23 September 2015 | Understanding Economic Transformation in Africa

Overseas Development Institute, London

Where do we go wrong, and what do we need to do differently to understand economic transformation in Africa?

Morten Jerven’s recent book “Africa, why economists get it wrong” questions what we really know about growth and economic transformation in Africa. He argues that mainstream economists have not used appropriate methodologies or sample periods, used data without critically assessing them, and focused on the wrong policies.

This event organised by ODI’s Supporting Economic Transformation (SET) programme discussed where we are going wrong and what should we be doing differently if we want to properly understand the prospects of economic transformation in low income countries. What are appropriate research methodologies, what data can be used, and what do we know about policies and institutions for economic transformation?

After an introduction to the main points in Jerven’s book, a number of speakers discussed the main questions: Blandina Kilama is an expert on economic transformation in Tanzania, Nick Crafts is a world leading authority on economic history, and Louise Fox is a leading voice on employment and labour markets in Africa.

Chair:

Dirk Willem te Velde – Director, Supporting Economic Transformation, ODI

Speakers:

Morten Jerven – Associate Professor in Global Change and International Relations, Norwegian University of Life Sciences; Associate Professor, School for International Studies, Simon Fraser University

Blandina Kilama – Senior Researcher, REPOA, Tanzania

Nick Crafts – Professor of Economics and Economic History at the University of Warwick

Louise Fox – Visiting Professor, University of California, Berkeley

Downloads:

Morten Jerven Event Report

Understanding Eco Transformation- Blandina Kilama

Jerven’s book on African growth in 10 points Dirk Willem te Velde

Comments on Jerven and Economists in the Tropics Louise Fox

Africa-why economists get it wrong-Jerven-ODI 2015

 

Photo credit: UNU-WIDER

Pedro Martins (UNECA) | Structural change: concepts, data and methodologies

Structural change is back in fashion. After a promising start in the mid-20th century – owing to the seminal works of Allan Fisher, Colin Clark, Simon Kuznets and Hollis Chenery – the topic was subsequently relegated to obscurity during the years of structural adjustment (a rather different concept!). It remained sidelined in the 2000s, when the attention of the research and policy communities was mainly devoted to the Millennium Development Goals and their focus on social outcomes.

15 September 2015.

Structural change is back in fashion. After a promising start in the mid-20th century – owing to the seminal works of Allan Fisher, Colin Clark, Simon Kuznets and Hollis Chenery – the topic was subsequently relegated to obscurity during the years of structural adjustment (a rather different concept!). It remained sidelined in the 2000s, when the attention of the research and policy communities was mainly devoted to the Millennium Development Goals and their focus on social outcomes.

Over the past five years, however, there has been a renewed interest in the study of structural change – partly due to concerns that recent growth patterns in developing countries are neither inclusive nor sustainable. The work of McMillan and Rodrik in 2011 did much to reignite the academic and policy debates. Structural change has also become part of the political lexicon and is increasingly captured in national and regional vision statements – for example, the African Union’s Agenda 2063 – as well as international policy agendas such as the forthcoming Sustainable Development Goals.

I have contributed to the topic with a comprehensive study assessing trends at the sub-regional level and a paper on Ethiopia. In this post, I provide a brief overview of the key concepts, data and methodologies that have been used in recent empirical studies.

Concepts

There is no universally agreed definition of structural change. In fact, many economists also refer to ‘structural transformation’, using the terms interchangeably. Judging by the way economists have tended to utilise the concept in practice, we can categorise existing perspectives into three broad groups: (i) ultra-narrow (production) focus, (ii) narrow (productivity) focus, and (iii) broad (socioeconomic) focus.

The first group assesses structural change merely in terms of shifts in the structure of production. Structural change happens when the economy shifts towards the production of goods and services associated with higher value added, which in turn stimulates economic growth. This usually entails a reduction in the weight of the agricultural sector in total output, and a concomitant increase in the share of industry and/or services. It is implicitly assumed that the market will automatically and efficiently facilitate any required reallocation of resources across sectors (e.g. capital, labour and land).

The second group evaluates structural change in terms of labour shifts from low-productivity sectors to higher-productivity sectors. This relocation of labour raises workers’ productivity, which contributes to accelerated economic growth. While the same sectoral patterns are expected, the explicit focus is on labour productivity rather than production alone. This stems from the observation that changes in the structure of employment often lag behind shifts in production. Many of the contemporary empirical studies fall in this category – such as McMillan, Rodrik and Verduzco-Gallo (2014), Roncolato and Kucera (2014), and de Vries, Timmer and de Vries (2015).

The third group goes beyond changes in the economic structure – such as production and employment – by also measuring changes in other aspects of society. For instance, structural change may entail a demographic transition (through lower fertility rates), changes in labour participation (through changing social preferences), and a spatial reorganisation of the population (through rural-urban migration). Given the interlinked nature of the process of structural change, it can be useful to consider as many dimensions as possible when conducting an empirical assessment. This is the approach followed in Martins (2014) and Martins (2015).

Data

The recent emphasis on structural change has led to a rapidly expanding body of theoretical and empirical work. Datasets with varying degrees of sectoral disaggregation and country coverage have been compiled. However, existing data sources present trade-offs that ought to be considered. I argue that the choice of data will depend on the purpose of the study – namely, that international sources are useful to carry out research at the regional and sub-regional levels, while national sources are better suited for country-level assessments.

