Do Minimum Wage Increases Really Kill Jobs? Evidence from the "Fight for $15" Era

The debate over minimum wage policy has raged for decades, with economists, policymakers, and business leaders offering sharply different predictions about its effects on employment. Critics warn that raising the minimum wage will force employers to cut jobs, while supporters argue that higher wages boost worker productivity and spending power. But what does the actual data tell us?

Using a comprehensive difference-in-differences analysis and Federal Reserve Economic Data covering 43 U.S. states from 2012-2020 of the "Fight for $15" movement between 2012 and 2020, I provide some evidence about how minimum wage increases actually affect employment in the real world.

The Perfect Natural Experiment

The period from 2012 to 2020 provided economists with an ideal "natural experiment" to study minimum wage effects. Here's why this timeframe was perfect for analysis:

• Federal Stability: The federal minimum wage remained frozen at $7.25 per hour since 2009, creating a stable baseline for comparison.
• State-Level Variation: The "Fight for $15" movement inspired 29 states to raise their minimum wages above the federal level, while 14 states kept theirs at $7.25.
• Staggered Timing: States didn't all change their wages at once. Instead, they implemented increases in different years (2013-2020), allowing us to compare states before and after their policy changes.
• Clean Controls: Unlike longer time periods that mix federal and state changes, this era gives us true "control" states that never raised their minimums.

The Data: A Comprehensive View

My analysis draws on high-quality federal economic data covering all 43 U.S. states from 2012 to 2020:

• Employment Data: Monthly employment figures from the Federal Reserve Economic Data (FRED) system, aggregated to annual averages and converted to logarithmic form to measure percentage changes.
• Minimum Wage Data: Official state minimum wage rates from FRED's comprehensive database, capturing all policy changes during this period.

Treatment Variation:

• 29 states increased minimum wages above federal levels
• 14 states maintained the $7.25 federal minimum throughout
• Peak adoption years were 2013 (10 states) and 2014 (7 states)

The Method: Difference-in-Differences Analysis

To isolate the causal effect of minimum wage increases on employment, I used a "difference-in-differences" approach— common for policy evaluation in economics.

The Logic: Compare employment changes in states that raised minimum wages to employment changes in states that didn't, before and after the policy changes. This method controls for:

• National economic trends affecting all states
• Permanent differences between states
• Other state-specific policies implemented at different times

Two Approaches:

1. Two-Way Fixed Effects (TWFE): Estimates the average employment effect across all minimum wage increases
2. Event Study: Tracks employment effects year-by-year around the time of policy adoption

Finding: Small Effects, Big Implications

The Overall Picture

Our analysis reveals that minimum wage increases during the "Fight for $15" era had small negative effects on employment:

• TWFE Results: 1.6% decrease in employment (marginally significant, p = 0.0504)
• Event Study: 0.83% decrease in the year of adoption (statistically significant, p = 0.0136)

The Event Study: A Detailed Timeline

The event study provides the most compelling evidence, tracking employment effects from 5 years before to 5 years after minimum wage adoption:

What This Graph Shows:

• Pre-Treatment (Years -5 to -2): The flat, statistically insignificant coefficients confirm that treated and control states had similar employment trends before policy changes—validating our research design.
• Year of Adoption (Year 0): A statistically significant drop of 0.83% in employment, shown by the negative coefficient that doesn't overlap with zero.
• Post-Treatment (Years 1-5): Effects persist but become statistically indistinguishable from zero, suggesting either adaptation by businesses or statistical uncertainty due to smaller samples.
• The Pattern: An immediate, modest employment reduction that appears to stabilize rather than compound over time.

Putting Results in Context

How Do These Findings Compare to Other Studies?

The results align closely with the modern economic consensus:

• Similar Magnitudes: Recent high-quality studies find employment elasticities between 0.01 and 0.04, consistent with my findings.
• Methodological Rigor: Unlike older studies that found larger effects, modern research using sophisticated methods (like my approach) consistently finds smaller impacts.
• Meta-Analysis Support: When economists correct for publication bias—the tendency for journals to publish more dramatic results—the employment effects of minimum wages largely disappear.

The Economic Significance

While statistically detectable, these effects are economically modest:

• Small Relative to Wage Gains: A 1.6% employment reduction is tiny compared to typical minimum wage increases of 15-30%.
• Net Worker Benefits: For every 100 low-wage workers, roughly 98 keep their jobs with higher pay, while 2 might lose employment.
• Aggregate Impact: The total income gained by workers receiving raises far exceeds income lost by those potentially losing jobs.

