In the past couple of weeks there has been quite stir about a proposal from the Republican US Representative from Wisconsin, Paul Ryan. Much of the initial analysis has focused on the usual partisan, and political dialogue about Federal spending and the role of the Government. There has been few close looks at the actually analysis used to put the budget together or the real ramifications of passing this kind of fiscal package.
One first attempt at analyzing some of the basic assumptions is a piece written by NY Times columnist, Paul Krugman. Krugman has never been shy about his political leanings and has often written explicitly about his liberal leaning. However, in this piece he did a pretty good job about setting his partisan leaning and focused basics. He points to the suspicious decrease in unemployment in the next 10 years from close to 9% to under 3%, something that is a) never achieved since World War 2 and b) is not possible in a sustainable economy. Krugman also points to the unrealistic decrease in non-defense, non-entitlement funding from 12% of GDP at current levels to 3.5% of GDP in 2021.
Still, as much as these 2 assumptions undermine the budget, there is still much to desired in looking fully at this budget or the potential ramifications. This void was filled yesterday by the incredibly credible, non-partisan research group Macroeconomics Advisers (Macro Advisers)in a post on their company blog. Macro Advisers test many of the assumptions used in analysis used by the authors of Rep Ryan's Budget, originally put together by the conservative Heritage Institute. After much simulation, Macro Advisers have founds several flaws in the analysis, and that the economic models were intentionally altered to produce favorable results for the Ryan budget. They point to everything from GDP growth expectations, unemployment projections, interest rate assumptions, and the relationship between spending contractions and capital investment.
The most disturbing part about the Macro Advisers analysis is that it points to the intentional fabrication of economic analysis to produce ideologically based policy that has the potential to derail economic recovery and effect the livelihood of the country. Policy makers need take fiscal policy more seriously and consider how it impacts the citizenry. Fiscal policy that impacts such a large percentage of the population should not be treated as a political game, but should consider all potential economic and social consequences.
Thursday, April 14, 2011
Thursday, January 6, 2011
NYT: Assessing the Housing Market by CASEY B. MULLIGAN
There was a great article issued in the New York Times yesterday about the difficulties in assessing the the national housing market. It was written by Casey B. Mulligan, and economic professor at the University of Chicago. Although I often disagree with the works that comes out of the Economics Department at the University of Chicago because of its theoretical conservative bent, Mulligan has stood out as a pragmatic centrists regarding economic policy (Examples of other pieces written by him that I recommend are: Sticky Wages, Sticky Prices and the Keynesians (Dec. 15, 2010); and Stop, Thief! (Nov. 19, 2010).
I have included his most recent article below, as a great example of boiling down the complexity in markets, and the policy that is drafted to affect them. I hope to include more of his works, along with my commentary and analysis in the future.
Assessing the Housing Sector
By CASEY B. MULLIGAN
The New York Times: January 5, 2011.
http://economix.blogs.nytimes.com/2011/01/05/assessing-the-housing-sector/?ref=business
Casey B. Mulligan is an economics professor at the University of Chicago.
A few economists are contending that our housing market is now in a “double dip,” based in part on last week’s report of housing price indexes for September and October that were lower than they were during the summer. In my opinion, the data on housing prices and construction do not show any significant housing market change during the second half of 2010.
When connecting the housing sector with the wider economy, three different measures of housing prices are helpful: inflation-adjusted housing prices, inflation-unadjusted housing prices and cost-adjusted housing prices.
Inflation-adjusted housing prices tell us how much the prices of homes have changed relative to the prices of other consumer goods. If, for example, we want to know whether demand for housing these days is any different than it was before the housing bubble, it helps to check whether, from the 1990s through 2010, housing prices failed to increase as much as other prices have.
In this case I look at a housing price index that has been normalized by a consumer price index.
Inflation adjustments are not appropriate for the purposes of analyzing foreclosures – a big drag on our economy – because the mortgage principal that pulls homeowners “under water” is not adjusted for inflation either. If unadjusted housing prices increase, even if more slowly than other consumer prices, that helps homeowners swim out of the water.
For this purpose, I look at an index of the dollar value of housing properties, without any adjustment for inflation.
For the purposes of understanding construction activity, it helps to know whether housing prices have increased more than the costs of building materials. The more that housing prices increase beyond the cost of materials, the more value that can be created by home construction activity.
For this purpose, I look at an index of housing prices that has been normalized by an index of building costs.