International sources have the benefit of providing harmonised (and thus internationally consistent) data for a large number of countries. For instance, the International Labour Organization (ILO) ensures that the employment data it publishes are consistent with its definitions of employment and working-age population. The United Nations Department of Economic and Social Affairs (DESA) ensures that the output data reported by member countries are published in accordance with the System of National Accounts – an internationally agreed standard for the compilation of economic activity measures. These secondary sources provide output and employment data by sector with a high degree of comparability across countries. Moreover, their extensive country coverage enables highly representative assessments of structural change at the regional and sub-regional levels.

However, in-depth country-level assessments should be based on the raw data produced by national sources. This allows a greater focus on internal consistency and more flexibility when conducting the assessment. For instance, international sources often apply modelling procedures to fill data gaps in order to facilitate country comparisons – e.g. ILO’s Global Employment Trends (GET) database and the Groningen Growth and Development Centre (GGDC) database. However, these gains in comparability might come at the cost of distortions introduced by the modelling (or even harmonisation) procedures. This may not matter much when assessing aggregate trends – as biases are likely to partially cancel each other out – but may affect conclusions at the country level. In addition, international sources often rely on a subset of available data sources – e.g. labour force surveys and/or population censuses – which could be complemented by other sources. These factors may account for some of the discrepancies in the results for Ethiopia (see table below). Greater scrutiny of (all) available data sources – including a deeper assessment of data quality – and the ability to tailor the analysis to a country’s policy needs, in terms of both sectoral emphasis and time horizons, are paramount in producing more precise, relevant and up-to-date estimates on the pace of structural change.

Table: Comparison of results for Ethiopia

StudyPeriodOverall growthCompound annual growth rate (%)
Output

per worker

growth (%)

Contribution from (%):Output

per worker

growth

Contribution from:
Within

sectors

Between

sectors

Within

sectors

Between

sectors

Martins (2014) *1999-20051787142.72.30.4
2005-20136576246.54.91.6
de Vries et al. (2015)2000-20105561394.52.71.7
McMillan et al. (2014)1990-20053221791.90.41.5
McMillan & Harttgen (2014)2000-2005119912.12.10.0

* The results have been updated by using the 2013 labour force survey.

There is also the issue of sectoral classification, which relates to the International Standard Industrial Classification of All Economic Activities (ISIC). Many countries are in the process of moving from ISIC revision 3.1 to ISIC revision 4, which creates a break in temporal comparability, since full correspondence is not possible between the two revisions. Only a meticulous country-specific investigation of the raw data can ensure that potential inconsistencies are minimised. This is particularly relevant for employment estimates.

Methodologies

Most studies on structural change are centred on the decomposition of labour productivity growth, which is typically measured by output per worker. This enables an assessment of the extent to which aggregate labour productivity has been driven by labour shifts across sectors vis-à-vis improvements within sectors – the latter being possible though skills upgrading, complementary capital investments, and/or increased organisational efficiency.

An alternative approach is to decompose output per capita growth rather than output per worker growth. This strategy enables an empirical assessment that is compatible with a broader view of structural change. In addition to evaluating the role of within-sector and between-sector productivity improvements, we are also able to assess the contributions of demographic change and the employment rate to economic growth. For instance, lower dependency ratios can generate a sizeable demographic dividend, while changing social preferences can – through economic inactivity – impact on employment rates, which in turn affect economic growth. Hence, this approach captures shifts in the structure of production, the structure of employment, the level of employment, and the size of the working-age population. In Ethiopia, demographic change accounted for about 10% of output per capita growth in 2005-2011, while a declining employment rate had a negative impact on economic performance. However, the latter was mainly due to young people staying longer in education, which is a positive development – especially if young people acquire skills that can boost labour productivity in the near future. In fact, these two dimensions are intrinsically connected. In order to benefit from a sizeable demographic dividend, African countries will have to scale up investments in human capital – as many Asian and Latin American countries have done in the past.

Conclusion

The recent proliferation of studies in this field has contributed to a better understanding of the pace and patterns of structural change in developing countries. Nonetheless, the findings emerging from the literature are sometimes ambiguous, owing to the use of different data sources, country samples, time frames, levels of sectoral aggregation, empirical methodologies, etc. This blog does not assess these discrepancies but provides a few thoughts on how to address some of the tensions arising from the different possible purposes of empirical studies in the structural change tradition.

 

Key references:

de Vries, Timmer and de Vries (2015) ‘Structural Transformation in Africa: Static Gains, Dynamic Losses’, Journal of Development Studies 51(6): 674–688.

Martins (2015) Sub-Regional Perspectives on Structural Change. CREDIT Research Paper 15/03, University of Nottingham.

Martins (2014) Structural Change in Ethiopia: An Employment Perspective. Policy Research Working Paper, WPS 6749. Washington, DC: World Bank Group.

McMillan and Harttgen (2014) What is Driving the African Growth Miracle? National Bureau of Economic Research (NBER) Working Paper No. 20077.

McMillan and Rodrik (2011) Globalization, Structural Change and Productivity Growth. NBER Working Paper No. 17143.

McMillan, Rodrik and Verduzco-Gallo (2014) ‘Globalization, Structural Change, and Productivity Growth, with an Update on Africa’, World Development 63: 11-32.

Roncolato and Kucera (2014) ‘Structural Drivers of Productivity and Employment Growth: A Decomposition Analysis for 81 Countries’, Cambridge Journal of Economics 38(2): 399-424.

