Dear ChatGPT: How do we measure the state of the American economy?

Measuring the state of the American economy involves tracking a range of indicators that reflect various aspects of economic performance, such as growth, employment, inflation, and financial stability. Some of the key measures include:

1. Gross Domestic Product (GDP)

What it measures: GDP represents the total value of all goods and services produced within the U.S. economy over a specific time period (usually quarterly or annually).

Why it's important: It serves as the broadest indicator of economic activity. A growing GDP typically signals a healthy, expanding economy, while a shrinking GDP suggests economic contraction or recession.

2. Unemployment Rate

What it measures: The percentage of people in the labor force who are actively seeking work but are unable to find employment.

Why it's important: High unemployment can indicate economic distress, while low unemployment suggests a healthy, robust labor market. However, it’s essential to look at other labor force metrics, such as participation rate and underemployment, to get a fuller picture.

3. Inflation Rate

What it measures: The rate at which the general price level of goods and services in the economy is rising, typically measured by the Consumer Price Index (CPI) or the Producer Price Index (PPI).

Why it's important: Moderate inflation is considered normal in a growing economy, but high inflation can erode purchasing power and signal overheating, while deflation can signal a stagnating economy.

4. Federal Reserve's Interest Rates

What it measures: The Federal Reserve (Fed) sets short-term interest rates to influence economic activity. The most common rate is the federal funds rate.

Why it's important: Interest rates directly affect borrowing costs for consumers and businesses. Lower rates stimulate economic activity by making borrowing cheaper, while higher rates are used to cool down an overheating economy and control inflation.

5. Stock Market Performance

What it measures: The performance of major stock indices like the S&P 500, Dow Jones Industrial Average, and NASDAQ can provide insight into the confidence of investors and the broader economy.

Why it's important: Stock market performance can reflect expectations of future economic growth or contraction. While it can be volatile, a strong, upward-moving stock market is generally a sign of investor optimism.

6. Consumer Confidence Index (CCI)

What it measures: This index, produced by the Conference Board, gauges consumer optimism regarding the economy, personal finances, and future spending habits.

Why it's important: High consumer confidence typically correlates with increased consumer spending, which is a significant driver of economic growth. Conversely, low confidence can signal potential slowdowns in demand.

7. Housing Market

What it measures: Data on housing starts, home prices, and sales volume are commonly used to assess the health of the housing market.

Why it's important: The housing market is a key component of economic activity, affecting construction, real estate, and consumer wealth. Rising home prices and new construction often indicate a growing economy, while a housing downturn can be a warning sign.

8. Trade Balance

What it measures: The difference between a country's exports and imports. A trade deficit occurs when imports exceed exports, and a trade surplus occurs when exports exceed imports.

Why it's important: Persistent trade deficits can indicate structural economic problems or dependence on foreign goods. On the other hand, a strong export economy can be a sign of global competitiveness and economic strength.

9. Wage Growth

What it measures: The rate at which wages for workers are increasing over time.

Why it's important: Rising wages are typically a sign of strong demand for labor and a healthy labor market, while stagnant wages may indicate economic challenges, particularly for lower-income workers.

10. Debt Levels (National Debt and Consumer Debt)

What it measures: The total amount of debt held by the government (national debt) and households (consumer debt).

Why it's important: High national debt can limit the government’s ability to respond to future crises and affect long-term fiscal sustainability. High consumer debt can indicate financial stress or signal overleveraging in the economy.

11. Business Investment (Capital Expenditures)

What it measures: Business spending on equipment, infrastructure, and other capital projects.

Why it's important: Increased business investment suggests confidence in the future and may lead to increased productivity and economic growth.

12. Productivity

What it measures: Productivity measures the output of goods and services per hour worked.

Why it's important: Higher productivity can lead to higher living standards and economic growth, as it means more is being produced with fewer resources.

13. Government Fiscal Health

What it measures: Government spending and tax revenue, as well as budget deficits or surpluses.

