China’s Demographic Shift

From Businessweek yesterday (excerpt):

Why China Is Suddenly Content with 7.5 Percent Growth

For years, there’s been one constant for people talking about the Chinese economy: GDP growth would exceed 8 percent. It didn’t much matter what happened in the rest of the world—the U.S. and other export markets might be thriving or might be struggling, but China would grow at least 8 percent, year in and year out. The country needed to create enough jobs for the millions of young people entering the workforce every year, and the Chinese leadership decided that anything below 8 percent would put job creation in jeopardy...

Getting More Women To Work

Malaysia’s female labour participation rate is quite frankly a disgrace. But getting more women into the workforce comes up against a host of factors, not least of which are cultural and religious. The effort however is well worth making – consider that more than half the women of working age are not in the work force. You could potentially increase the work force size by 25%, just by getting all the women into formal jobs, with the obvious impact on GDP/GNI. That’s a pipedream of course, but you can’t deny the potential impact involved.

And one effective way to do that is…paternity leave!

Demographics And Development

World Bank Chief Economist Justin Lifu Yin writes a concise article on the demographic transition and development policy (excerpt; emphasis added):

Youth Bulge: A Demographic Dividend or a Demographic Bomb in Developing Countries?

The youth bulge is a common phenomenon in many developing countries, and in particular, in the least developed countries. It is often due to a stage of development where a country achieves success in reducing infant mortality but mothers still have a high fertility rate. The result is that a large share of the population is comprised of children and young adults, and today’s children are tomorrow’s young adults...

Why Demographics Matter: Japan’s Lost Decade Was An Illusion

Via Lars Christensen, Daniel Gros points out Japan’s performance over the past two decades hasn’t been as bad as it looks (excerpt):

The Japan Myth

…How should one compare growth records among a group of similar, developed countries? The best measure is not overall GDP growth, but the growth of income per head of the working-age population (not per capita). This last element is important because only the working-age population represents an economy’s productive potential. If two countries achieve the same growth in average WAP income, one should conclude that both have been equally efficient in using their potential, even if their overall GDP growth rates differ.

When one looks at GDP/WAP (defined as population aged 20-60), one gets a surprising result: Japan has actually done better than the US or most European countries over the last decade. The reason is simple: Japan’s overall growth rates have been quite low, but growth was achieved despite a rapidly shrinking working-age population.

Making The Most Of The Demographic Dividend

From the World Bank blog:

Family planning, healthier economies

Countries like South Korea and Thailand have seen similar demographic formulas work to their advantage in recent decades: falling fertility rates lead to burgeoning adult working populations lead to greater economic productivity.

Economic Growth And The Demographic Dividend

I’ve been sold on the idea of a “demographic dividend” for well over a year now, and here’s some more evidence from a new IMF paper (abstract):

The Demographic Dividend: Evidence from the Indian States
Aiyar, Shekhar & Ashoka Mody

Large cohorts of young adults are poised to add to the working-age population of developing economies. Despite much interest in the consequent growth dividend, the size and circumstances of the potential gains remain under-explored. This study makes progress by focusing on India, which will be the largest individual contributor to the global demographic transition ahead. It exploits the variation in the age structure of the population across Indian states to identify the demographic dividend. The main finding is that there is a large and significant growth impact of both the level and growth rate of the working age ratio. This result is robust to a variety of empirical strategies, including a correction for inter-state migration. The results imply that a substantial fraction of the growth acceleration that India has experienced since the 1980s - sometimes ascribed exclusively to economic reforms - is attributable to changes in the country’s age structure. Moreover, the demographic dividend could add about 2 percentage points per annum to India’s per capita GDP growth over the next two decades. With the future expansion of the working age ratio concentrated in some of India’s poorest states, income convergence may well speed up, a theme likely to recur on the global stage.

Visualising Demographic Transitions

This site is gorgeous – you can actually see how the age profile of a population evolves over time.

To use, click on the alphabet to expand the country list, then click on the country and a date. The site generates the population tree automatically (works with non-Flash browsers, too) – successive clicking on dates causes the population tree image to transition smoothly (click on link for bigger pic):

Malaysia in the Top-30 by 2050?

