Analysing the DataGuide to Data Analysis Tools

This chapter discusses the tools that cities and city-regions can use toanalyse data collected on their economies. All of these have been triedand tested in actual city development strategies (CDSs). A few othertools not yet widely applied in city planning environments havealso been included.

Time Series Analysis

What Issues Are Addressed by Time Series Analysis?

The following questions can be addressed by a time series analysis:

� How is a local economy performing over time?● Population and other demographics (including education and

labour force● Income levels and distribution● Employment and unemployment levels (total economy and by

sector)● Economic output and exports (total economy and by sector)

� Which growth patterns reflect shocks and cycles and which are long-termtrends?

How Is Time Series Analysis Used?

Time series analysis is one of the most widely used tools in local economy assess-ments. It maps the development of key socioeconomic indicators over a period of time

and displays them in tables and graphs (line graphs or bar charts). Changes over time areexpressed through annual growth percentages and compound annual growth rates (CAGR)

and growth indexes. One of the great advantages of this analysis versus more static snapshottype tools is that it enables cities to determine whether an indicator for a specific year is a shock

or the result of a long-term trend. Table 8.1 is a basic example of the use of time series analysis forlooking at local employment levels (by sector) in a basic three-sector economy.

These tools arewidely used bylocal economicdevelopment (LED)practitioners:

General tools to help organ-ise and compare data:

� Time series analysis � Growth indexes� Composite indexes� Benchmarking� GIS mapping� PEST / trends analysis

Tools to help cities understand thestructure of their local economy:

� Sector share analysis� Value-added analysis� Economic base analysis� Location quotient� Specialisation index� Shift share analysis� Input-output analysis� Social accounting matrix � Cluster mapping� Value chain analysisTools to look at local endowments:

� Asset mappingTools to assess human capital:

� Skills audit� Tools to analyse

institutions:� Stakeholder analysis /

institutional mapping

See table 4.1, page 36

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In addition to tables, time series analysis is normally pre-sented using graphics, most often trend lines, histograms,and bar charts.

Time series analysis is often used in combination with sec-tor share analysis, value-added analysis, and benchmarking,

and is fundamental to developing growth indexes. In partic-ular, the trends of the key indicators in question are oftentracked against national trends, since this may revealwhether, for example, a recession is a local phenomenon ora reflection of a wider trend experienced at the national level.

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T A B L E 8 . 1 Time Series Analysis for Local Employment Levels in a Three-Sector Economy

CAGRSector 2002 %� 2003 %� 2004 %� 2005 %� (2002–2005)

Agriculture 2,009 — 2,000 – 0.4 1,980 – 1.0 1,974 – 0.3 – 0.6Manufacturing 11,350 — 11,600 +2.2 11,670 +0.6 11,145 – 4.5 – 0.6Services 14,500 — 15,330 +5.7 16,150 +5.3 16,553 +2.4 +4.5

TOTAL 27,859 — 28,930 +3.8 29,800 +3.0 29,652 – 0.5 +2.1

CASE STUDYMUNICH (Germany)

Time series analysis is one of the most frequently used tools in Munich’sannual local economy assessment. It is used to analyse and present trendsover time for key LED indicators, including employment levels, sectorgrowth, and unemployment levels. Although time series data are typicallyanalysed on a five-year cycle, analyses of medium-term trends (over peri-ods of 10 to 20 years) are also conducted. Munich places particular empha-sis on benchmarking its performance (by comparing its performance with

other German and international cities), so it analyses and presents compa-rable historical data for benchmark cities as well.

Munich presents the findings from its time series analysis in several ways,including bar charts, graphs, and tables. And the visual presentation of find-ings is accompanied by a clear narrative explanation of trends and the mainconclusions to be drawn from the analysis.

Tips

Compare with regional/national/ trends. Time series analysis can be espe-cially helpful when comparing local trends with regional and/or nationalones. However, because of the differing size of the economies surveyed, thedata are not always directly comparable, so it is useful to construct agrowth index (see page 75) to aid in comparisons.

Complement the data with insights from experts and literature. Time seriesanalysis displays trends for the economic indicators in question but doesnot explain or analyse them, and it does not provide predictive power. Byconsulting relevant experts and literature in addition to conducting a timeseries analysis, cities may be able to identify the factor(s)

that explain, for example, a sudden change in a particular sector (decline indemand, increased competition, or rapid structural changes) and be in abetter position to assess the future competitiveness of the local economy.

Making the best use of visual tools. Spreadsheet software like Microsoft Excelprovides valuable tools that can be used to plot graphs for time seriesanalysis. Visual tools can be powerful and communicate information effec-tively. However, keep the target audience in mind and develop graphs andexplanatory text appropriately.

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What Key Inputs Are Required for Time Series Analysis?

This analysis requires data for the selected socioeconomicindicator (including employment, gross domestic product[GDP], and population size) over several years.

Time series analysis is easy to conduct and does not requireany knowledge of econometrics beyond basic statistical analy-sis. This analysis does not require any special resources otherthan data and human resources, but spreadsheet software withthe capability to plot graphs is useful. When comparablenational and local level data are available, and other data col-lection is not required, the analysis has a low resource intensity.

Growth Indexes

What Issues Are Addressed by Growth Indexes?

The following questions can be addressed by growth indexes:

� How do various aspects of the local economy’s perform-ance compare with other economies over time?

� Which local growth patterns are driven by shocks orcycles? And which patterns are long-term trends?

How Are Growth Indexes Used?

The growth index is one of the most commonly used toolsin analysing the local economy. It is a simple and cost-effec-tive way to measure and compare local economic perform-ance with that of another economy (or economies), becauseit allows for direct comparison of a particular socioeconomicindicator between two or more economies over time. Thegrowth index is often used to apply benchmarking compar-isons to a basic time series analysis.

The growth index converts absolute data (includingemployment, output, and productivity) in a reference yearfor any number of economies into a common value (nor-mally 100). This enables simple comparisons of relativeperformance, particularly for absolute values that differ sub-stantially. To calculate a growth index, set the “year n” at100; for each subsequent year the formula is then:

Year n + 1 index = (year n + 1 / year n) x 100; Year n + 2index = (year n + 2 / year n + 1) x (year n + 1), etc.

These values can then be presented in a tabular format (seetable 8.2) and also using line charts and bar charts.

An advantage of using indexes to compare local indicatorswith regional or national indicators is that an index mayreveal, for example, whether an economic phenomenon (likerecession) represents a wider trend experienced on a nationallevel or if it is a more local event.

What Key Inputs Are Required for Growth Indexes?

This type of analysis requires at least two years of time seriesdata, for the local economy and for a reference economy

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Further Information

For guidelines to (and an example of) the use of time series analysis in the United States, see: http://www.economictoolbox.geog.psu.edu/index.php.

For guidelines on how to display time series data in a line graph, see: http://erc.msh.org/quality/foutools/foulngrf.cfm.

For a discussion of time series analysis as part of a forecasting exercise, see: http://www.statsoft.com/textbook/stathome.html.

For a discussion on the use of time series analysis in forecasting, see chapter 4 in Regional Economic Modeling: A Systematic Approach toEconomic Forecasting and Policy Analysis, by George I. Treyz (1993, Kluwer Academic Publishers).

T A B L E 8 . 2 An Example of the Presentation of Data Using the Growth Index

FDI Economy 1 FDI Economy 2 Index–FDI 1 Index–FDI 2

2002 50 350 100 1002003 60 390 120 1112004 68 440 136 1262005 81 470 162 134

FDI: foreign direct investment

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(often the national economy). Data availability depends onthe type of data to be indexed. Historical data on employ-ment and population tend to be more readily available thancity-level GDP data, for example.

This analysis does not require econometric knowledge. Itcan be conducted using basic spreadsheet software, so thistool is low in resource intensity.

Composite Indexes

What Issues Are Addressed by Composite Indexes?

This tool can be used to measure any socioeconomic issuerelevant to a local economy assessment and it allows prac-

titioners to summarise complex phenomena with a singleindicator.

How Are Composite Indexes Used?

A composite indicator aggregates a set of indicators to con-struct a single measurement of a socioeconomic phenome-non. Composite indicators are used extensively in socialscience research by governments, international organisa-tions, and universities to measure complex economic events.In particular, they are often used to assess investment climateas in the World Bank Ease of Doing Business Index; seeinvestment climate survey, page 66) and to analyse poverty

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CASE STUDYRAFAELA (Argentina)

The city of Rafaela calculated a growth index to make comparisons acrossfirms of different sizes (based on the number of employees, fewer than 5,6–10, 11–20, 21–50, 51–100, and more than 100 ) over a period of fouryears (1997–2001). The number of firms per category was converted intoa common value of 100 for the reference year (in this case 1997). The

employment growth trend was then tracked for the next three years, basedon the reference year, and presented in a histogram. By using the growthindex, Rafaela was able to compare employment growth across categoriesof firms—despite the fact that the number of firms in each category var-ied substantially during this time period.

Tips

Complement the data with insights from experts and literature. Growthindexes display trends for the indicators in question but do not explainor analyse them. And these indexes do not have predictive power. Byconsulting relevant experts and literature, in addition to a time seriesanalysis, it may be possible to identify the factor(s) that drive a suddenchange in a particular sector (increased competition, rapid structuralchanges, or decline in demand, for example)—which would help thecity to assess the likely future competitiveness of the local economy.

Making the best use of visual tools. Spreadsheet software like MicrosoftExcel offers valuable tools for plotting graphs of growth indexes. Thesevisual tools can be powerful and can often communicate informationmore clearly than detailed data tables. But graphs can also be misusedand (in some cases) make information less clear (as is sometimes truefor three-dimensional bar charts, for example). In general, simplegraphs with clear explanatory text work best.

Further Information

For guidelines on constructing a growth index, and anexample of its use in the United States, see: http://www.economictoolbox.geog. psu.edu/index.php.

