Global Economics - Fall 2023 Junior

Course # ECON 3003

6 Credits

Course Description

Econometrics II is an advanced quantitative methods course which builds on the skills and knowledge acquired in Econometrics I, and will prepare students for independent thesis research. This course has ten separate units and introduces microeconometric models, panel and time series models, models for correction of endogeneity and simultaneity bias as well as models for prediction and forecasting. In the first part, students will revise and extend their knowledge probability, statistics and matrix algebra before progressing to analysis of limited dependent variables with specific regression models needed to analyze binary, ordinal and count data of the type that is often found in analysis of survey data. The second part of the course introduces instrumental variables models used to correct for endogeneity, their extension to models with simultaneity and modeling approaches for prediction. The third part covers basic analysis of panel data as well as time-series regression analyses with special attention to issues of stationarity and seasonality, applicable in assessing the timing of effects and forecasting. In the third part, students will study the analysis of panel data using different regression models.

Course Learning Outcomes

On completing the course, the students will be able to:

• Interpret coefficients, odds ratios and marginal effects of linear and logistic regression models
• Recognize and define Poisson distributed, truncated and censored numeric and categorical dependent variables
• Analyze univariate and multivariate time series with different patterns of autocorrelation, trend and seasonal fluctuations for use in forecasting
• Interpret short and long run impacts of shocks using vector autoregression and vector error correction models.
• Analyze cross-section time series, panel and dynamic panel data using pooled ordinary least squares regressions, fixed and random effect regressions
• Use 2SLS with simultaneous equations to parameterize a supply and demand model
• Assess and improve predictive accuracy using penalized regression, principal component analysis and dynamic factor models

Course Assessments and Grading

Course # ECON 3004

6 Credits

Course description

The International Economics: Trade, Theory and Policy course will offer a comprehensive overview of the so-called “real" or trade part of international economics. The course will examine the causes and consequences of international trade, as well as provide an analysis of trade policy. Questions addressed will include, but not limited to: Why do nations trade? Who gains and who loses from trade? Is free trade optimal, or should be restricted in some cases? Should countries intervene to influence the structure of their international trade?

This course extensively uses tools from Microeconomics and Macroeconomics although set in a different and sometimes unfamiliar context. The course is not limited to theoretical models, though, and each topic will be backed by the empirical evidence, also based on examples from Central Asia economies. This will allow students to understand how theoretical models can be applied in real life.

Course learning outcomes

Upon successfully completing this course, students will be able to:

• Have a clear understanding of the driving forces of the international trade
• Seeing gains and losses from free international trade for different categories of people
• Understand the role of the Central Asian economies in the world economic system, including the knowledge of main advantages and disadvantages of the region
• Understand the interconnectedness between international economics and regional economic issues
• Adopt formal models and analytical tools to real life economic problems
• Be able to follow the contemporary international trade agenda

Course Assessments and Grading

Course # ECON 3006

6 Credits

Course description 

Natural Resource Economics applies microeconomic concepts and tools to issues that arise from the growth, use, depletion and degradation of natural systems and their components, including land, energy, air, water, and biodiversity. It also looks for solutions that exploit economic facets of human behavior to address these issues in ways that get the most done at minimum cost. You learn that economic objectives do not necessarily conflict with sustainability and environmental goals, and that markets can be harnessed to improve environmental quality and preservation of natural resource. We also discuss the limitations of economic analysis to provide policy guidance on Natural Resource issues. The course begins with a review of microeconomic concepts related to the function (or dysfunction) of markets. We look at standard (neoclassical) ways of understanding Natural Resource issues that arise when markets fail to deliver optimal/desirable outcomes with regard to natural resources, environmental quality, and the human benefits that derive from them. We then learn about market-based, regulatory, and community-based approaches to achieving economic efficiency and/or sustainability within these systems. We evaluate economic (and other) approaches to real-world problems in terms of efficient allocation, sustainable scale and just distribution.

Course learning outcomes  

Upon successfully completing this course, students will be able to:  

• Describe the economic aspects of natural resource issues
• Apply analytical tools (rhetorical, graphical, and mathematical) to describe the extent to which these issues constitute the failure of market systems
• Explain the difficulties arising in using economic analysis in natural resource policy design
• Recognize a number of real-world environmental policy problems, particularly those in the context of mountainous regions of Central Asia and evaluate in depth solutions to such problem using economic analysis.

