Recently, some friends asked me about non-farm payroll data, and I realized that many people still have some confusion about this concept. Today, let's talk about what big non-farm and small non-farm actually are, and why investors pay so much attention to them.

First, let's discuss big non-farm, officially called NFP (Non-Farm Payrolls), which is the official data released monthly by the U.S. Bureau of Labor Statistics. It includes three key indicators: non-farm employment, employment rate, and unemployment rate, covering employment in both the private and government sectors. Basically, it reflects the employment situation of the entire U.S. non-farm population. Small non-farm is different; it’s called the ADP National Employment Report, a private sector data released by ADP Research Institute, sampled from about 500k U.S. companies, covering employment information for roughly 35 million private sector employees.

Their release times also differ. Big non-farm is usually published on the first Friday of each month at 8:30 a.m. Eastern Time (Daylight Saving Time) or 9:30 a.m. (Standard Time), which is around 8:30 to 9:30 p.m. Taipei time; small non-farm is released on the first Wednesday of each month, two days earlier than big non-farm. Although small non-farm is private data, because the releasing organization is quite authoritative, it provides investors with a good reference indicator before the official non-farm data comes out.

So why is everyone so focused on non-farm payroll data? Essentially, this data reflects the health of the U.S. economy. Non-farm data covers manufacturing, services, construction, and other industries. When employment increases and the employment rate rises, it indicates the economy is improving, and consumption is expanding; conversely, if employment declines, it may signal economic slowdown or recession. Therefore, non-farm data has become an important standard for measuring the ups and downs of the U.S. economy and is a key indicator the Federal Reserve considers when setting interest rate policies. If employment data looks strong, the Fed might consider raising interest rates.

What should we pay attention to when analyzing non-farm data? First, the unemployment rate, but note that it has a lagging effect, so it’s best to analyze it together with other indicators like CPI. Non-farm employment accounts for over 80% of U.S. GDP, so when non-farm employment increases and the employment rate rises, economic growth accelerates, consumption expands, and unemployment naturally decreases. This tends to push the dollar higher, affecting the foreign exchange market, gold, and oil prices. Conversely, if non-farm data falls short of expectations, with fewer jobs added and unemployment rising, it signals economic slowdown, which can negatively impact the dollar, gold, and oil.

A small tip is to observe the trend of non-farm data rather than just the numbers themselves, such as evaluating the average employment growth over 12 months. This approach can be more helpful for investment decisions.

Non-farm data also has a direct impact on various financial markets. In the stock market, if non-farm data exceeds expectations and shows steady growth, investors tend to be optimistic about the economy, boosting market confidence and pushing stock prices higher. Conversely, if the data is disappointing, stock prices may fall. The foreign exchange market is similar: strong non-farm data usually indicates robust U.S. economic growth, prompting international capital to buy dollars, leading to dollar appreciation; weaker-than-expected data tends to weaken the dollar.

Although the cryptocurrency market isn’t directly affected by non-farm data, there are indirect effects. If non-farm data performs strongly, confidence in traditional markets increases, which might reduce investment in high-risk cryptocurrencies. But if the data disappoints, some investors may turn to cryptocurrencies and other alternative investments to hedge risks, making the crypto market more active. The same logic applies to indices: good non-farm data tends to push major indices higher, while poor data can lead to declines.

Of course, the magnitude of these effects depends on how much the actual data deviates from expectations and the current market conditions. Investors should always make comprehensive judgments and avoid impulsive actions based on a single data point. Learning how to analyze non-farm data, combining fundamental and technical analysis, and investing cautiously are skills every investor should master.