Tag Archives: unemployment

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

Gone with the Wind: America’s vanishing labour force

At first, the falling unemployment rate in the United States may have seemed to be only positive news when the April numbers were released a few days ago. However, as Mark Gongloff at Huffington Post noted, the decline was partly because the labour force participation rate “dropped to 63.6 percent, the lowest since December 1981”. In fact, even in absolute numbers, the American labour force is currently shrinking. The Economist makes the same correct analysis, and points out that compared with the Congression Budget Office’s (CBO) estimate back in 2008, the actual size of the labour force in 2012 is smaller by five million people :

True, the slide in the unemployment rate – a full percentage point since September – owes mostly to rising employment (as measured by the household survey). But the decline in unemployment has been helped by the failure of the labour force to grow more quickly. /…/ Yet in January, 2008, the Congressional Budget Office reckoned it would be some 5m larger by now, or 159.5m /…/”

In addition to a slow-growing labour force with a falling labour force participation rate, there is another growing problem: a relatively smaller potential workforce.

In last week’s issue of The Economist, there was an interesting article about the United States and China. It metaphorically hypothesized that had China been dipped in the river Styx to be given invulnerability, the country would perhaps had been “held” in its demography:

Alongside the other many problems it faces, China too has its deadly point of unseen weakness: demography. /…/ Between 2010 and 2050 China’s workforce will shrink as a share of the population by 11 percentage points, from 72% to 61%—a huge contraction, even allowing for the fact that the workforce share is exceptionally large now. That means China’s old-age dependency ratio (which compares the number of people over 65 with those aged 15 to 64) will soar. At the moment the ratio is 11—roughly half America’s level of 20. But by 2050, China’s old-age ratio will have risen fourfold to 42, surpassing America’s.

True, China’s demographic prospects from an economic viewpoint do indeed look glum. Considering several important factors such as population growth, median age and old-age dependency rate, America’s position does in comparison look better. But it is important to note the “in comparison”, because as stated, America has its own demographic issues. And aside from the fact that the labour force participation rate is falling, the number of persons in working-age (aged 15-64) in relation to the number of children and seniors is rapidly contracting, as this graph shows.

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

So to sum up this post in three points:

1) America’s labour force is growing at a slower rate
2) America’s labour force participation rate is falling
3) America’s potential labour force (persons in working-age) is shrinking relative the size of the rest of the population

1) implies slower economic growth, 2) means fewer workers are active on the labour market relative to the number of people who are likely to need support, and 3) will likely make the effects of 2) worse.

Hypothetically, with a faster growing labour force and a constant labour force participation rate, 3) alone would still constitute a worrying development. Thus, if these three issues are not taken seriously, America might really be heading for trouble.

Simon Hedlin