Why it's important: High government debt or persistent budget deficits can create long-term economic challenges, such as inflation or interest rate hikes, which can affect economic stability.

14. Regional Economic Conditions

What it measures: Economic performance can vary by region, and regional indicators like unemployment rates, housing data, and local GDP can provide a more localized view.

Why it's important: Economic conditions can differ greatly by state or metropolitan area. A national overview may mask significant disparities between regions.

15. Global Economic Factors

What it measures: External factors like global trade patterns, commodity prices (e.g., oil), and international economic conditions can impact the U.S. economy.

Why it's important: The U.S. economy is interconnected with the rest of the world. International trends like recessions in major trading partners or geopolitical events (e.g., war, trade wars) can affect U.S. economic growth.

Combining Data for a Holistic View

No single indicator can offer a complete picture of the U.S. economy. Instead, economists and policymakers analyze a combination of these measures to gauge the economy’s health and anticipate future trends. For instance:If GDP is growing, unemployment is low, and inflation is stable, the economy is typically considered strong.

If GDP is shrinking, unemployment is rising, and inflation is high, the economy might be heading into a recession or stagflation.

In short, measuring the state of the economy requires a multidimensional approach, using a variety of economic indicators to provide insight into both the current and future performance of the economy.

From 538: How I Acted Like A Pundit And Screwed Up On Donald Trump - Trump’s nomination shows the need for a more rigorous approach.

In 2305 today we looked at Nate Silver's bad call during the primary. Along with most other political observers, he discounted Trump's viability as a candidate for the Republican nomination. Here he tries to figure out what he got wrong and why. He calls himself a "data journalist" which suggests that his approach is rigorous and needs to be adjusted if proven faulty. Here's what he came up with.

- Click here for the article.

. . . I’m going to proceed in five sections:

1. Our early forecasts of Trump’s nomination chances weren’t based on a statistical model, which may have been most of the problem.

2. Trump’s nomination is just one event, and that makes it hard to judge the accuracy of a probabilistic forecast.

3. The historical evidence clearly suggested that Trump was an underdog, but the sample size probably wasn’t large enough to assign him quite so low a probability of winning.

4. Trump’s nomination is potentially a point in favor of “polls-only” as opposed to “fundamentals” models.

5. There’s a danger in hindsight bias, and in over correcting after an unexpected event such as Trump’s nomination.

From Campaigns and Elections: Organization and Analytics Help Take Down Trump in Iowa.

A nice inside look at how data driven campaigns are run. Obama started it all - at least in the digital age - but here is how things have progressed since then. This focuses on the recent fight between Rubio and Cruz.

- Click here for the article.

“The conventional wisdom has been destroyed. What you can do is rely on data,” Jeff Roe, Cruz’s campaign manager told the Washington Post at the time. In fact, Cruz’s camp had statisticians and behavioral psychologists from the firm embedded in order to help with what it called psychographic targeting, “which categorizes supporters into personality groups in order to target them with specially tailored messaging,” according to the company.

Rubio, meanwhile, was helped by hiring veteran hands from Mitt Romney’s successful 2012 bid for the GOP presidential nomination. Stuart Stevens, Romney’s top strategist in 2012, credited Rubio consultant Rich Beeson with forming data models that helped shape the Romney strategy four years ago.

“Every primary, Rich predicted the result before and was always right,” Stevens said. “His in-depth analytics then was tremendously important for us.” Beeson, a partner at FLS Connect, declined to comment for this piece.

Stevens also praised Roe, who has a reputation as an aggressive strategist.

“Data analytics helped them know where their vote is. I think Jeff Roe did a superb job,” said Stevens. “But Cruz really worked it. He did it the old fashioned way and voters rewarded him for it.”

Other consultants credited Cruz’s traditional campaign structure and put less emphasis on his data analytics for propelling him to victory.

