For those who don’t want to listen to a video, we’ve popped the script below.

We’ve chatted about why we care about GDP and the different ways of measuring it – be it through expenditure on final items, the value-added in each industry, or the income paid to those providing a “factor of production” such as work time or capital equipment.

In these chats we established that however we measure it, these measures of “expenditure” “production” and “income” all give us the same result for GDP – and this tells us that the amount we produce, the amount we consume, and the amount of income we have are really all the same thing.

This is a powerful result that has been used to say “Supply creates its own demand” or, as the bloke on my t-shirt says, that “demand creates its own supply”. Behind all of this we always need to model a process to truly understand how the macroeconomy works and what trade-offs there are.

[Gulnara]

However, out of context this result is also misleading – and can lead us to get the wrong impression about how society is progressing or the opportunities available to people.  Even ignoring other attributes that economists like to add in – the distribution of income, environmental change, and non-monetary value stemming from your interaction with your community – this perspective can miss important things even in terms of our narrow understanding of monetary incomes.

In this video we are going to think a little bit about that, and see what other measures we can look at from the national accounts that may give us a wider perspective on incomes.

GDP and income

[Back to Matt]

As we noted in a prior video, GDP can be measured by taking all expenditure on final items, taking all income paid to labour, capital, and through indirect taxes, or by calculating the value added in each industry and adding them up.  And each of these measures will give us the same number!

This measure is amazing at helping us understand the use of society’s scarce factors of production, gives us an insight into economic progress in a country, allows us to understand and compare production and incomes between countries, and provides a useful piece of information for asking about policy trade-offs when we use it with economic models.

However, when it comes to cross-country and time based comparisons we immediately run into an issue – prices change through time, and different countries use different currencies.

If we are looking at one country or countries with the same currency everything is in the same “unit of account” which makes it easy to add up – but how do we add up a New Zealand dollar and a Japanese Yen for building a GDP measure?  

When comparing across countries we need to adjust for the fact that different units of the currency can be exchanged for the same goods.  We can’t just use exchange rates here as they don’t give us the full picture due to non-tradable goods and services. Furthermore,these exchange rates can be excessively volatile and as assets currencies may be valued for reasons other than the strict ability to purchase goods and services. 

We aren’t going to focus on cross-country comparisons and therefore those types of measures right now, but will cover it in a future video.

Our focus here will be on the time based dimension. We know that a New Zealand dollar today cannot buy the same number of chocolate bars as a New Zealand dollar could when I was a kid – sadly. As a result, if we construct GDP just by counting the number of dollars spent on these items, then when comparing GDP numbers over time we will end up with a measure that captures both the increase in the price of goods and services as well as an increase in the actual number of them.

The measure that takes “current prices” to calculate GDP – the equivalent of just adding up the number of dollars spent – is called nominal GDP.  While the measure that adjusts for the fact that prices changed and attempts to measure the volume of GDP over time is called real GDP.

Getting from nominal to real GDP involves figuring out a way to remove the price effect, or what is termed deflating nominal GDP for price growth.  The GDP deflator captures the aggregate price effect through time, and by dividing nominal GDP by this deflator we get our measure of real GDP.

As we often care about the amount of stuff available to buy, rather than the number of New Zealand dollars floating around to spend on it, real GDP is normally the measure we are talking about when we talk about GDP as income.  However, both measures are useful and have their place for asking different questions.

To give this all a bit of context, let’s pull up the data and look at these measures for New Zealand.

[R session]

Here we have pulled some data off the Stats NZ website and made some quick and nasty graphs.  This first one shows us New Zealand GDP in nominal and real terms between 2000 and 2020.

Note how nominal GDP starts below real GDP and then climbs above it.  What does this mean?  Well it means the nominal GDP was growing faster than real GDP, which is what we would expect given that prices were rising!

Furthermore, they are equal during 2009 – implying that we are deflating nominal GDP to get real GDP in 2009 prices.

This is the income of the nation, but as we are looking at incomes we might want to know about the incomes per person – or per capita.  As populations are growing that looks like a second graph here.

This shows a similar trend, but you will notice that the lines are a bit flatter (or that the Y-axis increases by a smaller proportion).  This is because the population was growing during this period, so part of the increase in GDP was associated with there simply being more people.  

The real GDP per capita number tells us that, in New Zealand borders, significantly more output is created per person was created in 2020 than was the case in 2000 – 38% more to be precise!  This opens up a world of questions, are we using more inputs (i.e. working more, using more environmental capital, building up more physical capital) or have we just learned how to use what we have in a more efficient way – what is called multifactor productivity growth.

