January 29, 2014

Strategic Renewal


Strategic renewal includes the process, content, and outcome of refreshment or replacement of attributes of an organization that have the potential to substantially affect its long-term prospects. (Agarwal and Helfat, 2009)

 Four tests for deciding whether your company is ripe for strategic renewal:
1. Your profits are dominated by maturing businesses in which you see limited opportunities for growth.
2. There is a direct threat to your core source of profits.
3. An attractive opportunity  is outside your core markets.
4. New ways of making money by some firms are a threat to your core source of profits.


Strategic Renewal of Organizations
Organization Science, March April 2009 pp. 281-293

http://sloanreview.mit.edu/article/the-art-of-strategic-renewal/?social_token=18cdc238646c57a349dcca2aeb266af4&utm_source=facebook&utm_medium=social&utm_campaign=sm-direct




Strategic renewal is indicated by analysis of  firm's external environment.

January 13, 2014

PDCA requires IMPROVEMENT Model



PDCA requires IMPROVEMENT model.

Penn State came out with an Improvement Model. The model can be explained as




I   - Identify the Process for Improvement
M - Map the Process
P - Perform Analysis
R - Research, Evaluate and  Develop the improvement solution
O - Organize and Implement the solution
V - Verify the results,  make adjustments and standardize the process
E - Engage the Employees in Continuous Improvement

http://www.psu.edu/president/pia/innovation/insights007.pdf

Product-Process Feature-Performance and Efficiency Innovations




Product-Process Feature-Performance  Innovations




DEVELOPING A PRODUCT INNOVATION AND TECHNOLOGY STRATEGY FOR YOUR BUSINESS
Reference Paper #39 , 2010
By Dr. Robert G. Cooper and Dr. Scott J. Edgett
http://www.stage-gate.net/downloads/working_papers/wp_39.pdf


Product and Process Innovation
A System Dynamics-Based Analysis of the Interdependencies
Peter M. Milling and Joachim Stumpfe
2000 Conference Papers

In addition to numerous interactions with the environment the complexity of innovation processes in industrial
companies results from interactions between product and process innovation. An effective innovation management has to take these interdependencies into account coming to a congruent implementation of the different types of innovation.

http://www.systemdynamics.org/conferences/2000/PDFs/milling1.pdf


INNOVATION TYPOLOGIES
Rajesh Chandy  and Jaideep Prabhu
http://faculty.london.edu/rchandy/innovation%20typologies.pdf

Product-Process  Efficiency Innovations

Innovative Ideas - Improving Efficiency at Ontario Universities
2011 report
http://www.cou.on.ca/publications/reports/pdfs/innovative-ideas---improving-efficiency-at-ontario


Innovations for Resource-Efficient Production
http://www.bmbf.de/en/12596.php

Energy efficiency and U.S. competitiveness Applying product and process innovation to build long-term economic advantage.

The U.S. industrial and building sectors could extend the use of their still-productive
assets while improving energy efficiency by investing in technologies that make it easier to
manage energy use. These include variable-speed drives, motor-control systems, high-efficiency
motors and building-systems automation, among others.

We believe that there is no singular solution for achieving global competitiveness through energy
efficiency; and that while moderating consumer consumption and entirely new energy models are
part of the equation, so is immediate investment in existing infrastructure with widely available
technology. Such investment holds the promise of results and can be most easily implemented as
the responsibility falls largely on the private sector.
http://www02.abb.com/db/db0003/db002698.nsf/0/4cd0f07c8d298db7c1257a2f0050825d/$file/Energy+efficiency+and+U.S.+competitiveness.pdf



Energy Efficiency Innovation
http://www.urbanecology.org.au/topics/energyefficiencyinnovation.html


Doing the Right Things Right: Enhanced Effectiveness and Cost Savings
2006 artilce
http://www.psu.edu/president/pia/innovation/insights006.pdf

A STRUCTURED APPROACH TO ORGANIZATIONAL IMPROVEMENT
2003 article
http://www.psu.edu/president/pia/innovation/insights007.pdf



January 9, 2014

Data Driven Election Analysis and Forecasting



Psephology and forecasting election results based on opinion polls is well established. But is there a data driven election analysis?  The question came to mind as Goldman Sachs came out with a report on Indian Elections 2014 and the Congress Party central government minister were very angry and said why stock broking firm is making election forecasts. Can a security analyst use methods similar to the ones he uses to make stock market target price predictions and price trends. Such methods will be called data-based methods.

An interesting blog post in the regard is Using Data-Driven Models to Predict Election Results



Fundamental Analysis and Models for Forecasting Elections


Fundamental models for forecasting elections are models that can make forecasts of the results of elections using only economic and political data available months before the election.

These models provide accurate forecasts of the results of elections before polls on voting intentions can accurately forecast elections. Fundamental models are also useful in that they give  a better sense of what factors are driving the outcomes behind elections by indicating which types of economic and political data most meaningfully correlate with election outcomes.



