South Africa in the 21st Century

My exchange semester at Auckland University of Technology is officially over! Now I am waiting on my exam results and I am also about to head back to Germany tomorrow for the summer break. This is why today’s post is not going to be a long one. Rather I would like to share one of my research essays that I completed in my Growth and Development Economics paper at AUT.

The task was to choose one of the BRICS economies and write a research essay on their growth and development performance in the last decade. In particular, we were supposed to analyse the extent to which institutional development supported or hindered the process of economic development. The essay should include four parts: an introduction to the country, a section on growth and development trends, a section on institutional development and lastly a summary including policy implications.

I chose to focus on South Africa and I have to say that it was a very interesting assignment and it helped me significantly in developing my research skills. Doing all this research on South Africa also changed my impressions on the current state of human and economic development in the country. It has been 22 years since the end of apartheid but South Africa continues to face significant obstacles as highlighted in the essay. Despite a range of headwinds identified in the essay there is however the possibility for South Africa to regain its strength and play up to the expectations of becoming one of the future drivers of world economic growth as part of the BRICS as argued in the last part of the essay.

I hope you enjoy reading my research! The abstract of the essay is included below and complete file is available from here.




South Africa joined the BRICS for their 3rd BRICS Summit in 2011 after being predicted to become one of the future drivers of world economic growth. However, both in the area of economic growth and development as well as governance South Africa continues to face substantial challenges. The aim of the essay is to assess the country’s performance in these areas over the past decade. After a brief overview on South Africa the essay analyses growth and development trends with the use of the Human Development Index (HDI), the inequality adjusted HDI and the Multidimensional Poverty Index. In the second part, the essay focuses on institutional development employing the World Governance and Doing Business Indicators, as well as the Index of Economic Freedom.

The main findings are that South Africa’s economic development is impeded by sluggish growth rates and a contracting economy as well as the rising burden in fiscal debt and its servicing costs. South Africa’s society still faces racial and gender inequality as well as multidimensional poverty. The country’s potential human development in all three areas of health, education and income remains dampened by inequality which persists after the transition to a more open society and economy under the post-Apartheid regime. The country has suffered from a deterioration of institutional quality over the last years, especially in corruption coupled with an on-going underperformance in political stability. Furthermore, the ease of doing business is impeded by constraints in getting electricity and a deterioration of conditions regarding the access to credit.  A major concern is that business freedom, labour freedom and investment freedom have seen a long-term deterioration in conditions.

The essay’s policy recommendations centre on a holistic reform of South Africa’s institutional system in order to reshape incentives to invest in physical and human capital and to establish incentives for innovation. The recommendations derive from the World Bank Growth Commission’s 5 common growth ingredients of market incentives, trade openness, future orientation, macroeconomic stability and good governance with a focus on inclusive growth.

Droege, J. (2016). South Africa in the 21st Century. Auckland University of Technology, Auckland. Retrieved from 


The Life-Cycle Hypothesis and its Role in Household Saving

Today’s Growth and Development Class was about the role of savings and the resource gap. One of the readings for the lecture is Chapter 10 Investment and Savings in Perkins, Radelet, Lindauer and Block (2013). This is why I want to dedicate today’s post to one of the concepts of the chapter, namely the life-cycle model of household saving (pp. 376-379). It was formalized by Economist and Nobel-Prize laureate Franco Modigliani (1954, 1963).

The life-cycle model attempts to explain how savings and consumption tend to vary over a person’s lifespan. In particular, young adults are expected to save less or even dis-save (borrow) due to lower incomes and the costs of starting a family and raising children. Later in life people can save more because (1) incomes rise in working life and (2) expenditures on children fall in family life. At this stage most people start to accumulate their retirement savings. Finally, after retirement people are expected to dis-save again living of their accumulated wealth (Perkins et al., 2013). Overall, the manifestation of such a life-cycle model also depends on factors like the state of pensions, or public and private retirement plans in a country which determine whether there is a need to accumulate money for retirement or whether there is a tax-financed pension system.

This is the microeconomic part of the life-cycle model which can explain in-country differences in saving rates. However, there is also a macroeconomic part to it, meaning that one would expect cross-country differences in saving rates due to different demographic structures of societies in international comparison. According to Perkins et al. (2013) this stems from a demographic transition all societies pass through. It has the following three distinct phases:

  • Phase 1: High birth and death rates and therefore low population growth
  • Phase 2: Decreasing death rates coupled with constant high birth rates from phase 1 and therefore an increasing population growth rate
  • Phase 3: Decreasing birth rates with steady low death rates reached in phase 2 and therefore dropping population growth rates

These three phases have a significant influence on a country’s age dependency ratio, i.e. the share of the population below the working age (children) and above the working age (retired) to the working-age population. The World Bank’s formal definition is “the ratio of dependents – people younger than 15 or older than 64 – to the working-age people – those ages 15-64”. Their figures are reported as “the proportion of dependents per 100 working-age population” (2016).

