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A State-Ranker’s Guide to Writing 20/20 Economics Essays

So, you want to know how to improve your preliminary and HSC economics essay...

Cory Aitchison

State Ranks (Economics and Chemistry) & 99.95 ATAR

1. Introduction to this Guide

So, you want to know how to improve your preliminary and HSC economics essay writing? Look no further! In this guide, I’ll be covering key tips to help YOU smash the structure, amaze with your analysis, conquer the contemporary, and ultimately master the mystery of maximising your marks.

My name is Cory Aitchison, currently one of the Economics tutors at Project Academy . I completed the HSC in 2018, achieving a 99.95 ATAR as well as two state ranks — 6th in economics and 12th in chemistry. Graduating from Knox Grammar School, I also topped my grade in economics and was awarded Dux of the School for STEM. Believe it or not, at the beginning of Year 11 I initially struggled with economics due to the transition in conceptual thinking required in approaching economic assessments in comparison to my other subjects such as English. However, through Year 11 and Year 12, I built up key tips and strategies — that I’ll be sharing with you in this guide — to help me not only consistently achieve top marks in my internal assessments, but to ultimately go on to achieve the results I did in the HSC.

2. The Correct Way to Write

First off, you need to understand something: HSC economics essays are NOT english essays! They aren’t scientific discussions, nor geography reports, nor historical recounts. They’re unique and often quite different from other essays that you might’ve done previously in high school. The style of writing and approach to answering questions can be confusing at first, but follow these tips and you’ll be ready in no time:

Phrasing should be understandable and concise

Unlike some subjects where sophisticated phrasing is beneficial to getting marks, HSC economics essays should emphasise getting your point across with clarity. This means don’t run your sentences on for too long, be aware of any superfluous words, and make sure you actually understand yourself what you’re trying to say in a sentence.

For example:

GOOD: “An increase in interest rates should lead to decreased economic growth.”

NOT GOOD: “As a result of a rise or increase in interest rate levels from their previous values, the general state of economic activity in the domestic economy may begin to decrease and subsequently indicate the resultant situation of a decrease in economic growth.”

“Understandable” does not mean slang or lacking in terminology

Just because you want to get a point across, doesn’t mean you should resort to slang. In fact, using economic terminology is a strong way to boost your standing in the eyes of the marker — if you use it correctly! Always make sure you use full sentences, proper English grammar, and try and incorporate correct economic terms where possible.

GOOD: “This was a detrimental outcome for the economy.”

NOT GOOD: “This was a pretty bad outcome for the economy.”

GOOD: “The Australian Dollar depreciated.”

NOT GOOD: “The Australian Dollar decreased in value.”

Analysis should be done using low modality

Modality just refers to the confidence of your language — saying something “will” happen is strong modality, whereas saying something “might” happen is considered low modality. Since a large portion of economics is about applying theory, we have to make sure that we are aware that we are doing just that — talking about the theoretical, and so we can’t say for sure that anything will happen as predicted.

Some useful words include:

May, Might, Should, Could, Can theoretically

Don’t use words like:

Must, Will, Has to, Always

3. How to use Statistics

“What’s most important is that this contemporary is used to bring meaning or context to your argument…”

Using contemporary (statistics) can often seem straightforward at first, but using it effectively is usually harder than it looks. Contemporary generally refers to applying real-world facts to your analysis to help strengthen (or weaken) the theoretical arguments. This can include many different statistics or pieces of information, including:

• Historic economic indicators, such as GDP, inflation, GINI coefficients, exchange rates, or unemployment rates
• Trends or economic goals, such as long-term GDP growth rates, or the stability band for inflation
• Names of economic policies, such as examples of fiscal or microeconomic policies
• Specifics of economic policies, such as the amount spent on infrastructure in 2017

Whatever statistics you deem relevant to include in your essay, what’s most important is that this contemporary is used to bring meaning or context to your argument — just throwing around random numbers to show off your memorisation skills won’t impress the marker, and in fact might appear as if you were making them up on the spot. Rather, your use of contemporary should actively improve your analysis.

GOOD: “Following a period of growth consistently below the long-term trend-line of 3%, the depreciation of the AUD to 0.71USD in 2017 preceded an increase in economic growth to a 10-year high of 3.4% in 2018.”

