Classify activity before calculating a percentage
“Not working” is not enough information to classify someone as unemployed in a labour-force exercise. Work for pay or profit, job search and availability answer different questions. This lesson uses invented adult records and an explicitly limited classification rule. The figures describe only the supplied classroom populations, not a country, school, employer or real group of people.
The International Labour Organization distinguishes employment, unemployment and being outside the labour force, with employment taking priority when activities overlap. Its labour-force guidance also explains why studying or other main activities do not automatically determine status. The simplified rule below is supplied for these cases; complex temporary-absence and future-starter exceptions are outside the dataset.
The supplied rule and reference periods
- All records concern fictional adults aged 18 or over in the stated classroom population.
- Classify as employed anyone who did at least one hour of work for pay or profit during reference week W. The cards contain no unpaid contributing-family work or temporary-absence cases.
- Among those with no such employment, classify as unemployed only those who took a stated job-search step during the four weeks ending with W and were available to start during W.
- Classify all remaining records as outside the labour force. Apply this sequence so each record appears in exactly one group.
Availability during W is the explicit classroom criterion, not a claim that every survey uses that exact window. A blank search entry would be missing evidence, not “No.” Every base-case card below supplies the required facts. Unpaid household care and volunteering can be valuable activity without meeting this exercise’s employment definition.
Twelve anonymous adult record cards
| Card | Supplied activity | Paid/profit hours | Searched in stated window? | Available during W? | Classification |
|---|---|---|---|---|---|
| A01 | Paid repair task | 4 | No | Yes | Employed |
| A02 | Paid office work | 35 | No | Yes | Employed |
| A03 | Own business work for profit | 8 | No | Yes | Employed |
| A04 | Unpaid volunteering only | 0 | No | Yes | Outside labour force |
| A05 | No employment; submitted an application | 0 | Yes | Yes | Unemployed |
| A06 | No employment; searched but unavailable | 0 | Yes | No | Outside labour force |
| A07 | No employment; wants work but took no search step | 0 | No | Yes | Outside labour force |
| A08 | Adult studying and doing paid work | 2 | No | Yes | Employed |
| A09 | Adult studying, no employment, applied for work | 0 | Yes | Yes | Unemployed |
| A10 | Unpaid household care only | 0 | No | No | Outside labour force |
| A11 | One paid task | 1 | No | Yes | Employed |
| A12 | Paid work and search for another job | 6 | Yes | Yes | Employed |
These twelve cards contain 6 employed, 2 unemployed and 4 outside the labour force: 6 + 2 + 4 = 12. The labour force is 6 + 2 = 8. A12 is counted once, in employment, despite searching for another job. The cards are a complete small exercise, not a sample used to estimate the separate population below.
Worked case 1: education does not override paid work
A08 studies for most of the week but also performs two hours of paid work. Under the priority rule, A08 is employed. Classifying the person by their main time use would answer a different question. The two paid hours exceed the one-hour threshold even though the work is limited.
Compare A09: no employment, a stated search step and availability during W. A09 is unemployed under the supplied rule. “Both are studying, so both must be outside the labour force” therefore fails. Employment status does not itself describe educational participation, earnings adequacy, preferred hours or the quality of a job.
Worked case 2: separate desire, search and availability
A07 wants work and is available, but has not taken a search step in the stated window. With no employment and no applicable exception, A07 is outside the labour force in this exercise. Wanting work is not the same evidence as actively seeking it. A06 also falls outside, for a different reason: search is supplied but availability is not.
This label makes no judgement about effort, personal worth or all productive activity. A04’s volunteering and A10’s household care are explicitly useful kinds of activity that do not meet the pay-or-profit rule here. Do not rewrite “outside the labour force” as “idle” or “economically dependent”; the records do not establish either claim.
Worked case 3: three questions, three measures
A separate, complete fictional tally contains 200 adults: 120 employed, 30 unemployed and 50 outside the labour force. These totals are independently supplied, not projected from the twelve cards. First check 120 + 30 + 50 = 200. Labour force = employed + unemployed = 120 + 30 = 150.
| Question | Measure | Calculation | Result |
|---|---|---|---|
| What share of the labour force is unemployed? | Unemployment rate | 30 ÷ 150 × 100 | 20% |
| What share of the adult population is in the labour force? | Participation rate | 150 ÷ 200 × 100 | 75% |
| What share of the adult population is employed? | Employment-to-population ratio | 120 ÷ 200 × 100 | 60% |
Dividing unemployed adults by all 200 would give 15%, but that is not the unemployment rate: it answers a different denominator question. The 75% participation rate does not mean 75% are employed, because the labour force includes unemployed people. Similarly, 100% − 20% = 80% is the employed share of the labour force, not the employed share of all adults.
Three solved practice tasks
Practice 1: build a fresh tally
Task: A new complete set has 15 adults: 9 employed, 3 unemployed and 3 outside the labour force. Calculate unemployment and participation rates. Answer: Labour force = 12. Unemployment = 3 ÷ 12 × 100 = 25%; participation = 12 ÷ 15 × 100 = 80%. The employed population share is 60%, not 75%.
Practice 2: a lower rate without more employment
Task: Starting from the 200-adult tally, suppose 10 unemployed adults stop searching and move outside the labour force; every other status is unchanged. Answer: Employed remains 120, unemployed becomes 20, and outside becomes 60. Labour force falls to 140; unemployment is 20 ÷ 140 × 100 ≈ 14.3%, and participation is 70%. The lower unemployment rate does not show a gain in employment.
