Is it cheaper to live in poor economies?

If you are a rich country resident and travel to a poor country, you are continuously amazed how inexpensive life is there. One rationalizes this with the lower local wages which make domestic goods (and price discriminating imports) cheaper. Is this anecdotal evidence true in general? Does it hold across all countries?

Fadi Hassan finds that indeed rich countries have higher price levels. But once you go further down the development ladder, the statistical evidence is not that clear, and once you reach the lowest rungs, the cost of things could be increasing again. This analysis is performed using the ratio of purchasing power parity to the exchange rate, as measured in the Penn World Tables and finds that the best non-linear fit of the price-income relationship is not increasing for 40% of the countries. The challenge is now to understand why it is so.

Do not waste degrees of freedom with macro data

Dealing with microdata is relatively easy, as you have plenty of data points and can freely add explanatory variables with running the risk of running out of degrees of freedom. The story is different for macrodata, as series are much shorter, and one can quickly eat degrees of freedom by using lagged variables. The prime example here are the often abused vector autoregressions (VAR), that get larger and larger, and faster than new data points accumulate. The latest fad is to run regressions with time varying parameters, including in VARs, which is deadly for degrees of freedom as this is roughly equivalent to adding a boatload of dummy variables to the mix. Hence the need to be more parsimonious.

How parsimonious should one be? Joshua Chan, Gary Koop, Roberto Leon-Gonzalez and Rodney Strachan think the solution is in time-varying parsimony. The idea is that sometimes one needs a more complex model, and sometimes a few variables are sufficient. While this allows to spare degrees of freedom when one can do with few variables, this gain on paper is lost, and probably more than lost, by the implicit degrees of freedom used in selecting the right model. This is an old problem than is swept under the rug is many empirical applications, but in this case it becomes even more apparent because so many parameters and models are involved.

On the decline of the US manufacturing wage

It is always interesting to see how real wages evolve, as they allow to understand how much a worker can buy from his income. Usually, this is done by dividing the nominal wage by a price index, usually the commodities said worker would typically buy. The results may vary considerably, as a different price index is needed for different workers, and the basket of goods may also vary over time. The latter is particularly important when the sample period is long. It also depends whether you look a hourly, weekly or annual income, and how benefits are included.

John Pencavel reviews a centuries old literature on the topic that came to the conclusion that except for some periods on stagnation, real wages generally were upward bound. He then comes up with his own indexing procedure, and finds that real wages in the US manufacturing sector have declined by 40% since 1960. Wow, this seems to be a real big result, and this requires understanding how this was computed. Indeed, Pencavel does not measure the real wage in the conventional way, but rather the ratio of what workers get to what they could get if the firm made no profit. This does not necessarily mean that the buying power of the worker has decreased by 40%. but rather that a smaller share of firm income goes to labor. With the increased mechanization of manufacturing, this evolution should not surprise many people. But this is not necessarily a 40% fall in real wages as advertised in the paper's abstract.

Seasonal adjustment is difficult

As undergraduates, we are taught to make sure the macroeconomic data we are dealing with is seasonally adjusted. We are explained that statistical offices remove the seasonal factors is a way that is close to regressing the data on seasonal dummies and taking moving averages. If you really look into this, as so often, it turns out things are much more complex than that, and subtleties matter.

Stephen Pollock and Emi Mise do a technical review of the various methods and look at some alternatives. Broadly speaking, there are three strands of techniques. The first is based on ARIMA, the second removes seasonal frequencies found in a periodogram, and the third relies on clear distinctions between fundamental and seasonal components in spectral analysis. The difficulties are compounded by the fact that data usually has a trend, which may not be loglinear, and data thus requires pre- and post-treating. And as Pollock and Mise show, each of these methods matter, even for dating turning points. I can imagine this can become even more important when one throws data into a regression, especially if the series have been detrended in different ways. And it is rare to see statistical offices declare what method was used for that.

Price points, good diversity and price rigidity

Much of the real impact of monetary policy hinges on some sort of rigidity in some prices. As regular readers must have noticed, I am not convinced I am not convinced that prices a rigid to the point that it matters, and I am particularly appalled how price rigidity is introduced in theoretical models. Let us have a look at some of latest research on price rigidity.

