Job Market Indicators in Software Development Sector: A Data-Driven Analysis of Poland and the Baltics

Job Market Indicators in Software Development Sector: A Data-Driven Analysis of Poland and the Baltics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Job Market Indicators in Software Development Sector: A Data-Driven Analysis of Poland and the Baltics Dmitrij Żatuchin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5195074/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the dynamics of supply and demand within the software development sector in Poland and the Baltics during 2023, highlighting the underlying tension between talent availability and market needs. The concept of the "IT bench," similar to a sports team's reserves, illustrates how regional companies maintain a readily deployable pool of talent, underscoring the importance of flexibility and rapid response capabilities in today's volatile market. Using data from industry-specific sources, this research provides insights into demand fluctuations for key roles, such as Software Engineering, Tech Project Management, and Design, along with changes in median salaries in response to external economic pressures. Challenges such as wage growth and the impact of political and economic factors on business conditions are explored, providing context to the evolving dynamics of the software development job market. The methodology applies a collective dynamics framework using a 60-day rolling window to compute time-varying correlation matrices of job market indicators, including role-specific demand and salary trends. This analysis reveals significant correlations between job categories, indicating how shifts in demand for one role impact others. For example, the positive correlation between Software Engineering and Design roles reflects their interdependent nature. In contrast, the inverse correlation between Software Engineering and Sales & Business Development points to strategic shifts in hiring priorities. Key findings indicate that the software development industry is adapting to global tech layoffs and economic uncertainties by emphasising strategic talent management and flexible, project-based teams. The demand for developers in key technologies, such as Java, JavaScript, and Python, remains high, albeit with adjustments to salary expectations and employment conditions to better navigate market challenges. This research highlights the importance of a data-driven approach to recruitment and talent strategy, integrating technological tools with human insight to effectively adapt to the evolving market landscape. International Business Microeconomics software development supply and demand talent management economic conditions Poland Baltics Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The software development market in Poland and the Baltic states experienced substantial shifts throughout 2023, largely driven by global events such as the COVID-19 pandemic and the 2022 Russia-Ukraine War. The resulting geopolitical tensions from the war in Ukraine triggered economic disturbances across neighbouring countries, including Poland and the Baltic states, which share strong geographical and economic interdependencies within the European Union. These interdependencies have played a pivotal role in shaping the labor markets, talent mobility, and overall demand for software development professionals in the region (Das & Marjit, 2023; Sherif, 2024). Poland and the Baltic states—Estonia, Latvia, and Lithuania—form an important bloc within the European software landscape. Poland, as a major hub for IT outsourcing, benefits from a large, highly skilled workforce, whereas the Baltic states are known for their agility, innovation, and startup culture (James & Menzies, 2023). These two markets, while distinct, are influenced by similar economic conditions, and their interdependencies are evident in the movement of talent, shared labour policies, and similar responses to the global shifts in the software development sector. The demand for software development talent has been significantly influenced by regional and global crises, leading to both challenges and opportunities in terms of labour supply and workforce dynamics. The interdependencies between supply and demand for software development talent in Poland and the Baltics are shaped by shared labour dynamics and economic strategies. Poland's role as a significant IT outsourcing destination complements the Baltic states' innovation-driven market, resulting in cross-border collaboration and competition for talent (Das & Marjit, 2023). The IT bench concept, where companies maintain a reserve pool of talent for rapid deployment, has become increasingly relevant in both Poland and the Baltics as a response to fluctuating demand. This strategy ensures operational flexibility and allows firms to mitigate risks associated with sudden changes in market needs (James & Menzies, 2023). Recent research by James and Menzies (2023) has highlighted behavioural biases during economic crises that exacerbate market inefficiencies, including a lack of diversification in recruitment channels and an oversupply of certain software roles. These dynamics are evident in the software development market of Poland and the Baltics, where companies have often overestimated the long-term value of surplus talent, leading to a mismatch between demand for specialized roles—such as DevOps Engineers and Cloud Engineers—and an oversupply of generalists like Frontend Developers and JavaScript Engineers (James & Menzies, 2023). The 2023 wave of global tech layoffs, involving companies such as Google, Amazon, and Riot Games, has further complicated the talent landscape in the region (Stringer & Coral, 2023). These layoffs were primarily driven by reduced demand, the need to focus on fewer high-impact projects, and restructuring efforts to streamline operations (Das & Marjit, 2023). While these layoffs were concentrated in markets outside Eastern Europe, their ripple effects were felt in Poland and the Baltics, impacting demand for certain skills and necessitating a reevaluation of talent strategies to ensure competitiveness. In Poland, the number of IT job offers dropped significantly from 32.9 thousand in Q1 2023 to 19.1 thousand in Q4 2023, reflecting the broader impact of global economic pressures on local employment opportunities (Randstad, 2023). The latest data from the Polish Central Statistical Office (GUS, 2024), indicates that the average monthly gross salary in the ICT sector in August of this year was PLN 12,200, marking a 9.1% increase compared to the previous year, against a backdrop of 10.1% inflation. However, it notes that GUS data might not fully represent the IT sector as highly paid specialists tend to work on B2B contracts rather than employment contracts, showing a weakening in this area. In contrast, the Baltic states have observed a steady demand for specific skill sets, particularly in emerging technologies like AI and cybersecurity. However, the supply of such specialized talent remains constrained, highlighting a common issue between the two regions: a misalignment between the skills available and those required by an evolving tech sector (Ogburn 1957, James & Menzies, 2023). To address these challenges, companies have increasingly adopted "benching" strategies, akin to the portfolio optimization approaches used during financial market crises (James & Menzies, 2023). By creating flexible, on-demand talent pools, businesses in Poland and the Baltics can better manage workforce availability, respond quickly to project needs, and reduce risks associated with hiring freezes or sudden market shifts. This strategic pivot is essential in navigating the current volatility in the software development labor market, where both technical skills and soft competencies are critical for project success. This study aims to provide a comprehensive understanding of the interdependencies between supply and demand within the software development sector in Poland and the Baltics during 02.2023–02.2024. By leveraging insights from collective dynamics, cultural lag theory, and current market analyses, the study explores how global economic conditions, talent management strategies, and regional industry specifics shape the evolving software development workforce. The findings are expected to contribute to developing effective recruitment strategies that are both data-driven and flexible, enabling companies to navigate market uncertainties while maintaining operational efficiency. 