But we aren’t answering those here. Instead we want to ask, is this income?  Over to you Gully.

What is macroeconomic “income”

[Gulnara]

Thanks again Matt.

Describing GDP and income in the way above is typical for an introductory economics course, and feels very natural. However, it is also misleading. To truly think about income we need to ask what income actually is.

In Hicks 1946, John Hicks stated that income was “the maximum value which [a man] can consume during a week, and still expect to be as well off at the end of the week as he was in the beginning.”.  As one of the founders of the post-war “neo-classical synthesis”, and as a famous welfare economist, it is this definition of income that captures what an economist is thinking about when they say “income”.

It is then the equivalence between consumption + savings (potential consumption) and production at the macro level that leads us to view income as essentially GDP.

Income in this economist’s world is “potential consumption”, as it is this ability to consume final goods and services through time that generates wellbeing and welfare.

GDP on the other hand is a measure of the use of factors of production to generate value added/output – the rationale for looking at this is to understand the utilisation of factors of production, especially workers, in market activities. As noted by Matt above, the focus is also on the number of items. 

In this way, much of GDP is the act of creating some value associated with a final product that is consumed – but there may be cases where the factor of production is instead used in order to “maintain how well off they are” or where the residence of the individual who claims the output differs from the residence of the factors of production.  These differences will lead to a gap between our true concept of income and GDP – so let’s talk about them a bit more.

Maintaining assets

[Matt]

Thanks Gulnara.  The issue I want to talk about a bit more is that of maintenance.  

The “gross” in gross domestic product refers to the fact that ALL investment is included in the measure.

If you own a building and you use it to run a business then that refers to a capital item.  You may invest in that building by adding an extra floor, improving the air conditioning with services like www.newcastleairconditioning.co.uk, painting the building, or replacing the showers.  All this investment activity will be measured at once – however, some part of it refers to maintaining the productive use of the asset while some refers to increasing the productive use of the asset.

Why would we need to maintain anything?  Well, time takes its toll on all of us – especially capital assets.  This depreciation in the usefulness of the capital item is sometimes called “consumption of fixed capital”, and GDP includes investment that is solely about keeping the capital item as productive as it was before.

However, if we imagined someone producing something for themselves the decision to repair something doesn’t constitute a new thing for them to consume – so we wouldn’t view it as part of their “income” or an increase in their ability to consume while maintaining their current position.  Instead, this is an expense that is required to keep themselves in that position!

The measure that tries to adjust for this given estimates of depreciation and measures of the capital stock is Net Domestic Product.

[R session]

We will construct Net Domestic Product from the Stats NZ data and graph it here as well.  For this we have subtracted the Stats NZ estimate of “consumption of fixed capital” from GDP, and then used the same deflator to work out a real value.  True Net Domestic Product will look a little bit different, but this gets us close enough to chat about.

So what is this graph showing?

Net Domestic Product moves in a similar way to GDP, but is lower.  This makes sense as we are subtracting an “expense” that isn’t included in GDP.  By taking away the part of GDP that needs to be used to maintain capital equipment we have a more genuine measure of income relative to the definition Gulnara gave above.

This figure grew by 37.7% – slightly less than the growth in GDP over the same period.  Why is it different?  If the capital stock per person has risen, or the types of assets we hold depreciate more quickly – such as computers – then the amount of maintenance that needs to be paid will rise.  As a result, Net Domestic Product growing by less is also consistent with the changing nature of the economy over the past 20 years.

Opening the borders

[Gulnara]

Things become more complex when we open ourselves up to the rest of the world.

Three things happen when we admit there are other countries:

1. Domestic residents can own assets overseas, and so are able to consume things that are produced overseas – similarly foreign residents can own domestic assets, giving them a claim on some of this domestic production that is measured by GDP.
2. Domestic products might be sold overseas (exports), and foreign products may be purchased and consumed domestically (imports).  Imports are part of consumption, and must be funded to some degree by exports.  If the relative price of exports and imports increases , domestic residents can now consume more for the same amount of production.
3. Transfers, such as charitable giving and intermittences, involves residents in one country sacrificing consumption to provide those opportunities to others overseas.

In this way, GDP remains as a measure of what is “produced” within a country – it measures what is made with factors of production within national boundaries.  But this does not immediately mean that it reflects the claim on products for people who live within those borders!