For guidelines and an illustration on how to construct anduse a growth index, see: http://www.oecd.org/home/0,2987,en_2649_201185_1_1_1_1_1,00.html.

For an example of how growth indexes are used in a bench-marking analysis by the City of Glasgow (Scotland), seeGlasgow Economic Analysis and Benchmarking Study 2005(beginning at page 14): http://www.glasgoweconomicfacts.com/Glasgow%20Report%20BAK%20-%20Executive%20Summary%20Des%204.pdf.

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and deprivation (as in the UN-HABITAT City Develop-ment Index and UNDP’s Human Development Index).

The advantage of using this approach rather than a set ofindividual indicators is that a composite indicator provides abetter picture of complex issues and simplifies the rankingsand comparisons. Composite variables are also seen as a par-ticularly effective way to communicate trends of interest topolicy makers and local stakeholders.

Calculating composite indicators often involves using agroup of statistical methods called data reduction techniques.Two of the more common techniques are principal componentanalysis, which identifies groups of indicators whose scores (orbehaviour) are driven by the same underlying factor, andunobserved component analysis, which removes outlying indi-cators. A simpler way to construct composite indicators is toweight individual indicators according to importance and addthe results. Table 8.3 shows an example of how the compos-

ite index (total adjusted score) would be calculated using thismethod. (Based on a simple composite of three factors, theweighting of the factors should add up to 1.0.) 77

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CASE STUDYMEDELLÍN (Colombia)

In 2001 the city of Medellín created a quality of life (QoL) index to assesssocial exclusion and poverty in the city—and, since 2004, has monitoredthis index on an annual basis. The data for the QoL index, collected througha survey of 20,000 households, are used to measure the geographical dis-tribution of poverty and identify areas where policies to combat socialexclusion should be targeted.

Out of more than 50 indicators used to measure the quality of housing,access to public services and social security, asset endowment of the house-hold, and level of schooling of adults and children in the household, 17 indi-cators were selected as the base for the QoL index. The composite indicator

was calculated using principal component analysis.The resulting indexvalue ranges from 0 to 100 (with the lower scores indicating a lower qual-ity of life). For Medellín, the main advantage of using a QoL index is the abil-ity to account for the many factors that define poverty in one indicator.

Tips

Consider costs and benefits. Oversimplification can be a problem whenusing composite indicators, because socioeconomic situations areoften too complicated to be adequately captured within a singleindex. In some cases, presenting several key indicators (possibly inaddition to a composite) may give a more balanced picture.

Select the right indicators. A composite index is only as good as theindicators used to construct it. Therefore, it is important to think andassess carefully before aggregating indicators into a composite. Forexample, do the indicators measure elements of the same phenome-non? How might the indicators be interrelated? To test a compositeindex, replace a key indicator with an alternate indicator and makesure the results are not radically different.

Be careful with applying weights. From a technical standpoint, apply-ing weights is a simple process. But the process can be politically diffi-cult since results may be highly sensitive to the weights applied. So itis important to have a clear methodology in place for determininghow to weight various factors, and the weighting process should bedeveloped and tested with key experts and stakeholders.

T A B L E 8 . 3 Calculation of Composite Index Using a Weighting Method

Factor 1 Factor 2 Factor 3 Total

Raw score 5.0 7.0 9.0 21.0Weighting 0.3 0.1 0.6 1.0Adjusted score 1.5 0.7 5.4 7.6

Skyline view of Medellin, Colombia

Timothy Ross/The Image Works

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The composite indicator is often tracked over time usingtime series analysis.

What Key Inputs Are Required for Composite Index Studies?

Because composite indexes are normally only constructed tosimplify complex data, access to fairly extensive data is arequirement for using this tool. Such data may come frompublic statistical sources or may require the use of surveys (aninvestment climate survey, for example), depending on theinformation to be analysed.For basic composite indicators that use a weighting tech-nique, the analysis is relatively simple; it requires no econo-metric analysis and only a basic understanding of statistics.However, if the city plans to make extensive use of compos-ite indexes, it will be necessary to have an understanding ofmore complex data reduction techniques. Statistical softwarepackages like SPSS or STATA would be valuable to supportthe data reduction computation.

Benchmarking

What Issues Are Addressed by Benchmarking?

The following question can be addressed by benchmarking:How is the local economy performing compared with a ref-erence economy in a particular socioeconomic area? Forexample, one can look at employment growth, exports, firmcreation, GDP, investment, and innovation.

How Is Benchmarking Used?

Originally created as a business development tool, benchmark-ing analysis is today widely used by local and national govern-ments to assess competitiveness and formulate strategy.Benchmarking can be used to explain relative performanceversus quantitative outcome measures, but it can also be usedto compare qualitative factors and processes (such as how busi-ness support is provided). Benchmarking has become partic-

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Further Information

For guidelines and an example of how to create a composite variable, see: http://www.doingbusiness.org/documents/how_to_aggregate.pdf.

For guidelines on how to use the principal component analysis, see (under factor analysis): http://www.statsoft.com/textbook/stathome.html.

For guidelines on calculating the United Nations Development Programme’s (UNDP’s) Human Development Index, see: http://hdr.undp.org/docs/statistics/indices/technote_1.pdf.

For a critical review of the use of composite indicators in city rankings, see Grading Places: What Do the Business Climate Rankings ReallyTell Us?, by Peter Fisher: http://www.epinet.org/content.cfm?id=2052.

For an in-depth discussion and guidelines on calculating indexes, see State-of-the-Art Report on Composite Indicators for the Knowledge-based Economy, prepared by the Sixth Framework Programme of the European Commission: http://kei.publicstatistics.net/KEI%20D5_1.pdf.

For comprehensive guidance on composite variables, see the OECD’s Handbook on Constructing Composite Indicators: Methodology andUser Guide: http://www.olis.oecd.org/olis/2005doc.nsf/LinkTo/std-doc(2005)3.

For more information and links to a wide range of socioeconomic indexes (mostly calculated on a national basis), see: http://humandevelopment.bu.edu/use_exsisting_index/start_content.cfm.

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ularly popular as local economies increasingly design LEDstrategies using the competitive advantage framework.

The main benefit of benchmarking is that it helps citiesidentify areas of improvement—a good way to measureperformance of a local economy. Using this approach, it isoften easier to see how well the local economy is perform-ing when compared with an economy with similar growthconditions.

Benchmarking analysis can be conducted for the overalleconomy, but is more often used to analyse specific sectorsor topics. This is because benchmarking that relies heavily onquantitative statistical analysis can be resource intensive(with regard to acquiring data on the benchmark economyor economies in question).

Cities typically benchmark aspects of their economyagainst the wider region and nation or against similar cities.So one of the biggest challenges to conducting this analysisis to ensure that the comparisons made are appropriate. Thebenchmark economy and the local economy should be

similar—based on the areas in which the economies competeand the economic structure. Using a substantially differentbenchmark economy in the analysis is not likely to give anaccurate picture of regional performance or offer much guid-ance on what could be improved.

The data used for benchmarking analysis can be acquiredfrom several sources, including regional and/or national sta-tistical bureaus in the benchmark economy. Or it may bepossible to exchange information with the city or region cho-sen as a benchmark (e.g., directly with the benchmark city orworking through a city network). In some cases, the bench-mark economy may have conducted an investment climateor equivalent survey, and it may be a good idea to replicatethat survey for comparison in the local economy.

What Key Inputs Are Required for Benchmarking?

Benchmarking analysis can be demanding in several ways.First, the analysis requires data for at least two cities: the city

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CASE STUDYSAN FERNANDO (Philippines)

San Fernando, along with 65 other cities in the Philippines, is involved inthe Asian Institute of Management’s (AIM) Philippines Cities Competitive-ness Ranking Project (PCCRP), run by the Asian Institute of Management(AIM) Policy Center in collaboration with the United State Agency for Inter-national Development (USAID), the Asia Foundation, and GTZ.

The benchmarking tool used for the project is based on the annual Institute ofManagement Development’s IMD World Competitiveness Yearbook and cov-ers the following competitiveness issues: accessibility, cost competitiveness,dynamism of the local economy, human resources and training, in-frastructure, linkages, responsiveness of local government, and quality of life.

Data are derived from secondary sources, and a survey is undertaken specif-ically for the project. Scores for each indicator are converted into a 10-pointscale based on global and national benchmarks. The output from the PCCRPis seen as extremely valuable in assessing local economic competitiveness.But it is difficult to ensure that the project is fully institutionalised (orembedded in the ongoing local strategy process) so that updated data

remain available. The League of Cities of the Philippines, which overseesthe city development strategy (CDS) process in the country, is integratingthe PCCRP survey output into the CDS process.

Industrial facility in the Philippines

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studied and a reference economy. Getting access to thesedata and determining how the data were obtained can bechallenging. Many cities obtain data for benchmark analysisthrough city networks, and others use data from establishedindexes (including competitiveness and quality of lifeindexes).

Benchmarking does not require sophisticated economet-ric knowledge, but it does usually require basic statisticalknowledge, in addition to some basic training in bench-marking. These skills are needed to ensure that the tool isapplied effectively and that appropriate conclusions aredrawn from the analysis. Specific software or analyticalresources are not required. Overall, the tool has moderateresource intensity.

Geographical Information System (GIS) Mapping

What Issues Are Addressed by GIS Mapping?

The following questions can be addressed by GIS mapping:

� Where are firms located in the city or region?� How does the sector composition of the economy play

out spatially? Where are the concentrations of specific sectors?

� Where is industrial land located?� Where are economic activities in relation to the labour

force?� Where are economic activities in relation to critical infra-

structure?

How Is GIS Mapping Used?

GIS analysis is a computer-based tool that analyses spatialdata from a database and displays the results in the form of

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Tips

Choose comparable data. It is important to make sure that the databeing compared are strictly comparable, especially because of discrep-ancies in the measurements made by regional and national statisticsbureaus.