This age dependency ratio indicates in which phase a country is currently in. For example, poor countries in stage 2 of the demographic transition face high population growth and have a high number of young dependents (children) in relation to the working-age population. As workers need to care for many young dependents they are expected to save little or even dis-save. Once these countries transition into stage 3, the make-up of the population shifts from many young dependents to many adult workers. Hence the age dependency ratio is expected to fall which allows workers to save and the country to move from a lower income to a middle income per capita.

This link between a drop in the age dependency ratio and a rise in saving rates is what the life-cycle hypothesis predicts and what can actually be observed in some middle-income countries today. Due to dropping birth rates and a larger working-age population these middle-income countries tend to have high gross domestic saving rates. However, this is not the end of the demographic change. In the long run these countries are expected to have an ageing population. At some point there is an increasing number of retirees due to generations of low birth rates coupled with increasing life expectancy. This is what currently happens in East Asia and Pacific (for example in Korea, Japan, Thailand, China) which is ageing faster than any other region (world Bank, 2015). At this point of the demographic transition, the age dependency ratio is expected to pick up again and this is also one of the main reasons why rich countries tend to save less. Many of them have entered that stage of demographic transition, where a large share of the population are adult dependents (retirees) in relation to the working-age population. These rich countries tend to have (1) longer life expectancies together with (2) a large number of retirees drawing on accumulated savings. Overall, the life-cycle model therefore suggests lower saving rates both for low income and high income countries compared to middle-income countries.

One can test for the hypothesis of a relationship between the level of savings in a society and the demographic transition by plotting gross domestic savings (as percentage of GDP) against the age dependency ratio (as percentage of working-age population), as shown in the diagram below. For 2014 there are 155 observations on gross domestic savings and 194 observations for the age dependency ratio available from the World Development Indicators database. The average gross domestic saving rate across countries in 2014 was around 18.8 percent of GDP with a standard deviation of 18.7. The average age dependency ratio was at 59.0 percent with a standard deviation of 18.1. This means that, on average, for every 10 workers there were 5.9 people not of working age (either being young or old dependents). Gross domestic saving ranged from -50.5 percent (Liberia=LBR) to 76.3 percent (Equatorial Guinea=GNQ) of GDP. The United Arab Emirates (ARE) had the lowest age dependency ratio of only 17.5 percent, meaning that for every 10 workers there were less than 2 dependents. On the other hand, Niger (NER) had the highest age dependency ratio of 112.7 percent, meaning that for every 10 workers there were more than 11 dependents in 2014, which is expected to constrain the country’s saving capacity.

(Source: World Bank, 2016a; 2016b)

When regressing gross domestic saving on the age dependency ratio one can obtain the slope coefficient and the intercept for the line of best fit which is shown in the diagram. The regression results are included in the table below. It can be seen that a 10 percent increase in the age dependency ratio is associated with a 4 percent decrease in gross domestic saving. One should note though that (1) this does not infer causation and that (2) the R-squared of 15 percent reveals that there are large unexplained deviations from the trend line. Only 15 percent of the variability in gross domestic savings around the mean of 18.8 percent can be explained by the model.

  Model 1   
 Gross domestic saving Coef. Std.  Significance
Age dependency ratio -0.3976 0.0771 ***
_cons 41.9959 4.6710 ***
N 152
F(1, 150) 26.61
Prob > F 0.0000
R-squared 0.1507
Adj R-squared 0.145

Hence the life-cycle hypothesis seems to hold for some countries or might be a sign for an indirect link through some other factors but clearly does not hold universally for all countries. For example, the Republic of the Congo (COG) and Liberia (LIB) had similar age dependency ratios but significantly differing gross domestic saving ratios. The Republic of the Congo had an age dependency ratio of around 86 percent and gross domestic saving of 44 percent of GDP. Liberia had a similar age dependency ratio of around 84 percent but gross domestic saving of -50.45 percent of GDP. Both deviate significantly from the trend. The regression model would have predicted a gross domestic saving rate of 7.6 percent for Congo and a gross domestic saving rate of 8.6 percent for Liberia.

Another example are Hong Kong (HKG) and the Republic of Chad (TCD). They had similar gross domestic saving rates but largely differing age dependency ratios in 2014. Chad’s gross domestic saving rate stood at around 25.5 percent and Hong Kong’s gross domestic saving stood at around 24.1 percent of GDP. However, Chad’s age dependency ratio was at almost 102 percent of working-age population while Hong Kong’s age dependency ratio was only around 36 percent. Overall, while Hong Kong is close to the predicted saving rate of 27.7 percent of GDP, Chad has a significantly higher gross domestic saving rate than predicted by the model (1.5 percent of GDP).

One can also test for the hypothesis that the age dependency ratios for the young and the old have a different impact on gross domestic saving by separating age dependency into two variables (model 2). Firstly, the coefficient for the young age dependency ratio is more significant (1 percent level) than the one of the old (5 percent level). Second, a 10 percent increase in the old age dependency ratio is associated with a 5.4 percent decrease in gross domestic saving while a 10 percent increase in the young age dependency ratio is only associated with a 4.2 percent decrease in gross domestic saving. This contrasts with the findings of Perkins et al. (2013) which find a stronger relationship between the young age dependency ratio and the gross domestic saving rate. However, a stronger relationship between the age dependency ratio of the old and the gross domestic saving rate might be explained by the trend that children are increasingly a ‘luxury’ and people today tend to have already accumulated resources when deciding to have children and therefore the negative link between savings and children might weaken. Also, government support systems might decrease the cost of raising children and enable families to save despite raising children through policies like tax breaks or a negative income tax credit. However, the second model does not have a higher adjusted R-squared than the first one, indicating that separating the effects of young and old dependents does not increase the variability in gross domestic saving which can be explained by the model.

