Tag Archives: OECD

Manufacturing employment and the prime-age male LFP rate: What’s the relationship?

Recently I wrote about the decline in the U.S. prime-age male labor force participation (LFP) rate and discussed some of the factors that may have caused it. One of the demand-side factors that many people think played a role is the decline in manufacturing employment in the United States.

Manufacturing has typically been a male-dominated industry, especially for males with less formal education, but increases in automation and productivity have resulted in fewer manufacturing jobs in the United States over time. As manufacturing jobs disappeared, the story goes, so did a lot of economic opportunities for working-age men. The result has been men leaving the labor force.

However, the same decline in manufacturing employment occurred in other countries as well, yet many of them experienced much smaller declines in their prime-age male LFP rates. The table below shows the percent of employment in manufacturing in 1990 and 2012 for 10 OECD countries, as well as their 25 to 54 male LFP rates in 1990 and 2012. The manufacturing data come from the FRED website and the LFP data are from the OECD data site. The ten countries included here were chosen based on data availability and I think they provide a sample that can be reasonably compared to the United States.

country 25-54 LFP rate, manuf table

As shown in the table, all of the countries experienced a decline in manufacturing employment and labor force participation over this time period. Thus America was not unique in this regard.

But when changes in both variables are plotted on the same graph, the story that the decline in manufacturing employment caused the drop in male LFP rate doesn’t really hold up.

country 25-54 LFP rate, manuf scatter plot

The percentage point change in manufacturing employment is across the top on the x-axis and the percentage point change in the prime-age male LFP rate is on the y-axis. As shown in the graph the relationship between the two is negative in this sample, and the change in manufacturing employment explains almost 36% of the variation in LFP rate declines (the coefficient on the decline in manufacturing employment is -0.322 and the p-value is 0.08).

In other words, the countries that experienced the biggest drops in manufacturing employment experienced the smallest drops in their LFP rate, which is the opposite of what we would expect if the decline in manufacturing employment played a big role in the decline of the LFP rate across countries.

Of course, correlation does not mean causation and I find it hard to believe that declines in manufacturing employment actually improved LFP rates, all else equal. But I also think the less manufacturing, less labor force participation story is too simple, and this data supports that view.

America and Italy experienced similar declines in their male LFP rates but neither experienced the largest declines in manufacturing employment over this time period. What else is going on in America that caused its LFP decline to more closely resemble Italy’s than that of Canada, Australia and the UK, which are more similar to America along many dimensions?

Whatever the exact reasons are, it appears that American working-age males responded differently to the decline in manufacturing employment over the last 20 + years than similar males in similar countries. This could be due to our higher incarceration rate, the way our social safety net is constructed, differences between education systems, the strength of the economy overall or a number of other factors. But attributing the bulk of the blame to the decline of manufacturing employment doesn’t seem appropriate.

Economic policies and institutions matter

Economists often talk about the important role institutions and policies play in generating economic growth. A new paper that examines the role of urban governance and city-level productivity provides some additional, indirect evidence that institutions and policies impact economic productivity at the local level. (The focus of the paper is how administrative fragmentation affects city-level productivity, not what I present here, but I thought the following was interesting nonetheless.)

The authors graph the correlation between city population and city productivity for five different countries. There is a positive relationship between population and productivity in all of the countries, which is consistent with other studies that find a similar relationship. This relationship is largely due to agglomeration economies and the greater degree of specialization within large cities.

One of the figures from the study—for the U.S.—is shown below. City productivity is measured on the y-axis and the natural log of city population is on the x-axis. (Technical note for those interested: city productivity is measured as the coefficient on a city dummy variable in an individual-level log hourly wage/earnings regression that also controls for gender, age, age squared, education and occupation. This strips away observable characteristics of the population that may affect city productivity.)

US city productivity

Source: Ahrend, Rudiger, et al. “What makes cities more productive? Evidence from five OECD countries on the role of urban governance.” Journal of Regional Science 2017

 

As shown in the graph there is a relatively tight, positive relationship between size and productivity. The two noticeable outlies are El Paso and McAllen, TX, both of which are on the border with Mexico.

The next figure depicts the same information but for cities in Germany.

german city size, product graph

What’s interesting about this figure is that there is a cluster of outliers in the bottom left, which weakens the overall relationship. The cities in this cluster are less productive than one would expect based on their population. These cities also have another thing in common: They are located in or near what was East Germany. The authors comment on this:

“In Germany, the most noteworthy feature is probably the strong east-west divide, with city productivity premiums in eastern German cities being, on the whole, significantly below the levels found in western German cities of comparable size. In line with this finding, the city productivity premium in Berlin lies in between the trends in eastern and western Germany.”

The data used to construct these figures are from 2007, 17 years after the unification of Germany. After WWII and until 1990, East Germany was under communist control and had a centrally planned economy, complete with price controls and production quotas, while West Germany had a democratic government and market economy.