NOT GOOD: “Economic growth increased by 1 percentage point in 2017 to 2018”

NOT GOOD: “GDP was $1.32403 trillion in 2017”

GOOD: “The 2017 Budget’s Infrastructure Plan injected $42 billion into the economy — up 30% from 2016’s $31 billion, and 20% higher than the inflation-adjusted long-term expenditure.”

NOT GOOD: “The 2017 Budget’s Infrastructure Plan injected $42 billion into the economy”

That in mind, don’t think that these statistics have to be overly specific. As long as the general ideas gets across, it’s fine. You don’t need to say “$1,505,120” — just “$1.5 million” will suffice.

Ask yourself: if I get rid of the contemporary from my paragraphs, does the essay still have enough content?

Further, don’t get roped into the “contemporary trap” — where you fall into the mindset that “if I memorise all these statistics, my essay will get good marks”. Including numbers and contemporary at the expense of having a robust theoretical explanation and analysis will definitely be detrimental in getting you top marks. Particularly in trial exams and the HSC when you’ve got all these numbers floating in your head, it can be tempting to try and include as many as you can (often just because you can!). To avoid this, always try and focus your arguments on analysis and syllabus content first, contemporary second. Ask yourself: if I get rid of the contemporary from my paragraph, does the essay still have enough content?

4. Must Have Insightful “However”s

If you really want to extend your analysis and show the marker that you know your stuff, including insightful “however”s is a strong way to do it. What I mean by this is that for each of your paragraphs, try and include a counterpoint that highlights the flexible nature of economic theory. There are broadly two kinds of “however”s:

Theoretical “However”s

These are counterpoints that are based on theory — often there will be theoretical limitations for many of the concepts you come across in economics. It’s always important to include these limitations as it reinforces your knowledge of the actual content of economics.

“Although the Budget and fiscal policy can be effective at stimulating economic growth, it is also restricted by the “implementation time lag” limitation since it is only introduced annually.”

Contemporary “However”s

These are counterpoints that are based on contemporary — highlighting how although something should happen theoretically, this isn’t usually what is observed in reality. This can be particularly powerful in that it combines your knowledge of theory with your analysis of contemporary.

“Despite the expansionary stance that the RBA adopted in 2012–2016 for monetary policy, Australia’s annual GDP growth rate has remained below the trend rate of 3% — against the theoretical expectations. This could be attributed to factors such as …”

5. How to Interpret the Question

When you first look at a question, before you even put pen to paper, you need to come up with a plan of attack — how can you ensure that you answer the question correctly, and give the markers what they want? There are three main points to look for when interpreting essay questions:

Knowing your verbs

As you may (or may not) know, NESA has a bank of words that they like to pull from when writing questions, and these words impact how they want their question answered. These verbs should help steer your analysis onto the right path. For example:

Explain: “Relate causes and effects”

To answer these questions, you have to demonstrate a thorough understanding of how theory and events impact each other and the economy. This verb particularly emphasises the idea of a process — you need to be able to make clear links as to how each step leads to the next, rather than just jumping to the outcomes.

Analyse: “Draw out and relate implications”

These questions usually wants you to investigate the connections between different aspects of economic theory. Generally this involves showing a holistic understanding of how different areas (such as micro- and macroeconomic policies) come together to make a cohesive impact on the economy. It usually helps to think back to the syllabus and how the points are introduced when figuring out which ideas to link together.

Assess/Evaluate: “Make a judgement based on value/a criteria”

These require you to not only critically analyse a topic but also come to a conclusion given the arguments you provided. This type of question usually gets you to make a judgement of the effectiveness of some economic theory — such as the ability for economic policies to achieve their goals. Make sure you actually include this judgement in your answer — for example, say things like “strong impact”, “highly influential”, “extremely detrimental”.

Discuss: “Provide points for and/or against”

Similar to assess, discuss wants you to provide arguments towards and against a particular topic. Although it doesn’t require a specific judgement to be made, it does place greater emphasis on showing a well-rounded approach to the argument — providing relatively equal weightings towards both the positive and negative sides of the discussion.

Linking to the syllabus

When trying to understand what the question wants from you, I found the best way to approach it is to consider what points in the syllabus it is referring to (To do this, you need to have a solid understanding of the syllabus in the first place). Once you’ve located it, try drawing upon other topics in the vicinity of that dot point to help you answer the question.