Trade Policy and Economic Transformation

Marie-Agnes Jouanjean, Max Mendez-Parra and Dirk Willem te Velde, July 2015.
Trade has historically played a crucial role in the debate on economic transformation (ET), but the transmission mechanisms of different types of trade policies have not always been clearly articulated and empirical evidence is lacking in specific areas.

Marie-Agnes Jouanjean, Max Mendez-Parra and Dirk Willem te Velde, July 2015

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Trade has historically played a crucial role in the debate on economic transformation (ET), but the transmission mechanisms of different types of trade policies have not always been clearly articulated and empirical evidence is lacking in specific areas.

ET involves moving resources between sectors (for example, from agriculture to manufacturing); and improving productivity within sectors (for example, from subsistence agriculture to high-value crops), including through firm entry and exit, as well as within firms.

ET and trade are intimately linked through, for example: diversification of production and trade; discovery and development of new productive capabilities through trade; and creation higher domestic value added in trade.

Whilst there has been a firm belief in a strong link between trade and transforming productive structures, it remains too ambitious to identify a unique and unambiguous link between trade policy, trade, and economic structures. Import substitution strategies were used during the 1960s and 1970s to move resources from, typically, natural-resource-based sectors to manufactures. In the late 1980s and early 1990s, trade liberalisation was one of the most important policy tools under the so-called Washington Consensus.

This briefing discusses the effects of trade (related) policies and how trade policy affects ET through multiple channels: allocative efficiency, market size, competition, access to inputs and backward linkages, forward linkages, skills and technology, and political economy.

The briefing identifies several areas that require empirical work: (i) empirical evidence on the way in which trade affects the within and between components of productivity change and ET; (ii) building up more evidence on what drives exports of services, incl. the role of trade policy played in this; and (iii) the effects of different forms of global and regional value chains on ET.

Steve Wiggins (ODI) | Tracking agricultural transformation – if measuring productivity is hard, should we focus on rural wages?

As low income countries (LIC) grow and transform their economies, agriculture plays a key role. It has to raise production to feed increasing numbers living in towns, as well as to provide raw materials to domestic manufacturing — cotton for textiles, hides for shoemakers, palm oil for biscuit and cake makers, etc. For LICs lacking oil, gas, minerals, and substantial manufacturing, agriculture will probably be the largest source of exports to finance imported capital goods. With populations still largely rural, it helps domestic manufacturers if agricultural incomes rise since this expands the domestic market. Last, and certainly not least, agriculture has to free up labour for manufacturing and services — and, depending on the effectiveness of the financial system in rural areas — it may also transfer capital to other sectors.

30 July 2015.

As low income countries (LIC) grow and transform their economies, agriculture plays a key role. It has to raise production to feed increasing numbers living in towns, as well as to provide raw materials to domestic manufacturing — cotton for textiles, hides for shoemakers, palm oil for biscuit and cake makers, etc. For LICs lacking oil, gas, minerals, and substantial manufacturing, agriculture will probably be the largest source of exports to finance imported capital goods. With populations still largely rural, it helps domestic manufacturers if agricultural incomes rise since this expands the domestic market. Last, and certainly not least, agriculture has to free up labour for manufacturing and services — and, depending on the effectiveness of the financial system in rural areas — it may also transfer capital to other sectors.

These functions of agriculture in development, first set out by Johnston & Mellor in 1961, represent a stiff challenge for farming: more bricks from less straw. Yet it is a challenge that was met handsomely during the green revolutions of Asia that began in the late 1960s. Success is marked by:

  • Increased productivity of agriculture, above all of labour and land. This is the only way to increase production, raise farm incomes, and allow labour (and capital) to be released;
  • Labour moving out of agriculture into other work. Most obviously this can be seen in migration to towns and cities. But labour does not necessarily leave the village or household: instead, members of farm households increasingly spend their time on non-farm jobs. These jobs may be carried out at home, elsewhere in the village, or even by commuting to a nearby towns; and,
  • Increasingly active factor markets in rural areas — for seed, fertiliser, chemicals, veterinary drugs, animal feed, irrigation equipment, machinery; technical services; transport; savings, insurance, payments and credit; labour; and land.

Tracking these changes is not easy. Since rising productivity is so important, we would like to be able to make reasonably reliable estimates of agricultural productivity, ideally total factor productivity, but at least partial productivity of land and labour. Trying to measure both outputs and inputs in low-income countries is, however, difficult, as Carletto et al. 2015 explain. Problems abound:

  • Some agricultural output is consumed at home and never formally weighed and logged. Some crops, such as banana and cassava, are often harvested over extended periods, making it difficult to estimate by recall how much in total has been produced. When farmers do count their output, it is often in bags, bunches and bundles: measures that may be only roughly consistent in practice, the definition of which may change from district to district.
  • Small farmers tend to overstate their holdings, large farmers to understate theirs. Cultivable areas on sloping land may be exaggerated — it’s the horizontal plane that matters, not the sloping hypotenuse. Recording area to planted to particular crops can be complicated by intercropping.
  • Labour use estimates can only be rough estimates, when so much farm work involves a few hours at a time on a given field or in attending livestock, through production cycles that last for months.
  • When it comes to inputs, few farmers log their use of seed, fertiliser and chemicals. Capital costs of tools and their maintenance are difficult to capture, as are those of draught livestock services.
  • Most surveys rely on farmer recall which may vary in accuracy, and of course depends on farmers being prepared to reveal what they know. When farmers fear taxes, land redistribution, or exclusion from some social protection or development programme for not being sufficiently poor; or just do not wish their neighbours to become jealous; there are incentives to under-report on all counts. The alternative to recall is to measure: surveying land sizes, taking samples from fields for crop cutting at harvest time, etc. The cost of this, however, is usually prohibitive.
  • Moreover, agriculture varies from year to year as weather, pests and diseases, human health, conditions in markets and so on, mean that production and inputs may fluctuate significantly over time. Hence a very careful survey may just capture an unusual year and not provide an accurate guide to more typical years.