Since 1990, both areas have operated under the same country-level rules and institutions, but as shown above the productivity difference between the two regions persisted. This is evidence that it can take a considerable amount of time for an area to overcome damaging economic policies.

The Economics of Regulation Part 2: Quantifying Regulation

I recently wrote about a new study from economists John Dawson and John Seater that shows that federal regulations have slowed economic growth in the US by an average of 2% per year.  The study was novel and important enough from my perspective that it deserved some detailed coverage.  In this post, which is part two of a three part series (part one here), I go into some detail on the various ways that economists measure regulation.  This will help put into context the measure that Dawson and Seater used, which is the main innovation of their study.  The third part of the series will discuss the endogenous growth model in which they used their new measure of regulation to estimate its effect on economic growth.

From the macroeconomic perspective, the main policy interventions—that is, instruments wielded in a way to change individual or firm behavior—used by governments are taxes and regulations.  Others might include spending/deficit spending and monetary policy in that list, but a large percentage of economics studies on interventions intended to change behavior have focused on taxes, for one simple reason: taxes are relatively easy to quantify.  As a result, we know a lot more about taxes than we do about regulations, even if much of that knowledge is not well implemented.  Economists can calculate changes to marginal tax rates caused by specific policies, and by simultaneously tracking outcomes such as changes in tax revenue and the behavior of taxed and untaxed groups, deduce specific numbers with which to characterize the consequences of those taxation policies.  In short, with taxes, you have specific dollar values or percentages to work with. With regulations, not so much.

In fact, the actual burden of regulation is notoriously hidden, especially when directly compared to taxes that attempt to achieve the same policy objective.  For example, since fuel economy regulations (called Corporate Average Fuel Economy, or CAFE, standards) were first implemented in the 1970s, it has been broadly recognized that the goal of reducing gasoline consumption could be more efficiently achieved through a gasoline tax rather than vehicle design or performance standards.  However, it is much easier for a politician to tell her constituents that she will make auto manufacturers build more fuel-efficient cars than to tell constituents that they now face higher gasoline prices because of a fuel tax.  In econospeak, taxes are salient to voters—remembered as important and costly—whereas regulations are not. Even when comparing taxes to taxes, some, such as property taxes, are apparently more salient than others, such as payroll taxes, as this recent study shows.  If some taxes that workers pay on a regular basis are relatively unnoticed, how much easier is it to hide a tax in the form of a regulation?  Indeed, it is arguably because regulations are uniquely opaque as policy instruments that all presidents since Jimmy Carter have required some form of benefit-cost analysis on new regulations prior to their enactment (note, however, that the average quality of those analyses is astonishingly low).  Of course, it is for these same obfuscatory qualities that politicians seem to prefer regulations to taxes.

Despite the inherent difficulty, scholars have been analyzing the consequences of regulation for decades, leading to a fairly large literature. Studies typically examine the causal effect of a unique regulation or a small collection of related regulations, such as air quality standards stemming from the Clean Air Act.  Compared to the thousands of actual regulations that are in effect, the regulation typically studied is relatively limited in scope, even if its effects can be far-reaching.  Because most studies on regulation focus only on one or perhaps a few specific regulations, there is a lot of room for more research to be done.  Specifically, improved metrics of regulation, especially metrics that can be used either in multi-industry microeconomic studies or in macroeconomic contexts, could help advance our understanding of the overall effect of all regulations.

With that goal in mind, some attempts have been made to more comprehensively measure regulation through the use of surveys and legal studies.  The most famous example is probably the Doing Business index from the World Bank, while perhaps the most widely used in academic studies is the Indicators of Product Market Regulation from the OECD.  Since 2003, the World Bank has produced the Doing Business Index, which combines survey data with observational data into a single number designed to tell how much it would cost to “do business,” e.g. set up a company, get construction permits, get electricity, register property, etc., in set of 185 countries.  The Doing Business index is perhaps most useful for identifying good practices to follow in early to middle stages of economic development, when property rights and other beneficial institutions can be created and strengthened.

The OECD’s Indicators of Product Market Regulation database focuses more narrowly on types of regulation that are more relevant to developed economies.  Specifically, the original OECD data considered only product market and employment protection regulations, both of which are measured at “economy-wide” level—meaning the OECD measured whether those types of regulations existed in a given country, regardless of whether they were applicable to only certain individuals or particular industries.  The OECD later extended the data by adding barriers to entry, public ownership, vertical integration, market structure, and price controls for a small subset of broadly defined industries (gas, electricity, post, telecommunications, passenger air transport, railways, and road freight).  The OECD develops its database by surveying government officials in several countries and aggregating their responses, with weightings, into several indexes.