2 Methodology This study employed a mixed-method approach to analyze the talent dynamics within the software development sector across Poland and the Baltics. This analysis draws from diverse data sources, including quantitative data from MeetFrank, dedicated communication channels among demand generators and suppliers in the CEE region, and reports from the Software Development Association Poland (SoDA, 2023). These datasets provide both broad regional insights and specific trends within the talent market, encompassing salary dynamics, talent supply, and demand for different software development roles. Where necessary, data was anonymized to ensure privacy before further analysis. 2.1 Data Collection The primary data sources for this analysis include: MeetFrank: A talent platform providing real-time data on job market trends, salaries, and demand across key software development roles. Industry Communication Channels: Slack.com channels of regional communities, facilitating communication between demand generators (hiring companies) and suppliers (freelancers and agencies). These communications offered qualitative insights into market sentiments and recruitment strategies. Software Development Association Poland (SoDA) Reports: Industry reports from Q3 and Q4 2023, which provided additional context on the market, including the challenges and opportunities faced by IT service providers in Poland and the Baltics. The analysis incorporated both quantitative and qualitative data. The quantitative analysis utilized numerical metrics such as talent availability, salary changes, and job offer trends, while the qualitative analysis was based on Slack communications and industry reports to contextualize market dynamics. 2.2 Analytical Framework The study applied a theoretical framework based on strategic talent management and collective market dynamics. James and Menzies (2023) provided a comprehensive mathematical framework to analyze the correlation structure of talent supply and demand. Their framework, initially developed for financial crises, was adapted here to explore correlations between different roles within the software development job market. A 60-day rolling window was employed to compute time-varying correlation matrices, allowing us to measure coherence and market interdependencies during economic fluctuations. We used the following formula for time-varying correlation: $$\:\varPsi\:\left(t\right)=\frac{1}{S}\underset{\_}{R}\left(t\right){\underset{\_}{R}}^{T}\left(t\right)$$ Where \(\:\underset{\_}{R}\left(t\right)\) represents standardized returns (in our case, changes in job market indicators) and \(\:S\) is the window size of 60 days. 2.3 Data Processing and Analysis The analysis relied heavily on Python, which is well-suited for data analysis and manipulation due to its rich ecosystem of libraries. The main steps included: Data Transformation: Raw data from MeetFrank and Slack communications were transformed into structured formats using Pandas, enabling systematic analysis. Tuples were expanded into separate columns representing talent count, changes in talent count (delta), and average salary to allow granular analysis. Time-Series Analysis: Datetime modules were used to parse date ranges into Python datetime objects. This enabled us to conduct time-series analysis, identifying trends in salary dynamics and talent availability over Q4 2023. Statistical Computations: Calculations such as median, standard deviation, and trend analysis were performed using Pandas and NumPy. Furthermore, scipy.stats was employed for regression analysis to evaluate trends over time. Text Pattern Analysis: The re (regular expressions) module was used to extract structured information from unstructured Slack communications, identifying recurring patterns in discussions around supply and demand. Currency Conversion: Custom Python functions were developed to parse date ranges, extract currency rates, and perform PLN to EUR conversions. Monthly average exchange rates were used for reliable cross-period salary comparisons. Visualization: Visual representations of trends were generated using Matplotlib. Figures included treemaps of supply and demand proportions and line graphs of salary trends, providing intuitive insights into the job market dynamics. 2.4 Mixed-Methods Approach The mixed-methods approach was designed to provide a comprehensive understanding of the software development job market in Poland and the Baltics: 1. Quantitative Analysis: Numerical data from MeetFrank allowed us to compute trends in talent availability and salary changes. This included analysis of surplus talent, fluctuations in demand for specific roles, and identifying patterns over the quarter. The correlation matrix analysis applied from James and Menzies (2023) offered insights into the interdependencies between different job categories. For instance, we observed a positive correlation between Software Engineering and Design roles, highlighting inter-team dependencies. 2. Qualitative Insights: Slack communications provided context to the quantitative findings. For example, discussions on market sentiments and recruitment challenges provided valuable insights into why certain roles experienced surpluses while others faced shortages. Reports from SoDA helped verify these findings against broader industry trends, offering a holistic view of the software development market conditions during the given period. 2.5 Machine Learning for Predictive Analysis The final step in our methodology involved applying machine learning algorithms to predict future trends in talent supply and demand. We used clustering techniques to segment roles by skill demand, while linear regression models helped forecast changes in salary structures. These machine learning models were crucial in identifying potential mismatches in talent supply, thus providing actionable insights for strategic talent management. Through this rigorous methodological approach, combining quantitative data analysis with qualitative insights, we have produced a nuanced understanding of the software development job market dynamics in Poland and the Baltics. Python proved instrumental in transforming raw data into actionable insights, demonstrating its invaluable role in contemporary data science research. 3 Results Our analysis revealed several key findings that shed light on the current state of the software development job market in Poland and the Baltics. These results encompass three main areas: ( 1 ) the observed surplus in talent supply and its implications for demand dynamics, ( 2 ) trends in salary levels across different job categories, and ( 3 ) correlations between various job roles that indicate underlying market interdependencies. Each of these areas provides crucial insights into the evolving landscape of software development employment in the region. 3.1 Surplus in Talent Supply and Demand Dynamics Figure 1 illustrates the comparison between offered and supply prices for various job roles in the software development sector. The roles, such as "Senior Java," "Mid JavaScript," and "Mid PHP," represent different levels of expertise (senior, mid, junior) and specific technology stacks. The blue bars represent the average prices offered by companies, while the red bars indicate the average supply prices for each role. The width of each bar is proportional to the "Demand/Supply" ratio for the corresponding role, allowing for a clear visual comparison of market demand versus talent supply. Labels on each bar provide detailed information about the average price (in EUR) and the percentage difference in rates, allowing readers to easily observe the discrepancies between the offered and supply values across different job categories. The figure shows that roles such as "Mid Backend" and "Senior Python" exhibit significant variations between the offered and supply prices, indicating potential market imbalances for those specific skill sets. This visualization helps to understand both the pricing dynamics and the proportional demand for different roles, providing a comprehensive view of how companies' offerings align with the availability of specific talent types in the software development industry. Explanation of Seniority Levels Senior: Highly experienced professionals with extensive skills in their respective fields, often responsible for leading projects or mentoring less experienced team members. Mid: Professionals with a moderate level of experience who can work independently but may require guidance on complex tasks. Junior: Entry-level professionals with limited experience who often work under the direct supervision of more experienced colleagues. From Fig. 1 , several sector-specific observations can be made: Opportunities in Frontend Development Roles: The analysis of Frontend Developer roles, particularly at the senior and mid-levels, suggests a favorable market for employers. The supply price for these roles ("Senior Frontend: €31.05" and "Mid Frontend: €31.74") is slightly lower than the offered prices (€35.52 and €36.76 respectively). This indicates an oversupply in these roles, creating a buyer's market that provides companies the opportunity to hire top talent at competitive rates. Mixed Stability in Java and Python Developer Markets: The market for Java developers remains relatively stable. The offered prices for senior and mid-level Java roles indicate manageable levels of price difference, suggesting a balanced relationship between supply and demand. However, for Python developers, particularly at the senior level, there is a noticeable gap between the supply price (€46.86) and the offered price (€34.29). This gap reflects a potential mismatch, indicating that companies may be struggling to find qualified talent at acceptable price points in senior-level Python roles. Variability in Profit Margins for Commodity Roles: For commodity roles like Java, the relationship between offered and supply prices shows potential for profit margins, particularly in mid-level roles, suggesting that companies can still leverage cost efficiency. However, for roles like Python, particularly at the senior level, the supply price exceeds the offered price by a substantial margin, indicating constrained profit opportunities. This variability implies that profit margins are role-specific and not uniformly applicable across the entire tech sector. Thus, companies need to adjust their hiring strategies based on specific supply-demand conditions for each role. At the end of the year, we observed a significant surplus in talent supply, peaking at 200% relative to available job postings. This supply surplus is presented in Fig. 2 , where you can see that the available talent consistently outpaced the number of unique job offers. The 200% surplus means that the number of available candidates was twice the number of open positions, illustrating an imbalance in the software development job market in Poland and the Baltics. Explanation of Terms in Fig. 2 : Ask (Demand): Represents the total number of specific job requests made by companies during the analyzed period. This metric helps in understanding the demand for particular skills. Own (Supply): Represents the total number of distinct talent postings of various roles during the analyzed period, providing insights into the availability of talent in the market. 3.2 Observed Salary Trends and Economic Impacts Figure 3 illustrates salary levels across categories, including median values and changes over time. The 11% increase in available talent across all positions (Tech, PM, Sales, Design, Marketing) indicates a significant shift towards a more saturated job landscape, suggesting that companies have an increased capacity to fill roles efficiently. This increased supply, however, brings with it challenges related to competitive salary offerings and job security for professionals in the market. In the Fig. 3 , the delta symbol (Δ) represents the percentage change in either the median salary or the number of talents (i.e., individuals available or interested in the job category) over a specified period. The stagnation in wages amidst an overflow of available talent suggests cautious corporate spending and a recalibration of how companies value specific roles during times of economic uncertainty. Figure 4 presents the weekly average salary in EUR by job category. Salaries have generally shown little to no movement, with a few exceptions: Marketing saw a 15% decrease starting in October 2023. Sales and Tech roles experienced a marginal 1% increase. 3.3 Correlation Analysis of Job Categories The correlation matrix (Table 1 ) helps to understand the relationships between different job roles in the software development market. The following key interdependencies were observed: Positive Correlation between Software Engineering and Design Roles (0.258): This suggests a strategic interdependence between technical and creative roles, as projects often require both skill sets for successful execution. Inverse Relationship Between Technical and Sales Roles (-0.179): The correlation matrix reveals an inverse relationship, implying that when companies prioritize product development (technical roles), they may reduce their focus on market expansion (sales roles). Weak Correlations for Marketing & PR: The weak correlation between Marketing & PR and other roles indicates that these positions are more influenced by external market conditions than by internal project timelines. Table 1 Correlation Matrix of Software Development Job Roles (Source: Author’s own study) Software_Engineering Tech_Project_Management Marketing_PR Design Sales_BD Software_Engineering 1 -0.1556 -0.1405 0.2586 -0.1716 Tech_Project_Management -0.1556 1 -0.0137 0.0203 0.1570 Marketing_PR -0.1405 -0.0137 1 -0.1654 0.0937 Design 0.2586 0.0203 -0.1654 1 -0.0428 Sales_BD -0.1790 0.1570 0.0933 -0.0428 1 4 Discussion 4.1 Implications of Surplus in Talent Supply The observed 200% surplus in talent supply highlights a pronounced imbalance in the software development job market across Poland and the Baltics, which resonates with global trends identified by Derler and Winlaw (2023). As noted by Chhinzer (2023), organizations within sectors experiencing employment decline tend to favor a cost-containment approach during layoffs, whereas those in growth sectors emphasize preserving the employee-employer relationship. This suggests that in the current scenario, the surplus talent provides an opportunity for firms in the region to leverage workforce adjustments for greater cost efficiency while ensuring stability in critical roles. This surplus challenges traditional models of talent management (Collings & Mellahi, 2009) and necessitates a reevaluation of strategic human resource practices in the tech sector. The shift in bargaining power towards employers, while potentially leading to wage suppression (Farndale et al., 2010), also presents unique opportunities for strategic talent acquisition and long-term organizational enhancement (Blass, 2007). 4.2 Correlation Insights The correlation matrix analysis (Table 1 ) highlights several critical interdependencies between roles, which have significant implications for recruitment and team dynamics: Aligned Hiring of Technical and Design Roles: The positive correlation (0.258) between Software Engineering and Design roles suggests a strategic need for coordinated hiring. Aligning these roles fosters effective cross-functional collaboration, as Vaiman and Holden (2011) have pointed out, emphasizing that diverse skill sets within a team lead to greater innovation. Mellahi and Collings (2010) also underscore that the success of software projects hinges on integrating technical functionality with user-centric design—a synergy critical to product development and market success. Cyclical Nature of Technical vs. Sales Roles: The observed inverse relationship (-0.179) between Sales & Business Development and Software Engineering roles implies that companies might need to adopt a cyclical hiring approach to better align with evolving business strategies—focusing on either product development or market expansion. This aligns well with the insights of James and Menzies (2023), who compared such recruitment patterns to portfolio strategies in financial markets, where firms shift focus based on economic conditions. Das and Marjit (2023) also found that global economic shifts prompt firms to switch between growth and cost-control, further influencing cyclical hiring trends. Marketing Independence: The weak correlation observed between Marketing & PR roles and other positions underscores the independence of marketing activities within the software development industry. As highlighted by Maruping and Matook (2020), marketing functions are primarily influenced by external campaigns, product launches, and promotional events, rather than internal development cycles. This independent operation reflects their alignment with market demands rather than the immediate needs of project development, echoing earlier observations by Burke (1996). 