Wednesday, 21 April 2021

This is how the market responds to shortages of agricultural labour

The New Zealand Herald reported on Monday:

An Australian recruiter hopes the transtasman travel bubble will help fill huge shortages of labour on Australian farms.

In November the Australian Government began offering $2000 for New Zealanders to relocate to help with the shortage of horticulture and agriculture workers...

Like New Zealand, Australia is experiencing a huge shortfall in staff in the agriculture and horticulture sectors.

Incentives to attract workers such as free accommodation, food, increased pay rates and even cash bonuses are being offered.

The Queensland Strawberry Growers Association has offered cash prizes of up to $100,000 to entice workers to get involved in its winter harvest.

Think about the labour market for agricultural workers in Australia. If there is a shortage of workers, that means that the market wage must be below the equilibrium wage, as shown in the diagram below. The quantity of labour demanded (QD) exceeds the quantity of labour supplied (QS) at the market wage W1. There are more jobs available than there are workers to fill them. So, what do employers do? If they are willing and able to pay a higher wage, they could find themselves a willing employee, and offer to pay slightly more than W1, to ensure that they get that worker to work for them. So, employers will bid the wage up, until eventually the market reaches equilibrium at W0, where the quantity of labour demanded and quantity of labour supplied are both equal to Q0.

However, sometimes it isn't the wage that adjusts. Sometimes, the employer instead offers some other inducement to ensure that they aren't the employer who is short of workers. That's where the other incentives (free accommodation, food, cash bonuses, etc.) come in. If enough employers offer these additional incentives, that may encourage additional workers to enter the market, increasing the supply of workers. This is shown in the diagram below. The supply of labour has increased because of an influx of Kiwi agricultural workers, shifting the supply curve to the right at S1. Now the market is operating at equilibrium, with the wage at W1, and the quantity of labour supplied (and demanded) is equal to QD.

Of course, that would then have flow-on effects on the labour market for agricultural workers in New Zealand, where the supply will reduce and the shortage of agricultural workers will increase. That's going to make business very difficult for New Zealand farmers. So, it will be interesting to see if farmers in New Zealand are willing to match the incentives being offered in Australia, to prevent New Zealand-based workers from departing.

Tuesday, 20 April 2021

How should health benefits be aggregated?

In my ECONS102 class, in the health economics topic, we cover the value of a statistical life (increasingly now referred to as the value of a preventable fatality). The value of a statistical life (VSL) is a statistical construct that can be used in cost-benefit evaluations as a measure of the benefits of saving lives. So, for example, if government is considering building road safety improvements on a stretch of highway, the VSL can be used as a measure of the benefits of the improvements, which can then be weighed up against the costs of those improvements.

The VSL comes in for criticism though, mainly on moral or ideological grounds (often along the lines of 'you cannot place value on a human life'). However, the alternatives of ignoring the benefits of lives saved, or assuming those lives saved have infinite value, clearly lead to implausible implications for decision-making. For example, if government truly believed that lives had infinite value, they should immediately ban all vehicular travel, because regardless of the costs that such a ban would impose on society, those costs would be outweighed by the infinite benefits from lives saved due to fewer fatal motor vehicle accidents.

A similar critique was raised in this article from The Conversation yesterday by Ilan Noy (Victoria University of Wellington), in the context of measuring the burden of disasters:

But “value of life” prices can vary a lot between and even within countries. There is also an understandable public distaste for putting a price tag on human life. Governments typically don’t openly discuss these calculations, making it difficult to assess their legitimacy.

Noy they suggests: 

An alternative is a “life years lost index”. It is based on the World Health Organization (WHO) measure of “disability-adjusted life years” (DALY), calculated for a long list of diseases and published in a yearly account of the associated human costs.

In conventional measurements of the impact of disaster risk, the unit used is dollars. For this alternative index, the unit of measurement is “lost life years” — the loss of the equivalent of one year of full health.