It turns out that practice is messier than theory, because there are so many different houses in America and many different price trends. In practice, it matters which housing price index is used, regardless of which inflation or cost adjustment is used.
The Case-Shiller repeat sales index is one such index of existing homes. The Federal Housing Finance Agency has another index of existing homes (and there are others, as well). The Census Bureau has a quality-adjusted index of new home prices.
Chart 1 displays the three aforementioned home price indexes, measured quarterly without any inflation adjustment. The Case-Shiller index for the third quarter of 2010 (the first quarter without the government’s home buyer tax credit) was essentially the same as in the previous quarter. The other two indexes show slight decreases over the same time period, although well within the range of ups and downs over the previous six quarters.
By themselves, these data suggest that homeowners did not go significantly deeper under water in the third quarter and that the housing market trends were not dramatically different in the third quarter than in previous quarters.
Chart 2 displays the same three indexes, adjusted by the implicit price deflator for consumer spending. Because inflation has been low recently, it shows a similar pattern to Chart 1. By themselves, these series show no dramatic change in housing demand over the most recent quarter.
Chart 3 displays the same three indexes, adjusted by the producer price index for home building materials. Deflated this way, the Case-Shiller index actually shows a housing price increase from the second to the third quarter. That’s because building costs peaked in May and have been lower since then.
Without home prices falling by this measure, we do not expect construction activity to be lower than it was during 2009 (but, unsurprisingly, lower than it was during the short rush to sell homes before the tax credit expired).
You may notice that various housing price indexes disagree, and our most recent data is still three months old. Yet another approach is to look at home construction activity. Chart 4 displays monthly home construction activity through November 2010, measured as the number of housing permits, housing starts, homes under construction and homes completing construction.
Permits and starts are particularly interesting, because homes take time to build and we presume that many builders are looking ahead to the prices homes will command in the future, when the construction project is complete. Those series were actually higher in November 2010 than they were for several months before.
Predicting the future is difficult, but the price and construction data so far do not seem to suggest that home values will be significantly different this year than they were in 2010.
I have included his most recent article below, as a great example of boiling down the complexity in markets, and the policy that is drafted to affect them. I hope to include more of his works, along with my commentary and analysis in the future.
Assessing the Housing Sector
By CASEY B. MULLIGAN
The New York Times: January 5, 2011.
http://economix.blogs.nytimes.com/2011/01/05/assessing-the-housing-sector/?ref=business
Casey B. Mulligan is an economics professor at the University of Chicago.
A few economists are contending that our housing market is now in a “double dip,” based in part on last week’s report of housing price indexes for September and October that were lower than they were during the summer. In my opinion, the data on housing prices and construction do not show any significant housing market change during the second half of 2010.
When connecting the housing sector with the wider economy, three different measures of housing prices are helpful: inflation-adjusted housing prices, inflation-unadjusted housing prices and cost-adjusted housing prices.
Inflation-adjusted housing prices tell us how much the prices of homes have changed relative to the prices of other consumer goods. If, for example, we want to know whether demand for housing these days is any different than it was before the housing bubble, it helps to check whether, from the 1990s through 2010, housing prices failed to increase as much as other prices have.
In this case I look at a housing price index that has been normalized by a consumer price index.
Inflation adjustments are not appropriate for the purposes of analyzing foreclosures – a big drag on our economy – because the mortgage principal that pulls homeowners “under water” is not adjusted for inflation either. If unadjusted housing prices increase, even if more slowly than other consumer prices, that helps homeowners swim out of the water.
For this purpose, I look at an index of the dollar value of housing properties, without any adjustment for inflation.
For the purposes of understanding construction activity, it helps to know whether housing prices have increased more than the costs of building materials. The more that housing prices increase beyond the cost of materials, the more value that can be created by home construction activity.
For this purpose, I look at an index of housing prices that has been normalized by an index of building costs.
It turns out that practice is messier than theory, because there are so many different houses in America and many different price trends. In practice, it matters which housing price index is used, regardless of which inflation or cost adjustment is used.
The Case-Shiller repeat sales index is one such index of existing homes. The Federal Housing Finance Agency has another index of existing homes (and there are others, as well). The Census Bureau has a quality-adjusted index of new home prices.
Chart 1 displays the three aforementioned home price indexes, measured quarterly without any inflation adjustment. The Case-Shiller index for the third quarter of 2010 (the first quarter without the government’s home buyer tax credit) was essentially the same as in the previous quarter. The other two indexes show slight decreases over the same time period, although well within the range of ups and downs over the previous six quarters.
