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“COVID-19: economic setback for women?” (FT 12/9/2020)


COVID-19: economic setback for women?


One of the sad impacts of COVID-19 in Fiji is that the slow economic progress of women over the last thirty years in employment and incomes seems to be currently reversing in many key areas, although there is no national data as yet. Keep in mind that the wealth of women (and men) is, except for inheritance, an accumulation of savings from past incomes.

One of the most fundamental causes of gender inequality is the relative poverty of women, compared to men with the same economic attributes, despite the fact that they do on average far more work in terms of hours and days than men (last week’s article).

But despite their overall parity today in levels of education, fewer women are in income earning employment, they are more concentrated in lower paying occupations, they usually receive far lower incomes for similar work to men, and they are far more likely to be in less than full-time jobs which are always vulnerable to economic down-turns.

While I give some statistics in this article based on older data (2015-16), policy makers in Fiji (including government departments, NGOs, international organizations and donors) would gain enormously if they were to obtain “real time data” from just a few important organizations, all within government control:  Fiji National Provident Fund (FNPF), Fiji Revenue and Customs Services (FRCS), and Fiji Bureau of Statistics (FBS).

Another possible source of useful data is the RBF which could aggregate data from banks on the ownership of time and savings deposits.

All that is needed is the political will, encouraged by requests from all the stakeholders in women’s welfare, and especially the Ministry of Women and women-focused NGOs like FWRM and FRIEND.

Women Almost Equally Educated

One of the most extraordinary gender revolutions taking place in Fiji, virtually unnoticed and uncommented on by politicians preoccupied with themselves, has been the enormous progress of girls and women in education.

At primary and secondary school levels, girls have been out-performing boys in many subject areas. While they have been generally under-represented in the STEM subjects (science, technology, engineering and mathematics) they are catching up in these areas as well.

More importantly, FBS data for 2015-16 indicates that females comprised 47% of all those with Certificates, Diplomas and Degrees.

Given that this is the accumulated figure over time, then almost certainly there are more females currently graduating annually with higher qualifications than males.

BUT, and this is the big BUT, far fewer of these qualified females are in paid employment, only 40% as opposed to 85% of males.

Looked at alternatively, some 60% of females with these higher qualifications are not putting them to full use in income earning activities, compared to only 15% of males who are not using their qualifications thus. What an incredible “economic waste” for Fiji.

Of course, most of these economically under-utilized women are engaged in domestic work maintaining families, important in its own right.

But I am sure some good curious econometric researcher from our three universities could estimate the monetary cost to the Fiji economy of gender stereotypes which leads to qualified women not using their qualifications in paid work,

Fewer Paid Working Women than Men

Feminists globally complain about the gender bias of the term “economically active” as if those doing unpaid  household work are not “economically active” even though the time spent on this absolutely necessary work is higher in hours spent than paid work in the economy.

Nevertheless, let us assume that “economically active” refers to those who are in “paid work”, which is what gives rise to incomes which fund consumption and standards of living, and in turn gives rise to savings, whether in banks or superannuation accounts.

Table 1 gives you the negatives as well as the positives for females over the twelve years between 2004-05 and 2015-16 when the FBS conducted their national Employment and Unemployment Surveys.

First, females were just 30% of those in paid employment in 2004-05 with the proportion rising very slowly to just 33% by 2015-16.

Ipso facto, this fundamental lower involvement in paid economic activity, sharply reduces the capacity of females to earn incomes and accumulate wealth, although the latter is even worse when one takes their lower average incomes into account (see below).


Table 1    In Paid Employment (000) (2004-05 and 2015-16)

2004-05 2015-16 Ch. (000)
Females (000) 89 102 13
Males (000) 208 202 -6
Total (000) 297 303 7
Perc. Female 30 33
Source: FBS EUS for 2004-05 and 2015-16.


But nevertheless, their share of paid workers was rising and probably still rising before COVID-19 struck in early 2020.

I would surmise given that COVID-19 has devastated the female dominated tourism related industries,  that female share of employment has gone backwards these last six months and will continue doing so until the economy recovers.

The third column of “Change between 2004-05 and 2015-16” also shows that while women increased their number in paid work by 13 thousand, men in paid employment were unfortunately reduced by 6 thousand.

Note however that this “negative” aspect of women gaining ground “at the expense of men” only becomes a socially undesirable  “zero sum game” because of insufficient economic growth and inadequate numbers of additional jobs being created. If economic growth was healthy, then women could progress without it being at the expense of men.

Fewer Female Wages and Salaried Persons

While Table 1 gives one picture of “paid workers” in Fiji, unfortunately, many of the categories of workers in Fiji such as Self-employed, Family Workers and Community Workers (while large in numbers of Economically Active) do not work the full 40 hour week (or 240 days in the year) and large numbers work even less than 20 hours per week (or 120 days in the year).

It is these groups, a large proportion of whom are females, have absorbed the large numbers of young people entering the workforce because they are unable to find solid wage and salaried jobs.

They also earn terribly low incomes as well with women being much larger proportions of the lower paid workers, and who are vulnerable to economic downturns.

These criticisms are less valid for those who the FBS classifies as “Wage Earners” and “Salary Earners” for whom Table 2 gives a very similar picture for each year, and the changes over the twelve year period.

Again, the percentage of females who were Wages and Salaried persons rose from only 29% in 2004-05 to 34% in 2015-16, a small improvement.

The gains in employment for women over this period were 15 thousands extra jobs, while that for men was a much lower 5 thousand jobs (at least not negative).


