Tag Archives: growth

Income per capita and female labor force participation rates

The Financial Times has a great and highly recommended data blog for those of us who are interested in statistics and quantitative analysis. Its biggest disadvantage is that it is not updated frequently enough. The posts give much food for thought and a subject that was discussed last month is worth exploring further. The post was about the relationship between income per capita and the  female labor force participation rate and it can be found here.

Valentina Romei asks whether women in the workforce help boost economic growth. While it may or may not be true that female labor force participation is not an important determinant of growth, the graph that she presents in the post is not entirely convincing. It shows the relationship between the female labor force participation rate and income per capita in 2010. And a scatter plot without any control variables essentially assumes either that there are no fixed effects/other omitted variables or that the potential effect of the female labor force participation rate is so strong that it weighs more than all possible countering effects from other variables (this would still be a bias though, as we would underestimate the actual effect).

Romei concludes that her graph “shows a poor correlation between female labour participation and GDP per capita.” And yes, it does. But does that prove that the proportion of the women who take part in the workforce is unimportant? Not necessarily. Here is the relationship between income per capita and the unemployment rate based on data for all countries that are available in the World Bank’s database for 2010:

PPP and labor force participation rate

This graph does also show a weak relationship. But most people would probably agree that lower unemployment generally is good for economic growth. The point is: scatter plots are great because they are easy to comprehend, but they do not necessarily prove or disprove potential quantitative relationships. This is true in particular when countries and other bigger entities are being compared because they are already have such big initial differences.

Romei’s graph shows a weak relationship between female labor force participation rates and income per capita. One reason for that is that most of the countries with the highest female labor force participation rates do also happen to be poor. These are the ten countries in the world (according to the World Bank) with highest female labor force participation rates: Tanzania, Mozambique, Rwanda, Malawi, Burundi, Madagascar, Zimbabwe, Equatorial Guinea, Nepal, and Togo. Except for the extraction-based economy Equatorial Guinea, these are not precisely the richest countries in the world. One potential explanation is that in poor countries, many women have no choice but to work. This would be a case of reverse causation (where the dependent variable has an effect on the independent variable), and is one of those countering effects that was previously mentioned as a potential source of bias if we try to interpret Romei’s graph without further analysis.

What we are really interested in here is the counter-factual scenario. We know that Tanzania, Malawi and Zimbabwe are poor countries with high female labor force participation rates. What we want to know is whether they would higher, the same, or lower levels of income with a higher female labor force participation rate. Whether the United States has higher or lower income or/and female labor force participation rate than Spain is not particularly insightful; the interesting question is whether higher female labor force participation rate in the United States will boost American growth or not, everything else equal. And a cross-country scatter plot will not answer that question.

The following are a few very simple regression specifications. They are not meant to prove that higher female labor force participation causes higher income levels. The point is rather to show that an initially negative relationship quite easily can be turned into a positive relationship by taking some omitted variables into account.

ppp and fempar 1*** p<0.01. Robust (clustered where appropriate) standard errors in parentheses.

The dependent variable is income per capita in international dollars in terms of purchasing power parity (PPP). The main independent variable is female labor force participation rate in percent. The first column shows the result from the simplest of linear regressions. This result is different from the one depicted in Romei’s graph, because she finds a positive relationship between female labor force participation rate and income per capita whereas here the correlation is clearly negative. The result here implies that a one-percent increase in the female labor force participation rate is on average associated with a 51.91-dollar decrease in average annual income per capita. The reason that this result is negative whereas Romei’s is positive is probably because the data here is not only for 2010, but from 1990 to 2010 and it contains all observations available in the World Bank’s database for those years. See the following graph for comparison with Romei’s (although income per capita is the dependent variable, the axes are purposely swapped below for easier comparison with Romei’s original chart):

Female labor force participation rate and PPP per capita, 2010

The three other columns in the table show regressions that include different combinations of fixed effects. The result in the third column in the table is insignificant. The results in the two other columns, however, show a fairly large and highly significant relationship between income per capita in terms of PPP and female labor force participation. This implies that when we allow for differences between countries that are more or less constant over time (such as culture and geography) the initially negative relationship becomes positive such that higher female labor force participation rates are associated with higher levels of income.

Now, these results are not really proof of anything that concerns the main hypothesis about causality, because the analysis here lacks both a causal identification strategy and robustness tests. The table above simply shows that many important economic questions – such as the one related to female labor force participation and economic development – are not easy to answer. Scatter plots and regression outputs may look convincing, but sometimes they cannot, regrettably, tell us much. This is true in particular when we compare big and vastly different entities, such as countries.

Simon Hedlin

Longevity dilemma

Economic growth is not the only important growth issue. Another type of growth that should be discussed more often is the elderly-dependency growth; the fact that the number of seniors per potential worker is growing. Much like economic growth rates, the elderly-dependency growth rates vary greatly across countries. The economic impact and consequences for social security and budget deficits will also look different in one country from another. What they all have in common, however, is that neither has ever had to deal with the demographic problems of the magnitude that is expected.

for whom the growth tolls

Simon Hedlin

Are shrinking populations only good news?