Gross National Product or GNP is the measure that attempts to adjust for the income of domestic residents overseas, and the income of foreign residents that is generated in the domestic economy.  This is very similar to Gross National Income, or GNI – conceptually they should be the same, but in some accounts the residency definition or definition of primary income (income from factors of production) that are included can vary between the two.

However, secondary income (such as the intermittences noted above) are missing in these measures.  Adding in net transfers from above provides a measure termed GNDI (gross national disposable income) as an aggregate income measure.  By capturing all foreign transactions, the construction of this measure also captures changes in the terms of trade as “income” – in a way that GDP does not. (isi_box1_jun_2015.pdf (banrep.gov.co))

Taking GNDI, we run into the same issue that Matt talked about above – there is some investment that is just about maintaining the stock of capital.  Subtracting this depreciation then leaves us with two other measures that you may hear about, NNI or Net National Income, and NNDI or Net National Disposable Income.

Do all of these adjustments change much?  Let’s look at the New Zealand data with Matt!

[Matt on R part and summing up]

For these figures we are going to stick with the nominal data provided by Stats NZ. This is because real figures are not provided, and trying to back out the appropriate price deflators for these different indices will take a bit of time without necessarily adding much value.

With that out of the way, let’s plot everything from the Stats NZ experimental national accounts.

As we can see here there are potentially a few data issues in this experimental series – so I want us to focus on the relativities rather than the individual series shape over time.

GDP is at the top, with GNI and GDI catching up through time.  GNI and GNDI are also very close to each other.  NDI is then significantly lower than the others – which does make sense.

So why is GNI below GDI in New Zealand? Remember that we get from one to the other by subtracting the domestic production that is claimed by foreign residents and adding in foreign production that is claimed or owned by New Zealand residents. New Zealand on average borrows from the rest of the world, and it is the income flows associated with this that explains the gap!

However, the deflator does matter when we want to think about the key differences between GNDI and GDP – namely changes in the relative price of exports and imports, or the terms of trade.  For this we do want to consider how real measures may look different solely on this basis.

Here we get a much sharper change in the relative value of GDP and GNDI than when we looked at the nominal values.  Why is that?  In this instance New Zealand has seen a very substantial increase in its terms of trade – or the price of exports relative to the price of imports – over this period.  When we deflate the two income series, that terms of trade difference shows up as another part of the change in these measures.

Between 1992 and 2021 real GDP per capita rose 58% while real GNDI per capita rose 77% – as a result this is a very significant difference. So how do we think about which income measure to use?

The fact that New Zealand can now buy a lot more imports from the exports it sells is an increase in income in the GNDI sense – and it is not captured directly by real GDP.  How does this work?  If exports remained fixed and more imports were purchased for consumers to consume, then both C and M would increase in the GDP equation – cancelling each other out.

However, this is real income – having people overseas give New Zealanders more products for the same exports is exactly the same as exports becoming more productive in terms of the amount of goods and services available for New Zealanders to use.  As a result, understanding this change is extremely important!

Conclusion

As we’ve noted above, looking at national accounts to get an idea of material wellbeing can be complex – and MOTU has been undertaking work trying to get a more detailed understanding of this in recent years: Motu-Note-21-Material-Wellbeing-of-NZ-Households.pdf

However, by clearly articulating what GDP is, and how it may measure some things we don’t truly see as “income or potential consumption” we’ve been able to work out some alternative measures that already exist in the data to chat about what is happening.  

These measures give us additional information which may change the way we see New Zealand’s productivity performance, inequality within the country, New Zealand’s wealth and income relative to other countries and a multitude of other narratives that are given in the media and common discussion.  However, we’ll leave it here and we would be eager to hear if this perspective is useful in helping you understand a little more about our beautiful little island.

After recent discussions about “negative interest rates” across Australasia I thought it would be useful to talk about how these rates appear mechanically at a high level (in terms of financial system operations).

In class (and Gulnara’s posts here) the motivation of why negative interest rates might be appropriate in a policy sense was raised.  Furthermore, she did a great job of noting that it is unlikely that negative rates will cause additional savings (as some have claimed) and so theoretically we can continue to think about our investment model with negative interest rates.

For this post we will assume that the central bank is trying to influence interest rates towards a level that will “close the output gap” or “push Y to its sustainable level” and achieve their inflation target, and it just happens that this interest rate is negative.

The wrinkle is that we achieve this negative interest rate through a settlement cash mechanism – so we need to ask, how do negative rates in settlement cash accounts translate into lending and actual interest rates?

