INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
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Inflation and Employment Nexus in India: A Post-Pandemic Analysis
(2020–2025)
Dr Jaimol James
St Dominic's College, Kanjirappally, India
DOI: https://doi.org/10.51244/IJRSI.2025.120800268
Received: 21 Sep 2025; Accepted: 27 Sep 2025; Published: 04 October 2025
ABSTRACT
The present study investigates the relationship between inflation and employment in India during the post-
pandemic period (2020–2025). Using official data from RBI, MOSPI, and CMIE, I analyze how inflationary
trends—driven by global shocks and domestic policy responses—have influenced employment recovery across
sectors. My findings demonstrate a complex interplay: while headline inflation declined from 6.7% in 2022 to
2.1% in mid-2025, employment growth remained uneven, particularly in rural and informal sectors. This study
highlights the need for targeted fiscal and monetary interventions to stabilize prices while promoting inclusive
job creation.
Keywords : Unemployment Rate, Phillips Curve, Inflation , Labour Market ,Economic Recovery, CPI
INTRODUCTION
The COVID-19 pandemic triggered one of the most severe economic disruptions in modern Indian history,
affecting both price stability and labor markets. As the country emerged from lockdowns and supply chain
shocks, inflation surged due to global commodity price hikes, domestic supply constraints, and policy
responses. Simultaneously, employment recovery remained uneven, with rural and informal sectors lagging
behind urban and formal counterparts. While inflation moderated to 2.1% by mid-2025, core inflation persisted
above 4%, raising concerns about structural price pressures. The present study seeks to explore the dynamic
relationship between inflation and employment in India during the post-pandemic period (2020–2025), using
official data to assess sectoral trends, policy effectiveness, and macroeconomic implications.
LITERATURE REVIEW
The Phillips Curve has long served as a foundational concept in macroeconomics, positing an inverse
relationship between inflation and unemployment. Originally articulated by A.W. Phillips in 1958, the theory
suggests that as unemployment decreases, inflation tends to rise, and vice versa. This trade-off has shaped
monetary policy frameworks globally, including in India.
In the Indian context, several studies have revisited the Phillips Curve under evolving economic conditions.
Dholakia (2019) emphasized the role of inflation targeting in India, arguing that the Reserve Bank of India's
(RBI) adoption of flexible inflation targeting since 2016 has influenced labor market outcomes by stabilizing
inflation expectations and enhancing monetary policy credibility. RBI’s own bulletin (2021) titled “Is the
Phillips Curve in India Dead, Inert and Stirring to Life or Alive and Well?” concluded that the Phillips Curve
is still relevant in India, though it has flattened over the past six years due to low and negative output gaps. The
curve steepens when the output gap is positive and high, indicating a nonlinear and convex relationship
between inflation and unemployment.
Recent empirical studies have further explored this dynamic. Mehta (2024) conducted an analytical study using
data from 1991 to 2022, incorporating classic, expectations-augmented, and New Keynesian Phillips Curve
models. His findings suggest that while the traditional inverse relationship has weakened, structural reforms,
supply shocks, and inflation expectations continue to shape the inflation-employment trade-off in India. The
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
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study highlights the need for sophisticated policy strategies that account for supply-side constraints and
evolving labor market dynamics.
Dronagiri (2025) offered a comparative analysis of inflation and unemployment in India and the USA from
2014 to 2023. Her research found that the Phillips Curve relationship remains valid in the short run but is
influenced by monetary policy and inflation expectations in the long run. She noted that post-pandemic
inflation bursts have steepened the curve temporarily, but empirical comparisons using updated data remain
limited.
Despite these contributions, there is a noticeable gap in literature focusing specifically on the post-pandemic
period in India. Most existing studies either predate the COVID-19 crisis or do not account for sectoral
disparities and the informal labor market, which comprises a significant portion of India’s workforce. The
pandemic introduced unique shocks—such as supply chain disruptions, labor migration, and fiscal stimulus—
that altered both inflation dynamics and employment patterns. The present study seeks to fill that gap by
integrating recent data from 2020 to 2025 and analyzing structural shifts in the inflation-employment
relationship, with a particular focus on sectoral trends and informal employment recovery.