The problems are near intractable. We are, after all, dealing with semi-subsistence, semi-commercial, small-scale and diversified family farming that depends considerably on variable natural and human environments — complex, diverse and risk-prone as Robert Chambers (1989) once described them. All those adjectives and qualifications make measurement difficult.

The Integrated Surveys on Agriculture (ISA) now being added to Living Standards Measurement Surveys (LSMS) surveys are a commendable step forward, but they still rely overwhelmingly on farmers recalling a mass of detail for their plots and livestock, and so will remain subject to most of the shortcomings outlined. Indeed, the main (sole?) technical innovation being offered under ISA seems to be GPS mapping of field boundaries for more accurate assessment of land sizes.

All in all, estimating agricultural productivity is difficult. Even getting simple measures such as yield per hectare or gross value of production per worker is fraught with problems, let alone the valiant but surely vain attempts to estimate total factor productivity that require even bigger guesses to be made about capital inputs.

Does this mean that perhaps we should look for other indicators of agricultural transformation? How about rural labour moving out of farming? This indicator would be powerful, but labour surveys are few and far between, while it is hard to assess time spent on different activities by people who work on a portfolio of activities. Many surveys just record principle occupation, sometimes also recording secondary activities, but rarely reporting the share of labour spent on different activities.

If not labour use, then how about activity in rural factor markets? Almost all of this, however, is difficult to measure and may be sensitive. Take land markets. Few things indicate rural structural change more than land changing hands. But most such changes are informal and unregistered: moreover those involved may be reluctant to reveal such changes especially in countries where formal laws try to govern land transactions, rents, or set ceilings for ownership.

But one aspect of rural factor markets can be observed and could be usefully informative: rural wages, especially those for unskilled work. It is a reasonable bet that when farm productivity rises, when the rural non-farm economy thrives, and when the urban economy flourishes — all conditions for economics transformation — then demand for labour will rise and so will wages. Where, then, unskilled rural wages are rising rapidly, it is very likely that agriculture, rural economies and the overall economy are being transformed. Contemporary Asia has several examples.

Of course, they are imperfect measure of productivity change and transformation. Rural wages could rise when agricultural productivity is stagnant, as people are pulled out of farming by the attraction of better-paid jobs being created where manufacturing is thriving. Wages may, moreover, be in part determined by imperfect markets — monopoly power of either employers or workers, friction in labour markets — as well as by non-economic factors — such as social expectations and public policies. These factors may be more important when looking at wage levels than when looking at changes to wages.

Rural wages may be the canary in the cage. Just as the sick canary cannot tell us what the gas is, nor where it comes from, the silent bird signals something important is afoot. We should track wages, perhaps by establishing sentinel sites to observe them in key locations. When they show significant moves, then check for what the causes may be.

2 July 2015 | Trade, Global Value Chains and Economic Transformation

Overseas Development Institute, London.

Economic transformation involves the movement of factors of production toward higher productivity and/or value addition firms or sectors. It has traditionally been assessed through the degree of export diversification, taken as an outcome of the process.

Trade can support this process, e.g. through its impact on firm competitiveness – access to cheaper and better quality inputs, and opportunity to take advantage of economies of scale. The literature on global value chains (GVCs) further suggests a new way of looking at economic transformation (which was traditionally seen as moving from agriculture to manufacturing and services). Integration in global production networks allows countries to unlock their comparative advantage, but rather than focusing on producing all parts of the entire chain, it is now possible to focus on specific tasks and sub-sectors.

The discussion focused on the following questions:

  • Under which circumstances does trade openness foster export diversification through GVCs?
  • What are the determinants of GVC integration?
  • How and under which circumstances GVCs integration spills over beyond integrated sector and benefits the domestic economy, thereby supporting a sustained economic transformation?
  • What are the trade policy implications at the domestic, regional and global levels?

 

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23 June 2015 | Dani Rodrik: The future of economic transformation in developing countries

Leading economist Dani Rodrik shed light on the future of economic transformation in developing countries. For years, developing countries have tended to transition from agriculture to manufacturing to services. Yet recent evidence suggests that countries are running out of industrialisation options much sooner than expected.

Overseas Development Institute, London.

Leading economist Dani Rodrik shed light on the future of economic transformation in developing countries.

Economic transformation is needed for the type of growth that leads to poverty reduction. It leads to growth that generates income across the income distribution, is robust against price shocks and price cycles, and increases the opportunities and options for future economic growth.

Focusing on economic transformation involves understanding what determines growth and productivity at the micro and macro level. For example, how can resources be shifted to higher-value uses? How can diversification of a country’s productive capabilities, including exports, be encouraged?

But economic transformation in low-income countries is changing. For years, developing countries have tended to transition from agriculture to manufacturing to services. Yet recent evidence suggests that countries are running out of industrialisation options much sooner than expected. Is this a cause for concern? Or are there opportunities in agriculture and services that are just as effective at generating growth and ending poverty?