Choose reference economies. It is important to choose the referenceeconomy to be compared with the survey city purposefully, not solelydata availability. An important first step is often to be clear about thepurpose of the exercise. If a wide comparison for overall economic per-formance is the goal, choose a similar economy. But if there is a spe-cific issue, market segment, or sector to be benchmarked, then specificcompetitors may need to be chosen.

Contact benchmark economies. It is a good idea to contact governmentrepresentatives and institutions in the reference economy chosen asthe benchmark, because they may be willing to provide the data youneeded for the survey (if, of course, the findings are shared withthem).

Use as a powerful motivator. Benchmarking can be a powerful commu-nications and mobilisation device. And because it encourages stake-holders to look outside their own cities and provides hard data onrelative strengths and weaknesses, cities may want to consider bench-marking in areas where they want to encourage changes in thoughtand action among stakeholders.

Further Information

For useful background information and perspectives on sub-national benchmarking analysis, see BAK Basel Economics’paper Regional Benchmarking and Policymaking at:http://bakbasel.ch/wDeutsch/bak/publications/papers/999_regional_benchmarkingW3Dnavid W26149.shtml.

For an example of the application of a benchmark analysis,see Glasgow Economic Analysis and Benchmarking study2005 (beginning at page 14): http://www.glasgoweconomicfacts.com/default-2.htm.

For Web links to national statistical bureaus and com-parative data worldwide, see: http://unstats.un.org/unsd/methods/inter-natlinks/sd_natstat.htm.

For the World Bank’s knowledge assessment methodology(benchmarking tool for assessing knowledge economy is-sues), see: http://web. worldbank.org/WBSITE/EXTERNAL/WBI/WBIPROGRAMS/KFDLP/EXTUNIKAM/0,,menuPK:1414738~pagePK:64168427~piPK:64168435~theSitePK:1414721,00.html.

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a map. By overlaying geographical information (actual geo-graphic location of something on a map) and economic data(such as economic activity or employment data), GIS analy-sis can provide a powerful spatial analysis of the local econ-omy. Historically, GIS analysis has been most often used by gov-ernments in land planning and natural resource manage-ment; but this tool is increasingly used by LED practitioners.For example, GIS is now used to map out firm distributionas part of conducting a cluster analysis, and to identify areasfor upgrading and regeneration programmes. The examplein figure 8.1 shows how GIS mapping was used in China todefine the spatial patterns of economic growth as part of theLanzhou Municipality CDS process.

What Key Inputs Are Required for GIS Mapping?

Using this tool requires access to GIS software and trainingin how to use it. Some knowledge and understanding of spa-tial analytical techniques is also valuable. GIS does not requirespecific econometric knowledge, but this depends, in part, onthe types of analysis to be conducted using this software.

Using GIS tools requires fairly extensive data access. Thetype of data needed depends on the type of analysis to be

conducted. But it is always necessary to have detailed geo-graphic information (including a database of maps that arelinked to the latitude and longitude coordinates of specificlocations). When the appropriate data and software are avail-able, the analysis has a moderate resource intensity.

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CASE STUDYCOPENHAGEN (Denmark)

Copenhagen used the GIS analysis as part of a wider analysis of the locationpatterns of their research-intensive industry. A key aim was to identify anyindustrial and spatial clustering effects. Ten research-intensive businesssectors were identified (based on the expertise of the team of researchers),and a GIS analysis was performed on the database of 2,900 companies in 10sectors (including information technology, electronics, energy, and trans-port). The analysis was conducted on each sector individually.

First, a map was produced to show the location of the firms surveyed. Thena second map was produced to show the concentration of employment. Theresearchers for this study were looking for high concentrations of firms and

employment in a radius of 3 to 5 kilometres (a parameter based on theexpertise and judgement of the researchers). These steps were then com-plemented with an analysis of the factors behind location decisions for thefirms studied (based on survey and qualitative interview data)

F I G U R E 8 . 1 Spatial Patterns of Economic Growth, Lanzhou,China

Sewer in a clothing factory,

Copenhagen, Denmark

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PEST/Trends Analysis

What Issues Are Addressed by PEST/Trends Analysis?

The following questions can be addressed by PEST/trendsanalysis:

� What aspects of the macroeconomic environment couldimpact the local economy?

� What are the implications of these factors for the localeconomy (now and in the future)?

How Is PEST/Trends Analysis Used?

Trends analysis looks at aspects of the macroenvironment(those largely outside of the control of local stakeholders)that are most likely to affect the local economy in the future.One of the most commonly used trends analysis tools isPEST, which looks at the political, economic, social, andtechnological factors of the external environment, as shownin figure 8.2.

The PEST analysis (and trends analysis in general) typi-cally focuses on qualitative issues and is designed to ensurethat the competitiveness assessment and resulting strategies

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Further Information

For a high-level discussion of GIS and its uses, see: http://www.library.yale.edu/MapColl/oldsite/gis/whatis.htm.

For a step-by-step manual on the use of GIS software and related information, see: http://www.mapcruzin.com/learn_to_map/.

For a comprehensive manual on establishing local GIS capacity, produced by the Massachusetts Geographic Information System, see:http://www.mass.gov/mgis/Getting_Started_With_GIS.pdf.

For a manual for GIS developed by the United Nations Statistics Division, see: http://unstats.un.org/unsd/publication/SeriesF/SeriesF_79E.pdf.

F I G U R E 8 . 2 The PEST Trends Analysis Tool

POLITICAL

ECONOMIC

ENVIRONMENTALSOCIO -CULTURAL LOCALECONOMY

Tips

Use pictures for impact. GIS analysis can be powerful way to make

visual impressions of economic concepts that relate to geographic

location and concentration. For example, nonexpert audiences can

usually understand concepts illustrated by clusters—spatial concen-

trations of interconnected firms and institutions in related indus-

tries—shown on an actual map.

Be cautious interpreting the results. It is important not to be misled by

pictures derived from a GIS analysis. What appears to be an apparent

industry concentration shown on a map does not always indicate

some significant underlying economic factor. Be sure to seek out alter-

native explanations. For example, is there really a cluster, or is the

concentration shown on the map the result of local infrastructure or

zoning regulations?

Share the costs of GIS software and training. GIS tools are valuable in

many aspects of city planning, including environmental planning,

local economic development, and spatial planning. The costs for GIS

software and training can be substantial, but sharing resources across

departments may enable small cities to purchase this useful tool.

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take account of likely developments in the macroeconomicenvironment. A key advantage of PEST analysis is that itprovides a structured and simple way to organise, analyse,and present a wide range of information.

Trends and PEST analyses lend well to participa-tory approaches. They are normally used as inputs to SWOT (strengths, weaknesses, opportunities, and threats)analysis—in particular, to add a more dynamic perspectiveto the analysis. Trends and PEST are also used to identifypossible futures in scenario planning. (For a discussion ofSWOT analysis, see chapter 9 of this Resource Guide.)

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CASE STUDYIVANO-FRANKIVSK (Ukraine)

The city of Ivano-Frankivsk used PEST analysis as a tool to generate inputsfor their SWOT analysis. In particular, the PEST tool was used to identifyexternal opportunities and threats that might affect the city in the short andmedium term.

Nine members from the Strategy Development Committee participated ina brainstorming session. The discussion was facilitated by technical stafffrom the United States Agency for International Development’s LED projectand external consultants.

The committee members shared ideas on potential future changes acrossthe four areas specified by the PEST analysis, and then discussed whether,and how, these issues could present opportunities and/or threats for the

city. For example, could the future reform of customs regulations and taxcodes (legislative and political changes) present both threats and opportu-nities? Could reforms of this type reduce the city’s revenue base, but alsosimplify administration processes and increase transparency, consistency,and stability?

In this setting, PEST analysis was seen as useful in highlighting potentialdrivers of change in Ivano-Frankivsk’s external environment. However,given the rapid changes being made in the city (and considerable politicaluncertainty in Ukraine at the time of the assessment in 2005), it was diffi-cult for the committee to determine how the city might respond to, or pre-pare for, possible external changes.

Tips

PEST analysis is not an alternative to SWOT analysis. PEST and SWOTanalyses are often described as similar analytical tools, but they arenot. PEST focuses on analysing the external environment, so it cannotbe used on its own as a framework to assess the city’s overall competi-tiveness, as can be done with SWOT analysis. PEST is best used first, asan input to the SWOT process.

Make sure the analysis is dynamic. Analyses of the city’s external envi-ronment should aim to uncover key trends that are likely to affect thecity in the future. The focus of a PEST assessment should be forward-looking.

Carefully analyse each factor. A simple list of factors that affect thelocal economy has limited value. To maximise the value of a PESTanalysis exercise, it is important to think through what each of the fac-tors means and decide which are likely to have the greatest impact onthe local economy.

Further Information

For a brief discussion of and a free template for PEST analy-sis in a business context, see: http://www.businessballs.com/pestanalysisfreetemplate.htm.

For a discussion of the use of PEST analysis in a businesscontext, see: http://www.themanager.org/Models/PEST_Analysis.htm.

For a set of guidelines to and a free worksheet for a PESTanalysis, see: http://www.mindtools.com/pages/article/newTMC_09.htm.

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What Key Inputs Are Required for PEST/Trends Analysis?

Trends analysis can be effective with limited quantitative/statistical data input, and it commonly draws on qualitativeinput from an internal and/or external expert panel. A PESTanalysis can also be completed in a larger participatory forumwith a trained facilitator. The analysis can be conducted withrelatively limited resources and analytical capacity.

Sector Share Analysis

What Issues Are Addressed by Sector Share Analysis?

The following questions can be addressed by a sector shareanalysis:

� What is the basic structure of the city’s economy?� Which sectors contribute the most to GDP, overall

employment, and output?

How Is Sector Share Analysis Used?

Sector share analysis is probably the most common tool usedto analyse the city economic structure. It identifies signifi-cant industries in the city-region and provides important

insights into how global and national sectoral trends mightaffect the local economy.