By design, the OECD and Doing Business approaches do a good job of relating obscure macroeconomic data to actual people and businesses.  Consider the chart below, taken from the OECD description of how the Product Market Regulation database is created.  As I wrote last week and as the chart shows, the rather sanitized term “product market regulation” actually consists of several components that are directly relevant to a would-be entrepreneur (such as the opacity of a country’s licenses and permits system and administrative burdens for sole proprietorships) and to a consumer (such as price controls and barriers to foreign direct investment).  You can click on the chart below to see some of the other components that are considered in OECD’s product market regulation indicator.

oecd product regulation tree structure

Still, there are two major shortcomings of the OECD data (shortcomings that are equally applicable to similar indexes produced by the World Bank and others).  First, they cover relatively short time spans.  Changes in regulatory policy often require several years, if not decades, to implement, so the results of these changes may not be reflected in short time frames (to a degree, this can be overcome by measuring regulation for several different countries or different industries, so that results of different policies can be compared across countries or industries).

Second, and in my mind, more importantly, the Doing Business Index is not comprehensive.  Instead, it is focused on a few areas of regulation, and then only on whether regulations exist—not how complex or burdensome they are.  As Dawson and Seater explain:

[M]easures of regulation [such as the Doing Business Index and the OECD Indicators] generally proceed by constructing indices based on binary indicators of whether or not various kinds of regulation exist, assigning a value of 1 to each type of regulation that exists and a 0 to those that do not exist.  The index then is constructed as a weighted sum of all the binary indicators.  Such measures capture the existence of given types of regulation but cannot capture their extent or complexity.

Dawson and Seater go out of their way to mention at least twice that the OECD dataset ignores environmental and occupational health and safety regulations.  Theirs is a good point – in the US, at least, environmental regulations from the EPA alone accounted for about 15% of all restrictions published in federal regulations in 2010, and that percentage has consistently grown for the past decade, as can be seen in the graph below (created using data from RegData).  Occupational health and safety regulations take up a significant portion of the regulatory code as well.

env regs as percentage of total

In contrast, one could measure all federal regulations, not just a few select types.  But then the process requires some usage of the actual legal texts containing regulations.  There have been a few attempts to create all-inclusive time series measures of regulation based on the voluminous legal documents detailing regulatory activity at the federal level.   For the most part, studies have relied on the Federal Register, the government’s daily journal of newly proposed and final regulations.  For example, many scholars have counted pages in the Federal Register to test for the existence of the midnight regulations phenomenon—the observation that the administrations of outgoing presidents seem to produce abnormally large numbers of regulations during the lame-duck period

There are problems with using the Federal Register to measure regulation (I say this despite having used it in some of my own papers).  First and foremost, the Federal Register includes deregulatory activity.  When a regulatory agency eliminates words, paragraphs, or even entire chapters from the CFR, the agency has to notify the public of the changes.  The agency does this by printing a notice of proposed rulemaking in the Federal Register that explains the agencies intentions.  Then, once the public has had adequate time to comment on the agencies proposed actions, the agency has to publish a final rule in the Federal Register—another set of pages that detail the final actions the agency is taking.  Obviously, if one is counting pages published in the Federal Register and using that as a proxy for the growth of regulation, deregulatory activity that produces positive page counts would lead to incorrect measurements.  

Furthermore, pages published in the Federal Register may be a biased measure because the number of pages associated with individual rulemakings has increased over time as acts of Congress or executive orders have required more analyses. In his Ten-Thousand Commandments series, Wayne Crews mitigates this drawback to some degree by focusing only on pages devoted to final rules.  The Ten-Thousand Commandments series keeps track of both the annual number of final regulations published in the Federal Register and the annual number of Federal Register pages devoted to final regulations.

Dawson and Seater instead rely on the Code of Federal Regulations, another set of legal documents related to federal regulationsActually, the CFR would be better described as the books that contain the actual text of regulations in effect each year.  When a regulatory agency creates new regulations, or alters existing regulations, those changes are reflected in the next publication of the CFR.  Dawson and Seater collected data on the total number of pages in the CFR in each year from 1949 to 2005. I’ve graphed their data below.

dawson and seater cfr pages

*Dawson and Seater exclude Titles 1 – 3 and 32 from their total page counts because they argue that those Titles do not contain regulation, so comparing this graph with page count graphs produced elsewhere will show some discrepancies.

Perhaps the most significant advantage of the CFR over counting pages in the Federal Register is that it allows for decreases in regulations. However, using the CFR arguably has several advantages over indexes like the OECD product market regulation index and the World Bank Doing Business index.  First, using the CFR captures all federal regulation, not just a select few types.  Dawson and Seater point out:

Incomplete coverage leads to two problems: (1) omitted variables bias, and, in any time series study, (2) divergence between the time series behavior of subsets of regulation on the one hand and of total regulation on the other.

In other words, ignoring potentially important variables (such as environmental regulations) can cause estimates of the effect of regulation to be wrong.