“It was quite a concerted campaign, it wasn’t just data analytics,” said Bob Haus, an Iowa-based GOP consultant. “He traveled. He did all 99 counties and his message was consistent. There are some things data can tell you, but technology only gets you so far.”

Here's more on the topic:

- Cruz campaign credits psychological data and analytics for its rising success.

As Cecil Stinemetz walked up to a gray clapboard house in suburban Des Moines last week wearing his “Cruz 2016” cap, a program on his iPhone was determining what kind of person would answer the door.

Would she be a “relaxed leader”? A “temperamental conservative”? Maybe even a “true believer”?

Nope. It turned out that Birdie Harms, a 64-year-old grandmother, part-time real estate agent and longtime Republican, was, by the Ted Cruz campaign’s calculations, a “stoic traditionalist” — a conservative whose top concerns included President Obama’s use of executive orders on immigration.

Which meant that Stinemetz was instructed to talk to her in a tone that was “confident and warm and straight to the point” and ask about her concerns regarding the Obama administration’s positions on immigration, guns and other topics.

The outreach to Harms and others like her is part of a months-long effort by the Cruz campaign to profile and target potential supporters, an approach that campaign officials believe has helped propel the senator from Texas to the top tier among Republican presidential candidates in many states, including Iowa, where he is in first place, according to two recent polls. It’s also a multimillion-dollar bet that such efforts still matter in an age of pop-culture personalities and ­social-media messaging.

It's not magic. It's data science.

Thst's what Civis Analytics claims anyway.

I'd say this is the future - but it's really the present. And its a reason you might want to take a bunch of stats classes.

- Check it out.

From the Hill: Sanders sues Democratic Party

A Sanders staffer was found sneaking around in a data base the Democratic National Committee makes available to candidates- but which can be customized by each candidate. That apparently was the problem. The way each candidates customizes the data gives hints about how candidate strategy.

The DNC is suspending the Sander's camp's access to the database. The Sander campaign thinks the DNC wants to give an advantage to Clinton, so that's why they're suing.  

- Click here for the story.

Sen. Bernie Sanders’ (I-Vt.) campaign sued the Democratic National Committee in federal court Friday evening following the suspension of his campaign from the DNC’s voter database after a security breach.

The suit claims that the campaign is losing $600,000 in donations each day that it does not have access to the data, and adds that the “damage to the campaign’s political viability as a result of being unable to communicate with constituents and voters, is far more severe, and incapable of measurement.”

The DNC barred Sanders from accessing the party’s voter file, which includes much of his campaign’s voter data, after a campaign staffer improperly accessed private data belonging to front-runner Hillary Clinton’s campaign. The vendor hired by the party to maintain the data accidentally created the security vulnerability during an update, the DNC says.

The Sanders campaign fired a supervisory staffer involved in the incident and has gone on the warpath Friday claiming that the DNC overreacted and is trying to aid Clinton’s campaign.

The suit claims that the loss of the voter file could “significantly disadvantage, if not cripple, a Democratic candidate’s campaign for public office.” It also argues that the agreement between the candidate and the DNC mandates that a candidate get 10 days written notice to fix any issue before the party can restrict access.

Vox goes further. They discuss the ongoing tension between Sanders and the DNC. They also discuss the data base in question - NGP VAN.

- Click here for: The feud between Bernie Sanders and the DNC, explained.

NGP VAN is a data technology company that allows campaigns to view a whole host of information about voters across the country who have voted for Democrats in the past.

The DNC owns the basic voter file, which it shares with primary candidates running as Democrats. This includes all three presidential candidates as well as anyone running for lower offices on the national and state levels.

From there, each campaign can take those voters profiles and make all sorts of models with them, usually to predict how persuadable they are or how likely they are to vote, say on a scale from one to 100. Models can also be used to predict voters positions on specific issues, which helps campaigns target them.