Literature on Forecasting Elections

Arrow et al. (2008) notes that prediction markets resulted in an average forecasted error of 1.5 percentage points in the national popular vote in recent U.S. presidential elections, while the final Gallup poll resulted in an average error of 2.1 percentage points, and Berg et al. (2008a) finds similar results on the average accuracy of prediction markets and polls for forecasting the national popular vote just before U.S. presidential elections.

Holbrook and DeSart (1999), Kaplan and Barnett (2003), and Soumbatiants et al. (2006)  show that one can use polls taken just before an election to make fairly accurate forecasts of the state-level results in U.S. presidential elections, and Rothschild (2009) shows this for both polls and prediction markets.

While polls and prediction markets can both be used to make reasonably accurate forecasts
of election results just before an election, these methods are much less reliable when used months
before an election.

Gelman and King (1993) notes that polls for the national popular vote in U.S. presidential elections tend to oscillate wildly in the months before the election takes place, and as a result, polls can be highly unreliable indicators of the election outcomes months before the election.


Arrow et al. (2008) further notes that using prediction market data to make forecasts of the national
popular vote five months before a U.S. presidential election would have resulted in an average error
of over 5 percentage points in recent elections, and that using pre-election polls would have resulted
in even less accurate predictions.

Rothschild (2009) investigates the errors in both polls and prediction markets at forecasting probabilities of victory at the state level up to 130 days before the election, and notes that there is not enough liquidity to even have predictions for some states.

Researchers have investigated many unique methods of forecasting election results months
before the election that may hold more promise than using polls or prediction markets. Several
techniques have been explored for forecasting the results of elections such as using biographical
--------------------
See, for example, Arrow et al. 2008, Berg et al. 2008a; 2008b, Brown and Chappell 1999, Holbrook and DeSart 1999, Kaplan and Barnett 2003, Pickup and Johnston 2008, and Soumbatiants et al. 2006. Erikson and Wlezien (2008a) indicates that the inaccuracy of polls suggests a need to systematically adjust for biases
in polls.
---------------------

information (Armstrong and Graefe 2011), using measures of how well candidates would be
expected to handle particular issues (Graefe and Armstrong 2012), surveying experts or voters for
their predictions (Jones et al. 2007; Lewis-Beck and Tien 1999), using indices that reflect a variety
concerns such as whether there has been a major policy change, a major military failure or success,
and social unrest or a scandal (Armstrong and Cuzán 2006; Lichtman 2008), or even using pictures
or silent video clips of candidates (Armstrong et al. 2010; Benjamin and Shapiro 2009).

 However, while there is an extensive literature on forecasting elections using econometric models, so far the vast majority of this literature has focused on forecasting nationwide results. This holds for forecasting models of presidential elections, which typically focus on forecasting the national popular vote, and for forecasting models of congressional elections, which typically focus on forecasting the number of seats won by each major party in the two branches of Congress.

The focus on forecasting nationwide results for these elections is somewhat unsatisfying
because the results of these elections are typically determined at the state or local level. For
instance, the Electoral College elects the U.S. president, where each state has electors that equal its
congressional representation. Further, gubernatorial and senatorial elections are also state-level
elections. Despite this fact, so far only a handful of papers have addressed questions related to
forecasting election outcomes at the state level using econometric methods. Several papers related
to forecasting the results of the U.S. presidential election at the state level are of limited practical
use for forecasting elections because they focus on showing theoretically how one might make
______________
 See, for example, Abramowitz 2008, Alesina et al. 1996, Bartels and Zaller 2001, Campbell 2008, Cuzán and Bundrick 2008, Erikson and Wlezien 2008, Fair 2009, Haynes and Stone 2004, Hibbs 2008, Holbrook 2008, Lewis-Beck and Tien 2008, Lockerbie 2008, Norpoth 2008, and Sidman et al. 2008.
5 See, for example, Abramowitz 2010, Abramowitz and Segal 1986, Bafumi et al. 2010a, Campbell 2010, Coleman 1997, Cuzán 2010, Fair 2009, Kastellec et al. 2008, Lewis-Beck and Rice 1984; 1985, Lewis-Beck and Tien 2010, and Marra and Ostrom 1989
_______________________

forecasts of elections if certain data that is only available after elections were available before the
election (Rosenstone 1983; Holbrook 1991; Strumpf and Phillipe 1999).

Campbell (1992) and Campbell et al. (2006) illustrate how one can combine the results of
polls taken roughly two months before the election with a variety of other economic and political
indicators to make forecasts of the results of U.S. presidential elections at the state level.



Like Campbell (1992) and Campbell et al. (2006), Klarner (2008) uses information from pre-election polls on voting intentions in his forecasting model, though Klarner (2008) uses polls on voting intentions that are taken further in advance of the election than those considered by Campbell (1992) and Campbell et al. (2006). Klarner (2008) does not report the results of the errors in his out-of-sample forecasts in his paper.

 Bardwell and Lewis-Beck (2004) report results of a model that forecasts the results of Senate elections in Maine, and Klarner (2008) develops a model that forecasts the results of Senate elections for all states using information from pre-election polls on voting intentions.

