New hires
38in every 100
New hires are gone within a year
Nine never finish month one. For context, Aon's 2025 study puts overall Philippine attrition at 20% a year.
See the evidenceThe Great Filipino Workplace Mythbuster
Ten things we think we know about Filipino employees, tested against 275,000 employee records from 700+ Philippine companies.
- 275K employees
- 700+ companies
- 42M scheduled weekdays
Myth or not?
Employees wait for their 13th-month pay, then resign
Ten beliefs HR teams hold about Filipino employees. Call each one before you see the data.
6 busted
2 half true
2 confirmed
- 16resignations per 1,000 in December, the quietest month
- 2.4×the leave filed on a bridge day
- 38 in 100new hires gone within a year
- 90%of the Gen Z gap explained by tenure
- Month 6where terminations reverse
- 22% vs 12%exits after a raise alone, against no raise
- +62%undertime in the year before a resignation
Executive brief
Most of what we believe about why Filipinos quit is wrong
Ask an HR team in the Philippines when resignations peak, and most will say January, once the 13th-month pay has landed. Across four years of records from 328 companies, December turns out to be the quietest resignation month of the year. The busiest is July.
That is one of ten beliefs tested here against payroll, attendance and separation records. Six did not hold. Two did. Two turned out half right, and in both cases the half that fails is the half organizations plan around.
Three numbers worth taking to a leadership meeting
Pay
22%vs 12%
A raise on its own did not predict retention
Rank-and-file employees given a raise with no change in role, against those given no raise, who left within twelve months.
See the evidenceCalendar
16in Dec26in Jul
The January resignation wave does not exist
Resignations per 1,000 employees a month at large employers. The peak sits mid-year, not after the bonus.
See the evidence
How we tested
What sits behind every number
Anonymized, aggregated records from Sprout's client base. Most analyses cover January 2024 to August 2026; two extend back to January 2022. Each figure carries its own sample, shown under its chart.
700+Philippine companies
275Kemployees
42Mscheduled weekdays analyzed for attendance
10widely held beliefs tested
Records used Attendance logs Leave filings Payroll movements Separation records
Any figure from fewer than 5 companies or 100 employees is suppressed
Only companies active for an analysis's full window
Associations, not proven causes
Full methodology
At a glance
We tested ten common beliefs about Filipino employees
Two of these are easy to misread. The overtime finding shows that higher recorded overtime went with lower resignation, which is not the same as overtime keeping people. The pay finding shows that a raise alone did not predict staying, which is not the same as raises causing departures. Both cautions sit with the findings.
Chapter 1Three myths
The calendar
Filipino employees do manage the calendar. The data shows them doing it for long weekends, not for bonuses, and much less on Mondays than the stereotype suggests.
Chapter 1 · The calendar
The myth
Employees wait for their 13th-month pay, then resign
What the data saysDecember is the quietest resignation month of the year, at 16 resignations per 1,000 employees. July is the busiest, at 26.
Evidence
Resignations fall through Q4 and peak in July, not January
Monthly resignations per 1,000 employees, averaged 2022 to 2025
The cleaner test
Companies that pay half the 13th month mid-year saw no mid-year exodus
Resignations per 1,000 employees, May to August
Pay a mid-year tranche (25 companies)Pay once at year-end (142 companies)
There is nothing to wait for
Under Presidential Decree 851 and its revised guidelines, employees who resign before December keep their 13th-month pay. It is pro-rated and released with their final pay.
What people may be timing against is the discretionary Christmas bonus, a different thing entirely. Sprout's guide to 13th-month pay covers the computation.
One caveat
21SME resignations per 1,000 in December. SMEs dip in October and November too, then recover close to their annual average. The December trough is a large-employer pattern.
Behind the finding
Across four years, January ran 8% above its own normal, then 5% below, then 7% below, then 12% above. No direction at all. December through February accounted for 26%, 28%, 23% and 25% of each year's resignations, against the 25% an even spread would give you.
A quiet December does not tell you why it is quiet, which is why the split-payment test matters. DOLE's rules let employers release half the 13th month before the school year opens and the balance by 24 December. Twenty-five of the 167 companies in this cut do exactly that, and they resigned at 24, 18, 19 and 17 per 1,000 across May to August, against 27, 25, 29 and 23 at the companies paying only at year-end.
Why it matters
If your workforce plan assumes a January replacement rush, you are staffing for the wrong quarter. The real pressure point sits in the middle of the year, and it is being met with whatever resources are left over.
What to do
- Plot your own monthly separation curve against the twelve months before each point.