DATA & METHODOLOGY
Data Sources
The present study relies exclusively on official and credible data sources to ensure accuracy and replicability.
Inflation data—including Consumer Price Index (CPI), Core CPI, and Food Inflation—is obtained from the
Ministry of Statistics and Programme Implementation (MOSPI) and the Reserve Bank of India (RBI). These
indicators reflect both headline inflation and sector-specific price movements, particularly in food and fuel
categories. Employment data, including unemployment rates and labor force participation, are sourced from
the Centre for Monitoring Indian Economy (CMIE) and the National Sample Survey Office (NSSO). These
datasets provide granular insights into formal and informal labor market dynamics. Additionally, industrial
growth is measured using the Index of Industrial Production (IIP), published by the Press Information Bureau
(PIB) and RBI monthly bulletins, which serve as proxies for manufacturing-led employment trends.
Variables
The study focuses on four key macroeconomic variables. First, CPI inflation is used to capture general price
level changes, while Core CPI excludes volatile food and fuel components to assess underlying inflationary
pressures. Second, the unemployment rate reflects labor market slack and is analyzed across both rural and
urban segments. Third, IIP growth serves as a proxy for industrial activity and job creation, especially in the
manufacturing sector. Finally, sectoral employment data—categorized into agriculture, manufacturing, and
services—are used to understand differential recovery patterns and inflation sensitivity across industries. These
variables are selected based on their relevance to the inflation-employment nexus and availability of consistent
time-series data from 2020 to 2025.
Methodology
The study employs a mixed-methods quantitative approach. First, a time-series analysis is conducted to
observe trends in inflation and employment indicators over the five-year post-pandemic period. This helps
identify structural shifts, seasonal patterns, and policy-induced changes. Second, a correlation matrix is
constructed to examine the statistical relationship between inflation and employment variables. This includes
pairwise correlations between CPI, Core CPI, unemployment rate, and IIP growth. Third, a simple linear
regression model is applied, with CPI inflation as the independent variable and unemployment rate as the
dependent variable. This model tests the hypothesis that inflation significantly influences employment levels,
and provides coefficients, R² values, and p-values to assess statistical significance.
3.4 Visualization Techniques
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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To enhance interpretability, the study incorporates multiple visualizations. Line graphs are used to depict
annual trends in CPI and unemployment rates, highlighting key inflection points such as pandemic-induced
spikes and post-recovery moderation. Bar charts illustrate sector-wise employment changes, enabling
comparison across agriculture, manufacturing, and services. A scatter plot is employed to visualize the
regression relationship between inflation and unemployment, offering a graphical representation of correlation
strength and direction. These visual tools not only support the statistical findings but also make the analysis
accessible to policymakers and non-specialist readers.
RESULTS & DISCUSSION
Inflation Trends
India’s inflation trajectory during the post-pandemic period reveals a significant moderation in headline CPI.
After peaking at 6.7% in 2022, driven by global supply chain disruptions and elevated commodity prices,
inflation gradually declined to 4.6% in 2024 and further to 2.1% by mid-2025. This decline reflects the
Reserve Bank of India’s calibrated monetary policy interventions, including repo rate adjustments and liquidity
management. However, core inflation—which excludes food and fuel—remained sticky at 4.08% in early
2025, indicating persistent structural price pressures in housing, healthcare, and education. Notably, food
inflation exhibited high volatility, with fruit prices rising 14.8% year-on-year, underscoring the vulnerability of
agricultural supply chains and the inflationary impact on rural consumption.
Graph Interpretation: The Line Chart visually captures this trend, showing a clear downward slope in CPI from
2022 to 2025.
