The supply chain has been stretched to the limit over the last two years and there have been a number of reasons for that. From a lack of containers to surges in global economy activity, as consumers shifting from buying services to buying goods, the freight time and cost have increased significantly.
From the IMF – good video explaining how the supply chain works and the problems faced after two years of lockdowns. Has the supply chain got too complicated?
There are those that see the problem of unemployment in most economies (but especially the US) as a structural issue. This refers to the mismatch between the jobs that are available and the skills that people have. Cyclical unemployment can be reduced by boosting demand – dropping taxes and increasing government spending (fiscal policy) and lowering interest rates (monetary policy). However, if unemployment is mainly structural patience is needed to wait for the market to sort things out, and this takes time.
The Beveridge curve is an empirical relationship between job openings (vacancies) and unemployment. It serves as a simple representation of how efficient labour markets are in terms of matching unemployed workers to available job openings in the aggregate economy. Economists study movements in this curve to identify changes in the efficiency of the labour market. It is common to observe movements along this curve over the course of the business cycle. For instance, as the economy moves into a recession, unemployment goes up and firms post fewer vacancies, causing the equilibrium in the labor market to move downward along the curve (the red arrows in the figure above). Conversely, as the economy expands, firms look for new hires to increase their production and meet demand, which depletes the stock of the unemployed – see graph below.
Careful analysis of Beveridge Curve data by economists Murat Tasci and John Lindner at the Cleveland Federal Reserve shows that it’s behaving much the way it has in previous recessions: there are as few job vacancies as you’d expect, given how desperate people are for work – see graph below. The percentage of small businesses with so-called “hard-to-fill” job vacancies is near a twenty-five-year low, and open jobs are being filled quickly. And one recent study showed that companies’ “recruiting intensity” has dropped sharply, probably because the fall-off in demand means that they don’t have a pressing need for new workers.
The Beveridge Curve and COVID
The graph below shows the Beveridge Curve pre and post covid. The pre-covid curve is a typical which relates to theory above, however the post-covid curve has become a lot steeper in showing that changes in the unemployment rate are not as responsive to changes in the vacancies. If the matching process between workers and firms becomes less efficient, employers need to post more vacancies to fill a given number of positions. In terms of the model, an outward shift of the Beveridge curve can therefore be explained by a decline in match efficiency. Since match efficiency has declined, any reduction in unemployment now requires a much higher job opening rate than before the pandemic. During the pandemic, job creation has become more difficult, and firms have had to recruit more aggressively to find workers. Looking forward, a reduction of the unemployment rate to pre-COVID levels would require job openings to be at twice the level they were before.
Source: Revisiting the Beveridge Curve: Why has it shifter so dramatically. Economic Brief October 2021
Recently published by Stats NZ was the Tourism satellite account which presents information on tourism’s contribution to the New Zealand economy in terms of expenditure and employment. The March 2021 data is significant as it captures the impact of COVID-19 on the sector. As expected the international spending was down by 91.5% from the previous year with the total spend falling by 37.3%.
The table and graph below show the drop off of international tourism in 2021. Domestic tourism did increase by 2.6% but was never going to absorb the drop in international spending of $16,195m.
Over the same period direct employment in tourism fell by 33.1% from 218,580 full-time employees to 146,295.
Tourism – direct contribution to GDP 2020 – $16.2bn – 5.5% of GDP 2021 – $8.5bn – 2.9% of GDP
As New Zealand completes 7 days of lockdown and with community cases on the rise the question is when can the economy reopen again and will it be able to bounce back like in the third quarter last year – 14.1% growth. An expansionary fiscal and monetary policy – increase in government spending and lower interest rates – were largely responsible for this recovery. Were it not for the lockdown last Wednesday the Reserve Bank of New Zealand would have raised interest rates to slowdown an overheating economy which had recovered well after the initial lockdown.
So with interest rates still at an expansionary 0.25% and a promise from finance minister Grant Robertson to provide a fiscal stimulus there is every chance that the economy should return to pre-second lockdown growth and have a central bank looking to raise interest rates as supply constraints and pent up demand start to inflate prices. Graph below shows the GDP forecast from the Bank of New Zealand – note the bounce back in GDP and the lockdown in mid 2020. However a lot depends on how long the lockdown will last for.
