To find out whether your crew is off often or off for long, split the absence rate into two numbers: how many times people were away, and how long each absence lasted. The same rate can come from many people each missing a day here and there, or from two or three people each off for weeks with an injury, and the two need opposite fixes. Two farms with 12 people each can share the same 5 percent rate, one with 48 absences of a day and a half, the other with 6 absences of twelve working days.
With the absenteeism calculator you do the arithmetic from your time sheets. Then you fill in and date one page, the absence sheet, with the period you measured, what you counted as absence, the longest absences and their causes, and the jobs nobody else can cover.
Here is how that works with round numbers. Take a crew of 12 over a 24-week period, each person scheduled for 40 hours a week: 960 hours per person, 11,520 hours for the whole crew. Say 576 of those hours were lost to absence. That is an absence rate of 5 percent, or 48 lost hours per worker. Farm A loses them in 48 short absences of 12 hours each, a day and a half. Farm B loses them in 6 long absences of 96 hours each, twelve working days. Both lose the same 576 hours.
From the 5 percent alone, you cannot tell whether your farm looks like Farm A or Farm B. Many short absences usually mean the crew is too small for the work, or that people take days off on their own because the schedule never gives them one. A long absence usually means somebody got hurt or got sick, and if the job caused it, the next person on that job faces the same risk. Hiring an extra hand by the day can fill the gap in both cases, but it only solves the first. In the second, the risk is still in the job.
What is the absence rate made of?
The rate is how often times how long, divided by the hours scheduled. How often is the frequency: the number of absences divided by the number of people employed in the period. How long is the length: the hours lost divided by the number of absences. On Farm A, that is 4 absences per worker at 12 hours each, which comes to 48 lost hours per worker. Divide 48 by the 960 hours each person was scheduled and you get the 5 percent.
The rate rises by the same amount whether people are away twice as often or for twice as long, so to know which of the two moved it, you need frequency and length separately. The table sets the two farms side by side and adds two lines about people: how many were away at least once, and how many absences each of those people had.
| What was counted | Farm A | Farm B |
|---|---|---|
| Absences recorded | 48 | 6 |
| Absences per worker on the crew | 4 | 0.5 |
| Average length of one absence | 12 hours, a day and a half | 96 hours, twelve working days |
| People absent at least once | 9 of 12 | 4 of 12 |
| Absences for each of those people | 5.33 | 1.5 |
| Absence rate | 5 percent | 5 percent |
Rurivia calculation from a made-up example with round numbers, built for this article so that both farms lose the same 576 hours.
A real farm rarely looks like just Farm A or just Farm B, because the people behind the many absences are often the same people behind the long ones. A Brazilian study by Nágila Oenning and two colleagues, published in 2012 in the Revista Brasileira de Saúde Ocupacional, an occupational health journal, followed 782 service workers at an oil company for three years. They are not farm workers, but of the studies cited here it is the only one that counted, person by person, both how many times and how long.
Of the 72 workers with more than fifty days of absence, 27 had gone on sick leave ten times or more. Of the 109 with three days of absence or less, 79 had gone on leave just once. The authors conclude that the more sick leave episodes a worker had, the longer the absence ran.
On a real farm, splitting the rate shows you which people on your crew account for most of the hours lost, and for that you also count how many different people were away. On two crews of the same size, three people away ten times each and thirty people away once each give the same absences per worker, but on the first crew the problem is three people and on the second it is spread across thirty.
The oil company study keeps the two counts apart: the authors work out a sick leave frequency, which is episodes over people on the payroll, and separately a frequency of workers on leave, which is people who were absent over people on the payroll, and which came to 0.693 in that record. In other words, 69.3 percent of the workers had at least one sick leave. That is why the absence sheet carries both counts.
What you count as absence is a choice you make before counting. The International Labour Organization (ILO), the United Nations agency for work, defined absence for statistics in a 2008 resolution. Under it, absence hours are “the number of contractual hours of work not actually worked during a short reference period”, with contractual hours being “the time expected to be performed according to a contract for a paid-employment job”. On a farm, read that as the hours each person was due to work in the period you are measuring, whether or not there is a written contract.