Second, the number of pages in the CFR may reflect the complexity of regulations to some degree.  In contrast, the index metrics of regulation typically only consider whether a regulation exists—a binary variable equal to 1 or 0, with nothing in between.  Third, the CFR offers a long time series – almost three times as long as the OECD index, although it is shorter than the Federal Register time series.

Of course, there are downsides to using the CFR.  For one, it is possible that legal drafting standards and language norms have changed over the 57 years, which could introduce bias to their measure (Dawson and Seater brush this concern aside, but not convincingly in my opinion).  Second, the CFR is limited to only one country—the United States—whereas the OECD and World Bank products cover many countries.  Data on multiple countries (or multiple industries within a country, like RegData offers) allow comparisons of real-world outcomes and how they respond to different regulatory treatments.  In contrast, Dawson and Seater are limited to constructing a “counterfactual” economy – one that their model predicts would exist had regulations stayed at the level they were in 1949.  In my next post, I’ll go into more detail on the model they use to do this.

The Economics of Regulation Part 1: A New Study Shows That Regulatory Accumulation Hurts the Economy

In June, John Dawson and John Seater, economists at Appalachian State University and North Carolina State University, respectively, published a potentially important study (ungated version here) in the Journal of Economic Growth that shows the effects of regulatory accumulation on the US economy.  Several others have already summarized the study’s results (two examples here and here) with respect to how the accumulation of federal regulation caused substantial reductions in the growth rate of GDP.  So, while the results are important, I won’t dwell on them here.  The short summary is this: using a new measure of federal regulation in an endogenous growth model, Dawson and Seater find that, on average, federal regulation reduced economic growth in the US by about 2% annually in the period from 1949 to 2005.  Considering that economic growth is an exponential process, an average reduction of 2% over 57 years makes a big difference.  A relevant excerpt tells just how big of a difference:

 We can convert the reduction in output caused by regulation to more tangible terms by computing the dollar value of the loss involved.  […] In 2011, nominal GDP was $15.1 trillion.  Had regulation remained at its 1949 level, current GDP would have been about $53.9 trillion, an increase of $38.8 trillion.  With about 140 million households and 300 million people, an annual loss of $38.8 trillion converts to about $277,100 per household and $129,300 per person.

These are large numbers, but in fact they aren’t much different from what a bevy of previous studies have found about the effects of regulation.  The key differences between this study and most previous studies are the method of measuring regulation and the model used to estimate regulation’s effect on economic growth and total factor productivity.

In a multi-part series, I will focus on the tools that allowed Dawson and Seater to produce this study: 1. A new time series measure of total federal regulation, and 2. Models of endogenous growth.  My next post will go into detail on Dawson and Seater’s new time series measure of regulation, and compares it to other metrics that have been used.  Then I’ll follow up with a post discussing endogenous growth models, which consider that policy decisions can affect the accumulation of knowledge and the rates of innovation and entrepreneurship in an economy, and through these mechanisms affect economic growth.

Why should you care about something as obscure as a “time series measure of regulation” and “endogenous growth theory?”  Regulations—a form of law that lawyers call administrative law—create a hidden tax.  When the Department of Transportation creates new regulations that mandate that cars must become more fuel efficient, all cars become more expensive, in the same way that a tax on cars would make them more expensive.  Even worse, the accumulation of regulations over time stifle innovation, hinder entrepreneurship, and create unintended consequences by altering the prices of everyday purchases and activities.  For an example of hindering entrepreneurship, occupational licensing requirements in 17 states make it illegal for someone to braid hair for a living without first being licensed, a process which, in Pennsylvania at least, requires 300 hours of training, at least a 10th grade education, and passing a practical and a theory exam. Oh, and after you’ve paid for all that training, you still have to pay for a license.

And for an example of unintended consequences: Transportation Security Administration procedures in airports obviously slow down travel.  So now you have to leave work or home 30 minutes or even an hour earlier than you would have otherwise, and you lose the chance to spend another hour with your family or finishing some important project.  Furthermore, because of increased travel times when flying, some people choose to drive instead of fly.  Because driving involves a higher risk of accident and death than does flying, this shift, caused by regulation, of travelers from plane to car actually causes people to die (statistically speaking), as this paper showed.

Economists have realized the accumulation of regulation must be causing serious problems in the economy.  As a result, they have been trying to measure regulation in different ways, in order to include regulation in their models and better study its impact.  One famous measure of regulation, which I’ll discuss in more detail in my next post, is the OECD’s index of Product Market Regulation.  That rather sanitized term, “product market regulation,” actually consists of several components that are directly relevant to a would-be entrepreneur (such as the opacity of a country’s licenses and permits system and administrative burdens for sole proprietorships) and to a consumer (such as price controls, which can lead to shortages like we often see after hurricanes where anti-price gouging laws exist, and barriers to foreign direct investment, which could prevent multinational firms like Toyota from building a new facility and creating new jobs in a country).  But as you’ll see in the next post, that OECD measure (and many other measures) of regulation miss a lot of regulations that also directly affect every individual and business.  In any science, correct measurement is a necessary first step to empirical hypothesis testing.

