A taxi driver in Tamale recently gave me an interesting lesson in taxi ride pricing. It turns out that there are two different types of taxis on the road. The most common are drivers that rent the car from someone else. They pay for the fuel they use during the day, and pay a flat rate fee to the taxi owner, usually around 20 GHC per day. The other type of drivers are those that own the car they drive.
The two types of drivers bear different marginal costs for each taxi ride they give. The latter, who own the cars they drive, bear the cost of the fuel for the car, their own time (valued at nearly zero in Ghana) and the depreciation of the vehicle due to the ride. The former, who rent the car at a fixed rate, don’t bear the last cost—the vehicle depreciation. Therefore they are willing to accept lower prices for each taxi ride.
The driver who told me this of course owned his own car, and was trying to convince me that even though the cost of a taxi across Tamale is always 3 or 4 GHC, it wasn’t fair to pay him less than 5 cedis.
Unfortunately for him, as much as I like a good economic theory, I can also identify a good economic strategy: in this case, using the length of a taxi ride to badger an expat into paying more. (The driver also happened to take the longest possible route through town.) I paid him 4 GHC, consistent with providing incentives to honor the agreed-upon price.
After years of remaining a theoretical plaything for money nerds like me, nominal GDP targeting has suddenly become a thing. NGDP targeting has been getting love from economists across the political spectrum, which naturally means laypeople across the spectrum are responding with skepticism. Drudged up from what I remember from Prof. Burdekin's Money and Banking, I present the basics of NGDP targeting:
What is NGDP targeting?
NGDP targeting means that the central bank sets monetary policy to target nominal gross domestic product, that is, GDP that is not adjusted for price level. The beauty of NGDP targeting is that since both prices and output are part of NGDP, both of these things influence monetary policy even though only one metric is considered. Either NGDP level, or growth, can be targeted.
Why liberal economists like it: liberal economists tend to be more supportive of considering economic growth, not just inflation, when setting monetary policy. NGDP targeting naturally includes growth as an input.
Why conservative economists like it: conservative economists tend to prefer simple, transparent rules for monetary policy, as opposed to opaque systems that give broad discretion to policy makers. NGDP targeting is easy to understand and easy to see if policy makers have met their goal.
So how is it different?
The Federal Reserve has what’s called a dual mandate—they are charged with considering both prices and economic output when setting policy. (Some central banks, like the European Central Bank, are charged with only considering prices.) The Federal Reserve does not commit to following any particular formula for setting monetary policy, but it is largely believed to behave as if it follows the Taylor Rule, meaning it considers deviations from target inflation rates and target employment rates. Targeting NGDP would provide the Federal Reserve with a mandate to specifically target deviations from output targets.
There are other things a central bank can consider besides prices, employment, and output when setting policy, such as money growth and asset prices. A central bank with broad discretion can consider all of these things, but this comes at the cost of making monetary policy decisions less transparent.
But what about QE2??
So where do things like quantitative easing fit into all this? It is important to distinguish between the tools used to set monetary policy and the tools used to implement monetary policy. Tools used to set monetary policy, such as Taylor Rules, money growth target, or NGDP targeting, tell the central bank whether they need to tighten or loosen policy. The tools used to implement monetary policy generally remain the same regardless of what policy prescription tool you use. If interest rates are at zero, and inflation is low and unemployment high, you need quantitative easing to get to your targets. If interest rates are at zero and NGDP is below target—guess what, you still need quantitative easing to get to your target.
Targeting Growth vs. Level
If you are going to target NGDP, you have to decide whether to target NGDP growth rate or NGDP level. Most of the cool kids in economics prefer level targeting. Level targeting has the benefit of allowing for catch up periods: after the economy has had a downturn, it natural that it have a period of both higher growth and inflation to catch up to where it would have been pre-downturn.
So let’s do it?
To me, whether NGDP targeting should be implemented actually involves three different questions:
1. Should GDP be considered when setting monetary policy? The downside to considering GDP or other factors is that the central bank places less weight on price stability, which is bad if you are an inflation hawk. The benefit is that drops in GDP often lead drops in inflation or unemployment, so considering GDP can help the central bank adjust to new economic trends more quickly.