Both the VSL and the life years lost index can be used to measure the health burden of disasters. They are both ways that can be used to aggregate the health benefits of disaster prevention or mitigation, or the health costs of disasters.

You might believe that avoiding placing a value on lives lost, as the life years lost index does, is an improvement over the VSL. However, there is an implicit assumption that you are making when doing so, that you probably don't even realise that you are making, and which you would probably find just as distasteful as placing a value on human lives. The assumption is that every lost life year has the same value. That's a necessary assumption in order to add up the lost life years from different people.

That assumption doesn't seem so bad, right? But let's think about the implications of making that assumption. If a young person dies aged five years, when they had a life expectancy of 85 years, then that represents 80 lost life years. So, that young person's death would add 80 to the 'life years lost index'. If an older person dies aged 85 years, when they had a life expectancy of 90 years, then that represents 5 lost life years. So, that older person's death would add 5 to the 'life years lost index'. In other words, the young person's life is worth 16 times the older person's life, when you calculate the 'life years lost index'.

Moreover, if government was making decisions on the basis of the 'life years lost index', they would be well justified to devote excess resources to saving young people and protecting their health. After all, saving a young person leads to a significantly greater improvement in the index than does saving an older person. If you extend the index to considering disability, then any treatment that reduces disability or improves health among young people is similarly going to be preferred over the same treatment being offered to older people. Are you still feeling good about not valuing lives saved?

To be fair, the value of statistical life has exactly the opposite problem. It values all lives equally, so a life saved near the end of life is valued the same as a life saved near its beginning. That doesn't seem so bad on the surface, until you think about what that means for the value of a life year. Each year of additional life added for the older person I used in the example above would be valued at 16 times each year of additional life added for the young person. So, a government would be well justified to divert resources to treatments for older people that extend their lives, rather than the same treatments for younger people.

Aggregating health benefits is a difficult problem. These issues (and several related issues) are well covered in Kip Viscusi's excellent book Pricing Lives (which I reviewed here). There isn't a perfect solution to this dilemma, and economics cannot answer the question of which option - pricing lives saved (as the value of a statistical life does), or life years (as the life years lost index does) - is better. It is quite reasonable to hold an opinion either way, and quite unreasonable to impose your opinion on others. Critiques of VSL like the one that Noy uses do little to help people understand the issues, or to make an informed judgement for themselves.

Monday, 19 April 2021

Regressive regional fuel taxes

A few weeks ago, my ECONS102 class covered taxes. One aspect of that topic is consideration of whether income taxes are progressive or regressive. A progressive tax is one where higher income people pay a higher proportion of their income in tax than lower income people, while a regressive income tax is one where higher income people pay a lower proportion of their income in tax than lower income people. Note that whether income tax is progressive or regressive isn't determined by whether higher income people pay more tax or not. That happens under all but the most regressive tax systems. This is about the proportion of their income that higher income and lower income people pay.

Anyway, the same descriptors (progressive or regressive) not only apply to income taxes, but can also be applied to other taxes (as well as to subsidies, property rates, surcharges, and basically any sort of government fee). If lower income people will end up paying a higher proportion of their income in the tax (or other fee) than higher income people do, then the tax (or fee) is regressive.

To illustrate this point in class, I usually talk about cigarette taxes. However, they are far from the only example. Take this example from the New Zealand Herald this morning:

Efeso Collins raised eyebrows in 2018 when he opposed Auckland's regional fuel tax, designed to not only fund low and zero-carbon public transport but slash emissions.

After all, the Manukau ward councillor is a strong believer in taking action to address climate change.

But his opposition was rooted in arguments that have long divided climate action debates, about equity.

For people living in central Auckland, on high incomes, in walking or biking distance of their jobs, close to fast and frequent public transport, the fuel tax impact was minimal, and for many had the desired impact of a reduction in driving.

But for many in Collins' ward, on lower incomes with large families and poor public and active transport choices, many Māori and Pasifika, there was little choice but to continue driving.