What This Means for Policy

The Case for Gradual Increases

Our findings support the approach taken by most "Fight for $15" states: gradual, predictable minimum wage increases. The data suggests:

• Modest increases don't trigger mass layoffs
• Businesses can adapt through productivity improvements, reduced turnover, or small price adjustments
• Workers benefit substantially from higher wages with minimal employment risk

Addressing Common Concerns

• "Job Killer" Claims: The data doesn't support predictions of massive job losses from minimum wage increases.
• Small Business Impact: While some adjustment occurs, the effects are far smaller than often claimed by business groups.
• Economic Growth: States that raised minimum wages didn't experience employment collapses or economic downturns.

Limitations and Future Research

What This Study Doesn't Capture

• Substitution Effects: Our analysis measures net employment changes but may miss substitution between different types of workers.
• Long-Term Effects: While I track effects for up to 5 years, even longer-term impacts remain uncertain.
• Heterogeneous Effects: The impact likely varies across industries, regions, and demographic groups.

The Ongoing Debate

While my evidence supports modest minimum wage increases, the debate continues over:

• Optimal minimum wage levels
• Regional variation in appropriate wages
• Alternative policies to support low-wage workers

The Bottom Line

The "Fight for $15" era provided a remarkable natural experiment in minimum wage policy, and the results are reassuring for supporters of higher wages. Minimum wage increases during 2012-2020 had small negative employment effects that were far outweighed by the benefits to workers who kept their jobs.

This doesn't mean minimum wages can be raised without limit—there's certainly some level that would cause substantial job losses. But the evidence suggests that the moderate increases implemented by most states during this period struck a reasonable balance between supporting workers and maintaining employment opportunities.

For policymakers considering minimum wage increases, the message is clear: done thoughtfully and gradually, minimum wage increases can improve worker welfare without devastating employment. The data supports continued experimentation with higher minimum wages, especially in high-cost regions where current wages provide inadequate living standards.

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Full methodology and results are available upon request.

Assessing labor market transitions in Ghana using panel data

Understanding labor market transitions is important for policymakers and researchers in developing countries. Changes in economic activity status (employed, unemployed, out of labor force) vary significantly from one country to another and also within countries from one socio-economic group to another. Below I summarize analysis from the latest Ghana Annual Household Income and Expenditure Survey (AHIES) published by the Ghana Statistical Service. The AHIES is a panel survey that has followed over 10,000 individuals since 2022Q1 to assess changes in their livelihoods. I am currently analyzing trends and show here what has happened to economic activity between Q2 and Q3. 

Transitions in Ghana labor force status, 2022Q2 - 2022Q3

Source: Own analysis using AHIES from Ghana Statistical Service.

Note: Left hand side is 2022Q2 and right side is 2022Q3

• 81 percent of working age individuals (15-64-years) who were employed in 2022Q2 were still employed in 2022Q3; 7 percent went into unemployment, and 13 percent dropped out of the labor force. Among youth (15-35-years), 72 percent who were employed in 2022Q2 were still employed in 2022Q3, while 9 percent fell into unemployment, and a further 19 percent dropped out of the labor force. The corresponding values for non-youth were 83 percent, 5 percent, and 12 percent.
• 39 percent of working age individuals who were unemployed in 2022Q2 were employed in 2022Q3; 23 percent were still unemployed and 37 percent dropped out of the labor force. Among youth, 27 percent of those who were unemployed in 2022Q2 were still unemployed in 2022Q3, while 34 percent got employment, and a further 40 percent fell out of the labor force. 
• 22 percent of those who were outside the labor force are now employed; 10 percent are unemployed; and 68 percent are still out of the labor force.

One salient observation for a country looking to create jobs for youth is that, compared to non-youth (35 years and older), youth (15-35) are close to 20ppts less likely to transition from unemployment to employment; 10ppts more likely to stay in unemployment, and 5 ppts more likely to transition from unemployment to out-of-labor force. 

Similarly, we can visualize transitions across statuses in employment.

Source: Own analysis using AHIES from Ghana Statistical Service.

Note: Left hand side is 2022Q2 and right side is 2022Q3.

Several factors affect labor market transitions in developing countries. It would be interesting to assess the role of the following: (i) economic cycles; (ii) labor market efficiency; (iii) barriers to returning to work; (iv) sectoral and occupational job mismatches; and, in the longer run, (v) structural transformation. Policymakers and researchers need to understand these factors to design effective labor market policies and services that can help individuals navigate these transitions.