Table 2   With Wages and Salaries (000) (2004-05 and 2015-16)
2004-05 2015-16 Ch. (000)
Females (000) 53 68 15
Males (000) 128 133 5
Total (000) 180 201 21
Perc. Female 29 34
Source: FBS EUS for 2004-05 and 2015-16.


Note that while COVID-19 has definitely affected wage earners in the private sector, the majority of salaries persons in the civil service have not been affected as far as jobs are concerned although their superannuation contributions have been reduced.

Lower Female Shares of Total Income

As would be expected from the above tables, the female share of total income earned by households was only 27% in 2004-05, increasing to 29% in 2015-16.  Note that the $4 billion shown in this table is less than a half of the nominal GDP of around $10 billion between 2015 and 2016.  Of course, corporate incomes are not included here.


Table 3       Total Incomes Earned ($m) (2004-05 and 2015-16)
2004-05 2015-16 Ch. ($m)
Female ($m) 724 1200 477
Male ($m) 1989 2873 884
Total ($m) 2712 4073 1360
% Female 27 29 35
Source: FBS EUS for 2004-05 and 2015-16.


Again, notice that of the increase in Total Incomes over this twelve year period, females gained proportionately more (35%) of the increase than their normal share of income (29%), suggesting that there was some improvement taking place at the margins.

A very similar pattern but slightly better in terms of the percentage shares of females can be derived from the incomes of wages and salary earners.

But some good news for government

One of the positive areas of employment for females is the public sector and the 2015-16 EUS data has some fascinating results, when worker are differentiated by employment in the public sector, private sector, and private households (which are mostly family concerns).

While I am not sure whether all the public enterprise are included in this table for Row A which indicates that it some 50 thousand workers, an excellent outcome for females is that they comprised 41% of these workers, as opposed to only 31% of the 250 thousand Private Sector Employees (Row B). As would be expected private households and  family concerns had pretty good gender balance at 51%.


Table 4    Employment (type of employer) (000 and %)
Type of employer Female Males All % Female
A  Public (000) 20 29 50 41
B  Private (000) 77 172 250 31
C  Private Household (000) 18 18 36 51
All Paid Employment (000) 116 220 336 35
Source: FBS 2015-16 EUS.

But the good news for females does not end there.  Table 5 gives the Average Annual Incomes for all these categories by gender.  Females had lower average annual incomes by all the categories with a Gender Gap of -24% (in other words, the female average income was 24% less than the male average income.

But surprisingly, the Gender Gap in private households was a very large -42%, suggesting that female family workers were not being paid as well as the male family workers, despite most of them being members of the family.

Females employed by the Private employers had a very large incomes Gender Gap of -27%.

But the really good news again, is that the Gender Gap for females in the Public Sector was a mere -7%, which could have been partly contributed by lower years of experience (also due to motherhood gaps when mothers are on leave to take of infants) and qualifications, and not just gender discrimination.


Table 5    Average Annual Income ($ and %)
Type of employer Female Males All % Gender Gap
A  Public ($) 17424 18696 18182 -7
B  Private ($) 8970 12314 11279 -27
C  Private Household ($) 4297 7456 5859 -42
All 9707 12782 11722 -24
Source: FBS 2015-16 EUS.


I am currently engaged in with Dr Neelesh Gounder (USP Senior Lecturer in Economics) isolating the impact of various factors in order to assess whether there is really any gender discrimination going on is a fascinating exercise which  with solid preliminary results.

But overall, the really good news is that with the Fiji Government committing itself to many United Nations conventions and agreements for gender equality, these last two tables of statistics confirm that at least in its hiring and promotion policies, it is not just trying to achieve gender parity, but it is pretty close to it even by 2015-16.

I was pleasantly surprised to see a Fiji Times news item a few days ago that the Auditor General’s Office was demanding to know the gender composition of staff in the Ministry of Agriculture!

Of course, the private sector is far less amenable to government policies on gender equality.

Sadly, if females have suffered relatively more during this COVID-19 crisis, then some of the gains listed above in the private sector will be reversed, although I suspect that the gains in the public sector will largely remain.

So the real question for gender stakeholders, including the Ministry of Women remains: what exactly is the impact of COVID-19 on employment and incomes of women in Fiji?

Can Fiji see “real time” data?

I would suggest that for there to be a solid policies to bolster gender equality after COVID-19, gender stakeholders need to urgently request the Fiji Government to obtain “real-time data” on the current impact of COVID-19 on incomes and employment in Fiji.

Individual surveys by interest groups such as the Fiji Employers Federation, the FHTA and FTUC can help, but they are no substitute for comprehensive national level data.

The first port of call ought to be the FNPF which receives monthly returns from all the employers for contributors to FNPF.  This institution is totally controlled by Government and ought to be able to provide timely data on losses of jobs and reductions in incomes for all the FNPF contributors.

The second port of call, even if there will be some time lag, can be the FRCS which receives tax returns from Wages and Salaried persons and Businesses.

But both these institutions are focused on the formal sector and even the FNPF does not cover a very large number of people in paid employment throughout Fiji.

It is here that the national sample surveys of the Fiji Bureau of Statistics (like the Employment and Unemployment Surveys and the Household Income and Expenditure Surveys) are absolutely invaluable.

They provide treasure troves of data to government and civil society, absolutely useful for the formulation of evidence based policies, and assessment of the success or otherwise of government policies.

I would hope that the Minister for Women, Children and Poverty Alleviation is dialoguing with the Minister for the Economy and donors, to provide the mere $3 to $4 million needed to conduct the 2020-21 EUS, which could be immensely useful not just for her Ministry but also several others such as Rural Development, Labour and SME development.


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