 

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Media Coverage

The Broker Online, 30 June 2015

Oxfam blog, 26 June 2015

Chinese Special Economic Zones in Africa

Tang Xiaoyang, July 2015.
Africa is no longer satisfied with growth that is limited to traditional economic sectors, such as agriculture or mining. Policy-makers aiming to bring in more manufacturing, technology and innovations to the continent are attaching more importance to structural transformation in their vision of development.

Tang Xiaoyang, July 2015

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Africa is no longer satisfied with growth that is limited to traditional economic sectors, such as agriculture or mining. Policy-makers aiming to bring in more manufacturing, technology and innovations to the continent are attaching more importance to structural transformation in their vision of development.

In this context, a handful of ‘special economic zones’ (SEZs) established in Africa by Chinese firms are especially interesting. Firstly, these zones have concentrated an increasing number of manufacturing investment projects from China and other countries. Secondly, SEZs played a significant role in China’s own economic transformation during last three decades. Why did Chinese investors set up these zones in Africa? How do these zones affect the economic transformation in Africa? What can be done to maximise their positive impacts? This brief will offer a brief analysis to answer these three questions.

Using Hydroelectricity to Power Economic Transformation in Nepal

Gagan Thapa and Yurendra Basnett, May 2015.
Key messages from the brief include that hydropower can help Nepal decouple growth from rising carbon emissions and propel economic transformation. To do so will require creating agglomeration effects around hydropower development. Nepal should consider investing hydropower revenue to ensure that the country stays on a low-carbon economic growth pathway; to build the much needed transport infrastructure and power it with electricity; and to develop industries.

Gagan Thapa and Yurendra Basnett, May 2015

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Key messages from the brief include that hydropower can help Nepal decouple growth from rising carbon emissions and propel economic transformation. To do so will require creating agglomeration effects around hydropower development.

Nepal should consider investing hydropower revenue to ensure that the country stays on a low-carbon economic growth pathway; to build the much needed transport infrastructure and power it with electricity; and to develop industries.

There are important risks that could derail the benefits of hydropower development. These include financial volatility, corruption leading to further weakening of governance, and ineffective regulation.

Universal coverage of transmission lines will be important for ensuring inclusive access to energy.

Media Coverage

Kathmanudu Post, 09 December 2015

 

Dirk Willem te Velde (ODI) | The future of economic transformation in Africa

Having concluded the UN conference on financing for development in Addis Ababa in July and approaching the conclusion of new development goals at a UN summit in New York in September, this is a crucial time for the global community  to stand behind Africa’s priority objective of economic transformation. It will require  a sustained effort of discovering and experimenting with new ways of economic transformation, involving the right stakeholders from across society, led by African countries and supported by others as appropriate. The rewards are potentially huge, and early results look within reach.

17 July 2015

Having concluded the UN conference on financing for development in Addis Ababa in July and approaching the conclusion of new development goals at a UN summit in New York in September, this is a crucial time for the global community  to stand behind Africa’s priority objective of economic transformation. It will require  a sustained effort of discovering and experimenting with new ways of economic transformation, involving the right stakeholders from across society, led by African countries and supported by others as appropriate. The rewards are potentially huge, and early results look within reach.

Africa’s growth patterns have attracted much attention over the past two decades. Africa was termed ‘the hopeless continent’ in 2000, even though the available data showed that many African countries had in fact already turned a corner in GDP growth and GDP per capita in the mid-1990s, through policy reforms and as a result of fewer conflicts . Africa’s growth saw a further boost during the 2000s through high commodity prices and strong demand for natural resources from China. With growth at 5% a year in the early 2010s , Africa has become a key investment location.

Yet there have also been concerns that despite strong growth, African countries are not achieving economic transformation. Economic transformation is needed for the type of growth that leads to poverty reduction. This is growth that generates income broadly across the income distribution, is robust against price shocks and price cycles, and increases the opportunities and options for future economic growth. Focusing on economic transformation involves understanding determinants of growth and productivity at the micro and macro levels, including how resources shift to higher-value uses, and diversification of a country’s productive capabilities, including its exports.

Fortunately, there now are now ample reasons to be optimistic that several African countries are on the verge of a period of  economic transformation.

First, let’s look at the data. Over 1997-2012, data from the World Development Indicators show that  while manufacturing production increased on average by 2.3% annually across the world, it increased by 3.4% annually in sub-Saharan Africa, with examples such as Tanzania growing 7.9% annually over the same period. Overall, the share of sub-Saharan Africa in world manufacturing increased from 0.9% in 2000 to 1.1% in 2012.

Second, whilst the work by McMillan and Rodrik has shown that structural change in Africa was growth reducing over 1990-2005 as employment moved towards lower productivity sectors (e.g. agriculture), structural change accounted for half of Africa’s labour productivity growth between 2000 and 2010.

Third, the recent national account rebasing in six African countries, which found an additional $300 billion, suggests very clearly that we need to update our views on economic transformation. For example, the rebased gross domestic product (GDP) data recorded strong increases in value added in real estate and in information and communication technology (ICT) in countries such as Nigeria, Kenya, Uganda, and Zambia. They also show that the share of manufacturing in GDP increased by 1-5 percentage points in Nigeria, Ghana, Kenya, and Uganda.