From this weekend’s The Star:

Is 2050 our next big target?

...HSBC Bank plc issued the report, The World in 2050: Quantifying the Shift in the Global Economy, on Jan 4, and it's relevant to us here because Karen Ward, the bank's senior global economist and lead author of the report, seems pretty optimistic that Malaysia will fare well over the next four decades...

...According to the HSBC analysis, come 2050, Malaysia will be No. 20 among the world's top 30 economies, as ranked by the size of gross domestic product (GDP). That will mean climbing 17 rungs between now and 2050, the biggest jump recorded by any of these 30 countries. Thailand, the Netherlands, Switzerland, Hong Kong and South Africa are among those below Malaysia in the league table. Singapore will not even be in HSBC's top 30…

Preliminary Census 2010 Report

This actually came out last week, but I’ve been too bum lazy to touch on it. But the results, as preliminary as they are, yield some pretty interesting information – and not just for the population numbers:

Morgan Stanley Is Arguing That We Don’t Need Further Global Policy Stimulus – Because The Market Is Already Delivering It

Now this is a contrarian argument if I ever heard one (excerpt):

Global: How I Stopped Worrying and Learned to Love the Double-Dip Scare - Manoj Pradhan

...policy-type stimulus is already under way - without central banks having to lift a finger or utter a word. How? Markets are doing the job for the monetary policymakers, and very efficiently at that. For one thing, asset price inflation far outstripping growth and accelerating inflation expectations could have tempted central banks to hike, but recession fears and sovereign risks from Europe have led to a moderation in both. Further, the rally in Treasury bond markets (except in the euro area periphery) is exactly the kind of reaction that one would expect if policy rates were cut and /or central banks had ramped up QE programs to buy more government securities. As long as a recession doesn't materialise, this prevailing combination of softer risky asset prices and moderating inflation expectations has set the stage for AAA (ample, abundant and augmenting) liquidity to be provided for longer. Delayed tightening along with the rally in bond markets will support economic growth going forward.

It’s an interesting perspective of what’s going on, and on the same level as the argument advocated by Scott Sumner that low interest rates are indicating tight money, not loose (and vice versa).

Also in the same issue of the Global Economic Forum is an analysis of optimal policy options for ASEAN (excerpts):

ASEAN: The Leverage Lesson, Double Dip and the Rebalancing Game - Deyi Tan, Chetan Ahya & Shweta Singh

...In conventional lingo, the Chinese-style macro rebalancing means reducing savings (read: current account surplus) and increasing consumption. However, in ASEAN, we think rebalancing is more likely to come via investment. Gross domestic savings in 2004-07 had actually fell (with the exception of Malaysia) compared to the pre-Asia crisis period of 1994-97, and total consumption ratios (private plus public) mostly rose even as ASEAN economies adapted to Asia's new exporter status...

...Indeed, rather than poorer consumption, ASEAN's export surpluses had came primarily at the expense of weaker capex. Put another way, ASEAN economies were simply not channeling their savings into investment, but were exporting it abroad...

...Is there room for more consumption? Comparing ASEAN economies with countries of similar income levels indicates that ASEAN's gross domestic savings rates are much higher compared to the average. Prima facie, this would suggest scope for consumption as a rebalancing tool, too. We think it depends. In our view, the scope is bigger for economies such as Singapore, which have already achieved high-income status, rather than for upper middle-income economies such as Malaysia or lower-middle income economies such as Indonesia and Thailand. Typically, savings rates tend to have a positive relationship with the economy's income level. This is why lower-income economies suffer from the dilemma of needing investment to ‘take off', but lacking the savings to finance it. Hence, rather than increasing consumption, we think Malaysia, Indonesia and Thailand are better placed, tapping on their high savings rates to increase investment and raise potential growth trajectory...

...However, smaller economies like Singapore will still be able to maintain an export-oriented strategy. Singapore faces a theoretically infinite export demand market by virtue of its small size and can ‘free-ride' on the positive externalities from macro rebalancing efforts in other economies...

...We think economies like Indonesia and Singapore are slightly ahead in the rebalancing process compared to Thailand and Malaysia. To begin with, Indonesia's economy is relatively more balanced as its current account surplus is smaller than other economies...

