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Work is primary, secondary, tertiary or quaternary by what it does; a place's shares in each sector and its location quotients describe its economy.
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By the end of this lesson you will be able to classify a job by sector, find a place's employment structure, and measure a specialism with a location quotient.
You can find a percent of a whole and compare two percents in percentage points. You know that people move toward jobs, and that cities grow as they do. This lesson sorts those jobs by the kind of work they are, and shows how the mix of jobs changes as a place develops.
| Term | What it means |
|---|---|
| Primary sector | Work that takes resources from nature: farming, fishing, forestry, mining. |
| Secondary sector | Work that makes goods: manufacturing, processing, construction. |
| Tertiary sector | Work that provides services: retail, health care, teaching, transport. |
| Quaternary sector | Work that creates knowledge and information: research, software, data. |
| Employment structure | The percent of a place's workers in each sector. |
| Location quotient | A place's share of workers in a sector divided by the nation's share. |
| Multiplier effect | The extra jobs created when new workers spend their wages in a place. |
Every job belongs to a sector, decided by what the work does:
A place's employment structure is the percent of its workers in each sector. Its specialism is measured by
$$\text{location quotient} = \dfrac{\text{local share in a sector}}{\text{national share in that sector}}.$$
Another way: picture
Follow a can of tuna. A crew on a boat catches the fish, which is primary work. A cannery cooks and seals it, which is secondary. A truck driver, a grocery clerk and a cashier move and sell it, which is tertiary. A scientist tracking the tuna stock with satellite data does quaternary work.
Another way: steps
Economic sectors are connected by flows of materials, money and information. A resource at one location does not imply that every production stage stays there. In a fictional clothing chain, cotton farms supply a mill, the mill supplies a garment workshop, trucks connect the workshop to a port, and ships carry garments to overseas buyers. Farms extract or grow inputs, factories transform them, and transport and retail provide services. Designers exchange information with several nodes.
Trace the chain in both directions. A port closure can interrupt sales at the workshop and reduce orders at farms far inland; a poor harvest can interrupt factory inputs in another country. A local event therefore has regional and international consequences. Firms choose locations partly through transport costs, skills, infrastructure, access to markets and policy. The cheapest straight-line route may not be usable if it lacks a bridge, permits or capacity.
Work a capacity example: farms can supply 80 units a day, a mill can process 60, and the route can carry 100. With no stored inputs or alternative routes, output is limited to 60. Expanding the road alone does not remove the mill bottleneck. Also ask who gains and who pays: employment may increase near the mill while water demand rises upstream. A network explanation follows dependencies and distributions of effects instead of merely naming job sectors.
Geographers sort work into sectors because the mix of work in a place explains so much else: where its people live, how much they earn, what its landscape looks like and how it has changed.
The classification follows a product's journey. It starts with what is taken from nature, moves to what is made from it, then to the services that move and sell it, and finally to the knowledge that designs, studies and manages the whole chain.
The same product can be touched by all four sectors. A shrimp caught in the Gulf of Mexico is primary on the boat, secondary in the plant that peels and freezes it, and tertiary when a restaurant serves it.
So always classify the job, not the thing. Ask whether the worker is taking something from nature, making something, serving someone or creating knowledge. The answer places the job, whatever the product is.
Primary work takes resources directly from nature: growing crops, raising cattle, fishing, logging, mining coal and drilling for oil. It is tied to the places where those resources are, which is why farms spread across the Great Plains and oil wells cluster in West Texas and North Dakota.
Machines have replaced much primary labor. American farms grow far more food than a century ago with a small fraction of the workers, which freed millions of people for other sectors.
Secondary work turns raw materials into goods: steel into cars, wheat into flour, timber into houses. Factories locate where they can get materials, workers, energy and access to markets at the lowest cost.
In the United States, manufacturing employment peaked at almost $20$ million jobs in 1979, according to the Bureau of Labor Statistics. Automation and competition from factories abroad cut it to about $13$ million by the 2020s, even as factory output kept rising.
Tertiary work provides services: selling, teaching, nursing, driving, cooking, banking and repairing. It is the largest sector in every rich country, employing about four of every five American workers.
Many services must be close to the people they serve, so they follow population. Every town needs grocery stores, schools and clinics, which is why tertiary jobs are spread much more evenly than primary or secondary ones.
Quaternary work creates knowledge and information: scientific research, software, data analysis and consulting. It depends on highly educated workers and tends to cluster near universities and other firms in the same field.
Silicon Valley, the Research Triangle in North Carolina and the Boston area are American examples. Some geographers add a quinary sector for the top decision-makers in government and business, though many fold it into quaternary.