By themselves, these data suggest that homeowners did not go significantly deeper under water in the third quarter and that the housing market trends were not dramatically different in the third quarter than in previous quarters.
Chart 2 displays the same three indexes, adjusted by the implicit price deflator for consumer spending. Because inflation has been low recently, it shows a similar pattern to Chart 1. By themselves, these series show no dramatic change in housing demand over the most recent quarter.
Chart 3 displays the same three indexes, adjusted by the producer price index for home building materials. Deflated this way, the Case-Shiller index actually shows a housing price increase from the second to the third quarter. That’s because building costs peaked in May and have been lower since then.
Without home prices falling by this measure, we do not expect construction activity to be lower than it was during 2009 (but, unsurprisingly, lower than it was during the short rush to sell homes before the tax credit expired).
You may notice that various housing price indexes disagree, and our most recent data is still three months old. Yet another approach is to look at home construction activity. Chart 4 displays monthly home construction activity through November 2010, measured as the number of housing permits, housing starts, homes under construction and homes completing construction.
Permits and starts are particularly interesting, because homes take time to build and we presume that many builders are looking ahead to the prices homes will command in the future, when the construction project is complete. Those series were actually higher in November 2010 than they were for several months before.
Predicting the future is difficult, but the price and construction data so far do not seem to suggest that home values will be significantly different this year than they were in 2010.
Friday, July 23, 2010
This may come out of left field; however, this is something that I have been working on for the past 2 months. As an ever increasing amount of pressure is placed on the financing of public programs, there will be greater need to evaluate the cost effectiveness of public programs. In public health programs in particular, there is a push to measure the effectiveness in terms of Quality-Adjusted Life Years (QALYs). The measurement of public health programs in terms of QALYs has become common practice in Europe, and is a key part of the success of the National Health System in England. I have put together a brief on the use of QALYs in cost-effectiveness analysis.
Cost-Effectiveness Analysis
Driven by the increasing scarcity of public health program resources, and additional pressure for improved efficiency, a great deal of research has been conducted on the method for conducting cost-benefit (CBA) and cost-effectiveness (CEA) analysis for public health programs. Although there are differences in cost-benefit analysis and cost-effectiveness analysis, the substantial differences exist between the two analyses in the measurement of the benefit (the denominator), and not in the program costs (the numerator). The similarities in the measurement of program costs, increase the scale of literature that can be studied for the purpose PDA’s research on the inclusion of costs.
Seeking to standardize the methodology for cost-effectiveness analysis a Panel on Cost-Effectiveness in Health and Medicine (the Panel) was formed by the US Public Health Service and issued recommendations in 1996. (Weinstein et al. pp. 1253 – 1255) The recommendations of the Panel listed the costs that should be included:
• Costs of health care services
• Costs of patient time expended for the intervention
• Costs associated with care giving
• Other costs associated with illness, such as child care and travel expenses
• Economic costs borne by employers, other employees and the rest of society, including so-called friction costs associated with absenteeism and employee turnover
• And costs associated with non-health impacts of the intervention, such as on the educational system, the criminal justice system, or the environment.
The costs recommended to measure by the Panel include both indirect and direct costs associated with an intervention or treatment. (Henderson p. 126) The direct costs are those incurred in providing care; whereas, indirect costs are those not associated with the transactions for goods or services. Additional recommendations by the panel regarding the inclusion of costs were: costs included in the numerator should be measured in constant dollars, include time costs at the valued wage of workers, and should consider the opportunity cost. To account for the opportunity cost, it should measure the marginal cost of the intervention, not the total cost of the program. “Costs unaffected by the level of implementation of an intervention should generally be excluded from consideration.” (Weinstein et al. p. 1255)
Regardless of the recommendations passed by the Panel, the quality of implementation of cost-effectiveness analysis in the United States varies greatly. A review of 14 public health cost-effectiveness studies from 1998-2002 has shown different methodologies used for each. (Gross et al. pp.369) However, there is an amount of literature around best practice in cost-effectiveness analysis in public health, and the promotion of standardized practices.