- Move requisition approvals to the quarter before the peak your company actually has, not the one everyone assumes it has.
Chapter 1 · The calendar
The myth
Employees take more leave around long weekends
What the data saysLeave is filed on 10% of bridge days, against 4% of ordinary workdays. Unplanned absence rises too, from 18% to 20%.
Evidence
A bridge day draws more than twice the leave of an ordinary workday
Share of scheduled workdays with leave filed in advance
What a bridge day is
The single scheduled workday caught between a holiday and a weekend.
The multiplier
2.4×the ordinary-day leave rate. The largest and cleanest effect in the report.
Seniority
Bridge-day leave filed. The difference is whether the day gets filed in advance, not whether people want it off.
The day after a long weekend block shows elevated unplanned absence too, at 20%, which fits people stretching a break by one more day at short notice.
Why it matters
These dates are knowable years ahead from the holiday calendar alone, which makes this the most forecastable staffing exposure in the report. For customer-facing or shift-based teams, the coverage gaps are both predictable and avoidable.
What to do
- Map next year's bridge days now and set coverage against them.
- Watch the ratio of filed leave to unplanned absence on those dates. A high unplanned share means the leave policy is being routed around rather than used.
Chapter 1 · The calendar
The myth
Absence peaks on Mondays and Fridays
What the data saysMonday absence is higher. Just nowhere near high enough to treat Monday as a workforce problem.
Evidence
The bookend pattern is real, and small
Unplanned absence per 1,000 scheduled weekdays
One extra person per hundred
On a typical midweek day, about 18 in every 100 scheduled employees are out unplanned. On a Monday, about 19.
Lateness
2%Flat all week. Whatever happens on Mondays shows up as people not coming in, not as people arriving late.
Behind the finding
Unplanned absence here means any scheduled workday with no clock-in and no approved leave, which works out at 19% of scheduled weekdays across 42 million of them. That definition is broad: it catches forgotten time-ins, field staff who never clock in, and long absences nobody filed paperwork for. Every weekday is measured the same way, so the differences between days still hold.
Why it matters
Do not design policy around a stereotype. A one-point differential will not carry an attendance crackdown, a return-to-office push or a Monday shift premium.
What to do
- Track one-day absences separately from multi-day ones. They have different causes, and only one of them responds to policy.
Chapter 2Two myths
The people
What predicts departure is how long someone has been in the job, not when they were born. And the first year is brutal: nearly four in ten new hires are gone within twelve months.
Chapter 2 · The people
The myth
Gen Z are the biggest job hoppers
What the data saysThe problem is not generation. It is tenure. Hold length of service constant and about 90% of the apparent gap disappears.
Evidence, in four steps
At first glance, a clean staircase
Annual resignation rate, all tenures pooled
The point
Short-tenure employees resign at high rates regardless of when they were born.
A comparison that pools all tenures is not measuring generational attitudes to work. It is measuring the fact that Gen Z joined recently and Gen X did not, then blaming it on character.
Early tenure, every generation
43 to 50%Annual resignation rate in the first three months of a job, for all three generations. The variable worth watching is already in your HRIS.
Why it matters
Stop solving a generational problem the data says is a tenure problem. Screening on generational assumptions also sits uncomfortably against Republic Act 10911, the Anti-Age Discrimination in Employment Act.
What to do
- Put tenure band first in attrition reporting, ahead of department, generation or location.
- Where a generational gap appears, check whether it survives a tenure control before you act on it.
Chapter 2 · The people
The myth
New hires are most at risk in the first 90 days
What the data saysWithin three months, 19 in every 100 new hires have left. By twelve months, 38 have.
The first 90 days carry real risk. They also account for only half of first-year attrition, and the sharpest single movement in the data sits later, at the regularization decision.
Evidence
Terminations fall steadily, then reverse at month six
Separations per 1,000 new hires still employed, by month of service
TerminatedResigned
Of every 100 new hires
38 gone by month twelve. 19 left after day 90, the point where most onboarding attention stops.
Calculator
What first-year attrition costs you
There is no published Philippine benchmark for replacing an employee, which is part of why first-year attrition goes unpriced. Set your own numbers.
Cost per hire should cover recruiting, onboarding, ramp time and the productivity gap while the seat is empty. ₱60,000 is a placeholder, not a benchmark.
Month oneMonths two and threeMonths four to twelveStill there
28gone within month one
57gone within three months
114gone within twelve months
Replacement cost of first-year losses₱6.8M
Applies this report's measured rates: 9.3% by month one, 19% by month three, 38% by month twelve. Your own rates will differ. The figure sizes the question rather than answering it.
