Country Experience in Mongolia: Consumer Price Indices


Data computation of computer price indices is a challenging issue because different jurisdictions adopt unique methodologies when doing so. This study explores how CPI is computed in Mongolia by investigating four main issues: services in the CPI, new data sources for compiling CPI, the importance of understanding and meeting different users’ needs, and methodological issues in CPI Computation. Based on an assessment of these four key issues, this study demonstrates that Mongolia employs a thorough review of data analysis methods to produce monthly CPI information. These practices are based on global standards and replicate those adopted by countries that have similar social, political, and economic dynamics.

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Some key areas of CPI computation that need improvement include the tracking of price movements and the adoption of new survey instruments. It is important to address these issues because the Mongolian economy has significantly transformed in the last couple of years and traditional measurement methods may fail to capture real price movements. Thus, it is essential for the country to move away from adopting conservative methods of price computation and adopt more elaborate and dynamic instruments of the same. However, the Mongolian National Statistical Office, which is responsible for computing changes in CPI, needs to be prepared to shoulder the cost of implementing new systems. A big percentage of the expenditure may be related to the cost of training employees to use the new technology and the cost of buying new equipment. Although technology costs have declined over the years because of increased efficiencies in production, this study demonstrates that the Mongolian National Statistical Office could lead the way in exploiting the benefits of new technology better than small businesses because it can easily shoulder the cost of doing so.

CPI Country Experience in Mongolia

This report covers four main areas governing the computation of consumer price index (CPI) data in Mongolia. Discussions contained in this paper aim to explore issues relating to the computation of services, the challenges and opportunities created by the use of new data sources for compiling CPI, the process of understanding and meeting new consumer needs, and the methodological issues in CPI computation. Although the paper focuses on Mongolia, references will also be made to global challenges affecting CPI data production and management. The goal is to give an in-depth insight regarding the computation of CPI data in Mongolia and to provide suggestions about how to improve the process of CPI computation in the country.

Services in the CPI

The Mongolian National Statistical Office (NSO) is responsible for computing changes in CPI. Its responsibility also stretches to tracking inflation levels and reporting on the same (International Monetary Fund, 2012). In this regard, data relating to household expenses and associated family budgets are reviewed comprehensively. Services comprise a significant portion of the overall analysis of consumer price index (CPI) by the NSO because they form a growing consumer segment in most household expenses. In fact, according to Sohail, Hina, Amina, and Asma (2014), services comprise about 50% of all household expenses.

There are significant problems associated with examining this index because they attract several computational challenges. For example, it is difficult to keep track of the associated numbers because different units often vary across multiple periods. Similarly, there are difficulties associated with choosing the right sample of the analysis. Since services also have different qualities, assessing their true value could pose significant challenges to economists because of variations in the same. Based on this concern, Sohail et al. (2014) propose that all service-centered computations should account for variations. Another problem witnessed in the assessment of service quality is the frequent bundling of its value to that of other products. This marketing strategy, which is often pursued by several service providers, often makes it difficult for analysts to distinguish the true value of the service and the product.

The difficulty in determining changes in rental prices demonstrates the problem of tracking the price of services in the CPI. Rental properties pose a unique challenge for many analysts because different people use a multitude of factors to determine this index. The use of different types of software to undertake CPI further compounds the problem because new questions arise from the implementation of these technological tools in CPI measurement. For example, some problems are associated with assessing the true value of the digital economy and the value added to conventional services if an intermediary technology is used to complement service provision.

Part of the problem associated with quantifying changes in the price of services is the human factor moderating it. In other words, services are usually people-centered. Although many people would like to believe that specific services are representative of the price they pay for them, the truth is that the quality of services may change almost spontaneously. The ability of service standards to change quickly makes it difficult to track them. This problem stems from the difficulty of regulating service standards. Indeed, unlike goods and services, which are easy to track and assess, services are difficult to quantify and regulate. Furthermore, they are subjective in nature and people’s experiences of the same thing may vary extensively. Therefore, what one person may consider as “good service” may be unacceptable to others.

