Donating à la carte

Donating à la carte

Decisions are hard. Especially when the outcomes are important, the options are numerous, and relevant information is hard to find.

For many everyday decisions (where to eat, what clothes to wear, what to do tonight), I have a pretty good idea of what I can do, what I’d like to achieve, and how likely I am to do so. And despite most of these decisions being largely inconsequential I still consider them in great detail. I assume others are somewhat similar to me, delicately wasting their processing power on life’s minutiae.

Yet, of all the choices we make, one of the most impactful seems among the least rationally considered: donations.

We can donate to almost anything, anyone, at any time. But do we consider the options available before giving? And if so, how are we to weigh up the pain of a malnourished child to the impact of a polluted river; the needs of our local lifesaving club to the suffering of 100 battery hens; climate change to the housing needs of a woman escaping domestic violence?

We’re so overwhelmed by the immense number of possibilities that we often yield to impulse, emotion and social pressures. But these decisions deserve our most careful consideration. We have the power to change the status quo. And to ignore this is to choose to do nothing.

Thanks to the interwebs it’s now as easy to support locally as it is to support (almost) anywhere else in the world. So, we can cast as wide or as narrow a net as we like when looking to get behind a cause.


Modelling donations – a menu of causes

The model below presents the main options available for donations. Its aim is to help us make more conscious decisions, and explicitly remind us of what we’re ignoring.


Using the model – 2 paths to better donations

The model can be used in two ways:
1. Proactively – help guide your thinking when deciding on a cause, and
2. Re-actively –  recognise when a charity focuses on a particular group at the expense of others.


Method 1 is great for clarifying your personal values and systematically prioritising the areas you’d like to support.

Hypothetical example 1: the proactive method
I could start by acknowledging that I care more for people than animals and the environment. Then, I explicitly recognise a desire to help the local LGBTIQ community. Lastly, wanting to have an immediate impact, I support a charity which focuses on providing every-day services. This gives me the following combination:

Using the model as such can help articulate what I’m after, and find a charity which provides the desired service. If all charities were classified using this framework, then I could easily find an organisation to suit my needs.


Method 2 helps remind us of all the things we could be supporting before choosing a particular charity. When donating to a cause, we are implicitly choosing it above all others. The mapping exercise, i.e. explicitly acknowledging what we are focusing on, may highlight an excluded cause which, when considered, we find more worthy.

Hypothetical example 2: Check yo’self

If I’m a long-time supporter of an organisation sheltering dogs, it’s easy to continue doing so by focusing on the wonderful work the organisation does, and feeling great that I could help. However, by mapping their work to the model, I am forced to recognise there are many other animal species in need which I am implicitly ignoring. In fact, others’ need may be greater (either through the amount of cruelty experienced, or the sheer number being subjected to it); for example battery hens or caged pigs. With this realisation I can re-examine my values and act accordingly. If post-introspective I recognise I care more about the suffering of battery hens, then I can go back to Method 1 and better align my donations to reflect my values.


The Four Dimensions of Donations

The model has 4 main dimensions (with the key one broken down into subcategories)

1. The who (including ‘which subcategory’)
This helps differentiates between people, animals or the environment. Each of these key categories is broken down into further subcategories. For example, people can be dissected by religion, or sexuality, or age; animals by species; and the environment by ecosystem (rivers vs rain-forests vs oceans vs desserts, etc.).

2. The where (place)
This helps dictate the place and spread of the donating net. Are you interested in all specimens in the world equally, or do you have a particular attachment or concern over a region over all others?

3. The what (aspect)
Within each category there are different aspects which can be improved or supported. For people, helping improve health or education are pretty central, but there is also work done to support the arts, local sports clubs, churches or world peace. Animals and the Environment also have specific aspects which can be targeted, and these are presented in the model.

4. The how (support)
The how differentiates the different types of work which can help your cause. Should we act now, educate, try to change the decision makers, or continue researching to find better solutions? For example, if you want to help the world deal with climate change, would you prefer to support an organisation providing immediate direct work (e.g. decreasing emissions now) or should more funding be provided towards research in the hope that we discover a more efficient solution in the near future?