For example, if the question mentions “trends in Australia’s trade and financial flows”, then you know from the syllabus that you probably need to talk about value, composition and direction in order to get high marks. Further, it may also be worth it to bring in ideas from the Balance of Payments, as this is the next dot point along in the syllabus.

Digging into the source

For essay questions that provide a source for you to include in your answer, this is another goldmine from which you can discern what the marker really wants. If the source mentions microeconomic policy, it probably wasn’t on accident! Even if it may not be obvious how to link that to the question immediately, try and draw upon your knowledge and implications and see if there’s a different angle that you might be missing.

6. Putting it All together — Structuring your essay

My essays usually consisted of four main parts: an introduction, a background paragraph, body paragraphs, and a conclusion.

Introduction

Your introduction should not be long. I rarely wrote an introduction longer than three sentences.

First sentence: Answer the question (thesis)

Try and answer the question, while including the main key words of the question in your answer. Don’t directly restate it — instead, try and add meaning to it in a way that represents what you’re trying to get across in your essay.

For example: if the question was “Assess the impact of microeconomic policy in improving economic growth in Australia”, my first sentence might be “Microeconomic policy has had a significant impact in increasing aggregate supply and thus long-term economic growth in Australia since the 1960s”.

Next sentences: Introduce your arguments/paragraphs

In this part, it’s fine to almost list your paragraphs — there’s no need to do a whole sentence explaining each. That’s what the paragraphs themselves are for.

For example: using the same question as above, my next sentence might be “Although trade liberalisation may have been detrimental for short-term growth in manufacturing, policies such as competition policy and wage decentralisation have been highly effective in fostering economic growth in Australia”.

Background Paragraph

The aim of a background paragraph is threefold: to get across the main theory that underpins your argument; to establish the economic context for your argument; and to show the marker that you “know your stuff”.

For example, if the essay was on monetary policy, you may want to describe the process of Domestic Market Operations (how the reserve bank changes the cash rate) in your background paragraph, so that you don’t need to mention it each time you bring up changing stances. Further, it may be good to showcase the current economic climate — such as GDP growth rate and inflation — to give context to your analysis in your essay.

Some ideas for what to include in this paragraph include:

• Key theory such as DMOs or the rationale for macroeconomic policies
• Economic indicators that provide context to the time period that you’re working in, such as growth rates, inflation, unemployment rates, exchange rates, cash rates, etc.
• A brief description of the recent Budget (if talking about fiscal policy), including the stance and outcome

Bear in mind that this paragraph shouldn’t be too long — it isn’t the focus of your essay! Instead, aim for around 100–150 words at most. At this point in your essay, it may also be good to include a graph (more on this later).

Body Paragraphs

There’s no set rule for how many body paragraphs to include in your essay — I generally aim for at least 4, but there’s no real limit to how many you can (or should) write! Unlike english essays, it’s totally acceptable to just split a paragraph in two if you feel like the idea is too large to be written in one paragraph (as long as each paragraph makes sense on its own).

When writing a paragraph, I usually follow this structure:

Topic sentence

This is where you answer the question, and outline your argument or idea for this paragraph. If you are doing a discuss/assess/evaluate essay, try and make your judgement or side obvious. For example: “Trade liberalisation has been detrimental in its impact on economic growth in manufacturing industries”.

These sentences are where you bring together the theory and contemporary to build up your argument. Remember, the theory should be the focus, and contemporary a bonus. Try and weave a “story” into your analysis if you can — you should be showing the marker how everything fits together, how causes lead to effects, and ultimately bringing together relevant economic concepts to answer the question. Feel free to also include graphs here when they help strengthen your argument.

Fit in your “however” statements here. For discuss questions, this however section may take up a larger part of the paragraph if you choose to showcase two opposing arguments together.

Link your argument back to your overarching thesis, and answer the question. Following on from your “however” statement, it can often be a good idea to use linking words such as “nevertheless”, “notwithstanding”, or “despite this” to show that taking into account your arguments presented in the “however” statement, the overarching idea for the paragraph still remains.

Like the introduction, your conclusion should not be overly long. Rather, it should briefly restate the arguments made throughout your essay, and bring them all together again to reinforce how these points help answer the question.