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The problem usually boils down to people’s perception of the services they receive and the kind of value they believe they are getting from it, relative to the price they are willing to pay. For example, an American citizen may have a different kind of perception of quality service from a Mongolian citizen. While on the one hand a Mongolian could deem a certain service as satisfactorily offered, the American could deem the same service as inadequate. Their difference in perception also stretches to the price they are willing to pay for the same service and their assessments of whether they are getting value for money, or not. Using the above example, typically, the Mongols would believe that he is getting value for money (by virtue of being satisfied with the services offered), while the American would possibly be feeling “ripped off.”

The above example shows that tracking the price of services in CPI computation poses a challenge to the Mongolian NSO. The office also experiences compounded problems stemming from the fact that one company may provide the same service differently. This statement shifts the focus away from the customer, who is the recipient, to the service provider (the supplier). Since services are products of human-to-human relationships, it is common to find people who know each other enjoying the benefits of good service, while those who do not know each other suffering from an absence of the same. Therefore, the human element in service provision and reception plays a significant role in understanding the challenges associated with tracking the price of services.

The challenge associated with the inclusion of services in the consumer price index could be best demonstrated by the complexity of tracking changes in the prices of financial products in Mongolia. Two key issues emerge in this analysis. The first one is item domain issues, while the second one is price measurement problems (Ambukege, Justo, & Mushi, 2017). An analysis of domain issues alone reveals several complexities associated with categorizing service consumption. This review means that, while it may be right to assess consumption by analyzing one kind of service, it could be misleading to believe that this service exists in isolation.

As is the case in many financial services, several categories and subcategories could be deduced from one service alone, regardless of whether they lead to consumption, or not. For example, many Mongolian households could record several transactions that may fit the description of financial services. Some of them include seeking financial advice, seeking services associated with deposit and loan facilities, obtaining services provided by life insurance companies, and currency exchange (just to mention a few). It could be argued that some of these services may be attached to some capital-intensive activity. If this probability exists, it means that they should themselves be capitalized, or at least be categorized as not fitting the category of “consumption,” as far as CPI computation is concerned.

Overall, services pose unique challenges to CPI computation, based on their subjective and volatile dynamics. For example, those of the telecommunications sector is different from web-based services and similarly, those of the insurance industry are different from the challenges in the health sector. These issues could have a significant impact on the overall quality of the CPI index for Mongolia. However, it is important to point out that the challenges identified above are not unique to the East Asian country. In fact, different studies around the world have highlighted them as a global issue with little or no variance.

New Data Sources for Compiling CPI

New data for compiling the CPI could have several benefits, including a reduced response burden and efficiency gains. Other benefits associated with the same include better official price statistics and increased depth of information relating to CPI calculations. However, as is the case with the expansion of data, methodological, and practical measurement problems suffice. One type of information that has emerged as a new source of data for the Mongolian NSO is scanner data. De Haan, Willenborg, and Chessa (2016) alludes that different companies today recognize its importance and have accorded it the same value as other types of traditional information. The use of scanner data in CPI computations creates different issues for Mongolian economists, including computation (calculation) problems, which stem from the merger between traditional data sources and new pieces of information. Nonetheless, some selected countries have already included scanner data as one of their sources of information for CPI computation (De Haan et al., 2016).

The use of scanner data in compiling CPI measures creates selected benefits for economists. For example, an article by De Haan et al. (2016) specifies that this type of data poses three categories of benefits to statisticians, which include more data (which would lead to less variance), improved quality of data collected (less bias) and better methods of data collection. An analysis of the first category of benefits could be contextualized to the collection of CPI information for supermarkets. Deutsch (2016) has done the research, where he said that in most developed countries, the review of scanner data in supermarkets is based on three criteria: items, outlets, and weeks. This type of data exceeds the traditional ratio of 1,000 to 1.

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To demonstrate this fact, Deutsch (2016) uses an example of a supermarket in Mongolia, where CPI data for cereals are based on 675 individual price considerations. This figure implies that authorities make one observation per month for multiple items in every store. The observation would typically involve a sample of about 300 outlets. As opposed to the two or three observations per month completed using the traditional data collection technique, scanner data would allow the NSO to make up to six observations per month. In the above example, this finding would mean that over 200 cereal items per store would be subject to the observations and the sample would expand to about 3,000 outlets as opposed to the previous 300. If the scanner data are limited to about 21 days of operation, the NSO could make 1.8 million price observations. This analysis means that scanner data is rich in information and significantly provides analysts with more data for evaluation.