There have been papers that have investigated factors that affect vote choice in gubernatorial elections (Adams and Kenney 1989; Atkeson and Partin 1995; Carsey and Wright 1998; Hansen 1999; Howell and Vanderleeuw 1990; Niemi et al. 1995; Partin 1995; Peltzman 1987; Svoboda 1995),

Some Important Variables

Incumbency:  In the case of presidential elections, we expect voters to react differently depending on which party is the incumbent president. For this reason, we include a dummy variable that equals 1 (-1) if a Democrat (Republican) is president. However, since there is empirical evidence that voters are less likely to want to reelect members of the incumbent party if the incumbent party has been in office for multiple terms (Abramowitz 2008), we also include a variable that equals 1 (-1) if the Democrats (Republicans) have been in control of the presidency for at least eight consecutive years and 0 otherwise.
.

There is also empirical evidence that voters vote differently in Senate elections that takes place on a midterm than they do in Senate elections that take place the same year as a presidential election. Busch (1999), Chappell and Suzuki (1993) and Grofman et al. (1998) all suggest that voters are less likely to vote for members of the president’s party in Senate elections during a midterm than they are during a year when there is a presidential election.

Past Election Results: One of the best indicators of how states will vote in the future is differences
in how states voted in previous elections. Thus, in forecasting the Electoral College, we consider
variables that represent the difference between the fraction of the major party vote received by the
Democratic candidate in the state and the fraction of the major party vote received by the
Democratic candidate nationwide, in both of the two previous presidential elections.
While these variables are helpful in forecasting the results of future elections, these variables
can also sometimes give a misleading picture of the ideologies of the states. In some previous
presidential elections, there was a major third party candidate who took substantially more votes
from one major party candidate than another. For this reason, we include variables that address how
the vote shares of major third party candidates in previous elections should affect the forecasts we
make for future presidential elections. In particular, we consider the three different third party
candidates who received more than five percent of the national popular vote when they ran for
office.


State Ideology: In addition to past election results, we also find it helpful to include other measures
of ideology.

Different dummy variables for the different types of job experience that a candidate may have had has not been used before in the literature. The one other model for forecasting Senate elections at the state level that includes biographical information about the candidates instead considers a single variable that may assume
any one of several different arbitrarily chosen values depending on the previous experience of the
candidates (Klarner 2008).





While past election results are significant and meaningful in the presidential and senatorial
forecasting models, they are not statistically significant in the gubernatorial model. Including a
variable for past presidential elections analogous to that considered in the presidential and senatorial
models is not statistically significant in the gubernatorial model, perhaps a reflection of the fact that
gubernatorial elections and presidential elections involve different issues and voting patterns in one
of these types of elections are not especially predictive of voting patterns in the other. In addition,


The changes in income are better predictors of election results than gauges of the absolute level of performance of the economy such as, for example, absolute levels of
unemployment.




