Biases in Consumer Price Index Methodology in Pakistan: Suggestions for Improvements
Bibliographic record
Abstract
The issues relating to the complexity of the measurement of the Consumer Price Index (CPI) which is regarded as the best and most well known indicator of inflationary trends and without referring to which economic policies cannot be evaluated have long been debated. Any measurement error in CPI may over or understate inflation, which can have serious repercussions on monetary, fiscal and other economic management policies. The report of the Boskin Commission [Boskin, et al. (1996)] has identified the possible sources of bias in the CPI. These biases which this study has also corroborated through a primary survey of selected households relate to commodity and outlet substitution, quality adjustment and new product introduction as well as index calculation in the existing methodologies. In this paper these biases have been evaluated for Pakistan and ways to improve the construction of the Index have been suggested. Other issues in Pakistan relate to selecting a representative product (or good), defining average quality, data collection, weights determination and base year change. The use of the Geometric means index formula and Laspeyre’s Index to reduce the formula bias has been proposed in this study. 1. INTRODUCTION The Consumer Price Index (CPI) is an index number measuring the average price of consumer goods and services purchased by households. It is one of the several price indices calculated by national statistical agencies. The percent change in the CPI is considered as a measure of inflation. The CPI can be used to index (i.e., adjust for the effects of inflation) wages, salaries, pensions, or regulated or contracted prices. The CPI, along with the population census and the National Income and Product Accounts, is one of the most closely watched national economic statistics. The relative prices of different goods and services change frequently in a time interval due to various factors and these changes lead to change in the consumers’ buying behaviour. As there has been sizeable increase in the population of the lower and the middle class,1 demand patterns have tended to shift increasingly to services2 away from goods, and to characteristics of goods and services like better quality, variety and greater convenience. But all these factors, plus others, mean a larger part of what is produced and consumed in an economy is more complex to measure than it was a couple of decades ago when the economy largely consisted of smaller number of easier to measure items such as flour and onions.3 Inflation in a complex dynamic market economy is hard to measure. Further, the rapidly changing behaviour of economic agents puts tremendous pressure on a statistical system to keep up with the change and provide the coverage of context and scope. However, agencies which construct the CPI are constantly engaged in research to improve the measurement. The Federal Bureau of Statistics (FBS) is the main agency doing this work in Pakistan. Like several other developing countries4 the FBS has no research programme to improve the CPI estimation methodology. As a result it could not incorporate any remedial measure for several biases which were pointed out by the Boskin Commission more than a decade ago. The report of the Boskin Commission [Boskin, et al. (1998)] has focused a great deal of attention on the CPI issues. This report created much interest in research circles. It identified possible sources of bias in the CPI like substitution, outlet, quality and new product. This report has called into question the accuracy and relevancy of the CPI even when international standards are followed. Since the release of this report, major revisions in the CPI have been under consideration in various countries in the light of the issues raised in it. New Zealand, Australia, Canada, Japan and European countries have taken a lead in this regard. Many issues on CPI methodology, like outlet and substitution biases have been the object of considerable research in these countries. On the contrary developing countries are facing two main constraints in revising the construction of the CPI. The first one is the shortage of trained economists and statisticians in the area of price statistics, and the second is the concerned agencies’ limited funding capacity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".