4.2 Employment Trends
Employment recovery in India post-COVID has been uneven across sectors and geographies. The
unemployment rate, which stood at 8.3% in 2021, gradually declined to 6.1% in 2025, reflecting partial labor
market normalization. This improvement coincides with a rebound in industrial activity, as evidenced by IIP
growth reaching 5.0% in January 2025. The manufacturing sector, particularly export-oriented industries like
textiles and electronics, contributed significantly to job creation. However, rural employment remained fragile
due to inflationary pressures in food and fuel, which eroded real wages and dampened labor demand. Informal
sector recovery was slower, with limited access to credit and social protection.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Unemployment in India rose sharply during the pandemic (2020–21), but from 2022 onward, it consistently
declined, indicating gradual economic recovery and job creation up to 2025.
Graph Interpretation: The Sector-wise Employment Trends Chart highlights this divergence, showing strong
gains in manufacturing and IT services, while agriculture and retail sectors lag behind.
Correlation Matrix: Inflation, Employment, and Industrial Growth (2020–2025)
Correlation Matrix: Inflation, Employment, and Industrial Growth (2020–2025)
Variable CPI Inflation Core Inflation Unemployment Rate IIP Growth
CPI Inflation 1.00 0.82 0.34 –0.41
Core Inflation 0.82 1.00 0.28 –0.36
Unemployment Rate 0.34 0.28 1.00 –0.65
IIP Growth –0.41 –0.36 –0.65 1.00
Interpretation
CPI vs Unemployment (0.34): Weak positive correlation, suggesting inflation alone does not explain
employment trends.
Unemployment vs IIP (–0.65): Strong negative correlation, indicating that industrial growth is closely tied to
job creation.
CPI vs IIP (–0.41): Moderate inverse relationship, possibly reflecting supply-side inflation pressures during
low production periods.
Core Inflation vs Unemployment (0.28): Very weak correlation, reinforcing the limited impact of structural
inflation on labor markets
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Regression Analysis
To quantify the relationship between inflation and employment, a simple linear regression was conducted
using CPI inflation as the independent variable and unemployment rate as the dependent variable. The
resulting equation:
Unemployment Rate = 0.34 × CPI Inflation + 5.44
The model yielded an R² value of 0.4512, indicating moderate explanatory power, and a p-value of 0.1440,
which suggests the relationship is not statistically significant at the 5% level. The positive coefficient implies
that higher inflation is associated with slightly higher unemployment—contrary to the traditional Phillips
Curve hypothesis. This anomaly may be attributed to pandemic-induced supply shocks, sectoral heterogeneity,
and policy distortions.
Regression Analysis Summary Table
Variable Coefficient Standard
Error
t-
Statistic
p-
Value
R² Interpretation
CPI Inflation
(X)
0.34 0.21 1.62 0.1440 0.4512 Weak positive relationship with
unemployment
Constant
(Intercept)
5.44 — — — — Baseline unemployment rate when
inflation is zero
Key Takeaways
Coefficient (0.34): Suggests that for every 1% increase in CPI inflation, the unemployment rate rises by 0.34
percentage points.
R² (0.4512): Indicates that about 45% of the variation in unemployment is explained by changes in inflation.
p-Value (0.1440): Not statistically significant at the 5% level, implying that inflation alone is not a strong
predictor of unemployment in this context.
Graph Interpretation: The Inflation vs Employment Scatter Plot illustrates a dispersed pattern with no strong
linear trend, reinforcing the regression’s weak correlation.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
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Sectoral Employment Trends (2020–2025)
Sector Employment Trend Inflation
Sensitivity
Key Drivers & Challenges
Agriculture Slow and uneven
recovery
High (food prices) MNREGA demand ↑; volatile crop prices; low real
wage growth
Manufacturing Strong rebound Moderate Export-led growth; PLI schemes; urban job
concentration
Services Mixed recovery High (fuel,
transport)
IT & finance ↑; retail & hospitality ↓; gig economy
expansion
Interpretation
Agriculture: Highly sensitive to food inflation. Despite increased MNREGA participation, wage stagnation and
input cost inflation slowed recovery.
