Nghiên cứu 4Alpha: Liệu có sự ước tính quá cao có hệ thống về dữ liệu việc làm của Hoa Kỳ không?

4 Alpha Research Researcher: Kamiu

In todays global economy, the importance of employment data to global macro monetary policy makers and trading markets is self-evident. As an important indicator for measuring economic development, the US non-farm payrolls data has always attracted much attention. However, there has long been a questioning voice in the market: Why do US employment data and CPI trends deviate from each other, and why are there large differences between household surveys and corporate surveys? This disagreement has caused some people to doubt the non-farm payrolls data released by the US Department of Labor, believing that there may be errors or even systematic overestimation, especially with the repeated abnormalities in non-farm data since 2024 and the sharp drop in non-farm data in July 2024, the systematic doubts about non-farm data have further increased.

Next, we will explore the reasons behind this phenomenon and its possible impact on market analysis and policy making.

1. Why has the U.S. employment data long been suspected to be inaccurate or even systematically overestimated?

The non-farm payroll employment data released monthly by the U.S. Department of Labor (BLS) includes data such as the number of employed people and the unemployment rate, and has always been regarded as one of the most important macroeconomic indicators. The number of new non-farm payroll jobs reflects the number of new jobs in the non-agricultural sector in the United States, including all industries outside the government sector, such as manufacturing, services, and construction. This data helps to understand the expansion rate of the U.S. job market and the tightness of the labor market. The unemployment rate refers to the proportion of unemployed labor force to the total labor force over a certain period of time. It is another important indicator of economic health and reflects the degree of idleness in the labor market. The average hourly wage reflects the income level of American workers and is an important indicator for measuring consumer purchasing power and potential inflationary pressure.

Non-farm payrolls data has an important impact on financial markets, government policy making, and economic forecasts. Investors, economists, and policymakers closely monitor this report to assess the trend of the U.S. economy and make corresponding investments and decisions. The performance of non-farm payrolls data often affects the Federal Reserves monetary policy, which in turn affects global financial markets. However, in recent years, there has been a growing view that U.S. employment data is inaccurate and may be systematically overestimated, mainly due to the following reasons:

1. The discrepancies between non-agricultural data from different sources are getting bigger and bigger (details are given below), and the problem of lack of robustness of data is becoming more and more prominent, leading to doubts about the credibility of non-agricultural employment data;

2. There are potential contradictions between different macro data. Under the recent trend of significant decline in CPI data, the employment market continues to show a moderate growth trend. The specific comparison is as follows:

January 2024 :

CPI: According to the U.S. Bureau of Labor Statistics, CPI in January fell 0.1% month-on-month and increased 6.4% year-on-year.

Non-farm employment data: In January, the number of new non-farm jobs was 517,000, and the unemployment rate remained at 3.4%.

February 2024 :

CPI: CPI in February remained unchanged month-on-month and increased by 6.0% year-on-year.

Non-farm employment data: In February, the number of new non-farm jobs was 311,000, and the unemployment rate fell slightly to 3.3%.

March 2024 :

CPI: In March, CPI decreased by 0.2% month-on-month and increased by 5.2% year-on-year.

Non-farm payrolls: Non-farm payrolls increased by 235,000 in March and the unemployment rate remained unchanged.

April 2024 :

CPI: In April, CPI fell by 0.4% month-on-month and increased by 4.9% year-on-year.

Non-farm employment data: In April, the number of new non-farm jobs was 213,000, and the unemployment rate rose slightly to 3.4%.

May 2024 :

CPI: In May, CPI decreased by 0.3% month-on-month and increased by 4.0% year-on-year.

Non-farm employment data: In May, the number of new non-farm jobs was 184,000, and the unemployment rate remained at 3.4%.

June 2024 :

CPI: In June, CPI decreased by 0.2% month-on-month and increased by 3.2% year-on-year.

Non-farm employment data: In June, the number of new non-farm jobs was 176,000, and the unemployment rate fell slightly to 3.3%.

The above data depicts a slightly strange scenario, that is, in the first half of 2024, the US CPI showed a downward trend month by month, but the number of non-farm employment continued to rise moderately, showing strong resilience, which is inconsistent with the naive predictions made by observers based on the Phillips curve. Although the Phillips curve has been proven many times in history to have very limited ability to fit and predict actual situations, and its specific elasticity is also a long-standing topic of debate in the macroeconomics community, the continued deviation of data from the Phillips curve on a longer time scale from 2023 to the present will still cause the data itself to be questioned (this article will temporarily put aside the old-fashioned discussion on the statistical caliber of CPI);

3. The sub-data included in the non-agricultural data are contradictory. For example, in the non-agricultural employment data for May 2024, which is generally considered by the market to be the most bizarre in the past decade, the number of employed people recorded a significant increase, but the unemployment rate rose significantly when the labor force did not increase significantly, forming a self-contradiction that is difficult to justify (of course, the number of new non-agricultural jobs in May has been significantly revised downward in June, but this has further exacerbated the market and commentary circles doubts about the reliability of the initial data);

4. Starting from 2024, non-farm payrolls have been revised down several times. Since 2023, the non-farm payrolls data released by the U.S. Bureau of Labor Statistics has been revised down several times. For example, the non-farm payrolls data in May 2024 showed 272,000 new jobs, far exceeding the market expectation of 185,000, but the previous revisions to the non-farm payrolls data have caused the market to doubt the accuracy of this data. The Philadelphia Fed even suggested that the 2023 non-farm payrolls data may have overestimated the number of new jobs by as much as 800,000;

5. Non-farm payrolls data contradicts other employment survey data and continues to be higher than economists collective forecasts. In recent months, the Quarterly Employment and Wage Census (QCEW) and U.S. Private Employment (ADP) have long shown signs of a cooling in the U.S. job market, but non-farm data have consistently shown that U.S. employment has shown unexpected resilience. It is generally believed that non-farm payrolls data does not distinguish between formal and informal employment, while QCEW and other data focus more on formal employment statistics, with limited coverage of informal and part-time employment statistics.

