Posts Tagged ‘social development’

EFP Brief No. 223: Analysing Long-term Trends of a Post-industrialised Society: The Case of Finland

Tuesday, October 23rd, 2012

This study contributes to building FTA capacities for systemic and structural transformations. Increasing scientific and societal concerns have been raised about the adequacy of current measures of economic performance, in particular that of GDP. Current de-growth discussion summarises the implications. We do not propose a concrete vision but emphasise the need to make it a topic of futures discussions in EU development strategy. An empirical Finnish case study attests to the vital need to revise the current statistical evaluations of European welfare and economic growth processes.

The De-growth Scenario:
Policy Implications for the EU

The Commission on the Measurement of Economic Performance and Social Progress have been discussing new social welfare evaluation tools. European societies have been locked into socio-economic thought dominated by progressive growth economics. The hegemony of this kind of one-sided thinking has made imaginative thinking outside the box almost impossible. The de-growth topic has become a major international subject of debate, not just within the counter-globalisation movement but throughout the world. The big question is: What are the implications of ‘de-growth’ for the European Union and its policies? Do we need new sustainable macroeconomic policies that go beyond the Lisbon and Europe 2020 strategy?

In traditional mainstream economic policy, GDP (gross domestic product) and GDP per capita are often used as measures of national welfare. Although not originally designed for this task, they have become normative benchmarks of economic and social performance (Easterlein 1974). We have to acknowledge that relying on GDP can lead policymakers to draw wrong policy conclusions in the EU and in the EU member countries.

When Costs of Growth Exceed Benefits

For some time now, economists have been proposing a ‘threshold hypothesis’, the notion being that when macroeconomic systems expand beyond a certain size, the additional cost of economic growth exceeds the flow of additional welfare benefits (Daly & Cobb 1989). In order to support their findings, economists and scientists have developed a number of indexes to measure and compare the benefits and costs of growth (e.g., the index of sustainable economic welfare, ISEW and the genuine progress indicator GPI, etc.). In virtually every instance where an index of this type has been calculated for a particular country, the movement of the index appears to underline the validity of the threshold hypothesis. Philip Lawn (2003) has noted that by adopting a more inclusive concept of income and capital, these alternative new indexes are theoretically sound but require the continuous development of more robust valuation methods to be broadly accepted.

Making Indexes and Statistics Scientifically Sound

There is also ongoing scientific debate about the statistical correlations of gross domestic product (GDP), population, genuine progress indicator (GPI), index of sustainable economic welfare (ISEW), genuine saving (GS) and human development index (HDI) indicators. All these welfare indicators can be used in analysing the welfare and sustainability situation of the EU member countries. An interesting debate on the policy relevance of a set of indicators versus a single index has been going on for quite some time now. Both options have advantages and disadvantages:

  • A set of indicators is more appropriate for expert use, yet hard to communicate to the public and even more difficult to interpret because different indicators usually provide confusing signals.
  • A single index is a highly valuable instrument in political debates and setting targets as well as in communicating such targets to the public.

Nonetheless, the European Union’s macroeconomic planning and strategic decision-making requires active development of new relevant sustainability planning and evaluation tools and indexes. We cannot rely on just one index, GDP, in our welfare policy analyses.

New Approach in Statistical Analysis Needed

If the European Union wants to evaluate long-term sustainability of its macroeconomic development, new kinds of statistical analyses are needed. Our study is based on long-term statistics (years 1960-2009) for three key social welfare indicators; statistical analyses have been conducted for the same period for other variables (GS, HDI, and population) as well (Hoffrén 2001, Kekkonen 2010 and Lemmetyinen 2011). The long-term trends of key indicators have been analysed and a statistical correlation analysis between them has been carried out.

Our results support the validity of the threshold hypothesis, especially for the years following the oil crisis. Figure 1 demonstrates this in the case of Finland.
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Figure 1

Novel Sustainability Evaluation Method to Improve Social Welfare Systems in the EU

The idea of the article is to propose a novel sustainability evaluation methodology for the European Commission and EU member countries. This statistical approach is evidence-based and gives new evaluation and planning information about critical sustainability trends in European Union. In our case study, the focus is on Finland and its sustainability trends. A similar kind of indicator-based sustainability evaluation should be done for all EU27 countries to improve the quality of European Union’s long-term sustainability policy and especially its social welfare policy.