The Gini-Coefficient is derived from the same information used to create a Lorenz Curve. The co-efficient indicates the gap between two percentages: the percentage of population, and the percentage of income received by each percentage of the population. In order to calculate this you divide the area between the Lorenz Curve and the 45° line by the total area below the 45° line eg.
Area between the Lorenz Curve and the 45° line
Total area below the 45° line
The resulting number ranges between:
0 = perfect equality where say, 1% of the population = 1% of income, and
1 = maximum inequality where all the income of the economy is acquired by a single recipient. This figure has recently changed to 100 so the range is 0-100.
* The straight line (45° line) shows absolute equality of income. That is, 10% of the households earn 10% of income, 50% of households earn 50% of income.
Higher Inequality = more deaths from Covid-19.
There are various reason why this could be a plausible justification:
1. There is some research to suggest that a higher income inequality leads to a lower life expectancy. Thus lower income groups cannot easily afford healthcare in that economy and therefore tend to suffer more from covid-19 as they are in poor health. Also inequality and pre-existing conditions may worsen the effects of the virus.
2. Workers in relatively egalitarian countries tend to have more bargaining power with employers and therefore air concerns about work conditions etc. Sweden’s front line workers have not on average faced a higher risk from covid-19 than other workers. This is in contrast to results from America, Britain and Canada, which are more lightly regulated.
3. Where there is distrust amongst the population – weak social capital – the willingness of people to comply with virus-control measures, such as self-isolation or masks on public transport etc, is disappointing. This is evident in New South Wales with the number of arrests for non-compliance around covid-19 rules.
4. Low wage workers are prevalent in retail, public transit, and health care settings who cannot easily practice physical distancing. This greater exposure to the virus and less access to health services among the poor could explain why more economically unequal countries – not necessarily the poorest countries – experienced significantly higher mortality rates. Countries with a larger gap between rich and poor, like the United States, Russia, and Brazil are experiencing a more deadly pandemic.
High inequality is likely to continue to mean greater vulnerability to pandemics. Government’s have new challenges around inequality and pandemics including:
Vaccinating those that can’t book online / can’t get off work / have no form of transport
Economic incentives to stay at home if infectious
Investing more in children’s health which has long-term benefits.
Although a few years old now the mini-documentary below is very good and features many notable economists and economic thinkers. They basically look at the issue of financial stability, or the lack thereof, and discuss what is at the core of the problem. It includes Joseph Stiglitz, Gillian Tett, David Tuckett, Stephen Kinsella, John Kay, David Weinstein, Steve Keen and Dirk Bezemer. I have used this post to try and bring some reality to a lot of prescribed economics courses at high school level.
With the COVID crisis economists have got in wrong in many of their predictions. In New Zealand they stated that house prices would fall by 30%, unemployment would rise to between 15% to 30% and the downturn in NZ would be a lot worse than the GFC in 2008. Auckland house prices have risen by 17% since the outbreak, Unemployment is only at 4.7% and GDP growth expanded 1.6% in the March quarter. There is a very good podcast from Radio New Zealand’s Media Watch programme in which they discuss the problems of economists’ forecasts. Furthermore economists have long proven to be bad at predicting recessions.
A study by the IMF in 2018 looked at 153 recessions in 63 countries between 1992 and 2014 and found the vast bulk of them came as a surprise to economists.
The Queen famously asked why nobody noticed the 2008 Global Economic Crisis coming.
In his acceptance speech for the Nobel prize for economics, Friedrich Hayek said economists’ tendency to predict things with the certainty and language of science was misleading and “may have deplorable effects”.
The economic environment is said to be determined by agents or economic decision-makers. Today, an economy is a much more intricate machine which aims to allocate scarce resources to satisfy the utility of economic agents such as individuals, firms and government. The dominant model for many years has been “Dynamic Stochastic General Equilibrium” (DSGE) and it takes all the characteristics of an individual (this person is typically called the representative agent) which is then cloned and taken to represent the typical person in an economy.These agents make supposedly perfect decisions by optimising, working out the kinds of mathematical problems in an instant. However the rise of behavioural economics has shown that cognitive errors are now assumptions in many aspects of economics namely – heuristics, confirmation bias, overconfidence and distorted probability weights.
