Common Ways Misinformation About the Economy Spreads
We often classify information as either “true” or “false”. A falsehood is defined in relation to truth—so for lies to exist, there must be something called truth. If we analyse deeply, many things considered true can later turn out to be false. However, without going into philosophical debates, societies operate on conventions of truth. For example, if we say a coconut tree is a betel tree, that is a lie.
Conventions vary across countries, cultures, and even time periods. What is accepted as truth today may change tomorrow. Still, within a given society at a given time, certain facts are accepted as true, and lies are defined in relation to those facts.
This principle applies to economic data, statistics, and statements. We can classify them as “true” or “false”, but there is also a grey area called misinformation—information that is neither clearly true nor clearly false. Sometimes misinformation is an outright lie, but often it is not. If the truth is unclear, it is hard to confirm whether misinformation is a lie. This ambiguity makes misinformation easier to spread than blatant falsehoods.
Misinformation about the economy is commonly spread by social media operators, mainstream media, politicians, business leaders, and other parties. This article explains some common methods of spreading such misinformation, with examples.
Predictions vs Lies
Predictions about the future can be right or wrong. Wrong predictions are not lies or misinformation. Similarly, statements about future intentions cannot be classified as lies—they are either broken promises or incorrect forecasts.
To illustrate, let’s examine statements made by President Anura Kumara Dissanayake in Parliament on 19 December. These examples show how misinformation can arise, intentionally or unintentionally.
Example 1: “The highest foreign remittances in Sri Lanka’s history will be received this year.”
At the time of the statement, the year had not ended. By November, remittances totalled $7,197.1 million. The previous record was $7,241.5 million in 2016. Whether this year surpasses that figure will only be known later. Until then, this is a prediction—not a lie. Given December’s usual inflows, the prediction is almost certain to come true.
Example 2: “Tourism earnings in 2018 were about $3.8 billion. We expect that it will be surpassed.”
The first part is false. Official data shows 2018 earnings were $4.38 billion, not $3.8 billion. If this was an error, it is a serious one. The second part is a prediction, not a lie. However, given November’s earnings ($2.91 billion), surpassing $3.8 billion would require an impossible surge in December—about 6.5 million tourists in two weeks. This expectation is unrealistic and misleading.
Example 3: “Since 1950, our primary account had a surplus only on six occasions, all less than 1%. But for the first time, our surplus is 3.8% this year.”
The first part is correct: six occasions (1954, 1955, 1992, 2017, 2018, 2024). The second part is wrong—three of those years had surpluses above 1%, and 2024 recorded 2.2%. This is false information.
Example 4: “The bank overdraft in 2018 was 180 billion… by November 2025, the positive value was 1,202 billion.”
This is factually correct but omits key years (2022–2024), which account for most of the increase. Selective omission can mislead even when statements are technically true.
Summary of Common Methods
- Direct lies about facts – Assuming recipients won’t verify details.
- Presenting predictions as confirmed truths – Hard to disprove until time reveals the truth.
- Selective disclosure of information – Not false, but can create misleading impressions.
Summarised version of a Sinhala Face Book Post by ඉකොනොමැට්ටා