The impact of currency fluctuations on outsourcing businesses globally.
If you have a current offshore team in a different country/currency zone then you may find that the significant cost savings from outsourcing have vanished due to currency fluctuations that occur for reasons like earthquakes, war or oil- something which is outside the core competency of your business corporation. As off shoring companies incur cost in local currencies but gain revenue in American Dollars and Euro (mostly), they pass on these fluctuating costs to their customers but rarely pass along discounts on existing contracts. Sometimes the offshoring contract actually gains from currency fluctuations.The Indian rupee has fluctuated from 43.62 Rupees per USD (04-01-2005) to 48.58 (12-31-2008) to the current value of 44.65.This makes for a volatility component of almost 10 percentage points to the revenue and profit margins of an off shoring vendor. Inflation in India has been growing at 8.5 % and the annual increase in salaries has been around 10-15 % for the past few years. Offshoring vendors have been known to cut back on quality in recruitment when costs have risen historically, and the current attrition rate in Indian ITES is almost 17%.
This raises important questions for companies going for global bids for the offshoring contracts. Should macroeconomic indicators like currency fluctuations, wage-inflation be part of the request for proposal process (RFP). Would vendors be comfortable in disclosing the ratio of salary costs to billing revenue. Should dips in service quality be penalized by customer. Most importantly, while going in for a multi year contract, the projection of fore-casted savings may vary greatly due to extraneous factors.
Having a child lowers a woman’s lifetime earnings, but how much depends upon her skill level. In The Mommy Track Divides: The Impact of Childbearing on Wages of Women of Differing Skill Levels (NBER Working Paper No. 16582), co-authors Elizabeth Ty Wilde, Lily Batchelder, and David Ellwood estimate that having a child costs the average high skilled woman $230,000 in lost lifetime wages relative to similar women who never gave birth. By comparison, low skilled women experience a lifetime wage loss of only $49,000.
Using the 1979 National Longitudinal Survey of Youth (NLSY), Wilde et. al. divided women into high, medium, and low skill categories based on their Armed Forces Qualification Test (AFQT) scores. The authors use these skill categories, combined with earnings, labor force participation, and family formation data, to chart the labor market progress of women before and after childbirth, from ages 14-to-21 in 1979 through 41-to-49 in 2006, this study’s final sample year.
High scoring and low scoring women differed in a number of ways. While 70-75 percent of higher scoring women work full-time all year prior to their first birth, only 55-60 percent of low scoring women do. As they age, the high scoring women enjoy steeper wage growth than low scoring women; low scoring women’s wages do not change much if they reenter the labor market after they have their first child. Five years after the first birth, about 35 percent of each group is working full-time. However, the high scoring women who are not working full-time are more likely to be working part-time than the low scoring women, who are more likely to leave the workforce entirely.
and
Men’s earning profiles are relatively unaffected by having children although men who never have children earn less on average than those who do. High scoring women who have children late also tend to earn more than high scoring childless women. Their earnings advantage occurs before they have children and narrows substantially after they become mothers.
More than 71 Million individuals in the United States are admitted to
hospitals each year, according to the latest survey from the American
Hospital Association. Studies have concluded that in 2006 well over
$30 billion was spent on unnecessary hospital admissions. Each of
these unnecessary admissions took away one hospital bed from someone
else who needed it more.
The goal of the prize is to develop a predictive algorithm that can identify patients who will be admitted to the hospital within the next year, using historical claims data.
Official registration will open in 2011, after the launch of the prize. At that time, pre-registered teams will be notified to officially register for the competition. Teams must consent to be bound by final competition rules.
Registered teams will develop and test their algorithms. The winning algorithm will be able to predict patients at risk for an unplanned hospital admission with a high rate of accuracy. The first team to reach the accuracy threshold will have their algorithms confirmed by a judging panel. If confirmed, a winner will be declared.
The competition is expected to run for approximately two years. Registration will be open throughout the competition.