Behind the finding
Where an employee who will not be regularized is offered the chance to resign instead, the record shows a resignation. We cannot tell those cases apart, so we are not putting a number on how often it happens.
Resignations tick up at month six too, from 29 to 30 per 1,000, though that movement is small and less firmly established.
Why it matters
This is not an onboarding problem. It is a P&L problem. Hire 300 people a year and, at this rate, 114 will be gone within twelve months. You are paying to recruit many of the same seats twice.
What to do
- Put a documented checkpoint at month five, separate from the regularization paperwork.
- Track first-month attrition separately. At a tenth of each intake, it is a recruitment problem rather than a retention one.
- Review how probationary exits get recorded. Asking for a resignation removes your own ability to see what happened. Sprout's guide to employee termination covers what a defensible decision requires.
Chapter 3Five myths
The levers and the signal
Overtime does not drive people out. A raise on its own does not keep them. And the one thing that reliably comes before a resignation is quiet drift: people stop staying their full day, months before they go.
Chapter 3 · The levers
The myth
Heavy overtime pushes employees out
What the data saysHigher recorded overtime went with lower resignation, not higher. Among rank-and-file employees, the six-month resignation rate fell from 12% in the lightest overtime band to 8% above 40 hours a month.
Evidence Indicative
More overtime, fewer resignations
Rank-and-file: share who resigned within six months, by monthly overtime
Two readings fit
AMore engaged employees take more overtime.
BEmployees planning to leave stop volunteering for it.
We cannot tell which. Neither one says overtime keeps people.
Overtime is income
In the Philippines overtime premiums are fixed by law rather than left to employer discretion, so it is something many rank-and-file employees compete for rather than endure.
Why it matters
Raw overtime hours do not behave like the flight-risk signal many employers assume they are. A model built on them will point at the wrong people.
What to do
- Take overtime hours out of flight-risk scoring.
- If workload is the real concern, measure it directly through span of control, project load or self-reported capacity.
Chapter 3 · The levers
The myth
A pay increase helps retain employees
What the data saysAmong rank-and-file employees, 22% of those given a raise with no change in role had left within a year, against 12% of those given no raise at all.
Evidence, in three steps
A raise alone was not a reliable indicator that someone would stay
Rank-and-file: share of each group who had left within twelve months
Why this does not show that raises cause exits
If raises drove people out, bigger raises would drive out more. They do not. Three explanations fit, and we cannot tell which is right.
1Counter-offers. A counter-offer places the raise after the decision to leave, not before it.
2Merit-cycle timing. People who already intended to leave may have waited for the increase to land.
3The comparison group. No-increase includes companies without increase cycles and people too new to qualify.
What we can say. Across 12,763 rank-and-file employees who received one, a pay increase on its own did not keep people. Where the increase came with a promotion, departures fell back to roughly the no-raise baseline.
Why it matters
This is a budget question, not just a compensation question. If you are using off-cycle raises as a retention tool, find out whether you are buying retention or simply delaying a resignation.
What to do
- When an off-cycle increase is proposed to retain someone, record what else is changing: scope, title, reporting line.
- Track separations against increases over the following twelve months and see whether your own numbers behave like these.
Chapter 3 · The signals
The myth
Employees burn their leave credits before resigning
What the data saysEmployees who later resigned took slightly fewer leave days than colleagues who stayed, not more. The gap works out at about one day a year.
Evidence
No burst of leave before an exit
Leave days taken a month: range across the twelve months before leaving
Where the leave may go instead
Unused leave is commonly cashed out in the Philippines. Cash-out rather than consumption may be why no run-down shows up.
Data gap
Leave balance data did not pass validation, so we cannot see what employees did with accrued credits at exit. That gap is why this carries a Medium tag.
The belief is that departing employees run down their leave in the final months, which makes a sudden burst of vacation a useful tip-off. Filing records point the other way, though gently. Read this as the expected pattern failing to appear, rather than a strong effect running the other way.
Why it matters
A manager watching leave filings for signs of an impending resignation is watching the wrong field.
What to do
- Retire the assumption.
- If leave balance and conversion data exist in your own system, check the cash-out question directly.
Chapter 3 · The signals
The myth
Frequent absence is a warning sign that someone will resign
What the data saysEmployees who went on to resign ran about 5 points above stayers for the entire year, including the nine months in which they were not leaving.