A place's employment structure is the percent of its workers in each sector. A county with $600$ primary, $2400$ secondary and $9000$ tertiary workers has $12000$ in all, so its structure is $5$, $20$ and $75$ percent.
Always divide by all the workers. The shares must add to one hundred, which is a quick check that no sector was left out.
In the 1930s and 1940s the economists Allan Fisher and Colin Clark described how employment shifts as a country develops. Most workers start in primary jobs, move into secondary jobs as industry grows, and then into tertiary jobs as incomes rise and people spend more on services.
The United States followed this path. About $41$ percent of its workers farmed in 1900; by 2000, fewer than $2$ percent did, according to the U.S. Department of Agriculture.
A location quotient compares a place's specialism with the nation's. If $15$ percent of a county's workers are in manufacturing and $10$ percent of the nation's are, the quotient is $15$ divided by $10$, or $1.5$.
A quotient above one means the sector is more concentrated there than in the nation, and it probably sells to people elsewhere. A quotient below one means the place likely buys that sector's goods or services from outside.
Checking an answer. The shares add to one hundred. A location quotient near one means the place is typical; far above one means a strong specialism.
Classifying by what the work does is allowed because the sectors are defined by activity, not by product, so every job has exactly one sector.
Dividing a local share by a national share is allowed because both are percents of the same kind, workers in a sector out of all workers. The ratio says how many times more, or less, concentrated the sector is locally, whatever the sizes of the county and the nation.
When a factory opens, its workers spend their wages on housing, food and services. That spending creates jobs in shops, restaurants and schools, whose workers spend in turn. The total effect on a county is larger than the factory's own jobs.
Economists estimate the multiplier for each industry. The same logic works in reverse: when a plant closes, the jobs it supported can disappear too, which is why a single closing can shake a whole town.
Deindustrialization is a lasting fall in manufacturing jobs in a place. In the United States it hit the Manufacturing Belt, from New York to Illinois, hardest, earning the region the name Rust Belt from the 1970s onward.
Causes include automation, competition from factories abroad and firms moving to the South, where wages and land were cheaper. Some Rust Belt cities, such as Pittsburgh, rebuilt around health care, universities and technology; others have struggled for decades.
Countries at different stages of development have very different structures. Many low-income countries still have most workers in farming, often on small plots. Middle-income countries often have large manufacturing sectors, and rich countries are dominated by services.
Comparing structures is one way to measure development, but it has limits. A country rich in oil may earn high incomes with a small workforce in primary jobs, so the structure alone does not tell the whole story.
The most common slip is classifying by product rather than by work, so that anything to do with food counts as primary. Another is dividing a sector's count by another sector's count instead of by all workers.
A third is to put the national share on top of a location quotient, which turns a specialism into an apparent shortage. A fourth is to forget the plant's own workers when counting the total jobs a new plant brings.
Youngstown, Ohio, grew as a steel city, its mills lining the Mahoning River for miles. On September 19, 1977, remembered locally as Black Monday, Youngstown Sheet and Tube announced it would close its Campbell Works, eliminating about $5000$ jobs at once. Other mills followed over the next few years.
The multiplier effect ran in reverse. As steelworkers lost their wages, stores, restaurants and suppliers lost customers and cut jobs of their own. The city's population, about $170000$ in 1950, has fallen by more than half since, and many neighborhoods emptied.
Youngstown's employment structure shifted from secondary toward tertiary work, especially health care and education. Its story is one of many across the Rust Belt, and a reminder that a high location quotient in one industry is a strength in good years and a danger when that industry declines.
The Santa Clara Valley south of San Francisco was once known for fruit orchards, primary work that filled its fields with apricots and prunes. From the 1950s it became Silicon Valley, as firms such as Fairchild Semiconductor, founded in 1957, and Intel, founded in 1968, designed and built computer chips there.
Stanford University supplied engineers and research, and new firms were started by people who left older ones, a network that kept talent in the valley. Over time the chip factories moved to cheaper places, but the design, research and software work stayed, making it one of the world's great concentrations of quaternary jobs.
Its location quotients for software and research are far above one. The same concentration drives up housing costs, which pushes many service workers to live far away and commute long distances, showing how one sector's growth reshapes a whole region's geography.
It is natural to classify a job by the product it handles, so that anything to do with food or wood counts as primary. But the sectors are defined by what the work does. Catching a shrimp is primary, freezing it is secondary, and serving it is tertiary.