...However, in Thailand, fixed capex ratios fell further to 23.8% of GDP in 4Q09-1Q10 from 27.3% in 2004-07, and in Malaysia, it fell to 18.6% from 20.9%. In our view, the political climate will likely have to improve in Thailand to spur further investment. In Malaysia, policymakers will have to execute on their plans. Policymakers will have to shift their focus from physical to non-physical infrastructure such as education. A suitably qualified labour force will have to be built to unleash growth potential and drive government transformation. The subsidy systems, which have impeded progress on the competitiveness front, will have to be rolled back. A structural inflexion point in these areas will help to jump-start private investment momentum, both local and foreign.

In other words, we have to implement the NEM – in full. On a side note, I’ll add that only Indonesia and Malaysia of the countries covered have yet to undergo a demographic transition coming from falling birth rates and a lower dependency ratio – this factor will also boost consumption and growth over the long term, but will require considerable investment in education to fully leverage on.

Women: There’s No Progress Without Them

I’m late in commenting on this article – it’s been sitting tagged in my Google Reader for over a week now, but I’ve only just gotten round to posting on it (excerpts, emphasis added):

Women in workforce augur well for the economy

KUALA LUMPUR: The 2010 Asia-Pacific Human Development Report (AHDR) estimates that if women's employment rates were raised to 70%, countries like Malaysia, India and Indonesia would enjoy an increase in GDP between two and four per cent.

Such employment rate of women are only seen in the developed nations and the lack of women's participation in the workforce across Asia-Pacific costs the region an estimated US$89 billion every year...

Countries in the region that have done the most to tap women's talents and capacities have traveled farthest on many aspect of human development. Countries that tolerate deep inequalities fall short of equal citizenship and face social instability and economic loss...

Despite laws guaranteeing equal pay for work, the pay gap between men and women in Asia Pacific ranges between 54 per cent and 90 per cent.

In terms of economic power, a total of 67 per cent of East Asian women participate in the labour force, above the global average of 53 per cent, but South Asian women fall far behind, at only 36 per cent.

A majority of women in the region also, up to 85 per cent in South Asia, are in 'vulnerable' employment, such in the informal economy or low-end self-employment, far above the global average of 53 per cent.

More than 65 per cent of female employment in South Asia and more that 40 per cent in East Asia is in agriculture.

Yet, women in the Asia Pacific region head only seven per cent of farms, compared to 20 per cent in most other regions of the world.

Globally, Asia has the largest number of micro credit borrowers and highest percentage of poor women borrowers.

In Asia 98 per cent of micro credit borrowers in 2006 were women, compared with 66 per cent in Africa and 62 per cent in Latin America.

Meanwhile, the flow of women into business in Asia-Pacific is steady, up to 35 per cent of small or medium enterprises in the region are headed by women.

I’ve pointed out more than once that the female labour force participation rate in Malaysia is remarkably poor (here and here for instance). Note the last sentence I blocked out above – the global average is 53% and the East Asian average is 67%. Malaysia stands at just about 47%, worse than the global average and way below the regional norm. Wonder why Malaysia isn’t a high income economy and hasn’t been able to keep up with the Tiger economies? You’ve just found one big reason.

To be fair, this is largely a generational thing as the participation rate for females in the 25-34 age group is already very close to the East Asian average at 65%. The real big gap is in the older cohorts, with participation rates falling to under a quarter for females aged over 55 (compared to 60% for men). So this isn’t something that can be easily remedied through exhorting women to enter the work force – there’s a skill and experience gap that is probably to big to overcome.

On the flip side, we can look forward in 10-20 years to a much greater contribution from working Malaysian women towards economic growth and development in the future, as the population turns over and we get better average participation rates. That and the bulge of youngsters entering the work force in the next couple of decades is why I think demographic transition will be the main driver in transforming Malaysia into a high income economy - not new development models, or government incentives and subsidies, or lack thereof.

Singapore: The Future Has Grey Hairs

In exact counterpoint to my analysis of Malaysia’s demographics, Morgan Stanley (again! I love these guys) covers Singapore’s ageing work force, and the implications for economic growth in our southern neighbours:

Can Mr. Productivity Fight the ‘Silver Tsunami'?

