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Record W1600584503 · doi:10.30541/v47i3pp.267-285

Biases in Consumer Price Index Methodology in Pakistan: Suggestions for Improvements

2024· article· en· W1600584503 on OpenAlex

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aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
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Bibliographic record

VenueThe Pakistan Development Review · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsConsumer price index (South Africa)Index (typography)EconomicsEconometricsComputer scienceMacroeconomicsWorld Wide WebMonetary policy

Abstract

fetched live from OpenAlex

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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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.356
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it