This type of analysis takes data on employment and/oroutput (such as GDP and total production) in each sector andcalculates them as a share of the total economy. The resultsare then presented in a table or in a graph. Industry shares areusually based on widely-used standard classification codes(such as SIC, SITC, NAICS, or HS). However, data aresometimes collected only at a broad level of aggregation (suchas for primary, secondary, and tertiary industries).

Because the sector share analysis only provides a static pic-ture of the economy, it is most often used as a starting pointfor wider analysis. For example, it is often used with timeseries analysis, PEST/trend analysis, shift share analysis,input-output analysis, and location quotients. For cities thatare conducting a local economy assessment for the first time,sector share analysis is a critical starting tool that gives insightinto the local economic structure.

What Key Inputs Are Required for Sector Share Analysis?

The data required for sector share analysis are often availablefrom local, regional, or national statistics bureaus. Cities thatdo not already have these data may want to use an industrial

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CASE STUDYKARU and BOBO-DIOULASSO

KARU (Nigeria)

Karu used a sector share analysis in conducting its first local economyassessment in 2002. City administrators in Karu knew very little about thebasic composition of the local economy, so the sector share analysis was thesingle-most important tool used in the assessment. The analysis wasdesigned to identify the main sectors of the city’s economy and, in particu-lar, to assess the relative importance of the formal and informal sectors.

Because of the near absence of formal statistics to describe the economy ofKaru, data were collected using an industrial structure survey and an infor-mal sector survey. The sector share analysis then compared the contribu-tion of each of the sectors, by number of employees and the value of salesand output. Separate analyses were conducted for the formal and informalsectors, and these analyses were then compared to determine the relativeimportance of the different sectors.

The use of this analytical tool was considered highly successful because it notonly shed light on the basic structure of Karu’s economy, but it also helpedto identify important stakeholders in the private sector. In particular, theanalysis highlighted six main business sectors from which stakeholders wereinvited to participate in the city development strategy process.

BOBO-DIOULASSO (Burkina Faso)

Bobo-Dioulasso’s share analysis was derived from Burkina Faso’s nationalstatistics. Much of the sectoral data were extracted from breaking downnational aggregates at the local level to extract a rough overview of the sec-toral structure of the economy. Field data were then used to corroboratethe accuracy of the breakdowns of national data.

Although there is a risk of inaccuracy, sector share analysis has the benefitof generating significant sectoral data that would otherwise be difficult andcostly to obtain.

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structure survey to collect it. The following inputs areneeded for this analysis:

� Data on the output (GDP or total production, for exam-ple) of the firms in the local economy by sector.

� Data on the number of employees of the firms in the localeconomy by sector.

� An agreed sector classification system (in most cases citiesshould adopt the system currently used regionally ornationally).

� This analysis requires little or no external capacity or sta-tistical or econometric knowledge.

� Sector share analysis does not require any particularresources other than data and human resources. Whenrelevant local or regional data are available (and no addi-tional data collection is required), the analysis has lowresource intensity.

Value-Added Analysis

What Issues Are Addressed by Value-Added Analysis?

The following questions can be addressed by a value-addedanalysis:

� Which sectors and/or firms are the most important con-tributors to the local economy?

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Tips

Using modelling techniques to derive local sector structures. When local-level data are not available on output by sector, it may not be neces-sary to conduct an industrial census or survey. When both the regionaloutput data and city-level employment data are available, it is possi-ble to model city-level output share by sector—if the relationshipsbetween employment and output in sectors are broadly similar atboth levels of analysis. Although the results of modelling techniquesare less precise, modelling can be a cost-effective approach and so isused by most cities around the world.

Take the results further. As a stand-alone tool and a one-time snapshotof the industrial structure of the economy, sector share analysis doesnot identify which industries are growing fastest—or anything aboutthe factors that underpin the sectoral composition. So this tool may bemore effective when used along with qualitative information on theeconomy, acquired through interviews with industry experts and pub-lished reports, and other tools, such as benchmarking, time series, andPEST/ trend analysis.

Try analysing aggregated sectors to understand broad trends. Combin-ing a number of traditional sector definitions to create sector com-posites (for example, “knowledge sectors”) can be valuable tounderstanding how the local economy is structured with regard tobroader global trends.

Further Information

For detailed information on how to use and construct tablesand graphs, see: http://www.statsoft.com/textbook/stathome.html.

For guidelines on creating pie charts and graphs, see SouthAfrica’s Department of Provincial and Local Governmentguides to integrated development planning (Guide 4, Tool-box Part 2), at: http://www.thedplg.gov.za/subwebsites/Publications_b.htm.

For an introduction to industry classification and for guide-lines on creating a snapshot of the economy, see: http://cecd.aers.psu.edu/using_employment_data_to_better.htm.

For guidelines on how to create a snapshot of the econ-omy and an example of a sector share analysis for a U.S.county, see: http://www.economictoolbox.geog.psu.edu/index.php.

For a list of national statistical bureaus worldwide, see:http://www.oswego.edu/~economic/int-data.htm.

For the Organisation for Economic Co-operation andDevelopment’s (OECD) Structural and Demographic Busi-ness Statistics database (SDBS) with data on the sectoralcomposition for OECD member states, see: http://www.oecd.org/document/17/0,2340,en_2649_33715_36938705_1_1_1_1,00.html#SDBS.

For data on the sectoral composition of non-OECD mem-ber countries, the International Yearbook of Industrial Sta-tistics, published annually by UNIDO may be purchased at:http://www.unido.org/en/doc/3700.

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� How much of the output of local firms is produced withinthe city?

� In which sectors is our economy adding higher or lowervalue?

How Is Value-Added Analysis Used?

Value added is measured as the sales value of goods and serv-ices less the cost of inputs (materials, parts, and services) usedto facilitate production. Alternatively it can be seen as thesum of payments made to labour by firms in the city pluspayments made for these firms on investments. Value-addedanalysis provides several important indicators on the localeconomy. First, as gross value added (GVA), an alternativemeasure to gross domestic product (GDP), this tool is a fun-damental indicator of the overall economy. Value added isseen as an important measure of firm contribution to thelocal economy, because it looks at actual wealth creation, notjust employment or output. By taking value-added data ofeach firm and/or sector and calculating it as a share of thetotal economy, value-added analysis can also be a usefulapproach in conducting a sector share analysis. Moreover,dividing value added by one worker, one gets a commonlyused measure of productivity.

The most common way of estimating value added is byusing input-output tables, which essentially display the valueof input and output (goods and services sold) by supplier andbuyer respectively. (See section on input-output analysis forfurther information on this technique.) Another way to col-lect value-added data is by reviewing company accounts (seethe further information box on page 87 for an example),which may be available at a government agency; if not, rele-vant data may need to be collected using an industrial struc-ture survey tool.

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CASE STUDIESPOZNAN (Poland) and COPENHAGEN (Denmark)

Value added is often a preferred measure to other economic measures(including output and employment) in that it examines actual wealth cre-ation. However, because access to value-added data at the regional level isoften limited, mainly middle- and high-income cities use value-addedanalysis.

In Poznan, value-added analysis was used to demonstrate the diversity ofthe local economy and highlight areas of strength (its services sector, forexample). However, the analysis was only conducted at a high level of sec-toral aggregation (agriculture, industry and construction, and services)because of the lack of data—a problem frequently encountered at theregional level.

In Copenhagen, however, total regional value added was divided by the aver-age number of hours worked by employees in a year to serve as a proxy forlabour productivity. This then fed into a benchmarking exercise in which theCopenhagen city-region was compared with six other European cities in fourareas: innovation and specialisation, entrepreneurship, use of information andcommunication technologies, and human resources. The percent increase inlabour productivity between two years (1995 and 2000) was estimated foreach city in the benchmarking exercise and displayed in a histogram as a keymeasure of economic performance. The consultants conducting this analysissaw the great advantage of using the value-added measure (rather than GDPper capita, for example) because value added accounted for differences inaverage working hours in the benchmark economies

Tips

On using estimated data. When there are no city-level data on valueadded, it is possible to estimate the data using the national input-output account. This is a less costly way of obtaining value-added datathan through primary data collection; but the data are also likely to beless accurate. City-regions often are more dependent on external tradethan their national economy overall, so estimates based on nationaldata may underestimate the city-regions’ dependence on imports.More sophisticated modelling techniques supported by additionaldata can help improve accuracy.

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Value-added data at the regional or city level is often dif-ficult to obtain, and conducting an industrial census orindustrial structure survey is often too resource intensive.Therefore, value-added data are typically modelled (based onregional and national data).

Because the value-added analysis offers a simple snapshotof the contribution of firms to the economy, it is rarely usedas a stand-alone tool. It is almost always used in conjunctionwith sector share analysis and time series analysis.

What Key Inputs Are Required for Value-Added Analysis?

Value-added analysis requires detailed data on firm accounts,including the value of sales and the cost of inputs. Since thesedata are not always available at the local or regional level, thedata can be estimated using national input-output tables,although sometimes at the cost of accuracy (see tips box onpage 86). Alternatively, an industrial structure survey can beused to collect local-level data on firm accounts.

Although value-added analysis is relatively straightfor-ward, it requires some level of econometric knowledge andcomfort in reading input-output tables. However, it does notrequire any particular resources other than data and humanresources. When comparable data are available at local andnational levels (and no additional data collection is needed),the analysis has a low resource intensity.

Economic Base Analysis

What Issues Are Addressed by Economic Base Analysis?

The following questions can be addressed by an economicbase analysis:

� How much of the city’s economy is driven by meeting thelocal population needs versus selling products and serv-ices outside of the city (exports)?

� Which sectors and types of firms (by size) drive the city’sexport economy?

� What proportion of the labour force in the city works inthe export sectors?

How Is Economic Base Analysis Used?