Estimating the returns to education in Ghana

What determines earnings in Ghana? The Human Capital model postulates that the log of earnings of an individual is a function of that individual's productive characteristics. These individual characteristics help explain the marginal productivity and the returns to them.[1] In Mincer (1974) this model was formalized as in equation (1):

In equation (1), lnY_t is the log of earnings in year t, Educ is years of schooling, Exp is years of cumulative work experience, and X is a vector of other variables. We ran this model for Ghana using GLSS data with X including the variables shown in table 1. We build on Gundersen (2016) in specifying the model used in this analysis.[2]

We find that, conditional on age and age squared (as a proxy for experience), sex, parents’ education, occupation, public versus private sector employment, and marital status, an additional year of education boosts annual earnings by 5.7 percent. Experience has a statistically positive marginal effect on annual earnings, but this effect dissipates as experience grows. Being female is associated with poorer labor earnings - the estimated marginal effect of being female on one’s annual earnings is negative and can be interpreted as saying that, conditional on the other correlates, females are expected to earn 74 percent of male earnings per year. If we apply model (1) to explain variation in urban, rural, youth and nonyouth annual earnings, we observe a range of returns of 9 to 14 percent (figure 1). Females show higher conditional marginal returns to education than men; non-youth (ages 35 plus) have greater returns to education than youth (ages 15-35); and the rural/urban difference in returns to education is minimal.

Source: Own estimates using GLSS 7.

Note: The conditional marginal estimates of returns to education are derived from the log-linear model 1, in which we regress the natural logarithm of annual earnings on years of education, age and age squared (as a proxies for experience and experience squared), parents’ education, and marital status. All estimates are statistically significant at the 1 percent level of significance.

Other researchers find that globally (across 131 countries), average rates of return to education are 10.4 percent per year and that the returns are highest in Africa, where estimates average 12.8 percent per year.[3] They also find that in Africa, the highest returns exist at the tertiary level at 21.9 percent. Our estimates confirm this tertiary premium in Ghana - returns for those with 12-plus years of education are close to 20 percent per year (see figure 2). High returns at tertiary levels have remained in the 10-year period assessed, reflecting the relative scarcity of human capital with this level of education.

* p<0.05, ** p<0.01, *** p<0.001

Source: Own estimates based on data from GLSS 7. Note: The conditional marginal estimates of returns to education at each level are derived from a log linear regression of the natural logarithm of annual earnings on age and age squared (as a proxies for experience and experience squared); sex; parents’ education; and marital status. Education splines are generated with STATA’s ‘mkspline’ command, which creates knots specified at 0-6, 9-12, and 12-plus years of schooling, following Gundersen (2016). The average estimates as well as estimates for those with 12 plus years of education are statistically significant in both periods.

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[1] Gundersen, S., 2016. "Disappointing returns to education in Ghana: A test of the robustness of OLS estimates using propensity score matching." International Journal of Educational Development Volume 50, September 2016, Pages 74-89 https://www.sciencedirect.com/science/article/pii/S0738059316300608?via%3Dihub

[2] Mincer, J., 1974. “Schooling, Experience and Earnings.” Columbia University Press

[3] Psacharopoulos & Patrinos, 2018. “Returns to investment in education: a decennial review of the global literature.” Education Economics ISSN: 0964-5292 (Print) 1469-5782 (Online) Journal homepage: https://www.researchgate.net/publication/2528582_Returns_to_Investment_in_Education 

Skill-proximate occupations for non-post-secondary-educated workers in Ghana

In upcoming research, my colleague and I posit that the skill content of a worker’s current occupation is a high dimension piece of information that can function as a job market signal, particularly for low wage, non-post-secondary educated workers. Using the 2013 Skills Towards Employability and Productivity (STEP) Survey data for Ghana, we construct a skill vector for every occupation consisting of a skill measure incorporating routine versus nonroutine; and manual versus cognitive intensity. We then create an occupation relatedness measure across all occupations at the 3-digit ISCO level. The fact that many jobs in different industries share common skills but differ substantially in wages suggests that there may be incomplete information in the labor market and potential pathways for low educated workers to become skilled through alternative routes (STARs). The figure below illustrates the resulting relatedness plot of occupations with node size denoting employment shares and node color representing wages (dark blue being the highest).

Our results show that in 2016/17, there were approximately 1.2 million individuals whose skill profile based on current work is proximate to the skill profile of a higher paying occupation. We call these STARs after Blair et al (2020). Of these, 46 percent were workers with less than post-secondary education in low wage occupations who have skills to transition to a higher wage role in their wage category. Another 344,840 (28.3 percent) were workers with less than post-secondary education in middle wage occupations who have skills to transition to a higher wage role in their wage category. An estimated 292,151 (23.9 percent) were non-post-secondary-educated workers who have skillsets to transition to higher wage work. Finally, there were just 18,379 (1.5 percent) workers with less than post-secondary education who are in high-wage roles.

Shocks and Social Safety Net Program Participation in Ghana

My colleague and I just had our study published. The study discusses the association between household exposure to negative shocks and social safety net program participation in Ghana. To examine this issue, we link data from high-resolution geospatial maps of drought and flood risks to government administrative data on safety net program beneficiaries at the district level. We find that drought risk is positively associated with household participation in selected, main public social safety net programs. (The corresponding evidence for flood risk is weaker). We interpret the finding to be a result of pre-shock program coverage of drought-prone areas, in part achieved indirectly through the intentional targeting of poor areas by the programs.