During Obama’s 2012 campaign, for example, staffers used this type of modeling to figure out which undecided voters to target for canvassing and which voters seemed less motivated and could use an extra push.

The data and sources the system can pull in are incredibly precise because they rely on detailed information about every voter — rather than something like a poll, conducted with a small sample of respondents. For that reason, these models are very expensive to build.

Campaigns share the basic voter file, so they’re looking at all the same voters. But NGP VAN puts up firewalls between them so each campaign doesn’t have access to the other’s modeling.

What’s key to understand here is that the data hosted on NGP VAN's dictates a campaign’s entire ground game, which both of Obama’s campaigns claimed as their winning advantage. Losing access to the system means that a campaign loses its own predictive models dictating which voters to target – but it also means the campaign doesn’t have access to the names of Democratic voters.

Life expectancy in the US slips - comparatively

From the National Journal, which reports on a study released by the Institute for Health Metrics and Evaluation.

In absolute terms, life expectancy has increased, but we've slipped in comparative terms:

Compared with the rest of the industrialized world (OECD countries), America is falling behind. "These improvements are much less than what countries of similar income per capita have seen," the report states. The U.S. now ranks 39th and 40th out of 187 countries for life expectancy for males and females respectively.

And life expectancy is uneven across the nation:

The United States isn't uniformly underperforming in life expectancy. The county with the highest life-expectancy in the U.S. for males is Fairfax County, Va., where males live 81.67 years. That's better than the life expectancy of Japan and Switzerland, which are atop the list for worldwide longevity.

This is the second troubling aspect of the report: There's a huge disparity between the country's highest- and lowest-performing areas. For men, the difference in longevity in the top and lowest counties is 17.77 years. For women, that number is 12.37 years. Progress in national longevity can be attributed to increases in the highest-performing counties (and mainly among men). "Many counties have made no progress," the report states, "or for the period 1993 to 2002, there have been declines for females in several hundred counties."

The life expectancy of the U.S.'s poorest-performing counties is similar to the mortality rates of some of the world's poorer nations.

From a public policy perspective, the proper question is whether this report is likely to place life expectancy on the policy agenda. Given what we know about what factors drive items to the top of the agenda, this will happen only if life expectancy dips in areas of the country - and among constituencies - with political power. I have a hunch that is not the case.

From the Brookings Institute: Vital Statistics on Congress

The latest edition of an ongoing project. I'm tempted to create a separate section in class just on this material.

From Wired: Big Data and Analytics

There was talk in class today - in relation to the assigned book on the 2012 election - about whether data and analytics were good for campaigns. Is the public better served when candidates have such detailed information about what makes them tick? Should candidates have to physically confront potential voters in order to determine what their preferences are? Does data and analytics make manipulation more possible?
Its a legitimate subject.

This Wired author points out that the brave new world of big data and the analytical ability to process it provides all sorts of possibilities - some good, some bad. But you can't have one without the other:

If you think about all the hype generated about consumer privacy and enterprises collating and analyzing information for a more targeted and personal experience, customer segmentation and demographics, location-based and real-time marketing what the NSA exposure has taught us is that there really is no privacy in the 21st century and we should just get used to it. Our data is anonymized unless it’s being used specifically for our purpose and benefit but the fact is we are happily generating it for them to use in any case.

But Big Data is no longer creepy. Sorry but it’s not. You must live in painful ignorance if you think that every nuance of a digital interaction hasn’t been collected by someone and analyzed. What’s clear is that analytics and big data seem to be labelled as only for marketers to hound us with or for banks to sell us more debt laden products. We forget, for example, about the medical and scientific boundaries being broken that rely on data analytics and human generated information to help it along.

At some point there will be consumer based tools affordable enough for people to make sense of the data they generate themselves, and why not, it’s all part of the equation. Personal graph analysis will become a reality as much as its parent will be wielded by enterprises.