2. Briefly introduce how non-agricultural employment data is calculated

BLS compiles non-farm payrolls data based on a series of detailed surveys and statistical methods. The following are the key steps and methods for calculating non-farm payrolls:

1. Sample survey: BLS collects data through the household survey (Current Population Survey, CPS) and the enterprise survey (Current Employment Statistics, CES). The household survey is mainly used to calculate the unemployment rate and labor force participation rate, while the enterprise survey is used to calculate the number of job increases and average hourly wages;

2. Industry classification: Non-agricultural employment data divides employment into different industry categories, such as manufacturing, construction, and services, so as to analyze the employment situation of each industry in more detail;

3. Data adjustment: mainly includes seasonal adjustment and B/D adjustment:

• To ensure the accuracy of the data, BLS will seasonally adjust the data to eliminate the impact of seasonal factors on employment data. Specifically, first, BLS analyzes historical data to identify and quantify seasonal patterns. Seasonal patterns refer to fluctuations in employment data due to regular or predictable factors (such as holidays, weather changes, school holidays, etc.) in a specific time period. Secondly, BLS uses the S-ARIMA time series analysis method to fit the model parameters that make the residuals white noise using historical data, and seasonally differencing the original data to eliminate seasonal fluctuations.

• At the same time, since the CES survey cannot capture the employment changes of newly established and closed enterprises in real time, the BLS uses the Birth/Death Adjustment model to estimate these changes in order to more accurately reflect the actual situation of the employment market. Among them: the Birth Model estimates the jobs created by newly established enterprises. This model is based on historical data and takes into account the growth trends and macroeconomic conditions of different industries to predict the contribution of new enterprises to the employment market; the Death Model: estimates the jobs lost by closed enterprises. This model is also based on historical data to analyze the frequency and pattern of business closures, as well as the impact of macroeconomic conditions on business survival.

3. Conclusion: Is the US employment data intentionally overestimated?

The author believes that, in terms of being questioned, CPI and non-farm payrolls are similar in nature. These two monthly data with important macroeconomic significance have always been repeatedly questioned by the market whether they are manipulated to meet the needs of incumbent US politicians for support and votes, and thus question the independence of the Federal Reserve. Of course, the author cannot completely rule out the possibility of this conspiracy theory, but still believes that the anomalies and inconsistencies in non-farm payrolls in recent years are more due to the obsolete statistical methods, the structural changes in the US economic structure after the epidemic, and the increasing influx of illegal immigrants.

1. Outdated statistical methods

As described below, the operating model of the U.S. economy may have undergone structural changes, but the seasonal adjustment and B/D adjustment of CES data are highly dependent on historical data patterns, which may lead to huge deviations, among which the B/D adjustment has been criticized the most.

According to the data, of all the new non-agricultural jobs in May, 231,000 came from the B/D model, which is an estimate based on the establishment of new businesses. These jobs are not actually counted as having been generated, but are assumed to exist and directly included in the data. Since April 2023, the B/D model has added 1.9 million jobs, accounting for 56% of all new jobs in the same period. This means that more than half of the job growth in the past year came from adjustments, causing most market views to point directly to the B/D model as the culprit for the outrageous non-agricultural data in May 24, as shown in the figure below. In recent years, the percentage difference between CES and CPS results has become larger and larger, which is also considered to be ironclad evidence that the CES sampling method and statistical adjustment method have been seriously ineffective.

2. The U.S. economic structure has undergone structural changes after the epidemic

Before and after the COVID-19 public health incident, we can observe a sharp increase in the proportion of informal work and a rapid decline in the willingness of young people to work, and this phenomenon has continued to this day. At present, there is no particularly strong explanation for this phenomenon. Some people believe that the increase in the proportion of informal work and the decline in employment willingness may be caused by the long-term sequelae (LC) of the new crown, which reduces the overall labor capacity at the level of the entire population, but there is no conclusion yet. In any case, it is certain that the increase in the proportion of part-time work will greatly increase the difficulty of non-agricultural employment statistics. Since non-agricultural data is conducted in a sampling survey method, the same person working in multiple part-time jobs at the same time will inevitably lead to an overestimation of employment statistics compared to the actual situation, and eliminating these noises will lead to a disproportionate increase in survey costs. At the same time, a large number of people of working age withdraw from the labor force (the denominator of the unemployment rate), which will also lead to statistical distortions in the unemployment rate and the increase in employment.

3. Border control is ineffective and the influx of illegal immigrants is accelerating

This is closely related to the above-mentioned changes in economic structure, as illegal immigrants without legal status are significantly more likely to engage in informal work. At the same time, the employment of illegal immigrants will also lead to potential sampling bias.

The BLS non-farm payrolls data is based on the CES sampling survey, and if the sample does not adequately represent the employment of illegal immigrants, the survey results may deviate from the actual situation. For example, if the CES survey sampling (the sampling unit is the employer) covers more large companies that tend to employ legal workers and ignores small or underground companies where illegal immigrants are more likely to work, then the employment data is likely to be overestimated.

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This article is sourced from the internet: 4Alpha Research: Is there a systematic overestimation of US employment data?

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