Another day of overtime until 10 PM.
You squeeze onto the last subway, the strap of your bag digging into your shoulder, oblivious to whatever is playing in your earphones. Everyone in the carriage wears the same expression: exhausted, numb, heads slightly bowed.
A notification pops up—a WeChat Moments post from an old classmate back in your hometown: taking the kids fishing by the river on a weekend, set against a backdrop of sprawling blue skies and green grass.
You stare at that photo for a long time, and the thought that has surfaced countless times before pops up again:
“Maybe… I should just go back?”
This isn’t the first time you’ve had this thought.
Every time you work to the point of breaking, every time rent goes up, every time you go home for the New Year and are asked “When are you moving back?”—it surfaces.
But every time you calm down, you rationalize it away:
“I’ve held out for so many years; leaving now means admitting defeat.” “What would I even do back there? The salary wouldn’t even be half of what I make now.” “What about the kids’ future education?”
So you stay. You keep working overtime. You continue to rethink the same question during your next late-night breakdown.
Year after year, you think about it. Year after year, you stay put. It’s not that you lack an answer; it’s that you’ve never rigorously calculated the math.
The reason the “stay or leave” dilemma causes endless anxiety is that you are using feelings to make decisions.
When your mood is good, you think, “Tier-1 cities have more opportunities; let’s keep pushing.” When your mood crashes, you think, “I can’t live like this anymore.” These two emotional states take turns, trapping you in a perpetual loop.
Breaking this loop doesn’t require an epiphany. It requires a system—a structure that translates vague feelings into comparable numbers.
Today, I will give you a tool: the City Decision Matrix.
One piece of paper, five dimensions, two columns for comparison. After filling it out, the answer might not automatically jump off the page, but your dilemma will shift from a “tangled mess” to “I know exactly where the bottleneck is.”
Here are the five dimensions:
1. Income (Weight: TBD) If you stay in a Tier-1 city, what is your expected income over the next three years? What about if you return? Don’t look at the theoretical maximum; look at the highest probability number. Score each option from 1 to 10.
2. Cost of Living (Weight: TBD) Rent/mortgage, commute, food, children’s expenses. Calculate your average monthly outlay. What are they in the city versus back home? A lower cost equals a higher score.
3. Social Network (Weight: TBD) How many friends can you call out for dinner in the city? Back home? Where is your professional network denser? Loneliness is a hidden cost many underestimate.
4. Growth Potential (Weight: TBD) Where is the ceiling for your industry in a Tier-1 city versus your hometown? Where will the things you want to be doing in three years be easier to achieve?
5. Family Needs (Weight: TBD) Your parents’ health, your children’s educational stage, your partner’s career development—this isn’t just about you. Where are family needs better met?
For each dimension, assign a score from 1 to 10 for both “Stay” and “Return”.
The critical step: Assigning weights to each dimension. Which dimension matters most to you? Is income 30% or 15%? Are family needs 40% or 10%?
There is no standard answer for weights. They reflect your value system—what you fundamentally care about.
Imagine sitting at your desk tonight, pulling out a piece of paper, and starting to fill it out.
You quickly complete the “Income” column. A monthly salary of 20,000 RMB in the city, maybe 8,000 to 10,000 RMB back home. The score gap is obvious.
But when you hit “Family Needs,” you pause.
Your mother’s medical checkup last month showed high blood pressure; your father says he’s fine, but he visibly looks older than last year. Staying in the city, all you can do is wire money back every month. But some problems cannot be solved with money.
You score “Return” a 9 and “Stay” a 3 in this category.
Then you realize: When you adjust the weight of “Family Needs” to 35%, the total scores flip.
It turns out the issue isn’t that you can’t calculate the math; it’s that you’ve never dared to assign a high enough weight to the “Family” dimension.
Because once you admit that “family is more important than career,” you have to face a choice you’ve been avoiding.
This matrix will not make the decision for you.
What it does is dismantle that “indescribable anxiety” in your head into five distinct, analyzable dimensions. Writing down exactly what you think about each dimension is ten times clearer than letting it loop in your mind.
Moreover, the process of assigning weights helps you answer a much deeper question:
What do you actually care about in this lifetime?
A person who ranks income first and a person who ranks family first will produce entirely different tables. Neither table is wrong. But you must know which one you are filling out.
I’ve provided the template below. Print it out, find some quiet time tonight, and fill it in.
If you have a partner, have them fill one out too. Comparing your weights might be more revealing than the matrix itself—you will discover that, in many cases, you aren’t arguing about “staying or leaving,” but rather your priorities regarding “what matters most” are misaligned.
Lay both tables out side by side. Do not rush to reach a consensus. Just seeing each other’s weight distributions will make many unsaid things clear.
📋 Tool Name: City Decision Matrix Use Case: Struggling with whether to leave/move to a specific city. Frequency: Fill it out once before a major life decision. Execution Steps:
- List the five dimensions, score “Stay” and “Return” from 1-10.
- Assign weights to the five dimensions (totaling 100%).
- Calculate the weighted total score and identify the specific dimension where you are bottlenecked. Core Principle: Translate emotional dilemmas into quantifiable comparisons; let weights expose your true value system. Common Pitfall: Distributing weights evenly across all dimensions—which is equivalent to not assigning weights at all.
【 Insight 】—— Decision-making is a skill that can be practiced.
