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5. Discussion
5.2 On the value of data and the limitations of data policies
Economic theory establishes that good economic data are a precondition to
effective macroeconomic management: of GDP growth (oil and non-oil), of inflation rate, and of unemployment rates. This is particularly true in times of crisis for several reasons. First, due to macroeconomic policy timing, including monetary policies and fiscal policies, and second because of corrective policies that depend on well-timed availability of data. To be successful in offsetting the economic effects of crises, policy makers need to recognize these effects using real-time data. When there is a discontinuity between policy response and crisis impact, existing policies may not be effective enough to offset threats. Typically, this lag exists in the implementation of economic policies that are long and variable for monetary policies. However, we argue that the availability of clean and timely data can play a significant role in reducing this gap.
“businesses 600 billion USD each year […and that] stale data accounts for a large part of the losses”[46].
This highlights the importance of treating data as currency. Examining data as currency establishes the current values of entities within databases and datasets. This would not be a challenge if data values carried timestamp and provenance informa- tion. In reality, this metadata is often inconsistent, missing, or inaccurate. Likewise, the processing, and overprocessing, of data often results in incomplete, inaccurately- contextualized, and otherwise inadequate datasets. For Saudi Arabia, language is another challenge, keeping in mind that the language of the internet and web tech- nologies is vastly English. The majority of data is either bilingual, or translated from Arabic into English for processing, increasing the odds of duplication, errors, and redundancy.
With low quality data in mind, and during the COVID crisis, there was a sharp decline in the flow of foreign direct investments (FDIs). According to the UN Con- ference on Trade and Development (UNCTAD) [47], global FDI flows in 2020
dropped by about 35% of what it was in 2019, reaching its lowest since 2005. This has had dire ramifications on economic development, and recovery from this situation required policymakers to position their countries as attractively as possible for FDIs.
When it comes to the manufacturing sector in Saudi Arabia at the time of the Corona virus outbreak, the government responded to the economic crisis, as many countries around the world have: by announcing stimulus packages to support its economy. Reiterating from earlier, at the monetary level, SAMA unveiled an eco- nomic stimulus package worth 2% of its GDP to support the private sector, especially small and medium enterprises (SMEs) [31]. At the fiscal level, the government allo- cated SR 70 billion Saudi Riyals (Approx. $ 18.6B USD) to help businesses through exemptions and postponement of government taxes, fees, and customs and to provide liquidity for private sector enterprises.
Both fiscal and monetary policies should be coordinated in such circumstances to boost support for impacted households and firms. To promote this, the availability of real-time valid and reliable data and, most importantly, forecasted data, is crucial to the successful management of the crises. This includes data on national public debt, its relation to GDP, and detailed sectorial data that enable policy makers to determine the most affected sectors. Under this category, there should be clear and up-to-date data on sectors that have high economic multipliers. These are the sectors that are deeply interrelated with other economic sectors through backward and forward links. Like- wise, datasets of value should include sectors that depend heavily on imported raw material, sectors that are export-oriented, and sectors that are labour-intensive in order to evade severe impact on labour share and unemployment rates.
In turn, since the impact of policies ranges from short term to medium and long terms, relevant data should include solid predictions of economic variables that are based on reliable models. For example, although debt can push economic problems from the present to the future, doing so can aggravate the problem given that policies are often only as accurate as the input that delivered them initially are. It is important to observe here that the COVID crisis is related to real economy (supply and demand) not to financial problems; a distinction that highlights the lengthiness of recovery.
The policy taken by the Saudi government is ever so critical, but requires a serious consideration of policy timeframes to maximize its impact: is it a one-quarter shock? A two-quarter shock? Or perhaps more? The success of economic policies in offsetting the effects of a possible recession depend substantially on quick detection of the length of the recession. Such a policy should be consistent with the recessionary
effects of the nature of the pandemic. This means that estimates are required for the starting and recovery time of the recession to distribute the policy tools accordingly.
In this sense, access to raw data.
Well-structured, extensive, time-sensitive data is also an apparatus for post-policy measurements. In the case of the Corona crisis, access to adequate data facilitate the evaluation of stimulus packages effectiveness. This is done by measuring policy impact on real GDP and employment, whilst holding all other factors that affect real GDP and employment constant, as for example, other monetary policy tools, or business cycle changes. The mechanism behind such estimation is complex in nature and is affected by several factors that change supply and demand and, in effect, equilibrium GDP. In such a crisis, the success of policy is not as certain as regular times, leading to a wide variance in the estimation of the impact of stimulus packages.
Data used for such estimation are a significant factor in getting accurate results.
For example, theoretically, the policy aims at shifting money-supply curve to the right, but the effect depends mainly on the behaviour of firms and households as a response to this shift. This response needs further examination using massive data (Big Data) extracted from surveys and other data sources that thoroughly address the behavioural motivations and decisions of households and businesses.
With this in mind, and given the limited data sources currently available for the Saudi economy, we recommend strengthening the quality of micro-level data, mainly through surveys at subsector or firm level, to expound the impacts of COVID19. This includes data beyond social data; on firm cost structures, consumer expenditures, and firm production and revenue. Similarly, improving data organization and structure, and enhancing national data lifecycle through proper database design, management, and archiving is elemental. This includes having more disaggregated data at the manufacturing level.
Data need to be collected at a higher frequency than currently is, and disseminated at regular intervals for timely analysis. To be able to have a forward-looking perspec- tive, regular forecasts for many macroeconomic variables using solid and sophisti- cated modelling techniques such as advanced timeseries forecasting and regression models are necessary. Additionally, data should be tested frequently for accuracy, validity, and reliability as a means to improve quality. Likewise, accessibility is essen- tial for conducting research studies that support policy makers.
As one research points out,“[a]mong the lessons learned are that data on the same variables are gathered by multiple means, that data exist in many states and in many places”and that“[d]ata sharing is embraced in principle but little sharing actually occurs, due to interrelated factors such as lack of demand, lack of standards, and concerns about […] ownership, data quality, and ethics”[42]. To assess data quality, and begin evaluating data as currency, Saudi Arabia must pay attention to data quality, accessibility, and provenance. This includes understanding the
characteristics of data being generated; its frequency, flow, and reliability; for what certain and potential purpose(s); for whom, and with what access rights and
regulations.
Further research needs to look into how data criteria vary in different contexts, what policy frameworks need to be applied, with what measures, controls and limita- tions. And while Saudi has made massive strides in utilizing ICTs in the public health and e-government sectors, we also recommend assessing the value of data, where several models exist to measure data as currency (e.g. [46]), and applying appropriate standards and practices towards maintenance and sustainability.