...The Singapore economy is ageing and the pace of population greying will take on a new meaning in the coming decades (see factbox below for more details). Indeed, the dependency ratio (ratio of dependents to working-age population) has reached a historical low of 34.7% in 2010 amid a growing population base, implying that the economy is now in its final phase of reaping the ‘demographic dividend'. The current total fertility rate (TFR) has sunk as low as Japan's 1.27. This has been below the replacement rate of 2.1 since 1975. Ironically, the stark decline in TFR post 1950s and the sub-replacement fertility rate had helped to engineer an improvement in the dependency ratio via lower child dependency. Yet, the flip-side should soon rear its ugly head as the current working population grows old without replacement, leading to rising old-age dependency, arguably the more inferior sort of dependency.

They think that Singapore will eventually lose 1% of its growth potential over the next five years, a trend that I think will get worse as the population ages further. The greying of East Asia’s workforce will also likely slow growth in Taiwan, South Korea, and Hong Kong as well, with the only possible exception being China due to its still sub-optimal capital-labour ratio.

Malaysia has of course the opposite problem – our dependency ratio is high but mostly due to a higher proportion below the working age threshold. Which means over the next few decades, we’ll probably see a 1%-2% rise in our growth potential, provided of course that we have the right policies in place to harvest the “demographic dividend” of a rapidly increasing work force.

Panels and Pools: Malaysia's Demographics Part IV Continued

[Continued from last post]

Model III

In the last two attempts to make use of empirical evidence to suss out the relationship between the median age and dependency ratios of the population and income, we’ve looked at essentially two dimensional representations – Malaysian data across time, and a large sampling of countries at one point in time.

This post looks at a three-dimensional approach, of a large sample of countries across time (sorry, no graphs here). This is generally known as a panel or pooled approach, so if you ever come across the term, you’ll know what it means. Two further terms you need to know are “balanced” and “unbalanced”, which refers to data that is symmetrical (all countries have data for the sample period in question) or not.

Bear in mind that this is my first stab at working with panel data, so if you have any advice/contribution/criticisms to make, sound off in the comments – I want to hear from you!

I’ll skip all the technical stuff and go straight to the results (read the accompanying notes if you’re interested in the details):

1. LOG(GDP_MYS) = 0.87 + 3.90*LOG(AGE_MYS) - 4.04
2. LOG(GDP_MYS) = 0.66 - 2.11*LOG(RATIO_T_MYS) + 7.47
3. LOG(GDP_MYS) = 1.16 + 1.76*LOG(RATIO_O_MYS) + 12.66
4. LOG(GDP_MYS) = 0.89 - 1.83*LOG(RATIO_Y_MYS) + 7.17
5. LOG(GDP_MYS) = 0.87 + 3.93*LOG(AGE_MYS) + 0.02*LOG(RATIO_T_MYS) - 4.12
6. LOG(GDP_MYS) = 1.00 - 0.96*LOG(RATIO_Y_MYS) + 0.49*LOG(RATIO_O_MYS) + 1.69*LOG(AGE_MYS) + 3.54

I’ve only reported here the results for Malaysia; all the coefficients and regressions as a whole are statistically significant. As you can see (if you haven't gotten cross-eyed!), we again have general confirmation of the stylized facts:

1. A higher median age is associated with higher incomes
2. A lower dependency ratio is associated with higher incomes
3. A lower youth dependency ratio is associated with higher income
4. A higher old age dependency ratio is associated with higher income

The regressions that combine age with dependency ratios are less clear (just like in my first two attempts), with the possible interpretation that age has a bigger impact than any of the ratios. Unfortunately like in Model II, I'm still not getting plausible estimates for income - or I've screwed up somewhere. If I can figure it out, I'll revisit this topic in a future post.

It turns out that only the first, simplistic model (Malaysian data across time) comes close to providing reasonable estimates of income levels into the future. This suggests that the country specific factors driving Malaysian income are strong enough to overcome some of the disadvantages we face because of our demographic structure.

I have to reiterate this - Malaysia is a middle income country with a population structure of a low income country. I suspect that being an oil producer is one big leg up over that particular hurdle, although I haven't been able to statistically prove this - using a dummy for oil production across the whole sample didn't yield statistically significant coefficients. Other potential boosters that have generally been put forward in the literature include the ability to produce sufficient food (arable land) and institutional factors that have been the basis for inward investment.