Model 2
Gross domestic saving Coef. Std.  Significance
Age dependency ratio old -0.5435 0.2144 **
Age dependency ratio young -0.4193 0.0827 ***
_cons 44.8889 6.1364 ***
N 152
F(2, 149) 13.53
Prob > F 0.0000
R-squared 0.1537
Adj R-squared 0.1423

So while the life-cycle hypothesis explains differences in saving rates (1) within countries at microeconomic level and (2) across countries at macroeconomic level in theory, the hypothesis has only limited applicability in practice. There are clearly other factors (for example institutional development) that cause large deviations from the trend and can explain why some countries save a lot more than others.

Thanks for reading!




Aldo, A., and Modigliani, F. (1963). The Life-Cycle Hypothesis of Saving: Aggregate Implications and Tests. American Economic Review, 53(1), 55-84.

Modigliani F., and Brumberg, R. (1954). Utility Analysis and the Consumption Function: An Interpretation of Cross-Section Data. In K. Kurihara (ed.)., Post Keynesian Economics. New Brunswick, N.J.: Rutgers University Press.

Perkins, D.H., Radelet, S., Lindauer, D.L., and Block, S.A. (2013). Economics of Development (7th ed.). New York, N.Y.: W.W. Norton & Company.

World Bank (2015). Rapid Aging in East Asia and Pacific Will Shrink Workforce and Increase Public Spending. Retrieved from:

World Bank (2016a). Age dependency ratio (% of working-age population). Retrieved from:

Word Bank (2016b). Gross domestic savings (% of GDP). Retrieved from:


Botswana in the 21st Century

Botswana has been one of the role models for economic development despite unfavourable geographic conditions (landlocked, tropical climate) since it gained independence from Britain in 1965. Its success story can be attributed to both its natural resource endowments and institutional development. Despite colonialization precolonial tribal institutions were able to endure. These existing institutional structures encouraged the enforcement of property rights and limited the power of political elites in the country upon independence. It also enabled the development of sound political and economic institutions compared to other countries in sub-Saharan Africa. This is why the country could take advantage of its natural resource endowments. With some minimal requisite institutional development Botswana was able to make the country’ mineral wealth its comparative advantage in international trade. What is more, the government redistributed the gains from the exploitation of national resources by foreign multinationals, i.e. substantial tax revenues from diamond profits, to the population at large. The government invested in human capital formation and in turn achieved universal primary education. With the tax revenues it was also able to establish a basic social security system to avert crises such as the five-year drought from 1982 to 1987 (Todaro & Smith, 2012).

The post of today looks at how Botswana has developed in the 21st century, especially in the aftermath of the global recession 2008/09, and whether it continued its success story. In the first part, it will assess Botswana’s economic development with the use of the Human Development Index (HDI). In the second part, it will look at its governance and institutional development with the use of the World Governance Indicators (WGI).

Botswana 1
(Source: World Bank, 2016)

Botswana currently ranks 106 out of 189 countries in the HDI ranking with an index of 0.698 which is based on the country’s performance in the three dimensions income, education and health (UNDP, 2015a). In the income dimension, the HDI assesses GNI per capita for every country in the index. For Botswana GNI per capita in the 21st century is shown in the diagram above. It highlights that Botswana’s GNI per capita rose steadily by over 4 percent per annum over the period from 2005 to $12,772 in 2008. After a 5.57 percent drop in 2009 due to the global financial crisis it receded to high growth rates. In 2011, GNI per capita growth peaked at 7.62 percent, which catapulted the country’s per capita income above pre-downturn levels. In 2014, GNI per capita stood at $14,661. This is significantly above the average of upper middle income countries (UMC; four-tier World Bank classification) of $13,413 for the year 2014 (World Bank, 2016). However, Botswana has lost ground during the global financial crisis compared to the upper-middle income countries as its peers (as an aggregate group) experienced a significantly smaller drop in per capita income. Overall it can be concluded, though, that Botswana has performed well in the income category due to steady growth rates since 2005 if one excludes the year 2009. Incomes have seen a quite steady upward trend.