According to a paper entitled “Mindful Economics: The Production, Consumption, and Value of Beliefs” by Roland Bénabou and Jean Tirol research has shown that beliefs often fulfill important psychological and functional needs of the individual. Examples include:
confidence in ones’ abilities,
hope and anxiety reduction,
Therefore people hold beliefs because of the value they attach to them, as a result of the tradeoff between accuracy and desirability. As a consequence of this some of the beliefs do not consider prior knowledge of conditions or events that might be related to their beliefs – Bayseian Updating – this refers to people who are willing and able to modify their beliefs based on new, objective information. This non-Bayesian behaviour includes ignoring signals about their beliefs and denying what in turn will be the reality. Nevertheless motivated beliefs will respond to costs, benefits, and stakes involved in maintaining different self-views and world-views which leads to self-sustaining “social cognitions.”
Overconfidence Bénabou and Tirol suggest that overconfidence is the most common indicator of the motivated beliefs experience. Overconfidence can be seen as quite damaging although moderate confidence can be quite useful as it often enhances an individuals ability to act successfully on their own behalf and work well with others. Research has shown that psychologically “healthy” people display some degree of overoptimism and biased updating, while it is primarily depressed subjects who seem to be more objective.
If beliefs are shared between parties they may magnify each other and there is a tendency to follow the herd, especially if information is uncertain, incomplete, and asymmetric (some people are more informed than others). Basically, in a world of bounded rationality (the limits of the human brain in processing and understanding information), herding makes sense to most people. Herding is a fast and frugal heuristic (short-cut) that has been used by both human and non-human animals across the millennia. Some behavioural economists see herding as irrational because people aren’t basing their decisions on objective criteria. If herding is seen as rational it can result in price cascades leading to excessive booms and busts in the prices of financial assets. Case and Shiller (2003) surveyed the expectations of homeowners during the real-estate bubbles of 1988 and 2003. In both cases, 90 percent of respondents thought housing prices in their city would “increase over the next several years,” with an average expected gain for their own property of 9 to 15 percent per year over the next ten years.
The strategies of self-deception and dissonance-reduction used to protect valued beliefs are many and varied, Bénabou and Tirol group them into three main types: strategic ignorance, reality denial, and self-signaling.
Strategic ignorance is when a believer avoids information offering conflicting evidence.
Reality denial refers to troubling evidence that is rationalised away: house-price bulls might conjure up fanciful theories for why prices should behave unusually, and supporters of a disgraced politician might invent conspiracies or blame fake news.
Self-signaling is when the believer creates his own tools to interpret the facts in the way he wants: an unhealthy person, for example, might decide that going for a daily run proves he is well.
People derive utility from a sense of belonging to communities and having a positive self-image. Optimistic beliefs can also be valuable motivators to overcome self-control problems, as well as helpful in strategic interactions. In order to maintain this level of utility people tend to disregard Bayesian updating and are not willing to modify their beliefs based on new, objective information. Even if they did consider new information they will manipulate it to align with what their beliefs are.
Overconfidence is the most common indicator of the motivated beliefs experience and this can be impacted by the behaviour of others. Their confidence is often reinforced when people know that other people, including experts, and the rich and famous, are doing the same. In a world of bounded rationality, such behaviour may make sense – even though it can result in errors in decision making.
“To err is human; so is the failure to admit it” – The Economist June 10th 2017
“Mindful Economics: The Production, Consumption, and Value of Beliefs” by Roland Bénabou and Jean Tirol. Journal of Economic Perspectives—Volume 30, Number 3—Summer 2016—Pages 141–16
FT European Economics Commentary Martin Sandbu believes the COVID-19 pandemic is a once-in-a-lifetime chance to rebuild better economies that work for everyone. Sandbu author of ‘The Economics of Belonging’ – see previous post – talks here about the polarisation of rich societies since 1980. The main points of interest that he raises are below. Worth a look.
1980 – large number of jobs available in factories start to disappear.
Globalisation – not the main cause of unemployment but technology has taken a lot of the manual and clerical jobs (structural unemployment) and retail has gone online.
Tax systems have not redistributed income – unions have been in decline.
Rural areas worst effected – good jobs more prevalent in cities so rural areas suffer.
Low paid service jobs have been impacted by COVID-19. Also as they involve contact with others there is more exposure to the disease.
Pandemic catalyst for change. History tells us – US Great Depression = New Deal, 2nd WW = postwar welfare state.
Technology change is with us so the need to find new ways of working. Do we have a Universal Basic Income (UBI)?