Dawson and Seater have contributed a new measure of regulation that improves upon previously existing ones in many ways, although it also has its drawbacks.  And because their new measure of regulation offers many more years of observations than most other measures, it can be used in an endogenous growth model to estimate how regulation has affected the growth of the US economy.  Again, in endogenous growth models, policy decisions (such as how much regulation to create) affect economic growth if they affect the rates of accumulation of knowledge, innovation, and entrepreneurship. It’s by using their measure in an endogenous growth model that Dawson and Seater were able to estimate that individuals in the US would have been $129,300 richer if regulations had stayed at their 1949 level.  I’ll explain a bit more about endogenous growth theory in a second follow-up post.  But first things first—my next post will go into detail on measures of regulation and Dawson and Seater’s innovation.

Does economic freedom matter among wealthy countries?

In response to my last post, alert Neighborhood Effects reader Shane Phillips writes:

Are there charts like these that just compare the nations in the top quintile? It’s good to know that economic freedom leads to these positive outcomes, but knowing the difference between the Central African Republic, for example, and the US doesn’t really tell me as much as the US vs other modern, developed countries would.

This is a great question and the answer is yes, there has been some attempt to examine these relationships in a sub-set of wealthier countries. One area in which this has been done is in the literature looking at the effect of government size on economic growth. Government size, remember, is just one aspect of economic freedom (in the EFW, the other components are “legal system and property rights,” “sound money,” “freedom to trade internationally,” and “regulation”).

Speaking broadly, most economists who have looked at this, tend to approach the question with the following theoretical relationship in mind:

government size and growth in theory

In other words, at low levels of government spending, additional spending may be able to increase growth by financing things like property protection and public goods. But at higher levels of government spending, marginal increases in government size detract from growth as taxes become more distortionary and as government becomes less effective.

Andreas Bergh and Magnus Henrekson have a very nice survey of this literature. The whole study is worth a read, but one of the more important findings is that while the relationship is fairly ambiguous when all countries are included, it is less-so when you look at the sub-sample of wealthy countries:

The literature on the relationship between the size of government and economic growth is full of seemingly contradictory findings. This conflict is largely explained by variations in definitions and the countries studied. An alternative approach—of limiting the focus to studies of the relationship in rich countries, measuring government size as total taxes or total expenditure relative to GDP and relying on panel data estimations with variation over time—reveals a more consistent picture. The most recent studies find a significant negative correlation: An increase in government size by 10 percentage points is associated with a 0.5 to 1 percent lower annual growth rate.

To me this suggests that the theoretical prediction may not be far from the mark. It’s interesting to note, by the way, that government size is generally negatively correlated with other aspects of economic freedom. So the freer, more-developed countries are often the ones with the largest public sectors. This helps explain why the relationship isn’t consistent across a larger sample: some of the countries with the smallest size governments are also those with the most regulation, the most barriers to trade, etc.

What about economic freedom more broadly defined? Has this been studied among the subset of relatively wealthy and relatively economically-free countries? I’m unaware of any formal studies, but as it turns out I’ve done some simple correlations myself. In the chart below, I graph economic freedom along with per capita GDP in OECD countries. The relationship is positive and statistically significant, though I’d caution that it is a small sample size and I have no control variables.

economic freedom and per capita GDP in OECDOne nice thing about focusing on the subset of OECD countries is that doing so allows me to examine the relationship between economic freedom and median income (which isn’t readily available for non-OECD countries). Per capita measures are problematic because they are sensitive to outliers. A handful of super-wealthy people in the U.S. or Luxembourg may give the false impression that everyone is wealthy. The median, however, doesn’t have this problem because it is unaffected by the levels at the extremes of the sample. Here the relationship is in terms of median income:

economic freedom and median income in oecdAs before, the same caveat applies: This is statistically significant; but it is a small sample and I have no control variables.

Don’t make us drive these cattle over the cliff

First a brief note: I am now blogging at the American Spectator on economic issues. I invite you to visit the inaugural posts. Last week, I covered the fiscal cliff. Like many others, I also marvel at the audacity of the pork contained therein.

Lately the headlines have given me a flashback to 1990 and those first undergrad economics classes. And not just econ but also U.S. history and the American experience with price floors and ceilings. In this post I’ll discuss the floors.

As I note at The Spectacle one of the matters settled by the American Taxpayer Relief Act is the extension of dairy price supports from the 2008 farm bill. Now, Congress won’t be “forced to charge $8 gallon for milk.” To me, nothing screams government price-fixing more than this threat aimed to scare small children and the parents who buy their food.