Botswana 2.png
(Source: World Bank, 2016)

Education in the HDI is assessed by expected years of schooling and mean years of schooling. The former has increased from 11.7 years in 2000 to 12.5 years in 2014. The latter has increased from 7.6 to 8.9 over this period (UNDP, 2015b). Hence Botswana’s adult population today (aged 25 and above) has, on average, more than a year more of education than at the beginning of the century. Also government expenditure on education as percentage of GDP remains high and currently sits at 9.5 percent (UNDP, 2016). What is more, after having achieved universal primary education and widespread secondary education in the population, the country is now expanding post-school education as a means to develop human capital to the next level. This will in turn allow for sustained economic development in the future. The diagram above shows that the country’s gross tertiary enrolment ratio for both sexes went up from 6.9 percent in 2000 to 27.5 percent in 2014. The largest increase was seen over the period from 2007 to 2009 with an increase of more than 11.5 percentage points. Due to the crisis, however, it dropped and only picked up after 2011 again. The diagram also reveals that Botswana has caught up with the lower middle income countries (LMC) over the last years. In order to catch up with the UMCs, though, it needs to further invest in tertiary education to aim for enrolment levels close to 40 percent and beyond in the future.


Botswana 3
(Source: World Bank, 2016)

The HDI’s dimension health is assessed through the indicator ‘Life expectancy at birth’. The health dimension has been the problem child of Botswana at the turn of the century due to the HIV/AIDS pandemic. It caused life expectancy to decrease from more than 62 years in 1990 to below 50 years in 2000. In the 2000s, however, Botswana made enormous progress and surpassed its 1990 level of 62.7 years by the end of 2010. It rolled out antiretroviral treatment which is now available free of charge to all citizens (Todaro and Smith, 2012). This allowed Botswana to surpass the low income countries (LIC) average. With its current life expectancy of 64.4 years it is now in the middle of the low income and lower middle income average in life expectancy.

Botswana 4
(Source: UNDP, 2015a)

Overall, Botswana has caught up in human development mainly due to the large increase in life expectancy but also due to the steady rise in incomes. While the country’s HDI laid at 0.561 in 2000, it increased to 0.681 by 2010. Furthermore while the annual average HDI growth was actually negative from 1990 to 2000 it went up to 1.96 percent during the period 2000 to 2010. After 2010 it lowered to 0.61 percent on average per annum. In 2000, Botswana’s HDI was below the developing countries average but by 2010 it had surpassed the developing country average. Overall, Botswana’s HDI has also gotten close to the world average; the gap lowered to 0.013 index points in 2014 (UNDP, 2015a).

The second part of the post uses the WGI framework published by the World Bank annually since 2002. The bank developed this six-dimensional framework to assess (I) the process of government selection, monitoring and replacement, (II) the government’s capacity in policy making and implementation as well as (III) the environment for social and economic interaction. Each of these areas includes two governance measures shown in the diagrams below for the case of Botswana (Kaufmann, Kraay, & Mastruzzi, 2010).

Botswana 5
(Source: World Bank, 2015)

In the first area the two indicators are political stability and voice and accountability. Political stability has been extraordinarily high over the complete period. Botswana’s percentile rank has been consistently over 80 since 2006, meaning that 80 percent of the countries included in the WGIs have ranked below Botswana in this dimension in these years. This is due to the fact that the country’s multiparty democracy has been stable with elections every 5 years since 1965 and the success of the government to redistribute the gains from economic growth to the population at large (Todaro and Smith, 2012). Also relatively low corruption and a good human rights record contribute their share to political stability, making it one of the continent’s most stable nations (BBC, 2016). The most important threats to stability are regional dynamics and the high economic dependence on South Africa which can pose a transmission mechanism for economic or political crises. Also Botswana’s narrow economic base with high dependence on diamond tax revenues and the decline of the power of the long-term ruling political party might be potential sources of social instability in the future (Throup, 2011).

The second indicator voice and accountability has seen a fall over the period from 2004 to 2011. This has been the long-term trend despite a 2011 score that was highest of all South African Development Community (SADC) countries, indicating freedom of expression, freedom of association and free media. The downward sloping trend is attributed to the lack of legislation granting freedom of information or ensuring public access to information (OECD, 2014). A Freedom of Information Act was brought on its way in 2010 to improve access to public information but in August 2012 the ruling party opposed this bill so that public access to information remains a concern in Botswana. There have been more incidences of harassment and censorship lately and the government’s press relations have deteriorated since 2008 under the new president Ian Khama. Furthermore state-owned outlets (broadcast media distribution) as well as the state-owned largest newspaper Daily News, which is free to readers and is the only newspaper that covers rural areas, dominate the media market (Freedom House, 2016).

Botswana 6
(Source: World Bank, 2015)

In the second area the indicators are government effectiveness and regulatory quality. The former’s percentile rank has deteriorated since 2007 to 64.9 in 2014. The latter increased over the period from 2008 to 2012 but fell slightly thereafter. In general, government effectiveness has benefited from long-term planning and the government’s sound vision for the economy’s future since independence. The government has targeted broad economic development benefiting the society at large (Todaro and Smith, 2012). Government effectiveness has also been ensured, for example, through the Sustainable Budget Index, which is an implicit self-disciplinary rule that requires the government to invest mineral revenues in development and health and education (Iimi, 2006). These observations can explain why both indicators are still relatively high. On the other hand, the downward trend in government effectiveness is attributed to the deterioration of the quality of public services, of the civil service, of government policies and implementation capacity (Clausen, Deléchat, and Geartner, 2008). UNDP also points out that the public sector of Botswana has been relatively large in terms of employment and its share in the country’s GDP which in turn has created more opportunities for low effectiveness and inefficiency in the 21st century. The decline can therefore be associated with some wastage of resources in the government systems. Overall, however, UNDP (2009) sees the government’s capacity in policy making and implementation, e.g. poverty reduction and environmental programmes and HIV/AIDS projects, as solid and relevant after the turn of the century. Overall government effectiveness will be a key determinant of Botswana’s institutional strength in the future. The government’s effectiveness is currently below the standards of upper-middle income countries which Botswana is part of. It will need to improve its delivery of social outcomes (health and education) to catch up (Moody’s, 2015).