Edward Knottek uses supermarket scanner to find that price points are much more important than menu costs in determining prices. Price points are for example prices ending in 9, which make up 60% of retail prices. He also finds that in all but 10% of cases, prices return to the previous level after a sale. These two facts cannot be reconciled with menu costs being of relevance. Yet menu costs are the foundation, explicitly or implicitly of almost all models of price rigidity.

Saroj Bhattarai and Raphael Schoenle use producer prices and establish interesting patterns in decisions to change prices. They find that firms with a large variety of goods change prices more frequently, but by smaller amounts. If they change a price, they are more likely to decrease it, and the variance of positive price changes is larger. They also find that for a model to replicate such facts, one needs firm-specific menu costs and state-dependent pricing. This is definitely not Calvo pricing.

How to measure governance

There is now a cottage industry trying to measure how well countries are governed, in particular how bad corruption is, or how well the rule of law is imposed. These indicators are important, they can determine where foreign direct investment is taking place, whether development aid is disbursed, or whether policy reform has been successful. Thus, it is critical that governance be well measured.

Charles Oman and Christiane Arndt point out that most measures are based on perceptions, which are notoriously biased and difficult to change in the face of hard facts. But their is also substantial danger in that users tend to misread these indicators. For one, they are not precise and should only be interpreted as giving a rough picture. Even when intervals of error are provided, they are often ignored by users. Second, international comparison is difficult because the sources used to construct the index differ widely from one country to the next. Third, a single index is used for many different purposes, and each of those purposes should weigh differently the components of the index. Finally, there is no theory that tells us how to scale the measured before or during the determination of a governance index. And let us not forget most measures are subjective, as they are based on opinions. It frightens me when such an index is used in a regression, especially without any robustness test.

Maximizing the Human Development Index

We all recognize GDP per capita is far from a perfect measure of wellbeing in an economy, hence the Human Development Index (HDI) was developed. It aggregates indicators of health, education and income. The idea is to evaluate how well an individual can function in such an economy. But the elaboration of the HDI did not follow any formal theory in the selection of the precise indicators and their weighting. So what about doing the reverse: take the HDI seriously in theory?

Merwan Engineer and Ian King use a standard growth model, calibrated following Mankiw, Romer and Weil, and look for what it takes to maximize the HDI. And they find massive overinvestment into physical and human capital, which saving rates so much higher than what the Golden Rule would call for that consumption is almost reduced to zero. Because consumption is not part of HDI! That looks a crass oversight, as we generally assume, correctly I think, that people care about consumption for their standard of living.

Luminosity as an indicator of economic activity

When working with worldwide data, it si often frustrating that data quality and availability is far from uniform across countries. Especially for developing countries, or those with large informal sectors or notable self-sustenance, we have a very imperfect idea of how much economic activity there is. Hence, looking at other indicators that GDP can give us interesting clues.

Xi Chen and William Nordhaus make the case for luminosity. By looking at how brightly various locations shine at night, it allows you to infer something about economic activity and the level of development. Also, it allows to say something about regional distribution of economic activity. Of course, this is not going to be perfect, especially for developed economies where data is of much better quality to start with.

The standard data set for international macroeconomics data is the Penn World Tables. It also grade grades to its data, telling us how reliable it is. Unfortunately, these grades are largely ignored in empirical work. Chen and Nordhaus ask whether they can increase the quality of output measured with their luminosity data and they claim this is only useful for those labeled D and E. Yet they do not advocate using luminosity data for countrywide analysis. Indeed, data collection methods will eventually improve and traditional data will move up in the quality ladder. Luminosity data is far from perfect, it is just that in some countries official data is even worse at the moment. Chen and Nordhaus are more confident with luminosity as a proxy for regional activity in some cases, even if measurement error is even larger there.