By combining the four dimensions, you can have a much better understanding of how you would like to help.


The why

The model does not cover how we do or should decide which box to focus on. That will form another post, hopefully in the near future. But the aim for now was to raise awareness to the breadth of work available, with the hope that before making quick impulsive decisions, we consider what we can do, and hopefully do more with what we give.


To be improved…

It goes without saying that this model is probably missing a whole bunch of stuff. So please let me know what’s missing so I can update it as we discuss.

1st: Indigeneity and migrant status – from our UN correspondent! (How did I miss them?)
2nd: Biodiversity – Thanks Ms Sabrewing
3rd: Circumstance – From a recent dinner discussion, mentioning “Legacy”


The following documents were used in the development of this model:

Charity Navigator:
Government organisations
UK –
Australia –


Death Tree

Death Tree

Not quite as cool as the Death Star, this Death Tree breaks down the 153,580 deaths which occurred in Australia in 2014, by cause:

Interpreting the Tree
Size of the box shows relative number of deaths, compared to all other deaths.
The colour represents the sex divide.
The bluer boxes represent diseases which kill more men than women. The yellow boxes kill more women than men. The legend on the top right provides a guide as to the sex representation.

Navigating the Death Tree
Left clicking drills down into finer level causes (e.g.: Cancer breaks down into Lung cancer, colon cancer, breast cancer, etc.).
Right clicking drills back up.

All causes are classified using the International Classification of Diseases (ICD).

The Death Tree provides a bigger, more user-friendly representation.

In the meantime, here is a smaller embedded version:

If Australia were 100 stereotypes

If Australia were 100 stereotypes

For as long as I can remember it’s felt like we’ve been breaking down barriers and tearing down the regimes of acceptability. Traditional roles are no longer the norm, travelled by choice not momentum. Everybody’s road-fork a choice to be made.
But despite the changes, work occupations are still bastions of gender segregation.

Clichés and stereotypes such as tradies are men and teachers are women may not help fight outdated and ingrained social expectations, but they’re still the case in Australia, according to the latest ATO figures for 2013-14. Based on self-identified information from tax returns, there are 52,305 carpenters in Australia, of which only 127 are women. Similarly, only 94 of the 34,362 plumbers are women. That’s 0.2% and 0.3% of each occupation.

To simplify the situation using a popular meme: if Australia were 100 carpenters (or plumbers), none of them would be women. Not one.

IfAuswere 100 Carpenters

(I’ve not stats on how many are called Warren)

While I expected clichés to imitate life, I assumed they were exaggerating.

Overall, 637,402 Australians work in occupations where men make up at least 99% of the workforce. That’s 6% of all people with a known occupation. More broadly speaking, 35% of tax-paying men have roles where men make up at least 90% of the occupation. 50% occupy roles where men dominate by at least 80%. That’s to say that half of all working men live in roles where they outnumber women by (at least) 4 to 1. This includes occupations such as:

Top men occupations

What kind of impact is this environment and constant reinforcement having on half of all men?

While women dominated roles are less pronounced, 40% of women work in roles where women make up (at least) 80% of the workforce. This includes the following occupations:

Top women occupations

This lopsidedness on both sides means a minority of all workers (17%) fill roles which are equally distributed. (Evenly distributed is defined as 50 +/- 10%.)

Distribution of sex occupations

If we are to break down the gap between sexes, either roles need to become a lot less “gendered” or occupations need to become a lot more evenly rewarded. Whilst some movements has been made towards more equal distributions in the past 30 years, the information above shows there’s a huge way still to go.


All figures based on ATO statistics for 2013-14, Individuals Table 14A&14B.



De-constructing the ‘g’ gap

De-constructing the ‘g’ gap

Last week was International Women’s Day so everyone should be up to date with the latest estimate of the gender pay gap (17%), and very well versed on at least three theories behind it.

Now, then, might be the perfect time to ask why the social progress and workforce changes which occurred over the last 30 years have had no impact on the gap.