Fourth, Africa is now  covered with emerging manufacturing and services hotpots. Special economic zones built by the Chinese in Africa currently employ  around 20,000 people, many of them in jobs that were created over the last two years. One shining example is a Chinese shoe manufacturing company that was attracted to Ethiopia, where its factory now provides several thousand formal sector jobs. Ethiopia is investing to become a new manufacturing hub. In Tanzania, tourism ($2 billion, or 6% of GDP) has overtaken gold as the main exporter earner. Tanzania is designing a new five-year plan to industrialise the country based on its natural resources. In Nigeria, the ICT sector is more than 10% of GDP. The new administration has the opportunity to show investors it is serious about nudging Nigeria onto a truly transformational growth path. Kenya’s financial services have successfully attracted and provided much capital, with much potential to support the real sectors. Its mobile phone technology has transformed the livelihoods of ordinary people.

Of course, there are still major obstacles, but they look surmountable.

First, Rodrik’s finding of premature deindustrialisation, in which countries reach their manufacturing peak much earlier , suggests it will be harder for newcomers to industrialise. Yet even if the new peak is 15% of employment in manufacturing or 20-25% in value added, this still leaves many possibilities for labour to flow into African manufacturing, which currently absorbs less than 5-10% of total employment and is worth around 10% of GDP.

Second, there may be  potentially damaging, external cyclical factors such as a new Eurozone crisis, the end of monetary easing, or lower oil prices. Yet with Africa’s internal demand growing and new sources of growth emerging, and with recent experience in managing external shocks, the future is not as bleak as it could have been. It is easy to forget that African consumers gained $10 billion from the drop in oil prices over the last year.

Finally, leaders who prefer the status quo of holding-pattern growth are increasingly being superseded  by new leaders who take Africa’s transformational growth more seriously and who feel bolstered by the early signs of economic transformation.

In this important year for development, it is crucial for the global community to support  Africa’s economic transformation.

Sehr Syed (Liberia Institute of Statistics and Geo-Information Services) | Beyond the numbers: the case of Liberia

Working as an ODI Fellow in the statistics bureau for Liberia, the Liberia Institute of Statistics and Geo-Information Services (LISGIS), I experienced at first hand the challenges of producing and obtaining statistics in a developing country environment, from logistical, technical and political aspects. It is widely agreed that some data is better than no data, and that generally data is as good as it can be, given the context, but this is true only to the extent that the country context is understood. Liberia provides an instructive example of an extreme case where data collection, and resultant indicators, may suffer as a result of constraints, whether logistical, financial, cultural, educational or otherwise. I briefly discuss a very limited number of these below.

16 July 2015.

Working as an ODI Fellow in the statistics bureau for Liberia, the Liberia Institute of Statistics and Geo-Information Services (LISGIS), I experienced at first hand the challenges of producing and obtaining statistics in a developing country environment, from logistical, technical and political aspects. It is widely agreed that some data is better than no data, and that generally data is as good as it can be, given the context, but this is true only to the extent that the country context is understood. Liberia provides an instructive example of an extreme case where data collection, and resultant indicators, may suffer as a result of constraints, whether logistical, financial, cultural, educational or otherwise. I briefly discuss a very limited number of these below.

 

Background in Liberia

A 14-year civil war, caused in part by the marginalisation of a large portion of the Liberian population from political power, ended in 2003, when a peace agreement was negotiated and signed, and a transitional government was put into place. The United Nations Mission in Liberia (UNMIL) was established to ensure the peace process was implemented and law and order maintained throughout the country. Liberia had by then suffered an estimated 270,000 deaths and the destruction of vital institutions and infrastructure, and the country’s economy had come to a halt. In 2005, Madam Ellen Johnson Sirleaf was democratically elected as the first female president of Africa.

 

Transformation strategies

The country’s first poverty reduction strategy, named Lift Liberia, was designed and implemented by Madam Sirleaf’s government to raise Liberia from post-conflict emergency reconstruction and position it for future growth. Madam Sirleaf was re-elected in 2011; during her second term as president, a new long-term vision for Liberia’s socioeconomic development was articulated: Liberia Rising 2030. This national vision sees Liberia’s economy transformed to middle-income status by 2030. In 2012 the government outlined a five-year development strategy, the Agenda for Transformation (AfT), whose goals and objectives represent the first steps towards achieving the goals of Liberia Rising.

 

Statistics and data in Liberia

Most of the data from surveys and censuses taken before 1989, prior to the civil war, were lost during the conflict. After the war, capacity constraints limited the collection of socioeconomic and geo-information data. In response, the government re-established LISGIS as an autonomous agency responsible for producing statistics and spatial data for Liberia. In recent years, LISGIS has been working to implement the National Strategy for the Development of Statistics (NSDS), approved in 2008, which aims to establish a robust national statistics system through rebuilding statistical capacity and strengthening coordination across data collection agencies.

Since 2008 LISGIS has achieved many objectives set out by NSDS, including the completion of major data collection activities. Among these are the Core Welfare Indicators Questionnaire (2007, 2010), Liberia Demographic and Health Survey (2007), censuses (National Population and Housing Census 2008, Human Resource for Health Census 2009, and School Census 2007, 2009 and 2011), Labour Force Survey (2010), National Accounts Survey (2008 and 2012) and Agricultural Crop Survey (2008, 2009, 2010, 2011). These recent efforts have helped to narrow the statistical gap that resulted from the conflict.