It is difficult to measure poverty

Measuring poverty is very difficult. First, it is a relative concept and requires the definition of a standard or threshold. Second, as people are usually not normally distributed, any single measure misses some aspect of the distribution. Third, the item whose distribution is measured may not be the appropriate one to represent poverty. Most of the time this is income, but temporary low income is very different from permanent low income, and in both cases, purchasing power may differ dramatically on location, social policies and period. All these difficulties have lead to a plethora of poverty measures. In fact, if you look at the program of any economic inequality conference, there will be plenty of papers on new measures by authors hopeful that their names will stick to a new index or coefficient.

Walter Bossert, Satya Chakravarty and Conchita d'Ambrosio come up with a new measure that emphasizes the persistence of poverty. They are very careful in making their measure following three axioms: the measure corresponds to static poverty in the one period-case, a measure is worse is poverty spells are longer ans spells out of poverty are shorter, and two decomposability axioms too complex to describe here.

The measure they propose is a weighted sum of per period poverty measures, where weight are proportional to the current poverty spell. Using the European Community Household Panel, they find that their measure does not change rankings much whether poverty spell weights are used or not. But I bet they would change quite a bit for the US.

How high in the true US unemployment rate?

Everybody seems to agree that the official measure of the US unemployment rate is too low, but it is uncertain by how much. Indeed, the method of measurement is very poor, as it is based on a survey which is rife with errors. Follow-ups have uncovered frightening errors both on the side of the interviewers and interviewees. And even those reinterviews are not reliable, because they are too small, too infrequent and still full of errors. Thus they cannot be used as a standard for evaluating measurement error bias. But the survey is a short panel, meaning that interviewees are followed for a few months, which allows to improve the accuracy of the measures.

Shuaizhang Feng and Yingyao Hu do this by assuming that there are latent variables underlying the process, and these variables have some level of persistence and measurement error probabilities that varies across groups (race, gender, age). The resulting "true" unemployment rates are on average 35% higher than official ones, more during recessions. Right now, the unemployment rate would be close to 15%. Employment rates do not differ significantly, indicating that unemployed often claim not to be in the labor force, especially females.

A measure of guilt

How do you measure guilt? There is no market price for it, and most of the time it has no measurable expression. One would need some rather particular circumstances to find a way to measure guilt. Hongbin Li, Mark Rosenzweig and Junsen Zhang found it.

During the Cultural Revolution in China, many urban families had to send some of their children to work among farmers. Assignments were to a large extend random, and parents had to choose which child to send. As this send-off was viewed as an unjustified punishment, many parents felt guilt and tried to compensate later in life with transfers, as a marriage gift or other. To get a clean data set, Li, Rosenzweig and Zhang set out to recruit twins and compare those who were separated during this period, as presumably everything else prior should have been identical. And it turns out that those that were sent off received substantially more from their parents, even when they were subsequently better off than their siblings. And this result is robust to all sorts of controls, for example party membership (there was indoctrination on farms, and party members were more likely to gain favors in the system, which parents may want to buy into).

I hope they will be using this data set for other studies. Otherwise, this would have been a tremendous effort for only a proof of concept. Because I see little use for the result beyond that.

Measuring seasonality

When analysis business cycles, it is usual to factor out any seasonal influences from the data. Theoretically, this makes perfect sense if one can distinguish well long-term trends, fluctuations at business cycle frequencies and seasonal factors, both in separating them in the data and in the fact that they do not influence each other. In practice, the filtering is less than perfect, and there is at least some evidence that business cycles have an impact on trends.

Tommaso Proietti proposes a measure of the influence of seasonal cycles on the business cycle. Indeed, one does not observe the seasonally adjusted data, only an estimate of it. This means that there is some uncertainty about the true adjusted data, and thus any analysis of it should carry this uncertainty with it. This is of special importance when estimating the output gap, as it is of high policy relevance and it has been shown that policy makers needs to take into account the uncertainty about its measurement. The bandpass and HP filters both exhibit quite some uncertainty in the measurement of the cyclical components. This means in particular that one should carry the analysis from data that has not been seasonally adjusted, using a procedure suggested in the paper. I am not qualified to judge whether this is the best method, but this should at least make us more careful with the data.