4.3 Economic Context and Talent Retention Strategies The observed stagnation in salary levels across the software development job market (Figs. 2 and 3 ) reflects broader economic trends. Marketing roles experienced a 15% decrease in salaries, illustrating a shift towards reallocating budgets from non-core to core activities—a pattern that has been observed in other studies, including Hansen (2007). Conversely, the relative stability of Tech and Sales roles points to their criticality to long-term business success, even amidst economic uncertainty (Farndale et al., 2010). The increase in available talent without corresponding salary increases implies a focus on cost optimization, resonating with the labor market strategies detailed by Blass (2007). The broader economic context, particularly the impact of global tech layoffs (TechCrunch), has significantly influenced talent availability and employment dynamics in Poland and the Baltics. Outsourcing trends discussed by Das and Marjit (2023) have further intensified competition within the local talent market. As firms increasingly adopt flexible workforce models akin to the on-demand talent pools described by James and Menzies (2023), they are better positioned to navigate economic fluctuations while optimizing operational efficiency. Moreover, Derler & Winlaw (2023) emphasizes that while the current surplus presents an opportunity to attract high-quality talent, retention will require more than competitive salaries. Effective retention strategies should include career growth opportunities, development programs, and a focus on work-life balance—elements essential for long-term employee satisfaction and organizational success. 5 Conclusion and Future Research This study contributes to the literature on talent management in the software development sector by providing a data-driven analysis of market dynamics in Poland and the Baltics during a period of significant global economic uncertainty. By integrating quantitative trend analysis with qualitative insights from industry communications, we offer a nuanced understanding of how regional software development markets respond to global pressures. However, our study is limited by its focus on a specific time period and geographic region, which may limit the generalizability of findings to other contexts or time frames. The results of this study offer several practical recommendations for industry practitioners and decision-makers regarding talent management: Cross-Disciplinary Hiring for Enhanced Collaboration: Coordinating the recruitment of technical and creative roles, as indicated by the positive correlation between Software Engineering and Design, can significantly enhance cross-functional collaboration and project innovation. Collings, Scullion, and Vaiman (2011) similarly emphasized that integrating diverse competencies within a team improves overall output and boosts project success, particularly in software development where technical and user interface elements must align. Adopting Cyclical Hiring Aligned with Business Phases: The identified cyclical pattern between technical and sales roles highlights the importance of adapting hiring strategies to align with the company’s current phase—whether it be a focus on product innovation or market expansion. Borkowska (2005) supports this approach, noting that aligning workforce adjustments with business objectives enhances organizational flexibility. Furthermore, the role of macroeconomic trends, as highlighted by Das and Marjit (2023), emphasizes the need for an agile talent strategy to remain resilient during economic instability. Maintaining Stability in Project Management Roles: The weak correlation between Tech Project Management and other roles underscores the necessity of maintaining a stable project management workforce, irrespective of variations in other hiring domains. Listwan (2009) similarly argued for the critical role of stable leadership in guiding both technical and business teams, ensuring consistent performance during periods of growth and contraction. Future research should focus on: The limitations of the study by expanding the temporal and geographic scope of analysis, potentially incorporating comparative studies across different regions or economic cycles The effectiveness of comprehensive retention strategies amidst economic volatility, expanding on theories by Collings and Mellahi (2009) and addressing current challenges in balancing talent availability with business sustainability. References Blass E (2007) Talent management: Maximizing talent for future performance. Routledge, London Borkowska S (2005) Zarządzanie talentami. IPiSS, Seria Studia i Monografie, Warszawa Burke A (1997) Developing high-potential employees in the new business reality. 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Randstad Industry Report, Q4 2023 Sherif A (2024) Number of tech employees laid off worldwide from 2020 to 2023 by company. [online] https://www.statista.com/statistics/1127080/worldwide-tech-layoffs-covid-19-biggest/ [Accessed 14 May 2024] Software Development Association Poland (2023) Barometr Nastrojów SoDA Q3 2023, Barometr Nastrojów IT Q4 2023 . [online] https://sodapl.com/raporty/ [Accessed 22 February 2024] Stringer A, Corrall C (2024) A comprehensive list of 2024 tech layoffs. [online] https://techcrunch.com/2024/02/21/tech-layoffs-2023-list/ [Accessed 14 May 2024] Vale R (2023) Forecasting the 2022-23 tech layoffs using epidemiological models. https://doi.org/10.48550/arXiv.2305.05210 Additional Declarations The authors declare no competing interests. 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study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5195074/v1/b4d313ed93a5422ccf081bdd.png"},{"id":65883176,"identity":"51bcecc0-06a7-47f8-b888-62221e24f5d8","added_by":"auto","created_at":"2024-10-04 02:44:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBi-weekly Trend of Demand and Supply from February 2023 to February 2024.\u003cbr\u003e\n Source:\u003c/strong\u003e Author’s own study\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5195074/v1/886cc372d397a978e8513f8a.png"},{"id":65883376,"identity":"7ac85dbb-749c-467f-9d7a-3abea2de502a","added_by":"auto","created_at":"2024-10-04 02:52:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSalary Level by Category with Median and Change\u003cbr\u003e\n Source:\u003c/strong\u003eAuthor’s own study\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5195074/v1/125c57b5794eca1c757072e5.png"},{"id":65883179,"identity":"1be308ab-91ed-41da-960d-9bdd4b3914c8","added_by":"auto","created_at":"2024-10-04 02:44:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeekly average salary in EUR by job category\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Author’s own study\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5195074/v1/2cbfca23e7c44f1c99bd448a.png"},{"id":65883831,"identity":"3e97d555-5730-4b6d-9660-8ff2e0b2384f","added_by":"auto","created_at":"2024-10-04 03:00:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":663813,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5195074/v1/1b11c581-76d4-49f8-b0a5-e6fdb96645e5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eJob Market Indicators in Software Development Sector: A Data-Driven Analysis of Poland and the Baltics\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe software development market in Poland and the Baltic states experienced substantial shifts throughout 2023, largely driven by global events such as the COVID-19 pandemic and the 2022 Russia-Ukraine War. The resulting geopolitical tensions from the war in Ukraine triggered economic disturbances across neighbouring countries, including Poland and the Baltic states, which share strong geographical and economic interdependencies within the European Union. These interdependencies have played a pivotal role in shaping the labor markets, talent mobility, and overall demand for software development professionals in the region (Das \u0026amp; Marjit, 2023; Sherif, 2024).\u003c/p\u003e \u003cp\u003ePoland and the Baltic states\u0026mdash;Estonia, Latvia, and Lithuania\u0026mdash;form an important bloc within the European software landscape. Poland, as a major hub for IT outsourcing, benefits from a large, highly skilled workforce, whereas the Baltic states are known for their agility, innovation, and startup culture (James \u0026amp; Menzies, 2023). These two markets, while distinct, are influenced by similar economic conditions, and their interdependencies are evident in the movement of talent, shared labour policies, and similar responses to the global shifts in the software development sector. The demand for software development talent has been significantly influenced by regional and global crises, leading to both challenges and opportunities in terms of labour supply and workforce dynamics.\u003c/p\u003e \u003cp\u003eThe interdependencies between supply and demand for software development talent in Poland and the Baltics are shaped by shared labour dynamics and economic strategies. Poland's role as a significant IT outsourcing destination complements the Baltic states' innovation-driven market, resulting in cross-border collaboration and competition for talent (Das \u0026amp; Marjit, 2023). The IT bench concept, where companies maintain a reserve pool of talent for rapid deployment, has become increasingly relevant in both Poland and the Baltics as a response to fluctuating demand. This strategy ensures operational flexibility and allows firms to mitigate risks associated with sudden changes in market needs (James \u0026amp; Menzies, 2023).