Although the benefits of using scanner data in CPI are well documented, there is a need to harmonize laws governing the process because several economists have noted disparities in the legal framework governing their implementation. For example, Deutsch (2016) advocated for the harmonization of the European Union (EU) rules governing the same because there is a high possibility that different countries would make different interpretations of the same data. Nonetheless, many observers agree that before such harmonization occurs, it is essential to exploit the benefits of scanner data because traditional data manual collection techniques are prone to massive errors and inefficiencies (International Monetary Fund, 2012).

Many people have welcomed the benefits of using scanner data (highlighted above), but a review of the experiences of retailers when implementing the technology reveals that the organizations willing to use them need to be prepared to shoulder the cost of implementing such a system. A big percentage of this expenditure involves the cost of training employees to use the new technology and the price of buying new equipment. Although these costs have declined over the years because of the increased availability of scanner data technology in Mongolia, experts say large organizations could exploit its benefits better because they can easily shoulder the cost of new technology compared to smaller businesses.

Nonetheless, some issues associated with the use of scanner data have emerged because studies that have investigated the use of big data in the CPI computation in Mongolia reveal that the development could bring methodological and practical issues in implementation (International Monetary Fund, 2012). For example, traditional data analysis techniques are unable to cope with the sheer volume of information generated from the use of scanner data. Therefore, there is a need to deploy advanced analysis techniques to manage such data. In other words, there is a need to use advanced software in the analysis of scanned data. Therefore, economists may need to review calculation formulas for the regular CPIs as organizations reorient their processes to align with the technological requirements of big data use. This view is highlighted in several studies, including those of the International Monetary Fund (2012) and the International Monetary Fund (2013).

Understanding and Meeting Different User Needs

Experts have traditionally used CPI evaluation to analyze the cost of living and to determine the compensation that affected people could be subject to receive. However, CPI has recently transformed and the index serves a greater purpose, which is characterized by the expansion of its need scope. An assessment of inflation levels is one of many new uses of CPI today. The Mongolian experience is similar because authorities have used CPI to examine inflation levels and evaluate the country’s economic performance. Since the government pays keen interest in the welfare of its people, it has used the CPI for purposes of indexing money (International Monetary Fund, 2012). The goal has been to understand if the citizens are able to maintain their purchasing power through varied economic environments. At the same time, the government has used different measures of the CPI to review how large monetary reserves could help improve the welfare of the citizens. For example, part of the monetary indexation strategy has been to use the CPI to index wages and social programs.

This strategy has also included the indexation of interests and rents. The goal has often been to regulate contractual payments involving the citizens. Through this process, the government has been striving to maintain the standards of living for its people. These reviews show that the CPI has not only influenced the fiscal policy needs of Mongolia’s economy, but also the monetary policy requirements as well. The use of CPI data for fiscal and monetary purposes in Mongolia is partly demonstrated by the fact that the government has used it as an instrument to deflate the economy during times of hyperactivity and index specific contracts, which have a significant impact on people’s economic welfare (International Monetary Fund, 2012). The use of the CPI to meet the needs of Mongolians augurs well with common practices regarding the use of the same concept because studies by Deutsch (2016) and Ambukege et al. (2017) have shown that CPIs are often used for compensation purposes, indexation of wages, and managing social transfers and pensions.

Although the CPI is useful for different types of users, there are selected implications when using the index to serve different segments of the Mongolian population. For example, CPI calculations could influence how users interpret the findings. In some countries, CPI is calculated monthly, while in others, such calculations happen annually. The monthly calculations would provide a more in-depth and accurate picture of the index compared to the annual review. Most publications of CPI in Mongolia are done monthly. This system means that data users should be aware of the benefits and limitations of such a system and account for the same in their use of the data. For example, fiscal policy experts are always aware of this fact and only compare this type of data with other countries or regions that share the same methodology for calculation (Deutsch, 2016).

Methodological issues would also stretch into determining the number of samples used to come up with final CPI numbers. For example, America develops its CPI figures by basing its calculations on 23,000 retail and service companies (International Monetary Fund, 2012). Mongolia uses a different approach because it is a relatively smaller economy and does not have a robust service sector market as America does. Therefore, its CPI calculations hinge on a lower sample of retail outlets. At the same time, the country’s calculations predominantly focus on the commodity sector because the agriculture and mining sectors are its most vibrant economic subcategories (International Monetary Fund, 2013).

