Countries are promoting the catch-phrases such as ‘evidence-based policy’, geared to deal with different public policy issues. Cambodia is not an exception. The Partner organizations have long been providing training courses to support the development of human capital and institutional capacity. They aim to equip government officials with the ability to draft policy based on reliable data and sound evidence.
Yet, the question now is not only what data we have but also how good it is. In the absence of quality data, it is easy to lose sight of the truth. This data issue is mirrored across much of the developing countries, not only in Africa. Most importantly, leaders use these figures to argue for how to allocate the state’s limited resources and justify various policy goals and choices.
Gross Domestic Product (GDP) is the most commonly used indicator to measure an economy’s success or failure. But there are problems with its estimates, argued Morten Jerven, an assistant professor at Simon Fraser University, in his book, Poor Numbers: How We Are Misled by African Development Statistics and What To Do About It.
Two major issues pointed out by Jerven are the problems of knowledge and governance.
Notably, GDP’s ranking differs, depending on which source is consulted. Looking closely at three sources of national income data, namely the World Development Indicators, the Penn World Tables, and Angus Maddison’s datasets, the author found discrepancies in the ranking results. For example, Mozambique’s GDP ranks as the eighth poorest by the World Bank and the twelve richest by Maddison (p. 9). Therefore, it is challenging to measure actual economic changes. Jerven also notes that many base years have not been updated, so it is difficult to gauge the progress and take the necessary policy measures. In Kenya, for example, its national accounts base year is 2001, so any output produced this year will be valued at 2001 price (p.26).
Particularly interesting is the way in which the author discusses the problem on the side of data collector/enumerators. Some vested interests are actually driving the data collectors that give statistical significance in their political favour. Moreover, some data users do not know what is good data and what is bad data, or worse, they do not care. He then provides three cases, including Nigeria’s population estimates, Nigeria’s agricultural data and Tanzania’s structural adjustment program.
Jerven ends on a positive note, discussing the policy implications and offering policy alternatives for capacity building to produce better data. In the scholarship of African economic development, Jerven contributes a critical starting point to understand how and why quality data matters and raises the question about the meaning and measurement of available data. Therefore, we need to view these numbers with some degree of caution.
What about Cambodia? Do we have timely and accurate data?
Cambodia faces the same issue. With the base year of 2000, one would account for rice output for the year 2021 as it would be valued in 2000, while neighbouring Vietnam uses the base year of 2010. So, the current GDP estimate no longer accurately reflects the status quo of Cambodia’s economy. A recent report by UNESCAP recommends using the Supply and Use Table (SUT) framework to construct new benchmark GDP for Cambodia. While this could be a step forward to produce better data, there are newer, different measurements, such as the Human Development Index (HDI) and the Multidimensional Development Index (MDI). There is no doubt that no measurement is perfect. Yet, a combination of these reports would allow the government, donor community and the public to better understand the dynamics of Cambodia’s economic development data instead of relying on the traditional approach of using GDP to measure people’s living standards.