December 10, 2025
Analysis of the impact of the Performance Management Program (PGD) on the productivity of the Federal Executive
Author: Alberto de Campos e Silva — Advisor: Lucas Henrique de Souza
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This work used Data Analysis in People Management to measure the productivity of the Performance Management Program (PGD) in the federal executive branch and compare it with managers’ perception. Servers with and without PGD were analyzed, detailing the work modalities (in-person, partial, remote) to measure the policy’s impact, according to Gertler et al. (2018). The measured productivity was confronted with questionnaire data to compare perceived and measured effectiveness.
Performance management has evolved historically (Brandão and Guimarães, 2001), gaining strength in the public sector in recent decades. Studies such as those by McClelland and Dailey (1972) already explored the relationship between competencies and performance. Heinrich (2002) cites the Job Training Partnership Act (JTPA) program of 1982 in the USA as a milestone in results-based management. In Brazil, Decree 5.707/2006 was a precursor, establishing the National Personnel Development Policy to align development and performance. Competency management, understood as the individual’s delivery capacity, has become a pillar of performance management (Silva et al., 2015).
The Performance Management Program (PGD) was formalized by Normative Instruction No. 1, of August 31, 2018, but its adoption was accelerated by the COVID-19 pandemic in 2020 to enable remote work. The PGD replaces attendance tracking with a focus on results and quality (Brazil, 2022). The Ministry of Management and Innovation in Public Services (MGI) recommends its implementation, which reinforces the need for studies that evaluate the efficiency of public policies and their costs and benefits (Oliveira et al., 2024).
The PGD is a management system that aligns organizational strategies, unit deliverables, and individual work plans, connecting the employee’s activity to the institution’s objectives (Oliveira et al., 2020). Despite its importance, the literature on its quantitative impact is scarce, focusing on perceptions and literature reviews. There is conceptual confusion between PGD (management tool) and remote work (work modality). The literature points to employees’ preference for remote work due to its flexibility, but also reports resistance from senior management and difficulties in team control (Alves, 2024; Lima and Fajardo, 2022; Maranhão and Pantoja, 2024).
A study that quantifies the effectiveness of PGD in a federal public foundation provides subsidies for MGI and senior management. People Management Data Analysis (HR Analytics) is the appropriate approach, as it promotes strategic alignment and evidence-based decision-making (Suri and Lakhanpal, 2024). By providing an objective measure of the impact of PGD, this research fills a gap in the literature and offers a methodological model for future evaluations of management policies in the public sector.
The research is descriptive, seeking to analyze the relationships between variables without researcher interference (Gil, 2025). A mixed approach was adopted, combining quantitative data (productivity) and qualitative data (questionnaires, interviews) collected in parallel (Creswell et al., 2021). The design was a Field Survey, focused on the association between variables such as work modality and productivity, without any claim of causality. The data collection techniques were questionnaires and primary data survey, processed and analyzed (Malhotra, 2019).
The methodology was divided into three phases. The first applied questionnaires to all servers and managers to capture the perception of the effectiveness of the PGD. Voluntary participation (132 out of 314 respondents, 42% rate) introduced a selection bias, focusing the study on the internal analysis of the agency. The second, quantitative phase, measured individual productivity by analyzing 529,160 documents from the Electronic Information System (SEI) produced by 299 employees in 102 sectors, between 2023 and May 2025. The turnover of servers in the PGD, required by the agency, allowed for the natural creation of treatment and control groups, enabling a robust impact assessment (Gertler et al., 2018).
The initial analysis of the SEI data showed great heterogeneity in document production across sectors, making direct comparison unfeasible. To address this, the data were normalized, transforming each sector’s production onto a standardized scale where the monthly average was set to one. This allowed the linear regression model to measure the impact of PGD in percentage terms. The Ordinary Least Squares (OLS) model was employed to estimate the coefficients for each PGD modality (in-person, partial, remote) compared to the control group (no PGD).
The third phase, qualitative and exploratory, investigated inflection points in the productivity time series. Two free and unstructured interviews were conducted with key managers (the head of the PGD sector and a technical coordinator) to hypothesize about external factors, such as regulatory changes, that could have influenced productivity. The findings of this phase, due to their exploratory nature, serve as a guide for future investigations (Jung and Ramos, 2024).