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The CPI calculation is not the only feature, which would influence how people interpret data because the methods used by authorities to communicate CPI outcomes would have a similar effect. Notably, timely communication is essential for all users of CPI data because the data contained is often time-sensitive. Therefore, if not communicated well, the information could be useless. For example, inflation statistics relating to the Mongolian economy in the year 2000 are irrelevant to an economist in the year 2017 because the data used today are different from the past.

Proper communication is also useful in making sure that user needs are effectively met because people may make assumptions about such data if they are not fully furnished with information relating to the methodological processes adopted. For example, it would be useful for authors of such data to state the associated limitations of data collection to allow users to act on them accordingly. Basing such interactions on proper and comprehensive communication networks would also give users an opportunity to give their views regarding the index and how best to improve their usefulness. When these issues are analyzed in the Mongolian context, they are likely to have an effect on people’s household experience. The probability that they would improve the accuracy of economists and analysts who rely on such data is also high because relevant authorities would be more aware of the needs of each user group. They would also understand that it is in their best interest to communicate well with the users because CPI data is supposed to benefit them the most.

Methodological Issues in CPI Computation

Multiple researchers such as Sohail et al. (2014) and Deutsch (2016) have explored methodological issues in CPI computation and said the two main questions characterize them. The first one is determining the main set of prices to use when making computations and the second one is establishing the most appropriate way to average price movements (Sohail et al., 2014; Deutsch, 2016). Many statistical methods capture the gist of the two issues by providing the experiences of different countries. In Mongolia, the diversity in approach is one of the main contributors to methodological issues in CPI computation. A broader assessment of issues affecting the CPI computation reveals that other problems include weighting issues, sampling problems, and the calculation of elementary and high-level price indexes (International Monetary Fund, 2012). Affiliated problems include adjusting for quality changes, treatment of seasonal products, and the process of accounting for missing observations (International Monetary Fund, 2012).

The National Statistics Office (NSO) disseminates Mongolia’s annual GDP based on current prices. At the same time, it disseminates the country’s expenditure based on the gross national product (GNP). Nonetheless, the organization does not reveal certain types of information, such as the sequence of accounts it uses to compute data, and the annual supply and quarterly GDP used for similar purposes (International Monetary Fund, 2013). This action means that Mongolia keeps certain aspects of its CPI computational processes private. While doing so, the organization still makes sure its processes align with international standards of CPI computation (especially with regards to the computation of goods and services).

An analysis of methodological issues relating to the basis for recording reveals the same pattern because the recording of transactions follows international best practice as well. In line with this provision, Mongolia uses market-based pricing of goods and services to undertake its computational processes (International Monetary Fund, 2013). All transactions undertaken in the country are also documented on an accrual basis, except for situations where they involve government projects. In line with the recommendations of the 1993 SNA, the NSO records inter-firm transactions net as opposed to gross.

The Mongolian NSO conducts a family budget survey every five years, but it publishes data relating to CPI monthly. Urban dwellers complete most of these surveys (International Monetary Fund, 2013). Although the NSO conducts these assessments by following international standards on the same, the process still has several problems. The first one is the biased collection of price data mostly from urban areas and a few from rural ones. The second problem is the failure to disclose the survey instruments and data used across several levels of computation (International Monetary Fund, 2013). This challenge makes it difficult to review the quality and reliability of the information provided by the NSO.

A detailed review of the accuracy and reliability of information obtained by the Mongolian NSO reveals that the main source of data for CPI computations is the integrated housing and income expenditure survey (HIES) (International Monetary Fund, 2013). The weights used today are also based on this survey. Here, it is important to note that, rarely do they update the weights based on price changes; instead, they align the computations with the price base period (International Monetary Fund, 2013). Generally, few outlets are excluded from the price assessment. At the same time, the NSO applies few imputations for houses that are occupied by their rightful owners.

An assessment of the source data for CPI computations in Mongolia reveals that they do not account for sampling errors in the HIES (International Monetary Fund, 2013). Furthermore, consumption expenditure relating to alcohol and tobacco use is not compared with supply data coming from the main retail outlets that sell these products. Lastly, a review of the statistical techniques used in CPI computation reveals that the NSO often carries forward prices for temporarily missing goods and seasonal products on their books, regardless of the implicitness or explicitness of quality adjustments techniques to be followed when computing for items that have been rendered obsolete or unavailable.