Practice 3: a zero denominator
Task: All 8 adults in another fictional group are outside the labour force. Find participation and unemployment rates. Answer: Participation is 0 ÷ 8 × 100 = 0%. The unemployment-rate denominator is zero, so the rate is undefined, not 0%. A reported percentage must have a meaningful denominator.
Check an employment-data explanation
Keep the activity rule, reference periods, population and percentage denominator beside the result. Round only at the end and state the rounding used. A count or rate cannot, on its own, establish why conditions changed, whether earnings are adequate or whether a person needs support. Those are different questions requiring different evidence.
Connect population change to a business and a development question
The adult labour-force cards above answer employment questions. The following separate fictional town dataset adds demographic structure and a business decision. The earlier twelve cards and 200-adult tally are not samples used to estimate it. These are invented aggregate counts with no records about real people.
| Item | Period A | Period B |
|---|---|---|
| Residents below age 15 | 300 | 360 |
| Residents ages 15–64 | 600 | 660 |
| Residents ages 65 and over | 100 | 120 |
| Total residents | 1000 | 1140 |
| Working-age grouping employed under the supplied survey rule | 420 | 450 |
| Working-age grouping unemployed | 60 | 90 |
| Working-age grouping outside labour force | 120 | 120 |
| Reedline Bakery capacity per day | 100 loaves | 100 loaves |
| Confirmed daily customer orders | 80 loaves | 90 loaves |
| Unit price; variable cost; fixed daily cost | KSh50; KSh30; KSh300 | Unchanged |
The three employment-status counts within each 15–64 grouping sum to that group: 420 + 60 + 120 = 600 and 450 + 90 + 120 = 660. This is a supplied grouping for the exercise, not a claim that nobody outside it works. Do not apply the earlier adult age boundary mechanically to this different table.
Completed demographic calculations
Total population grows by 1,140 − 1,000 = 140, or 14%. The working-age grouping grows by 60, or 10%. For a conventional age-structure comparison here, define the age-dependency ratio as (below 15 + 65 and over) ÷ ages 15–64 × 100. Period A gives (300 + 100) ÷ 600 × 100 = 66.7; B gives 480 ÷ 660 × 100 = 72.7 age-grouped dependants per 100 people in the middle group, rounded to one decimal. The ratio describes ages, not proof of who earns or is supported by whom.
Employment rises by thirty, yet unemployment in the supplied working-age labour force also rises: 60 ÷ (420 + 60) = 12.5% in A and 90 ÷ (450 + 90) = 16.7% in B. A growing working-age population creates a potential labour supply, but paid work, skills, productive resources and access to opportunities affect what follows. Population growth alone cannot establish a demographic dividend or unemployment’s cause.
The business decision uses orders, not headcount alone
Reedline’s confirmed orders rise from 80 to 90, an increase of 12.5%, rather than the town’s 14%. At KSh20 contribution per loaf, A’s model profit is 80 × 20 − 300 = KSh1,300; B’s is 90 × 20 − 300 = KSh1,500. Both fit the stated 100-loaf capacity. Assuming demand rises exactly with population would predict 91.2 loaves, which is neither a whole-loaf order count nor evidence of actual orders. Income, prices, tastes and competitors could matter.
More younger residents can increase future demand for learning services; more older residents can alter service needs. A larger population also calls for water, transport and housing capacity. Those are planning questions to investigate, not outcomes proved by age totals. Health, educational participation, earnings and infrastructure evidence would be needed to judge development. The age ratio must not be used to label individuals unproductive.
Independent capacity and data-limit task
Task: a later snapshot adds 60 residents but gives no age breakdown. Orders become 108 loaves daily, with all bakery costs and capacity unchanged. Can you calculate the new age-dependency ratio? Can all orders be fulfilled?
Answer: the ratio cannot be determined without the three age counts. Orders exceed capacity by eight. At the maximum 100 sold loaves, conditional daily profit is 100 × 20 − 300 = KSh1,700; profit at 108 cannot be claimed under the 100-loaf capacity model. Additional capacity or changed fulfilment would require a supplied plan and its costs.
Background definitions: World Bank age-dependency definition. These sources do not supply the fictional figures or establish current Kenyan rules or syllabus placement.
Frequently asked questions
Does no paid work automatically mean unemployment?
No. Under this rule the person must also have searched in the stated window and been available during the reference week.
Can an adult student be employed?
Yes. A08 has two hours of paid work, so employment takes priority under the supplied classification rule.
Why count a job seeker with paid work as employed?
The priority rule makes the groups mutually exclusive. Searching for another job does not remove the employment recorded during week W.
Is someone outside the labour force doing nothing?
No. The category can include unpaid care, volunteering and other activity. It describes the supplied labour-market criteria, not a person’s worth.
Why exclude outside adults from the unemployment denominator?
The unemployment rate asks about unemployed people within the labour force. Participation and employment-to-population measures use the wider adult population.
Can unemployment fall without employment rising?
Yes. In the practice scenario people stop searching, so both unemployment and the labour force fall while the employed count stays unchanged.
Are these national or official survey estimates?
No. They are original fictional records under a stated classroom rule. Real statistical estimates require their own definitions, survey design and evidence.
References
International Labour Organization: labour-force classification. The teaching cases, questions, tables and answers are original.
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