Course Assessments and Grading 

Course # DMNS 3032E

6 Credits

Course Description

This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources.

• Framework for Data Collection and Analysis (9 hours)

This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. Furthermore this course will provide you with a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source. Finally we will introduce different large scale data collection efforts done by private industry and government agencies, and review the learned concepts through these examples. This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products.

• Data Collection: Online, Telephone and Face-to-face (21 hours)

This course presents research conducted to increase our understanding of how data collection decisions affect survey errors. This is not a “how–to-do-it” course on data collection, but instead reviews the literature on survey design decisions and data quality in order to sensitize learners to how alternative survey designs might impact the data obtained from those surveys. The course reviews a range of survey data collection methods that are both interview-based (face-to-face and telephone) and self-administered (paper questionnaires that are mailed and those that are implemented online, i.e. as web surveys). Mixed mode designs are also covered as well as several hybrid modes for collecting sensitive information e.g., self-administering the sensitive questions in what is otherwise a face-to-face interview. The course also covers newer methods such as mobile web and SMS (text message) interviews, and examines alternative data sources such as social media. It concentrates on the impact these techniques have on the quality of survey data, including error from measurement, nonresponse, and coverage, and assesses the tradeoffs between these error sources when researchers choose a mode or survey design.

• Questionnaire Design for Social Surveys (16 hours)

This course will cover the basic elements of designing and evaluating questionnaires. We will review the process of responding to questions, challenges and options for asking questions about behavioral frequencies, practical techniques for evaluating questions, mode specific questionnaire characteristics, and review methods of standardized and conversational interviewing.

• Sampling People, Networks and Records (21 hours)

Good data collection is built on good samples. But the samples can be chosen in many ways. Samples can be haphazard or convenient selections of persons, or records, or networks, or other units, but one questions the quality of such samples, especially what these selection methods mean for drawing good conclusions about a population after data collection and analysis is done. Samples can be more carefully selected based on a researcher’s judgment, but one then questions whether that judgment can be biased by personal factors. Samples can also be draw in statistically rigorous and careful ways, using random selection and control methods to provide sound representation and cost control. It is these last kinds of samples that will be discussed in this course. We will examine simple random sampling that can be used for sampling persons or records, cluster sampling that can be used to sample groups of persons or records or networks, stratification which can be applied to simple random and cluster samples, systematic selection, and stratified multistage samples. The course concludes with a brief overview of how to estimate and summarize the uncertainty of randomized sampling.

• Dealing with Missing Data (17 hours)

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®.

• Combining and Analyzing Complex Data (9 hours)

In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.

• Survey Data Collection and Analytics Capstone Project (10 hours)

The Capstone Project offers qualified learners to the opportunity to apply their knowledge by analyzing and comparing multiple data sources on the same topic. Students will develop a research question, access and analyze relevant data, and critically examine the quality of each data source.

Course Learning Outcomes

After completing the course students should be able to:

• Identify data sources to match a research question
• Break a research question down into quantifiable parts
• Administer face-to-face and telephone interviews
• Assess the tradeoffs between these error sources when researchers choose a mode or survey design.
• Design questionnaires for social surveys
• Determine when to apply simple random sampling, cluster sampling, stratification, systematic selection, and stratified multistage samples
• Estimate and summarize the uncertainty of random sampling
• Solve nonresponse and missing data problems through weighting and imputation
• Link records using statistical matching methods

Course Assessments and Grading

Course # ECON 3031E

3 Credits

Course Description

This course builds on an understanding of entrepreneurship and the vital role played by entrepreneurs and entrepreneurship in the development of economy. In addition, the course provides a platform for undertaking research in the field of economics of entrepreneurship and implementing economic analysis of entrepreneurial ventures. Students are introduced to the concept and theory of entrepreneurship. Students use methods of applied entrepreneurial research, assess performance of entrepreneurial ventures, analyze public policy, market regulation and taxation and their impact on entrepreneurial activity.