\u003c/p\u003e \u003cp\u003eRecent research by James and Menzies (2023) has highlighted behavioural biases during economic crises that exacerbate market inefficiencies, including a lack of diversification in recruitment channels and an oversupply of certain software roles. These dynamics are evident in the software development market of Poland and the Baltics, where companies have often overestimated the long-term value of surplus talent, leading to a mismatch between demand for specialized roles\u0026mdash;such as DevOps Engineers and Cloud Engineers\u0026mdash;and an oversupply of generalists like Frontend Developers and JavaScript Engineers (James \u0026amp; Menzies, 2023).\u003c/p\u003e \u003cp\u003eThe 2023 wave of global tech layoffs, involving companies such as Google, Amazon, and Riot Games, has further complicated the talent landscape in the region (Stringer \u0026amp; Coral, 2023). These layoffs were primarily driven by reduced demand, the need to focus on fewer high-impact projects, and restructuring efforts to streamline operations (Das \u0026amp; Marjit, 2023). While these layoffs were concentrated in markets outside Eastern Europe, their ripple effects were felt in Poland and the Baltics, impacting demand for certain skills and necessitating a reevaluation of talent strategies to ensure competitiveness.\u003c/p\u003e \u003cp\u003eIn Poland, the number of IT job offers dropped significantly from 32.9 thousand in Q1 2023 to 19.1 thousand in Q4 2023, reflecting the broader impact of global economic pressures on local employment opportunities (Randstad, 2023). The latest data from the Polish Central Statistical Office (GUS, 2024), indicates that the average monthly gross salary in the ICT sector in August of this year was PLN 12,200, marking a 9.1% increase compared to the previous year, against a backdrop of 10.1% inflation. However, it notes that GUS data might not fully represent the IT sector as highly paid specialists tend to work on B2B contracts rather than employment contracts, showing a weakening in this area. In contrast, the Baltic states have observed a steady demand for specific skill sets, particularly in emerging technologies like AI and cybersecurity. However, the supply of such specialized talent remains constrained, highlighting a common issue between the two regions: a misalignment between the skills available and those required by an evolving tech sector (Ogburn 1957, James \u0026amp; Menzies, 2023). To address these challenges, companies have increasingly adopted \"benching\" strategies, akin to the portfolio optimization approaches used during financial market crises (James \u0026amp; Menzies, 2023). By creating flexible, on-demand talent pools, businesses in Poland and the Baltics can better manage workforce availability, respond quickly to project needs, and reduce risks associated with hiring freezes or sudden market shifts.\u003c/p\u003e \u003cp\u003eThis strategic pivot is essential in navigating the current volatility in the software development labor market, where both technical skills and soft competencies are critical for project success.\u003c/p\u003e \u003cp\u003eThis study aims to provide a comprehensive understanding of the interdependencies between supply and demand within the software development sector in Poland and the Baltics during 02.2023\u0026ndash;02.2024. By leveraging insights from collective dynamics, cultural lag theory, and current market analyses, the study explores how global economic conditions, talent management strategies, and regional industry specifics shape the evolving software development workforce. The findings are expected to contribute to developing effective recruitment strategies that are both data-driven and flexible, enabling companies to navigate market uncertainties while maintaining operational efficiency.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cp\u003eThis study employed a mixed-method approach to analyze the talent dynamics within the software development sector across Poland and the Baltics. This analysis draws from diverse data sources, including quantitative data from MeetFrank, dedicated communication channels among demand generators and suppliers in the CEE region, and reports from the Software Development Association Poland (SoDA, 2023). These datasets provide both broad regional insights and specific trends within the talent market, encompassing salary dynamics, talent supply, and demand for different software development roles. Where necessary, data was anonymized to ensure privacy before further analysis.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Data Collection\u003c/h2\u003e\n\u003cp\u003eThe primary data sources for this analysis include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eMeetFrank: A talent platform providing real-time data on job market trends, salaries, and demand across key software development roles.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIndustry Communication Channels: Slack.com channels of regional communities, facilitating communication between demand generators (hiring companies) and suppliers (freelancers and agencies). These communications offered qualitative insights into market sentiments and recruitment strategies.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSoftware Development Association Poland (SoDA) Reports: Industry reports from Q3 and Q4 2023, which provided additional context on the market, including the challenges and opportunities faced by IT service providers in Poland and the Baltics.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe analysis incorporated both quantitative and qualitative data. The quantitative analysis utilized numerical metrics such as talent availability, salary changes, and job offer trends, while the qualitative analysis was based on Slack communications and industry reports to contextualize market dynamics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Analytical Framework\u003c/h2\u003e\n\u003cp\u003eThe study applied a theoretical framework based on strategic talent management and collective market dynamics. James and Menzies (2023) provided a comprehensive mathematical framework to analyze the correlation structure of talent supply and demand. Their framework, initially developed for financial crises, was adapted here to explore correlations between different roles within the software development job market. A 60-day rolling window was employed to compute time-varying correlation matrices, allowing us to measure coherence and market interdependencies during economic fluctuations.\u003c/p\u003e\n\u003cp\u003eWe used the following formula for time-varying correlation:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:\\varPsi\\:\\left(t\\right)=\\frac{1}{S}\\underset{\\_}{R}\\left(t\\right){\\underset{\\_}{R}}^{T}\\left(t\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\underset{\\_}{R}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e represents standardized returns (in our case, changes in job market indicators) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\)\u003c/span\u003e\u003c/span\u003e is the window size of 60 days.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Data Processing and Analysis\u003c/h2\u003e\n\u003cp\u003eThe analysis relied heavily on Python, which is well-suited for data analysis and manipulation due to its rich ecosystem of libraries. The main steps included:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eData Transformation: Raw data from MeetFrank and Slack communications were transformed into structured formats using Pandas, enabling systematic analysis. Tuples were expanded into separate columns representing talent count, changes in talent count (delta), and average salary to allow granular analysis.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTime-Series Analysis: Datetime modules were used to parse date ranges into Python datetime objects. This enabled us to conduct time-series analysis, identifying trends in salary dynamics and talent availability over Q4 2023.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStatistical Computations: Calculations such as median, standard deviation, and trend analysis were performed using Pandas and NumPy. Furthermore, scipy.stats was employed for regression analysis to evaluate trends over time.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eText Pattern Analysis: The re (regular expressions) module was used to extract structured information from unstructured Slack communications, identifying recurring patterns in discussions around supply and demand.