BIbliography

Abramowitz, Alan I. 1988. “Explaining Senate Election Outcomes.” American Political Science
Review 82, 2, 385-403.
Abramowitz, Alan I. 2008. “Forecasting the 2008 Presidential Election with the Time-for-Change
Model.” P.S. Political Science and Politics 41, 4, 691-695.
Abramowitz, Alan I. 2010. “How Large a Wave? Using the Generic Ballot to Forecast the 2010
Midterm Elections.” P.S. Political Science and Politics 43, 4, 631-632.
Abramowitz, Alan I. and Jeffrey A. Segal. 1986. “Determinants of the Outcomes of U.S. Senate
Elections.” Journal of Politics 48, 2, 433-439.
Abrams, Burton A. and James L. Butkiewicz. 1995. “The Influence of State-Level Economic
Conditions on the 1992 U.S. Presidential Election.” Public Choice 85, 1, 1-10.
Adams, James D. and Lawrence W. Kenny. 1989. “The Retention of State Governors.” Public
Choice 62, 1, 1-13.
Alesina, Alberto, John Londregan, and Howard Rosenthal. 1996. “The 1992, 1994, and 1996
Elections: A Comment and a Forecast.” Public Choice 88, 1, 115-125.
Alvarev, Michael R. and Jonathan Nagler. 1995. “Economics, Issues, and the Perot Candidacy:
Voter Choice in the 1992 Presidential Election.” American Journal of Political Science 39, 3, 714-
744.
Ansolabehere, Stephen and James M. Snyder Jr. 2002. “The Incumbency Advantage in U.S.
Elections: An Analysis of State and Federal Offices, 1942-2000.” Election Law Journal 1, 315-339.
29
Armstrong, J. Scott and Alfred G. Cuzán. 2006. “Index Methods for Forecasting: An Application to
the American Presidential Elections.” Foresight: The International Journal of Applied Forecasting
3, 10-13.
Armstrong, J. Scott and Andreas Graefe. 2011. “Predicting Elections from Biographical Information
About Candidates: A Test of the Index Method.” Journal of Business Research 64, 7, 699-706.
Armstrong, J. Scott, Kesten C. Green, Randall J. Jones, and Malcolm Wright. 2010. “Predicting
Elections from Politicians’ Faces.” International Journal of Public Opinion Research 22, 511-522.
Arrow, Kenneth J., Robert Forsythe, Michael Gorham, Robert Hahn, Robin Hanson, John O.
Ledyard, Saul Levmore, Robert Litan, Paul Milgrom, Forrest D. Nelson, George R. Neumann,
Marco Ottaviani, Thomas C. Shelling, Robert J. Shiller, Vernon L. Smith, Erik Snowberg, Cass R.
Sunstein, Paul C. Tetlock, Philip E. Tetlock, Hal R. Varian, Justin Wolfers, and Eric Zitzewitz.
2008. “The Promise of Prediction Markets.” Science 320, 5878, 877-878.
Atkeson, Lonna Rae and Randall W. Partin. 1995. “Economic and Referendum Voting: A
Comparison of Gubernatorial and Senatorial Elections.” American Political Science Review 89, 1,
99-107.
Bafumi, Joseph, Robert S. Erikson, and Christopher Wlezien. 2010a. “Forecasting House Seats
from Generic Congressional Polls: The 2010 Midterm Election.” P.S. Political Science and Politics
43, 4, 633-636.
Bafumi, Joseph, Robert S. Erikson, and Christopher Wlezien. 2010b. “Ideological Balancing,
Generic Polls and Midterm Congressional Elections.” Journal of Politics 72, 3, 705-719.
Bardwell, Kedron and Michael S. Lewis-Beck. 2004. “State-Level Forecasts of U.S. Senate
Elections.” P.S. Political Science and Politics 37, 4, 821-826.
Bartels, Larry M. and John Zaller. 2001. “Presidential Vote Models: A Recount” P.S. Political
Science and Politics 34, 1, 9-20.
30
Benjamin, Daniel J. and Jesse M. Shapiro. 2009. “Thin-Slice Forecasts of Gubernatorial Elections.”
Review of Economics and Statistics 91, 3, 523-536.
Berg, Joyce, Robert Forsythe, Forrest Nelson, and Thomas Rietz. 2008a. “Results from a Dozen
Years of Elections Futures Markets Research.” In Handbook of Experimental Economics Results,
Volume 1, eds. Charles R. Plott and Vernon L. Smith. North-Holland: Amsterdam.
Berg, Joyce A., Forrest D. Nelson, and Thomas E. Reitz. 2008b. “Prediction Market Accuracy in
the Long Run.” International Journal of Forecasting 24, 2, 285-300.
Brown, Lloyd B. and Henry W. Chappell Jr. 1999. “Forecasting Presidential Elections Using
History and Polls.” International Journal of Forecasting 15, 2, 127-135.
Bullock, Charles S. III. 1988. “Regional Realignment from an Officeholding Perspective.” Journal
of Politics 50, 3, 553-574.
Busch, Andrew E. 1999. Horses in Midstream: U.S. Midterm Elections and Their Consequences,
1894-1998. University of Pittsburgh Press: Pittsburgh.
Campbell, James E. 1992. “Forecasting the Presidential Vote in the States.” American Journal of
Political Science 36, 2, 386-407.
Campbell, James E. 2008. “The Trial-Heat Forecast of the 2008 Presidential Vote: Performance and
Value Considerations in an Open-Seat Election.” P.S. Political Science and Politics 41, 4, 697-701.
Campbell, James E. 2010. “The Seats in Trouble Forecast of the 2010 Elections to the U.S. House.”
P.S. Political Science and Politics 43, 4, 627-630.
Campbell, James E., Syed Ali, and Farida Jalalzai. 2006. “Forecasting the Presidential Vote in the
States, 1948-2004: An Update, Revision, and Extension of a State-Level Presidential Forecasting
Model.” Journal of Political Marketing 5, 1, 33-57.
31
Campbell, James E. and Joe A. Summers. 1990. “Presidential Coattails in Senate Elections.”
American Political Science Review 84, 2, 513-524.
Canes-Wrone, Brandice, David W. Brady, and John F. Cogan. 2002. “Out of Step, Out of Office:
Electoral Accountability and House Members’ Voting.” American Political Science Review 96, 1,
127-140.
Carsey, Thomas M. and Gerald C. Wright. 1998. “State and National Factors in Gubernatorial and
Senatorial Elections.” American Journal of Political Science 42, 3, 994-1002.
Chappell, Henry W. Jr. and Motoshi Suzuki. 1993. “Aggregate Vote Functions for the U.S.
Presidency, Senate and House.” Journal of Politics 55, 1, 207-217.
Coleman, John J. 1997. “The Importance of Being Republican: Forecasting Party Fortunes in House
Midterm Elections.” Journal of Politics 59, 2, 497-519.
Cuzán, Alfred G and Charles M. Bundrick. 2008. “Forecasting the 2008 Presidential Election: A
Challenge for the Fiscal Model.” P.S. Political Science and Politics 41, 4, 717-722.
Cuzán, Alfred G. 2010. “Will the Republicans Retake the House in 2010?” P.S. Political Science
and Politics 43, 4, 639-641.
Erikson, Robert S. 1988. “The Puzzle of Midterm Loss.” Journal of Politics 50, 4, 1011-1029.
Erikson, Robert S. and Christopher Wlezien. 2008a. “Are Political Markets Really Superior to Polls
as Election Predictors?” Public Opinion Quarterly 72, 2, 190-215.
Erikson, Robert S. and Christopher Wlezien. 2008b. “Leading Economic Indicators, the Polls, and
the Presidential Vote.” P.S. Political Science and Politics 41, 4, 703-707.
32
Fair, Ray C. 2009. “Presidential and Congressional Vote-Share Equations.” American Journal of
Political Science 53, 1, 55-72.
Fiorina, Morris. 2003. Divided Government. Pearson Longman: New York.
Garand, James C. 1988. “Localism and Regionalism in Presidential Elections.” Western Political
Quarterly 41, 1, 85-103.
Gelman, Andrew and Gary King. 1993. “Why Are American Presidential Election Campaign Polls
So Variable When Votes Are So Predictable?” British Journal of Political Science 23, 4, 409-451.
