Strengthening national statistical capacities

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About nine years ago, a book, written by Morten Jerven, then an assistant professor in the School of International Studies at Simon Fraser University in Vancouver, sparked a debate about the accuracy of economic data and the development of Africa. Entitled “Poor Numbers: How We Are Fooled by Africa’s Development Statistics and What to Do About It,” the book exposed “huge gaps and alarming gaps” in African numbers. Javan’s work also found that only three of 23 national statistical offices believed their own gross domestic product (GDP) calculations covered the entire economy and 18 believed they were underestimated. The book further revealed that in 2011, only 17 of the 47 selected African countries had prepared their own new GDP estimates and 10 had a “base year” that was in the previous decade. Another conclusion was that more than half of the ranking of African economies up to 2009 was a pure estimate.

Although Jeven’s study focused on Africa, the problems identified in the work still hold true for many other developing countries in Asia. Many African countries have yet to get rid of questionable data, as overestimation and underestimation is a regular occurrence there. Some Asian countries also face the problem of updating and authenticating data despite the fact that a number of measures have been taken to improve data quality, especially macroeconomic indicators.

The quality of economic data largely depends on the process of data collection and compilation. Great care is required to collect and compile raw data at ground level. The next step is compilation where entering raw data is critical. Wrong entry can lead to distorted or inadequate final data. Staff of national statistical agencies as well as other national institutions must be properly trained in order to be able to collect and compile data accurately. At the collection stage, teams should focus on the reality of the situation and collect what they find. In the compilation phase, it is also crucial to enter the data collected at ground level. The data collector and compiler must ensure the authenticity of the data without thinking about the output. In the data processing phase, it is necessary to randomly cross-check the entries and to go back to the compilers and even the collectors if necessary. Cross-checking helps reduce the possibility of errors and omissions.

All of this work requires meticulous effort. With the modernization of data collection and compilation procedures, it is now easier to follow the phases. For example, the Bangladesh Bureau of Statistics (BBS) has a GPS tracking system for field staff. When working in the field to collect raw data using a tablet or other electronic device, the device can be tracked from headquarters to know what locations or locations staff are currently working. This type of technology helps ensure the accuracy and transparency of the data. Again, it is also necessary to deploy the right people in the right places. The provision of national statistics is above all the prerogative of statisticians. They know the statistical method better than anyone. By appointing more statisticians, the national statistical office can improve data quality. In addition, regular training of government officials and non-statisticians is necessary.

However, it is ultimately the decision of policy makers that matters in providing quality data in a transparent manner. What decision-makers think and how they want to generate quality national statistics and relevant data are the two central questions in this regard. There is no doubt that policymakers sometimes feel uncomfortable with national statistics that describe the poor performance of the economy or one of its sectors. In Bangladesh, there are cases where the publication of national statistics is delayed mainly due to the lack of authorization from the concerned authorities. For example, BBS released the final GDP estimate for FY20 almost a year later, in August of this year. He also made public the provisional GDP estimate for fiscal year 21, which is usually released before the end of a fiscal year and cited in the budget speech. This type of delay also raises the question of the authenticity of the data and sometimes even gives rise to speculation about the manipulation of the data.

Not only the national statistical agency, but other important national institutions also have problems with data and statistics. An English daily published a report last week on the central bank’s overestimation of the country’s foreign exchange reserve. According to the report, the International Monetary Fund (IMF) found that the Bangladesh Bank overestimated its foreign exchange reserves by $ 7.2 billion “by including non-reserve assets underestimating the associated risks.” The IMF has a clear foreign reserve policy for all member countries. Typically, international reserves include foreign currencies, other assets denominated in foreign currencies, gold reserves, special drawing rights (SDRs), and IMF reserve positions.

Although no response from the central bank in this regard is yet available, this type of overestimation is worrying and misleading. The overestimation gives a false impression of the strength of the foreign exchange reserve. For example, a foreign exchange reserve worth $ 48 billion (end of August) was considered sufficient to cover seven months of importing goods and services, according to the July Balance of Payments (BoP) table. -August prepared by the Bangladesh Bank. Deducting $ 7.0 billion from the “overestimated” amount, the actual reserve should be $ 41 billion, which equates to payments for about six months of importing goods and services. Of course, the overestimation of the reserve makes the monetary authority more comfortable in the management of exchange rates, but the downside is that it exposes the economy to the risk of absorbing any undue pressure. We can remember the official celebratory event, organized by the former governor almost a decade ago, to mark the achievement of a foreign exchange reserve worth 10 billion dollars.

The central problem is that, whether it is the national statistical agency or the central bank, it is essential to put more emphasis on strengthening national statistical capacities and data management. It is difficult to ignore a number related to the national economy, and it is more difficult to overcome the damage due to a wrong number.

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