That definition is deliberately wide: the reasons it lists run from illness and injury through to “bad weather, public or other holidays, or another reason”. Your own count does not need all of it, because a morning lost to rain or a holiday is not something you can fix.
Do many short absences or a few long ones carry the loss?
Three sets of real absence records show how different the two patterns can be. None of them is a farm in your country, but two come from sugarcane, where the work runs against a harvest deadline, as farm work does.
| Where the records came from | How many absences per worker | How long each one lasted |
|---|---|---|
| 1,230 doctor’s notes from 400 sugarcane workers in western São Paulo, Brazil, over nine months | about three per worker | three quarters of them for a single day |
| 9,443 absence records from a sugar agribusiness with 3,150 workers in Peru, over two years | about three per worker | 4.24 days on average; the longest 6.64 percent (ten working days or more) averaged 27.3 days |
| 2,564 sick-leave episodes from 782 service workers at an oil company in Brazil, over three years | 3.3 per worker; 69.3 percent of workers had at least one | 6.6 days on average; leaves for work-caused illness were rare but averaged 167 days each |
The sugarcane study, published in 2014 by Aline Ceccato and colleagues in Cadernos de Saúde Pública, a Brazilian public health journal, is the clearest case of the short pattern. Of those doctor’s notes, its authors report that “seventy-five percent of medical excuses were for one day”. No single one of those absences would have stood out, and together they made up most of the record.
The Peruvian study, published in 2018 by Wilfor Aguirre Quispe and a colleague in Horizonte Médico, a Peruvian medical journal, shows what a few long absences do inside a record that is mostly short. Its authors write that only 6.64 percent were prolonged absence, with a mean of 27.3 days, against an overall mean of 4.24 days per absence.
Out of every 100 absences in that record, 6.64 on average were long, at 27.3 days each, which adds up to about 181 days. All 100 together, at 4.24 days on average, add up to about 424 days. So fewer than 7 absences in every 100 account for about 43 percent of the days lost. The authors do not print that figure; it comes from their two averages.
The oil company study also sorted the sick leaves by type of illness, and there the two numbers moved in opposite directions. The study measured a duration index, the average number of days per leave. It found that Occupational disease showed a high duration index and a low frequency, which the authors read as its chronic and degenerative character, while non-occupational illness showed a greater frequency of episodes and a low duration index.
Why does the same rate need two opposite fixes?
On a farm with many short absences, the cause is usually the schedule and how people feel about the work. A 2019 study by Tesfaye Mekonnen and colleagues in BMC Research Notes, a medical research journal, followed 444 workers on flower farms in Ethiopia. It found that having come to work sick was the strongest predictor of being off sick later, roughly tripling the odds.
The authors put it this way: “working while sick may exacerbate workers’ health conditions resulting in subsequent repeated away off work because of lack of the necessary recuperation”. Workers unhappy with the job were also more likely to be off sick, at about 1.6 times the odds. The fix here is not a stricter attendance rule but a crew sized so that one person can be missing, and days off that are on the calendar instead of being taken by surprise.
On a farm with a few long absences, the cause is usually safety and the way the work is set up. In the Peruvian study, the authors name what made an absence run long: occupational accidents, moderate to intense physical activity involving load handling, and atypical work schedules were the most important factors in prolonged absence. That means accidents, lifting and carrying, and rotating or night shifts. How much lifting a job takes, how the shifts rotate and, often, what leads to an accident are decisions someone made about the work, not traits of the people doing it.
The sugarcane authors record that other studies had tied the high incidence of musculoskeletal absence to a payment-by-production system, with heavy workloads and no pauses, which is what growers call piece rates, and they note that their own findings agree.
The two patterns are also connected, because repeated short absences can turn into a long one. In the oil company study, most leaves for work-caused illness had been preceded by sick leave for the same or an equivalent diagnosis, filed as non-occupational illness. In one case, the recognition that the work had caused it came only after the fifteenth episode with the same or a similar diagnosis.
So many short absences point to the schedule as long as they are spread across different people and different complaints. When the same person keeps coming back with the same ache, that may be a long absence starting, and an extra hand by the day is no longer the answer. You see it by sorting the doctor’s notes by complaint and checking whether the repeats come from the same person or the same job.
How much is a lost hour really worth?