Chris Edwards explains how America’s dairy subsidy programs work in Milk Madness. Since the 1930’s the federal government  has set the minimum price to be charged for dairy. A misguided idea from the start, the point of the program was to ensure that dairy farmers weren’t hurt by falling prices during the Great Depression. When market prices fall below the government set price the government agrees to buy up any excess butter, dry milk or cheese that is produced. Thusly, dairy prices are kept artificially high which stimulates more demand.

According to Edwards’ study, the OECD found that U.S. dairy policies create a 26 percent “implicit tax” on milk, a regressive tax that affects low-income families in particular. Taxpayers pay to keep food prices artificially high, generate waste, and prevent local farmers from entering a caretlized market.

Now for the cows. The recession revealed that the nation has an oversupply of them. The New York Times reports that rapid expansion in the U.S. dairy market driven by increased global demand for milk products came to a sudden halt in 2008. Farmers were left with cows that needed to be milked regardless of the slump in world prices. The excess dry milk was then sold to the government but only at a price that was set above what the market demanded.

In other words, in a world without price supports, farmers could have sold the milk for less at market and consumers would have enjoyed cheaper butter, cheese and baby formula. Instead, the government stepped in, bought $91 million in milk powder so the farmer could get an above-market price and keep supporting an excess of milk cows. Rather than downsize the dairy based on market signals (and sell part of the herd to other dairy farmers, or the butcher) farmers take the subsidy and keep one too many cows pumping out more milk than is demanded.

It turns out auctioning a herd is not something all farmers are anxious to do. Some may look for additional governmental assistance to keep their cattle fed in spite of dropping prices, increased feed costs, and bad weather. To be sure eliminating farm subsidies would produce a temporary shock (a windfall for farmers and sticker shock for consumers), but in the long run as markets adjust everyone benefits.Dairy cows in the sale ring at the Warragul cattle sales, Victoria, [2]

New Zealand did it. Thirty years later and costs are lower for consumers, farmers are thrivingenvironmental practices have improved, and organic farming is growing. While politicians and the farm lobby may continue pushing for inefficient agricultural policy in spite of the nation’s fiscal path,as Robert Samuelson at Real Clear Politics writes, “If we can’t kill farm subsidies, what can we kill?”

 

Does UK Double-Dip Prove that Austerity Doesn’t Work?

The U.K. has slipped back into recession and Paul Krugman thinks this is evidence that austerity doesn’t work. Is it?

There are three questions with austerity:

  1. Will it work? Will it actually cut the debt?
  2. Will it hurt? Will it harm the economy or might it actually be stimulative?
  3. What mix of spending cuts and tax increases yield the best answers to questions 1 and 2.

Here is what the data says (and there is a lot of it):

  1. Sadly, most austerity efforts fail. According to research by Alberto Alesina, about 84 percent of fiscal reforms fail to substantially reduce a nation’s debt-to-GDP level.
  2. We’ve known for a while that austerity can be stimulative. Even left-of-center economists such as David Romer have acknowledged this possibility. But the evidence on this is decidedly mixed. As Alesina put it in his Mercatus working paper, austerity is about as likely to be stimulative as…well…stimulus. And we know the economics profession is quite divided on stimulus. So you shouldn’t hold your breath hoping austerity will boost economic growth. But remember, that’s not why we should be pursing austerity. We should pursue austerity because we know that we are on an unsustainable fiscal path and that in the long run, too much debt is very bad for growth. Furthermore, we know that the longer we put off reforms, the more painful they will have to be.
  3. Lots and lots of papers* have now studied this question and the evidence is rather clear: the types of austerity that are most-likely to a) cut the debt and b) not kill the economy are those that are heavily weighted toward spending reductions and not tax increases. I am aware of not one study that found the opposite. In fact, we know more. The most successful reforms are those that go after the most politically sensitive items: government employment and entitlement programs. Lastly, there is evidence that markets react positively when politicians signal their seriousness by going against their partisan inclinations. In other words, the most credible spending reductions are those that are undertaken by left-of-center governments. So slash away, Mr. Obama!

photo by: 401K/Flickr

I summarized these issues in this summary and in this presentation.

Given what we know about austerity, my advice to the UK would be: tweak your austerity measures so that they are more spending-cut-focused and less revenue-increase-focused. And go after the most politically-sensitive items. I wish I knew more about what they actually did, but my knowledge of this is limited and I’ve frankly heard conflicting reports (apparently in the UK, there are just as many arguments over the proper baseline as there are here in the U.S.!).

