Nathan and Dave Green debate this question. Grok says:
The North American Free Trade Agreement (NAFTA), implemented in 1994, had a mixed impact on the standard of living for the average American, with evidence suggesting modest overall benefits but significant variation across groups and regions. Here’s a concise breakdown based on available data and analysis:
Positive Impacts:
Economic Growth and Consumer Benefits: NAFTA increased trade between the U.S., Canada, and Mexico, boosting U.S. GDP by an estimated 0.5% to 1% over time (e.g., Congressional Budget Office, 2003). This growth came from lower trade barriers, increasing access to cheaper goods, particularly from Mexico. For consumers, this meant lower prices for items like produce, electronics, and clothing, effectively raising purchasing power.
Job Creation in Some Sectors: Export-oriented industries, such as agriculture, automotive, and services, saw job growth. U.S. exports to Mexico and Canada grew from $337 billion in 1993 to over $1 trillion by 2017 (USTR data), supporting jobs in these sectors.
Wage Effects in Certain Industries: Workers in export-driven sectors often saw wage gains due to increased demand. Studies (e.g., Hufbauer and Schott, 2005) estimate a net positive effect on U.S. wages, though small, averaging 0.1-0.3% higher by the 2000s.
Negative Impacts:
Job Losses in Manufacturing: NAFTA contributed to the displacement of roughly 700,000 U.S. jobs by 2010, primarily in manufacturing (Economic Policy Institute, 2011). Industries like textiles and electronics faced competition from lower-wage Mexican labor, hitting regions like the Rust Belt hard. Displaced workers often moved to lower-paying service jobs, reducing their standard of living.
Wage Suppression in Some Sectors: Increased competition from Mexican labor put downward pressure on wages for low-skill manufacturing workers. Autor et al. (2016) found that trade shocks from NAFTA and other agreements reduced wages for some blue-collar workers by 1-2% in affected regions.
Regional Disparities: Communities dependent on manufacturing (e.g., Michigan, Ohio) faced economic decline, with ripple effects on local services and infrastructure. Meanwhile, border states like Texas benefited from trade logistics and cross-border commerce.
Net Effect on Standard of Living:
Modest Gains for the Average American: Most studies conclude NAFTA had a small positive effect on the average American’s standard of living, driven by lower consumer prices and slight GDP growth. The U.S. International Trade Commission (2016) estimated a net welfare gain of $56 billion annually by 2016.
Uneven Distribution: Gains were uneven, with higher-skill workers, urban consumers, and export-sector employees benefiting more. Low-skill manufacturing workers and certain rural communities faced losses, with inadequate retraining programs exacerbating the impact.
Broader Context: NAFTA’s effects are hard to isolate from other factors like automation, China’s rise in global trade, and domestic policy changes, which also influenced jobs and wages.
Conclusion:
NAFTA likely improved the standard of living for the average American by a small margin through cheaper goods and economic growth, but it harmed specific groups, particularly low-skill manufacturing workers in certain regions. The net positive effect masks significant losers, highlighting why perceptions of NAFTA remain polarized.
Nathan says that changing the composition of the population is a moral question. Well, there’s a moral question too in what type of economy we want? Do we want an economy run for maximum economic efficiency according to certain economic models or do we start with other goals?
Some cite efficiency as a rationale for free trade. For adherents of this faith, the sole objective of trade policy is market efficiency. Lower tariffs and nontariff barriers reduce the costs of producing and distributing goods and services; that, in turn, makes society as a whole better off — or so the argument goes. Of course, there are advantages to lower prices when considering Americans as consumers. The problem is that adherents of this view seldom consider how this single – minded pursuit of efficiency affects the men and women who actually do the producing.
According to the definitions preferred by these efficiency – minded free traders, the downside of trade for American producers is not evidence against their approach but rather is an unfortunate but necessary side effect. That’s because free trade is always taken as a given, not as an approach to be questioned. Rather than envisioning the type of society desired and then, in light of that conception of the common good, fashioning a trade policy to fit that vision, economists tend to do the opposite: they start from the proposition that free trade should reign and then argue that society should adapt. Most acknowledge that lowering trade barriers causes economic disruption, but very few suggest that the rules of trade should be calibrated to help society better manage those effects. On the right, libertarians deny that these bad effects are a problem, because the benefits of cheap consumer goods for the masses supposedly outweigh the costs, and factory workers, in their view, can be retrained to write computer programs. On the left, progressives promote trade adjustment assistance and other wealth – transfer schemes as a means of smoothing globalization’s rough edges.
Nathan Cofnas writes: “There are currently 6.8 million unemployed Americans and 8 million job openings.”
Grok says:
Nathan Cofnas’s claim that there are “6.8 million unemployed Americans and 8 million job openings” suggests a labor market with more opportunities than unemployed workers, implying a relatively strong economy. This aligns with government statistics like the U-3 unemployment rate, which was reported at 4.2% in November 2024, indicating low unemployment. However, Eugene Ludwig’s Politico article argues that such statistics misrepresent economic reality, painting an overly optimistic picture that contrasts with public perception and lived experiences.