Graefe, Andreas and J. Scott Armstrong. 2012. “Predicting Elections from the Most Important
Issue: A Test of the Take-the-Best Heuristic.” Journal of Behavioral Decision Making 25, 1, 41-48.
Grofman, Bernard, Thomas L. Burnell, and William Koetzle. 1998. “Why Gain in the Senate but
Midterm Loss in the House? Evidence from a Natural Experiment.” Legislative Studies Quarterly
23, 1, 79-89.
Hansen, Susan B. 1999. ““Life Is Not Fair”: Governors’ Job Performance Ratings and State
Economies.” Political Research Quarterly 52, 1, 167-188.
Haynes, Stephen E. and Joe A. Stone. 2004. “‘Guns and Butter’ in U.S. Presidential Elections.”
Economics Bulletin 1, 5.
Hibbs, Douglas A. Jr. 2008. “Implications for the ‘Bread and Peace’ Model for the 2008 US
Presidential Election.” Public Choice 137, 1, 1-10.
Highton, Benjamin. 2000. “Senate Elections in the United States, 1920-94.” British Journal of
Political Science 30, 3, 483-506.
Holbrook, Thomas M. 1991. “Presidential Elections in Space and Time.” American Journal of
Political Science 35, 1, 91-109.
33
Holbrook, Thomas M. 2008. “Incumbency, National Conditions, and the 2008 Presidential
Election.” P.S. Political Science and Politics 41, 4, 709-712.
Holbrook, Thomas M. and Jay A. DeSart. 1999. “Using State Polls to Forecast Presidential Election
Outcomes in the American States.” International Journal of Forecasting 15, 2, 137-142.
Holbrook-Provow, Thomas M. 1987. “National Factors in Gubernatorial Elections.” American
Politics Research 15, 4, 471-483.
Howell, Susan E. and James M. Vanderleeuw. 1990. “Economic Effects on State Governors.”
American Politics Research 18, 2, 158-168.
Jones, Randall J. Jr., J. Scott Armstrong, and Alfred G. Cuzán. 2007. “Forecasting Elections Using
Expert Surveys: An Application Using U.S. Presidential Elections.” Working Paper.
Kaplan, Edward H and Arnold Barnett. 2003. “A New Approach to Estimating the Probability of
Winning the Presidency.” Operations Research 51, 1, 32-40.
Kastellec, Jonathan P., Andrew Gelman, and Jamie P. Chandler. 2008. “The Playing Field Shifts:
Predicting the Seats-Votes Curve in the 2008 U.S. House Elections.” P.S. Political Science and
Politics 41, 4, 729-732.
Kiewiet, D. Roderick. 1979. “Approval Voting: The Case of the 1968 Election.” Polity 12, 1, 170-
181.
Klarner, Carl. 2008. “Forecasting the 2008 U.S. House, Senate, and Presidential Elections at the
District and State Level.” P.S. Political Science and Politics 41, 4, 723-728.
Lacy, Dean and Barry C. Burden. 1999. “The Vote-Stealing and Turnout Effects of Ross Perot in
the 1992 U.S. Presidential Election.” American Journal of Political Science 43, 1, 233-235.
34
Lewis-Beck, Michael S. and Tom W. Rice. 1983. “Localism in Presidential Elections: The Home
State Advantage.” American Journal of Political Science 27, 3, 548-556.
Lewis-Beck, Michael S. and Tom W. Rice. 1984. “Forecasting U.S. House Elections.” Legislative
Studies Quarterly 9, 3, 475-486.
Lewis-Beck, Michael S. and Tom W. Rice. 1985. “Are Senate Election Outcomes Predictable?”
P.S. Political Science and Politics 18, 4, 745-754.
Lewis-Beck, Michael S. and Charles Tien. 1999. “Voters as Forecasters: A Micromodel of Election
Prediction.” International Journal of Forecasting 15, 2, 175-184.
Lewis-Beck, Michael S. and Charles Tien. 2008. “The Job of a President and the Jobs Model
Forecast: Obama for ’08?” P.S. Political Science and Politics 41, 4, 687-690.
Lewis-Beck, Michael S. and Charles Tien. 2010. “The Referendum Model: A 2010 Congressional
Forecast.” P.S. Political Science and Politics 43, 4, 637-642.
Leyden, Kevin M. and Stephen M. Borrelli. 1995. “The Effect of State Economic Conditions on
Gubernatorial Elections: Does Unified Government Make a Difference?” Political Research
Quarterly 48, 2, 275-290.
Lichtman, Alan J. 2008. “The Keys to the White House: An Index Forecast for 2008.” International
Journal of Forecasting 24, 2, 301-309.
Lockerbie, Brad. 2008. “Election Forecasting: The Future of the Presidency and the House.” P.S.
Political Science and Politics 41, 4, 713-716.
Marra, Robin F. and Charles M. Ostrom Jr. 1989. “Explaining Seat Change in the U.S. House of
Representatives.” American Journal of Political Science 33, 3, 541-569.
Mayer, Jeremy D. 2002. “Nixon Rides the Backlash to Victory.” The Historian 64, 2, 351-366.
35
Niemi, Richard G., Harold W. Stanley, and Ronald J. Vogel. 1995. “State Economies and State
Taxes: Do Voters Hold Governors Accountable?” American Journal of Political Science 39, 4, 936-
957.
Norpoth, Helmut. 2008. “On the Razor’s Edge: The Forecast of the Primary Model.” P.S. Political
Science and Politics 41, 4, 683-686.
Partin, Ronald W. 1995. “Economic Conditions and Gubernatorial Elections: Is the State Executive
Held Accountable?” American Politics Research 23, 1, 81-95.
Peltzman, Sam. 1987. “Economic Conditions and Gubernatorial Elections.” American Economic
Review Papers and Proceedings 77, 2, 293-297.
Pickup, Mark and Richard Johnston. 2008. “Campaign Trial Heats as Election Forecasts:
Measurement Error and Bias in 2004 Presidential Campaign Polls.” International Journal of
Forecasting 24, 2, 272-284.
Piereson, James E. 1977. “Sources of Candidate Success in Gubernatorial Elections, 1910-1970.”
Journal of Politics 39, 4, 939-958.
Rosenstone, Steven J. 1983. Forecasting Presidential Elections. Yale University Press: New
Haven.
Rothschild, David. 2009. “Forecasting Elections: Comparing Prediction Markets, Polls, and their
Biases.” Public Opinion Quarterly 73, 5, 895-916.
Sidman, Andrew H., Maxwell Mak, and Matthew J. Lebo. 2008. “Forecasting Non-Incumbent
Presidential Elections: Lessons Learned from the 2000 Election.” International Journal of
Forecasting 24, 2, 237-258.
36
Simon, Dennis M. 1989. “Presidents, Governors, and Electoral Accountability.” Journal of Politics
51, 2, 286-304.
Soumbatiants, Souren, Henry W. Chappell Jr., and Eric Johnson. 2006. “Using State Polls to
Forecast U.S. Presidential Election Outcomes.” Public Choice 127, 1, 207-223.
Stanley, Harold W. 1988. “Southern Partisan Changes: Dealignment, Realignment, or Both?”
Journal of Politics 50, 1, 64-88.
Strumpf, Koleman, S. and John R. Phillipe Jr. 1999. “Estimating Presidential Elections: The
Importance of State Fixed Effects and the Role of National Versus Local Information.” Economics
and Politics 11, 1, 33-50.
Squire, Peverill. 1989. “Challengers in U.S. Senate Elections.” Legislative Studies Quarterly 14, 4,
531-547.
Squire, Peverill. 1992a. “Challenger Profile and Gubernatorial Elections.” Western Political
Quarterly 45, 1, 125-142.
Squire, Peverill. 1992b. “Challenger Quality and Voting Behavior in U.S. Senate Elections.”
Legislative Studies Quarterly 17, 2, 247-263.
Svoboda, Craig J. 1995. “Retrospective Voting in Gubernatorial Elections: 1982 and 1986.”
Political Research Quarterly 48, 1, 135-150.
Tompkins, Mark E. 1984. “The Electoral Fortunes of Gubernatorial Incumbents: 1947-1981.”
Journal of Politics 46, 2, 520-543.
Tompkins, Mark E. 1988. “Have Gubernatorial Elections Become More Distinctive Contests?”
Journal of Politics 50, 1, 192-205.