Despite the significant narrowing of data gaps and statistical capacity, data in Liberia still suffer from substantial limitations due to physical and logistical constraints imposed by the country’s geography and lack of infrastructure, and due to low capacity, both of which affect quality of data collected.

 

Monitoring progress of the Agenda for Transformation

Five key pillars are at the heart of AfT, one of which is economic transformation. The economic transformation plan identifies and focuses on improvements in seven key areas: private sector development, macroeconomic issues, infrastructure, agriculture and food security, forestry, mineral development, and management and capacity development needs.

Initially, around 220 indicators were proposed in order to monitor progress of AfT, including a significant number aiming to measure economic transformation.

Besides this original suggestion including superfluous and inefficient indicators, there is a clear lack of understanding of the country’s statistical limitations that results from weak bilateral relations between LISGIS and designers of the monitoring and evaluation for AfT. Over a period of almost two years, the number of indicators was significantly refined and reduced to approximately 50. Indicators come from a variety of data sources, from LISGIS and elsewhere. Many data sources are not vetted by the official statistics bureau, LISGIS, due to a lack of coordination, funding and capacity.

 

Challenges with data and lessons from the field

Challenges in collecting accurate, robust data are ubiquitous; in developing countries that have weak statistical systems, low capacity levels and vast, remote and hard-to-reach areas, the challenges are even more pronounced.

A significant amount of my time at LISGIS has been spent on the 2014-2015 Household Income and Expenditure Survey (HIES). The HIES is a multi-topic household survey which collects information on household consumption and income in key areas such as health, education, employment, and food security. The primary objectives of the HIES are to address major shortcomings in gross domestic product (GDP) and consumer price index (CPI) estimates for Liberia, and to produce detailed poverty analysis and robust and nationally representative agriculture statistics. Collecting data for the HIES presented a number of challenges.

Impacts of seasonality and Ebola virus disease on data collection and analysis

Liberia has two extreme seasons: a dry season, from approximately November to April, and a rainy season, from May to October; induced by the rain, the harvest season for staple crops runs from July until December. In order to capture effects of seasonality on key indicators, data  was scheduled to take place over a 12-month period. The spread of the Ebola virus disease rapidly accelerated in August 2014, and our teams were pulled out of the field for their civil and health safety and in accordance with government recommendations and countrywide travel restrictions.

As a result, only six months of data collection were completed, instead of the planned 12 months. Although the sample was only about half the target sample size, it was designed to be nationally representative on a quarterly basis, and data can be used to produce estimates with a fair amount of precision. However, estimates may suffer from bias attributed to lack of seasonality. In particular, the harvest season and major festive period (including predominant Liberian holidays and Christmas) fell outside of the data collection period. This resulted in the following:

Consumer Price Index: A new consumer basket and weights will not represent real annual consumption patterns in Liberia; however, using a six-month dataset is far superior to previous CPI methodology, which was based on four neighbouring countries and outdated weights. This six-month data will be used in the interim until a 12-month survey can be conducted, and it should be heavily caveated.

National Accounts: Compiling the household component of National Accounts using six months of data is far from ideal; furthermore, National Accounts data in constant prices should also be used with caution due to the suboptimal deflator available. Updates will  be made as soon as a 12-month survey is completed.

Poverty: Poverty measurement is highly sensitive to the effects of seasonality, and therefore poverty indices will not be released based on the six-months data.

Physical terrain

In terms of surface area, the majority of Liberia is remote and hard to reach, yet it must be visited to ensure that data are nationally representative and inclusive of such vulnerable remote populations. HIES field teams have often walked long stretches, easily up to 12 hours, in order to reach a cluster selected in the sample; they have taken motorbikes, crossed rivers by canoe or by raft (a piece of wood), pulling themselves across using a rope tied between two trees across the river; they have also walked across many logs over water, or across makeshift bridges of logs as narrow in diameter as 10cm.  Enumerators need to be physically fit and highly motivated to complete the work, and a high level of monitoring by the head office is required to ensure that work is conducted according to plan. In reality, delays in obtaining funding, and overstretched project staff in headquarters, mean that monitoring is infrequent.

 

Further challenges with National Accounts estimates

Liberia’s current GDP estimates are  The sources of information for estimating different components of GDP include the financial and non-financial companies, the government, non-profit organisations, and households. Information from the first three sources is generally available in some form; however, significant deficiencies  exist in the source data. National Establishment Censuses (2007, 2012) and National Accounts surveys (2008, 2012) have been conducted to provide source data for the business component of National Accounts. Refusals and incomplete interviews are common, particularly when questions are revenue related, despite the training and data collection expertise of LISGIS personnel. Businesses highly distrust LISGIS because they fear information will be passed on to government revenue authorities. Furthermore, information on the activities of non-profit organisations is unreliable due to weak capacity and enforcement, and data related to households’ activities and behaviour are mostly non-existent. For the first time, detailed household data from the HIES 2014 will be available to prepare the household component of National Accounts.

Morten Jerven (Simon Fraser University) | Mind the Gap: What do we know about economic transformation in low-income countries?

One of the stylised facts in debates on data quality and data availability is that over the past few years we have seen more and better data on many, if not most, aspects of development. But when it comes to economic statistics, and particularly statistics on economic transformation, there is a lack of good data.

16 July 2015.

Morten Jerven

One of the stylised facts in debates on data quality and data availability is that over the past few years we have seen more and better data on many, if not most, aspects of development. But when it comes to economic statistics, and particularly statistics on economic transformation, there is a lack of good data.

