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...

The Determinants of Saving

In my inbox today, from the NBER (abstract):

The Determinants and Long-term Projections of Saving Rates in Developing Asia
Charles Yuji Horioka, Akiko Terada-Hagiwara

In this paper, we present data on trends over time in domestic saving rates in twelve economies in developing Asia during the 1966-2007 period and analyze the determinants of these trends. We find that domestic saving rates in developing Asia have, in general, been high and rising but that there have been substantial differences from economy to economy and that the main determinants of these trends appear to have been the age structure of the population (especially the aged dependency ratio), income levels, and the level of financial sector development. We then project future trends in domestic saving rates in developing Asia for the 2011-2030 period based on our estimation results and find that the domestic saving rate in developing Asia as a whole will remain roughly constant during the next two decades despite rapid population aging in some economies in developing Asia because population aging will occur much later in other economies and because the negative impact of population aging on the domestic saving rate will be largely offset by the positive impact of higher income levels.

X Marks The Spot: Demographics and the Middle East

I think William Pesek has this pegged just right (excerpt):

Sex Ratio Does Magic in China Amid Egypt Effect: William Pesek

Feb. 24 (Bloomberg) -- The world abhors China’s one-child policy. Officials in Beijing must be quietly toasting its very existence as the Middle East burns.

A common thread linking events in Egypt, Bahrain, Tunisia, Libya and elsewhere is big populations of disaffected youth. They’re angry about greed, corruption, the rich-poor divide and unaccountable leaders. Many Chinese harbor similar gripes, yet demographics works in the Communist Party’s favor.

Had China not instituted population control in 1979, there would be tens of millions more underemployed and aggrieved young men milling about in China’s cities. Just the type to foment revolution -- a Tiananmen Square 2.0. Only, they were never born. Turns out, the policy is a boon for Chinese regime control.

The longer-term implications are far less advantageous. China’s working-age population will start shrinking in 2020, denting growth. For now, though, demographics is a key reason China isn’t Egypt. It also could mean slower yuan gains amid fear that less growth will fan unrest…

Part-Time Work Regulations Implemented

If you recall my post on Dr Fong Chan Onn’s thoughts on the minimum wage, one of the things that he recommends and foresees is greater part time work and a more flexible labour force.

Well, the changes to the Employment Act 1955 enacted in August will come into force tomorrow:

New rules on part-time work

PUTRAJAYA: Only those working between 30% and 70% of the eight-hour stretch daily will be considered part-timers under new rules from next month, said Human Resource Minister Datuk Dr S. Subramaniam…

…He said staff working below 30% of the eight-hour stretch would be categorised as casual workers while those putting in more than 70% were full-timers.

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