2. Should the central bank target levels rather than growth rates? When it comes to prices, levels are arbitrary—it’s change in prices that matter. That’s the primary logic behind targeting inflation rather than price level. However, as discussed above, it is natural for an economy that has been sluggish for a while to have a period of higher growth and higher inflation to “catch up” after a downturn, so monetary policy that accommodates this can make sense. This accommodation can actually help end a downturn more quickly—if during a downturn, people know they can expect extra high inflation in the future, they have an incentive to spend more now, stimulating the economy. The flip side to all this, which I haven’t seen discussed much, is that monetary policy lags the trends a bit. This sounds great when you are coming out of a recession, but what about when you are coming out of an expansion? Would the people keen to see accommodating monetary policy continue as growth picks up be equally keen to see tight monetary policy stay in place when growth comes to an end?
3. Should the central bank have a clearly defined rule? The benefit of a rule is that it provides transparency and sets expectations, and expectations are essential for effective monetary policy. The downside of a rule is less flexibility, and loss of credibility if the central bank fails to meet its targets.
If you answered yes to all of the above, then NGDP level targeting is for you. Personally, I agree that a central bank with a dual mandate should consider GDP. However, I think that the central bank should have some flexibility in targeting levels versus growth, and that growth is sometimes the more appropriate metric. Moreover, I think that a central bank like the Federal Reserve, that has a high level of trust and credibility, has more to lose from committing to a rule like NGDP level targeting than it has to gain, since failure to achieve the target would hurt its credibility. As it is, the Federal Reserve can consider the policy prescriptions of an NGDP target, while also considering Taylor Rules, asset prices, and other economic data when setting monetary policy.
Recently, in a small village print shop with an old copy machine discarded from Canada or Belgium or some such place, I received a printing bill for twice the amount I expected-- because I printed my document from a pen drive.
Newcomers to Ghana are often warned of the dangers of "promiscuous" pen drives. which can spread viruses. (The term seems increasingly appropriate the more I consider the mechanics of using a pen drive.)
It is interesting to see that shops recognize the risk and tax it by charging higher rates on printing from pen drives. I was a bit put out, however, because the additional rate is charged per page, even though the virus risk is no different for printing a one-page document than a 100-page document. I'm looking forward to finding the shop that charges a flat rate fee for using a pen drive. I'll admit the deterrent is effective though: next time, I will email my document to myself.
How do you find an office in a town in Ghana? Mostly, you walk into town, chat up everyone you meet, tell them you are looking for an office, and give them your contact number. People are usually happy to help. Be outgoing, and soon you can have a beautiful, blue office like ours.
Chris Blattman recently blogged about the moral absurdity of running regressions where the dependent variable is “war deaths”.
While looking at death, illness, hunger, and poverty through the lens of statistics may seem rather reptilian, I think many researchers have emotional reactions to the data they work with. For me, these connections hit hard and unexpectedly, often when I am tired and working late, and they come despite efforts to be dispassionate about the data I am looking at.
Survey editing is prime territory for emotional connections to data. When editing surveys, you see the story of an individual respondent in a way that you don’t when you are looking at columns of aggregated data . Once, I was reading a survey where a respondent reported that a household member had experienced a headache. I turned the page to the question on outcomes of health events. The headache had resulted in death for that household member—despite the family spending an amount equal to roughly one-fourth of Ghana’s annual GDP per capita on health care for that individual. The shock of the outcome hit me almost physically. Another respondent reported testing positive for HIV. Sitting alone in the Tamale office at night, I struggled to pull myself together, shoo the bugs out of my computer keyboard, and make my way home.
The “death” outcome became a dependent variable in regressions I later ran looking at determinants of health outcomes. Luckily, there were very few events of death in my sample. We also looked at a number of food insecurity events: individuals going to bed hungry, or not eating for an entire day, for example. These were, unfortunately, common among our respondents. I don’t deal well with feeling hungry myself, and for me, food insecurity statistics evoke desperately sad, human images: a man’s disappointment at foregoing his favorite fish; a young student trying to sleep before an exam while feeling the distracting ache of hunger; an elderly woman going without food for a day so her grandchildren can eat; a mother having to tell her thin children there is no food today.
These emotional connections often seem like a distraction, something that prevents us from approaching our analysis logically and dispassionately. In all honesty, part of my attraction to quantitative research tools might be to protect myself from these types of emotional connections to problems. But it our ability to have these connections, even through layers of statistics, is tied to a very deep belief in the importance of what we are doing, and that counts for something. Hey, at least I’m not working in finance.