Negative rates and settlement cash

Let’s take our example from before and assume that the OCR is cut by 400 basis points to -2%.  In this case the borrowing financial institution will “pay” -1.75% and the depositing financial institution “receives” -2.25%.

Why would an institution deposit funds in an account to earn -2.25%?  Why would financial institutions not just ramp up their borrowing at -1.75%?

The reason for that intuition we feel is that we are thinking about money – money yields a nominal interest rate of 0%.  If the banks could just independently hold money at 0% they would borrow endlessly at -1.75% and would never save at -2.25%.  

However, the settlement cash account is designed to include cash holdings for banks.  So if a bank borrows a dollar from settlement cash and tries to hold it as a cash asset, it returns as a deposit in the settlement cash account.  So in this way the system cannot be escaped.

What about interbank lending?  One bank could lend to another bank at 0% and this would be a better return for the lender than -2.25%.  But that bank who is borrowing is paying 0% instead of -1.75% is actually worse off.  As a result, the interbank market will reinforce this dynamic as long as the settlement cash account is determining the opportunity cost of marginal lending.

Given the banks cannot avoid these negative rates in settlement cash accounts, they would then be expected to pass these on to both their depositors and borrowers (in order to achieve the same interest margin) through lower/negative interest rates!

However, they haven’t been. Deposit rates refuse to budge below zero in countries with negative cash rates, and although banks are accepting lower margins they have also reduced lending rates by less than they would have otherwise.

In this situation, there are two ways that banks can turn around and try to avoid these negative rates – and therefore avoid passing on negative rates to depositors and lenders:

1. Investing in instruments that are zero yielding that don’t get captured in settlement cash (eg overpaying tax – e.g. Sweden).  We need to be careful here not to include assets that have an expected negative capital gain (as that equates to a negative yield – such as gold).
2. More carefully ensuring that deposits and loans match on a day-to-day basis.

Both of these have one thing in common – banks are willing to slow down their matching of lending and borrowing, and accept lower liquidity, in order to avoid the cost associated with negative interest rates.

If this is the case, then when rates become negative enough (when they aren’t just representing a small service fee) financial institutions may respond to negative interest rates by being more careful to match their deposits and lending on a day-to-day basis.  Suddenly when you want a loan or to deposit funds the bank will start saying “you meet our criterion, but we have a waiting line for approving loans/deposits and it will occur when you come to the front of that line”.

This would reduce the volatility of money and in turn restrict economic activity.  This is a mechanism that may lead to negative interest rates reducing growth – if the negative interest rates also involve an increase in non-interest restrictions to lending.

But a negative cash rate pays people to lend!

Good point and very consistent with what we’ve done in class. As a result, this discussion requires further explanation.

When cash rates are negative it is true that a financial institution will now be willing to lend at a lower interest rate (given that they can “borrow” from settlement cash at a more attractive rate), so at face value there is no reason to think such negative rates will restrict lending.

But we also need to remember that the borrowing also credits a banks account.  If the person being lent to then deposits the funds in another banks account that other bank will be a bit unhappy about having to pay the negative interest rate on it – and if the person immediately spends it those funds will be deposited.

As a result, banks will want to restrict deposits.  In so far as a bank restricts credit being paid into their account, they can create an issue for loans – which in turn could restrict how quickly loans could take place.

When interest rates are positive you have same issue from lender, but with negative it is the deposit taker that pulls back.  With positive interest rates, lenders are increasingly unwilling to lend as rates go up (due to the uncertain cost of mismatch), with negative rates the deposit taker becomes unwilling to accept funds as rates go down – both break down intermediation, but with positive rates that is the goal – with negative it is not.

Furthermore, even if the loan came back to the same bank as a deposit if the zero lower bound on deposit rates is binding (which it seems to be – with deposits rates in countries with negative cash rates staying at zero) for financial institutions then charging lower interest rates on investment will simply reduce the net interest margin of banks.  With negative spillovers from uncoordinated lending in this sense, tacit collusion between banks might see them agree to cut lending to support margins – making negative rates at best useless and and worst a device that leads to uncompetitive behaviour.

Overall, thinking about how cash rates translate into negative interest rates – and the broader concerns in Eggertson etal 2018 – indicates that negative interest rates may not be as useful as our models in class suggest, where all that mattered was the investment function and the related impulse to investment this suggested.

However, this is an area economists are still trying to understand using data – so hopefully in the future we will have a better idea of what elements are relatively more important!