Whatever the causes may be, we're ahead of the game in some respects and have the potential to sustain above-average growth going well beyond that of our regional peers - Malaysia's labour force as a proportion of the population will keep on rising for decades to come.

Technical Notes

1. GDP data from the IMF World Economic Outlook Database (April 2009)
   
2. Population estimates from the US Census Bureau International Data Base
   
3. Sample size is 179 countries, with age, ratio and gdp data from 1991 to 2008 (unweighted, unbalanced panel; 3097 observations)
   
4. GDP data is in current international dollars
   
5. Fixed effect dummies are used to model individual country variations (second intercept term in the equations above) - no time effects (fixed or random) are assumed
   

The Adventures of Scattershot Billy: Malaysia's Demographics Part III Continued

[Continued from last post]

Model II

This second attempt at finding a relationship between population characteristics and income uses a both a broader and narrower approach than that in the first post in the series. Rather than taking one country and tracking the data over time, a cross-sectional approach uses more countries (to ensure more universal applicability) but at only one point in time.

I've culled and cross referenced the IMF's World Economic Outlook database (for income) and the US Census Bureau's International Data Base to see if a relationship exists between my two population structure variables of choice (median age and dependency ratio) and income (in this case, GDP per capita in international dollars). I have a sample size of 181 countries with the data I'm looking for, out of the approximately 210 available from both databases - all data is from 2007, as 2008 data for some countries still consists of estimates.

The best way to show the relationship between the data is through scatter diagrams, with each data point representing an individual country:

Some observations:

1. It's clear that a low population median age is associated with low income, but the opposite cannot be said to hold - a high population median age does not guarantee a high income economy. This is partly due to the effect of some countries in the sample which have generally done badly, or are only just starting to take off after policy/strategy mistakes, e.g. the ex-Soviet bloc countries, which had relatively mature stable populations but had not benefited from following a market system until recently. However, this also means that any regression attempt must be qualified by a relatively large confidence range.
2. The dependency ratios substantiate the stylized facts - "young" countries tend to have lower incomes, while "older" countries tend to have higher incomes. But as with the median age, the experience between countries varies widely, depending on their particular circumstances. It's interesting to note that there's a "floor" to the youth ratio and a "ceiling" for the old age ratio, beyond which countries rarely cross.

To minimize problems with this wide variance in experience, I trimmed the sample by cutting out the top 10 with the highest GDP per capita. Hopefully, this will give me a more "normal" relationship between the variables and income that would be more typical of the experience most countries would undergo. Eyeballing the scatter charts, there's little difference to see except between age and income:

...which looks better behaved, and will hopefully yield results more representative of "true" relationship.

Running the regressions (based on the trimmed sample), we get:

1. LOG(GDP_TRIM) = 3.40*LOG(AGE_TRIM) - 2.36
2. LOG(GDP_TRIM) = -3.32*LOG(RATIO_T_TRIM) + 6.92
3. LOG(GDP_TRIM) = -2.03*LOG(RATIO_Y_TRIM) + 7.05
4. LOG(GDP_TRIM) = 1.35*LOG(RATIO_O_TRIM) + 11.84
5. LOG(GDP_TRIM) = 2.73*LOG(AGE_TRIM) - 0.81*LOG(RATIO_T_TRIM) - 0.61

...which gives the same interpretation that we found in the single country example in the first post in this series i.e. higher median age and a higher old age dependency ratio are associated with higher income, while the opposite relationship exists for the total dependency and youth dependency ratios. Comparing the two sets of estimates, the biggest differences in the coefficients are with median age (3.40 here against 6.86) and to a lesser extent, with the old age dependency ratio (1.35 against 2.80).

The best two fits are with regression 1 and regression 4, but having said that, none of these regressions yield future income estimates that make plausible sense. So while this approach has yielded some nice visual representations, it's a failure as a conduit for forecasting Malaysia's future income.