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCurrency Conversion: Custom Python functions were developed to parse date ranges, extract currency rates, and perform PLN to EUR conversions. Monthly average exchange rates were used for reliable cross-period salary comparisons.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVisualization: Visual representations of trends were generated using Matplotlib. Figures included treemaps of supply and demand proportions and line graphs of salary trends, providing intuitive insights into the job market dynamics.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Mixed-Methods Approach\u003c/h2\u003e\n\u003cp\u003eThe mixed-methods approach was designed to provide a comprehensive understanding of the software development job market in Poland and the Baltics:\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. Quantitative Analysis:\u003c/h3\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003e\n\u003cp\u003eNumerical data from MeetFrank allowed us to compute trends in talent availability and salary changes. This included analysis of surplus talent, fluctuations in demand for specific roles, and identifying patterns over the quarter.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe correlation matrix analysis applied from James and Menzies (2023) offered insights into the interdependencies between different job categories. For instance, we observed a positive correlation between Software Engineering and Design roles, highlighting inter-team dependencies.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e2. Qualitative Insights:\u003c/h3\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003e\n\u003cp\u003eSlack communications provided context to the quantitative findings. For example, discussions on market sentiments and recruitment challenges provided valuable insights into why certain roles experienced surpluses while others faced shortages.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReports from SoDA helped verify these findings against broader industry trends, offering a holistic view of the software development market conditions during the given period.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Machine Learning for Predictive Analysis\u003c/h2\u003e\n\u003cp\u003eThe final step in our methodology involved applying machine learning algorithms to predict future trends in talent supply and demand. We used clustering techniques to segment roles by skill demand, while linear regression models helped forecast changes in salary structures. These machine learning models were crucial in identifying potential mismatches in talent supply, thus providing actionable insights for strategic talent management.\u003c/p\u003e\n\u003cp\u003eThrough this rigorous methodological approach, combining quantitative data analysis with qualitative insights, we have produced a nuanced understanding of the software development job market dynamics in Poland and the Baltics. Python proved instrumental in transforming raw data into actionable insights, demonstrating its invaluable role in contemporary data science research.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eOur analysis revealed several key findings that shed light on the current state of the software development job market in Poland and the Baltics. These results encompass three main areas: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) the observed surplus in talent supply and its implications for demand dynamics, (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) trends in salary levels across different job categories, and (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) correlations between various job roles that indicate underlying market interdependencies. Each of these areas provides crucial insights into the evolving landscape of software development employment in the region.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Surplus in Talent Supply and Demand Dynamics\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the comparison between offered and supply prices for various job roles in the software development sector. The roles, such as \"Senior Java,\" \"Mid JavaScript,\" and \"Mid PHP,\" represent different levels of expertise (senior, mid, junior) and specific technology stacks.\u003c/p\u003e\n\u003cp\u003eThe blue bars represent the average prices offered by companies, while the red bars indicate the average supply prices for each role. The width of each bar is proportional to the \"Demand/Supply\" ratio for the corresponding role, allowing for a clear visual comparison of market demand versus talent supply.\u003c/p\u003e\n\u003cp\u003eLabels on each bar provide detailed information about the average price (in EUR) and the percentage difference in rates, allowing readers to easily observe the discrepancies between the offered and supply values across different job categories. The figure shows that roles such as \"Mid Backend\" and \"Senior Python\" exhibit significant variations between the offered and supply prices, indicating potential market imbalances for those specific skill sets.\u003c/p\u003e\n\u003cp\u003eThis visualization helps to understand both the pricing dynamics and the proportional demand for different roles, providing a comprehensive view of how companies' offerings align with the availability of specific talent types in the software development industry.\u003c/p\u003e\n\u003cp\u003eExplanation of Seniority Levels\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSenior: Highly experienced professionals with extensive skills in their respective fields, often responsible for leading projects or mentoring less experienced team members.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMid: Professionals with a moderate level of experience who can work independently but may require guidance on complex tasks.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eJunior: Entry-level professionals with limited experience who often work under the direct supervision of more experienced colleagues.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, several sector-specific observations can be made:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003e\n\u003cp\u003eOpportunities in Frontend Development Roles: The analysis of Frontend Developer roles, particularly at the senior and mid-levels, suggests a favorable market for employers. The supply price for these roles (\"Senior Frontend: \u0026euro;31.05\" and \"Mid Frontend: \u0026euro;31.74\") is slightly lower than the offered prices (\u0026euro;35.52 and \u0026euro;36.76 respectively). This indicates an oversupply in these roles, creating a buyer's market that provides companies the opportunity to hire top talent at competitive rates.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMixed Stability in Java and Python Developer Markets: The market for Java developers remains relatively stable. The offered prices for senior and mid-level Java roles indicate manageable levels of price difference, suggesting a balanced relationship between supply and demand. However, for Python developers, particularly at the senior level, there is a noticeable gap between the supply price (\u0026euro;46.86) and the offered price (\u0026euro;34.29). This gap reflects a potential mismatch, indicating that companies may be struggling to find qualified talent at acceptable price points in senior-level Python roles.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eVariability in Profit Margins for Commodity Roles: For commodity roles like Java, the relationship between offered and supply prices shows potential for profit margins, particularly in mid-level roles, suggesting that companies can still leverage cost efficiency. However, for roles like Python, particularly at the senior level, the supply price exceeds the offered price by a substantial margin, indicating constrained profit opportunities. This variability implies that profit margins are role-specific and not uniformly applicable across the entire tech sector. Thus, companies need to adjust their hiring strategies based on specific supply-demand conditions for each role.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the end of the year, we observed a significant surplus in talent supply, peaking at 200% relative to available job postings. This supply surplus is presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, where you can see that the available talent consistently outpaced the number of unique job offers. The 200% surplus means that the number of available candidates was twice the number of open positions, illustrating an imbalance in the software development job market in Poland and the Baltics.