Economic base analysis (also called export base analysis) isdesigned to analyse the broad economic structure of the localeconomy. It does this by dividing the economy into twosectors: 1) the basic or export sector (which includes all output—goods and services—sold outside the borders of thecity or region), and 2) non-basic sector (which includes alloutput that is sold within the local economy, for example,output from local grocery retailing, hairdressing, restaurants,and other local services). Economic base analysis originates

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Further Information

The U.K. Department of Trade and Industry collects value-added data on 800 U.K. and 500 European firms through its Value AddedScoreboard. For more information, see: http://www.innovation.gov.uk/value_added/home.asp?p=home.

For guidelines on creating a local economy “snapshot,” see: http://cecd.aers.psu.edu/using_employment_data_to_better.htm.

For information on how to calculate value added based on the United Nations System of National Accounts, see: http://unstats.un.org/unsd/sna1993/tocLev8.asp?L1=16&L2=5.

For an example of the use of value-added data in sector share analysis in the U.S. state of Oklahoma, see: http://pods.dasnr.okstate.edu/docushare/dsweb/Get/Document-982/F-910web.pdf.

Many textbooks in regional science cover value added in relation to input-output tables. For information on how to read value added andto calculate GDP from input tables, see Regional Economics and Policy (3rd edition), by H. Armstrong and J. Taylor, published 2000 byBlackwell Publishing.

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from economic base theory, which stipulates that inflow ofmoney generated from the export sector is the main sourceof growth in an economy and determines the rate of employ-ment and employment growth of the non-basic sectors serv-ing local consumption. It is seen as a valuable tool, becauseit can be used to make relatively simple forecasts on incomeand employment for the local economy.

Constructing an economic base analysis involves twomain steps:

1. Determining the basic and non-basic sectors: There aretwo broad approaches to assigning local economic activ-ity to basic and nonbasic sectors. One method, theassumption technique, assumes that certain sectors, suchas mining and manufacturing, are wholly basic, whileothers, services, for example, are wholly non-basic. Thisis quick and easy for analytical purposes, but inaccurate—increasingly so as the services sector becomes more glob-alised. The second and more common method involvescalculating location quotients for each sector (employ-ment shares of a sector vis-à-vis a reference economy, nor-mally the national economy) and assuming that any

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Tips

Overestimating importance of exports. An important caveat of eco-nomic base analysis and its theoretical underpinnings is that it seesthe exports as the sole driver of economic growth, neglecting the roleof, for example, investments and productivity. When assessing thelocal economy using economic base analysis, it is important to recog-nise that there is considerable scope for growth outside of export; thisis particularly true for larger cities and city-regions.

Be careful comparing economic bases across locations. Economic baseanalysis is designed for understanding a city’s economic structure,mainly for conducting forecasting and assessing the possible impactsof possible future scenarios on the city (see scenario planning for fur-ther information). As such, it is generally not appropriate to comparebasic versus non-basic structures or even base multipliers across cities.

Be clear on city boundaries. The economic base analysis is focused onmaking the distinction between local and external economies. There-fore, defining clearly the “local economy” is critical. Is the local econ-omy a city? A city-region? What constitutes the wider externaleconomy? Is it a region? A nation?

CASE STUDYMUNICH (Germany)

Munich used economic base analysis to forecast employment levels (fortotal employment and at a disaggregated sectoral and spatial level) for thecity and its wider city-region for the years 2005–2015. Munich conductsemployment forecasts every five to six years, in an effort to understandlikely changes to economic structure and employment.

Economic base analysis formed the backbone for the forecast model usedin Munich. Econometric modelling was required since employment data arenot captured by the official census in Germany and are not available at aspatially disaggregated level. The analysis divided the economy into threesegments: the basic sector (exported products and services), the non-basicsector (local products and services), and the other services sector (whichincludes the public sector and other institutions with employment patternsthat do not necessarily follow market rules). Local economic activity was

assigned to this third sector based on the assumption technique, whereaseconomic activity was assigned to the basic and non-basic sector based onlocation quotient (measured by employment levels in Munich versus othermetropolitan regions in Germany).

Capturing growth trends in the basic sector was central to determining theemployment forecasts for Munich. These growth forecasts were deter-mined through a shift share analysis—in which the model first examinedthe impact of development trends in the overall national economy and thenconsidered potential regional variation above/below the national trend forthe various industrial sectors in the basic sector. A simpler approach, basedon regional population growth estimates, was used to determine thegrowth forecasts for the non-basic sector.

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employment above the reference economy average isbasic.

2. Calculating base multipliers: The base multiplier calcu-lates the ratio of the total employment in the local econ-omy to the basic employment in the economy:

Base multiplier = total employment year x / basic employment year x

The base multiplier gives an understanding of howchanges in employment in the basic sector will influencethe overall economy.

Note: Economic base analysis can be calculated on both output andincome, but it is typically calculated based on employment data (oftenreadily available at the local level).

What Key Inputs Are Required for Economic Base Analysis?

The data required for this analysis may not be readily avail-able from national and local statistical sources. For thisanalysis, the following are needed:

� Data on national and local-level employment by sector.See location quotient below for further information ondata requirements for this approach.

� To analyse economic base on output or value added, cor-responding local-level data on this are needed, or thelocal-level data will need to be modelled based on regionalor national data.

This analysis is moderately complex and requires capac-ity in econometric analysis. It does not require any particu-lar resources other than data and human resources, althoughspreadsheet software or other statistical software (STATA,SPSS) would be useful. If comparable data at national andlocal levels are available and no additional data collection isrequired, the analysis has moderate resource intensity.

Location Quotient

What Issues Are Addressed by the Location Quotient?

The following questions can be addressed by the locationquotient:

� How specialised is the city’s economy?� In which sectors is the local economy specialised and con-

centrated?

How Is the Location Quotient Used?

Location quotient is one of the most widely used measuresof specialisation and industrial concentration of a local econ-omy. The location quotient takes the relative size of any sec-tor (most commonly measured by employment or output)and compares it with equivalent rate in a reference economy(usually the national level). Therefore, this tool calculateshow closely the local economy mirrors the structure of thenational economy and in which sectors the local economy ismore or less specialised. Typically, this is the primary toolused to identify where clusters may exist in the local econ-omy (see cluster mapping, page 99, for further information).

This tool is also often used to identify the import andexport sectors in the local economy, as a part of economic

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Further Information

For a discussion of the theory and application of economicbase analysis, see: http://garnet.acns.fsu.edu/~tchapin/urp5261/topics/econbase.htm.

For guidelines and equations for economic base and mul-tiplier models, see: http://www.rri.wvu.edu/WebBook/Schaffer/chap02.html.

For an example of the use of economic base analysis by theCanadian city of Dryden, see: http://www.dryden.ca/website.nsf/DrydenSocioEconomicFinalReportFeb05.pdf?OpenFileResource#search=%22%22economic%20base%22%20dryden%22.

For an accessible step-by-step guide to multiplier analysis,see: http://www2.sjsu.edu/faculty/watkins/EPM01.htm.

Most textbooks in regional science or local economic devel-opment have good explanations of the role of economic basetheory and analysis. See, for example: Local Economic Devel-opment: Analysis and Practice, by John P. Blair, published1995 by Sage Publications.

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base analysis (with exports defined as sales outside of the localeconomy, not necessarily outside of the country). Surplusemployment or output in any sector of the local economyrelative to the reference economy is assumed to be involvedin exports, because it is in excess of what is needed to servelocal demand.

The location quotient is simply the ratio of the per-centage of local employment or output in any sector to theequivalent percentage for the reference economy. It can beexpressed as:

LQ =ei /e

Ei /E

where ei = local employment in industry i, e = total localemployment, Ei = national (or reference economy) employ-ment in industry i, andE = total national (or reference econ-omy) employment.

A ratio of 1 indicates that the local economy and the ref-erence economy have an identical share of an industry; a

score of greater than or smaller than 1 means that the localeconomy has a greater or smaller share of that sector than thereference economy, respectively. Therefore, by reviewingthese ratios, the major import and export sectors in the localeconomy are easily identified.

Locational Gini index, adjusted geographic concentrationindex, and entropy indexes and decompositions are similar,but more complex, spatial indexes used to measure industryconcentration (see table 8.5). For information on an alterna-tive measure of specialisation, see specialisation index.

What Key Inputs Are Required for the Location Quotient?

The data required for this analysis are often readily availablefrom national and local statistical sources (especially employ-ment data). The following data are needed for this type ofanalysis:

� Output and/or employment data for the city, by sector

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CASE STUDYTORONTO (Canada)

The city of Toronto has structured its most recent economic developmentstrategy in 2000 around clusters, so analysing location quotients of mainsectors in the city economy was fundamental to developing its city strat-egy. The city first identified the clusters that were most important to theToronto economy in terms of employment. For these 10 clusters, anemployment-based location quotient was then calculated (based on aver-age employment concentrations for Canada and the United States com-bined). This allowed for an assessment of the degree to which Toronto couldbe considered specialised on a North American basis in priority clusters.

Toronto took the analysis further by combining this level of specialisationwith benchmarks of relative growth rates and overall employment. Thisapproach allowed for: 1) an assessment of Toronto’s relative competitive-ness performance versus other cities in each sector; and 2) an assessment ofthe relative importance and competitiveness of each sector for the city. Thisanalysis found that Toronto’s relative specialisation varied substantiallyamong the 10 sectors, with biotech and business and professional servicesshowing particularly high relative specialisation in the city.

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� Output and/or employment data at the national orregional level, by sector

Location quotient analysis is moderately complex andrequires some minimal capacity in econometric analysis.Other than data and human resources, this type of analysisdoes not require particular resources, although spreadsheetsoftware is useful. If comparable data at national and local lev-

els are available and additional data collection is not required,location quotient analysis has a moderate resource intensity.

Specialisation Index

What Issues Are Addressed by the Specialisation Index?

The following questions can be addressed by the specialisa-tion index:

� How specialised or diversified is the local economy?� To what extent is the local market dominated by a few

firms or a few sectors?� How reliant is the city on certain firms or sectors?

How Are Specialisation Indexes Used?

