A few weeks ago, we pointed out some new additions to Wolfram|Alpha from the World Bank’s World Development Indicators database, primarily focused on labor and employment around the world. We’ve also incorporated data about doing business in most countries, collected by a World Bank project called, appropriately, “Doing Business”.
This data covers a wide range of issues, including import/export costs, business tax rates, and the time required to complete various business-related activities. Try the following examples to get a better sense of the breadth of this dataset:
- Export costs in Brazil vs. Argentina »
- New businesses registered in Japan vs. India »
- Credit bureau coverage in Asia »
- Europe total business tax rate »
- How much does it cost to start a business in India, Mexico, Singapore »
- Time required to start a business »
Wolfram|Alpha can also rank countries according to a number of business-related indices. Ask about the ease of doing business in Eurozone countries, for example, and you’ll see that Ireland and Greece occupy the ends of this spectrum. In this case, a higher index score indicates a regulatory environment that is generally unfriendly to business operation; in light of Greece’s recent economic woes, it should come as no surprise to see that country at the bottom of the list. (Note that for all the properties in this dataset, you can click the “Definition” button in the input interpretation or in other pods to get more details on each property.)
New York City. Los Angeles. Chicago. Each of these cities is renowned for a diverse array of cultural, entertainment, culinary, and other experiences—as well as for legendary traffic delays. But just how bad do native commuters have it? And if you drive to work in a different city, how does your commute stack up? Wolfram|Alpha can’t yet guide you through the traffic, but it can visualize and compare statistics about traffic and urban transportation in more than 100 US urban areas, with data from the Texas Transportation Institute’s Urban Mobility Report.
Ask Wolfram|Alpha about traffic in NYC, LA, and Chicago, for example, to see how they compare:
Recently, CNNMoney published stories highlighting the “20 most profitable companies” and those considered to be the “20 biggest money losers”. Companies such as Exxon Mobil, AT&T, Apple, and Verizon were ranked by their 2010 profits, with each of the 20 profitable companies bringing in well over a billion dollars.
Using the information provided by both articles, there are many opportunities to gain more data on each of the companies from Wolfram|Alpha. For example, Exxon Mobil topped the list of most profitable companies, but has it always been profitable? Entering “Exxon earnings” into Wolfram|Alpha produces data and graphs documenting its earnings history.
In 2010, our friends at the World Bank opened up their highly regarded World Development Indicators (WDI) database, making hundreds of economics, education, health, and other indicators free to download and explore. As part of our own mission to make data more accessible and comprehensible, we’re pleased to announce that we’ve been steadily adding WDI and other World Bank data to Wolfram|Alpha, so you can answer thousands of new questions about key components of global development.
One of the first sets we tackled was data on labor and employment, which means Wolfram|Alpha can now generate some quite detailed computations and comparisons of employment-related data for most of the world’s countries and territories. Try an input like “fraction of people working in agriculture in US, Russia, and Japan” to see find out how much less agrarian these economies have become over time. More »
There are more than four million births per year in the US alone. And just in time for spring, a time associated with new life, Wolfram|Alpha’s research team has introduced a unique set of tools to help soon-to-be mothers and fathers better understand what is happening to their developing fetuses throughout pregnancy.
One of the most common methods of monitoring fetal development is through ultrasounds. Besides providing first glimpses of the baby, ultrasound images also provide doctors and technicians with important information about the fetus’s physical development. This information is useful in helping doctors diagnose, predict, and potentially avoid complications further down the line in pregnancy. To find out the typical measurements of a fetus for a given gestational age (e.g. 21 weeks), try entering something like “pregnant 21 weeks” into Wolfram|Alpha.
For the gestational age of 21 weeks, Wolfram|Alpha can tell you the estimated fetal weight, the normal range of fetal weights, and the estimated dates of conception and birth.
By popular demand, Wolfram|Alpha recently expanded population data for most of the world’s countries, based in part on statistics from the United Nations Population Division. Populations are shaped by factors such as disease, war, genocide, and famine as well as more benign phenomena such as immigration. One of the more common user requests in this area has been to support queries like “China population distribution”, which now returns an age pyramid and detailed table of population by age and sex:
You can also query for specific age groups, as indicated on the pyramid, or just query for a single age, and Wolfram|Alpha will return data for the appropriate five-year age “bin”:
Last year, we showed you how Wolfram|Alpha could help you explore some interesting historical statistics about federal income taxes in the United States. We’ve picked up the latest available figures from the Internal Revenue Service (IRS) through the 2008 tax year, so you can revisit that data and see if previous years’ trends have held up.
Wolfram|Alpha still can’t do your taxes (and if you haven’t finished them yet, don’t forget you’ve got until Monday to file)… but it can compute some very interesting new facts about income taxes in the US. There’s been a lot of discussion and debate this year about state-level corporate and individual taxes and their impact on budgets and the overall business climate in any given state. So we’ve added data on the maximum and minimum individual and corporate tax rates in each US state, which means Wolfram|Alpha can now compute rankings and summary statistics about “income tax rates in US states” or perform a comparison of the “highest corporate income tax rates in Illinois, Iowa, and Indiana”.