So, you see, we have heroes and villains even in data analytics but it’s all a matter of perspective. The NSA are deemed evil for breaching our liberties and analyzing data without our consent to understand terrorist activities, and medical science is a force of good for helping us cure diseases using data sourced from all manner of places.

New data source: Measuring American Legislatures

Measuring American Legislatures is a new website that offers data on all state legislators servinf from 1993-2011.

Texas on the Brink points out where Texas lags behind other states in education and health care.

This is the sixth edition of the document, produced by the Legislative Study Group.

The Texas Tribune comments here.

Is hate speech related to ethnic violence?

A group called Hatebase think that it is and monitors social media to determine whether ethnic conflict is about to flare up:

From Foreign Policy:

In the months leading up to the Rwandan genocide of 1994, the radio station Radio Television Libre des Mille Collines blanketed the country with anti-Tutsi propaganda, inciting its Hutu listeners to "exterminate the cockroaches." During the genocide, the station took on an even more active role, reading out lists of people to be killed and their locations.

The role played by the station only became widely understood outside of Rwanda after the violence was over. Three of its former executives were eventually indicted by a U.N. tribunal for their part in the genocide, but what if the world had been monitoring Milles Collines before the killing started?

That's the idea behind Hatebase, a new initiative from the Sentinel Project, a Canadian group that aims to use social media and other technology to identify early warning signals for ethnic conflict.

There are two main features to Hatebase. The first is a Wikipedia-like interface which allows users to identify hate speech terms by region and the group they refer to. This could have some value for researchers, but Hatebase's developers are especially excited by the second main feature, which allows users to identify instances when they've heard these terms used.

"The real value is the sightings, says Hatebase's developer Timothy Quinn. "As soon as you have logged incidents of hate speech you can start mapping that stuff, looking at frequency, severity, the migration of terms geographically. There's a whole lot of value when people start mapping it against the real world

."

Is the level of violent crime related to the level of lead in the environment?

There's evidence that a relationship exists between exposure to high levels of lead - it used to commonly be in paint and gas - and propensity to commit violent crime. The data correlate strongly.  

The lines are shifted a bit so they seem to overlap more than they normally would. The reasoning is likely that someone who was exposed to lead as a child would be more likely to commit violent crimes as an adult.

The question now is whether the relationship is spurious, and there is a different factor that drives the change - perhaps something that drives both independently. But researchers have isolated a molecule in lead that reduces IQ. Does that also increase the likelihood that someone will become a violent criminal? If so, that's your link.

Over the past few decades, laws have been passed limiting the amount of lead in the environment. Could these be taking effect? The prison population has been reduced recently, and there has a been a long term trend downward in the percent of people going to prison. A commentator calls this one of the major unreported public policy stories of the year.





















Many factors are associated with this shift, but there's a definite shift in the tendency of younger males - the one's likely to commit violent crimes - to be sentenced to prison. Something seems to have shifted about 35 years ago - which was about the time leaded gasoline and lead paint were phased out.

From Slate: The Death of the Hunch

Here's a look into the current state of campaigning, at least on Obama's side. It involves randomized tests, data, and empirical analysis:

The Obama campaign’s “experiment-informed programs”—known as EIP in the lefty tactical circles where they’ve become the vogue in recent years—are designed to track the impact of campaign messages as voters process them in the real world, instead of relying solely on artificial environments like focus groups and surveys. The method combines the two most exciting developments in electioneering practice over the last decade: the use of randomized, controlled experiments able to isolate cause and effect in political activity and the microtargeting statistical models that can calculate the probability a voter will hold a particular view based on hundreds of variables.

Obama’s campaign has already begun rolling out messages to small test audiences. Analysts then rely on an extensive, ongoing microtargeting operation to discern which slivers of the electorate are most responsive, and to which messages. This cycle of trial and error offers empirically minded electioneers an upgrade over the current régime of approaching voters based on hunches.
Here's a related story - regarding the habits of shoppers and how to influence them.