The usual instinct is to value a lost hour at the hourly wage, and on a farm that undercounts it. A 2017 study of Canadian firms, not farms, by Zhang and colleagues in Health Economics Review found that “the productivity loss due to worker absence exceeds the wage for team workers, especially in small firms”. Team workers here are people whose work depends on the rest of a crew.
The authors also say why the small operation is the one exposed: “while large firms can hire extra employees to ensure that a given output level can be maintained if a team worker is absent, small firms may not be able to afford this expense”.
Two conditions common on farms widen the gap between the wage and the loss. One is crew work that depends on skills that are hard to replace: there, “the value of lost output to the firm from an absence will exceed the daily wage of the absent worker and could be as large as the total output of the team”. The other is a deadline, because “the cost of an absence will exceed the wage when a firm incurs a penalty if it misses an output target due to the absence”. Farms rarely pay that kind of penalty, but harvest works the same way: work not done while the weather allows costs you part of the crop.
So the same lost hour is worth little in a quiet month and much more in the third week of harvest, and the absences do not fall evenly. In the sugarcane record, doctor’s notes for muscle and joint problems were “more frequent at the end of the sugarcane harvest than during the intercrop season”, meaning the months between harvests.
The Canadian authors estimate the loss with a multiplier, a factor for how many times the daily wage one absent day really costs: “the productivity loss can be estimated by calculating the measured number of absent workdays due to health problems, multiplied by the daily wage and the multiplier”. Nobody has published that multiplier for your farm. What you can act on is replacement, because the loss falls back toward the wage when “replacements are found who are either inexpensive or are close substitutes for the absent worker”.
So count the jobs on your place where nobody else could step in. That count, more than the hourly wage, settles whether a day off costs you one day’s pay or part of the crop.
Where can the number mislead you?
In four ways. The first is what you counted as absence. In the oil company study the average leave lasted 6.6 days with maternity leave included, and the duration index fell to 5.64 days once maternity leave was taken out. One change in what was counted took almost fifteen percent off the result, and nothing changed on the crew.
That is why you write down what counts as absence before you calculate; written afterwards, it becomes a defense of whatever number came out. The international definition includes a lot: “annual leave, public holidays, sick leave, parental leave or maternity/paternity leave, other leave for personal or family reasons or civic duty”. Count all of that and a farm that gives generous holidays looks like a farm with a health problem.
The second is what you divide by. It is scheduled time, not paid time and not calendar days, and a period with more scheduled days gives a smaller rate for exactly the same absences.
The third is when in the year you measure. Eight weeks at the peak of harvest and eight weeks in the off-season are not two readings of the same thing. Compare like periods, and write on the absence sheet which period you chose.
The fourth is that a falling rate is not always good news. Looking at workers on unstable contracts, who were absent less, the Peruvian authors concluded that less job stability and fewer labor rights push the worker not to be absent for too many days, which cuts the time their health has to recover and raises the relapses.
A rate that falls because people are afraid to stay home sick is a cost put off, and, going by the Ethiopian study, it comes back as more sick days later. There is no target rate to aim at, either. In the same 2008 resolution, the ILO concedes that “the relevance of the various measures of working time in a given State depends on the nature of its workforce, labour markets and user needs”, a State here meaning a country. In practice, the only comparison worth making is with your own previous period.
Where to start
Set aside an hour with the time sheets from the period that just ended and the doctor’s notes, if you keep them. Without doctor’s notes, the time sheets alone still have how often and how long. Enter the numbers in the calculator and copy its results to the top of the absence sheet: the rate, absences per worker, the average length and how many people were away. The nine lines below are what the calculator cannot work out for you. Absences go on the sheet by week and not by name, because the sheet is for fixing how the work is organized, not a file on anyone. The only names are next to the jobs nobody else can cover.
Absence often comes before people leave: the person who quits in June was often away a lot in April. That is why this sheet comes before working out what turnover costs, which you can do with the turnover calculator in a separate article.
If you have a farm org chart, write next to each job who can stand in for it; if you do not have one yet, the list of one-person jobs on this sheet is a start, and there is a separate article on drawing one on a single page. The turnover and org chart articles are among the articles on managing people. Setting this period beside the last and correcting what you find is a habit the rest of farm management runs on too.