—————————————

*Most of the following papers directly test the question of whether spending-cut-focused reforms or tax-cut-focused reforms are more successful and more expansionary. A few test related questions but provide corroborating evidence for this question. All of them suggest that spending-cut-focused reforms work better and are more likely to aid the economy. The papers are in chronological order, but I’d recommend starting with the latest:

Francesco Giavazzi and Marco Pagano, “Can Sever Fiscal Contractions Be Expansionary? Tales of Two Small European Countries,” NBER Macroeconomics Annual, (Cambridge, MA: MIT Press, 1990), 95-122.

Alberto Alesina and Roberto Perotti, “Reducing Budget Deficits,” 1994-95 Discussion Paper Series No. 759 (1995);

Alberto Alesina and Silvia Ardagna, “Fiscal Expansions and Adjustments in OECD Countries,” Economic Policy, No. 21, (1995): 207-47;

Francesco Giavazzi and Marco Pagano, “Non-Keynesian Effects of Fiscal Policy Changes: International Evidence and the Swedish Experience,” Swedish Economic Policy Review, Vol. 3, No. 1 (1996): 67-112;

John McDermott and Robert Wescott, “An Empirical Analysis of Fiscal Adjustments,” International Monetary Fund Staff Papers, Vol. 43 (1996): 725-753;

Alberto Alesina and Roberto Perotti, “Fiscal Adjustments in OECD Countries: Composition and Macroeconomic Effects,” NBER Working Paper 5730 (1997);

Alberto Alesina, Roberto Perotti, and Jose Tavares, “The Political Economy of Fiscal Adjustments,” Brookings Papers on Economic Activity (1998);

Alberto Alesina and Silvia Ardagna, “Tales of Fiscal Adjustment,” Economic Policy, Vol. 13, No. 27 (1998): 489-545;

Roberto Perotti, “Fiscal Policy in Good Times and Bad,” The Quarterly Journal of Economics, Vol. 114 (1999): 1399-1436;

Juergen von Hagen and Rolf Strauch, “Fiscal Consolidations: Quality, Economic Conditions, and Success,” Public Choice, Vol. 109, No. 3-4 (2001): 327-46;

Alberto Alesina, Silvia Ardagna, Roberto Perotti, and Fabio Schiantarelli, “Fiscal Policy, Profits, and Investment,” American Economic Review, Vol. 92, No. 3 (2002): 571-589;

Juergen von Hagen, Hughes Halite, and Rolf Starch, “Budgetary Consolidation in Europe: Quality, Economic Conditions, and Persistence,” Journal of the Japanese and International Economics, Vol. 16 (2002): 512-35;

Silvia Adrian, “Fiscal Stabilizations: When Do They Work and Why?” European Economic Review, Vol. 48, No. 5 (2004): 1047-74;

Jose Tavares, “Does Right or Left Matter? Cabinets, Credibility and Fiscal Adjustments,” Journal of Public Economics, Vol. 88 (2004): 2447-2468;

Luisa Lambertini and Jose Tavares, “Exchange Rates and Fiscal Adjustments: Evidence from the OECD and Implicates for the EMU,” Contributions to Macroeconomics, Vol. 5, No. 11 (2005);

Boris Cournede and Frederic Gonand, “Restoring Fiscal Sustainability in the Euro Area: Raise Taxes or Curb Spending?OECD Economics Department Working Papers, No. 520 (2006);

Stephanie Guichard, Mike Kennedy, Eckhard Wurzel, and Christophe Andre, “What Promotes Fiscal Consolidation: OECD Country Experiences,” OECD Economics Department Working Papers, No. 553 (2007);

OECD, “IV. Fiscal Consolidation: Lessons from Past Experience,” in OECD Economic Outlook, 2007;

Andrew Biggs, Kevin Hassett, and Matthew Jensen, “A Guide for Deficit Reduction in the United States Based on Historical Consolidations That Worked,” AEI Economic Policy Working Paper No. 2010-04, (2010);

Ben Broadbent and Kevin Daly, “Limiting the Fallout from Fiscal Adjustment,” Goldman Sachs Global Economics Paper, No. 195 (2010);

David Leigh, Pete Devries, Charles Freedman, Jaime Guajardo, Douglas Laxton, and Andrea Pescatori, “Will It Hurt? Macroeconomic Effects of Fiscal Consolidation,” in World Economic Outlook: Recovery, Risk and Rebalancing (Washington, DC: International Monetary Fund, 2010);

 

 

Is Cutting the Deficit an Electoral Loser?

Here is an NBER article I hope all presidential candidates read:

The conventional wisdom regarding the political consequences of large reductions of budget deficits is that they are very costly for the governments which implement them: they are punished by voters at the following elections. In the present paper, instead, we find no evidence that governments which quickly reduce budget deficits are systematically voted out of office in a sample of 19 OECD countries from 1975 to 2008.  We also take into consideration issues of reverse causality, namely the possibility that only “strong and popular” governments can implement fiscal adjustments and thus they are not voted out of office “despite” having reduced the deficits.  In the end we conclude that many governments can reduce deficits avoiding an electoral defeat.