The trouble with single respondents in household surveys

Much of the development literature is based on the administration of household surveys, with typically one member of the household answering all question on the behalf of all members of the household. Of crucial importance here is whether is actually matters who answers.

Monica Fisher, Jeffrey Reimer and Edward R. Carr use survey data from Malawi and verify whether the estimates of the spouse's income by the head of household were accurate. It turns out not: they are statistically not reliable. While interviewing only one person may save time and cost, it makes a substantial part on a survey absolutely useless. Worse, they make important analysis unreliable. In their example, one cannot establish the determinants of poverty. I wonder how many results in the development literature need to be reexamined.

Facts for heterogeneous agent macroeconomics

I rarely discuss material published in journals because I usually have seen it before in a working paper form. But sometimes you come across a great article, and in this case a special issue of the Review of Economic Dynamics. Nowadays, macroeconomics, at least the fresh water variety, is all about agent heterogeneity, and thus it is important to understand well the data these models are supposed to replicate. The special issue does this for nine countries, in an effort that tries to use uniform definitions and treatment of the data. In addition, data and programs are made available.

There is too much to write about for the whole special issue, so I will concentrate on the introduction by Dirk Krueger, Fabrizio Perri, Luigi Pistaferri and Giovanni Violante. They highlight that:

1. Wage disparity is lower where the labor market faces more institutional constraints.
   
2. The college premium is high everywhere.
   
3. Income inequality has increased.
   
4. Earnings inequality is larger than wage inequality.
   
5. Asset income and private transfers have no impact on inequality.
   
6. Government transfers affect inequality at the bottom, taxes at the top of the distribution.
   
7. Inequality in disposable income is larger than inequality in consumption.
   
8. Long-run changes in the inequality of discposable income are also larger than for consumption.
   
9. In recessions, inequality of earnings is more pronounced at the bottom of the distribution.
   
10. The same holds true for consumption.
    
11. Recessions have no impact on wealth inequality (we have to wait and see for the last one, though).
    
12. Inequality over the life cycle varies considerably across countries.
    

I found of particular interest the effort to reconcile micro-level consumption data with the national accounts. It is well known that there are discrepancies in the US and the UK for the growth of consumption, but apparently not elsewhere.

Lottery wins and health

Who has not dreamt of winning big at the lottery? Not only would one be able to afford all sorts of goodies, one would have less worries. This translates into better mental health and better physical health, knowing how the two are closely linked. It appears, however, that lottery winner do not enjoy better health. Why?

Bénédicte Apouey and Andrew Clark are not particularly interested in lottery winners, but studying them allows to understand the consequences of exogenous income changes on health. They use British data, which avoids the problems with US data, where income has a direct effect on health because of limited access to health care (and because also some lottery winners get murdered). But the problem is that lottery players have different characteristics to begin with. For example, they are less risk averse than others. This implies that they are also indulging in other risky behaviour, like drinking or smoking. Also, we all know lotteries are an institutionalized scam, so beyond being risk taking, lottery players must not be able to understand the consequences of playing. This impairment can have also implications regarding unhealthy behaviour.

So what do we learn from all this? These results are important in the context of the literature discussing more generally the consequences of income fluctuations (macro and micro) on health. The problem in this literature is that everything is endogenous and cannot properly be instrumented for. Thus the idea of using lottery winning, which should be exogenous. But as Apouey and Clark show, that only works if 1) one makes the distinction between physical and mental health, 2) one needs to take into account that lottery players have a different attitude with regard to health to begin with, 3) any aggregate comparisons are going to be fruitless. In other words, looking at lottery players is the wrong avenue unless you have a lot more information about them then we currently have.

PS: This is a FEEM working paper. FEEM specializes in environmental economics. I am still looking for a link between the topic of this paper and environmental economics.

Even producer prices are flexible

It seems that I am on a vendetta against the myth of rigid prices, but I find it frustrating that macroeconomics keeps insisting on models where price rigidity is crucial despite the evidence that prices are not rigid. I have given plenty of evidence on this blog that retail prices are indeed flexible, in the United States and elsewhere, and even evidence that wages are more flexible than assumed. Too many links to list them here.