If you’re concerned about the US economy, you probably caught last week’s news that Standard & Poor’s Case–Shiller home price index for 20 large cities continued to decline in January. If you’re curious to know more about recent housing trends in the US, you can not only ask Wolfram|Alpha about the 20-city index, but also for details on any of the major metropolitan areas included in that composite. For example, query “Las Vegas, Phoenix, Los Angeles, Miami Case–Shiller index”, and you can see just how big the housing “bubble” was in each of these four cities.
Wolfram|Alpha recently added information about the minimum wage in U.S. states (from 1967 through today) based on data from the U.S. Department of Labor. This means you can ask Wolfram|Alpha about simple historical facts, like the U.S. minimum wage in 1980, or perform simple analyses, like comparing the current minimum wage in Ohio and Alaska.
Renowned physicist Enrico Fermi’s name is synonymous with a type of estimation problem often illustrated by the classic question, “How many piano tuners are there in Chicago?” Finding a “Fermi estimate” of this number would typically involve multiplying a series of rough estimates, such as the population of Chicago, an approximate number of households owning pianos, the frequency with which a typical piano might be tuned, and so on. It’s unlikely that anyone would arrive at a precise, correct answer through this method, but a Fermi estimate should at least be able to generate an answer that is approximately the right order of magnitude.
A Fermi estimate usually seeks to measure a quantity that would be extremely difficult, if not impossible, to actually measure. “Piano tuners in Chicago” may have fallen into that category several decades ago, but as Wolfram|Alpha can now demonstrate, things have changed:
We recently overhauled our data on jobs and salaries in the United States, adding Bureau of Labor Statistics (BLS) data on more than 800 detailed occupations at the national, state, and metropolitan area levels. Which means Wolfram|Alpha can’t quite get you to an exact measurement of the number of piano tuners in Chicago (and presumably, many of them must at least dabble in other instruments), but it can come surprisingly close.
Wolfram|Alpha can also compute a number of interesting statistics that aren’t obvious from the source data, such as the fact that Chicago has quite a high density of musical instrument tuners and repairers—roughly 2.3 times the national average workforce fraction for this occupation—and that their median wage is roughly 1.3 times the national average. And it can also provide helpful context for any occupation, computing employment and wage information for related jobs and sub-specialties, according to BLS classifications.
You can also perform all kinds of interesting comparisons, of course: try asking Wolfram|Alpha to “compare producers and actors employment in California”, for example, or “garbage collectors vs waiters salaries in New York City”. Or if you’re contemplating a cross-country move, you might be interested to see a comparison between “computer programmers salaries in Seattle vs Philadelphia”.
And if you need to access salary and job-related data often, you can create your own Wolfram|Alpha Widgets tailored for specific jobs and regions. You can easily customize widgets, like the one below, and embed them in your website and share with your social networks.
Wolfram|Alpha launched with an extensive database of United States economic data, derived from the Federal Reserve Bank’s FRED database. Over the past year, we’ve continued to improve our handling of this data in a variety of ways—teaching Wolfram|Alpha to return more related statistics along with any specific result, improving our linguistic abilities so we can answer more complex questions, and increasing the frequency with which we update this data. Wolfram|Alpha is now refreshing its collection of FRED-derived data on a daily basis, so you can always access the latest available data on the national economy.
We’ve also begun to expand our coverage of economic data for smaller geographic areas in the United States, starting with state-level statistics. This means Wolfram|Alpha users can now query for the latest available information on a variety of economic topics, including gross state product, unemployment, health insurance coverage, and housing-related data.
As always, one of the strengths of Wolfram|Alpha is that it allows you to compare and analyze different pieces of data—and with this data set, you can quickly uncover strong correlations between various economic properties. It’s easy to see that the house price index tends to move together with employment and state tax collections. You can also use Wolfram|Alpha to run simple calculations of productivity in U.S. states, or to find out a given state’s share of the national economy or workforce.
To make it even easier to explore this data, you can also use the new Wolfram|Alpha Widget Builder to create simple tools for analyzing and comparing the economic properties of states. To get you started, here’s a small selection of widgets focused on US state economies—ranging from the serious to the slightly silly. Try them out:
Feel free to customize and share these on Twitter or Facebook, in your blog, or anywhere else—and let us know in the comments if you create any useful new widgets of your own.
New curated data flows into Wolfram|Alpha every day. One addition that we haven’t highlighted before is crime data from the U.S. Department of Justice Statistics, including historical information on crimes and crime rates for all 50 states and thousands of individual cities.
A simple query for “U.S. Crime” will return the nation’s overall crime rate (the number of crimes per 100,000 people) and details on individual categories of violent and property crimes.
But Wolfram|Alpha’s true strength shows when you perform more-advanced comparisons and computations. For example, try comparing the crime statistics for two cities, such as “Crime Seattle vs. New York”; you can see clearly that although crime rates have fallen gradually over the last fifteen years, Seattle’s crime rate has maintained a level around 2.5 times that of New York City. More »
Soon everyone will have access to the first version of Wolfram|Alpha. Already some have asked: “What kinds of questions can Wolfram|Alpha help me answer?” “Will there be examples for me to use?” “How will I get started?”
As we make our final preparations to release Wolfram|Alpha over the next week, we thought it might be helpful to discuss questions like these in this blog.
Looking at the Examples by Topic page provides a good framework. You will be able to navigate from the Wolfram|Alpha home page to Examples: