Methods and Procedures

1. Methods and Procedures Chapter 9

2. Distinctions between Methods and Methodology Methods - tools or techniques applied in the research process Procedures – a way we put the tools and techniques together in sequence or combinations to achieve objectives For methodology, the appropriate methods and procedures need to be carefully selected and applied to achieve the objectives and produce reliable knowledge

3. Treatment of Methods and Procedures • Perspective on planning the project and the written proposal • Proposal contains planned methods and procedures • Final report contains the actual methods and procedures • The proposal contained planned methods and procedures and there may be reason to alter the plans as the research proceeds.

4. Organization of the Chapter • Purposes of Methods and Procedures • A Historical Perspective on Empirical Methods • Models in Economic Research • Types of Empirical Methods • Data Considerations • Procedural Suggestions • Summary

5. 1. Purposes of Methods and Procedures • To provide the plan, its description and how the objectives will be achieved • It is the what, why and how of the research project • How: order, steps, model specification, analytical methods, data collection and administration and input, how tested, how results being interpreted etc.. • How does the methods relate to the activities

6. Methods and Procedures • Levels of details – depends on the type of work (extension, industry) and expectations of your committee • To specify the approach for testing hypothesis • Can be both qualitative or quantitative • Directly addresses the objectives • Research objectives are derived from the research problem • The research methods and procedures are driven by the problem and objective not the other way around • But if the objective is to test a new technique then it is different

7. Methods and Procedures • Choice of methods and procedures important Focus on : • Identifying meaningful researchable problems • Specifying appropriate objectives • Developing appropriate methods and procedures to achieve those objective

8. Sample Problem Clearly, if 1.3 billion Chinese people continue to use plastic bags on a regular basis, there will be dire consequences on China’s environment, as there already is in the major cities of Beijing, Shanghai, Guangzhou and Tianjin. Actions must be taken to reverse this destructive trend, before its impacts become irreversible.

9. Sample Objectives • The objective of this study is to determine consumer preferences for shopping bags made from alternative materials and to determine the tradeoffs among the important purchasing attributes for the purchaser of these alternative-material bags.

10. Specific Objectives • (1) to evaluate the attributes of shopping bags which are important to consumers, (2) to determine the socio-economic demographics which might affect their buying preferences, and, (3) to discuss the results and marketing implications.

11. Sample Methods And Procedure • (1) developed a conjoint choice experiment survey to collect data on consumer preferences, (2) conducted the survey and collected data from several markets in Tianjin, (3) analyzed the data with latent class method and, (4) made conclusions and examine the implications. • http://www.ctahr.hawaii.edu/nrem/staff/downloads/20091006_Formatted.pdf

12. Historical Perspective on Empirical Methods • The experimental method by Mills • Two experimental techniques • Method of agreement • Method of difference

13. Method of Agreement • Two classifications- • Positive Canon of Agreement -PCA • Negative Canon of Agreement • PCA - When multiple occurrences of a given phenomenon have one condition in common that condition is regarded as the cause of the phenomenon • Relationship established between condition C and phenomenon X

14. Negative Canon of Agreement • Reasons from the absence of both the conditions and phenomenon • The experiment without C results in the absence of phenomenon X • Provides stronger relationship is the canon of agreement

15. Method of Difference • Combination of the Positive and Negative Canons of Agreement • Control case – without C , no X • Experimental case – with C, presence of X This is much stronger evidence of relationship between C and X than the previous Canons

16. Causation vs Relationship • Experimental methods do not have the capability to establish causation • Our techniques be they observational or statistical, can establish only associations • They help us determine, often probabilistically, whether things are related to one another

17. Evidence of causation is derived by first developing hypotheses of direction of causation from conceptual reasoning (theory) • Then examine for evidence of the expected relationship. • If empirical evidence exists to support the relationships, then it supports the hypothesis of causation. • The causative implications come from the conceptual reasoning (theory) rather than the empirical evidence • Example: Higher avocado production is hypothesized to be influenced by higher price

18. Research is concerned with isolating and quantifying effects of individual conditions • In economics, we try to understand and/or quantify the effects of the different variables on a particular economic phenomenon independent of the effects of the other relevant variable. • E.g. higher price negatively effects on consumption – ceteris parabis • Understanding each is important to understand the collective influence

19. Control of variable in economics is difficult because of the complexity of systems and phenomena studied • Necessitate the adoption of statistics to control for the effects of variables in economics • Through techniques such as regression analysis, a means for statistical ‘control’ of other forces was provided • The field of econometrics grew from this emphasis which greatly helped with the advent of high speed computers and technology • Econometrics – technique based on economic theory and applying statistics

20. Models in Economic Research • Economic models are abstractions from reality, developed in whole or part from theory, often expressed in mathematical format to provide: • Explanations and predictions • Discovery • Description and illustration • They could be use with or without data • When the models are constructed with the intent of estimating structure or parameters, the model constitutes a form of hypothesis

21. Purpose of a Model • Explain relationships or system works • To identify factors or forces that drive a phenomenon • Explain with specificity how those forces act and interact to cause the phenomenon • Adaptation of theory to a set of phenomenon forms a model • The model can use to predict the direction of change and how policy instruments can affect change

22. Model SpecificationP (i) = f (C, M, T, D, A, GE, HI, ED, CO) P (i) = Probability of choosing product profile A vs. B, C = Shopping bag cost, taking values of 0.3 CNY, 1.5 CNY, or 3.0 CNY. M = Types of materials, biodegradable plastics, degradable plastics, paper, and cloth. T = Number of reuse times, taking values of 1, 5 and 30. D = Time it takes for the material to naturally degrade, taking the values of 1.5 month, 3 months, and 100 years. A = Age group: 16 to 18, 19 to 29, 30 to 39, 40 to 49, 50 and above. GE = Gender: Male or Female. HI = Household income group (per month) : <3,000 CNY, 3,000 to 5,000 CNY, and > 5,000 CNY. ED = Educational attainment group: elementary school diploma, junior high school diploma, high school diploma, bachelor degree and above. CO = Plastic bag consumption per week, per household: <10, 10 to 20, and >20.

23. EXPLAIN RELATIONSHIPS P (i) = f (C, M, T, D, A, GE, HI, ED, CO) - ? + - ? +/-

25. Predict Direction and Policy

26. Models in Economic Research • When we merge data and theory in a model with a particular intent, we build an empirical model. • Empirical models can be classified as econometric, optimization, or simulation

27. Classification of Empirical Models • Econometric – stochastic (some probability of error and positive – from actual data) • Optimization – normative (from desired objectives, stochastic (probability distributions are derived from outside the model or non-stochastic) • Simulation –positive and non-stochastic

28. Notes and Caveats • Optimization and simulation models typically involve no statistical hypothesis testing but are often the most effective in cases in which there is no way to observe the phenomena being studies. • They are not mutually exclusive. Econometric models can be used for simulation. • Models have limitations – means to an end • “Employment of mathematical methods does not substitute for rigorous theoretical formulations. Rather, it itself requires more precise understanding of the constructs of which the discipline is constituted” Breimyer 1991. • Economists often mistaken statistical significance for scientific significance.

29. Methods • The Descriptive Method • Statistical and Econometric Tools • Simple statistical estimations and determinations of fit and distribution (t-test, F-test) • Single equation multivariate statistical analysis (regression, probit, logit estimated with OLS etc.) • Structural econometric models (Systems of simultaneous equations, economy model, 2SLS) • Time-series methods/models (concentrates on the behavior of (economic) variables or systems of variables through time)