Source:
Fundamental Models for Forecasting Elections*
Patrick Hummel David Rothschild
PHummel@alumni.gsb.stanford.edu, David@ReseachDMR.com
http://patrickhummel.webs.com www.ResearchDMR.com
https://www.aeaweb.org/aea/2013conference/program/retrieve.php?pdfid=88.

In case of India Mint a business daily published in collaboration with Wall Street Journal promised data driven election analysis article till Lok Sabha elections. One of them is
http://www.livemint.com/Politics/xBhIIn5Nxtuwvho6I6kT0I/Election-analysis-Decoding-corners-of-contests.html


http://www.fivethirtyeight.com/

http://fivethirtyeight.blogs.nytimes.com/

http://votamatic.org/

Select a Worthy Objective - Become Confident First - Motivated Next - Put in the Effort - Reap the Success




What are your objectives and plans for the future? The objectives can be ranked in the order of worthiness. They can also be ranked in the order your confidence regarding achieving them. What is confidence? Rosabeth Moss Kanter, Professor of Harvard Business School explains confidence as an expectation of positive outcome based on your resources and abilities. Even though confidence is based your resources and abilities, it is not a personality trait which means you are always confident or diffident..Confidence for each objective is based on an assessment of the situation or the environment. So confidence is an assessment whether the environment is sufficiently fertile for your effort to give you the success with the resources and abilities that you have. If you assessment makes you confident of success,  that will  spark motivation.