Lower burden of employing workers – less income tax, payroll tax and generally make it cheaper to hire people in to better jobs. Make up the shortfall in revenue elsewhere.
With the significant increase in inequality – introduction of a wealth tax. Also a tax on carbon emissions and redistribute to help the worse off.
Greater need to overcome regional inequality within countries
Need to the political will to make economies work better for everyone.
Below is a useful graph looking at the 2020 GDP levels in most developed countries. New Zealand had a quick rebound with its elimination strategy, a supportive fiscal response and an expansionary monetary policy. The 2020 GDP figures considered the scale of lost activity from the COVID-19 lockdown as well as the rebound when restrictions were lifted. There seemed to be the trend that early lockdowns led to better GDP figures. Taiwan (2.98%) and China (2.3%) were the only countries to experience positive growth levels with New Zealand down 2.9% compared to 2019. Taiwan’s investment into public health infrastructure pre-COVID-19 enabled them to avoid a national lockdown. Early screening, effective methods for isolation/quarantine, digital technologies for identifying potential cases and mass mask use led to a much more controlled environment. China did experience a positive growth rate (2.3%) but this was well below 7% which they have been averaging since 2010.
However it is important to be aware that some countries were more impacted by COVID-19 than others, not only because of their hesitation to lockdown but also their reliance on certain sectors for GDP growth. Countries like Spain, who are very dependent on the tourist industry were hit hard by the pandemic. Many emerging and developing countries were already experiencing weaker growth before the pandemic struck.
This is a very good video on inflation from The Economist – it discusses why over the past two decades inflation has remained low in good times and bad. There is a brief look at historical rates of inflation and policy with reference to Bill Phillips (Phillips Curve) and Paul Volker (US Fed Chairman) who increased the prime interest rate to 21.5% in 1981 to tackle inflation. Also low interest rates and government fiscal stimulus could start to see an upward movement in the inflation figure. Very useful for Unit 4 of the CIE AS and A2 Economics course.
Below is a useful diagram from McKinsey & Company that compares the money used to assist the economies after the outbreak of Covid-19 and the GFC in 2017. Governments allocated US$10 trillion for economic stimulus in just two months—and for some countries, their response as a percentage of GDP was nearly ten times what it was in the financial crisis of 2008–09.
Countries in Europe have allocated around US$4 trillion which is approximately 30 times than that of the Marshall Plan in today’s value – the Marshall Plan was valued at $15bn in 1948. The size of government responses are unprecedented and they, with central banks, are moving into new territory. Global debt is estimated to reach US$300 trillion by the March quarter in 2021 with global GDP taking a huge hit. However unlike the GFC there seems to be an end point once an effective vaccine has been found but many jobs and businesses have gone and it will take time before new ones appear.
The Economist Free Exchange recently ran an article looking at the various taxonomies that are used to categorise models of capitalism. The book entitled “Varieties of Capitalism” (2001), distinguished between liberal market economies (LMEs) and co-ordinated market economies (CMEs).
LMEs’ rely on market mechanisms to allocate resources and determine wages, and on financial markets to allocate capital. E.G. America, Britain and Canada CMEs, like social organisations such as trade unions, and of bank finance. E.G. Germany, Sweden, Austria and the Netherlands
Western economies tend to sit on a continuum between these two models – below is a table outlining the main criteria each:
Which system is better during a pandemic?
During the pandemic, CMEs have generally had a more sound strategy for containing the spread of the virus. This may be generated by unity and consistency than by the strength of the intervention that is chosen. Some countries, e.g. Sweden, avoided lockdowns completely but seemed to get a lot of public support and relied on voluntary social distancing. New Zealand implemented a lockdown policy from the outset and relied a lot on contract tracing as well as strict system of managed isolation. LMEs such as the USA and the UK have had a policy which have been on the whole disorganised and not taken the virus seriously.
However in such situations and because of their innovative nature LMEs are more likely to focus on treatments and vaccines.
Of 34 vaccine candidates tracked by the World Health Organisation CMEs = 4 LMEs = 13 (AstraZeneca, an Anglo-Swedish drugmaker working with Oxford University, straddles both categories).
CMEs are likely to have a lower death count but LMEs seem to hold the upper hand with regard to a vaccines. Maybe a global coalition and co-ordination is needed in future to get the best of both systems.