\u003c/p\u003e\n\u003cp\u003eExplanation of Terms in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAsk (Demand): Represents the total number of specific job requests made by companies during the analyzed period. This metric helps in understanding the demand for particular skills.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOwn (Supply): Represents the total number of distinct talent postings of various roles during the analyzed period, providing insights into the availability of talent in the market.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Observed Salary Trends and Economic Impacts\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates salary levels across categories, including median values and changes over time. The 11% increase in available talent across all positions (Tech, PM, Sales, Design, Marketing) indicates a significant shift towards a more saturated job landscape, suggesting that companies have an increased capacity to fill roles efficiently. This increased supply, however, brings with it challenges related to competitive salary offerings and job security for professionals in the market. In the Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the delta symbol (\u0026Delta;) represents the percentage change in either the median salary or the number of talents (i.e., individuals available or interested in the job category) over a specified period.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe stagnation in wages amidst an overflow of available talent suggests cautious corporate spending and a recalibration of how companies value specific roles during times of economic uncertainty.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the weekly average salary in EUR by job category. Salaries have generally shown little to no movement, with a few exceptions:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003e\n\u003cp\u003eMarketing saw a 15% decrease starting in October 2023.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSales and Tech roles experienced a marginal 1% increase.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Correlation Analysis of Job Categories\u003c/h2\u003e\n\u003cp\u003eThe correlation matrix (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) helps to understand the relationships between different job roles in the software development market.\u003c/p\u003e\n\u003cp\u003eThe following key interdependencies were observed:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003ePositive Correlation between Software Engineering and Design Roles (0.258): This suggests a strategic interdependence between technical and creative roles, as projects often require both skill sets for successful execution.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInverse Relationship Between Technical and Sales Roles (-0.179): The correlation matrix reveals an inverse relationship, implying that when companies prioritize product development (technical roles), they may reduce their focus on market expansion (sales roles).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWeak Correlations for Marketing \u0026amp; PR: The weak correlation between Marketing \u0026amp; PR and other roles indicates that these positions are more influenced by external market conditions than by internal project timelines.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation Matrix of Software Development Job Roles\u003c/p\u003e\n\u003cdiv class=\"Credit\"\u003e\n\u003cp\u003e(Source: Author\u0026rsquo;s own study)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSoftware_Engineering\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTech_Project_Management\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMarketing_PR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDesign\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSales_BD\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSoftware_Engineering\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1716\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTech_Project_Management\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1570\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarketing_PR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0937\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDesign\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0428\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSales_BD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1790\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1570\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0933\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Implications of Surplus in Talent Supply\u003c/h2\u003e \u003cp\u003eThe observed 200% surplus in talent supply highlights a pronounced imbalance in the software development job market across Poland and the Baltics, which resonates with global trends identified by Derler and Winlaw (2023). As noted by Chhinzer (2023), organizations within sectors experiencing employment decline tend to favor a cost-containment approach during layoffs, whereas those in growth sectors emphasize preserving the employee-employer relationship. This suggests that in the current scenario, the surplus talent provides an opportunity for firms in the region to leverage workforce adjustments for greater cost efficiency while ensuring stability in critical roles.\u003c/p\u003e \u003cp\u003eThis surplus challenges traditional models of talent management (Collings \u0026amp; Mellahi, 2009) and necessitates a reevaluation of strategic human resource practices in the tech sector. The shift in bargaining power towards employers, while potentially leading to wage suppression (Farndale et al., 2010), also presents unique opportunities for strategic talent acquisition and long-term organizational enhancement (Blass, 2007).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Correlation Insights\u003c/h2\u003e \u003cp\u003eThe correlation matrix analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) highlights several critical interdependencies between roles, which have significant implications for recruitment and team dynamics:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAligned Hiring of Technical and Design Roles:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe positive correlation (0.258) between Software Engineering and Design roles suggests a strategic need for coordinated hiring. Aligning these roles fosters effective cross-functional collaboration, as Vaiman and Holden (2011) have pointed out, emphasizing that diverse skill sets within a team lead to greater innovation. Mellahi and Collings (2010) also underscore that the success of software projects hinges on integrating technical functionality with user-centric design\u0026mdash;a synergy critical to product development and market success.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCyclical Nature of Technical vs. Sales Roles:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe observed inverse relationship (-0.179) between Sales \u0026amp; Business Development and Software Engineering roles implies that companies might need to adopt a cyclical hiring approach to better align with evolving business strategies\u0026mdash;focusing on either product development or market expansion. This aligns well with the insights of James and Menzies (2023), who compared such recruitment patterns to portfolio strategies in financial markets, where firms shift focus based on economic conditions. Das and Marjit (2023) also found that global economic shifts prompt firms to switch between growth and cost-control, further influencing cyclical hiring trends.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMarketing Independence:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe weak correlation observed between Marketing \u0026amp; PR roles and other positions underscores the independence of marketing activities within the software development industry. As highlighted by Maruping and Matook (2020), marketing functions are primarily influenced by external campaigns, product launches, and promotional events, rather than internal development cycles. This independent operation reflects their alignment with market demands rather than the immediate needs of project development, echoing earlier observations by Burke (1996).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Economic Context and Talent Retention Strategies\u003c/h2\u003e \u003cp\u003eThe observed stagnation in salary levels across the software development job market (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reflects broader economic trends. Marketing roles experienced a 15% decrease in salaries, illustrating a shift towards reallocating budgets from non-core to core activities\u0026mdash;a pattern that has been observed in other studies, including Hansen (2007). Conversely, the relative stability of Tech and Sales roles points to their criticality to long-term business success, even amidst economic uncertainty (Farndale et al., 2010). The increase in available talent without corresponding salary increases implies a focus on cost optimization, resonating with the labor market strategies detailed by Blass (2007).