A Powerpoint Presentation describing the Application of Data Science in Development Economics

1. Application of
Data Science in
Development
Economics
Bibhu Prasad Mohanty
CHRIST Deemed to be University

2. Agenda
KEY TOPICS DISCUSSED IN
THIS PRESENTATION
• 1. Data Science and Development Economics
• 2. Data-Driven Policy Making
• 3. Economic Empowerment through Data
Science
• 4. Challenges and Opportunities

3. Economics and Data
Science
Economics and Data Science are closely-related
subjects. Data Science as a formal discipline
evolved in the 2000s but it made considerable
headway as a new and emerging field of study in
the 1970s when economists and researchers
began widening the scope of statistics from a
subject that mainly involved describing the data to
a subject that would include not only collection or
description of data but also its analysis, using
scientific methods and tools.

4. • Data science has become a powerful tool to help economists understand and predict
economic phenomena.
• With the rise of data-driven decision-making and the increasing availability of data,
economists are increasingly turning to data science to better understand the
economic environment and to create more accurate predictions of economic trends.
• Data science can help economists to create models that can explain complex
economic processes, identify key drivers of economic growth, and enable more
precise forecasting of economic outcomes.
• It has become an increasingly popular field in recent years, due to the ubiquity of
data that is now available. This data can be used to better understand and make
predictions about economic trends and behaviour.
• As a result, many economists have begun to explore the possibilities of applying data
science to their field

5. Introduction
• Data science encompasses the process of deriving
significant knowledge from data through the utilization
of diverse methodologies such as statistical analysis
and machine learning.
• Regarding its contribution to the field of Development
Economics, data science plays a pivotal role in
expediting the conversion of unprocessed data into
actionable information conducive to fostering economic
growth.

6. Practical Applications:
Empowering
Development through
Data Driven Strategies
Poverty Reduction and Targeting:
• The identification of vulnerable populations can
be accomplished through the analysis of
extensive datasets derived from surveys,
satellite imagery, and mobile phone usage.
• This data science approach enables the specific
targeting of populations facing poverty or food
insecurity, leading to more efficient allocation of
resources and interventions.
• Moreover, the utilization of machine learning
models can predict the risk of poverty by
analyzing various factors such as income,
education, and location. This proactive measure
allows for the prevention of poverty before it
occurs.

7. 2. Predictive Modeling for
Economic Trends:
Objective: Forecasting economic trends to make
proactive decisions.
Benefits of Predictive Modeling in Development
Economics:
• Timely Decision-Making: Allows policymakers to
make decisions proactively based on anticipated
economic changes.
• Resource Optimization: Helps allocate resources
efficiently by anticipating areas that may require
additional support or intervention.
Application: Utilizing time series analysis and
machine learning algorithms to predict
changes in key economic indicators such as
GDP growth, inflation rates, and
unemployment.

8. 3. Healthcare and
Education:
Disease control and outbreak prediction: Analyzing disease
prevalence data, travel patterns, and environmental factors can
predict potential outbreaks and help optimize resource
allocation for healthcare delivery.
Monitoring education quality: Analyzing student performance
data and teacher characteristics can identify areas needing
improvement and track progress towards educational goals.

9. 4. Economic Growth and Job Creation:
• Assessing the impact of development policies:
Data science can evaluate the effectiveness of
government programs and initiatives on
economic growth, job creation, and poverty
reduction.
• Matching job seekers with employers: By
analyzing skills and qualifications, data science
can facilitate better job matching, improving
employment outcomes for individuals and
promoting economic growth.

10. •
•

11. Financial
Inclusion and
Microfinance
• Creditworthiness assessment: Data science
can analyze alternative data sources (e.g.,
mobile phone transactions) to assess
creditworthiness for individuals with limited
formal financial history, expanding access
to financial services.
• Fraud detection and risk management:
Analyzing transaction data and identifying
patterns can help financial institutions
detect fraudulent activities and manage risk
more effectively.

12. Challenges and
Considerations
• Data availability and quality: Access to reliable and
timely data is crucial for effective analysis. Data gaps
and inconsistencies can limit the accuracy and
generalizability of insights.
• Ethical considerations: Ensuring data privacy, avoiding
bias, and using data responsibly are essential ethical
concerns in development economics.
• Capacity building: Empowering local communities and
institutions to collect, analyze, and interpret data is
crucial for long-term sustainability and impact.

13. In conclusion, predictive modeling emerges as a
formidable ally in the realm of development
economics, offering a proactive lens into the future.
By forecasting economic trends such as GDP growth,
inflation rates, and unemployment, we empower
decision-makers to navigate challenges with
foresight. The ability to optimize resource allocation
based on predictive insights not only enhances
efficiency but also amplifies the impact of
development initiatives. While challenges exist, the
success stories shared today underscore the
transformative potential of predictive modeling in
shaping well-informed, resilient, and adaptive
economic strategies. Embracing this tool signals a
paradigm shift towards a future where data-driven
foresight becomes integral to sustainable economic
growth and development.