What is motivation. Motivation is an internal urge or force that make you put in the effort, to invest your time and resources, and to persist in sticking to the plan till the last minute in case of competitive events. In case of non competitive activities a confident man will persist till he reaches the goal. An example of non-competitive activity is learning a skill like car driving.  It is not confidence or positive thinking itself that produces success; it is the investment of resources and the effort. But if  enough confidence is not there, it’s too easy to give up prematurely or not get started at all. Hopelessness and despair prevent action from taking place or even though action starts, it will not be done properly with enthusiasm.

Rosabeth Kanter identified certain barriers to confidence building. Individuals and team leaders have to consider them and eliminate or minimize these barriers to go on and achieve their objectives.

Barriers to Confidence Development. These barriers may appear at the starting of an activity or during the course of an activity.

Self-defeating assumptions.
Goals that are too big or too distant.
Declaring victory too soon.
Do-it-yourself-ing.
Blaming someone else.
Defensiveness.
Neglecting to anticipate setbacks.
Over-confidence.

Self-defeating assumptions.
 If you start with a list of weak points and then highlight them in doing your analysis, the likely conclusion will dampen your confidence. It is important to write strong points that will give success first.

Goals that are too big or too distant.
The goals that look feasible with a focused effort and available resources and result in quick successes will build confidence. The goals that are remote and very big will not inspire the same confidence. That is why many times, social organizations are small dedicated teams when they are highlighting big and distant goals, but whenever a short term feasible goal appears, their following increases many times. After that goal is reached once again the popular enthusiasm subsides and the team becomes small. But the dedicated team is essential for the long time survival of an organization.

Declaring victory too soon
Small successes are important but don't declare victory till the real victory is achieved. Celebrate small successes, but inform the audience the further steps needed for complete victory and explain the plan in the context of the success being celebrated.

Do-it-yourself-ing or Promoting stars and Belittling many
In a team activity, don't get into the act of doing and winning all by yourself. No team game is won by a single person. Even though some games are won by the superb performance of an individual, the general confidence of the team is low. It is important to train the team members to be a high performance team. Creating a culture in which everyone is more likely to succeed, through recognizing their strengths and further mentoring them to use those strengths in the anticipated challenges is important. Giving recognition and support  to others boosts happiness and self-esteem. Numerous research studies support this hypothesis.A supportive environment makes it easier for teams to succeed in very challenging situations. In industry, Toyota successfully developed a supportive environment where neighbors support each other in meeting quantity as well as quality targets.

Blaming someone else
Failures are inevitable events in any project. An organization which takes failure as a defective output and looks inwards to take corrective actions will develop confidence. An organization whose leaders do not take responsibility for the failure and look inwards but blame others will lose confidence.


Defensiveness
Defensiveness is starting a project with an apology. Who asked you for it? Start any project clearly explaining the worth of it. Start a project based on cost-benefit analysis. Only when critics emerge, you can answer and bring out limitations under which you preferred the project.

Neglecting to anticipate setbacks
Do scenario planning with your team. Give opportunity to team members to point out what can go wrong. Then outline your contingency planning. Keep contingency resources and inform the team about it. Whenever some setbacks are anticipated and planned, the actual happening of the setback will not demoralise the team but makes them put extra effort to tide over the setback.

Over-confidence
Overconfidence is described as arrogance by Prof Kanter. When the team management becomes arrogant, it neglects its team members. When a business team becomes arrogant it neglects its customers. The early signs of failure are ignored and the corrective action is not taken in time. What gives success?

Remember, it is not confidence. It is your work. Overconfidence kills your motive to work. Confidence provides motive to work and succeed.


Hear the Athletic Director and head coach of the Varsity Soccer team at Ryerson University, Dr. Ivan Joseph stating some of the above points in his talk on being confident and becoming confident
___________________

___________________

Read the blog post of Prof Rosabeth Kanter in Harvard Blogs.
http://blogs.hbr.org/2014/01/overcome-the-eight-barriers-to-confidence/




Presently in India Lok Sabha Elections are due in May 2014.

Confidence is the key to the effort put in by party leaders and workers. Why?

See this theoretical proposition.
In an election year that favors one party, that party campaigns heavily in opponents’ districts that are closest to swinging toward the favored party.
http://www.apsanet.org/~lss/Newsletter/jan2009/Klarner.pdf




India 2014 Lok Sabha Elections. Which party worker can be more confident?


AAP

Success in Delhi Elections. Came Second. But formed the government.
Many reputed persons new to politics are joining the party.
Some members including former MPs and MLAs are joining the party.
Lakhs of young people are joining.
For Delhi, they could collect the target campaign fund of Rs. 20 Cr.
For Lok Sabha their target is one crore (10 million) for lok sabha seat.



