Whenever I speak with people about their personal finance situation, they always wonder whether they are doing well or poorly. Personal finance is personal and therefore it is quite possible that someone would feel like they’re doing well on an income or wealth level while another person would feel differently at those levels. Nevertheless, the existence of an objective figure will help to put people’s income and savings levels in the proper perspective. It should allow people to know with some level of certainty whether they are doing as well as their peers. In pursuit of such a measure, I launched the Ghana Earnings and Savings Survey (GESS) to try to get an idea of the level of earnings and savings of people working in Ghana.

The survey was launched on June 8, 2023 with a target of 1,000 respondents by September 30, 2023. However, thanks to the sharing of the survey widely on social media and chatgroups by some kind individuals, the survey surpassed 700 respondents in 24-hours. There was clearly a high demand for this information and I realized I would need some help if I was to get results out in a timely manner. I therefore reached out to Alfred Appiah, a Data Scientist based in Canada, to analyse and visualise the data. With his help I was able to post some early results to sustain interest in the survey. I am deeply indebted to his expertise for making what otherwise would’ve been a difficult task a breeze. The survey ended up with 1,802 respondents before it was closed on June 16.

Survey Design and Limitations

The survey starts with gathering demographic data such as Nationality, Age, Marital Status, Number of Dependents, Gender, Type of Employment, Education, Region of Residence and Industry. Upon deploying the survey I received a lot of useful feedback, especially on the importance of having a Number of Years Worked variable. Unfortunately, the survey was filled so quickly that it would have been unfair to introduce that question after many had already filled it. Therefore, we are left with using Age as a proxy to determine the relationship between working years and income. However, this feedback and others would ensure that the second version of this survey would be much better without being too lengthy.

The measurement of monthly income used 10 different income levels ranging from GH¢1,000 and below to GH¢50,000 and above. Note that this measurement of income was supposed to include all earnings including salary, allowances, side gigs, or profits from business ventures. This is an important point because the results of the survey are supposed to help people understand how they compare to their peers in terms of monthly earnings as a whole, and salaries only cannot reflect that. It’s also to accomodate for self-employed people, entrepreneurs and people employed in the informal sector to be able to participate in the survey.

On the matter of savings, there were two measures – a relative and an objective measure. The relative measure was How many months’ worth of expenses do you have saved up? and the idea was to understand how long people could survive on their current savings without any external help were they to be out of work. This measure is relative because GH¢20,000 could represent 10 months of expenditure to someone while it would only cover two months for another person. The results of this measure would allow someone to compare their financial buffer with that of their peers.

The other measure of savings was an objective cedi value measured by the question Including your retirement benefits but excluding any physical assets? How much do you have saved up?. The purpose of including retirement balances such as a Provident Fund balance, a Tier 2 Pension balance or any other arrangement with a pension fund is because it would be misleading to only look at bank balances or investment accounts when these pension funds usually represent the bulk of a formal employee’s savings. Not including them could also create the perception that formal workers and informal workers have similar levels of savings, which could be very far from the truth. Physical assets were excluded because the cedi value of these assets relies on judgment and therefore it could distort the results. Nevertheless, physical assets are a measure of wealth and therefore I conclude the survey by asking about home ownership and car ownership.

Sampling Bias and Limitations

Because the survey was deployed online, the demographic leans heavily towards young, unmarried, graduate men in Accra aged 26 to 35. There really is not much to say except that the sample is not very representative of the general working population. That is however far from an indication that the survey is not useful. With the use of crosstabs we are able to segregate respondents according to their demographic profile in order to truly compare like for like. There were suggestions for us to make the sample more representative by weighting it using population data. That is a very useful exercise however, that is beyond the scope of our work.

The survey was fully anonymous and there is no way to trace who supplied the information. This also means that it is possible that a respondent filled out the survey more than once as no unique information was collected. As this survey was voluntarily filled online, I have decided to release the full survey results in a CSV file to the general public. Alfred will also post the script to his GitHub. The purpose of the release is to allow academics, graduate students, independent researchers and the media to carry out their own research using the data and to bring out any interesting relationships we may have missed or not covered.

Demographic Profile

Let’s start the results with a look at the demographic profile of the respondents. Figure GESS 1.1 presents the demographic results of the study as it shows that the sample size is heavily skewed towards people aged 26-35 (73.6%), Male (68.2%), Unmarried (74.8%), Resident in Greater Accra (77.8%), and with a tertiary education (65.2%).

Figure GESS 1.2 shows the industries in which the respondents work. Financial Services, Technology and Health are the top 3 industries in which the respondents work, accounting for 41% of all respondents.

Earnings

Figure GESS 1.3 shows the overall results with respect to earnings. Most respondents (29%) earn between GH¢2,500 to GH¢4,999 a month, with a total of 55% of respondents earning less than GH¢5,000 per month. Only 1% earn GH¢50,000 and above, but a total of 21% earn at least GH¢10,000 per month. Note again that these are overall earnings which could result from much more than a person’s basic salary. It would be useful in the next survey to understand what proportion of people’s earnings comes from their primary employment.

