Canada Oil and Gas Employment Statistics

19 March 2025 - Written by ML

1. Introduction

This report examines employment trends in Canada’s oil and gas extraction industry, leveraging monthly, seasonally adjusted data from Statistics Canada (Table 14-10-0331-01, Vector ID: 1645360662, Industry: 211, 2111). The data, extracted via a methodology detailed here, spans multiple years and is analyzed through various visualizations and tables generated in R. Our goal is to identify historical patterns, assess seasonality, and explore recent trends, providing insights into the industry’s workforce dynamics.

2. Historical Trend

We begin with an overview of employment over time, depicted in the line chart below:

Figure 1: Historical Employment in Oil and Gas Extraction (Monthly, Seasonally Adjusted)

Figure 1: Historical Employment in Oil and Gas Extraction (Monthly, Seasonally Adjusted)

Key Observations: - Long-Term Volatility: The chart reveals periods of significant growth interspersed with declines, reflecting the industry’s sensitivity to external factors that may relate to changes in oil prices and economic cycles. - Recent Trends: A closer inspection suggests a stabilization in recent years, though fluctuations persist, hinting at ongoing market or policy influences.

3. Seasonal pattern

To investigate seasonality, the time series is decomposed using the STL method1 by Cleveland et al. (Cleveland et al., 1990), as shown below:

Figure 2: STL Decomposition of Employment Data

Figure 2: STL Decomposition of Employment Data

Insights: - Seasonal Component: The decomposition highlights a recurring seasonal pattern, with consistent peaks and troughs each year. This suggests that employment in the oil and gas sector may be influenced by seasonal operational cycles, such as drilling schedules or weather-related factors. - Trend Component: The trend line smooths out short-term fluctuations, reinforcing the long-term volatility observed in Figure 1.

To quantify seasonality, we compare monthly employment to long-term averages:

Table 1: Differences from Long-Term Monthly Averages (Sample)
month 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
January 6312 3978 -3537 1687 -1000 -3 -1838 -1778 -1539 -2279
February 5899 3562 -2861 298 -563 -941 -2264 -1684 84 -1528
March 6864 2489 -2745 302 -507 -9 -2433 -1622 -84 -2253
April 6318 3114 -1606 134 -933 -669 -2496 -1654 608 -2815
May 5666 2351 -1145 931 -340 -1036 -3315 -1113 637 -2636
June 5490 1336 592 786 112 -2074 -3916 -1394 640 -1571
July 5692 1198 614 1128 460 -2318 -5564 -965 1287 -1534
August 5312 958 1052 766 590 -2562 -5491 -692 1638 -1572
September 6214 312 1596 -154 838 -2340 -5430 -1286 1900 -1650
October 6769 -270 1377 114 361 -3086 -3066 -1061 78 -1219
November 5385 -460 2380 -588 334 -2632 -2102 -878 -442 -1000
December 6264 -999 1301 -779 271 -1694 -2594 74 -897 -948

Findings: - Monthly Variations: Table 1 shows how each month’s employment deviates from its long-term average. For instance, winter months like January and February often exhibit positive differences, possibly due to heightened activity, while summer months may show declines. - Consistency: These patterns align with the seasonal component in Figure 2, confirming a cyclical nature to employment.

4. Last 5 Years trend

Focusing on the past five years, we plot monthly employment by year:

Figure 3: Employment by Month (Last 5 Years)

Figure 3: Employment by Month (Last 5 Years)

Observations: - Yearly Comparisons: Figure 3 reveals distinct trajectories for each year. For example, 2024 shows a relatively stable pattern compared to earlier years, which may indicate a recovery or adaptation phase. - Seasonal Echoes: The seasonal peaks and troughs from Section 3 are evident, though their magnitude varies by year, suggesting external influences modulate the baseline cycle.

A detailed table for the past ten years, including year-over-year (YoY) changes for 2023-2024, provides further context:

Table 2: Employment by Month and Year with YoY Change (Sample)
month 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 yoy_pct
January 62504 60170 52655 57879 55192 56189 54354 54414 54653 53913 -1%
February 61867 59530 53107 56266 55405 55027 53704 54284 56052 54440 -3%
March 62686 58311 53077 56124 55315 55813 53389 54200 55738 53569 -4%
April 62169 58965 54245 55985 54918 55182 53355 54197 56459 53036 -6%
May 61412 58097 54601 56677 55406 54710 52431 54633 56383 53110 -6%
June 61378 57224 56480 56674 56000 53814 51972 54494 56528 54317 -4%
July 61631 57137 56553 57067 56399 53621 50375 54974 57226 54405 -5%
August 61222 56868 56962 56676 56500 53348 50419 55218 57548 54338 -6%
September 62210 56309 57592 55843 56835 53656 50567 54710 57896 54347 -6%
October 62744 55705 57352 56089 56336 52889 52909 54914 56053 54756 -2%
November 61172 55327 58167 55199 56121 53155 53685 54909 55345 54787 -1%
December 61806 54543 56843 54763 55813 53848 52948 55616 54645 54594 0%

Insights: - YoY Changes: Table 2 highlights a contraction in employment across each month in 2024.

5. Distribution by Month

Box and violin plots illustrate the distribution of employment across months:

Figure 4: Box Plots of Employment by Month

Figure 4: Box Plots of Employment by Month

Figure 5: Violin Plots of Employment by Month

Figure 5: Violin Plots of Employment by Month

Analysis: - Variability: Figure 4 (box plots) shows median employment levels and outliers, indicating months with unusual spikes or drops. Figure 5 (violin plots) adds density information, revealing the spread and concentration of employment values. - Seasonal Confirmation: Both plots reinforce the seasonal patterns, with wider distributions in months like February and narrower ones in mid-year, reflecting operational cycles.

6. Discussion

The analysis reveals a multifaceted picture of employment in Canada’s oil and gas extraction industry: - Historical Volatility: Figure 1 underscores the sector’s responsiveness to global oil markets, with notable declines likely tied to price drops (e.g., 2014-2016) and recoveries linked to demand surges. - Seasonality: Figures 2, 4, and 5, alongside Table 1, confirm a consistent seasonal cycle, possibly driven by operational factors like winter drilling or summer maintenance. - Recent Stabilization: Figure 3 and Table 2 suggest a leveling off in recent years, potentially due to technological advancements reducing labor needs or policy shifts stabilizing investment.

Contributing Factors: - Market Dynamics: Fluctuations in crude oil prices directly impact hiring. - Technology: Automation and enhanced extraction methods may reduce workforce demand over time. - Policy: Carbon taxes and renewable energy incentives could influence long-term employment trends.

7. Next Steps

Future analyses could: - Overlay economic indicators (e.g., oil prices, GDP) with employment data to test correlations. - Extend the dataset to include regional breakdowns or non-seasonally adjusted figures for a broader perspective. - Investigate the impact of specific policy changes using event studies.

8. Conclusion

Employment in Canada’s oil and gas extraction industry exhibits significant variability, driven by historical volatility, seasonal cycles, and recent stabilization trends. The visualizations and tables provide a robust foundation for understanding these dynamics, highlighting the need for continued monitoring as economic and regulatory landscapes evolve.

References

Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on LOESS. Journal of Official Statistics, 6(1), 3–73.

  1. [The STL (Seasonal-Trend Decomposition using LOESS) method is a robust and versatile approach to decomposing a time series into three distinct components: trend, seasonal, and remainder (or residual). It was developed by Robert B. Cleveland and colleagues in 1990 and is useful for time series with strong seasonal patterns that may vary over time.]↩︎