Source: The Economist – Which is the best market model? 12th September 2020
Below is a link to a very good podcast from the BBC ‘The Real Story’. Dan Damon discuss what should be done about rising unemployment in the age of Covid-19? Contributors include Australian economist Steve Keen author of ‘Debunking Economics’. Topics of debate include:
Universal Basic Income
Modern Monetary Theory
How much debt can a government sustain in propping up an economy?
Should a government subsidise companies taking-on workers?
Also features a very good interview with Daniel Susskind – author of ‘A World Without Work: Technology, Automation and How We Should Respond’
It is 53 minutes long but can take your mind off the commute to work.
Physicists and mathematicians have puzzled over the three-body problem – the question of how three objects orbit one another according to Newton’s laws. No single equation can predict how three bodies will move in relation to one another and whether their orbits will repeat or devolve into chaos.
John Mauldin of Mauldin Economics wrote about the eight-body problem in economics in which we cannot predict how the economy will react when eight variables change. He lists the following:
What is certain is that as government fiscal intervention starts to lose its effectiveness it will be inevitable that monetary policy will continue to remain very accommodating with bond buybacks and record low interest rates. COVID-19 has turned conventional economic thinking upside down.
The inflation rate in New Zealand, as in many countries, is on a downward trajectory – it will take a lot of stimulus form the Reserve Bank to meet its policy target agreement of maintaining the CPI between 1-3%. Westpac have forecast a drop to 0.2% in 2021 and to remain below 1% until the middle of 2022. There have been some obvious reasons for less pressure on inflation:
Demand for goods and services both in NZ and overseas has dropped significantly and tamed any inflation. Most notably there has been a major drop in oil prices.
The use of ecommerce and, without the overheads of rents / staff, prices are often much lower than the high street.
With zero net migration and as excess capacity in long term rental market prices haven’t moved. Add to this the Government’s rent freeze.
A lack of tourist dollars has meant a shift inwards of the aggregate demand curve as exports of services fall – AD = C+I+G+(X-M).
With people having the growing uncertainty of job security there has been little additional spending or borrowing with the threat of redundancy hanging over them.
The wage subsidy has kept some companies afloat but there has been no room for wages increases/negotiations for such uncertain times. Therefore consumer spending has been limited compared to previous years.
Important to note that inflation figures that are quoted are usually on a yearly basis so it is the change in prices from today to this time last year. It will be interesting to see what state the economy will be in this time next year.
I was surprised to see the official unemployment figures issued today – down from 4.2% to 4.0%. However this reflects those workers that were laid off but unable to seek further employment due to the Level 4 lockdown but still included in the labour force. Remember the unemployment calculation is those people who are unemployed and actively seeking employment.
According to the ASB a better measure in the current environment would be underutilisation – It is defined such that jobseekers outside the labour force are captured (unlike the unemployment rate) and includes people working part-time who would like to work more hours. Utilisation rose from 10.4% to 12%. The unadjusted LCI, more of a ‘raw’ measure of wage costs, rose just 0.4% qoq, with annual growth slowing from 3.8% to 3.1%. Average hourly earnings from the QES slowed to 2.5% yoy for private sector workers, a multi-year low.
End of wage subsidy
Although these were positive signs for unemployment figures later in the year it is inevitable that these figures will deteriorate when the wage subsidy ends and we return to an economy which isn’t propped up by government spending. Unemployment is forecast to peak at 9.8% in September.
I have blogged before about Modern Monetary Theory. Basically it says that you can print your own currency by having your own central bank, run large deficits, have full employment, have no inflationary pressure and do this year after year. However while large deficits and monetary stimulus make some sense during a short deflationary economic contraction, sustaining those policies for years, will lead to inflation and economic stagnation – stagflation. The video below is from BBC Reel where Stephanie Kelton, author of The Deficit Myth, argues that we need to rethink our attitudes towards government spending. Worth a look – great graphics.
Although in New Zealand the containment of the Covid-19 has so far been successful, with no international visitors the tourism sector has seen a sharp downturn. Those that have suffered most are the smaller operators and bars, restaurants, accommodation providers. Even with the wage subsidy a lot of these firms have been forced out of business. Domestic tourism will be essentially for the survival of a lot of the tourist spots around the country. The return of overseas visitors is some way off and even when restrictions are lifted visitor numbers are likely to be limited.