Asking what the worker actually does, take from nature, make, serve or create knowledge, places every job correctly. It also explains why a food-producing county can have most of its workers in services.
Classify a farmer growing corn in Iowa.
$\text{primary}$
Taking a crop from the land.
Classify a worker turning corn into ethanol at a plant.
$\text{secondary}$
Making a product.
Classify a driver trucking the ethanol to a refinery.
$\text{tertiary}$
A transport service.
Classify a scientist breeding drought-tolerant corn.
$\text{quaternary}$
Creating knowledge.
A county has $900$ primary, $2700$ secondary and $5400$ tertiary workers. Find the total.
$900 + 2700 + 5400 = 9000$
All three sectors.
Find the primary share.
$\dfrac{900}{9000} \times 100 = 10\%$
Part over whole.
Find the secondary share.
$\dfrac{2700}{9000} \times 100 = 30\%$
Part over whole.
Find the tertiary share.
$\dfrac{5400}{9000} \times 100 = 60\%$
Part over whole.
Check the total of the shares.
$10 + 30 + 60 = 100$
Nothing left out.
A county has $2000$ of its $10000$ workers in manufacturing. Find its share.
$\dfrac{2000}{10000} \times 100 = 20\%$
Local share.
The nation has $8$ percent in manufacturing. Record the national share.
$8\%$
The comparison.
Find the location quotient.
$\dfrac{20}{8} = 2.5$
Local over national.
Read the quotient.
$2.5 > 1$
Two and a half times as concentrated.
Say what that suggests.
$\text{the county sells goods to other places}$
More than it needs locally.
Name a risk of the specialism.
$\text{one closing can shake the county}$
Many jobs depend on one sector.
Find the county's share.
$\dfrac{1200}{8000} \times 100 = 15\%$
Local share.
Divide by the nation's share.
$\dfrac{15}{12}$
Local over national.
Evaluate the quotient.
In which economic sector does a logger cutting timber in Oregon work?
Complete the worked solution: in 1960 a country had $640$ thousand primary workers out of $1600$ thousand in all. By 2020 it had $300$ thousand out of $2000$ thousand. Find the primary share at each date and the drop in percentage points.
Find the primary share in 1960.
$\dfrac{\text{primary}}{\text{all}} \times 100 =$ s
Part over whole.
Find the primary share in 2020.
$\dfrac{\text{primary}}{\text{all}} \times 100 =$ t
Part over whole.
Find the drop in the share.
$\text{earlier} - \text{later} =$ g
Percentage points.
Name the pattern the drop fits.
$\text{the Clark-Fisher shift}$
Work moves out of primary jobs as a country develops.
Match each economic sector to the kind of work it does.
| taking resources such as crops, fish, timber or ore from nature | making goods from raw materials | providing services to people and businesses | creating knowledge, research and information | |
|---|---|---|---|---|
| primary | ||||
| secondary | ||||
| tertiary | ||||
| quaternary |
A county has $4200$ workers in primary jobs, $5600$ in secondary jobs and $4200$ in tertiary jobs. Fill in the percent of its workers in each sector.
| share | |
|---|---|
| primary (%) | |
| secondary (%) | |
| tertiary (%) |
A region has $8500$ manufacturing jobs today and is losing $250$ of them each year. If that loss stays steady, write the number of manufacturing jobs as a function of the years $y$ from today.
Answer:
A county has $1800$ of its $12000$ workers in manufacturing. Across the nation, $10$ percent of workers are in manufacturing. What is the county's location quotient for manufacturing?
Answer:
Suppose a car plant in Tennessee hires $2500$ workers, and a study estimates that each plant job supports $0.8$ other jobs in the county, in shops, restaurants, trucking and schools. How many jobs does the plant bring to the county in all?
Answer: jobs
A fictional mine supplies 4 tons daily. Its processor can handle twice that, but the sole export bridge carries half the mine's output. Buyers are overseas. There is no storage or other route. Mark every supported sentence for a network analysis, including the condition limiting its conclusion.
This task has no paper form; do it on a device.
Lesson test: one question per skill, one attempt each, no hints. Your answers are checked when you submit.
A region has $30000$ manufacturing jobs today and is losing $1200$ of them each year. If that loss stays steady, write the number of manufacturing jobs as a function of the years $y$ from today.
Answer:
You can use economic sectors. Explain why the same shrimp can be primary, secondary and tertiary work.
27. Your turn: a county has $1200$ of its $8000$ workers in health care, and the nation has $12$ percent. What is the county's location quotient for health care?, step 3
$1.25$
A little more concentrated than the nation.