Evidence, in four steps
The gap is flat for nine months, then widens from month three
Share of scheduled days lost to unplanned absence, by months before leaving
The level is not the signal. The change is. A gap that is always there is not a warning: most employees carrying it never leave, which is why absence-based flight-risk flags produce so many false alarms.
Behind the finding
The final-month figure is inflated by employees already serving notice. The usable part is the movement between months three and two. HR teams who have tried absence-based flags usually conclude the signal does not work, because a level-based rule surfaces the same standing population all year.
Why it matters
This is why so many retention dashboards disappoint. An alert built on a threshold surfaces the chronically absent, who are a management issue rather than a flight risk. An alert built on movement surfaces something different, and much smaller.
What to do
- Replace threshold rules with baseline-comparison rules.
- Compare each employee's current quarter against their own preceding four quarters, and flag sustained change rather than high level.
Chapter 3 · The signals
The myth
Employees check out before they resign
What the data saysOver their final year, employees who later resigned went from 19 to 30 minutes of undertime a month, a 62% rise. Those who stayed held steady at about 17 minutes.
Evidence
Leavers drift. Stayers do not move.
Undertime, minutes a month
ResignedStayed
Undertime means
Time clocked out before the end of a scheduled shift. It is the cleanest early signal in this study because the comparison group barely moves across a full year.
Modest per person
11 minof extra early leaving across a whole month. A population-level flag, not something a manager spots unaided.
The warning window
The only finding in this report that gives you advance notice
The rise begins around month nine before departure and continues steadily. The window between the signal becoming clear and the resignation arriving is roughly two to three months: enough time for a conversation, and more than enough to avoid a counter-offer being the only option left.
Behind the finding
The sharp spike in the final month is employees already serving notice, so it is a consequence rather than a warning. The usable signal is the earlier, gentler rise. Tardiness follows the same shape: stayers flat at 15 to 16 late arrivals per 1,000 scheduled days, leavers climbing from 19 to 29.
Why it matters
Two to three months is enough time for a conversation, and more than enough to avoid a counter-offer being the only remaining option.
What to do
- Add undertime to whatever early-warning view you have, measured as change against each employee's own baseline rather than a fixed threshold.
- Back-test it: of the people your rule flags, what share resign within six months, against the base rate? That single ratio tells you whether the flag is worth running.
The bigger pattern
Stop asking what kind of employee is likely to leave. Start asking what has changed.
Ten separate corrections point at one idea, and it is the thing most worth carrying out of this report.
What most dashboards watch
Categories
Every belief that failed names a category and claims that belonging to it predicts departure. Tap one to see what the data said.
Categories fit dashboard columns, survive a change of HRIS, and let a whole workforce be discussed in one meeting. That convenience is most of why they persist.
What carried signal
Two things that do not fit a column
How long someone has been in the job
Resignation falls with every band of tenure, for every generation.
Whether their own behavior has changed
The absence gap between leavers and stayers only moves in the final three months.
A quarterly average by department will never show either, which is why both stayed invisible while the categories were watched closely for years.
An organization that monitors groups will keep finding patterns that evaporate under scrutiny. One that learns what normal looks like for each employee gets roughly two to three months of warning.
Blind spot check
Are you watching categories, or changes?
Six questions, one for each belief that did not hold up. Answer for how your organization actually operates today. Nothing is stored or sent.
Recommendations
What to do, by who owns the decision
Most of these need a budget holder rather than an HR process change. Pick your seat, or see everything on one timeline. Add the ones you will act on to a plan you can copy.
8 actions · 3 owners
Tick the actions you will take. They collect in a plan you can copy and share.
0 of 8 actions in your plan
Where Sprout fits
Every signal here came from records you already keep
Attendance logs, leave filings, separation dates and payroll movements hold the regularization cliff, the undertime drift and the resignation calendar. The hard part is turning them into insight early enough to act on, because the comparison that matters, one employee against their own prior pattern, is the one nobody has time to run across separate systems and exports.
Records you already keepThe foundation every solution above reads fromAttendanceLeavePayrollSeparations
Your next step
Before you lock your 2027 workforce plan and merit budget
The most useful thing to do with this report is check it against your own numbers rather than ours. The comparison needs only separation dates, hire dates and attendance records, all of which you already hold, and it usually takes a single session.
What we would look at together
- Your own monthly separation curve, and where it actually peaks
- First-year attrition by tenure month, including months five to seven
- What your off-cycle increases did over the following twelve months
- Whether an undertime baseline flag would have caught last year's leavers
Sources and notes
Where every number comes from
The primary data is Sprout's own. Everything external is listed here with where it is used, followed by the method and the limits that change how a finding should be read.