Aggregate Demand / Supply Graph

Graphs are a great way to add extra spice to your essay — not only does it help strengthen your explanations of economic theory, it also makes it look like you wrote more pages than you actually did! Graphs, such as aggregate demand graphs, business cycle graphs, and Phillips curves, can be great in reinforcing your ideas when you mention them in your essay. They usually come either in background paragraphs or body paragraphs, and it’s usually best to draw them about a quarter to a third of the page in size. It’s also good practice to label them as “Figure 1” or “Graph 1”, and refer to them as such in your actual paragraph.

Although they can be beneficial, don’t try and force them either. Not all essays have appropriate graphs, and trying to include as many as you can without regards for their relevance may come across negatively in the eyes of the marker.

8. How to Answer Source Questions

If your essay question involves a source, try and refer to it multiple times throughout your essay. For example, this can be in the background paragraph and two of your body paragraphs. Rather than just adding in an “…as seen in the source” to one of your sentences, try and actively analyse it — show the marker that you understand why they included it, and how it actually helps strengthen your arguments.

9. Plan You Essay

Don’t be afraid to use the first page of your answer booklet as a planning page. Taking a couple minutes before you answer the question to lay out your scaffold for body paragraphs is a great first step to helping ensure that you actually end up answering the question to the best of your abilities. It also serves as a great reminder to keep checking as you finish each paragraph to ensure that you actually wrote what you intended. Just make sure to make it clear to the marker that those scribbles on the page are just a plan, and not your actual essay!

10. How to Prepare for Essays in the Exam

I find it much better to prepare paragraphs and ideas that you can draw upon to help “build up” a response during the exam itself.

Don’t go into the exam with a pre-prepared essay that you are ready to regurgitate — not only are there too many possibilities to prepare for, but it’s also unlikely that you’ll actually answer the question well with a pre-prepared response.

Instead of memorising sets of essays before the exam, I find it much better to prepare paragraphs and ideas that you can draw upon to help “build up” a response during the exam itself. What I mean by this, is that in your mind you have a “bank of different paragraphs” and ideas from all the topics in the syllabus, and when you read the exam, you start drawing from different paragraphs here and there to best formulate a response that answers the question. This allows you to be flexible in answering almost any question they can throw at you.

On top of this, ensure you have a solid foundation in both the theory and contemporary — knowing what statistics or topics to include in your essay is useless knowledge unless you have the actual content to back it up.

Now that you know the basics of how to write a good HSC economics essay, it’s time to start practising! Have a go, try out different styles, and find what works best for you. Good luck!

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How to Bring More Predictive Power to Economic Forecasts

May 9, 2023 • 7 min read.

A new study co-authored by Wharton’s Jules van Binsbergen uses a machine learning algorithm to produce more accurate and granular forecasts for GDP growth, employment, and interest-rate decisions.

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A study by experts at Wharton and elsewhere captures changing economic sentiment over 170 years to predict fundamentals such as GDP growth, employment, and monetary policy decisions. The study leads conventional forecasting models on several counts: in time series by more than a century; in capturing important information existing models fail to capture; in granularity that tracks nationwide and state-level variations; and in cross-sectional variations.

The study’s findings are published in a paper titled “ (Almost) 200 Years of News-Based Economic Sentiment ,” authored by Wharton finance professor Jules H. van Binsbergen , London Business School finance professor Svetlana Bryzgalova , London Business School doctoral student Mayukh Mukhopadhyay , and Nanyang Business School finance professor Varun Sharma .

The study customized a machine learning technique to create an algorithm that analyzed more than a billion articles across 200 million pages of 13,000 U.S. local newspapers from 1850 to 2017; the authors claimed that dataset is 95 times larger than the total amount of all of the English-language Wikipedia entries combined. The inclusion of local newspapers enabled them to measure sentiment on a granular basis at the city, county, and state levels.

“If you want to better understand the role that sentiment has on economic activity, GDP growth, labor market decisions, and other fundamentals, it’s always better to have more granular and longer time series data,” Binsbergen said. “We show this sentiment has predictive power for economic activity over and above the standard predictors such as the so-called yield spread, or the difference between long-term and short-term interest rates as well as lagged GDP data (or economic growth after it occurred).”

Leading Conventional Forecasts

Significantly, the study’s model leads the Philadelphia Fed’s Survey of Professional Forecasters in predicting the next quarter’s consensus value of macroeconomic fundamentals, and it betters the Michigan Survey of Consumer Sentiment in the length of time (the Michigan Survey is available from 1966) and in providing state-level variations; the common component of national news and local news is only 35%, and the remainder is made of local news.