Specialisation indexes measure the relative concentration ordiversification of a city’s economy. They can be used tounderstand concentration in, and reliance on, certain sectorsand/or firms. Two examples of specialisation indexes follow:

1. Herfindahl-Hirschman Index (HHI). HHI is a measureof market concentration that indicates the extent to whichthe market in question is dominated by a few firms. The

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Tips

Get data that are progressively more detailed. The level of data aggrega-tion matters—both in terms of how much work is involved and howmuch is gained from the analysis. It may be useful to start at a highlevel of aggregation (using two-digit standard industrial codes, forexample) to identify the broad pattern of specialisation. For sectorswhere a specialisation or strong export sector is detected, location quo-tients can then be calculated at a finer level of aggregation. This willhelp cities to identify specific subsectors that are driving the values seenat higher levels of aggregation.

Be cautious of underestimates for exports. It is important to rememberthat the location quotient tends to underestimate the size of the export(basic) sector, because it assumes there is no cross-hauling (no simulta-neous import and export of the same type of product in a region).

Be sensitive to spatial scale of measurement. The score for the locationquotient will depend on the spatial scale of measurement. This is par-ticularly important to take into account when using regional employ-ment data to measure specialisation at the city level. If the differencebetween the economic structure and the size of the region and the cityis substantial, then the location quotient may give an incorrect impres-sion of specialisation.

Probe for implications. It is critical to examine the location quotientresults and probe for the wider implications. Is the local economyhighly dependent on particular sectors? Is this dependence a strength?Or, how might this dependence be problematic?

Further Information

For information on how to calculate the location quotient,see the Florida State University Web page at: http://garnet.acns.fsu.edu/ ~tchapin/urp5261/topics/econbase/lq.htm.

For guidelines on how to calculate the location quotient, see:http://www.rri.wvu.edu/WebBook/Schaffer/chap02.html#Heading14.

For an example of how location quotients are constructedand presented for U.S. counties, see: http://www.economictoolbox.geog. psu.edu/index.php.

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measure has traditionally been used by competition boardsto supervise mergers and other structural changes in theprivate sector, but has also been used by cities to determinethe extent to which the local economy relies upon the per-formance of a few firms. The formula for HHI is:

For the purposes of a local economy assessment, si can becalculated as the output or (more likely) employment ofa firm or sector i in the market, and n is the number offirms or sectors. So the employment share of each firm orsector is squared and the resulting numbers are added up.A resulting score between 1000 and 1800 indicates amoderate concentration; a score above 1800 indicates ahigh degree of concentration.

2. Tress index. A tress index measures the degree of concen-tration of a city’s economy on a sector basis. A tress indexof employment contribution is determined by calculatingeach sector’s contribution to the local economy; multiply-ing each sector’s total employment by its appropriateweighting (that is, according to its share of the economytotal); and calculating the sum totals of the weighted val-ues for each sector. The totals will vary depending on thenumber of sectors in the economy and are then normally

indexed (on a 0 to 100 index). (See growth indexes onpage 75 for further information on this technique).

See location quotient for other measures to calculate spe-cialisation.

What Key Inputs Are Required for Specialisation Indexes?

Computing the specialisation indexes is relatively straight-forward and requires no specific econometric knowledge or

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CASE STUDYRAFAELA (Argentina)

In Rafaela, the Herfindahl-Hirschman index is created in relation to theindustrial census the city conducts every five to six years. (See industrialstructure survey on page 62 for further information on the industrial cen-sus.) Rafaela sees the HHI as an important tool because it indicates theextent to which the local economy relies upon the performance of certainkey firms. Ideally, Rafaela would like to compute the index for each sector.However, the small number of businesses in the city makes it difficult toproduce statistically robust results.

To compensate for this shortcoming, the HHI is incorporated into a wideranalysis of the composition of the local economy in Rafaela. Specifically, the

tool is complemented with a descriptive data analysis of the evolution ofthe market share for the top 4, 10, and 20 firms. The analysis also examinesthe evolution of sales, employment figures, and expansion rates by size offirm.

In 2000, the HHI was calculated by estimating each local (surveyed) firm’sshare of total sales, squaring it, and adding them all up. The resulting indexof 1216 indicated a moderate degree of concentration.

Tips

Complement with qualitative analysis. Quantitative data on firm andsector specialisation provide important insights into the structure ofthe local economy. However, in order to assess and understand theimplications of apparent concentrations and specialisations, it is criti-cal to also understand details on the activities and scope of these sec-tors and firms in the city, in addition to global trends.

Be sensitive to spatial scale of measurement. The score for specialisationindexes will depend substantially on the spatial scale of measurement.If the difference between the economic structure and the size of theregion and the city is substantial, then the specialisation indexes maygive an incorrect impression of specialisation.

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software tools (with the exception of basic spreadsheet soft-ware). The analysis does, however, require detailed informa-tion on employment (and possibly output) for firms andsectors in the city economy. When the appropriate data areavailable, this tool has a relatively low resource intensity.

Shift Share Analysis

What Issues Are Addressed by Shift Share Analysis?

The following questions can be addressed by a shift shareanalysis:

� How well are the different sectors in the local economyperforming relative to a reference economy?

� How much of the employment growth experienced by alocal economy can be accounted for by the nationalgrowth rate?

� How much of the employment growth experienced by alocal economy can be accounted for by the mix of indus-tries in a local economy?

� How much of the employment growth experienced by alocal economy can be accounted for by local factors?

How Is Shift Share Analysis Used?

The shift share analysis assesses the performance of the sec-tors of a local economy—typically measured by employmentgrowth—relative to a larger reference economy (most com-monly the national economy). This tool is seen as a relativelysimple and effective way to measure the competitiveness ofboth individual sectors and the overall economy.

Specifically, the shift share analysis calculates how muchof the employment growth experienced by a local economyin a specific time period can be accounted for by: 1) the econ-omy’s mix of sectors, because different sectors grow at differ-ent rates; 2) the national growth rate, because a certainsimilarity between national and local employment growth isa reasonable expectation; and 3) local factors, because a localeconomy may possess a competitive advantage in certain sec-tors. If a city’s employment in a sector is growing at a fasterrate than the national employment growth in the sectorwould suggest, the local economy is assumed to possess acompetitive advantage in that sector.

Calculating the shift term for a sector involves first calcu-lating the growth rates for the local economy and for the ref-erence economy (either the regional or national economy).

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Further Information

For basic information and an online calculator for estimating the HHI, see: http://www.unclaw.com/chin/teaching/antitrust/herfindahl.htm.

For information on how to calculate the HHI, see: http://www.usdoj.gov/atr/public/testimony/hhi.htm.

For an overview of the Tress index and how to use it, see the Development Bank of Southern Africa’s (DBSA) Guidelines to RegionalAnalysis, at: http://www.dbsa.org/Research/Pages/Publications.aspx.

For a critical discussion of entropy indexes as measures of industry concentration, see: http://faculty.smu.edu/maasoumi/Pdf%20Files/MS2rev1.pdf.

The following are examples of specialisation indexes currently in use in local-level economic assessments:

● To see how the HHI is applied in Rafaela (Argentina), see Censo Industrial Rafaela 2000 under “publicaciones” at:http://www.rafaela.gov.ar/es/Publicaciones-ampliar.aspx?p=20 (document in Spanish).

● For the application of the Tress index in the city of Tshwane (South Africa), see page 9 of the status quo analysis at:http://www.tshwane.gov.za/idp2004.cfm.

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This is calculated as:

Growth rate = (e2 – e1)/e1

where e2 = employment at time period 2, and e1 = employ-ment in time period 1. The shift term is then calculated as:

Growth rate sector x (local economy) – growth rate sectorx (reference economy)

If the shift term is positive, the local economy is growingfaster than the reference economy in the specific sector. Buta negative shift term indicates that the local economy isgrowing slower than the reference economy in the sector.

It is important to note that a sector may experience adeclining employment rate and simultaneously increasingoutput, so a local economy may actually possess a competi-tive advantage in that sector (despite shift share analysisresults indicating otherwise). Therefore, the traditionalemployment-based shift share analysis has been extended to

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CASE STUDYGLASGOW (Scotland)

Shift share analysis was used in Glasgow as part of a 2005 economic assess-ment study, Glasgow Economic Analysis and Benchmark Report, conducted byBAK Basel Economics. The shift share analysis was used specifically to under-stand the drivers of growth in the two most important sectors of the Glasgoweconomy: finance and business services, and the public sector.

Specifically the analysis focused on disaggregating the regional growth ratein these two sectors into four main contribution effects (global, structural,national, and regional). Conducting an analysis at these four levels of disag-gregation required substantial economic data as well as fairly sophisticatedanalytical techniques and expert resources. The results of the analysis wereparticularly useful for Glasgow in that they stripped out some of the struc-tural issues that affected growth in the city and helped to isolate city-specificcompetitiveness issues, often masked in a more static type of analysis. Forexample, the shift share analysis in Glasgow found that although the city per-formed well overall in business services (as was the conventional wisdom),

the regional effect was particularly strong for information technology serv-ices, slightly above average in banking, and particularly weak in real estate.

Port and city view of Glasgow, Scotland

Tips

Be sensitive to spatial scale of measurement. The outcome of the shift

share analysis will depend on the spatial scale of measurement. This is

particularly important to take into account when using regional

employment data to measure specialisation at the city level. If the dif-

ference between the economic structure and the size of the region and

the city is substantial, then the shift share analysis is likely to give an

incorrect impression of specialisation.

Complement the analysis with industry experts and literature. The shift

share analysis only indicates which sectors of the local economy seem

to possess a competitive advantage, not the source of this competi-

tive advantage. By consulting industry experts and literature, it

may be possible to identify the factor(s) underpinning a local com-

petitive advantage—for example, quality of local endowments,

availability of factors of production, and market access for particular

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include average factor (capital and labour) productivity forsector and/or region.

What Key Inputs Are Required for Shift Share Analysis?