The main reason for discussing this is to show that, although our models in class are useful – they are not always the whole story. And given unique circumstances it is important to think about other margins that matter. Remember the Joan Robinson quote from the start of the semester – we are learning how to question things, not incontrovertible facts.

In that vein, today we are going to talk about how the central bank does influence the nominal interest rate in New Zealand (as compared to our still useful discussion of bond purchases in class).  By doing so we will also be able to ask about “negative interest rates” in a later post.

It should be noted that none of the content I cover here is assessed – you will be assessed on what we do in class and in the lecture notes and readings. Instead the purpose of this is to add a bit more detail about things for students who are interested.

Cash rates and interest rates

A history of monetary policy implementation in New Zealand is given by the RBNZ here.  However, we will abstract from most of that and just look at where things are right now.  For this the RBNZ provides an excellent teaching resource on settlement cash accounts rate here, and the RBA also provides a nice explanation of the market here.

The corridor described in these pieces refers to the gap between the rate paid to those who deposit funds in the settlement cash account (the ESAS) and the rate changed to those who borrow from the settlement cash account.  There have been significant changes recently in the corridor set around the OCR (due to COVID) – so we will assume our own corridor for simplicity below. 

Across the banking system as a whole we would expect the funds deposited in financial institutions (for simplicity I’ll call them all banks – even though there are many other types of institutions) and the funds lent out to be equal.  We described this process when looking at the money multiplier in class.  However, there will be individual banks who temporarily have deposits that are greater than the loans they’ve written and vice-versa.  The settlement cash account provides a mechanism to help these banks balance the books.

The “cash rate” we are then discussing is the rate that determines these rates from the settlement cash account.  The rate for deposits will be lower than the rate for loans, implying that the central bank earns a “margin” from their settlement activity.  Furthermore, they will lend or borrow as much as necessary at this rate.

This is the background we need to work out how this cash rate influences the lending and deposit rate of banks.  If, at the end of a day, a bank has lent more out than it has brought in with deposits it can borrow the difference from the central bank at the settlement cash lending rate.  If instead they have brought in more in deposits than they have lent out, they can leave the remainder in the settlement cash account and earn the deposit rate.

Given the recent rate corridor changes, let’s use an example where OCR + 25 basis points is charged for someone borrowing from settlement cash, and OCR – 25 basis points is charged for someone lending to settlement cash.  

Take an OCR of 2% (200 basis points), here a bank who is expecting to have to borrow from their settlement account knows that financing will cost 2.25%.  As a result, if a new borrower comes to them they will not charge below a (risk adjusted) 2.25% to lend to that borrower as the cash rate determines their opportunity cost.

Similarly, someone who looks like they are going to have a credit in their settlement cash account may see a new depositor turn up.  They know that they could earn 1.75% putting that money in the settlement cash account and so would not be willing to offer a higher interest rate to that depositor.

Now take things a step further.  One of these banks is lending and one of them is borrowing.  They could essentially transact with each other, through interbank lending, in order to benefit from closing this margin.  For example, the bank that is borrowing from settlement cash could offer 2% to borrow from the other bank that has excess deposits, and the other bank would be keen to take it.

In this way, the settlement cash rate influences the opportunity cost of lending and borrowing for financial institutions, and in turn influences the interest rates they charge lenders, provide to depositors, and choose to interact with each other.

But what happens when the cash rate goes negative?  We will think about that more in a future post.

In doing so we found that the outcomes in a competitive market allowed gains from trade – buyers who valued the products more than the sellers were trading with each other. But there were issues:

• It assumed there were lots of potential sellers of a homogenous product whose choices have no influence on the choice of other sellers.
• It assumed there were no systematic biases in consumer choices and ignored agency problems in production.
• It took for granted full, or at least symmetric, information about the product and the market.
• It didn’t incorporate the way the choice to produce or consume could impact upon a third party (externalities).
• It assumed that the institutional structure ensured the product was excludable.

These assumptions do hold in some circumstances – and even when they don’t there could be a good reason we start with it (eg assuming a systematic bias without evidence is just assuming people are stupid – which isn’t a good starting point for trying to objectively understand their choices).

But we would like to think about other types of market structures we observe.

Since then we have built some more tools to think about choice – comparative advantage, finance, game theory, and emergent macro-phenomenon.

Armed with these tools we can return to our original market model and ask “what happens when there is only one firm – with the same motives and desires as the many firms before”. This is the case of monopoly.