\u003c/p\u003e \u003cp\u003eThe broader economic context, particularly the impact of global tech layoffs (TechCrunch), has significantly influenced talent availability and employment dynamics in Poland and the Baltics. Outsourcing trends discussed by Das and Marjit (2023) have further intensified competition within the local talent market. As firms increasingly adopt flexible workforce models akin to the on-demand talent pools described by James and Menzies (2023), they are better positioned to navigate economic fluctuations while optimizing operational efficiency.\u003c/p\u003e \u003cp\u003eMoreover, Derler \u0026amp; Winlaw (2023) emphasizes that while the current surplus presents an opportunity to attract high-quality talent, retention will require more than competitive salaries. Effective retention strategies should include career growth opportunities, development programs, and a focus on work-life balance\u0026mdash;elements essential for long-term employee satisfaction and organizational success.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion and Future Research","content":"\u003cp\u003eThis study contributes to the literature on talent management in the software development sector by providing a data-driven analysis of market dynamics in Poland and the Baltics during a period of significant global economic uncertainty. By integrating quantitative trend analysis with qualitative insights from industry communications, we offer a nuanced understanding of how regional software development markets respond to global pressures. However, our study is limited by its focus on a specific time period and geographic region, which may limit the generalizability of findings to other contexts or time frames.\u003c/p\u003e\n\u003cp\u003eThe results of this study offer several practical recommendations for industry practitioners and decision-makers regarding talent management:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eCross-Disciplinary Hiring for Enhanced Collaboration:\u003cbr /\u003eCoordinating the recruitment of technical and creative roles, as indicated by the positive correlation between Software Engineering and Design, can significantly enhance cross-functional collaboration and project innovation. Collings, Scullion, and Vaiman (2011) similarly emphasized that integrating diverse competencies within a team improves overall output and boosts project success, particularly in software development where technical and user interface elements must align.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAdopting Cyclical Hiring Aligned with Business Phases:\u003cbr /\u003eThe identified cyclical pattern between technical and sales roles highlights the importance of adapting hiring strategies to align with the company\u0026rsquo;s current phase\u0026mdash;whether it be a focus on product innovation or market expansion. Borkowska (2005) supports this approach, noting that aligning workforce adjustments with business objectives enhances organizational flexibility. Furthermore, the role of macroeconomic trends, as highlighted by Das and Marjit (2023), emphasizes the need for an agile talent strategy to remain resilient during economic instability.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMaintaining Stability in Project Management Roles:\u003cbr /\u003eThe weak correlation between Tech Project Management and other roles underscores the necessity of maintaining a stable project management workforce, irrespective of variations in other hiring domains. Listwan (2009) similarly argued for the critical role of stable leadership in guiding both technical and business teams, ensuring consistent performance during periods of growth and contraction.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFuture research should focus on:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe limitations of the study by expanding the temporal and geographic scope of analysis, potentially incorporating comparative studies across different regions or economic cycles\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe effectiveness of comprehensive retention strategies amidst economic volatility, expanding on theories by Collings and Mellahi (2009) and addressing current challenges in balancing talent availability with business sustainability.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBlass E (2007) Talent management: Maximizing talent for future performance. 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[online] \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://techcrunch.com/2024/02/21/tech-layoffs-2023-list/\u003c/span\u003e\u003cspan address=\"https://techcrunch.com/2024/02/21/tech-layoffs-2023-list/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Accessed 14 May 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVale R (2023) Forecasting the 2022-23 tech layoffs using epidemiological models. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.2305.05210\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2305.05210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"software development, supply and demand, talent management, economic conditions, Poland, Baltics","lastPublishedDoi":"10.21203/rs.3.rs-5195074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5195074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the dynamics of supply and demand within the software development sector in Poland and the Baltics during 2023, highlighting the underlying tension between talent availability and market needs. The concept of the \"IT bench,\" similar to a sports team's reserves, illustrates how regional companies maintain a readily deployable pool of talent, underscoring the importance of flexibility and rapid response capabilities in today's volatile market.\u003c/p\u003e \u003cp\u003eUsing data from industry-specific sources, this research provides insights into demand fluctuations for key roles, such as Software Engineering, Tech Project Management, and Design, along with changes in median salaries in response to external economic pressures. Challenges such as wage growth and the impact of political and economic factors on business conditions are explored, providing context to the evolving dynamics of the software development job market.\u003c/p\u003e \u003cp\u003eThe methodology applies a collective dynamics framework using a 60-day rolling window to compute time-varying correlation matrices of job market indicators, including role-specific demand and salary trends. This analysis reveals significant correlations between job categories, indicating how shifts in demand for one role impact others. For example, the positive correlation between Software Engineering and Design roles reflects their interdependent nature. In contrast, the inverse correlation between Software Engineering and Sales \u0026amp; Business Development points to strategic shifts in hiring priorities.\u003c/p\u003e \u003cp\u003eKey findings indicate that the software development industry is adapting to global tech layoffs and economic uncertainties by emphasising strategic talent management and flexible, project-based teams. The demand for developers in key technologies, such as Java, JavaScript, and Python, remains high, albeit with adjustments to salary expectations and employment conditions to better navigate market challenges.\u003c/p\u003e \u003cp\u003eThis research highlights the importance of a data-driven approach to recruitment and talent strategy, integrating technological tools with human insight to effectively adapt to the evolving market landscape.\u003c/p\u003e","manuscriptTitle":"Job Market Indicators in Software Development Sector: A Data-Driven Analysis of Poland and the Baltics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-04 02:44:48","doi":"10.21203/rs.3.rs-5195074/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd629ee5-1cd4-403a-8ad0-45ba72e849a3","owner":[],"postedDate":"October 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38476097,"name":"International Business"},{"id":38476098,"name":"Microeconomics"}],"tags":[],"updatedAt":"2024-10-04T02:44:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-04 02:44:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5195074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5195074","identity":"rs-5195074","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}