Comparison
Unemployment Metrics:
Cofnas: Cites 6.8 million unemployed, consistent with official U-3 data, which counts only those actively seeking work and not discouraged workers or the underemployed. The 8 million job openings suggest a tight labor market where jobs outnumber job seekers.Ludwig: Critiques U-3 as misleading because it counts underemployed workers (e.g., part-time workers seeking full-time jobs) and those earning poverty wages as “employed.” His research at the Ludwig Institute estimates a “functional unemployment” rate of 23.7%, including those who are underemployed, discouraged, or earning insufficient wages (e.g., below $25,000 annually). This starkly contrasts with Cofnas’s implied optimism, highlighting a much bleaker labor market for many Americans.
Economic Strength:
Cofnas: The surplus of job openings (8 million vs. 6.8 million unemployed) suggests economic vigor, as businesses are hiring and opportunities exist. This aligns with narratives of a robust post-pandemic recovery often cited by policymakers.Ludwig: Argues that government metrics like GDP obscure disparities. While GDP growth indicates prosperity, it doesn’t reflect how gains are distributed. Ludwig notes that since 2013, only those with advanced degrees or in affluent regions (e.g., San Francisco, Boston) have seen significant gains, while lower-income and less-educated workers, or those in struggling regions (e.g., Youngstown, Ohio), have stagnated or declined. This challenges the idea that job openings alone signal broad prosperity.
Contrast
Perception vs. Reality:
Cofnas: His figures reflect government data, which Ludwig argues Democrats overly trusted, leading to a disconnect with voters who felt economically strained. Cofnas’s statement doesn’t address whether job openings are accessible, well-paying, or geographically relevant to the unemployed.Ludwig: Emphasizes that public perception of economic decline is more accurate than government stats. He cites visible signs like homelessness and derelict regions, which resonate with voters’ experiences. His alternative metrics (e.g., functional unemployment, true cost-of-living increases) align with Republican skepticism of rosy official numbers, explaining voter frustration in 2024.
Wage and Inflation Insights:
Cofnas: Doesn’t address wages or inflation, focusing solely on unemployment and job openings. His data implies workers can find jobs, but not whether those jobs pay enough to meet living costs.Ludwig: Highlights flaws in wage and inflation metrics. Official weekly earnings ($61,900 median) exclude part-time and unemployed workers, inflating perceptions of income. Ludwig’s estimate ($52,300 median) shows workers earn 16% less than reported. On inflation, the Consumer Price Index (CPI) underestimates cost-of-living increases for low- and middle-income households, who spend more on necessities like groceries and rent. Ludwig’s alternative indicator shows a 35% higher cost-of-living rise since 2001, with a 9.4% increase in 2023 alone, outpacing wage growth and eroding purchasing power by 4.3% that year.
Methodological Critique:
Cofnas: Relies on unadjusted government statistics without questioning their validity, reflecting the Washington insider perspective Ludwig critiques.Ludwig: Advocates for new metrics to capture economic realities, like including underemployment and poverty wages in unemployment rates or focusing inflation measures on necessities. He argues that current methodologies, unchanged for decades, mislead policymakers and fail to address disparities.
Synthesis
Cofnas’s statement reflects the optimistic narrative of government statistics, suggesting a labor market with ample opportunities (8 million openings for 6.8 million unemployed). Ludwig, however, reveals the limitations of these numbers, showing that unemployment is functionally higher (23.7% vs. 4.2%), wages are lower ($52,300 vs. $61,900), and inflation hits harder for most Americans than the CPI suggests. While Cofnas’s data might appeal to those citing headline figures, Ludwig’s analysis explains why voters felt economically worse off, aligning with visible struggles like homelessness and regional decline. Ludwig’s call for updated metrics underscores the need to bridge the gap between statistical optimism and ground-level reality, a disconnect that Cofnas’s figures don’t address.
Dave Green is unbearable. He goes on and on how about the standard of living for the median American is now lower than it was in the 1990s. Grok says:
Yes, the standard of living for the median American is generally higher now than in 1995, but the picture is nuanced. Key factors include:
Income and Purchasing Power: Real median household income has risen from about $56,000 in 1995 to around $81,000 in 2023 (adjusted for inflation, 2023 dollars). Real per capita GDP has also grown significantly, from ~$39,000 to ~$65,000 (2023 dollars). This reflects higher purchasing power overall.Technology and Goods: Access to advanced technology (smartphones, internet, streaming) and cheaper goods (electronics, clothing) has improved quality of life. Many products unavailable or costly in 1995 are now affordable and ubiquitous.
Healthcare and Life Expectancy: Life expectancy increased from 75.8 years in 1995 to 77.5 years by 2023, with better medical treatments and access to care, though disparities persist. Healthcare costs, however, have outpaced inflation.
Housing and Debt: Housing costs have risen faster than incomes (median home price: $147,000 in 1995 vs. $412,000 in 2023, adjusted). Student and consumer debt have also grown, straining some households.
Work and Leisure: Real wages for the median worker have grown modestly (~10-15% since 1995), but job polarization has reduced middle-skill opportunities. Remote work and flexible arrangements, enabled by tech, are more common, enhancing work-life balance for some.