From Thomas Malthus to Paul Ehrlich and thereafter many predictions have been made regarding the effects of population growth. Malthus thought that people would be kept at subsistence level, and that productivity gains would result in population growth until the economy converged again to subsistence level. In The Population Bomb published in 1968, Ehrlich argued that overpopulation would lead to mass starvation.

What we see today is a different story. Productivity gains have made obesity a bigger problem than hunger in many countries. And almost everywhere, fertility rates are falling. Some countries do even experience negative population growth. From an environmental perspective this is likely to be good news. Fewer people, holding everything else constant, implies less pollution and less extraction levels of our planet’s scarce resources. A shrinking population does, however, lead to a few economic dilemmas.

One is debt. As The Economist’s Buttonwood columnist Philip Coggan points out:

First, debt is easier to service if your nominal income is rising, but nominal income growth has been very sluggish in [countries such as] Japan. Second, debt does not decline as the population falls; so the debt per capita rises, making individuals even more cautious. You are not going to go on a spending spree if you have high debts already and you are worried how you will afford retirement.

Another economic problem is the effects that the changing population structure has on social security. Having a pension system where today’s workers pay for today’s retirees has worked during times of a growing economy and an increasing population. But when growth rates in many countries are slowing down and the number of workers shrink relative to the number of retirees, the pyramid turns upside-down. Indeed, as has been discussed before in Buttonwood’s notebook, and on this blog, the projected changes in dependency ratios are striking. The next post will attempt to illustrate this problem graphically.

Simon Hedlin

An inverted environmental Kuznets curve?

The so-called environmental Kuznets curve (named after economist Simon Kuznets) is based on a hypothesis that environmental quality and economic development are related in such a way that their relationship produces an inverted-U curve.  Supposedly, countries with lower levels of income emit low levels of pollutants. When they industrialize and become richer their emissions will increase up to a certain point when countries either start to invest in the environment or switch to less resource intensive means of production, which leads to falling emissions. In a graph where emissions are plotted on the vertical axis and economic development (usually income per capita) is plotted on the horizontal axis, this would lead to a curve resembling an inverted U. The empirical evidence for the existence of such a curve is mixed. Richard Carson  finds that it is in fact effective regulation and diffusion of technological change that cause environmental quality to go up, and that these two factors are associated with higher income levels. If Carson is correct, it would imply that the underlying reasons behind an environmental Kuznets curve are all but clear.

A very simple empirical test also casts doubts on the existence of such a curve. For this test all data available in the World Bank’s database – in total for 68 countries – was used to estimate the relationship between so-called biochemical oxygen demand (BOD), and income per capita (in purchasing power parities (PPP) and constant international dollars). BOD refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants. BOD satisfies both the condition of being a local pollutant and of having short-term costs – two conditions normally claimed to be required for an environmental Kuznets curve to exist.

In a linear regression test with time-lag, PPP per capita is associated with a reduction in BOD a decade later. However, when a quadratic term for PPP is introduced into the model, it seemed as if the relationship was quadratic rather than linear. But in sharp contrast to environmental Kuznets curves, this relationship is plotted as a normal U, not an inverted one. This test thus suggests an “inverted environmental Kuznets curve.”

inverted environmental kuznets curve

It should be noted as a significant caveat that data on both BOD and PPP are sketchy, but this test at least suggests that BOD falls sharply with higher levels of income per capita until a certain point when BOD starts rising again. It could be something worth looking into further.

Simon Hedlin

Not quite there yet

Most people who have taken a Principles of Economics 101 course are familiar with the convergence hypothesis, i.e. that poorer economies in terms of per capita should grow at faster rates than wealthier economies eventually resulting in a convergence. Few would probably defend the absolute version of the convergence hypothesis today, but if there is somebody who does, the following graph will at least show that we are not quite there yet.


(Feel free to use this figure for your own purposes, but please do not forget to mention this blog as the source.)

Simon Hedlin

Rogoff on BP oil spill and international regulation

An economist’s nuanced and well-balanced thoughts about technology, energy “consumption” and economic growth is found on Project Syndicate’s webpage. The author of the article is former IMF chief economist Kenneth Rogoff. One might not necessarily agree, but the article is still interesting to read:

If ever there were a wake-up call for Western society to rethink its dependence on ever-accelerating technological innovation for ever-expanding fuel consumption, surely the BP oil spill should be it. Even China, with its “boom now, deal with the environment later” strategy should be taking a hard look at the Gulf of Mexico.

Economics teaches us that when there is huge uncertainty about catastrophic risks, it is dangerous to rely too much on the price mechanism to get incentives right. Unfortunately, economists know much less about how to adapt regulation over time to complex systems with constantly evolving risks, much less how to design regulatory resilient institutions. Until these problems are better understood, we may be doomed to a world of regulation that perpetually overshoots or undershoots its goals.

The finance industry already is warning that new regulation may overshoot – that is, have the unintended effect of sharply impeding growth. Now, we may soon face the same concerns over energy policy, and not just for oil.

Given the huge financial stakes involved, achieving global consensus will be difficult, as the Copenhagen climate-change fiasco proved. The advanced countries, which can best afford to restrain long-term growth, must lead by example. The balance of technology, complexity, and regulation is without doubt one of the greatest challenges that the world must face in twenty-first century. We can ill afford to keep getting it wrong.