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Public Policy Analysis

Public Policy Analysis. MPA 404 Lecture 8. Previous Lecture.

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Public Policy Analysis

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  1. Public Policy Analysis MPA 404 Lecture 8

  2. Previous Lecture • We went through some of the ways that a particular public policy can have effects upon citizens and their participation in the socio economic activities of the country. For example, we discussed how pluralistic society can help achieve some of the participatory goals. • Ten general attributes of good policy making.

  3. Quantitative Methods for Policy Analysis • What are they used for? They are used for defining a policy problem, evaluating impact of a certain policy and present solutions (potential or alternative). • What kind of quantitative methods are used for policy analysis? From simple ones to very complex one’s. Techniques such as simple modeling and input-output analysis to statistical inference and risk analysis are used as part of quantitative inquiry into the art of public policy. • What do we intend to achieve with the use of these methods? a) Demonstrate whether a causal relationship (specifically in terms of statistical probabilities) exists between policy designs and policy outcomes; b) Gather data in terms of magnitude, which in turn demonstrates the degree of effect of one variable upon another and which parameters were affected to what degree/

  4. Try to find a solution or an alternative to the present scheme of things in case its not working. • Historically, modern research methods are traced to Laswell’s opinions about public policy. Before Laswell there were methods of analysis, but they were rather simple and their scope was limited. Sophisticated techniques like input-output analysis, operations research and econometrics are post-WWII phenomenon. • Although quiet a few of them (like system engineering and input-output analysis) were used for military purposes, these gradually found its way in other fields of analysis like public policy, economics, agriculture, etc. • Perhaps the biggest boost and incentive to the use these kinds of methods came when their efficacy and place was recognized in government circles. For example, in the US, the government established the Council of Economic Advisors to the President for the purpose of applying the latest techniques of analysis to the economy.

  5. With time, it was realized that the application of these methods can be realized in other areas too. So, for example, these advanced techniques of analysis were being applied to problems such as environmental analysis. • With the expanding role of the government in terms of social and general welfare programs came the expanding scope of public policy analysis and the establishment of dedicated public policy departments in universities. The US was leader in this respect. These developments were ably complemented by advances in computing technology and statistical/mathematical modeling (especially in the sphere of economics). • The application of sophisticated methods of quantitative technique has had its criticism. Trying to interpret everything based on statistical and mathematical techniques has been shown to be a short of reality in many instances. The latest instance being the great recession that hit the western nations in late 2007 and whose jitters are still being felt. Critics point out that for all the advances in modeling and computing, sophisticated models and despite the employment of an army of quant's (those who are master’s at application of advanced quantitative techniques), not one of them was able to predict the coming recession.

  6. It has often been pointed out by critics that quantitative methods lack the humane, philosophical, intuitive side of policy making and doesn’t take into account these factors. For example, it is heavily reliant on tangible results rather than intangible results (remember the discussion about educating a child and its later positive effects). Econometrics and like programs may fail to catch those intangibles. • There is now an increasing movement from positivism (over-reliance on advanced quantitative techniques) towards post-positivism, which is a movement of incorporation of views (such as moral and ethical parameters) to complement the statistical techniques. • In general, if an individual wants to be a good public policy analyst, having a very good knowledge of statistics and statistical techniques (as applied to public policy analysis) is necessary (if not a must). But knowing the bare minimum of the basis of statistical theory is a must.

  7. Frequently used quantitative methods • The statistical techniques are based on data drawn from samples of population, about which a hypothesis is conceived. That hypothesis is then tested through a quantitative technique in order to see whether the result, as posited by theory, is true or false. This whole process is termed as ‘Hypothesis Testing’, with two probable conceptions: null or alternative hypothesis. • The statistical techniques can be bifurcated into two classes by use of data: descriptive statistics and inferential statistics. The former has more to do with organizing statistics while the latter is concerned with inferring a pattern from data about the sample. • Univariate or Bivariate Analysis Univariate is in the same category as descriptive statistics, with concepts like median, mode and standard deviation. It gives us a unique value (‘Uni’ means one) from the dataset. Bivariate analysis is more in tune with the inferential statistics category.

  8. What people try to do is that they try to get a statistical relation between two variables (‘Bi’ implies two), independent and dependent variable. The statistical test’s are conducted in order to determine the strength, direction and nature of relationship between the dependent or independent variable. For example, one of the most famous causal relationships in economics is between money supply and inflation. But the strength of the relationship varies from country to country or situation to situation. In other words, increasing money supply may have a drastic effect upon inflation or a mild one. Moreover, depending upon the nature of the variables (are they numbers, ratio’s, etc), different inferential techniques for testing come into play. • Analysis of Variance (ANOVA): It is often the case that no two groups are the same when it comes to specific characteristics. What ANOVA does is determine whether samples are from populations with same means. Through the use of F-test, it tends to determine whether bias (if any) is attributed the way of sampling, or a judgment error (how sample population has been conceptualized). Used for obtaining results in comparative studies. Book example of on job vs. school training and its effect upon income.

  9. Discussion: Why couldn’t the quant's predict the recession

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