Relative to the above assertions, one of the recent methodological issues under review in Mongolia is how to compute price changes. Some researchers have highlighted this problem by saying it is prevalent in instances where inflation needs to be determined (International Monetary Fund, 2013). In some cases, the country’s central bank experts admit that although there have been low inflation rates in the country, they are not sure whether the numbers represent changes in the cost of living or a mere change in the prices of goods and services within a small category basket. These kinds of questions emerge in different ways and in multiple kinds of dilemmas. For example, Deutsch (2016) presents a methodological dilemma that often plagues economists. He ponders on how the country should compute CPI when there is a change in the quality of a specific product, while the price remains constant. This example could be simulated through an evaluation of changes in the price of telecommunications services because there are cases where calling prices decline, while the quality of services remains the same. Similarly, there are cases where calling rates increase, while the quality of services declines.

Another problem associated with the computation of CPI data in Mongolia is an attitude problem among some of the economists and experts mandated to do this task. For example, the lack of diversity in the staff makes it difficult for the NSO to cope with changes in the environment. This problem is a limitation for the country because it has specific regulations that limit transactions involving the housing market and how much businesses should pay their employees. Notably, many changes in the economy seem to have occurred within the transition period from a communist-based economy to a market-based economy. One notable case that has been recently realized is the involvement of the government in the housing sector and its quest to regulate what should be done to property owners and tenants whenever the rent exceeds the stipulated amount (International Monetary Fund, 2013). Since the future may accommodate further deregulation of the economy, it is pertinent for the methodological approaches adopted by Mongolia to reflect these changes.

A comprehensive review of the methodological soundness of Mongolia’s CPI framework reveals that the country’s processes align with international concepts and definitions on the same. Particularly, they align with the concepts and definitions of the 1993 systems of accounts (SNA) (International Monetary Fund, 2013). A review of the scope used to compute CPI data reveals that the country does not cover several accounts and tables as recommended by the Intersecretariat Working Group on National Accounts. The group’s mandate is necessary for the implementation of the 1993 SNA.


Although Mongolia experiences different issues surrounding how they compute their CPI numbers, it is important to point out that the CPI data they produce on a monthly basis are the products of a thorough review of the numbers. The employees working at the national bureau office are also doing a good job by following the questionnaire, as should be the case. Similar to many countries with the same political, social, and economic dynamics of Mongolia, the methodological choice of CPI computation is largely borrowed from international best practices. A deeper investigation into the same practices reveals that most of the methodological processes in the country are borrowed from the International Labor Organization (ILO), and specific western nations, such as Italy and the US (International Monetary Fund, 2013).

Generally, some of the main issues that currently need attention in the East Asian nation include the “obsession” by some of the experts to measure price movements strictly and accurately. They should realize that the focus of CPI is not to get the most accurate price, but to track price changes using the best way. This diversion of focus may be detrimental to users who want to understand CPI changes in the first place. Similarly, there is a weakness among some of the economists in the country to embrace new types of surveys. This limitation makes it difficult for them to account for the changing dynamics and realities of measuring CPI. It is essential to review this problem because rapid changes in the Mongolian economy have rendered some of the traditional measures of assessment obsolete. The current problem presents a situation where the country computes CPI in a conservative way. This way, they fail to understand the root causes of price changes.


Ambukege, G., Justo, G., & Mushi, J. (2017). Neuro fuzzy modeling for prediction of consumer price index. International Journal of Artificial Intelligence and Applications, 8(5), 33-44.

De Haan, J., Willenborg, L., & Chessa, A. (2016). An overview of price index methods for scanner data. Web.

Deutsch, T. (2016). Statistical capacity building of official statisticians in practice: Case of the consumer price index. Journal of Official Statistics, 32(4), 827-848.

International Monetary Fund. (2012). Mongolia: 2012 Article IV consultation and third post-program monitoring. Washington, DC: International Monetary Fund.

International Monetary Fund. (2013). Mongolia: Report on the observance of standards and codes: Data module, response by the authorities, and detailed assessment using the data quality assessment framework (DQAF). Washington, DC: International Monetary Fund.

Sohail, M., Hina, N., Amina, M., & Asma, S. (2014). Issues in the measurement and construction of the consumer price index in Pakistan. Washington, DC: Intl Food Policy Res Inst.

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