De-growth Strategy for the European Union

For some authors, the very idea of sustainable development seems to be a contradiction in terms. It is not a big surprise that practice has shown unequivocally that it is not possible to reconcile economic growth with environmental sustainability. Some parts of the global scientific community, for instance those participating in the UNEP (see IPSRM-UNEP 2010), think that the Western lifestyle is damaging not only its own environment but also that of the poorer countries and the planet as a whole. In this context, the proposal of ‘sustainable de-growth’ has emerged as a strategy that aims to generate new social values and new policies capable of satisfying human requirements whilst reducing the consumption of resources. De-growth is a political, economic, and social movement based on environmentalist, anti-consumerist and anti-capitalist ideas. ‘Sustainable de-growth’ or ‘de-growth’ is not yet a formalised theory but rather a focal point for social movements, academia or politics to rally around (Latouche 2006).

Questioning the Consumption Paradigm

De-growth supporters have advocated the downscaling of production and consumption – the contraction of economies – as overconsumption lies at the root of long-term environmental issues and social inequalities. Key to the concept of de-growth is that reducing consumption does not require individual martyring and a decrease in well-being. Rather, ‘de-growthists’ aim to maximise happiness and well-being through non-consumptive means: sharing work, consuming less while devoting more time to art, music, family, culture and community. De-growth – in contrast to the idea of dematerialisation, which aims at a reduction of resource use while the economy continues to grow – goes further and means that significant reductions of resource use require fundamental changes in the production and consumption system.

The de-growth movement opposes economic growth, which has created many more poor people and has inevitably led to environmental degradation. From this perspective, the de-growth strategy opposes the Europe 2020 policy. In any case, the de-growth movement’s future success will depend on its capacity to generate coherent political responses and empirical results to shore up its proposals. This study contributes to tackling this challenge facing the de-growth movement.

The Finnish Case: Evidence for the Anti-Growth Strategy

In the case of Finland, we observe a negative correlation between GDP and GPI after the oil crisis years. Growth of GDP appears not to be connected with improved GPI development. GDP still correlates positively with GS and HDI. However, the correlation rates are much lower now than before the oil crisis.

When we discuss de-growth policy and its potential content, we must bear in mind that there are various aspects of welfare beyond economic growth alone. In the Finnish case, we can note that the linkage between GDP growth and welfare indicators is not as strong as it was before the oil-crisis period. Yet, we must also remember that the GDP indicator too includes immaterial and qualitative aspects of welfare. If we think of de-growth from this perspective, it is not a rational aim to radically minimise GDP growth. Probably we should try to find a “golden middle of the road solution”, which is a rather unadventurous or inoffensive path that does not go only one way or the other (neither de-growth nor growth mania).

Another policy conclusion from our empirical analysis is that GPI is a critical indicator for the de-growth movement because the GPI indicator provides empirical foundations for the anti-growth movement and its form of welfare thinking. In Figure 1, the trade-off curve of GDP and GPI is plotted for Finland for the years 1960-2009. The turning point of GDP and GNI  (Gross National Income) trends was in 1988. This year can be seen as a benchmark year because in 1988 Finland reached the peak level of welfare as measured by the GPI. Although GDP has grown in Finland, GPI has not increased since. Socially and politically the situation is most problematic.

Dynamics of Economic & Social Development Have Changed Dramatically

In the study, a long time series (years 1960-2009) was initially analysed by Pearson correlation analysis. Subsequently, the time periods before the oil crisis (years 1960-1972) and the time period after the oil crisis (1973-2009) were analysed in the same way. Six welfare indicators were correlated.