Course Learning Outcomes

By the end of this course the students will be able to:

• Explain the role of entrepreneurship in economies
• Analyze entrepreneurial markets and behavior
• Demonstrate an understanding of entrepreneurial financing
• Identify research topic in the field of entrepreneurship and develop research proposal on the topic
• Present an analysis of public policy

Course Assessment and Grading

Course # DMNS 4180E

6 Credits

Course Description

Linear programming is one of the most versatile and powerful mathematical programming techniques that can be employed for efficiently solving a stylized class of decision problems.
Linear programming has been used with marked success to solve optimization problems in areas such as economics (including banking and finance), business administration and management, agriculture and energy, contract bidding, nutrition (diet) planning, health care, public decision making, facility location, transportation, strategic planning, and so on.
In this course, students will study mathematics that deals with various real-world situations which leads to mathematical models involving linear optimization problems, geometrical solution of two-variable problem, simplex algorithm, dual problems, sensitivity analysis and transportation problem.

Course Learning Outcomes

Upon successfully completing this course, students will be able to:

• Formulate a mathematical model of a real-life problem.
• Solve the formulated mathematical problem with the use of different algorithms.
• Translate the results back into the context of the original problem.
• Solve linear programming problems with R studio.

Course Assessments and Grading

Course # ECON 3063E

6 Credits

Course description

This course aims at helping students to develop 1) historical perspective on economic theorizing and 2) sensitivity to assumptions and premises of alternative economic approaches. We will first walk through the museum of economic thought and then discuss controversial issues of the global world such as inequality, climate change, etc. and try to approach these issues from the research perspective: how these problems can be studied, what is the applicable methodology in specific cases. Our main concern will be to find out how different concerns, theories, methods, and types of evidence sit together, which is essential for the capstone project in economics.

Course learning outcomes

Upon successfully completing this course, students will be able to:

• Associate certain thinkers, ideas, and concepts with major theoretical approaches in the history of economic thought,
• Explain the logic of major theoretical approaches in the history of economic thought,
• Compare and assess applicability of theoretically informed research strategies to specific cases/problematiques,
• Justify the choice of theoretically informed research strategy for their own research.

Course Assessments and Grading

Course # ECON 3005

6 Credits

Course Description

This course provides an overview of the main topics in international monetary economics. During classes, students examine the theoretical concepts of international monetary economics and discuss world economy practical examples, including cases in developed countries, emerging markets and the Central Asian economies. All the topics discussed in the course are illustrated with relevant practical examples.

Course Learning Outcomes

After completing the course students should be able to:

• Discuss specific policy issues such as the choice of exchange rate regime, the desirability of free capital flows or the creation of optimal currency areas.
• Identify causes and consequences of international financial crises.
• Discuss central bank actions and decisions in relation to international financial markets and the consequences of monetary policy changes for the domestic economy.
• Compare country specific experiences in the field of exchange rate policies and regimes.
• Critically interpret press reports discussing world financial markets.
• Critically comment on current debates on international economic finance policies

Course Assessments and Grading

Course # ECON 4133E

3 Credits 

General course information

This is the syllabus for a blended learning course. A part of the course will be delivered online. The digital content of the course and part of the assignments will be provided by CERGE-EI Foundation Moodle. Part of the course will be delivered on campus. During on-campus sessions, the UCA course instructor will monitor your progress, provide opportunities for discussing the course materials, and conduct the exercise session.

The course will be facilitated by Muboriz Mirzoshoev. The UCA course instructor will track the course completion on the platform of the CERGE-EI Foundation Moodle and will be responsible monitoring the quizzes, home assignments, and exams. Upon completion of the course, you will receive an official certification/course completion confirmation from the CERGE-EI Foundation.

Course Description

Students will learn basic concepts, definitions, facts, and trends in modern labor economics. We will discuss how labor demand and supply are determined and what mechanisms set the equilibria. The course will also address other selected topics in labor economics, including human capital, gender, race, unemployment, and mobility. Real-life examples will supplement the theoretical part of the course, allowing students to apply their newly acquired knowledge directly.

