Saudi Arabia: Annual Growth Indicator, 1987–2024
Source: SA.xlsx (Country: Saudi Arabia, SAU). Values are annual percent change; the indicator name is not given in the file.
Shocks and Recovery
The biggest drops and jumps, and what followed.
Largest declines
| Year | Value |
|---|---|
| 2020 | -23.90% |
| 2009 | -11.68% |
| 2010 | -2.73% |
| 2012 | -1.04% |
| 2002 | -0.94% |
Largest gains
| Year | Value |
|---|---|
| 1988 | 18.65% |
| 1987 | 16.08% |
| 2021 | 14.73% |
| 1989 | 12.13% |
| 2022 | 10.64% |
Key readings
- The 2009 fall (-11.7%) and the 2020 fall (-23.9%) are the two deepest. The 2020 drop was about twice as deep as 2009.
- Rebound: 2021 posted 14.7%, the third highest year on record, followed by 10.6%, 7.7% and 6.8% in 2022 to 2024.
- Stability: from 2014 to 2019 values stayed between 1.2% and 3.6%, the calmest run in the series.
- 6 negative years in total: 2002, 2005, 2009, 2010, 2012, 2020.
Report on Historical Growth Patterns, Threshold Performance, and the Way Forward
1. Executive summary
The dashboard summarizes 38 years of an unspecified growth measure. Its clearest finding is that positive growth was much more common than negative growth: 32 of the 38 years—approximately 84%—cleared the dashboard’s 0% threshold, while six years recorded negative growth. The largest individual growth band was “7% and above,” containing 12 years.�
This points to a historical record with frequent growth and a meaningful number of strong-growth years. However, the dashboard shows the distribution of outcomes, not their chronological order. It therefore cannot establish whether growth has recently accelerated, whether downturns clustered into particular periods, or whether the underlying measure became more stable over time.
The most useful next step is to connect these summary charts to the underlying year-by-year data. That would turn a description of historical frequency into an assessment of trends, risks, and future priorities.
2. Scope and interpretation
The screenshot contains two complementary views:
A doughnut chart showing the share of years in four growth bands.
A funnel showing how many years cleared progressively higher growth thresholds.�
The screenshot does not identify the underlying variable, geography, sector, data source, or period covered. Consequently, this report refers to “growth” without assuming that it means GDP, company revenue, investment returns, production, or another measure.
This distinction matters: the charts support conclusions about the frequency of different outcomes, but not explanations of what caused them.
3. Distribution of growth outcomes
The growth-band chart divides the 38 years as follows. Percentages below are calculated from the displayed counts.�
Growth was common, but its strength varied
The two middle bands together account for 20 years, or 52.6% of the sample. Thus, although the highest band is the largest single category, more than half of all observations fall in the combined 0%–7% range.�
This is an important distinction. The record contains substantial strong growth, but it is not dominated by years of 7% or more. That highest band represents just under one-third of the sample—not a majority.
Negative years were a minority
Six negative-growth years represent approximately one in every six observations. The other 32 years fall in the chart’s nonnegative growth bands.�
The frequency of negative years alone does not reveal their severity. A small contraction and a very large contraction count equally in this chart, despite potentially having very different consequences.
The most common band is not the average growth rate
The “7% and above” category is the modal band: it contains more years than any other individual category. That does not establish that average growth was above 7%. Exact annual values are required to calculate the mean, median, range, and volatility.
4. What the growth funnel reveals
The funnel asks a different question: how often did growth exceed a given benchmark? Its categories are cumulative, so a year above 5% is also included among years above 3% and above 0%. They should not be added together.�
The lowest visible funnel segment appears to show five years above 10%, equivalent to approximately 13.2% of the sample. Because this label is small in the screenshot, that reading should be checked against the original dashboard before formal publication.�
Higher growth was progressively less frequent
The funnel narrows from 32 years above 0%, to 22 above 3%, and then to 15 above 5%. This separates three aspects of performance:
Frequency of growth: growth above zero occurred in a large majority of years.
Frequency of stronger growth: more than half of the sample exceeded 3%.
Frequency of high growth: fewer than half exceeded 5%.�
The implication is that “growth occurred” and “growth was strong” are materially different descriptions of this record.
Threshold definitions need clarification
The doughnut uses labels such as “0 to 3%” and “3 to 7%,” while the funnel uses “Above 0%” and “Above 3%.” The screenshot does not state how observations exactly equal to 0%, 3%, or 7% are assigned.�
The published methodology should specify whether each boundary is inclusive or exclusive. This is particularly important when reconciling the band counts with the threshold counts.
5. What the charts tell us about history—and what they do not
The dashboard provides a historical frequency profile rather than a historical timeline.
It establishes that the 38-year sample included negative, lower-growth, middle-range, and high-growth outcomes. It does not identify when those outcomes occurred or how long particular conditions lasted.�
Several very different histories could produce the same charts:
Negative years might be concentrated in an early period, followed by sustained improvement.
They might be spread across the full period.
Strong years might occur in long runs or as isolated rebounds.
These are illustrative possibilities, not findings from the screenshot.
Accordingly, the available evidence cannot establish:
A long-term upward or downward trend.
The timing or duration of downturns.
Whether high growth was sustained.
The speed of recovery after negative years.
Whether recent performance differs from the earlier record.
The causes of any changes.
A historical narrative naming particular crises, reforms, policies, or market developments would require the actual dates and the identity of the growth series.
6. Way forward
The following is a proposed analytical work programme, not a forecast derived from the chart.
First: establish the data foundation
Document:
What is growing and how growth is calculated.
The first and last years covered.
The original source and any revisions.
Whether the series is nominal or inflation-adjusted, where relevant.
The treatment of missing observations and threshold boundaries.
Second: restore the time dimension
Add a year-by-year line chart with a zero-growth reference line. Use it to identify the timing of contractions, periods of sustained growth, and changes between earlier and later years.
Compare subperiods rather than relying only on the full-sample distribution. The central question is whether the historical pattern has changed.
Third: measure magnitude as well as frequency
Calculate average and median growth, volatility, the largest contraction, the strongest expansion, and the longest runs of positive and negative growth.
If the underlying measure supports compounding, also calculate cumulative growth and the compounded annual growth rate. Counts alone cannot show how much the underlying level increased over the full period.
Fourth: investigate persistence and recovery
Examine what typically followed a negative-growth year and whether high-growth years tended to persist. This would help distinguish sustained improvement from temporary rebounds.
Fifth: connect strategy to the identified series
Once the subject is known, the practical priorities should be framed around three questions:
How can the frequency and severity of negative outcomes be reduced?
What distinguishes lower-growth years from stronger-growth years?
Which conditions support sustained strong growth rather than isolated peaks?
The dashboard provides a useful starting point: it shows how often different outcomes occurred. A credible forward-looking strategy requires the missing chronology, magnitudes, and context—not simply an extension of the historical percentages.
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