The tax made up a much larger share of their income, essentially subsidising projects miles away like the City Rail Link in the central city.

The regional fuel tax in Auckland is an excise tax on fuel, set at ten cents per litre of fuel. People who spend a higher proportion of their income on fuel, will also spend a higher proportion of their income on the regional fuel tax. If lower income people spend a higher proportion of their income on fuel than higher income people do, then the regional fuel tax is regressive.

Collins essentially argues that this is the case because lower income people live further from central Auckland, therefore travel further and spend more on transport. It's fair to make the assertion that the fuel tax is regressive, but I'd want to see that backed up by some evidence. Maybe lower income people drive less (they are lower income after all, and driving is already expensive), and make more use of public transport. If we want to really know whether the regional fuel tax is regressive or not, it pays to look at some data.

Statistics New Zealand collects data on household expenditures, and it is reported in two places: NZ.Stat and Shinyapps. Unfortunately, neither of these sources quite do what we want, which is to disaggregate spending on fuel by income group. However, the detailed data on Shinyapps suggests that a little more than 70 percent of spending in the category "Private transport supplies and services" is petrol and other fuels and lubricants. That is one of the categories that NZ.Stat uses in disaggregating spending by income group, so we'll use it as a proxy for spending on fuel. [*]

In 2019 (the latest Household Expenditure Survey data), the top income group (Decile 10; incomes above $199,400 per year) spent $116.70 per week on "Private transport supplies and services". Using the bottom of the income bin ($199,400), that spending works out to about 3.0 percent of their annual income.

Moving down the income distribution to the middle, the fifth income group (Decile 5; incomes between $60,800 and $77,199) spent $72.50 per week on "Private transport supplies and services". Using the middle of that income bin ($69,000), that spending works out to about 5.5 percent of their annual income.

Moving further down to the bottom of the income distribution, the bottom income group (Decile 1; incomes below $23,700 per year) spent $38.60 per week. Using the top of that income bin ($23,700), that spending works out to about 8.5 percent of their annual income.

It is pretty clear that lower income households spend a higher proportion of their income on "Private transport supplies and services". It therefore seems highly likely that they spend a higher proportion of their income on fuel, and so they also spend a higher proportion of their income on the regional fuel tax. The regional fuel tax is regressive.


[*] Spending on "Private transport supplies and services" is not a perfect proxy for spending on fuel. As a category, in addition to petrol, and fuels and other lubricants, it includes vehicle parts and accessories, vehicle servicing and repairs, and "other private transport services". Those other spending categories make up around 30 percent of the spending on "Private transport supplies and services". However, provided the share of this category that is spent on fuel doesn't differ too much by income, then we should be ok. So, we're essentially assuming that, for all income groups, about 70 percent of their spending in the "Private transport supplies and services" category is on petrol, fuel and other lubricants.

Sunday, 11 April 2021

Reduced exports due to border restrictions and the domestic market for strawberries

Last week, my ECONS102 class covered international trade, including the effects of trade restrictions on economic welfare. Usually, the examples I use involve the government interfering in the market, through the use of quotas or tariffs, and those trade policies invariably lead to a loss of economic welfare (a deadweight loss). However, sometimes other things get in the way of international trade, such as this recent example from HortNews:

Strawberry prices fell 43% in November 2020 as Covid-19 border restrictions reduced exports, Stats NZ said.

Consumer prices manager Katrina Dewbery says that fewer exports have meant there is more supply available for domestic consumption.

Prices averaged $3.45/250g punnet in November, down from $6.04 in October.

“Prices are lower than we typically see for a November month with December generally being when they are cheapest. Some people may be seeing even cheaper prices during the first half of December,” Dewbery said.

There was no government intervention here, but a lack of capacity to export strawberries due to the COVID-19 border restrictions reduced the quantity that could be exported. We could interpret that as being similar to an export quota on strawberries (where the quantity of exports was restricted to less than it would have been with open borders), so let's look at the effect on the market for strawberries.