One key observation of this indicator study is that the dynamics of economic and social development in Finland have changed dramatically. We can expect similar structural changes to also have occurred elsewhere in the European Union. The GDP indicator was correlated in a different way before and after the oil crisis. The changes in the correlation tables are considerable, indicating substantial structural changes. We find support for the following analytical conclusions:

  • In the long run, the GDP correlates positively with five other indexes of the Finnish case study.
  • Before the oil crisis, positive correlations were strong between the GDP index and the other indices analysed.
  • After the oil crisis, however, our statistical analysis clearly supports the threshold hypothesis in the Finnish case. Especially the correlation between GDP and GPI has shifted dramatically in Finland after the peak year 1988.
  • A single aggregate index, such as GDP, is certainly a valuable means of communication for policy purposes. At the expert level, however, a set of indicators is a more appropriate toolbox, even though it may be harder to communicate and more difficult to interpret because of different and sometimes opposing signals. As this case study shows, a single aggregate index can lead to very problematic policy choices in the EU member countries.
  • There is a need to develop a sustainable de-growth strategy that goes beyond the Lisbon and Europe 2020 strategies. Many European governments may face a new situation where welfare indicators are developing in an undesirable direction although the GDP indicator shows economic growth and successful economic performance. This phenomenon was also observed in the Finnish case study.
  • Despite all theoretically and empirically motivated criticism of GDP as a social welfare and progress indicator, the GDP’s role in economics, public policy, politics and society seems to remain influential also in the future.

The European Union’s macroeconomic planning and strategic decision-making urgently calls for new sustainability planning and evaluation tools and indexes. We cannot rely on just one old and much criticised GDP index in our European welfare policy analyses. Relying on inadequate signals in coordinating common EU policies may very well lead member countries to make wrong policy decisions. We now need new macro-aggregates, such as ISEW and GPI, to foster our socio-economic performance and competitiveness.

In evidence-based policy making, the European Union should pay more attention to the underlying motivation of growth policy because what we understand as economic growth today does not necessarily contribute to welfare in any linear fashion. Our study is important because it shows that, if we evaluate welfare by the GPI index, this is precisely what has been happening in Finland: there is no longer any immediate link between economic growth and general social welfare. Especially under the Europe 2020 strategy process we need broader evidence that the political decisions taken are actually leading Europe toward improved welfare. The possibility that the threshold hypothesis adequately describes the reality in the European Union countries should be taken more seriously in various policy fields.

Confirming the Commission on the Measurement of Economic Performance and Social Progress

In a recent study, the Nobel prize-winning economists and professors Joseph E. Stiglitz, Amartya Sen and Jean-Paul Fitoussi (2009) (SSF report) urge the adoption of new assessment tools that incorporate a broader concern for human welfare than just economic growth. By their reckoning and insights, much of the contemporary economic disaster owes to the misbegotten assumption that policy makers simply had to focus on nurturing economic growth, trusting that this would maximise prosperity for all. The case study of Finland shows that this taken-for-granted assumption is too simplistic. In this light, the policy recommendations of SSF Report are highly policy relevant for the European Commission and EU member countries to achieve greater social welfare to actually improve the lives of their citizens.

Authors: Jukka Hoffrén            jukka.hoffren@stat.fi  Jari Kaivo-oja    jari.kaivo-oja@tse.fi   Samuli Aho            samuli.aho@tse.fi
Sponsors: Finland Futures Research Centre (FFRC), University of Turku, Finland Statistics Finland, Finland
Type: National FTA exercise, Finland
Organizer: Finland Futures Research Centre (FFRC), Electrocity, Tykistönkatu 4 D, 7th Floor, FIN-20520 TURKU
Duration: 2011
Budget: n.a.
Time Horizon: 2020
Date of Brief: October 2012

Download: EFP Brief No. 223: Analysing Long-term Trends of a Post-industrialised Society: The Case of Finland

Sources and References


Aldrich, J. (1995): Correlations genuine and spurious in Pearson and Yule. Statistical Science 10 (4), pp. 364–376.

Daly, H. & Cobb, J. (1989): For the Common Good. Beacon Press, Boston.

Easterlin, R. (1974): Does economic growth improve the human lot? In: David, P., Weber, R. (Eds.), Nations and Households in Economic Growth. Academic Press, New York.

Hoffrén, J. (2001): Measuring the Eco-efficiency of Welfare Generation in a National Economy. The Case of Finland. Statistics Finland Research Reports 233. Helsinki. And update by Hoffrén (2011).

Kekkonen, E. (2010): Hyvinvoinnin ja edistymisen kuvaaminen yhdistelmäindikaattorilla: Suomen kestävän yhteiskunnan indeksin laskenta. Master’s Thesis. University of Helsinki. Department of Economics. Helsinki.