One firm – one price

Our monopoly has the same motivation as the perfectly competitive firm. It minimises its costs and maximises its profit. So the monopoly we are thinking about is not subject to the agency problems we have discussed could occur – and any X-inefficiency due to this is put to the side.

Given we are comparing like-with-like we want to think about why the aggregated choice of perfectly competitive firms, and the choice of a monopolist over the same industry would differ.

For this we can use the following pieces of understanding:

• A profit maximising firm will set MR = MC (marginal revenue equal to marginal cost).
• For a competitive firm, their choice of price does not influence the price they face OR the price faced by other firms. As a result, MR = P. In a competitive industry this implies that the overall supply curve is simply the MC curve (above average variable costs).
• To put another way – if a competitive firm set a higher price it loses all its customers, if it sets a lower price it simply loses revenue (as it can sell as much as it wants at a given price), so they only ever have an incentive to set that one price.
• For a monopoly, when they produce more they lower the price they face – as a result, making one more unit and selling it involves selling at a lower price to all the customers who would have purchased at a higher price (non-discrimination).
• As a result, selling an extra unit will provide an increase of revenue equal to the price the unit is sold for minus the lost revenue from their existing customers – so MR < P.
• Given this, MR = MC < P and the monopolist will only sell output up until the point where they make a margin on cost (where cost includes the opportunity cost of production).
• The monopolist WANTS to sell to another customer at a price below the current price. A customer WANTS to buy at this price. But they do not transact because the monopolist would have to cut the price for all other customers. Therefore, some transactions that have gains from trade do not occur.

That is our process for thinking about the monopoly – no graphs and no real math. This also tells us we can ensure all gainful trade occurs by allowing first degree (perfect) price discrimination – but then all of the surplus will be received by the monopolist, and none by the consumer. So we may view this as inequitable.

Graphs

So to make sure we understand, lets do this with our graphs.

In this first graph we can see that the monopolist charges a mark-up on cost, and will produce less at a higher price that the competitive firm did (where demand equals marginal cost). The “lost surplus” for these missing transactions is coloured in yellow – we call this deadweight loss, and often term it the inefficiency in the market.

Our second graph here adds another perspective. If we have normal profits at the competitive equilibrium, then a monopolist will make supernormal profits. We know that these profits would incentivise entry – and so for entry to not occur there must be some specific nature of the industry (eg economies of scale and natural monopolies) or barrier to entry (either government or firm induced) that prevent a second firm from showing up.

Summing up

Our model of monopoly leads to an outcome we would – in many ways – view as “worse” than the competitive situation. Transactions do not occur when there are gains from trade associated with them.

But this isn’t due to some specific motivation – the monopolist is no more evil than a competitive firm in our example, there are no differences in motivates and desires. Instead the trade-off they face is different – and they respond to that.

We did noticed that entry and the potential for competition are important – and this takes us to our final topic for the course, Oligopolies!

Finance seems like an exciting topic, with a lot of the economic metrics we see flashed around day to day related to financial markets rather than the abstract markets we’ve been talking about so far. Financial security through oil and gas OT cybersecurity systems can work wonders to safeguard online financial information.

However, what do we mean when we talk about finance here, and how does it fit into what we’ve talked about so far? For the murky details of how we “do” finance I will leave you to work through the lectures (and slides here and here) – this post is just about motivating why we do those calculations.

What are we talking about with finance?

Lets not fixate on stock markets, interest rates, and some of the exciting financial gambles you see in movies. Lets not use some of the common definition of “financing an expenditure” or “dealing with large sums of money. And instead lets start at the beginning and work out what this term means for us in economics.

So far in the course we’ve been fixated on the amount of stuff we have the opportunity to consume – the real things. Ice creams, cars, coffees – all the good stuff. And in week three we described how we may want to think about these intertemporally – namely our consumption at different points in time.

With that we needed a way to magically shift consumption through time – we needed saving and borrowing.

Finance describes how we transfer and value monetary flows through time, which are then used to shift when these consumption opportunities occur. For more o n business financial management, you can use software like this online check stub maker which will make your payroll process easier.

Finance is the study of financial markets, where financial markets define how we can shift monetary sums through time and how they translate – where the translation is complicated by i) the fact we value things differently through time, ii) the payment of a nominal interest rate, iii) the change in prices [eg giving the idea that a dollar now is valued differently to a dollar in the future].

What’s this got to do with microeconomics?

Everything.

A lot of what we’ve discussed so far in class was static. There was one period of time, there was an amount of income, and we used that for consumption.