From a certain perspective that's understandable - this approach would best describe the "global population", and ignore any country specific differences that may influence income. Given that Malaysia is a little unusual in having a "younger"-than-normal population for its income level, we're not likely to get usable forecasts. On the other hand, we should at least allow for this more "global" experience to influence any other forecasts we make, as there is alwasy the possibility that Malaysia in future would "revert" back to the mean of the global experience.

That leads to the last approach, where we combine time series (from the first post) with cross-section data (this post). Coming up next.

Technical Notes

1. GDP data from the IMF World Economic Outlook Database (April 2009)
2. Population estimates from the US Census Bureau International Data Base

Income, Age and Dependencies: Malaysia's Demographics Part II

In my first post on Malaysia's demographic profile, I covered the theory regarding demographic transitions and the demographic dividend. This post will focus on some of the empirical evidence.

To wit: Is there a long term causal relationship between the age profile of a country, and it's potential to generate income? That's a hard (and complicated) question to answer, so in the best traditions of economics, I'm going to grossly simplify: Is there a statistical relationship between either the median age of a population or its dependency ratio, with GDP per capita?

Changing the question completely avoids the problems attempting to model the distribution of age within a population by focusing on simple numeric measures of a population structure, with the obvious pitfall of getting it all wrong if those measures are not actually related to income. Note that I'm also completely avoiding the issue of causality.

My underlying hypothesis here is that the higher the median age the greater the potential for generating income while the dependency ratio should have the opposite effect; and I'm also assuming that the relationship is linear. In real life, because of the different impact of the old age cohorts (high savings and consumption) compared to children (must be fully supported by the working population), I suspect the relationship between income with age and dependency would in actuality be non-linear, but particularly for the dependency ratio. If you recall the discussion in the last post, at the 1st and 4th stages of the demographic transition, the population is stable, which means there won't be a demographic impact on incomes at those stages. However, there are very few countries (Japan comes to mind), who have high enough median age populations to test this, so I'm pretty sure I'm safe assuming linearity in stages 2 and 3.

Moving on to the question of methodology, there's a number of approaches to answer the income-age/income-dependency question:

1. The single country approach - simplest and least demanding to do, using time series data from a single country only;
2. The cross-sectional approach - combines the data from a number of countries from a single year which is a more universal application, but ignores the probability that the sought-for relationship may actually change across time, as well as ignoring country-specific effects (government policies, availability of arable land etc);
3. The pooled/panel approach - combines both cross-sectional and time series data. Great if you can manage it, but has horrendous data requirements depending on how many countries are included in the sample (preferably all).

I'm going to cover all three approaches (hey, I'm ambitious), and hopefully we'll come up with an answer that makes sense, as well as provide some forecasts as to if and when Malaysia may reach high-income status. Since the material is pretty extensive, I'm going to split this into three quick posts, rather than just the one I had originally planned, to make reading a little easier.

Model I

Here's the dataset I'm working with (details at the end of the post):

Note the different trends of the youth and old age dependency ratios (both relatively trend-stationary), which result in a non-stationary total dependency ratio. Also, I have high correlations between most of the variables, so there is the potential for multicollinearity problems (which tested out as being confirmed between median age and the old age ratio):

	GDP_MYS	AGE_MYS	RATIO_T_MYS	RATIO_O_MYS	RATIO_Y_MYS
GDP_MYS	1.00	0.98	-0.95	0.98	-0.96
AGE_MYS	0.98	1.00	-0.74	0.99	-0.97
RATIO_T_MYS	-0.95	-0.74	1.00	-0.63	0.88
RATIO_O_MYS	0.98	0.99	-0.63	1.00	-0.92
RATIO_Y_MYS	-0.96	-0.97	0.88	-0.92	1.00

Next, I ran a series of regressions - GDP against all the variables, singly or in combination. Here are the results:

1. LOG(GDP_MYS) = 6.86*LOG(AGE_MYS) - 12.42 + [AR(1)=0.47]
2. LOG(GDP_MYS) = -3.60*LOG(RATIO_T_MYS) + 7.55 + [AR(1)=0.72]
3. LOG(GDP_MYS) = -2.88*LOG(RATIO_Y_MYS) + 7.54 + [AR(1)=0.71]
4. LOG(GDP_MYS) = 2.80*LOG(RATIO_O_MYS) + 16.65 + [AR(1)=0.71]
5. LOG(GDP_MYS) = 10.78*LOG(AGE_MYS) + 2.53*LOG(RATIO_T_MYS) - 23.62
6. LOG(GDP_MYS) = 8.74*LOG(AGE_MYS) + 3.28*LOG(RATIO_Y_MYS) + 2.14*LOG(RATIO_O_MYS) - 10.77

All coefficients are statistically significant at the 5% level, with the regressions showing very high r-squared figures, and diagnostics that (mainly) work out ok. The AR(1) terms, where present, are to correct for serial correlation.