The increasing awareness, numerous policies, university attendance explosion, increasing maternity leave and participation rates, industrial and occupational distributions, and a myriad other variables have increased or decreased to varying degrees. Mostly towards gender parity. Yet the pay gap for full-time women has not deviated more than +/- 2 percentage points since Lionel Ritchie first sang “Hello…. is it me you’re looking for?!”[1].

Gender Pay gap 83 15

The gap itself is a complex issue with many moving parts. This is a look at a few of those parts, and a general wondering: how is it not improving?


An aging workforce

One of the biggest changes in the workforce has been workers’ age. Average full-time wages increase rapidly with age, until they begin plateauing around 30, finally peaking in the late 30s to mid 40s (depending on occupation and role).  On average, workers under 25 earn 40% less than those 25 years and over[2].

Historically, one of the reasons behind the wage gap has been that working women are dis-proportionally younger than men, and therefore lower paid (i.e. junior staff on junior wages).  But the age demographic across the sexes has become a lot more equal over the last few decades.

While the overall percentage of workers under 25 has halved since 1983, women’s compositional distribution has changed much more than men’s (as shown by the graph below)[3].

Aging workforce by Sex

This suggests junior wages, or wages from young staff who are yet to reach role maturity, should have a much smaller impact on the overall average wage than it used to. Thus, diminishing one reason why there may be a pay-gap.

On the other end of the maturity spectrum, the proportion of women over 40 has almost doubled since 1983.  This suggests a greater proportion of women are returning to full-time employment after giving birth, continuing to build on their careers, with advanced wages.

Aging workforce by catergories

The changes in women’s labour force have been so substantial that the average age gap between the sexes is less than a quarter of what it used to be: down from 4.5 years in 1983 to 1 year gap in 2015.

This said, women have not achieved age parity in the workforce, but it’s certainly a lot closer than it used to be. Yet, the pay-gap has not changed since ‘Return of the Jedi‘ hit the cinema screens.


Women learning it for themselves

One potential reason behind the maturing female workforce is the increasing number of women attending university.  University attendance has increased across the board, but women’s increase has doubled men’s.  While women had not achieved tertiary education parity by the early 80s, they well and truly have by 2015.  In 2013, 58% of all Australians studying at university are women. This is just as true for postgrads as it is for undergrads. In fact, women have been the majority at uni since 1987[4].

This increase in university attendance has flowed to the labour force.  Full-time working women are now 45% more likely to have graduated from university than their male counterparts.

Uni Attainment in LF by sex

However, not all graduates are created equal. Some fields of study pay more than others, and the figures above don’t provide that level of detail. But overall graduates earn substantially more than workers with no university qualifications, and women are increasingly dominating this sphere.

This further suggests a move towards pay parity.  Yet, the pay-gap has not changed since Bob Hawke first became Prime Minister of Australia.


From doing to managing – occupational changes

Higher paid occupations (e.g. managers and professionals) now make up much larger proportions of the workforce than they did in the mid80s. Whether it’s due to social progress, the growing number of university educated women, or any other reason, the proportion of women in these roles has increased faster than men’s over the period in question.  The proportions of full-time women in the two highest paid occupations, professional and managerial roles, have increased 12 and 6 percentage points respectively. Men, on the other hand, increased 7 and 2 percentage points.

To balance this out, the proportions of women filling admin, labouring and sales roles (the three lowest paid occupations) have decreased by 11, 4 and 3 percentage points, to men’s 2, 4 and 1.

The graph below compares the proportion of women and men by occupation in 2015 to 1986. Higher paid occupations have generally increased, and low paid decreased… and women have faired better at both ends of the spectrum.


Occupation can still account for some of the current disparity. Despite the move towards higher paid roles, women are still over represented in some of the lower paid occupations; e.g. 25% of women fill admin roles, as opposed to 7% of men. But the changes over the past 30 years should have had an impact on the pay disparity.

Yet, the pay gap has not changed since the average price for a Melbourne house was $52,000[5].