But once we admit that:

• there are multiple periods of time – some where we would really like to borrow from the future or past, and some where we don’t value additional consumption all that much,
• that we don’t necessarily have information about what will happen in the future (and so there is risk associated with a choice that uses or allocates funds that would have been available in the future),
• and that there are a variety of ways of allocating these funds through time (holding cash, holding bonds, holding other assets).

We can see that this is an important issue to explicitly think about. Once we start working in this space this expands what we can do when using our economic tools to manage payroll in 2022.

In this way, finance is an application of microeconomics that explicitly models time and the intertemporal consumption choice – or trying to understand the dynamic process behind people’s choices about when to consume and save.

Furthermore, these choices are important for macroeconomists – as they look at this behaviour when trying to understand the dynamics of the economy at large. In this way, finance is a central subject for both microeconomists and macroeconomists.

This topic is awesome, and really wish we could give it more space – in future economics you will.

So what are we thinking about here.

In our first six weeks we built a model where people’s actions didn’t influence the incentives of others – and so couldn’t influence their actions. For example, if I decided to go buck-wild buying potato chips, it wouldn’t change the the price of chips.

However, this isn’t always the case. If a firm decides to set a higher price it can still sell some product, and by doing so it will lead to other firms seeing a direct increase in their own demand. If I decide to not show up to a hockey game, our teams ability to play will be worsened by being down a player (arguably) – then other players may then have an incentive to just not bother showing up.

These are real life situations we can relate to where people’s actions are interdependent. And this is the key of what game theory is – where your payoff depends on the actions of other, and their expectations of your actions, this creates scope for strategic behaviour.

The two games we look at today in class are a prisoner’s dilemma, and a coordination game. Once you understand those games you will start to see them everywhere. See you there.

As you know this is a course on microeconomics … so it is a bit random to teach macroeconomics. However, as this is the only economics course many students do we think it is a good idea to introduce some of the jargon you will see a lot in your work life!

Usually this is a week of lectures, but given the semester is a week shorter we teach this in a single lecture – and it will be assessed as such.

So what is macroeconomic, why do we care, and what are we measuring?

What is macroeconomics

So far we’ve done microeconomics – we have looked at the behaviour or choices of individuals when faced with a trade-off due to scarcity. We have recognised that prices provide incentives – be they explicit prices or shadow prices – and that the key point to keep in mind was the opportunity cost of a choice.

This is cool. Macroeconomics wants to do the same – but the questions in macroeconomics are LARGE. Instead of asking about a few individuals, or a single market, macroeconomics asks about activity in many markets across a large area (eg an entire country).

The data we use for macroeconomics (to make testable hypotheses) are aggregates of individuals – this could be the total sum, the spread between individuals (the variance), and related changes or growth rates in these amounts.

We would like to use microeconomic intuitions – and the importance of scarcity and opportunity cost – when analysing these problems. However, these are emergent phenomenon where some of the smaller market distortions can multiple to be very large over a whole economy. So the method we often take is to start with the aggregates, decompose them, and then try to understand the behaviour of these aggregated amounts.

This is not a perfect process, and if we tried to implement policy on this basis it could be problematic (as if we haven’t described the behaviour, we don’t know how the relationships will change if we change the policy!). But it is a start – if you would like to do more, come along to ECON141 next semester

Why do we care

I’ll keep this short – a lot of you have explicitly told me you are doing economics because you are interested in where the economy as a whole is going, you are concerned about poverty or unemployment, and that you want to work in policy.

Microeconomics is essential for this – but at the same time so is macroeconomics. Microeconomics gives you the raw tools to “think systematically” in the way economists do – macroeconomics gives you specific understanding of many of the big questions that motivate a lot of economics students.

When I came in economics I was motivated by macro – even though I ended up preferring micro – and I have appreciated the importance of both since.

What do we measure

This was the main focus on our class – we measured certain aggregates: GDP, unemployment, inflation, business cycles and trends, and labour productivity. We understand, in a rough sense, what each of those things mean.

GDP = Production within a certain region (eg New Zealand)

Unemployment = Those willing to work at the current wage who can’t [Note: We also mentioned reporting bias and discouraged workers]

Inflation = Growth in the price level [Note: We also talked about our interest in a measure where this growth is in an “average basket” and how we want to take out changes in prices relative to each other – our changes in scarcity in a micro sense – to think about this idea]

Business cycles = The movement in GDP around a “trend” level due to coordination issues [Note: We stated this was the big focus of the ECON141 course]

Labour productivity = Output produced per hour of work [Note: We discussed how this has risen significantly through the last 150 years, and that this growth is another core issue of interest in macro]

That is a lot of ground to cover in one lecture! But we did it – and after seeing this idea of a “coordination issue” we are now going to do a lecture on game theory which has coordination games. Buckle in.