First, consider the case without any border restrictions. This is shown in the diagram below. New Zealand is an exporting country, which means that New Zealand has a comparative advantage producing strawberries. That means that New Zealand can produce strawberries at a lower opportunity cost than other countries. On a supply-and-demand diagram like the one below, it means that the domestic market equilibrium price of strawberries (PD) would be below the price of strawberries on the world market (PW). Because the domestic price is lower than the world price, if New Zealand is open to trade there are opportunities for traders to buy strawberries in the domestic market (at the price PD), and sell it on the world market (at the price PW) and make a profit (or maybe the suppliers themselves sell directly to the world market for the price PW). In other words, there are incentives to export strawberries. The domestic consumers would end up having to pay the price PW for strawberries as well, since they would be competing with the world price (and who would sell at the lower price PD when they could sell on the world market for PW instead?). At this higher price, the domestic consumers choose to purchase Qd0 strawberries, while the domestic suppliers sell Qs0 strawberries (assuming that the world market could absorb any quantity of strawberries that was produced). The difference (Qs0 - Qd0) is the quantity of strawberries that is exported. Essentially the demand curve with exports follows the red line in the diagram.

In terms of economic welfare, if there was no international trade in strawberries, the market would operate at the domestic equilibrium, with price PD and quantity Q0. Consumer surplus (the gains to domestic strawberry consumers) would be the area AEPD, the producer surplus (the gains to domestic strawberry producers) would be the area PDEF, and total welfare (the sum of consumer surplus and producer surplus, or the gains to society overall) would be the area AEF. With trade, the consumer surplus decreases to ABPW, the producer surplus increases to PWCF, and total welfare increases to ABCF. Since total welfare is larger (by the area BCE), this represents the gains from trade.

Now consider what would happen if the quantity of strawberry exports was restricted below (Qs0 - Qd0). This is shown in the diagram below as an export quota. Let's say that the quantity of exports is reduced to the amount between B and G on the diagram (about half the amount of unrestricted exports). Now consider what happens to the demand curve (including exports). The upper part represents the domestic consumers with high willingness-to-pay for strawberries. Then there is a limited quantity of exports that can get through the border restrictions, at the world price PW. After that, there are still profit opportunities for domestic suppliers (that is, there are still some domestic consumers who are willing to pay more than what it costs the suppliers to produce strawberries). So, the demand curve (including the export quota) pivots at the point G, and follows a parallel path to the original demand curve (i.e. the demand curve including exports follows the red line in the diagram). The domestic price is the price where supply is equal to demand (P1). The domestic consumers choose to purchase Qd1 strawberries at the price P1, while the domestic suppliers sell Qs1 strawberries at that price. The difference (Qs1 - Qd1) is the quantity of exports. Notice that the price of strawberries that consumers pay has fallen, just as the article linked above noted.

Now consider the areas of economic welfare. The consumer surplus is larger than it was without the restricted exports (it is now the area AJP1), the producer surplus is smaller than it was without the restricted exports (it is now the area P1HF plus the area KLHJ. The first area (P1HF) is producer surplus as if the farmers sold all of their products to the domestic market, while the second area (KLHJ) is the extra profits the farmers get from selling the limited amount of exports that are able to get through the border restrictions. Total welfare is smaller than without the restricted exports (it is now the area AJHF+KLHJ). There is a deadweight loss (a loss of total welfare arising from the restricted exports) equal to the area [BKJ + LCH] - these areas were part of total welfare with trade and no restricted exports, but have now been lost.

The lost exports make strawberry farmers worse off, as well as society overall (in terms of economic welfare in total). However, strawberry consumers are the unwitting recipients of a gain. The interesting thing here is that the government is not responsible for the deadweight loss - this is a deadweight loss caused by a more general disruption in international trade. And it was not just strawberries that were affected - domestic consumers will have been made better off in all exported commodities that cannot be stored for long periods of time.