This type of analysis requires local and national (or regional)employment data by sector for at least two separate years. Forthe extended version of the shift share analysis, productivitydata are also needed, overall or by factor (capital and labour)on a national (or regional) and local level.Other than data and human capacity, the only resourcerequired to conduct shift share analysis is basic spreadsheetsoftware. The analysis is of moderate complexity andrequires some limited econometric capacity.

Input-Output Analysis

What Issues Are Addressed by Input-Output Analysis?

The following questions can be addressed by an input-out-put analysis:

� What are the economic linkages between sectors in thelocal economy?

� How do changes in one sector affect other sectors in thelocal economy?

� How do changes in sectors affect overall economic activ-ity in the local economy?

How Is Input-Output Analysis Used?

This tool is widely used in local economy analysis to deter-mine linkages between sectors in the economy, by breakingdown inputs into each sector (by contributing sector) andoutputs from each producing sector (to consuming sectors).An input-output table therefore provides a summary of thetransactions occurring within an economy over a selectedtime period, showing, for a given industry, the industriesfrom which it purchases and the industries to which it sells.Input-output tables also show the use of industry productionin private and government consumption, and the use ininvestment and sales outside of the region (exports). Table8.4 provides a basic example of how a simple input-outputtable might look for a typical three-sector economy.

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Further Information

For a step-by-step guide for shift share analysis developed by Penn State’s Centre for Economic and Community Development, see toolnumber 4, at: http://cecd.aers.psu.edu/using_employment_data_to_better.htm.

For a calculator developed by the Department of Geography at the University of Sheffield in the United Kingdom, for use in conduct-ing shift share analysis, see: http://www.shef.ac.uk/geography/teaching/shift share/shift share.html.

For an explanation and practical application of a shift share analysis for a U.S. county, see: http://www.economictoolbox.geog.psu.edu/index.php.

For an in-depth and rigorous approach to shift share analysis, see Regional Impact Models by W. A. Schaffer at: http://www.rri.wvu.edu/WebBook/Schaffer/index.html.

For a practical application of shift share analysis in the U.S. county of Lauderdale (Mississippi), see pp. 24 to 29, at: http://www.embdc.org/researchpublications.html.

Most textbooks in regional science and/or local economic development generally cover the shift share analysis extensively. For a discus-sion and a practical application of the Total Factor Productivity approach to shift share analysis, see Regional Economic Development:Analysis and Planning Strategy, by R. J. Stimson, R. R. Stough, and B. H. Roberts, published 2002 by Springer-Verlag.

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The input-output analysis is an essential tool in local eco-nomic planning that can be used for a range of analyses. It isuseful in understanding the structure of the local economy(especially the linkages between sectors and firms), andtherefore it is frequently used in relation to value chain andcluster-mapping exercises. (See cluster mapping on page 99and value chain analysis on page 101, for further information).

This tool is also important for seeing how changes in oneindustry affect another. It is therefore of particular use ineconomic forecasting or scenario planning as part of the eco-nomic assessment and strategy process. (See scenario plan-ning on page 118 for further information).

What Key Inputs Are Required for Input-Output Analysis?

This type of analysis typically requires detailed statisticalinformation on sectors in the local or regional economy.

These data need to be disaggregated by sector and availableat the regional level. (Note: Where statistical data are notavailable, data can be collected through a detailed survey ofall industry sectors in the local economy. However, this canbe time and resource intensive.)

Input-output analysis is complex and requires significantcapacity in econometric analysis.

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T A B L E 8 . 4 Example of an Input-Output Table for a Three-Sector Economy

Inputs Inputs to Inputs TotalEconomic to agri- manufac- to Final out-activities culture turing services demand put

Agriculture 5 15 5 20 45Manufacturing 10 40 20 50 120Services 20 80 100 50 250Labour 20 20 40 0 80

CASE STUDYBRISBANE (Australia)

The city of Brisbane used input-output analysis for projecting the regionaleconomy in its 1999 and 2006 city strategies. For example, the 1999 strat-egy developed a detailed input-output transaction table that traced out themonetary value of transactions between industry sectors in the regionaleconomy.

The analysis provided both a concise, descriptive snapshot of the economyat that particular point in time and a matrix representation of the regional

economic structure. In particular, the analysis was used to demonstrate thenature and impact of transactions between industry sectors and to showhow these transactions could change over time. Because of the complexityof the analysis, Brisbane used third-party specialist capacity to conduct theinput-output analysis.

Tips

Exert caution with using estimated data. Input-output tables can be

estimated using national input-output accounts. This is a less costly

way of getting value-added data than through primary data collec-

tion, but the data may also be less accurate. Regions tend to be far

more dependent on external trade than the national economy, so esti-

mates based on national data may underestimate the dependence on

imports

Be aware of structural inter- and intra-industry changes. If the input-

output links between sectors and firms are changing rapidly, then the

role of input-output analysis in forecasting is quickly made redundant.

In cases where the local economy is believed to be undergoing signifi-

cant structural change, it may be best to consider collecting additional

information about these changes through surveys to account for them

in a forecasting exercise.

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Social Accounting Matrix

What issues Are Addressed by the Social Accounting Matrix?

The following questions can be addressed by the socialaccounting matrix:

� What are the economic linkages between sectors in thelocal economy?

� How do changes in one sector affect other sectors in thelocal economy?

� How do changes in sectors affect overall economic activ-ity in the local economy?

How Is the Social Accounting Matrix Used?

A social accounting matrix (SAM) is a general equilibriummodel of the economy based on the principles of input-out-put analysis—essentially, it is an extension of the input-out-put model. The SAM uses a system of accounts frameworkto track economic flows between the supply side and institu-

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Further Information

For a guide on constructing regional input-output tables (including examples) refer to the Web site of The National Institute of Eco-nomic and Industry Research in Australia: http://www.nieir.com.au/index.php?option=com.content&task=view&is=28&Itemid=114.

For a discussion of the theoretical underpinnings and application of input-output analysis, see chapter 11 in An Introduction to RegionalEconomics, by E. M. Hoover and F. Giarratani, at: http://www.rri.wvu.edu/WebBook/Giarratani/chaptereleven.htm.

For an in-depth discussion of the construction and application of input-output tables, see: http://www.rri.wvu.edu/WebBook/Schaffer/index.html.

For a discussion of the application of input-output tables in cluster mapping, see: http://www.rri.wvu.edu/WebBook/Bergman-Feser/chapter3.htm#3.3.3.

Most textbooks in regional science and/or local economic development generally provide extensive discussion of the input-output analy-sis. See, for example, Local Economic Development: Analysis and Practice, by John P. Blair, published 1995 by Sage Publications.

For an overview of current issues in input-output analysis, see the International Input-Output Association (IIOA) Web site at: http://www.iioa.org/.

CASE STUDYBOBO-DIOULASSO (Burkina Faso)

A social accounting matrix (SAM) was extensively used in Bobo-Dioulassoduring the city development strategy (CDS) process, which was guided bythe OECD and MDP ECOLOC process. The Bobo-Dioulasso SAM is a simplifiedinput-output table, showing the economic interaction among sectors in thearea. This approach was particularly well-suited to Bobo-Dioulasso becauseof the low level of statistical complexity required and because of this tool’scapacity to accommodate information on different socioeconomic aspectsstemming from the surveys conducted within the CDS framework.

The SAM was used to uncover accounting transactions and linkages in thecity and its surrounding area and was a useful tool in identifying economicactivity complexes in Bobo-Dioulasso. However, a drawback of using theSAM was that, even though it was an excellent method for identifying eco-nomic interaction, it was less effective at uncovering the more social andstructural linkages.

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tional accounts. It therefore helps to illustrate how income isderived from production activities and how it is distributedto the various socioeconomic groups in the economy.

A basic SAM model contains three institutions: factors ofproduction, household types, and production sectors. Pro-

duction sectors (agriculture, manufacturing, and so forth)pay factors of production (labour and capital) for servicesrendered. The factors pass this money along to differenttypes of households (rural households versus urban house-holds). The households, in turn, pay the production sectorsfor purchases of food, clothing, and so forth.

One benefit of using a SAM is that it allows for substan-tial flexibility in defining the data to be analysed. It is possi-ble to adopt a wide range of units of measurement andalternative definitions, while maintaining internal consis-tency in the model (that is, inputs must equal outputs). Forexample, it is possible to disaggregate the model at whateverlevel of detail is desired to explore specific issues such as linksbetween growth, income allocation, and poverty.

What Key Inputs Are Required for SAM?

Analysis using a SAM typically requires detailed statisticalinformation on the local economy, as well as on governmentand household spending. The data should be available atleast at the regional level to allow for effective local-levelanalysis. (Note: Where statistical data are not available, datacan be collected through a detailed survey of all industry sec-

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Tips

Exert caution with using estimated data. SAM tables can be estimatedusing national input-output accounts. This is a less costly way toobtain value-added data than through primary data collection, but the data may also be less accurate. City-regions are often far moredependent on external trade than the national economy, so estimatesbased on national data may underestimate the dependence onimports.

Be aware of structural inter- and intra-industry changes. If the linkagesbetween sectors, firms, and households change rapidly, the role ofSAM analysis in forecasting quickly becomes redundant. In caseswhere the local economy is believed to be undergoing significantstructural change, it may be best to consider collecting additionalinformation about these changes through surveys to account for themin a forecasting exercise.

Further Information

For an introduction to using SAM at a subnational level, see: http://www.implan.com/library/documents/elements_of_the_implan_sam.pdf.

For a paper describing the methods and data sources for constructing SAMs for small cities, see: Schwarm, Walter, and Harvey Cutler.2005. “Multiple Labor Groups and Their Effects on Small City and Town SAMs and CGE Models.” Review of Urban and Regional Devel-opment Studies 17(2): 162–176 (downloadable via Blackwell-Synergy: http://www.blackwell-synergy.com/doi/abs/10.1111/1467-940X.00069).