But there is also an argument out there that retail prices are only part of the question, producer prices are where the real action is, and those are definitely rigid. If you are such a firm believer is price rigidity, you are in for a big surprise that should make you lose your faith. Pinelopi Koujianou Goldberg and Rebecca Hellerstein show that producer prices are about as flexible as consumer prices that include temporary sales, and more flexible than consumer prices excluding sales periods. So, what is going to be the next line of defense of the religion of price rigidity?

Why growth regressions are so scary

A lot of empirical research on growth and development has been done with cross-country regressions. This has been in particular favored by the availability of the Penn World Tables, which are a careful exercise of providing macroeconomic data that is comparable across countries. But it is obvious that for many countries, the data remains of poor quality, and the makers of the Penn World Tables label each data point with a quality grade. Unfortunately, nobody takes those labels into accounts.

Why would they matter? Because data of poor quality may be subject to major revisions, for example. And major revisions of some data points (which have a tendency to be outliers) can lead to changes in the empirical results. This is what Simon Johnson, William Larson, Chris Papageorgiou and Arvind Subramanian explore by trying to replicate dozens of studies using the Penn World Tables, using various versions of the database. The results are sobering. Essentially, the results of every study using the panel data can be reversed. Studies only using long-run averages are much safer, though. Do not use the Penn World Tables blindly, and do not believe results using them before scrutinizing them yourself.

Are ethnic differences across nations relevant?

It is well know that ethno-liguistic diversity is bad for the development of an economy, for example because it leads to leaders favoring their ethnicity, thus members of an ethnicity only vote for their own, and the political and economic process only is about rent seeking. I am exaggerate, but in some countries this unfortunately close to the truth. The negative impact of ethno-linguistic diversity has been shown over and over in cross-country regressions.

Could ethno-liguistic similarity across countries also matter? For one, one could imagine that it could foster closer ties and thus lower trade barriers, which is good for development. It is also likely to has spurred fewer interethnic wars, thus leading to more trust that is necessary for commerce. On the other hand, civil unrest can spill over to a country with ethnic or linguistic affinity. In any case, before this can be studied, one needs a good measure of ethno-linguistic similarity.

Olaf de Groot delivers this by measuring the percentage of shared identity characteristics of two individuals randomly drawn from two different populations. And using this measure to study conflict spillovers, it appears to be a very good predictor. Let's see whether this measure will be useful for other studies.

Program evaluation: estimation vs. simulation

Simulations are a tool that is more and more used to evaluate policies. This is in particular important when these policies have never been implemented before and thus there is no historical data to draw on. How good are such simulations? One way to check this is to do an ex-ante simulation of a policy change that has actually been implemented thereafter, and then estimate ex-post its effect.

Fabian Bornhorst does this with the PROGRESA program in Mexico. There, some families receive transfers if their children go to school. Bornhorst first draws a model of occupational choice and then estimates it using data that was available before PROGRESA was implemented. He then looks at outcomes if PROGRESA is applied in this model economy. This is then compared to actual outcomes. How well does the simulation fare? Not bad at all, but not perfectly either. It tends to indicate somewhat stronger outcomes than those actually observed. Also, it does not capture some differences across groups, but this can mainly be attributed to the level of heterogeneity within the simulated model, so this is fixable. Overall, very encouraging.

Measuring unemployment with Google

Unexpected statistics sometimes work surprisingly well in measuring in a more timely manner than official numbers some important economics aggregates. The prime example is the recession index of The Economist, which is based on the frequency of the word "recession" in the Wall Street Journal and the New York Times. This measure agrees well with the NBER business cycle dates, and does so much earlier than official data is released or the NBER ruled. Of course, one could argue that there is an endogeneity issue with this index, and journalists may talk up a recession, and then it really happens.

Nikos Askitas and Klaus Zimmermann offer another indicator that does not suffer from such endogeneity problem. They claim that the German unemployment rate is appropriately measured by Google searches using some keywords, like (translating) "unemployment office or agency", "unemployment rate", "personnel consultant" or the most popular job sites. What can Google not do?