# **5. Discussion and conclusion**

In this paper, we examine the spatial price adjustment methodology in poverty measurement with a focus on the potential use of the CPI price data by applying the CPD method. Previous studies have highlighted the importance of the information about detailed product quality in the price data in the spatial comparison of the cost of living. Taking advantage of the data availability in Ghana, we estimate spatial prices in the country by applying the CPD method to the CPI raw price data. We also develop different approaches to measuring housing costs and combine them with the CPD method. The resulting spatial price indexes are compared to other indexes based on different items (food only, with and without housing), different index calculations (for example, Laspeyres, Paasche, and Fisher), and different price data (the market price survey).

Our analysis indeed underscores the importance of detailed product quality information in the price data. Expanding quality information in the price data from the market survey data to the item-level CPI price data to the variety-level CPI price data, we observe a convergence of regional price levels, due to the falling price level of Greater Accra relative to other regions (or conversely due to the increasing price levels of other regions relative to Greater Accra). This is because of the differences in the quality of goods and services available across the regions. Not controlling for these quality differences leads to a biased estimate of the price levels, which biases the price-level estimates of regions with higher-quality items (like Greater Accra) upward and those with lower-quality items downward.

Our Ghana case study suggests that CPI price data appear to be useful for constructing a spatial deflator poverty measurement, conditional on its availability and quality. The key conditions include the geographic coverage of price data collection and the detailed product specification list that guides data collection in a consistent manner across regions. For the primary purpose of the CPI—that is, the monitoring of temporal price changes—price data collection from rural areas is not necessary. However, using the CPI price data for spatial price adjustment requires rural areas to be well covered for poverty measurement and analysis.

Admittedly, most low- and middle-income countries do not have the CPI price data readily suitable for spatial price adjustment. Gibson rightly points out in the handbook published by the United Nations Statistics Division (2005, 184), The final choice, of relying on existing price collection efforts, is unlikely to work in many settings. The Consumer Price Index in many countries relies almost solely on urban prices, so these would not be applicable for calculating either poverty lines or spatial deflators and for imputing the value of consumption for rural households. Moreover, as explained above, the commodity weighting in a CPI is much more towards the consumption pattern of richer households, so the index values are unlikely to be relevant to poverty-related analysis.

and recommends as follows: Given the need for price data and the concerns about both unit values and relying on existing price collection efforts, it would be worthwhile for statistical agencies to invest more effort in gathering prices from local stores and markets and opinions about prices when their household surveys are fielded.

While this remains true even today, there are a few countries that have considerably invested in CPI data collection even in Africa, such as Ghana, Rwanda, Ethiopia, and Zimbabwe. Importantly, these countries also already use the CPI price data for spatial price adjustment for official poverty 23