Latouche, S. (2006): Le Pari de la Décroissance. Fayard. Paris.

Lawn, P.A. (2003): A theoretical foundation to support the Index of Sustainable Economic Welfare (ISEW), Genuine Progress Indicator (GPI), and other related indexes. Ecological Economics 44 (2003), pp. 105-118.

Lemmetyinen, I (2011): Genuine Savings – indikaattori Suomelle. Master Thesis. Aalto University. Helsinki School of Economics. Helsinki.

Rättö, H. (2008): Hyvinvointi ja hyvinvoinnin mittaamisen kehittäminen. Statistics Finland. Research Reports 250. And update version by Hoffrén (2011). Helsinki.

Stiglitz, J.E., Sen, A. & Fitoussi, J.-P. (2009): Report by the Commission on the Measurement of Economic Performance and Social Progress. Commission on the Measurement of Economic Performance and Social Progress. France.

EFP Brief No. 169: Foresight Toolbox for Small and Medium-sized Enterprises

Tuesday, May 24th, 2011

“Foresight-Toolbox für den Mittelstand” is a research project to evaluate the specific needs of and identify suitable methodological approaches for strategic planning in small and medium-sized enterprises (SMEs). The project’s centrepiece is a web-based toolbox at www.zukunft-im-mittelstand.de, which participants may use to create foresight processes and which includes downloadable descriptions of methods and various tools. User habits and stored processes are the empirical base for the present research. Also, ten qualitative issue-focused interviews with various-sized SMEs from different industries completed the insights gained from SME-specific future-oriented work.

Meeting the Needs of SMEs

The present research aims to evaluate the specific needs of and identify adequate methods for foresight and strategic planning in the context of SMEs. According to an earlier research project implemented by Z_punkt The Foresight Company, “Corporate Foresight im Mittelstand” (2008), innovative and successful enterprises use systematic foresight and future-oriented work methodologies more often than less innovative and successful enterprises. Based on this empirical finding, the current project focuses on the perspective of SMEs to develop foresight instruments that match the requirements of SMEs. Our main hypothesis suggests that selection and combination of foresight methods differ according to the indicators enterprise size and industry. The procedure contains both quantitative analysis and qualitative analysis.

Quantitative and Qualitative Research Combined

The web-platform www.zukunft-im-mittelstand.de provides a set of 17 different foresight methods to create individual foresight processes. User habits are evaluated based on the main variables size (number of employees) and industry (manufacturing, service or retail). Individual methods and combined processes are analysed in two different steps. The quantitative data analysis focuses on the following questions:

  • What describes a typical SME foresight process?
  • What is the methodological preference?
  • Which methods or processes are used to reach specific aims?
  • What preferences can be determined based on size and branch?

Qualitative Research

The quantitative data analysis is supplemented by qualitative interviews with decision-makers of ten SMEs of different sizes and industries. Using the methodological approach of problem-centred interviews, a semi-narrative, guideline-based interview technique, the participants explain their practices of strategic planning, specific needs and requirements, and typical problems and solutions of foresight practice. The findings of the qualitative research are enriched by case studies and interpreted in a comparative study along the following lines:

  • What is the relevance of foresight and strategic planning in general?
  • Which methods or processes are used to reach specific aims?
  • How can foresight and strategic planning be integrated into an enterprise’s structure and decision-making process?
  • What approaches can be identified based on size and industry?
  • What would define an ideal process in the specific context of SMEs?

Web-based Foresight Toolbox

The Foresight Toolbox, as the centrepiece of the project, has been online at www.zukunft-im-mittelstand.de since July 2009. Access is free and enables individuals to design foresight processes using downloadable explanations and tools that support strategic practice. The toolbox concept includes 17 different foresight methods structured in five logical steps. For each method, simple and expert versions are available, which differ with regard to complexity and effort necessary. The offered selection of methods represents the state-of-the-art of science-based futurology and fulfils the requirements of strategic planning in the context of SMEs.

In principle, there are no restrictions regarding the combination of methods for foresight processes. All methods can be mixed with each other, however, participants are provided with information on the best possible combinations. In addition to full foresight processes, users may also download individual methods or tools that they find interesting and useful for their specific requirements.