What we have done this week is actually quite similar – we have asked about gains from trade when the production possibilities were actually a bit different. Given that we have noted that, with different productive opportunities specialisation can generate gains from trade!

The example we use for this is international trade – and that is what we have done in the lectures here and here.

As a practice exercise, can you describe the current shock associated with COVID in terms of a Production Possibility Frontier diagram based on the Australia and NZ trade example in lectures. Graphically what does the shock look like? Could the shock increase New Zealand’s comparative advantage in making Wool? What does this mean?

I’ll put up an answer to this after you test on Monday – as this content is not in this test.

Furthermore, we are focusing on a set of assumptions – so lets start with those:

Assumptions

In the past 4 weeks we’ve built a “profit maximising” firm and a “utility maximising” consumer. For a single good or service we are going to assume it is provided by many of these sorts of firms and demanded by many of these sorts of customers.

The product provided is the same irrespective of the firm. The customers and firms have perfect information. And, in the long-run, the number of firms and the scale of the firm can change.

Why do I put these at the front? Because when we describe a real market some of these assumptions may not hold, and yet our results depend on them. However, the example with these assumptions allows us to ask “so how does the result change as we change these assumptions”. Also it gives us a specific way of thinking about gains from trade – and when market (and government) structures prevent trade that would generate gains from trade for the buyer and the seller.

Equilibrium

We have individual demand curves and individual supply curves. Add the many individuals together and you have … market demand and supply curves. Congratulations.

Our individuals were given the price – they were all forms of price takers. This is because there were many others and so the individual quantity demanded and supplied by a single agent was not sufficient to influence the price.

Given that how do we make sense of this scissors thing as a way of determining that price?

When they cross, the quantity demanded and the quantity supplied are the same – all firms are selling everything they make, while all customers and buying everything they want too. So there is no incentive for them to try to shift the price down or up, as the firm is selling the product for the highest price they can while the consumer is buying it for the lowest price they can. Nice.

What happens when prices are higher? Well the quantity supplied is greater than the quantity demanded. Each firm then faces a choice – sell a rationed amount of output for that price, or cut the price by a tiny amount and sell as much as they want. Seeing this they all start cutting prices, and the price falls.

When prices are lower we face a similar issue from the demand side – seeing they can only buy a rationed amount individual consumers offer a slightly higher price to satisfy their demand, gradually driving prices up.

This is our tendency. But so what?

Surplus

Instead of thinking about the price, lets think about the quantity.

At every quantity left of equilibrium there is a potential buyer who values the product more than a potential seller. As a result, there are gains from trade.

At every quantity right of equilibrium every potential buyer values the product LESS than every potential seller. So there are no gains from trade.

At equilibrium we have all the leftward transactions happening and none of the rightward ones – so we have the maximum gains from trade. This is the idea behind surplus that we discussed in class:

We can see that, with perfect competition (the assumptions discussed above) all the potential situations with gains from trade occur! Nifty.

Now we may have reasons why we want some transfer between the firms and consumers, and we could do that by changing the price (eg an unfair initial allocation of resources). However, a different price would lead to some “rationing” and a lower quantity – which in turn comes with a deadweight loss. This is the lost value associated with transactions that have some gains from trade not occurring.

So even if there is value from the transfer associated with changing the price – there is an unintended consequence associated with the transactions that do not occur.

Questions

• Is Matt’s hypothetical cafe, which is located close to the train station and involves chats about economics, a perfectly competitive firm?
• What are potential market failures when we have a perfectly competitive market? (Hint: Think about the two ways we discussed missing property rights)
• Do we always want to maximise surplus?
• How do rationing rules influence how we think about deadweight loss?
• How might “non-price” means for purchasing rationed products undermine our ability to help the poor by forcing firms to set low prices?

Added explanation

Just like in the previous discussion of simulating choice it is possible to think of this as a form of simulation. When we discussed what was happening “outside of equilibrium” we talked about how agents would change their behaviour and that would describe our tendency back to that equilibrium.

The Youtube channel Primer does a great job of this with an actual simulation of little blobs – I suggest giving it a watch for another perspective on how this idea works!