of-sale data in the form of scanner data for the compilation of sub-national PPPs.

New Characteristics and Hedonic Price Index Numbers

Changes in product characteristics on the extensive margin are an important and hitherto neglected dimension of quality change. Standard techniques for quality-adjusting price indices cannot handle such changes satisfactorily, which leads to an economically and statistically significant bias in the measurement of prices and real output. We combine theoretical insights from index numbers and demand for characteristics to develop a new method for incorporating changes on the extensive characteristic margin. Applied to data on new car sales in the U.K., our method leads to revisions in estimated inflation rates for this commodity group that are both plausible and quantitatively important.

A RESEARCH PAPER ON A STUDY ON PRICE INDICES METHODS IN INDIA

It is expected that the price index numbers which are universally used as the scientific tool of measures of price changes all over the world must reflect the real and true position of prices in economy every time since they are the only arithmetic source of measuring the price fluctuations and economic fluctuations thereby. But unfortunately, they do not seem to fulfill these expectations fully especially in India. Many times it has happened that price index numbers have failed in reflecting real economic conditions through price changes. So, this research is attempt to study all the different price indices model and try to find out the effective way out.

Repeat Assessed Values Model for Housing Price Index

Abstract This study proposes an innovative methodology, named Repeat Appraised Price Model (RAV), useful for determining the price index numbers for real estate markets and the corresponding index numbers of hedonic prices of main real estate characteristics in the case of a lack of data. The methodological approach proposed in this paper aims to appraise the time series of price index numbers. It integrates the principles of the method of repeat sales with the peculiarities of the Hedonic Price Method, overcoming the problem of an almost total absence of repeat sales for the same property in a given time range; on the other hand, the technique aims to overcome the limitation of the repeat sales technique concerning the inability to take into account the characteristics of individual properties.