Does the lack of high quality statistics matter? One of the things I used to say when I addressed this question in discussions following the publication of Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It was that in the average low-income country in sub-Saharan Africa, even if a government wanted an industrial policy or plan for improvements in employment or agricultural performance, there would be no statistics to formulate or evaluate such a plan.

Data gaps

In Poor Numbers I focused on the highly visible gross domestic product (GDP) measure, but the problem of soft and outdated GDP benchmarks is just a symptom of paucity of data on economic production and consumption. Of the 77 countries that are classified as low-income countries, less than a handful are able to produce economic statistics of the quality and regularity required in order to be members of the International Monetary Fund’s (IMF’s) Special Data Dissemination System. A minority of these countries have benchmarks for calculating economic growth and inflation that are less than ten years old, and other data are simply missing. I surveyed the availability of labour statistics for African countries and found that only 5 or 6 countries (depending on the data source, IMF or the International Labour Organization (ILO)) have annual labour data. Perhaps more tellingly of our knowledge problem in labour statistics, ILO had no metadata for 19 of the 54 countries. So not only is there a lack of data, but there is even a lack of data on the size of that data gap. In agricultural statistics, there is a similar dearth of regular high quality data, and while we have more regular and reliable data on household budget expenditures, we know much less about production, particularly in small- and medium-sized enterprises.

What do we know?

It is beyond doubt that there is more growth, investment and trade in low-income countries now compared to a decade or two ago. The bottom line is that there is no longer any bottom billion. However, where does this growth come from? Is it associated with economic transformation, or are we seeing an intensification in external trade and activities in primary sectors? Our enthusiasm over ‘Africa Rising’ or catch-up growth in low-income countries should crucially depend on the extent to which this growth is sustainable and therefore accompanied by qualitatively observable differences in how goods and services are produced and how this production is organised.

What we do know is that some of these countries are richer than we thought. On 5 November 2010, Ghana Statistical Service announced new and revised GDP estimates. As a result, the size of the economy was adjusted upward by over 60%, suggesting that in previous GDP estimates, economic activities worth about $13 billion had been missed. While this change in GDP was exceptionally large, it turned out not to be an isolated case. On 7 April 2014, Nigeria’s National Bureau of Statistics declared that its GDP estimates also were being revised upward to $510 billion, an 89% increase from the old estimate for 2013.

So where does that leave us? It should have come as no surprise that the previous GDP numbers were a poor guide to levels of expenditures and income. The benchmark years in Ghana and Nigeria were updated from 1993 and 1990 to 2006 and 2010 respectively. It then appeared that a lot of new economic activity was missed. Research that made use of data from Demographic Health Surveys to correct for missing data in the national accounts showed that low rates of growth were at odds with higher rates of  since the 1990s.

The new benchmark data do, among other things, also provide us with an updated view of the economic structures of these economies. Some analysts have taken the new numbers to mean that that there is structural change, but to compare an old benchmark with a new benchmark is like comparing apples and oranges. It is hard to know whether the new economic structures reflect different prices, weights, definitions and data availability, and to what extent they reflect real growth in some sectors. What we can say, is that the new estimates give us a picture of economies that are more diversified into manufacturing and service activities   in the picture we had with the outdated benchmark years.

Overall, it is notable that tertiary sectors looks much larger in the new estimates – whereas the changes in agriculture and industry are too small to say that we know they are significant. These new GDP estimates were in many cases prepared without new data on production, and without specific surveys of business, industry and agriculture; and data were mostly drawn from surveys on household expenditure. It is symptomatic of the data availability problem that recent studies of industrialisation in sub-Saharan Africa are using data from health surveys in the absence of classic data sources on employment and production.

The way forward

It is easy to lament the status of knowledge and call for better and more data. Surely, more frequent data on economic activity would satisfy the needs of investors, central banks, commercial operators and some scholars and analysts, but these data demands have to be weighed against other priorities. Data collection is time-consuming and expensive, and capacity at statistical offices is stretched.

In the meantime, we need more hands at work. Part of the problem has been neglect of the study of economic change in many low-income countries. Since the 1990s, overwhelmingly the focus has been on poverty and poverty eradication. Since the 2000s, the focus broadened with the Millennium Development Goals. This focus is reflected in the statistical record. The Sustainable Development Goals do in part put industrialisation, employment and agricultural productivity back on the development agenda.

A lot can be done by triangulating data sources and unearthing new sources, through the careful work of the document historian. It is at the same time symptomatic and promising that the main  from datasets meant to monitor health and demographic trends.

It remains true that levels of employment, or the share of employment of the labour force, will depend on the survey type and the questions asked. World Bank researchers found that for Tanzania, labour force participation rates vary by as much as 10 percentage points across four different surveys. In Nigeria, the reported unemployment rate just fell 75% as the definition of ‘employed’ was reduced from 40 to 20 hours per week. Similarly diverging results from one data source to another may be seen in the levels and trends in agricultural production. This underlines the importance of doing research close to the source of data. Comparisons of levels and rates of taxation suffer in similar ways – but recent work has been done to combine and compare all possible data sources and datasets, not only to fill the gaps in existing datasets but also to use different sources to decide upon the most plausible observation.

While waiting for the new data sources, researchers, analysts and policy-makers will have to make do with the data. The SET portal on Data and Statistics is promising in this regard.