Course Assessments and Grading

Course # ECON 4131E

3 Credits 

TBA

Course # EAES 3052

6 Credits 

Course Description

Environmental governance refers to how and why societies and governments manage the relationship between human beings and the natural world. To study environmental governance is to study the rationales, rhetoric and structures of environmental management systems, and to compare these systems to understand why certain environmental problems are managed as they are, what approaches to environmental management are more (or less) successful, and for whom and in what ways they are (or are not) successful. This course seeks to provide tools for describing, discussing and analyzing the issues that underpin environmental management problems.

Course Learning Outcomes

Upon the completion of the course, students will be able to:  

• Apply key concepts and theories of institutional analysis in relation to environmental issues.
• Evaluate the effectiveness of four major institutional forms (state, market, civil society, and global governance bodies) in addressing environmental problems.
• Analyze a particular environmental management problem through the lens of an applicable governance model in writing.

Course Assessments and Grading

Course # EAES 2130E

3 Credits 

Course Description 

This Elective Course explores the human and environmental interactions in Central Asia. The interaction of the people inhabiting the region with its environment has a long history. This course focuses on the major environmental issues that have both resulted from human interaction, from habitation in the region and have also altered the relationship between humans and the environment. It will also examine how the environment and settlement in various parts of Central Asia were affected by colonial, Soviet, and present political, economic, social, and demographic changes. Guided by the new approaches of Environmental Humanities the course stresses the multidisciplinary approaches to understanding human habitation and environment in the region. Throughout the course students will analyze primary and secondary sources: documentaries, photographs, policy papers, and fictional works, to have a better understanding of the issues affecting human relationships with the environment in Central Asia 

Course Learning Outcomes 

Upon completion of the course, students will be able to: 

Analyse how socio-economic and political changes in modern Central Asia had an impact on human  interactions with environment. 

Discuss the ways  human impacts on ecosystems in different parts of the region 

Identify and critically analyse the environmental issues that affect settlement of population 

Identify appropriate sources to analyse environmental changes, and interventions in the region 

Discuss different approaches to understanding major environmental changes in Central Asia 

Course Assessments and Grading 

Course # MDIA 4083E

6 Credits 

Course Description 

This course increases students’ knowledge and skills in using communication to advance different environmental discourses by connecting the local with the global.  Students study a range of visual and written texts to learn how environmental communication is used by different actors in society to achieve certain outcomes. The role of communication is studied at the intersections of other key issues such as biodiversity, sustainable development, and climate change.  Through the evaluation and creation of a range of texts students gain an understanding of how various contexts and media shape environmental communication discourses in the public sphere. Using holistic and systems thinking students conduct research, identify target audience and design effective messages that place community concerns at the centre. 

Course Learning Outcomes

Upon the completion of the course, students will be able to:  

• Examine the ways in which different political, cultural, economic and geographical contexts shape environmental communication discourses in the public sphere.
• Evaluate a range of texts and assess their effectiveness on the intended audience.
• Examine how visual texts act as cultural prism that shape our understanding of nature. 
• Discuss the role of media in reporting key environmental issues in different societies while connecting the local with the global. 
• Design communication responses to engage a variety of audiences about environmental issues.

Course Assessments and Grading

Course # COMP 4001E

6 Credits 

Course Description 

 This Human-Computer Interaction (HCI) course provides students with a comprehensive grasp of the principles and techniques required for crafting effective user interfaces. It encompasses aspects such as physical capabilities, cognitive models, and social models that shape the design of interactions. Through this course, students will gain insights into variables like color, perception, ergonomics, attention, memory, and cultural considerations, which will significantly enhance their capacity to design interfaces tailored to a wide range of user requirements. Consequently, students completing this HCI program will acquire a diverse skill set and foundational knowledge crucial for the creation of user-friendly, accessible, and secure digital interfaces.  

Course Learning Outcomes 

 Upon the completion of this course, students should be able to: 

• Apply principles of good design for people from the perspectives of age and disabilities.
• Analyze techniques for user centered design for a medium sized software.
• Evaluate the usability of medium size software user interface.

Course Assessments and Grading 

Course # COMP 2081

6 Credits 

Course Description 

Want to get started in the world of coding and build websites as a career? This certificate, designed by the software engineering experts at Meta—the creators of Facebook and Instagram, will prepare you for a career as a front-end developer. 

Upon completion, you’ll get access to the Meta Career Programs Job Board—a job search platform that connects you with 200+ employers who have committed to sourcing talent through Meta’s certificate programs, as well as career support resources to help you with your job search.  