The analysis of the questionnaires revealed that managers perceive PGD as beneficial for productivity, associating it with increased motivation. There was disagreement in units 1 and 3, with less favorable perceptions, while unit 4 consistently demonstrated positive support. This managerial perception aligns with other studies in the public sector. In the pilot project of the Instituto Federal Goiano, the “increase in productivity” was highlighted by servers and supervisors (Moraes et al., 2022). In the Secretariat of Science, Technology, Innovation and Strategic Inputs in Health (SCTIE/MS), 52.9% of servers felt an increase in productivity, a perception corroborated by managers (Lima and Fajardo, 2022). Research at the Secretariat of State for Economy of the DF and at the Regional Electoral Court of Maranhão also found similar results (Paro et al., 2024; Ribeiro et al., 2024).
The quantitative analysis by linear regression (OLS) demonstrated, with high statistical significance (p < 0.001), that servers in PGD produced, on average, 14.7% more than non-participants. Although the model explains a small part of the variance (R² = 0.049) and the residuals do not follow a normal distribution, the large number of observations (7,689) and the Central Limit Theorem guarantee the robustness of the estimated coefficients for impact measurement (Fávero and Belfiore, 2023). The model is, therefore, reliable for stating that PGD is associated with a significant productivity gain in the agency.
Disaggregating by modality, Partial PGD was the most productive, with a 21% increase compared to the control group, followed by In-Person PGD (19%) and Full Remote PGD (9%), suggesting that hybrid models can optimize benefits. However, the main finding was the extreme variability of results between sectors. The scatter plot showed that, depending on the unit, PGD could either increase or decrease productivity. This highlights that the effectiveness of PGD depends on the context of each team and the management applied, advising against a centralized and uniform policy for the entire organization.
The time series analysis of productivity revealed a drop in October 2024 and a recovery starting in February 2025. Qualitative interviews raised hypotheses for these fluctuations. The PGD manager pointed to the editing of a new normative act on October 30, 2024, and the implementation of the Petrvs system in December 2024. The new rule restricted civil servants in commissioned positions to in-person modalities, which, according to the second interviewee, may have led to a reorganization of work, concentrating tasks among managers (now obligatorily in-person) and potentially removing responsibilities from civil servants working remotely.
This hypothesis suggests that the drop in productivity in full-time remote work and the increase in in-person work do not reflect a change in program efficiency, but a consequence of the functional segregation imposed by the new rule. Managers’ productivity increased due to work concentration, while remote workers’ productivity decreased due to a possible reduction in demand. The recovery from March 2025 onwards may indicate an adaptation to this dynamic or the effects of the Petrvs system. The finding underscores how organizational and external normative factors influence the results of a management policy.
The core of the discussion is the dissonance between measured productivity and managers’ perception. The analysis by board revealed a paradox: units 1 and 2, with the highest PGD productivity gains (28% and 23%, respectively), expressed the most negative perceptions. In contrast, unit 3, where the impact of PGD tended to be negative, had a positive evaluation. Only unit 4, the most productive in volume in SEI, aligned positive perception and measured productivity gain (10%). This misalignment suggests that managers have difficulty evaluating performance objectively, relying on intuition or the feeling of losing control over the remote team.
This disconnection is a critical finding, as strategic decisions about remote work may be based on flawed perceptions. The literature corroborates that many challenges of teleworking are often pre-existing management deficiencies, amplified by the remote environment. Resistance from superiors and difficulty in organizing and controlling activities remotely are cultural barriers that remote work exposes (Oliveira et al., 2020). The results of this study, aligned with other quantitative research that also found productivity gains (Marquese, 2019; Teixeira and Bacinello, 2024), reinforce that remote work is an effective tool, but its success depends on a management culture oriented by data and trust.
The analysis suggests that the normative change of October 2024, restricting managers to in-person work, may have been detrimental to overall productivity. The concentration of work on managers and the underutilization of remote servers indicate a possible management failure resulting from the rule. Recom
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