Alesina described similar findings in his 2010 Mercatus Working paper. There he wrote:

One of the most striking results of Alesina and Ardagna (2010) is that fiscal adjustments (reductions) on the spending side are almost as likely to be associated with high growth (i.e. a successful episode) as fiscal expansions on the spending side,

Maybe We Need a Super Democrat?

The Super Committee has failed. What now?

As I have said before, it is very difficult to look at the long-run fiscal projections and conclude that the impending debt crisis is anything but a major spending problem. According to the CBO, when my daughter graduates from college, federal revenue will be right at its historical average of 18.4 percent of GDP. At the same time, federal spending will consume more than 35 percent of GDP—more than 15 percentage points above the 20 percent average that has prevailed my entire life.

So the long-run explosion in spending—which is driven almost entirely by entitlements and interest payments—must be arrested. How?

In my view, it takes a Democrat.

Only Nixon could go to China. Only Carter could deregulate. Only Reagan could sign the first arms reduction treaties. Only Clinton could sign welfare reform. Lasting and meaningful reforms often require politicians to cross the ideological divide. Given the partisan divide right now, it is very difficult for me to imagine that any Republican president would be successful in reducing entitlement spending. But a Democrat could do it.

And one piece of evidence for this is a 2004 paper in the Journal of Public Economics by the economist José Tavares. He writes:

In a panel of large fiscal adjustments in OECD countries during the last 40 years, we find evidence that left-wing and right-wing cabinets are partisan: the left tends to reduce the deficit by raising tax revenues while the right relies mostly on spending cuts. Our testable hypothesis is that cabinets can signal commitment by undertaking fiscal adjustments in ways that are not favored by their constituencies. In other words, the left gains credibility when it cuts spending while the right becomes more credible when it increases tax revenues. Probit estimates of the determinants of persistence in fiscal adjustments confirm that spending cuts by the left and tax increases by the right are associated with persistent adjustments.

So if it is spending cuts that we need, then these cuts are likely to be more sustainable (“persistent”) if they are executed by a left-leaning government.

Unfortunately, I don’t see much evidence that President Obama is keen to follow this course. His best shot at it came when his own deficit-reduction panel (the Bowles-Simpson Commission) endorsed a mostly-spending-cuts approach. He ignored them.

Do Revenues Need to be Part of the Debt Solution?

“You can’t reduce the deficit to the levels that it needs to be reduced without having some revenues in the mix.”

So said President Obama in his press conference yesterday. Is the President correct?

We are not the first nation to wrestle with unsustainable debts. And fortunately for us, we can learn from the measures that others have taken. That is why the work of Harvard’s Alberto Alesina and Silvia Ardagna is so important. Examining 37 years of data from 21 similarly-situated nations (fellow members of the OECD) they identified 107 episodes of “fiscal adjustment” (basically efforts to get debt levels under control).  They then broke these down according to how successful they were (did they manage to rein in the debt?) and how they impacted the economy (did they cause the economy to expand or to contract?). 

Let’s first look at the instances in which austerity worked. As shown by the two left bars in the graph below, in cases where austerity actually succeeded in reducing debts, spending as a share of GDP fell by about 2 percentage points while revenue also fell by half a percentage point. In other words, contrary to the President’s assertion, successful austerity does not seem to require a revenue increase. Contrast this with the instances in which austerity failed to reduce debts. This is shown by the two right bars below. Among the instances in which austerity didn’t work, the spending reductions were more modest (only .8 percentage point reduction) and revenue increased—rather substantially (1.41 percent of GDP). 

Alesina and Ardagna also looked at what happened to the economy after austerity. Sometimes it expanded rapidly; sometimes it didn’t. The results of their analysis is below. Among the instances in which austerity was followed by significant economic growth, spending had been reduced by about 2.19 percentage points as a share of GDP while revenue had only been raised 0.34 percentage points. Meanwhile, among the instances in which austerity was not followed by significant growth, spending was reduced much less (0.7 percent of GDP) and revenue was increased much more (1.2 percent of GDP). 

 

 I should make one more point. Republicans sometimes use the phrase “cut and grow” to imply that spending reductions will give the economy a lift. I think this overstates the case. As Alesina put it in his Mercatus Working Paper (p. 5):

Fiscal adjustments (reductions) on the spending side are almost as likely to be associated with high growth (i.e. a successful episode) as fiscal expansions on the spending side.

In other words, spending cuts are about as likely as spending increases to lead to rapid growth. Readers of this blog probably know that spending increases typically don’t lead to large and sustainable growth spurts. So we shouldn’t cut spending because we think it will make the economy grow. We should cut spending because it is mathematically impossible for government to constantly outpace the growth of the private sector on which it depends. And as Herbert Stein famously remarked, something that can’t go on forever, won’t.