By the end, you’ll put your new skills to work by completing a real-world project where you’ll create your own front-end web application. 

Meta Front-End Developer Professional Certificate program consists of - 9 course series: 

1. Introduction to Front-End Development
2. Programming with JavaScript
3. Version Control (optional, not graded by UCA)
4. HTML and CSS in depth
5. React Basics
6. Advanced React
7. Principles of UX/UI Design (optional, not graded by UCA)
8. Front-End Developer Capstone
9. Coding Interview Preparation (optional, not graded by UCA)

Note: There will be only one mandatory offline class session with UCA instructor at the beginning. Depending of course progress the number of sessions might change. 

Course Learning Outcomes  

Upon the completion of this course, students should be able to: 

• Create a responsive website using HTML to structure content, CSS to handle visual style, and JavaScript to develop interactive experiences.  
• Learn Bootstrap CSS Framework to create webpages and work with GitHub repositories and version control.
• Create robust and reusable components with advanced techniques and learn different patterns to reuse common behavior.
• Interact with a remote server and fetch and post data via an API.
• Seamlessly test React applications with React Testing Library.
• Integrate commonly used React libraries to streamline your application development.

Course Assessments and Grading 

Course # ECON 4132E

6 Credits 

This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources.

Course Learning Outcomes

Upon the completion of the course, students will be able to:

• Identify data sources to match a research question
• Break a research question down into quantifiable parts
• Administer face-to-face and telephone interviews
• Assess the tradeoffs between these error sources when researchers choose a mode or survey design.
• Design questionnaires for social surveys
• Determine when to apply simple random sampling, cluster sampling, stratification, systematic selection, and stratified multistage samples
• Estimate and summarize the uncertainty of random sampling
• Solve nonresponse and missing data problems through weighting and imputation
• Link records using statistical matching methods

Course Assessments and Grading

Course # DMNS 4180E

6 Credits 

Course Description 

Linear programming is one of the most versatile and powerful mathematical programming techniques that can be employed for efficiently solving a stylized class of decision problems. Linear programming has been used with marked success to solve optimization problems in areas such as economics (including banking and finance), business administration and management, agriculture and energy, contract bidding, nutrition (diet) planning, health care, public decision making, facility location, transportation, strategic planning, and so on.  In this course, students will study mathematics that deals with various real-world situations which leads to mathematical models involving linear optimization problems, geometrical solution of two-variable problem, simplex algorithm, dual problems, sensitivity analysis and transportation problem. 

Course Learning Outcomes 

Upon completion of this course, students should be able to: 

• Formulate a mathematical model of a real-life problem.
• Solve the formulated mathematical problem with the use of different algorithms.
• Translate the results back into the context of the original problem. 
• Solve linear programming problems with R studio.

Course Assessments and Grading 

Course # EAES 4751E

6 Credits 

Course Description  

Programming in Python is an introductory course that covers programming techniques and tools to manipulate, manage, and analyze relevant data. The course focuses on the Python programming language that students will use to solve statistical analysis and GIS problems, apply Machine Learning and Deep Learning techniques, and create a website using Django framework. The tasks will be accomplished by identifying and using existing Python packages as well as appropriate open-source software extensions. The course introduces basic to advanced statistical functions, data visualization, and data manipulation techniques. The relevant functions in data science are explained. The main goal of this course is to give students an understanding of the breadth of different programming applications. In particular, students will be taught how to design and write effective code using Python to perform routine and specialized data manipulation, management, statistical analysis, GIS analysis, and web application development tasks.  

Course Learning Outcomes 

Upon the completion of the course, students will be able to:  

• Explain the theoretical concepts of different data types
• Conceptualize and create loops and if/else statements in Python
• Create specialized functions in Python to handle results
• Manipulate data for descriptive statistical analysis in Python
• Use Django framework for development of different types of websites, in particular, a highly customizable app, such as an internet magazine website
• Use special packages, such as panda, to create graphs and convert plain text to formatted text.
• Using the packages NumPy, Matplotlib, Pandas and Skikit-Learn for various mathematical calculations, data manipulation, graphing and creating machine learning algorithms.

Course Assignments and Grading