I know this is terribly wonkish, and not at all easy to follow if you've never studied statistics, but bear with me. To read the equations, simply treat the numbers before each variable on the right hand side as the precentage change to GDP per capita, from every percentage change in that particular variable. So from equation 1, a +1% rise in the median age results in GDP per capita rising +6.86%. If the number is negative, then the relationship between that variable and GDP is also negative.

More generally, the results tend to bear out my intuition:

1. An increase in the median age is associated with an increase in GDP per capita
2. A reduction in the total dependency ratio (both youth and old age cohorts) is associated with an increase in GDP per capita
3. A reduction in the youth dependency ratio is associated with an increase in GDP per capita
4. An increase in the old age dependency ratio is associated with an increase in GDP per capita
5. The last two results show the wrong signs for the dependency ratio coefficients, which is an artifact of the mutlicollinearity problem

The forecasts generated by the regressions are summarised in the chart below:

...which is an awfully wide range of estimates. Model selection criteria points squarely at equation 6, which just so happens to generate the highest forecast GDP per capita - tripling GDP per capita from 2010 to 2020. That doesn't seem plausible - although I should point out I'm forecasting 40 years forward from just 18 years of realised data.

Of the single variable regressions, the next best is equation 4 (GDP with the old age ratio), followed by equation 1 (GDP with median age). Both have GDP per capita doubling by 2020, although the forecasts diverge substantially after that.

Will that be enough for Malaysia to achieve high-income status? It will be close. The standard by which income is judged is the World Bank's classification, which converts local currency nominal GNI per capita to USD using the Atlas method. As of 2008, Malaysia's GNI per capita is around USD6,970, about 41.5% below the high-income threshold of USD11,906. Assuming 3% inflation per annum, that means by 2020 we have to hit GNI per capita of about USD16,975. In PPP-corrected terms (assuming price levels increase by the same ratio), that's about 34,270 in current international dollars, or ever so slightly above the 2020 forecast of equation 4 (34,187) and not too far from equation 1 (31,231).

In other words, it will be a close run thing. Given the uncertainty built into the forecasts and my assumptions, we could potentially pass the high-income barrier before 2020, and equally we might pass it well after. It really depends.

On the other hand, it also seems pretty clear that Malaysia's demographic transition will play a role in accelerating the process.

Technical Notes:
1. GDP data from the IMF World Economic Outlook Database (April 2009)
2. Population estimates from the US Census Bureau International Data Base

It's The People, Stupid! Malaysia's Demographics Part I

[When I first started this blog a year ago, I made a list of some of the things I wanted to talk about regarding the structure of the Malaysian economy. I had twelve items on that list, but in the past year I've only managed to cover five. This post starts off with one of those uncovered topics - one down, six to go! P.S. There will be two versions of this post. This first once presents a normative argument, with some backing data. The second post will be for the more statistically inclined]

Here's a few predictions about the future of Malaysia:

1. By 2020, the level of crime will have increased about 13%-14%, irrespective of the policies of the government (whichever one we will happen to have by then), or the level of enforcement.

2. Property and housing will be a very good long term investment.

3. We will still be considered a middle income country, but only just.

There's a common thread underlying these predictions - demographics. I got prompted to look at this from a few paragraphs in the introduction to Freakonomics (crime in the US fell in the mid-1990s because of the impact of Roe vs Wade on legalised abortion two decades earlier), as well as rereading this fascinating old article from the IMF's Finance and Development magazine. It's a stylized fact that a bigger working population relative to the population as a whole is associated with (I won't say "causes") higher per capita incomes.