Data Sets
Registered teams will be granted access to two separate datasets of de-identified patient claims data for developing and testing algorithms: a training dataset and a quiz/test dataset. The datasets will be comprised of de-identified patient data. The datasets will include:
Outpatient encounter data
Hospitalization encounter data
Medication dispensing claims data, including medications
Outpatient laboratory data, including test outcome values
The data for each de-identified patient will be organized into two sections: “Historical Data” and “Admission Data.” Historical Data will represent three years of past claims data. This section of the dataset will be used to predict if that patient is going to be admitted during the Admission Data period. Admission Data represents previous claims data and will contain whether or not a hospital admission occurred for that patient; it will be a binary flag.
The training dataset includes several thousand anonymized patients and will be made available, securely and in full, to any registered team for the purpose of developing effective screening algorithms.
The quiz/test dataset is a smaller set of anonymized patients. Teams will only receive the Historical Data section of these datasets and the two datasets will be mixed together so that teams will not be aware of which de-identified patients are in which set. Teams will make predictions based on these data sets and submit their predictions to HPN through the official Heritage Health Prize web site. HPN will use the Quiz Dataset for the initial assessment of the Team’s algorithms. HPN will evaluate and report back scores to the teams through the prize website’s leader board.
Scores from the final Test Dataset will not be made available to teams until the accuracy thresholds are passed. The test dataset will be used in the final judging and results will be kept hidden. These scores are used to preserve the integrity of scoring and to help validate the predictive algorithms.
Teams can begin developing and testing their algorithms as soon as they are registered and ready. Teams will log onto the official Heritage Health Prize website and submit their predictions online. Comparisons will be run automatically and team accuracy scores will be posted on the leader board. This score will be only on a portion of the predictions submitted (the Quiz Dataset), the additional results will be kept back (the Test Dataset).
Once a team successfully scores above the accuracy thresholds on the online testing (quiz dataset), final judging will occur. There will be three parts to this judging. First, the judges will confirm that the potential winning team’s algorithm accurately predicts patient admissions in the Test Dataset (again, above the thresholds for accuracy).
Next, the judging panel will confirm that the algorithm does not identify patients and use external data sources to derive its predictions. Lastly, the panel will confirm that the team’s algorithm is authentic and derives its predictive power from the datasets, not from hand-coding results to improve scores. If the algorithm meets these three criteria, it will be declared the winner.
Failure to meet any one of these three parts will disqualify the team and the contest will continue. The judges reserve the right to award second and third place prizes if deemed applicable.
When performing the exercise, subjects are given a list of 56 adjectives and picks five or six that they feel describe their own personality. Peers of the subject are then given the same list, and each picks five or six adjectives that describe the subject. These adjectives are then mapped onto a grid
A Johari window consists of the following 56 adjectives used as possible descriptions of the participant. In alphabetical order they are:
When performing the exercise, subjects are given a list of 56 adjectives and picks five or six that they feel describe their own personality. Peers of the subject are then given the same list, and each picks five or six adjectives that describe the subject. These adjectives are then mapped onto a grid
A Johari window consists of the following 56 adjectives used as possible descriptions of the participant. In alphabetical order they are:
I met a traveller from an antique land
Who said: Two vast and trunkless legs of stone
Stand in the desert. Near them, on the sand,
Half sunk, a shattered visage lies, whose frown
And wrinkled lip, and sneer of cold command
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them and the heart that fed.
And on the pedestal these words appear:
“My name is Ozymandias, king of kings:
Look on my works, ye Mighty, and despair!”
Nothing beside remains. Round the decay
Of that colossal wreck, boundless and bare
The lone and level sands stretch far away.[1]
In Egypt’s sandy silence, all alone,
Stands a gigantic Leg, which far off throws
The only shadow that the Desert knows:
“I am great OZYMANDIAS,” saith the stone,
“The King of Kings; this mighty City shows
“The wonders of my hand.” The City’s gone,
Nought but the Leg remaining to disclose
The site of this forgotten Babylon.
We wonder, and some Hunter may express
Wonder like ours, when thro’ the wilderness
Where London stood, holding the Wolf in chace,
He meets some fragments huge, and stops to guess
What powerful but unrecorded race
Once dwelt in that annihilated place.
The training dataset includes several thousand anonymized patients and will be made available, securely and in full, to any registered team for the purpose of developing effective screening algorithms.
DECISION STATS 