The paper explained how its algorithm does a better job than standard forecasting models in predicting future GDP per capita growth. A one standard deviation increase in sentiment corresponded to 2% additional GDP growth over the next year in the sample period from 1850 to 2017, it stated. Furthermore, for the period from 1947 to 2019, for which it has yield curve data (a common recession indicator), it showed that a one standard deviation increase in sentiment led to 0.29% of additional GDP growth one quarter ahead, which corresponds to 1.1% annualized growth. “Overall, our results indicate the importance of sentiment in understanding the business cycle,” the paper noted.

The study’s measure operates mainly through the labor channel rather than the capital channel, as it predicts employment, consumption, and services but neither investment nor industrial production. Similarly, it showed that economic sentiment operates through the real economy and does not predict inflation.

Degrees of Predictive Power

“Our measure seems to have mainly predictive power – not on the investment side, or what machines and other capital goods companies invest in – but rather on the labor market side,” Binsbergen said. “It seems to have predictive power for hiring decisions [at companies]. Higher sentiment implies more hiring and lower sentiment predicts lower hiring.”

“If you want to better understand the role that sentiment has on economic activity, GDP growth, labor market decisions and other fundamentals, it’s always better to have more granular and longer time series data.” — Jules H. van Binsbergen

The study also found that sentiment has a large influence on the key policy rate, particularly during recessions: a one-standard-deviation decrease in sentiment over the past two quarters leads to a 25-basis-point decrease in the policy rate. “Driving these effects to influence interest-rate decisions are two factors, the paper explained: One is economic sentiment and its ability to predict GDP growth and employment; and the other is how sentiment could induce the Federal Reserve to deviate from the so-called Taylor Rule, which links interest rate decisions to changes in inflation and economic growth.

Economic sentiment, however, does not seem to have predictive power over stock market returns, but Binsbergen explained how that might play out. “One way to interpret our findings is that the stock market seems to understand what the influence of sentiment will be on economic activity, and has already priced it in. Since it is already captured in the price today, it doesn’t predict price movements in the future.”

The economic sentiment tracked by the study coincided with many prominent economic crises such as the Great Depression, the Great Recession, and the recession of 1973–1975, the paper noted.

Tracking Words, and Their Context and Intensity

The algorithm in the study worked by looking at the context in which a word/phrase is used, not the physical distance between words in a given sentence. It selected one billion words at random for each five-year period in the sample from 1850 to 2020 “to ensure that the corpus is balanced across history and that each of the time periods has consistent representation.”

The algorithm was trained to respond to a dictionary of words and phrases related to the economy. The word “economy” was most closely associated with words and phrases such as economic growth, inflation, recession, and economic recovery. Similarly, it relied on “seed words” that reflect positive sentiment such as expansion, boom, growth, and prosperity, while those for negative sentiment were words such as recession, bankrupt, shrinking, and unemployment. The paper captured the top 1,000 words and phrases in a word cloud:

Tracking the “Secular Stagnation Period”

Significantly, the sentiment tracked a downward trend that began in the 1970s, or what is known in macroeconomic literature as a “secular stagnation period,” the paper noted. “We show that this trend is not driven by the coverage of economic news but rather relates to the overall mood,” it added. It pointed out that after the U.S. elections of 1992, many studies have focused on “the apparent negativity bias in media” and that “the media disproportionately focuses on negative economic news.”

The paper attempted to explain that dominance of negative news. “Because the public needs to be made aware of important risks in society, a certain level of general negative bias in the news coverage is expected, particularly if one adheres to the view that traditional media fulfills a watchdog/surveillance function,” it stated. But it found that to be an inadequate explanation, and added, “The world of news, especially that of printed newspapers, has become increasingly competitive over the years. It is natural, therefore, to expect that in order to attract a larger audience, many outlets have been increasingly focusing on negative news.”

Hurdles in Identifying Information Sources

Is there a way to use the algorithm in the study to capture sentiment in the future, too? “In principle, there is nothing against keeping on updating our database and using the same algorithm,” Binsbergen said. But the problem is in determining what represents an authoritative source of information when it comes to news reporting, he noted. “If I just gave you the local newspapers, I think you would object. I think you would say, ‘Is that enough? Is that everything?’” At the same time, including other sources of information, such as on social media, is also fraught with hurdles such as negativity and fake news, he added. Determining the right mix of information sources remains a challenge, he indicated.