The direct costs associated with smoking cessation interventions can be decomposed into four components: the screening costs; the costs associated with advising smokers; motivating unwilling smokers; and the direct intervention costs incurred in helping smokers quit. (Cromwell et al. p. 1760) These costs can also be characterized as the cost identification, or “the calculation of the cost of an intervention as the opportunity cost of resources consumed.” (Grosse et al. 370) These costs would differentiate themselves among the five categories of intervention: minimal, brief, full, individual intensive and group intensive. The need to differentiate among the variation of the five categories is due to the relationship between intervention intensity, cost and effectiveness. As the intensity of a tobacco intervention program increases, so do the costs and the effectiveness, affecting both sides of the cost-effectiveness ratio. (Parrott and Godfrey p. 948) Examples of specific costs to be included are: labor costs, materials and supplies, pharmacotherapy or NRT, professional services, screening, administrative, patient time, and more.
The indirect costs measured are the productivity costs that are not associated with the intervention, also called the social costs. (Henderson p. 126) These costs are the future costs of intervention, as compared with no intervention, and reflect the cost of the illness. Inclusion of indirect productivity costs increase the relative cost-effectiveness of interventions that promote greater survival among younger or working-age adults relative to interventions that promote survival at older ages, but in general do not affect cost-effectiveness rankings. (Grosse et al. 370) Examples of individual productivity costs are the economic value of lost leisure time, or the morbidity that results in time lost from work. The productivity costs of the intervention program are not limited to the individual participant, but can extend to external systems, such as the non-health impacts on the education system, criminal justice system or the environment. (Weinstein et al. p. 1255)
Quality-Adjusted Life Years
The use of utility units as the denominator in cost-effectiveness analysis varies among multiple sources and studies. The Panel on the Cost Effectiveness of Health and Medicine has recommended that cost-effectiveness analysis use quality adjusted life years as a measurement of benefit from public health programs. (Weinstein et al. 1996) Quality-adjusted life years (QALY) are a representation of “a patients’ perception of the reduction in value of one year in perfect health due to pain, disability, and suffering caused by illness.” (Henderson, p. 127) Perceptions are measured as a weight, determined from surveys where individuals are asked a series of questions about their perception of their current health status, and their health status preference. The advantage of consistent measurement of benefits through QALY allows for the cost per benefit measured across different health interventions, assisting decision-makers to determine the most efficient way to furnish health benefits. (Neumann and Greenberg, 2009)
How a QALY is calculated
Patient x has a serious, life-threatening condition.
• If he continues receiving standard treatment he will live for 1 year and his quality of life will be 0.4 (0 or below = worst possible health, 1= best possible health)
• If he receives the new drug he will live for 1 year 3 months (1.25 years), with a quality of life of 0.6.
The new treatment is compared with standard care in terms of the QALYs gained:
• Standard treatment: 1 (year’s extra life) x 0.4 = 0.4 QALY
• New treatment: 1.25 (1 year, 3 months extra life) x 0.6 = 0.75 QALY
Therefore, the new treatment leads to 0.35 additional QALYs (that is: 0.75 -0.4 QALY = 0.35 QALYs).
• The cost of the new drug is assumed to be £10,000, standard treatment costs £3000.
The difference in treatment costs (£7000) is divided by the QALYs gained (0.35) to calculate the cost per QALY. So the new treatment would cost £20,000 per QALY. (NHS, 2010)
Concerns with QALYs
Concern has been expressed about the use of QALYs in cost-effectiveness analysis due to the individual measurement of health preference weights, population measured for health preference, and the equity of measurement. In determining the health preference weight, many researchers use a scale 0-1, with 1 being perfect health, and 0 being death; however, the questions used to measure preference may vary, affecting individual responses. (Neumann and Greenberg, 2009) Additionally, the population surveyed to determine health preference can dramatically affect the preference weight. Consensus has not been achieved on whether health preference should reflect those that are ill, or those that are not, each having different measured health preference. Individuals with illness value their health state, even if diminished, more than those that are not ill. (Neumann and Greenberg, 2009) The variation in the measurement of health preference weights limits the ability to compare the cost-effectiveness across multiple forms of health interventions and treatments. (McGregor and Caro 2006) Finally, debate exists on the issue of fairness in the use of QALYs to measure cost-effectiveness. Because QALY is a measurement of marginal utility over time, programs that assist disabled or elderly populations will always have a worse cost per QALY ratio, than those that benefit younger populations. (Grosse et al. 2007) Some researchers argue that due to concerns over fairness, alternative measures should be used, such as willingness-to-pay; although similar methodological issues exist for alternatives.