Botswana 7
(Source: World Bank, 2015)

In the third area the two indicators are control of corruption and rule of law. Both indicators are requisites for markets to function properly and to shape incentives for actors to engage in economic activities. They also allow society to function smoothly. Both indicators have been relatively high over the complete period from 2000. In 2014, more than 70 percent of the countries ranked below Botswana in terms of controlling corruption and the rule of law. However, the former has seen a decline from 2013 to 2014 while the latter improved over that time. Overall, the indicators have remained relatively stable, positioning Botswana well above the average. Botswana has also long been the top performer in the rule of law compared to the other African countries (The Economist, 2015). One of the current legal issues is access to justice for minorities infected or affected by HIV/AIDS as legislation is not currently promoting human rights in these regards (UNDP, 2013).

In summary, the post looked at both Botswana’s economic development with the use of the HDI and the country’s institutional development in the 21st century, supplemented by World Bank statistics. Botswana has made enormous progress in terms of economic development, especially in national incomes and health (life expectancy). The 2008/09 recession did have a significant impact on the country’s development though. Its income and education outcomes for example took a hit. However, the country’s HDI still grew rapidly over the period from 2000 to 2010 as a whole after negative growth in the decade before. It also grew moderately after 2010. Overall, Botswana caught up and lowered the gap between its HDI and the world average remarkably by 2014. In terms of economic development Botswana therefore arguably has continued to be a success story. In terms of the country’s institutional development, it can be noted that it has ranked above average in all 6 dimensions even at the beginning of the century but one could observe deterioration of conditions in some areas since the 2000s. Key areas of concern are now voice and accountability with the need for freedom of information as well as a return to higher government effectiveness. Also the most recent drop in the control of corruption needs to be monitored closely. Progress has been made in the rule of law in international comparison recently and in regulatory quality from 2008 to 2012. Botswana’s strongest area remains its political stability. Ultimately, high long-term stability has enabled Botswana’s progress after independence in 1965 and it continues to be the driver, for example, for foreign investment as investors seek high returns coupled with low risk and politically stable economies. This has been and continues to be Botswana’s key to success. Botswana faces more challenges than I could address here but it does provide a short overview on why the country’s success story continues.

Thanks for reading!


BBC (2016, 1 February). Botswana country profile [online]. BBC News. Retrieved from:

Clausen, J., Deléchat, C., and Geartner, M. (2008). Botswana: Selected Issues (IMF Country Report No. 08/57). Washington, D.C.: International Monetary Fund.

Freedom House (2016). Botswana: Freedom of the Press 2013. Retrieved from:

Iimi, A. (2006). Did Botswana Excape from the Resource Curse? (IMF Working Paper 06/138) Washington, D.C.: International Monetary Fund.

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The Worldwide Governance Indicators: Methodology and Analytical Issues. Washington, D.C.: Brookings Institute. Retrieved from:

Moody’s (2015, 11 December). Moody’s affirms Botswana’s A2 government bond rating; outlook stable. Retrieved from:–PR_341051

OECD (2014). OECD Investment Policy Reviews: Botswana 2014. Paris: OECD Publishing. Retrieved from:

Todaro, M.P., and Smith, S.C. (2012). Economic Development (11th ed.). Boston, M.A.: Addison-Wesley.

UNDP (2009). Assessment of Development Results: Evaluation of UNDP Contribution Botswana [pdf]. New York, N.Y.: United Nations Development Programme. Retrieved from:

UNDP (2013). Rule of Law and Access to Justice in Eastern and Southern Africa: Showcasing Innovations and Good Practices [pdf]. Addis Ababa: UNDP Regional Service Centre for Africa. Retrieved from:

UNDP (2015a). 2015 Human Development Statistical Tables [Data file]. Retrieved from:

UNDP (2015b). Human Development Report 2015: Briefing Note Botswana [pdf]. Retrieved from:

UNDP (2016). Botswana Human Development Indicators. Retrieved from:

The Economist (2015, 3 June). African governance: Law comes first – Botswana is rated best on the continent in rule of law and governance, again [online]. The Economist. Retrieved from:

Throup, D. (2011). Botswana: Assessing Risks to Stability. Washington, D.C.: Center for Strategic International Studies. Retrieved from:

World Bank (2015). World Governance Indicators [Data]. Retrieved from World Governance Indicators (WGI) database:

World Bank (2016a). World Development Indicators: Botswana [Data file]. Retrieved from:

World Bank (2016b). World Development Indicators [Data]. Retrieved from World Development Indicators (WDI) database:

Urbanization vs GDP per Capita revisited

Yesterday I looked at urbanization vs income per capita across Asian countries. Today I want to get into more detail and extend my analysis to the regions of America, Europe, Africa and Asia (revisited). The data comes from the World Development Indicators database and I have included my cleaned-up dataset at the end of the post. I have chosen GDP per capita in PPP at constant 2011 international dollar and urban population as a percentage of total population. For the year 2014 the indicators are available for 179 countries.