A dynamic view of this relationship can be explained by the demographic transition model (the Wikipedia article on this is pretty good - no, I haven't read any primary sources on this yet):

Stage 1: preindustrial society; population stable

Stage 2: better health care causes death rates to drop and improves infant survival; population growth

Stage 3: urbanisation, increasing incomes, and other factors cause the fertility rate to drop; population growth slows

Stage 4: industrial society; population again stable, but at a higher level

Stage 5: countries where the birth rate drops to low; population shrinks

There is therefore the potential for what's called the "demographic dividend", a transitory phase where a larger working population coupled with lower fertility rates means excess income that can be used for investment and asset accumulation. The IMF article further argues that there is a second dividend arising from a larger, wealthier old-age cohort who invest their retirement funds, thus further boosting the pool of funds available for investment.

In short, countries in stage 3 and stage 4 tend to have higher growth and then higher incomes, relative to those in earlier stages. The IMF article estimates that as much as 44% (about 1.9% p.a.) of South-East Asia's growth between 1970-2000 came from this transition. This article makes an even more comprehensive argument that demographic change was behind the East Asia growth "miracle".

To illustrate, here's Thailand's population pyramids between 1965 and 2050 (from the IMF article):

So what's the point of all this?

Malaysia is actually the diamond in the rough (or if you prefer, the thorn among the roses). When other countries in the region began actively trying to restrict population growth after the 1960s, Malaysia only paid lip service to the idea then went the other direction with the 1984-85 National Population Policy. If you aren't old enough to remember this, the goal was to increase the population to 70 million by 2100 - with lots of accompanying jokes about encouraging polygamy, natch.

As a result, we are far behind the curve in getting into stage 3 of the demographic transition compared to our regional peers. To compare, here are the population pyramids for Singapore, Hong Kong, Korea, Taiwan, Thailand, Indonesia and Malaysia in 1990:

...and the same countries in 2010:

(All the above from Nationmaster.com: you can find more population pyramids on the site, look under age distribution under each country page).

Note the substantial difference in age structure between the Asian Tigers and the other three countries shown here. Also, Malaysia appears to be behind even Indonesia in starting the stage 3 demographic transition.

Two other ways of looking at the same data is to use the median age of the population, and the dependency ratio, which is defined as the percentage of the population outside the 15-64 age bracket (using the same sample of countries):

Malaysia will have the same population median age in 2050, that Singapore had in 2000! This explains a lot of why Malaysia has lagged the Asian Tigers in growth - imagine a 2% disadvantage in per capita income growth every year, for a whole generation. On that basis, it's actually more surprising we are a middle-income country at all, as our population age profile is more typical of a stage 2 low-income economy.

The implication is also that Malaysia's growth potential over the next couple of decades is far higher than most of the region - apart from Indonesia, the rest of the economies in this (unscientific) sample will be subject to rapid population aging after about 2015. But the key to realizing that growth potential of course is putting in place the correct policies to take advantage of the demographic dividend when it comes.

Getting back to the predictions I made at the top of this post, the reasons why should be clear:

1. The level of crime will increase 13%-14%, simply because the supply of potential criminals will increase by the same portion (I'm assuming males in the 15-29 age bracket). The confidence interval for this would necessarily be large, as the other determinant of crime is the state of economy (and believe it or not, not enforcement).

2. Population growth in the working-age cohorts will increase demand for relatively scarce housing and residential land.

3. Based on some models I'm working with and the assumptions I'm making, I'm getting estimates of between 2023-2030 (when the median age will be between 28-30) when Malaysia will pass the middle-income barrier. That's when the current crop of youngsters will have entered the workforce and started earning and consuming (assuming of course, we manage to generate enough jobs and entrepreneurial opportunities for them).

Technical Notes:
1. Demographic Transition, Wikipedia article
2. Demographic Dividend, Wikipedia article
3. "What is the Demographic Dividend", Lee, Ronald & Andrew Mason, Finance & Development Magazine, Sept 2006 Vol43 No3, International Monetary Fund
4. "Demographic Dividend", Barker, JF, Online article, Population Growth & Migration website (2004)
5. Population pyramids from Nationmaster.com
6. Global population data and estimates from the US Census Bureau's International Data Base