Based on the Foresight Toolbox, decision-makers should be able to perform a professional process of strategic planning in pursuit of various business aims. The Foresight Toolbox conveys both methodological knowledge and competence for implementing and communicating future strategies.

First Step: Defining Aims and Focus of the Foresight Process

In technology foresight practice, foresight processes begin with the definition of specific goals and aims. This first step designates and limits observation scope and structures the following process. The toolbox offers a set of four different objectives that comprise the different fields of strategic relevance, including the level of products or services and organisational development or market dispositions:

  • Find future strategies
  • Develop ideas for innovations
  • Open new markets / target groups
  • Early detection of changes in markets

Furthermore, the platform provides a checklist with guiding questions to create the framework conditions for a successful foresight process.

Second Step: Research

In the second step, relevant empirical data on the future has to be researched. Different observation scopes focused on various aspects of the organisational environment are offered. Methods include observation techniques, for instance the STEEP observation scheme, and use a large number of data sources ranging from online and media research to Delphi surveys. The obtained data is the basis for the next process steps. The Foresight Toolbox contains four research methods:

  • Environmental Scanning: Examine environmental frameworks and drivers
  • Market Scanning: Examine customer needs and market trends
  • Context Scanning: Examine the immediate context of product use
  • Competition Scanning: Examine strategies and changes concerning competitors

Third Step: Analysis

The analysis stage aims to transform the obtained data into future-relevant information. The interpretation process is framed by four different business-related categories. Analysis aims to achieve a basic understanding of trends, drivers and shaping factors concerning future business development. It also gives an insight into potential impacts and uncertainties as well as into the constellation of relevant actors that have an influence on future development. The Foresight Toolbox offers the following analysis methods:

  • Impact Analysis: Identify the most powerful factors
  • Uncertainty Analysis: Recognise incalculable future developments
  • Stakeholder Analysis: Detect the most influential actors and their strategies
  • Trend Analysis: Understand the signs and drivers of change

Fourth Step: Projection

Projections are used to transform analysis results into concrete constructions of the future. The proposed methods vary in their level of concretisation, from practice-oriented to more abstract approaches to different futures. Being aware that the projection step is at the methodological heart of scientific foresight practice, the Foresight Toolbox has been designed to translate science-based approaches and make them relevant and understandable for SMEs. According to the specific application context, the methods refer to normative or descriptive aspects of future construction. The Foresight Toolbox contains five projection methods:

  • Scenario Technique: Develop alternative visions of the future
  • Roadmapping: Map milestones of future developments
  • Trend Extrapolation: Describe predictable future developments
  • Visioning: Develop desirable futures and define objectives
  • Backcasting: Retrace the path to a desirable future

Fifth Step: Implication

The final process stage is (ideally) closely linked to the first step of defining aims and closes the circle. Here, the results and gathered findings have to be applied, implemented and translated into strategic decisions, innovations or organisational change processes. The four implication methods include practical tools for decision-making and the internal communication of results:

  • Strategy Development: Identify options and determine the best strategy
  • Development of Product Ideas: Create and select innovative ideas
  • Portfolio Development: Make own areas of business future-proof
  • Assessment of Market Potentials: Describe future markets and assess their volume

State of Research

The research project is still in progress. This brief is only able to provide a short overview of the interim results of our quantitative and qualitative research.

800 Participants So Far

Between July 2009 (when the platform went online) and February 2010, some 800 participants used the Foresight Toolbox. A majority downloaded selected individual methods. In addition, some 180 completed foresight processes were saved.

An evaluation and analysis of user habits, stored processes, popularity of individual methods and tools and focus group-related priorities will follow in February or March 2010.

Comparative Study Based on Qualitative Interviews and Case Studies

The ten qualitative interviews were conducted between November 2009 and January 2010. In addition to the case studies of each participating SME, typical features, characteristics and significant variations were analysed in a comparative study based on the factors size and industry. Please find below a brief outline of the comparative study.

Size and Industry Matter

In the following, we present preliminary results from the study. To put it most concisely, the organisational features ‘business size’ and ‘type of industry’ make a difference. We have organized our brief summary of how size and industry affect foresight activities along six factors: foresight relevance, time horizon, objectives and perspective, knowledge sources, securing strategic decisions and implementation of foresight results.