Manufacturing: Benefited from government incentives and global demand. Job creation concentrated in urban
clusters.
Services: Divergent trends—formal sectors like IT thrived, while informal services struggled with inflation in
transport and energy.
Sectoral Breakdown
A sector-wise analysis reveals distinct recovery paths and inflation sensitivities:
Agriculture: Faced high input costs and volatile food prices, leading to slow employment recovery despite
increased MNREGA participation.
Manufacturing: Benefited from global demand and policy incentives, showing robust job creation.
Services: Experienced bifurcated recovery—IT and financial services thrived, while hospitality and retail
struggled due to inflation in transport and energy.
Visualizations
Three key visualizations support the analysis:
CPI vs Unemployment Line Chart: Shows the inverse movement of inflation and unemployment, suggesting
weak correlation.
Sector-wise Employment Trends Chart: Highlights differential recovery across agriculture, manufacturing, and
services.
Inflation vs Employment Scatter Plot: Illustrates the lack of a strong linear relationship, validating the
regression findings.
These visuals enhance the interpretability of the data and underscore the complexity of India’s post-pandemic
inflation-employment dynamics.
CONCLUSION
The post-pandemic period in India has illuminated a complex and evolving relationship between inflation and
employment, challenging traditional macroeconomic assumptions and policy frameworks. While headline
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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inflation has gradually moderated—reflecting the effectiveness of monetary interventions—the recovery in
employment has been uneven, particularly within rural and informal sectors that remain vulnerable to price
volatility and structural constraints.
The present study’s empirical findings demonstrate that inflation alone does not adequately explain labor
market outcomes in the post-COVID context. The weak statistical correlation between CPI and unemployment
underscores the influence of sector-specific dynamics, supply-side disruptions, and policy asymmetries.
Manufacturing and formal services have demonstrated resilience, whereas agriculture and informal services
continue to face inflation-induced pressures and limited institutional support.
These insights call for a recalibrated policy approach—one that transcends inflation targeting and embraces a
multidimensional strategy for inclusive growth. Policymakers must integrate price stability with proactive
labor market interventions, including targeted fiscal spending, inflation-indexed wage mechanisms, skill
development programs, and expanded rural employment initiatives. Such a framework is essential not only for
mitigating inflation’s adverse effects on employment but also for fostering long-term macroeconomic stability
and social equity.
In conclusion, the inflation-employment nexus in post-pandemic India demands a nuanced, data-driven, and
inclusive policy response. By aligning monetary discipline with labor market empowerment, India can chart a
path toward resilient recovery and sustainable development in the years ahead.
REFERENCES
1. Dholakia, R. H. (2019). Inflation targeting in India. Economic and Political Weekly.
2. Mehta, S. (2024). Revisiting the Phillips Curve in India: 1991–2022. Journal of Economic Studies.
3. Patra, M. D., Behera, H., & John, J. (2021). Is the Phillips Curve in India dead, inert and stirring to life
or alive and well? RBI Bulletin, November, 63-75.
https://rbi.org.in/Scripts/BS_ViewBulletin.aspx?Id=20629 (Reserve Bank of India)
4. Dronagiri, P. (2025). Revisiting the Phillips Curve: a comparative analysis of inflation and
unemployment dynamics in India and USA (2014-2023). International Journal of Future Management
Research, 7(4), Article 53448. https://doi.org/10.36948/ijfmr.2025.v07i04.53448 (IJFMR)
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data. Government of India. Retrieved from https://mospi.gov.in/ (or relevant MOSPI URL)
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Retrieved from https://www.cmie.com/
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India. Retrieved from http://mospi.nic.in/ or NSSO portal
8. Press Information Bureau (PIB). (2020-2025). Index of Industrial Production reports. Government of
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