Sprout client workforce records
Primary data · Sprout Solutions
Anonymized, aggregated attendance logs, leave filings, payroll movements and separation records. Up to 274,946 employees across 700+ companies, January 2022 to August 2026.
Every chart in this report
2025 Salary Increase and Turnover Study
Industry research · Aon
Puts overall Philippine attrition at 20% a year, the highest in Southeast Asia.
Presidential Decree 851
Law · 13th-month pay
Requires the 13th-month pay. Employees who resign before December receive it pro-rated with final pay.
Revised guidelines on the 13th-month pay law
Regulation · Implementing guidelines
Covers pro-rating on separation and the split mid-year payment used in the cleaner test.
Republic Act 10911
Law · Anti-Age Discrimination in Employment Act
Cited to describe the exposure attached to generational screening, not to interpret its application to a specific employer.
DOLE Labor Advisory No. 06-2020
Regulation · Release of final pay
Sets the 30-day rule for releasing final pay, relevant to the notice-period caveat on the final month before an exit.
Republic Act 10173, Data Privacy Act of 2012
Law · Data governance
All records are processed in aggregate under Sprout's client agreements and in line with the Act.
Sprout guides used in this report
Sprout Solutions
Method
The sample behind each analysis
Each analysis uses only companies continuously active on the platform for its full window, so platform growth and client churn do not show up as workforce trends. That is why the base changes from chart to chart. Bars show the number of companies.
Monthly separations; attrition by generationJan 2022 to Aug 2026
Attendance, holidays, first-year survival274,946 employees · Jan 2024 to Aug 2026
13th-month disbursement timingJan 2022 to Aug 2026
Overtime and later attritionRank-and-file, overtime-eligible
Pay increases and retention63,581 rank-and-file
Behavior before resignationMatched cohort: 27,124 leavers, 24,526 stayers
What counts as a separation
Resigned 71%Terminated 29%
Absence without official leave is under 1%. Where this report says employees resigned, it means voluntary resignation specifically.
Confidence tags
HighA large sample holding steady across the period.
MediumA clear pattern with at least one plausible alternative this data cannot rule out.
IndicativeThe direction is consistent wherever visible, but the sample is too thin to put weight on precise figures.
Reading the rates
The same employees can produce very different numbers depending on the form. Each chart says which it uses.
43%+Annualized. Resignation rate for people in their first three months of service.
11%Cumulative. Share of new hires who have resigned by the end of month three.
Nothing here establishes that one thing causes another. Where a competing explanation is live, it is named rather than resolved.
Known limits that change how a finding reads
Suppression
Any cell drawing on fewer than five companies or 100 employees is masked rather than removed. Suppression is heaviest in the industry cuts and the overtime analysis, where several dimensions combine in one table.
Comparison groups
The pre-separation analysis matched leavers against stayers on industry, company size, rank and tenure band, at roughly one stayer for every two leavers. Some combinations had too few eligible employees to reach the full ratio.
Inference limits
Separation type comes from the employer-recorded status field. No reason-for-leaving field was available, which is why the month-six discussion stops short of quantifying how many recorded resignations were employer-initiated.
Notice periods
Resignation timing reflects the last working day on record, not the date of decision. Under a standard 30-day notice period, decisions precede recorded exits by roughly a month.
Absence measurement
Unplanned absence means a scheduled workday with no time-in and no leave filed in advance. At 19% of scheduled weekdays, the baseline likely also captures missing time-ins, field and flexible staff, and long absences never formally filed. Comparisons hold because everything is measured identically; the figure itself is not a national absenteeism rate.
Industry and sample composition
About 30% of companies carry no industry tag, so industry could not be a reporting dimension. Outsourcing and offshoring is the largest tagged segment at 14% of companies. Day-of-week analysis excludes rotating shifts. The sample covers formal-sector employers using HR technology and over-represents organized, mid-to-large, urban employers, so it is not representative of the Philippine labour force as a whole.
Missing fields
Overtime data was unavailable in the pre-separation cohort, so the checking-out finding rests on absence, undertime and tardiness. Leave balance and conversion data did not pass validation and is excluded, which limits the leave-burn finding.
Data governance
All records are processed in aggregate under Sprout's client agreements and in line with the Data Privacy Act of 2012. Nothing in this report identifies a client, a workplace or an individual. See Sprout's guide to the Data Privacy Act, privacy policy and security commitments.