Urbanization is an indicator for structural transformation because the relative decline of the agricultural sector compared to the industrial and services sector implies a substantial migration of labour from rural to urban areas in a country (Perkins et al., 2013). Based on this, there should be a positive correlation between a country’s level of urbanization and its standard of living. This is of interest to policy makers because a high urban population rate despite low GDP per capita is a hint towards inefficiencies in for example the labour market or urban policies. As there is enough urban labour available for expanding the industrial and services economy there must be a weakening in the link of recruitment of rural labour into the cities and their productivity in the urban sector thereafter.

urban 1.png

The first diagram summarizes the association between urbanization and GDP per capita for the entire dataset. Thereby the y-axis is measured on a log2 scale in order to transform the correlation between GDP per capita and urbanization into a linear trend. Overall it can be inferred that a rise in urban population tends to be accompanied by an increase in income per capita in the dataset. In general, urbanization ranges from just unter 10 percent (Trinidad and Tobago) to 100 percent (Hong Kong, Macau and Singapore). Meanwhile GDP per capita ranges from 567 Dollar (Central African Republic) to 134,182 Dollar (Qatar). I have also chosen to give certain datapoints as an example. In global comparison it can be observed that outliers tend to be developing countries, small island countries (Trinidad and Tobago), city-states (Singapore, Hong Kong and Macau) and oil-rich countries such as Qatar. Furthermore conflict and violence seem to weaken the transmission link between urbanization and a country’s standard of living (Congo, Palestine). It can be concluded that countries suffering from negative externalities are signficantly below the trend line whereas countries suffering from positive environmental influences or other factors are significantly above the trend line.

urban 2

The second diagram looks at the North and South American continent in depth. The United States and Canada are major positive outliers in regional comparison. The remainder is relatively closely scattered around the trend line. The sub-regional trend for South America is somewhat flatter where high urban population rates have not resulted in standards of living comparable to high income countries. Uruguay is the best example in these regards with an urban population of around 95 percent but a GDP per capita of only circa 19,924 Dollar. This observation for the complete South American region might indicate regional conflicts that weaken the transmission mechanism between migration to cities and increases in GDP per capita.

urban 3

The next continent I want to look at is Europe. The lowest European GDP per capita in the dataset has Moldova at 4,754 Dollar with an urban population of almost 45 percent. The highest GDP per capita can be found in Luxembourg at around 91,408 Dollar. The lowest urban population share has Bosnia and Herzegovina at just under 40 percent whereas the highest urban population share of almost 98 percent can be found in Belgium. There are considerable regional differences across the continent. Western and Northern Europe are closely scattered around the trend on the right whereas Eastern European countries are more loosely scattered around the trend line on the left. Southern Europe remains diverse in terms of urbanization rates. It can be seen that outliers tend to occur in sections of lower GDP per capita below the trend line (Moldova and Ukraine). Especially  Ukraine serves as an example of conflict offsetting the link between urbanization and increases in standard of living.

urban 4

The next regional diagram deals with Africa. For simplicity, only Western African countries are labelled as I want to focus on the significant lower GDP per capita in this region at the same urban population shares compared to the rest of Africa, especially Southern Africa (yellow trend line). Here again, the considerable regional differences suggest that there are regional conflicts or other negative environmental influences regarding West Africa and positive external factors for Southern Africa shifting its trend line significantly upwards. The lowest GDP per capita in this region has the Central African Republic (also the global minimum as mentioned before). The highest GDP per capita has Equatorial Guinea at around 33,142 Dollar. What is more, the minimum and maximum regional GDP per capita occur both in Central Africa. This suggests that there are considerable differences in Central Africa causing this divergence. Equatorial Guinea and Central African Republic have an identical urban population share of 39.8 percent. Hence the gap in their standard of living is likely influenced by country-specific and not regional-specific factors. The lowest share in urban population has Burundi at around 12 percent. This is the second lowest global rate after Trinidad and Tobago (Caribbean). Gabon has the highest African urban population of circa 87 percent.