Visitor arrivals in New Zealand
Before Covid-19, Tourism was New Zealand’s largest export industry in terms of foreign exchange earnings. It directly employed 8.4 per cent of the New Zealand workforce. For the year ended March 2019:
the indirect value added of industries supporting tourism generated an additional $11.2 billion, or 4.0 percent of GDP.
tourism as whole generated a direct contribution to gross domestic product (GDP) of $16.2 billion, or 5.8 percent of GDP.
international tourism expenditure increased 5.2 percent ($843 million) to $17.2 billion, and contributed 20.4 percent to New Zealand’s total exports of goods and services.
As the economy struggles along people will be concerned about job security and look to be a lot more cautious with spending. However having been restricted during the lockdown there is the hope that New Zealanders will want to travel domestically.
I came across this material on the blog ‘Sex, Drugs and Economics’ which discusses Bruce Wydick’s post on his blog ‘Across Two Worlds’. This is very useful for NCEA Level 3 and CIE AS Level Unit 2 both of which look at Income Elasticity of Demand.
Wydick looks at who is most likely to do well and who is likely to suffer in a post-covid environment. A typical recession is generally caused by supply-side factors (oil crisis years of 1973 – prices up by 400% – 1979 – prices up by 200%) or demand-side impact (loss of business confidence and consumer confidence). Covid-19 is very different as it is a complete shut-down of certain businesses and it forced people to stop buying things that they normal do. Wydick puts goods and services into two categories:
Snap-Back goods and services – things we couldn’t buy during the Level 4 lock-down period but were purchased when we went to Level 3. Pent up demand meant that purchases of these goods and service might have been higher than normal – buying less now means buying more later.
Gone Forever – as it states. Invariably this generally refers to services like air travel, tourism, haircuts, public transport and entertainment. When it becomes safe to have a haircut you still only get one haircut as the rest of your haircuts have disappeared and there is no catch-up spending like with snap-back goods.
These are the differences between goods with low versus high income elasticity. Income elasticity of demand measures the responsiveness of quantity demanded to changes in income. We can have different types of normal goods. If a 10% increase in income brought about a 10% increase in quantity demanded, we can say the income elasticity of demand is unitary. If EY>1 we classify the good as a luxury, and if EY<1, a necessity.
Income elasticity of demand will also affect the pattern of demand over time. For normal luxury goods, whose income elasticity of demand exceeds +1, as incomes rise, the proportion of a consumer’s income spent on that product will go up. For normal necessities (income elasticity of demand is positive but less than 1 and for inferior goods (where the income elasticity of demand is negative) – then as income rises, the share or proportion of their budget on these products will fall. Wydick puts the different types of purchases in a simple 2 x 2 matrix“Snap-Back” vs. “Gone Forever” and High vs. Low income elasticity.
It then becomes easy to see which industries are in the most trouble in 2020. So, when goods and services are both “gone forever” and have a high income elasticity, we can expect the impact of the coronavirus pandemic to be most severe. Wydick identifies air travel, tourism, sporting events, hospitality, and transport (but not public transport). Everything else either snaps back and experiences some catch-up spending, or isn’t as affected by lower incomes. Goods that have a high income elasticity means that when you lose your job during the recession, you and others like you are even less likely to buy these things. For New Zealand the decline of the tourism industry is a significant hit to GDP and employment in this sector.
New Zealand has been seen by many as a country which has so far done well to restrict the spread of Covid-19 and hopefully limit the longer term impact on the economy. Like many countries the economic consequences have been significant with the contraction of GDP and rising unemployment. New Zealand is now in a deep recession – negative GDP for two consecutive quarters – with GDP set decline by 17% through the six months of the year. By comparison NZ only fell by 2.7% during the GFC in 2008 and part of 2009.
The graph (from Westpac) below shows the importance of government spending in 2020 and continuing into 2021. But the reduction in household spending, residential construction and business investment are a major concern and invariable this will lead to a further loss of job. However the forecast for GDP in 2021 is more promising with household spending and government consumption being the engines of growth. Although some are saying that the recovery will be faster than after the GFC one has to remember that the GDP figures will be a lot higher as they coming from a very low base – even negative. So even a small increase in economic activity will give you a very large percentage change from the previous year. The government have spent approximately $22bn in support measure which is equivalent to around 7% of annual GDP and no doubt there is more to come.
Aggregate demand is crucial here and it is important for both Cambridge and NCEA students to understand its components and how it generates growth – see midmap below.