Changing occupations by sex2

Mining the AGEING boom

Unlike the other variables examined, women have not clearly moved to the higher paid industries over the past 30 years. In fact, women have slightly decreased proportional representation for the three highest paid industries when compared to men (i.e. mining, financial and professional services). Mining is of particular interest as it is the highest income by almost $40k, and the boom meant its growth outstripped every other industry’s growth. These two aspects combine to help stretch the pay-gap further apart.

Changes in Industry distribution, by sex, 1984 to 2015

Changing industry by sex 3

The industry experiencing the largest increase in women participation has been Health Care and Social Assistance, potentially on the back of Australia’s ageing population’s demand. While its income is only marginally below the average for all industries, the increase in Health and Social assistants means women have not migrated towards the higher paid industries in large numbers.

Having said that, as previously described, women have gained much ground in GP representation.  The proportion of women GPs has increased from 22% in 1984 to 43% in 2014*.  This means that whilst women continue to be over represented in lower paid industries, they are filling higher paid roles within these industries.


Ask for more

Data on workforce by “method of setting pay” (i.e. opportunity to negotiate pay, which some suggest promotes disparity) has been hard to come by. This aspect may be extended upon in future.


Yet, not.

Most of these changes suggest the gender pay gap should have decreased over the past 30 years. At the very least it shows the variability of many contributing aspects. To not have achieved pay parity is understandable, as there are still many obstacles to overcome, and underlying contexts/assumptions/social norms to change. And many tricky issues to figure out on how to get there.

  • Should more women work in higher paid industries, or should we (financially) value women-dominated industries more?
  • Are health and education paid less because they are women heavy industries, or are they women-dominated because they are lower paid?
  • Should we increase paternity leave to support women to stay, or support them to return after birth-giving?
  • Should women be encouraged to negotiate more or should industrial frameworks diminish the impact negotiation has on individual’s pay?

But to not have made any improvement, despite all the changes that have occurred seems odd. Changes in age  alone should have had massive impacts.

Yet, not. No change, improvement or otherwise.

This post has no answers or suggestions… just a baffled look to greater minds to tells us why…




[1] Based on Full-time ordinary wages, by sex from Average Wages, ABS:



[4] and



Sex education

Sex education

Gender inequality, with regards to high school graduation rates, has not been this pronounced since pre WWII days.
According to Census 2011 data, boys born prior to 1959 were more likely to finish high school than girls, but the reverse has been the case from that year onwards.  The gap continued to increase for 20 years before steadying.  However, after a small bump in recent years the gap between the graduation rates mirrors the levels seen in the mid 1930s, with girls out-graduating boys by 10 percentage points*.
But let’s not get caught in the detail. The greater story is the educational revolution which has occurred over the past 80 years, in Australia and around the world.  While less than 20% of the community finished high school a mere 2 or 3 generations ago (less than 1 in 7 for girls), around 75% of today’s kids do so.   
Graph stops in 1992 as those born since have not had an equal opportunity to graduate yet.
Unfortunately, we seem to have plateaued. The fast growth in graduation experienced between those born in 1915 to those born in 1975 has come to a complete halt. The last 17 years have shown no significant movement, with those born in 1975 being just as likely to graduate as those born in the 90s.  Have we peaked? Or is this purely a revolutionary intermission?
And is 75% enough?
A similar halt to action occurred for boys born between 1955 and 1966, where for a period of over 10 years the rates of graduation did not improve.  That time, however, the effect was only felt by boys, while in the same period girls improved by 12 percentage points.  What was it about the 1970s that didn’t encourage boys to improve their likelihood to graduate from school?  I have no idea, if anyone does, please let me know!
This story is not meant to suggest boys are hard done by, but rather to promote the overall improvements in access to education, in particular with regards to gender equality. To highlight one of the many improvements achieved in an impressive time frame  As well as to highlight that it was not so long ago that many of the things many take for granted nowadays were only afforded to a very privileged few.
* While girls’ graduation rates are currently 10% points higher than boys’, the same 10 points back in the 1930s (because of the small overall graduation rates) meant  boys were almost twice as likely to graduate as girls… oils ain’t oils.

All data presented in this post was sourced from the Australian Bureau of Statistics’ 2011 Census: