The discrepancies in the new oil

Islam Zween

Islam Zween

If three agencies are assigned to gather data on the number of the unemployed people in a certain country, can they produce and analyse the same data if they differ on the very basic definition of an unemployed person?

For some, a person must meet two conditions to be counted as jobless: no work during the survey period and no job search undertaken during the month that preceded the data collection. Others would add one or more conditions to meet the criteria, such as the time of holding a survey and the individual’s availability; namely, anyone who’s available for full-time or part-time employment in the past six months.

The direct cause of the data discrepancies between the three government agencies is that they all pursue different and separate calculation methods. Accordingly, the produced data isn’t usable even if the three bodies have got the job done by ticking key boxes of quality data: timely, relevant, and of course not fabricated for personal gains. In short, we call these systematic errors in data collection.

None of these criteria is absolute, and sometimes trade-offs must be made among them. Timeliness and accuracy, for example, are two famous metrics that always compete against one another in data collection, which leads to discrepancies. Some data firms often produce preliminary reports and then they revise up or down the numbers in a later stage when they issue another report contradicting the first data, as they were racing against time to become first and potentially make headlines.

The success of data-driven decision-making depends on data quality, which refers to the degree of usable data. Investors, businesspeople, venture capitalists and of course the public expect qualitative and wide-range government data, which should complement their own data.

The Customer Price Index (CPI) is an emphatic case in point. It has gone through different conceptual decisions by the relevant authorities in several countries, which resulted in discrepancies in data produced and analysed afterwards. The positive thing is the index is centred around the cost of living for individuals. So, we are talking here about one specific concept, which supposedly should lead to identical quality data when different agencies produce their annual or quarterly report.

But what if one agency raises this very legitimate question: shall we consider the recent hikes in interest rates over the past few years -- before they slightly went down in September – and their impact on the monthly rents and mortgages as one of the main indicators used by the CPI just like food prices, for example?

In economics, the CPI data faces a special problem, which is that the represented product or industry must be defined with sufficient precision to permit identification of quality improvement or deterioration in the priced items. So, if let’s be very specific and consider the element of homeownership in particular, since mortgages represent today so large a portion of family expenditures not just in the West but Arab countries as well.

Different state and independent agencies disagree on this real dilemma then: If a homeowner pays utility bills of his/her house, home repairs and insurance, so this should count as part of family expenditures and be taken into consideration when an agency produces and analyses CPI data.

But the other camp refuses to count the homeownership element as such since a house is an investment unlike the basket of items purchased for consumption; hence the monthly mortgage and related expenses should be excluded in data collection to avoid discrepancies in measure cost of living. There’s no right way unfortunately to fix this problem.

Also, one of the common data discrepancies firms often face is when statisticians phrase (and sometimes frame) their questions that are asked in a public survey. Even minor changes in wording or rearrangement of the placement of questions can affect survey results.

No wonder that a country like the United States has just one reliable federal data agency with a unified approach: the US Census Bureau. It produces annually the American Community Survey (ACS), which has become the most relied-on source for up-to-date data for tens of thousands of businesses in the US as it gathers unified and detailed data on nearly everything that shapes the macroeconomic perspective of the country including jobs, unemployment, inflation, GDP and home ownership. The consistent and transparent data is crucial for these businesses to make important decisions or fine tune their businesses according to the macro-outlook of the country.

So, unified approaches create ‘value’ of data, which hence becomes usable and not merely published by this or that agency.

This complex reality was part of the challenges Argaam faced when it started working on developing the Argaam Macro platform, which we launched with the aim of providing accurate and standardized data on economic indicators in Saudi Arabia, supported by in-depth and objective economic reports. We hope that this will contribute to the development of entrepreneurship and innovation in the kingdom, given the critical importance of data and its analysis. No wonder they call quality and usable data the ‘new oil’.