Although there are several concerns regarding the use of QALYs as a measure of health benefit, the need of a standardized measure for health benefit is consistently referenced. Even with variation in methodologies, flexible use of QALYs could be beneficial for decision-makers. (Neumann and Greenberg, 2009) International partnerships have been formed to resolve issues regarding standardization of methodology, and acceptability, and resources have been created to assist those interested in pursuing QALY cost-effectiveness studies.
Cost-Effectiveness Analysis
Driven by the increasing scarcity of public health program resources, and additional pressure for improved efficiency, a great deal of research has been conducted on the method for conducting cost-benefit (CBA) and cost-effectiveness (CEA) analysis for public health programs. Although there are differences in cost-benefit analysis and cost-effectiveness analysis, the substantial differences exist between the two analyses in the measurement of the benefit (the denominator), and not in the program costs (the numerator). The similarities in the measurement of program costs, increase the scale of literature that can be studied for the purpose PDA’s research on the inclusion of costs.
Seeking to standardize the methodology for cost-effectiveness analysis a Panel on Cost-Effectiveness in Health and Medicine (the Panel) was formed by the US Public Health Service and issued recommendations in 1996. (Weinstein et al. pp. 1253 – 1255) The recommendations of the Panel listed the costs that should be included:
• Costs of health care services
• Costs of patient time expended for the intervention
• Costs associated with care giving
• Other costs associated with illness, such as child care and travel expenses
• Economic costs borne by employers, other employees and the rest of society, including so-called friction costs associated with absenteeism and employee turnover
• And costs associated with non-health impacts of the intervention, such as on the educational system, the criminal justice system, or the environment.
The costs recommended to measure by the Panel include both indirect and direct costs associated with an intervention or treatment. (Henderson p. 126) The direct costs are those incurred in providing care; whereas, indirect costs are those not associated with the transactions for goods or services. Additional recommendations by the panel regarding the inclusion of costs were: costs included in the numerator should be measured in constant dollars, include time costs at the valued wage of workers, and should consider the opportunity cost. To account for the opportunity cost, it should measure the marginal cost of the intervention, not the total cost of the program. “Costs unaffected by the level of implementation of an intervention should generally be excluded from consideration.” (Weinstein et al. p. 1255)
Regardless of the recommendations passed by the Panel, the quality of implementation of cost-effectiveness analysis in the United States varies greatly. A review of 14 public health cost-effectiveness studies from 1998-2002 has shown different methodologies used for each. (Gross et al. pp.369) However, there is an amount of literature around best practice in cost-effectiveness analysis in public health, and the promotion of standardized practices.
The direct costs associated with smoking cessation interventions can be decomposed into four components: the screening costs; the costs associated with advising smokers; motivating unwilling smokers; and the direct intervention costs incurred in helping smokers quit. (Cromwell et al. p. 1760) These costs can also be characterized as the cost identification, or “the calculation of the cost of an intervention as the opportunity cost of resources consumed.” (Grosse et al. 370) These costs would differentiate themselves among the five categories of intervention: minimal, brief, full, individual intensive and group intensive. The need to differentiate among the variation of the five categories is due to the relationship between intervention intensity, cost and effectiveness. As the intensity of a tobacco intervention program increases, so do the costs and the effectiveness, affecting both sides of the cost-effectiveness ratio. (Parrott and Godfrey p. 948) Examples of specific costs to be included are: labor costs, materials and supplies, pharmacotherapy or NRT, professional services, screening, administrative, patient time, and more.
The indirect costs measured are the productivity costs that are not associated with the intervention, also called the social costs. (Henderson p. 126) These costs are the future costs of intervention, as compared with no intervention, and reflect the cost of the illness. Inclusion of indirect productivity costs increase the relative cost-effectiveness of interventions that promote greater survival among younger or working-age adults relative to interventions that promote survival at older ages, but in general do not affect cost-effectiveness rankings. (Grosse et al. 370) Examples of individual productivity costs are the economic value of lost leisure time, or the morbidity that results in time lost from work. The productivity costs of the intervention program are not limited to the individual participant, but can extend to external systems, such as the non-health impacts on the education system, criminal justice system or the environment. (Weinstein et al. p. 1255)
Quality-Adjusted Life Years
The use of utility units as the denominator in cost-effectiveness analysis varies among multiple sources and studies. The Panel on the Cost Effectiveness of Health and Medicine has recommended that cost-effectiveness analysis use quality adjusted life years as a measurement of benefit from public health programs. (Weinstein et al. 1996) Quality-adjusted life years (QALY) are a representation of “a patients’ perception of the reduction in value of one year in perfect health due to pain, disability, and suffering caused by illness.” (Henderson, p. 127) Perceptions are measured as a weight, determined from surveys where individuals are asked a series of questions about their perception of their current health status, and their health status preference. The advantage of consistent measurement of benefits through QALY allows for the cost per benefit measured across different health interventions, assisting decision-makers to determine the most efficient way to furnish health benefits. (Neumann and Greenberg, 2009)
How a QALY is calculated
Patient x has a serious, life-threatening condition.