30. Simple Regression

33. Multiple Regression Y = a + b1*X1 + b2*X2 + ... + bp*Xp http://videolectures.net/ssmt09_kittel_mra/

34. Model SpecificationP (i) = f (C, M, T, D, A, GE, HI, ED, CO) P (i) = Probability of choosing product profile A vs. B, C = Shopping bag cost, taking values of 0.3 CNY, 1.5 CNY, or 3.0 CNY. M = Types of materials, biodegradable plastics, degradable plastics, paper, and cloth. T = Number of reuse times, taking values of 1, 5 and 30. D = Time it takes for the material to naturally degrade, taking the values of 1.5 month, 3 months, and 100 years. A = Age group: 16 to 18, 19 to 29, 30 to 39, 40 to 49, 50 and above. GE = Gender: Male or Female. HI = Household income group (per month) : <3,000 CNY, 3,000 to 5,000 CNY, and > 5,000 CNY. ED = Educational attainment group: elementary school diploma, junior high school diploma, high school diploma, bachelor degree and above. CO = Plastic bag consumption per week, per household: <10, 10 to 20, and >20.

35. EXPLAIN RELATIONSHIPS P (i) = f (C, M, T, D, A, GE, HI, ED, CO) - ? + - ? +/-

36. Parameter Estimates

37. Results • Class 1 are degradable plastics (+ sign) and non-degradable plastics (-), cost (-) and degradation period (-). Therefore, Class 1 respondents prefer bags made of degradable material, lower cost, and less time for the material to degrade naturally. These signs are expected and significant at the 0.05 or 0.01 levels. • For Class 2, the significant attributes found in this group are degradable (+) and non-degradable plastics (-), reuse times (+), and degradation period (-). Again, the signs are expected and they are all significant at the 0.01 level. Cost has the expected negative correlation in this class, but was not significant. Class 2 respondents prefer degradable plastics and bags that can be used many times, and do not prefer non-degradable plastic bags that take a long time to degrade. Cost and bags made of either cloth or paper are not important for this group. • For Class 3, the significant attributes are cloth (+), non-degradable plastics (-), paper (+), and time it takes to degrade (-). These parameters are all significant at the 0.05 level. • In Class 4, all parameters except for degradable plastics and paper are significant and have the expected signs. • Class 5 respondents do not prefer high cost (-). They prefer paper (+), and higher number of times the bag can be reused (+). These parameters are significant at the 0.05 or 0.01 level.

38. Operations Research Tools • Optimization Techniques • Linear Programming –use for research and management applications; min or max obj. ftns s.t some constraints. • Nonlinear Programming – obj. ftns non-linear • Simulation Techniques –positivistic, nonstochastic • Budgeting –enterprise budget (B/C) • Mathematical Simulation –I/O, CGE to simulate an outcome • Probabilistic Simulation- use information on probabilities as the basis of their simulations.

42. Financial Analysis Enterprise Budget Cash Flow Analysis Sensitivity Analysis

43. Enterprise Budget Analysis Political Risk Insurance = $200 farm/month

44. Enterprise Budget The enterprise budget for Humpback grouper shows positive returns only when the live fish are transported and sold in Hong Kong at average wholesale prices Revenue = $157,500 per cycle/farm Total cost = $81,588 Net Return = $44, 410 cycle/farm Net Return to Enterprise = $532,920 cycle

45. Financial Analysis Enterprise Budget Cash Flow Analysis Sensitivity Analysis

46. Cash Flow Analysis

47. Cash Flow Analysis Minimum Investments: $140,000/farm $2.1 million/enterprise (10-year) IRR of baseline model ($60/kg) for humpback grouper =38%

48. Methods for Financial Analysis Enterprise Budget Cash Flow Analysis Sensitivity Analysis

49. Sensitivity Analysis:Market Prices vs. Fish Survival Rates Baseline farmgate price of $25/kg Low: $15 & High $35 Baseline wholesale price of $60/kg Low: $40/kg & High: $80/kg Survival Rates between 50% and 90%

50. Sensitivity Analysis: Market Prices vs. Fish Survival Rates