Size Makes a Difference

Strategic Planning vs. Ad Hoc Decision-making

Foresight relevance – Regardless of business size, all decision-makers consider strategic planning to be very relevant. Exact definitions of strategy, however, differ. Larger enterprises may specify objectives and goals for strategic work, smaller firms more often act under the requirements of the situation.

Time horizon – The time horizon of foresight and strategic planning increases in line with organisation size.

Consideration of Non-economic Factors Grows with Size

Objectives and perspective – All businesses focus on the economic aspects of their environment. Larger enterprises are more likely to also include more secondary aspects in their observation scheme. Social development, demographic change and political and legal frameworks acquire special relevance for enterprises that participate in transnational business networks.

Internal Sources and Social Networks

Most Important Knowledge Sources

Knowledge sources – Independent of size, all participants use internal knowledge from all hierarchy levels as the most important knowledge source for their future-oriented work. Some of the larger firms already have experience with bringing in different forms of external consulting.

For all participants, the most important data comes from publicly available sources (general or business media) and formal or informal social networks.

Decision-making Based on Personal Experience

Securing Strategic Decisions – Only some larger SMEs have a developed monitoring system or access to continuous foresight updates. Most of the participating decision-makers rely on personal experience or even intuition or “gut feeling”. Only a minority of SMEs systematically have alternative options in place in the event of strategic failure.

Foresight Characterised by Conserving

Resources and Short-term Implementation

Implementation of foresight results – Foresight in the context of SMEs is mainly characterised by conserving financial and personal resources and short-term implementation. Short decision-making processes make it possible to transform strategic positions efficiently into action. However, smaller enterprises are greatly limited with regard to changing management processes.

Manufacturing Has an Edge over Service and Retail Industry in Use of Foresight

Foresight More Advanced

Foresight relevance – SMEs have a large variety of foresight approaches. In general, businesses in the manufacturing sector have a longer tradition and a more advanced approach to systematic future-oriented work than service or retail enterprises.

Time horizon – Machine building companies, in particular, show a higher tendency for long-term strategic planning. Service enterprises often set time perspectives according to their projects’ time horizon. Due to the SMEs’ specific short-term strategies, most decision-makers emphasise the importance of anticipating disruptive events or breaks in long-term market developments.

Broader Scope of Factors Considered

Objectives and perspective – Businesses show a comparable level of systematisation across all sectors. That said, manufacturing companies tend to see themselves as active parts of the entire value chain. Hence, their observation patterns differ in that more secondary factors are included.

More Systematic in Utilizing Internal Knowledge Sources

Knowledge sources – Here, manufacturing companies also show a more systematic approach to using internal knowledge sources. Some have developed pay and incentive systems for product innovations or innovative technological solutions. In the service and retail sector, where problem-solving skills are used in personal interaction, knowledge is limited to individuals.

Coping with Uncertainty Easier for Manufacturing

Securing Strategic Decisions – Securing is defined as the key problem of foresight-based decision-making. Foresight methods in the SME context aim to reduce uncertainty to manageable levels. Small firms in the service sector argue that strategic work has to be measured by future reality and consider this a criticism of foresight efficiency. Decision-makers from manufacturing enterprises find it easier to accept the fact of an unknown future.

Implementation Varies with Corporate Culture

Implementation of foresight results – Regardless of industry, structures and routines for implementation and internal communication vary according to corporate culture.

Authors: Beate Schulz-Montag                  schulz@z-punkt.de

Kai Jannek                                jannek@z-punkt.de

Tim Volkmann                          volkmann@z-punkt.de    

            Sponsors: Federal Ministry of Education and Research (BMBF)

Project management: VDI/VDE Innovation + Technik GmbH

Type: Publicly funded project within the framework of “Innovations- und Technikanalyse”
Organizer: Z_punkt GmbH The Foresight Company
Duration: 08/2008 – 03/2010 Budget: ca. 140,000 € Time Horizon: N/A Date of Brief: Dec. 2010

 

Download EFP Brief No. 169: Foresight Toolbox for Small and Medium-sized Enterprises

Sources and References

www.zukunft-im-mittelstand.de