BJP
Stupendous success in two state assemblies.
Success in one more assembly.
Their PM nominee Narendra Modi is still the preferred number one choice in the country (TOI opinion poll results 8 Jan 2014)
Success of their mega rallies.
Announcement of 272+ mission
More allies willing to come on board
Even foreign media declaring the likely success of the party in elections to come to power.

Congress
Has 12 state governments.
Has the ability to campaign across the country.
Twice it came back to power at the centre after big defeats.
Money is not a problem for election campaigning.










January 6, 2014

Management - Top YouTube Videos - January 2014




About HR Careers - HR Professionals Explaining
______________________

______________________

January 2, 2014

Economics of Advertising - Economics for the CEO

Advertising is a pure form of selling cost. This chapter illustrates the general approach of managerial economics to various kinds of marketing or selling outlays.

Nature of Advertising Costs

Advertising costs are designed to increase the demand for the firm's products. Advertising expenditures shift the demand curve to the right of where it would otherwise be.

Selling costs are incurred to the get the business. Production and distribution costs create the product and take it to the market.

Pure selling costs are designed to shift the demand schedule, i.e., to obtain sales that would not otherwise have been obtained at the same price. Selling expenses have no functional relationship to production output. They are a cause of sales or some part of sales. In the short run, sales and hence profit depend on the combinations of price, product improvement or specified functionality and quality, advertising outlay and other selling activities. These four influences are interactive. Advertising can shift the demand curve to the right and also can make demand less elastic.

Promotional Elasticity of Demand

Promotional elasticity of demand measures the responsiveness of sales to changes in the amount of advertising with constant price. Like other elasticity measures, it is the ratio of proportionate chagne in sales to the proportionate change in the advertising that causes the change.

Long-Run Aspect of Advertising

Advertising has long-run impacts apart from shifting the short term demand to the right. It helps a firm to attain strategic advantage in market position and gives it a security that contributes to the long-run profit maximization.

The further discussion in the chapter is focused on three issues.

1. The level of the total advertising expenditure over a period of years.
2. Fluctuations in the annual outlays over the course of a business cycle.
3. Measuring the effects of advertising for planning and control purposes.



1. The Level of Advertising Expenditure

The theory of monopolistic competition provides an opportunity to analyze advertising expenditure and its effect on profit for a monopolist.

Simple theory of behavior of advertising expenditure and its impact: Ad expenditure includes all pure selling costs in the theory. The marginal cost of advertising comes down initially and then it goes up in the short run. A firm can go on advertising till the marginal cost of advertising becomes equal to gross profit (gross profit assumed to be constant).


Methods for determining total advertising budget: While the theoretical rule of marginal cost is equal to gross profit on a unit is rational, firms use more simple thumb rules. They are further discussed in the chapter.

a. Percentage of sales approach
b. All-you-can-afford approach
c. Return on investment approach
d. Objective and task approach
e. Competitive parity approach

2. Cyclical Fluctuations

3. Measuring the effects of advertising for planning and control purposes.

One has to measure how much the firm's demand schedule has shifted as a result of a specified amount of advertising outlay. Historical data if available can be studied in two ways. (i) by comparing firms with different advertising outlays and (ii) by comparing a firm's performance over time with different advertising outlays.

While Joel Dean's book authored in 1951 has pointed out that economics of advertising is an important managerial economics issue for top management, it has not provided much content. One has to see the contribution of subsequent authors in this topic to get more useful direction.

Reference: Managerial Economics by Joel Dean

For a Recent Update on Economics of Advertising
The Economics of Advertising: Introduction - Kyle Bagwell - Prof - Columbia University
http://www.stanford.edu/~kbagwell/Bagwell_Web/advertisingintrofv2.pdf

Originally posted at
http://knol.google.com/k/narayana-rao/economics-of-advertising-economics-for/2utb2lsm2k7a/3134


Updated:2.1.2014
Published in the blog 11.12.2011

Managerial Economics of Basic Price - Joel Dean - Review Notes

Joel Dean stated that the firms activities can be viewed as three important ones.

1. Product development and improvement (innovation).
2. Sales promotion, and
3. Pricing

The objective of this chapter is to develop philosophy of price making and to explain how economic analysis and market research can be used to improve practical pricing.

The discussion focuses on manufacturers, who have some flexibility to set prices as their products are differentiated and also the setting is a multi-product firm.

Pricing objectives

While survival of the firm making the required return on capital is the broadest objective, on a more specific level various objectives can be rate of growth, market share, etc.

Pricing theory

Pricing theory indicated that concepts of demand and demand schedule, competitors actions, and cost of the product are relevant for pricing decisions.

Pricing research

Research needs to be done to information on the structure of competition, the behavior of the firm's costs, and the nature of demand.

Economic theory of monopoly pricing is discussed in the chapter.

In the case of new products pioneer pricing and maturity pricing are discussed.

Pricing of problems of oligopoly are discussed. The topic of cost-plus pricing is also covered in the chapter



_______________

_______________

Updated 2 Jan 2-14