In any event, Binsbergen is happy that their study has advanced forecasting techniques. “There are people that forecast GDP for a living, and they include professional forecasters at banks and asset managers and other places,” he said. “It seems that our sentiment measure leads their forecasts, meaning that it seems to run more information than what professional forecasters seem to be using in their forecasts.”

That said, it is not possible to predict economic fundamentals with absolute precision. “Our measure does have decent predictability, but within plausible ranges,” said Binsbergen, who will be discussing the study further in a webinar hosted by the Jacobs Levy Equity Management Center for Quantitative Financial Research . “If we could perfectly predict the next crash, then the crash would happen today.”

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Economic forecasting: why it matters and why it’s so often wrong

Lecturer in Economics, University of the Witwatersrand

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Anybody who has seen a convertible caught in a rainstorm understands the importance of weather forecasts. And the expectation that the forecast is in fact accurate and reliable. Economies, like weather systems, are complex. But instead of having a foundation based on predictable laws of physics, they are built on human behaviour. Some would argue predictable human behaviour.

With all the information available to Homo Economicus, one naturally wonders how he (according to this Latin name, he is male) makes choices. Being completely rational and unbiased, which is the entity most likely to guide his decisions?

He is most likely, being rational and unbiased, to follow the forecasts of the entity that is reliable and has been consistently accurate over a period of time. In the New York Times bestseller Predictably Irrational , Professor Dan Ariely explains that most people are very unlike Homo Economicus. If anything, they are completely irrational. Despite this, they behave in a predictable fashion.

Why predictability matters

Predictability builds confidence and certainty in an economy. Individuals feel more optimistic. Their decisions become more efficient. Inaccurate forecasts, whether they underestimate or overestimate, incur additional costs. They lead the economy astray. Forecasts are therefore crucial for all economic and business activity. Looking into the future involves uncertainty and risk and the fact that forecasts may be inaccurate creates a serious dilemma for policy makers. As a recent headline so aptly infers: economists’ crystal balls are cracked.

The national treasury in any country is, among other things, responsible for managing the government’s finances and allocating funds to the relevant departments. When Homo Economicus listens to the budget speech and the national treasury forecasts, he forms his expectations, which determine his behaviour. This is regardless of his occupation. The quality, accuracy and reliability of these forecasts are therefore of utmost importance.

The challenges of forward-looking forecasts

The forward-looking nature of policy deliberations stems from the fact that there are lags associated with the response of economic activity to variations in macroeconomic policy changes.

Forecasting accuracy, especially over longer horizons, is equally important for fiscal policy credibility. The role of forecasting becomes crucial. The primary focus for forecasters should not be making highly specific or detailed numerical forecasts. Rather they should provide a general indication of the likely environment in which forward-looking policy needs to be formulated .

Macroeconomic forecasts done by any entity – private sector companies like Thompson Reuters , agencies such as the International Monetary Fund or the national treasury office – begin with an internal forecasting process. These preliminary estimates can be generated through:

using complex models

pure judgement based on the perturbations of recent events.

For example, if central banks want to achieve a target for inflation they need to be able to forecast inflation and adjust policy accordingly.

The “official” forecasts of the National Treasury of South Africa are usually made biannually. The first, just before the start of the financial year, coincides with the day the finance minister delivers his budget speech. The second happens when the medium term budget is reviewed towards the end of the year.

A common practice is to provide estimates of revenue and expenditure for the current year as well an extra three years. In addition, the treasury includes its own estimates of broad macroeconomic indicators such as GDP growth and inflation. For the current year, the forecasts of these indicators take the form of a point estimate. For years beyond the budget year, these broad macroeconomic parameters take the form of projections rather than definitive estimates.

Before publishing, the preliminary projections are frequently compared to those made by the private sector, namely Consensus, Reuters and Bloomberg. If they differ significantly, they are often reconsidered and updated .

Without a crystal ball, the forecasts are ultimately a mixture of science that has been generated through carefully evaluated econometric systems. And a healthy dose of artful judgement and revision.

While the private sector in most industrial countries generates dozens of macroeconomic forecasts, official forecasts generally only emanate from two or three government agencies. Thus the quality, accuracy and reliability of the forecasts by central banks and the treasury become paramount.