urban 5

Finally, I want to revisit my analysis of urbanization and GDP per capita across Asian countries with an improved diagram. The smallest urban population have Sri Lanka and Nepal (only 18 percent). In comparison the regional maximum can be found in Singapore, Macao and Hong Kong of 100 percent due to their city-state status. However, they are closely followed by the countries Qatar (99), Kuwait (98), Japan (93) Israel (92). The most densely urban populations are found in Western Asia (yellow) and Eastern Asia (red). The smallest urban populations are in the areas of Southern (green) and Central Asia (lilac). Southeastern Asia is more diverse in terms of urban population shares. However, Acemoglu, Johnson and Robinson (2001) point out that South-East Asia in fact has a history of high population density and urbanization but that intervention of Europe has reversed this to a certain extent. This might explain the diversity of urbanization rates in Southeastern Asia. Also, Southeastern Asia’s neigbouring regions may have some influence, i.e. mainly urban from Eastern and mainly rural from Southern Asia.  In Asia the lowest GDP per capita has Afghanistan of only 1,844 Dollar. Given its urban population share of 26 percent it is below the trend line. Again, this is an indicator for conflict and negative environmental factors dampening the correlation between urbanization and GDP per capita. The highest GDP per capita has Qatar at 134182 Dollar followed by Macau (133,341 Dollar). These are also the highest GDP per capita in global comparison. Macau’s GDP for example is equivalent to 754 percent of the world’s average (Tradingeconomics, 2016). Finally, it can be seen that Asia has the greatest divergence in urbanization rates and GDP per capita compared to America, Europe and Africa. This, certainly, is a major challenge for the Asian continent.

Thanks for reading and feel free to comment my analysis!


My dataset can be found here: Urbanisation vs GDP per capita WDI 2014

Acemoglu, D., Johnson, S., and Robinson, J.A. (2001). Reversal Of Fortune: Geography And Institutions In The Making Of The Modern World Income Distribution, Quarterly Journal of Economics, 2002, v107(4,Nov), pp.1231-1294.

Perkins, D.H., Radelet, S., Lindauer, D.L., and Block, S.A. (2013). Economics of Development, Seventh Edition. New York, NY: W.W. Norton & Company.

Tradingeconomics (2016). Macau: GDP per Capita PPP. [online ] Available at: [Accessed 11/04/2016].

World Bank (2016). World Development Indicators. [Data] Retrieved from World Development Indicators (WDI) database: [Accessed 11/04/2016].

Urbanization vs Income per Capita in Asian Countries

A country that undergoes structural change will face greater urbanisation rates, i.e. migration to cities, and a relative shift in the composition of the economy towards the secondary and tertiary sector. That’s why I analyse urbanisation across Asian countries today. In particular, I have plotted urban population as percentage of total population against GDP per capita at PPP (in constant 2011 international $) for the year 2014 for all Asian countries for which data is available in the World Development Indicators database.

Urbanisation GDP.png

The diagram confirms that there is a positive correlation between urbanization and income per capita. Hence economic growth which leads to higher incomes tends to go together with an urbanisation of the country’s population.

Secondly, one can see that there is a geographical split across Asia. The trend lines (logarithmic) of Central, Southern and Western Asia have a similar flatter slope and the trend lines of Eastern and Southeastern Asia have a similar steeper slope. This means that a 1 percent increase in urbanisation of the total population seems to increase GDP per capita by a grater amount in Eastern and Southeastern Asia than in Central, Southern and Western Asia. This gap might be explained the observation that many Asian countries “have struggled to make the most of the opportunity urbanization provides them to transform their economies to join the ranks of richer nations in both prosperity and livability” (World Bank, 2015).

There are two outliers that differ greatly from the average. Firstly, Sri Lanka has a considerably lower urban population than its GDP per capita level would suggest. Secondly, Palestine (called West Bank and Gaza in the WDI database) has a considerably higher urban population compared to what its GDP per capita level would predict. In these two examples there might well be other factors that distort the normal relationship between urbanisation and GDP per capita such as conflict or adverse regulations and policies.

Lastly, the graph shows a clear difference in the overall performance of regions. Southern Asia and Central Asia are at the lower end of the spectrum in terms of urbanisation and income levels. Southeastern Asia is very diverse in economic development und urbanisation outcomes. Eastern Asia and Western Asia are at the higher end of the spectrum. Most notably, the trend in Eastern Asia is driven by Japan, Hong Kong and Macau (measured separately from China). Western Asia’s performance is driven by the oil rich countries such as United Arab Emirates, Saudi Arabia and Qatar.

That’s me for today! Thanks for reading!


World Bank (2015). Leveraging Urbanization in Sri Lanka. [Online] Available at: [Accessed 11/04/2016].

World Bank (2016). World Development Indicators. [Data] Retrieved from World Development Indicators (WDI) database: [Accessed 11/04/2016].

The Lewis Labour Surplus Model and Agricultural Productivity Growth

Today I revised for my Growth and Development Economics test next week. One of the topics covered is Theories of Economic Growth and Development. It includes the Basic Growth Model, the Harrod-Domar Growth Model, Solow Growth Model, Lewis Labour Surplus Model and the Neoclassical Two-Sector Model. Fortunately, the Harris-Todaro Model will not be tested in the midterm; only in the final exam. The main textbook for the class is Economics of Development by Perkins, Radelet, Lindauer and Block (2013) and the reading for this section is Chapter 4 and Chapter 16 in case you want to have a look! During revision of the Lewis Labour Surplus Model today I recognised that the textbook mentions population growth and a rise in agricultural productivity as two comparative statics exercises. However, Perkins et al. only show the impact of an increase in population in the Lewis Labour Surplus Model on page 596. So I thought that I’d like to think through the rise in agricultural productivity myself and attempted to replicate an appropriate model!