Caveats: Inequality has widened, with top earners gaining disproportionately. Millennials and younger generations face challenges like delayed homeownership and higher debt burdens. Subjective well-being metrics (e.g., happiness surveys) show mixed results, with some reporting less optimism than in the 1990s.
Overall, material and technological improvements tilt toward a higher standard of living, but housing costs, debt, and inequality create trade-offs for the median American.
Dave Green says that GDP per capita is a poor proxy “longitudinally” for wealth. What does that mean?
Grok says:
When Dave Green says GDP per capita is a poor proxy “longitudinally” for wealth, he means it’s not a reliable measure for tracking changes in wealth over time. “Longitudinally” refers to analyzing data across multiple time periods to observe trends or changes, as opposed to a single snapshot (“cross-sectionally”).
GDP per capita, which is the total economic output of a country divided by its population, reflects average income or production at a given moment. However, Green’s point is that it doesn’t effectively capture how wealth—accumulated assets, savings, or net worth—evolves over time. Here’s why this might be the case:
Wealth vs. Income: GDP per capita measures income generated in a year, not accumulated wealth (e.g., property, investments, or savings). Wealth can grow or shrink independently of annual income, especially over long periods.
Inequality: GDP per capita is an average and doesn’t account for wealth distribution. Over time, wealth may concentrate among a few, while the majority’s wealth stagnates, even if GDP per capita rises.
Inflation and Cost of Living: Longitudinal comparisons of GDP per capita may not adjust well for inflation or changes in purchasing power, which affect real wealth.
Non-Market Factors: Wealth includes assets like real estate or inheritances, which GDP per capita doesn’t capture. These can significantly influence wealth trends over time.
Economic Shocks or Policies: Tax policies, recessions, or booms can disproportionately affect wealth accumulation (e.g., stock market gains or housing crashes) in ways GDP per capita doesn’t reflect.
In short, Green is arguing that GDP per capita misses key dynamics of wealth accumulation when you track it over time, making it a flawed metric for understanding long-term changes in economic well-being. For a more accurate picture, you’d need to look at metrics like net worth, wealth distribution, or asset ownership trends.
When we are told that there are 7.5 million job openings in America, how reliable is that figure?
Grok says:
The figure of 7.5 million job openings in America, typically sourced from the U.S. Bureau of Labor Statistics (BLS) Job Openings and Labor Turnover Survey (JOLTS), has some reliability but comes with caveats. Here’s a breakdown of its reliability based on available information:
Source and Methodology: The JOLTS data is collected from a sample of about 21,000 businesses and government establishments, representing a subset of the U.S. economy. The BLS uses statistical modeling to estimate total job openings, defined as positions open on the last business day of the month that meet specific criteria (work available, start within 30 days, active recruitment). This sampling approach introduces potential errors, as it relies on extrapolating from a limited dataset.
Response Rate Concerns: Posts on X highlight a significant issue: the JOLTS survey response rate is low, around 33% in recent years, meaning roughly 67% of the data is estimated or imputed. Low response rates can reduce accuracy, as the BLS must rely on assumptions or historical data to fill gaps, potentially skewing results.
Revisions: The JOLTS data is frequently revised, often downward. For example, February 2025 job openings were revised down by 88,000 to 7.5 million, and December 2024 data was revised down by 92,000 to 7.5 million. These revisions suggest initial estimates may overestimate openings due to incomplete data or modeling errors. Over the last 21 months, 16 reports were revised downward, indicating systematic overestimation.
Economic Context and Interpretation: The 7.5 million figure for February 2025 aligns with a broader trend of declining job openings from a peak of 12.2 million in March 2022. However, it’s still high historically (pre-2021, openings rarely exceeded 8 million). This suggests resilience in labor demand but doesn’t capture whether these openings are for high-quality, well-paying jobs or low-wage, high-turnover positions. Additionally, external factors like policy uncertainty (e.g., tariffs) or labor force participation (down from pre-COVID levels) may distort employer demand or reporting.
Skepticism from Observers: Some X posts question the data’s validity, calling it “guesswork” or “made up” due to the low response rate and frequent revisions. While these claims may exaggerate, they reflect legitimate concerns about the precision of estimates when two-thirds of the data is imputed.
Counterpoints: Despite limitations, JOLTS remains a widely used indicator, corroborated by other labor market metrics like unemployment rates (4.2% in April 2025) and payroll growth (177,000 jobs added in April). These suggest a solid, though cooling, labor market, lending some credibility to the job openings figure. The BLS also employs rigorous statistical methods to adjust for non-responses and seasonal factors, though these adjustments aren’t foolproof.
Conclusion: The 7.5 million job openings figure is a useful but imperfect estimate. Its reliability is undermined by low survey response rates, frequent downward revisions, and reliance on statistical imputation for a significant portion of the data. While it broadly reflects labor market trends, it may overstate or misrepresent the true number of openings, especially in specific industries or regions.
Nathan says that Americans are already poorer due to Trump’s tariffs.