• If he continues receiving standard treatment he will live for 1 year and his quality of life will be 0.4 (0 or below = worst possible health, 1= best possible health)
• If he receives the new drug he will live for 1 year 3 months (1.25 years), with a quality of life of 0.6.
The new treatment is compared with standard care in terms of the QALYs gained:
• Standard treatment: 1 (year’s extra life) x 0.4 = 0.4 QALY
• New treatment: 1.25 (1 year, 3 months extra life) x 0.6 = 0.75 QALY
Therefore, the new treatment leads to 0.35 additional QALYs (that is: 0.75 -0.4 QALY = 0.35 QALYs).
• The cost of the new drug is assumed to be £10,000, standard treatment costs £3000.
The difference in treatment costs (£7000) is divided by the QALYs gained (0.35) to calculate the cost per QALY. So the new treatment would cost £20,000 per QALY. (NHS, 2010)
Concerns with QALYs
Concern has been expressed about the use of QALYs in cost-effectiveness analysis due to the individual measurement of health preference weights, population measured for health preference, and the equity of measurement. In determining the health preference weight, many researchers use a scale 0-1, with 1 being perfect health, and 0 being death; however, the questions used to measure preference may vary, affecting individual responses. (Neumann and Greenberg, 2009) Additionally, the population surveyed to determine health preference can dramatically affect the preference weight. Consensus has not been achieved on whether health preference should reflect those that are ill, or those that are not, each having different measured health preference. Individuals with illness value their health state, even if diminished, more than those that are not ill. (Neumann and Greenberg, 2009) The variation in the measurement of health preference weights limits the ability to compare the cost-effectiveness across multiple forms of health interventions and treatments. (McGregor and Caro 2006) Finally, debate exists on the issue of fairness in the use of QALYs to measure cost-effectiveness. Because QALY is a measurement of marginal utility over time, programs that assist disabled or elderly populations will always have a worse cost per QALY ratio, than those that benefit younger populations. (Grosse et al. 2007) Some researchers argue that due to concerns over fairness, alternative measures should be used, such as willingness-to-pay; although similar methodological issues exist for alternatives.
Although there are several concerns regarding the use of QALYs as a measure of health benefit, the need of a standardized measure for health benefit is consistently referenced. Even with variation in methodologies, flexible use of QALYs could be beneficial for decision-makers. (Neumann and Greenberg, 2009) International partnerships have been formed to resolve issues regarding standardization of methodology, and acceptability, and resources have been created to assist those interested in pursuing QALY cost-effectiveness studies.
Thursday, June 24, 2010
Welcome
As we have seen during the Health Care Debate, facts do not always sway the minds of the citizens. Then, as many times, public opinion was manipulated by media campaigns, and ideas that are designed to trigger an emotional, often irrational, response to a proposed idea in hopes of rallying support or opposition. The discussion that follows becomes devoid of a balanced viewpoint, thoughtful analysis, or often any form of critical thinking. This represents the failure of dialogue in democracy. If our democracy is to work, then the level of dialogue, and the quality of discussion needs to improve, focusing on debates and discussions around the value of policy consequences, and not emotional reactions.
The purpose of this blog is to examine issues in the public interest, and examine them with a thoughtful research based approach. By know means will I attempt to replicate academic literature, that is often out of touch with the political reality, but will attempt to translate, convert or synthesize proposed policy solutions and ideas based on their merit, feasibility and consequences. By no means do I suggest that I know the answers, or that my analysis will be perfect, but I do hope that my posts will assist in elevating the discussion.
The purpose of this blog is to examine issues in the public interest, and examine them with a thoughtful research based approach. By know means will I attempt to replicate academic literature, that is often out of touch with the political reality, but will attempt to translate, convert or synthesize proposed policy solutions and ideas based on their merit, feasibility and consequences. By no means do I suggest that I know the answers, or that my analysis will be perfect, but I do hope that my posts will assist in elevating the discussion.
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