Getting it wrong more times than getting it right

Studies reveal the inability of forecasters to correctly or timeously predict outcomes. Based on a sample of 63 industrial and developing countries, a study by Prakash Loungani at the International Monetary Fund is revealing. It showed that private sector forecasters were only able to predict two of the 60 recessions that occurred over the sample while the majority remained undetected.

Professor Philip Tetlock also spent many years researching the accuracy of forecasts – specifically in a political realm. After collecting and analysing 28 000 predictions from 284 experts, he found that the average expert’s forecasts were only slightly more accurate than random guessing.

This ubiquitous failure to make predictions in an uncertain world has called to question the reliability of “official” forecasts generated by government and inter-governmental institutions as well as those from the private sector.

A fundamental component of the global environment is uncertainty. Rather than expecting forecasts to foresee events, Homo Economicus should rationally accept that uncertainty exists. As Makridakis et al suggest: use forecasts to estimate the uncertainty, and then double the range. This is because people are conditioned to underestimate uncertainty and should therefore not believe any predictions about the future. Rather develop plans that will be sensitive to surprises.

Despite their poor track record, forecasters still play a vital role. They are integral to establishing even a general sense of the future. The practice of forecasting can therefore not be relegated.

As author Cullen Roche explains :

It’s sort of like preparing to set sail across an ocean. You would never claim to be able to predict the weather or conditions you might encounter, but you can understand your path, potential weather patterns and how your vessel operates so you can increase the odds that you will make if there in one piece.

• Economic forecasts
• Budget speeches
• Central banks
• SA Budget 2016
• International Monetary Fund (IMF)

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Too much theory leads economists to bad predictions

The man who knew too much. Alan Greenspan pictured in 1990. Photo by Terry Ashe/Life/Getty

by Peter A Coclanis   + BIO

Whether it was the physicist Niels Bohr or the baseball player Yogi Berra who said it – or, most likely, someone else – it is indeed hard to make predictions, especially about the future. This is certainly so regarding economic, social and political phenomena. If you don’t believe me, just ask the Nobel prizewinning economist Paul Krugman, who, writing in The New York Times on the night of Donald Trump’s election victory in November 2016, predicted an imminent global recession, from which global markets might ‘never’ recover. We’re still waiting. One is reminded of the quip by another Nobel prizewinning economist, Paul S Samuelson: ‘Wall Street indexes predicted nine of the past five recessions!’

And Krugman isn’t alone. In November 2006, Alan Greenspan, who earlier in the year had stepped down from his position at the US Federal Reserve, explained that ‘the worst is behind us’ with regard to the housing slump. He could not have been more wrong. Clearly, even smart people often get caught with egg on their faces when making predictions or even conjectures about what lies ahead. Humans are keen on foreknowledge, to which its place in numerous religions attests, and demand for diviners has long spilt over into the economic, social and political realms, which certain types of people are happy to supply. Although no modus operandi is failsafe, and no amount of training or experience can ensure success, as a historian I am convinced that the risks of making predictions can be reduced through the employment of a few simple historical tools, and by knowing a bit more about the past.

Before getting into history and the historian’s tool kit, however, let me point out that Krugman and Greenspan were following time-honoured traditions in making erroneous predictions. The economist Ravi Batra, for example, wrote popular books in 1989 and 1999 incorrectly predicting global depressions in 1990 and 2000 respectively, and in 1992 the economist Lester Thurow of MIT (sometimes referred to as ‘Less than Thurow’ by his detractors) wrote a bestseller called Head to Head, wherein he predicted that China ‘will not have a big impact on the world economy in the first half of the 21st century’.

And, lest one claim that I’m picking on economists, let me mention a few luminaries from other social sciences. In this regard, the political scientist Francis Fukuyama can be considered Exhibit A. In celebrated publications appearing between 1989 and 1992, Fukuyama explained to readers that history had reached its final stage of development with the triumph of liberal democracy and freemarket capitalism over authoritarianism and socialism, and the anticipated spread of both liberal democracy and freemarket capitalism around the globe. Oops.

Closely related to forecasting per se is what might be called the authoritative pronouncement with strong implications. In 1960, the sociologist Daniel Bell wrote a book arguing that the age of ideology had ended in the West, and in a book published that same year his friend, the political sociologist Seymour Martin Lipset, claimed that ‘the fundamental political problems of the industrial revolution have been solved’. And a few years earlier in The Affluent Society (1958), the Harvard economist John Kenneth Galbraith suggested that poverty in the US was no longer a major structural problem, but ‘more nearly an afterthought’.