Now that we have a picture of the overall earnings, let’s dig into how much each demographic earns. Figure GESS 1.4 breaks down the earnings according to Age, Sex, Marital Status, Number of Dependents, and Highest Level of Education.

As expected, we seem to see a relationship between Age, Education, Marital Status, Educational Level and earnings. We however did not see a difference in earnings between males and females. This presents an interesting question of what a representative sample based on gender would look like i.e. 50% male – 50% female rather than the 70% male – 30% female sample we are working with. (Researchers, please jump on the data for more analysis!).

Now let’s look at other interesting earnings comparisons. Figure GESS 1.5 looks at earnings levels between people working for Ghana-based and foreign-based companies; Figure GESS 1.6 looks at earnings levels between Public and Private Sector employees; and Figure GESS 1.7 looks at earnings levels across industries.

As expected, people that work in companies based abroad are 8.5 times more likely to make over GH¢20,000 per month than those working in Ghana-based companies. (Note that we are referring to people resident in Ghana but are working for companies based outside the country, not Ghanaians resident abroad).

We see that a total of 25% of people in the private sector make at least GH¢10,000 per month compared to 11% of people in the public sector. Also while half of the people in the private sector make more than GH¢5,000 per month, only a little over a third of people in the public sector make a similar amount.

Unsurprisingly, the Mining, Oil & Gas Industry has the highest proportion of high-income earners as a total of 47% of respondents make at least GH¢10,000 per month. This is followed by Technology and Telecommunications (34%), Non-Profits (32%), Financial Services (30%), Legal Services (23%), and Communications (19%).

Savings

As already mentioned, I considered both the number of months of expenses that people have saved up as well as the cedi value of savings. Let’s first dive into the overall months of expenses saved up as seen in Figure GESS 1.8.

It comes as a bit of a surprise that 39% of the respondents have savings equivalent to zero months of expenditure. This means that if they were to lose their income sources, they would be unable to meet any expense without external help. In other words, they have no financial buffer. 31% of respondents have between 1 to 3 months of expenditure saved up. In total, only 30% of respondents would be able to meet expenses without external assistance for over 3 months. Such low levels of financial buffers in a country without a social safety net is disturbing.

What is not surprising however is that the lower the income you earn, the lower the level of savings you are likely to have. This is shown in Figure GESS 1.9. A total of 64% of people earning over GH¢20,000 have at least 4 months of expenses saved up compared to only 16% of people earning below GH¢5,000.

Figure GESS 1.10 presents the data on the cedi savings data. Keep in mind that these savings include amounts in retirement accounts but excludes any physical assets. More than half of respondents have less than GH¢10,000 saved up. Having at least GH¢50,000 in total savings puts you in the top quartile (25%) of respondents.

Now let’s break down the savings amounts by demographic. Figure GESS 1.11 shows that even for people aged 35 and above, more than a third of them (35.7%) have less than GH¢10,000 saved up. Once again we do not see much of a difference between genders in terms of savings. However married people, people with more than 4 dependents, and people with post-graduate degrees tend to be those with the highest level of savings in cedi terms.

Car Ownership

Overall, 34% of respondents owned cars. Note that we did not ask the method by which these cars were acquired but we did observe some trends in car ownership. For example, the older the respondent, the more likely they were to own a car (GESS 1.12); the higher a person’s earnings, the more likely they were to own a car (GESS 1.13); and the higher the level of savings, the more likely one was to own a car (GESS 1.14).

Home Ownership

Only 10% of our respondents owned a home, which is to be understood given the youthful bend of our sample. Nevertheless, Figure GESS 1.15 shows that for people earning GH¢20,000 a month and above, 35% of them own a home.

Acknowledgement

My biggest appreciation goes to my collaborator, Alfred Appiah, who I have to thank for the analysis and visualization done in record time to allow me to share the results with you. I also want to thank all the people who advised me on changes to make before deploying the survey including Courage Martey, Senam Amematekpor, Desmond Bredu, Enoch Edem-Kojo, and Lawrenda Enam Adzomani. If you think my survey design is bad now, you should have seen it before their invaluable contributions. I also want to thank Mimi Anane-Appiah, Andy Akoto, Prince Dagadu, Bridget Otoo, Gary Al Smith, Kalyjay, AsieduMends, Patrick Agama, William Duncan and so many others for sharing the survey and ensuring that it reached a very wide audience. For all those who I could not mention, the mistake is all mine, my deepest apologies and appreciation.

Downloading the Data

Please visit Alfred Appiah’s GitHub to download the raw data as well as all the beautiful charts I used and many more.

Referencing the Data

My small request is that anyone who uses the data should cite it as the Ghana Earnings and Savings Survey (GESS 1). This ensures consistency in naming that would make everyone be able to track how the data is used across different studies.