Let’s start with the agricultural sector. Its output can be derived from the Agricultural Production Function with the only two inputs agricultural labour and land, as well as a measure for agricultural productivity A. It is the usual Cobb-Douglas Production Function with diminishing returns to scale.

Agricultural production function 1.png Agricultural production function 2

In particular, there is a perfectly elastic segment where an additional worker does not add any extra output (food) to total agricultural output. In this segment the marginal product (MP) of agricultural labour must therefore be equal to zero. Also note that we have a decreasing quantity of labour in the agricultural sector as we move from the left to the right and an increasing quantity of labour in the urban sector. This is because we have only these two sectors in the model which employ the total workforce of the economy. So let’s now introduce our rise in agricultural productivity. Mathematically A increases to A’, where A’ > A. Graphically this means that the Agricultural Production Function (panel a) shifts upward. This will increase (1) total agricultural output and (2) the length of the segment at which the marginal product of agricultural labour is equal to zero. In short: a rise in agricultural productivity increases the amount of food produced and reduces the amount of agricultural labour needed to produce the maximum capacity.

Let’s move on to the rural labour market (panel b). In the standard Lewis Labour Surplus Model there is the so-called subsistence wage or minimum wage which is equal to the average product (AP) of agricultural labour.  Hence rural wages are institutionally fixed and not determined in the market as long as there is a labour surplus in the rural sector. So what effect has the increase in agricultural productivity on the rural labour market?

Agricultural production function 3Agricultural production function 4There are two factors at work. Firstly, the average product of agricultural labour increases from AP to AP’. This is intuitive because, on average, the same amount of agricultural labour can now produce more. This drives up the subsistence wage and thereby reduces rural poverty. Secondly, the marginal product will increase because we need less agricultural labour for maximum food output. So each of the now fewer workers in the agricultural sector will increase output by more than before. This is also rather intuitive – despite the maths – because e.g. labour-saving technology such as large tractors makes each of the fewer workers more productive. In panel b this is depicted by the pivot and shift of the MP curve to MP’. MP’ is now flat up until g’ and is steeper than MP. Another important aspect here is the Lewis turning point, i.e. the point where rural wages equal the average product (subsistence wages) and marginal product of labour. To the left of this point rural wages are institutionally fixed; to the right of it wages are determined in the market and equal to the marginal product of agricultural labour. The agricultural productivity increase postpones the Lewis turning point at a higher overall subsistence wage.

One key observation here is that the agricultural productivity increase drives up the amount of surplus labour from g to g’ in panel b, too. This means that a country can shift more labour to the urban sector which unambiguously increases total GDP (due to longer constant agricultural output and increasing industrial output). Furthermore this structural change happens in an environment of less rural poverty which is desirable from a policy makers point of view.

The third panel shows the urban labour market. The labour supply curve is actually taken from the labour supply curve facing the industrial sector in panel b (Perkins et al., 2013). This is just the supply curve of panel b shifted upwards by an amount equal to the rural subsistence wage plus a premium for the costs of migration to the urban sector. Hence the supply curve in the urban sector is congruent to the supply curve in the rural sector. This is a necessary condition for the model to work because otherwise people would have an incentive to migrate. In particular, in the segment where rural surplus labour exists, subsistence wages (AP of agricultural labour) plus the costs of migration will equal urban wages and in the remaining part, the MP of agricultural labour will equal urban wages.

What is the effect of the agricultural productivity increase on the urban labour market? It will actually increase urban wages by the same amount as rural subsistence wages increase. This is quite intuitive because the increase in rural wages must be transmitted into the urban labour market to keep urban labour from migrating to the rural sector. Because the supply curves of panel b and c are congruent the marginal product of urban labour will shift and pivot in the same fashion as in the rural sector once rural labour surplus is exhausted.

Untitled Diagram (1)

In conclusion, the agricultural productivity increase drives up both rural and urban incomes. In addition, there is more surplus labour available to industrial employers (i.e. longer unambiguous GDP growth until g’) but once the pool is exhausted employers face a more inelastic urban labour supply curve and must increase wages by a greater amount to hire the same amount of additional workers compared to before. This is essentially caused by the steeper marginal productivity of agricultural labour. This faster wager growth will make it more costly for employers to hire workers. On the other hand firms might be able to offset these costs by increasing industrial productivity. Also, higher wages will mean that more income available to spend. As rural and urban workers become more wealthy, the proportion of income spent on food will fall (Engel’s law) and more income will be left over for other things. One might then argue that this could increase the demand for goods in the industrial sector. This could offset the increase in wages. However, more plausible to me is that industrial employers will seek to introduce productivity-enhancing and labour-saving technology to offset the wage increases. More on wages and productivity can be found in Gregor Mankiw’s blog post on How are wages and productivity related? (2006) where he explains why real wages and productivity should line up in theory but might actually not line up in the data.

I hope you enjoyed today’s post. Feel free to comment on my analysis and thanks for reading!


Mankiw, G. (2006). How are wages and productivity related? [online] Available at: [Accessed 10/04/2016].

Perkins, D.H., Radelet, S., Lindauer, D.L., and Block, S.A. (2013). Economics of Development, Seventh Edition. New York, NY: W.W. Norton & Company.