Afterthought or not, let’s return to history and the historian’s tool kit, which for a variety of reasons in recent years have become a bit less déclassé in the minds of economists and other social scientists. This, after a long period in which not only history but also historically oriented work w ithin the social sciences were often disparaged for being insufficiently theoretical, overly inductive, nonaxiomatic – indeed, rather ad hoc – and too concerned with ‘the anecdotal’, with ‘mere’ events, and with ‘isolated’ facts, rather than with the intentionally simplified generalisations known as ‘stylised facts’ that many social scientists prefer.

History was for antiquarians, ‘so yesterday’, a phrase popular with young people in recent years before the term itself became passé , and certainly no place for high-flyers in economics and the other social sciences. In economics, as a result, both economic history and (especially) the history of economic thought withered for a generation or two.

S o what accounts for the recent change of course? For starters, there was the Great Recession – or ‘Lesser Depression’, as Krugman called it in 2011 – which seemed to a few influential economists such as Ben Bernanke, Carmen Reinhart, Ken Rogoff and Barry Eichengreen similar in many ways to other financial crises in the past. But there were other factors too, including the general retreat from globalisation, and the renascence of both nationalist and authoritarian movements around the world, which sounded the death knell for Fukuyama’s benign new world. Then, too, there was the astounding (if rather unlikely) international success of the French economist Thomas Piketty’s Capital in the Twenty-First Century (2013), which traces the trajectory of economic inequality over the past two centuries in the course of mounting a case against inequality today. As ‘history’ returned, so too has a degree of acceptance of historical approaches among social scientists, who sense, however vaguely, that though history might not repeat itself, it often rhymes, as Mark Twain (might have) put it.

Had economics not largely abandoned the history of economic thought, more practitioners would have recalled what Joseph Schumpeter had to say about history. In his History of Economic Analysis (1954), the great Austrian economist noted that what distinguished ‘scientific’ economists from others is ‘a command of techniques that we class under three heads: history, statistics, and “theory”.’ According to Schumpeter: ‘The three together make up what we shall call Economic Analysis … Of these fundamental fields, economic history – which issues into and includes present-day facts – is by far the most important.’

Not theory, not statistics, but history – what happened and why. While theory and statistics can help explain ‘why’ questions, first comes systematic study of the ‘who, what, where, when and how’ questions – purportedly quotidian questions to which many economists have, to their detriment, long given short shrift. Had they not spurned or, at best, passed lightly over history, more economists would have sensed in the run-up to the 2007-9 financial crisis that the situation, as Reinhart and Rogoff suggest, maybe wasn’t so different from earlier financial crises after all.

To be sure, Reinhart and Rogoff were not arguing that the 2007-9 financial crisis was exactly the same as earlier financial crises. Rather, they believe that the present is not free-floating but bounded, that the past matters, and that it can provide important lessons to those who study it in a systematic, or at least disciplined, manner. In other words, economists – not to mention sociologists and political scientists – would do well to supplement their stock-in-trade, analytical rigour, by thinking more historically. Here, they could do worse than to begin by familiarising themselves with Richard Neustadt and Ernest May’s classic Thinking in Time: The Uses of History for Decision Makers (1986) , which would equip them with tools that would help to prevent forecasting bloopers and authoritative-seeming blunders due to egregiously incomplete information, misguided linear extrapolation, misleading historical analogies and spurious ‘stylised facts’.

Thinking historically, of course, entails both temporal and contextual dimensions and, in addition, often requires a significant amount of empirical work. Indeed, finding, assembling, analysing and drawing accurate conclusions from the bodies of evidence that historians call data is not for the weak of heart or, more to the point, for those short of time.

So, bottom line: economic forecasters would profit from thinking a bit more about history before gazing into their crystal balls, or at least before telling us what they see. Don’t get me wrong – I realise how hard making predictions is, especially about the future. So, one last point: if economic seers don’t want to think more historically or use empirical data more rigorously, they should at least hedge their bets. As a piece in The Wall Street Journal advised last year, put the chances of something happening at 40 per cent. If that something does in fact happen, one looks good. If it doesn’t, one can always say: ‘Hey, look, all I meant was that it was a strong possibility.’ Krugman might have dodged a bullet in 2016 had he followed that tack.

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