For a general reference on SAM, see: Graham Pyatt and Jeffrey Round. 1985. “Social Accounting Matrices: A Basis for Planning,” at:http://www.worldbank.org/reference/ (type in author name in “Dpcuments and Reports” field).

For a detailed discussion on the construction of SAM in South Africa and comparisons with 11 other countries, see the Statistics SouthAfrica discussion paper at: http://www.statssa.gov.za/Publications/DiscussSAM/DiscussSAM.pdf.

For an example of the use of SAM as a regional economic model in Alaskan Fisheries, see: http://www.st.nmfs.gov/st5/documents/Review_of_Regional_Economic_Models_in_Fisheries.pdf.

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tors in the local economy. However, this can be time andresource intensive. There are also a variety of statisticalapproaches to model local-level data.)

SAM analysis is complex and requires significant capacityin econometric analysis.

Cluster Mapping

What Issues Are Addressed by Cluster Mapping?

The following questions can be addressed by cluster mapping:

� What are the main clusters of economic activity in thelocal economy?

� What are the nature and strength of links between thefirms and supporting institutions in a cluster?

� To what extent are the firms and institutions in a clustergeographically concentrated?

How Is Cluster Mapping Used?

Cluster mapping (also referred to as cluster analysis) identi-fies groups of tightly linked firms in related industries in alocal economy. Popularised by management theoristMichael Porter, it has become one of the most popular toolsin regional planning since the 1990s. A cluster generallyrefers to a group of firms and supporting institutions, suchas universities and research centres, that operate in relatedsectors, are interlinked through trade and knowledgeexchange, and operate in proximity to each other.

Identifying clusters in a local economy generally involvestwo types of analysis: 1) assessing the degree of geographical

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CASE STUDYMEDELLÍN (Colombia)

As part of a wider analysis of seven priority clusters in Medellín, the city’sChamber of Commerce carried out a cluster mapping exercise. The resultsof this exercise form an important input into the strategic developmentplan for Medellín to be finalised in 2007. The seven clusters identifiedthrough the cluster mapping exercise were named as priority industries inthe final strategy.

The aim of the cluster mapping exercise was to identify clusters with thegreatest actual and/or potential contribution to economic growth in thecity. The city first identified seven industries (based on data on regionalvalue added, exports, output, and employment) that fulfilled the above criteria. This analysis was carried out at a disaggregated data level, usingthe International Standard Industrial Classification (ISIC) of all economicactivities.

Then all sectors and research institutions related to each of these seven sec-tors were identified based on: 1) a review of existing secondary sources, 2)statistical analysis, and 3) a series of workshops with industry experts andrepresentatives from the largest firms in the seven sectors. Once the clus-ters had been identified, their competitiveness was assessed using Porter’sDiamond method (see the competitive advantage framework in chapter 9of this Resource Guide for further information on this approach). Construction work, Colombia

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concentration of the group or sectors of firms; and 2) assess-ing the strength and nature of interfirm linkages. There is nostandardised methodology for this analysis, but it most ofteninvolves both quantitative and qualitative techniques. Thedegree of geographical concentration is typically calculated

using location quotients or other indices of specialisation. Acomplement or alternative is to plot the location of firms andsupporting institutions in geographical space using GISmapping.

The linkages between firms can be quantified using input-output tables—displaying the value of input and output(goods and services sold) by supplier and buyer, respectively.Additionally, linkages can be assessed through a more qual-itative and participatory mapping exercise of the relation-ships among key firms and related institutions in the clusterbased on input from workshops, focus groups, or interviews.

What Key Inputs Are Required for Cluster Mapping?

A highly quantitative cluster mapping exercise is data inten-sive and may involve data that are not readily available at citylevel. Overall, a cluster mapping analysis requires:

� Qualitative or quantitative information on the firms oper-ating in the local economy, such as the interfirm flow ofgoods and know-how, employment by firm and/or activ-ity; and

� Data on output and employment for firms in the localand reference (most national or regional) economy, if cal-culating clusters based on location quotient.

This analysis is moderately complex and requires capac-ity in econometric analysis as well as an understanding of

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Tips

Interpret the results of quantitative analysis with caution. A high loca-tion quotient for an industry sector does not necessarily indicate theexistence of a cluster. Cities are likely to have high concentrations ofsome activities (services, for example) whether or not there is anyactual clustering. Similarly, a low location quotient does not necessar-ily preclude the existence of a cluster in the region. And although a GISanalysis may suggest the possible existence of clusters, it is importantto explore other possible explanations. Clusters are defined not just bya physical concentration of firms, but also by their activities and inter-relationships.

Complement quantitative data with qualitative insights. Quantitativetools such as input-output analysis can help to determine the degreeof trade linkages between sectors in a city or region, but a clustermapping exercise that focuses exclusively on quantitative tools mayomit important qualitative aspects of a cluster. For example, input-output analysis does not account for the role of supporting institutions(such as higher education institutions) or the potential importance ofnontrade linkages between firms (knowledge sharing, for example).

Further Information

For a useful introduction to methods and concepts relevant for cluster analysis and mapping, see: Industrial and Regional Clusters: Con-cepts and Comparative Applications, by E. M. Bergman and E. J. Feser, at: http://www.rri.wvu.edu/WebBook/Bergman-Feser/contents.htm.

For information on the UNIDO cluster development programme that focuses on developing countries, see: http://www.unido.org/doc/4297.

For examples of cluster processes in developing countries, see: the Global Cluster Initiative Survey (GCIS) on cluster initiatives in Devel-oping and Transition Economies, at: http://www.cluster-research.org/devtra.htm.

Cluster mapping is not typically addressed in regional science and/or local economic development textbooks. R. J. Stimson, R. R. Stough,and B. H. Roberts dedicated a chapter to the various methods used in cluster mapping in Regional Economic Development: Analysis andPlanning Strategy.

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cluster theory and theories of competitive advantage. If thecluster analysis includes spatial mapping, GIS data and tech-nology are required. Cluster mapping has a moderate to highresource intensity.

Value Chain Analysis

What Issues Are Addressed by Value Chain Analysis?

The following questions can be addressed by a value chainanalysis:

� How are the different activities involved in productionand distribution structured in the local economy (overalland within specific sectors)?

● How and to what extent are the firms in the valuechain interlinked in terms of flows of goods and services?

● Where in the value chain do the firms in the localeconomy sit?

● In what kind of activities are the firms in the localeconomy involved?

� What areas need improving to make local firms morecompetitive (product quality, innovation, reliability ofsupply and logistics, and so forth)?

� What are the obstacles for local firms in moving up thevalue chain?

How Is Value Chain Analysis Used?

A value chain describes a series of stages that create and buildvalue in products and services. Value chain analysis, devel-oped by management theorist Michael Porter, has mostcommonly been used in the private sector as a tool to buildcompetitive advantage by identifying opportunities to buildprocess efficiencies (by cutting costs, for example) or findingsources of differentiation. The process is now increasinglyused as a tool in the context of local economy assessments,particularly in terms of working with key sectors in the localeconomy to help firms understand their current position inglobal value chains and identify obstacles and opportunitiesfor upgrading to more lucrative parts of the chain.

Value chain analysis slices up the production chain intothe different activities (logistics, sales, marketing, produc-tion, research and development) and looks at the spread offirms in the different activities, the relationships betweenthem, and the extent to which the firms in the different activ-ities control the value chain. Figure 8.3 shows a basic valuechain within a regional development model.

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CASE STUDYDURBAN (South Africa)

The city of Durban puts substantial emphasis on identifying and support-ing key sectors as part of the local economic development strategy. Durbanmakes regular use of value chain mapping in order to assess the dynamicinteraction between local firms and between groups of local firms andnational and global value chains.

Specifically, Durban has found value chain assessment useful for two ele-ments of the assessment. First, it helps them to understand the degree towhich local firms are operating in lower value and marginal activities or aremoving into high value and more complex activities. Second, it allows themto understand the active, or in some cases missing, linkages among localfirms. Because the value chain tool requires substantial depth of data gath-

ering and analysis, it is most often used to analyse a specific set of targetsectors rather than for the economy overall. Typically, the analysis involvesboth quantitative and qualitative analysis and has been used successfullyas part of participatory assessment techniques with the private sector.

Durban first used value chain analysis as part of a major economic strategy.The study, Durban at the Crossroads, mapped out Durban’s priority clustersand conducted value chain analysis of the most important of these. Ithelped to identify the strengths and weaknesses of Durban’s clusters andto establish the interventions required to ensure sustainable competitive-ness for the city’s industries.

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There is no standardised methodology for conducting avalue chain analysis. Instead, it often uses a range of quanti-tative and qualitative tools. Input-output analysis can beused to diagram and quantify the relationships between firmsin an industry value chain. A more qualitative and participa-tory mapping exercise of the relationships among firms ineach value chain, as well as the degree of power of the firms(in different activities represented in the chain), can be usedin addition to (or instead of) input-output analysis.

The value chain analysis is also often integrated into clus-ter mapping exercises. (For more information on clustermapping, see page 99.)

What Key Inputs Are Required for Value Chain Analysis?

The qualitative data required for value chain analysis are typ-ically readily available; in most cases capturing this informa-tion requires facilitating a participatory process with theindustry sector in question. However, rigorous quantitativeanalysis is data intensive and data may not be readily avail-able at city level. Overall, value chain analysis requires:

� Qualitative or quantitative information on the firms inthe value chain and the linkages between them, for exam-

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F I G U R E 8 . 3 A Basic Value Chain Within a Regional Development Model

THE ENABLINGENVIRONMENT

PRODUCTION PACKAGING LOGISTICS MARKETING SALES

ORGANISATION

RESEARCH/INNOVATION

COMMUNICATION AND INFORMATION

TRAINING ANDDEVELOPMENT

FINANCING

MARKETSPRODUCTS

and SERVICES

VALUE CHAIN

Source: Kaiser Associates Economic Development.

Tips

Use local resources and industry experts. Value chain analysis will in

many cases involve participation from the private sector. Local busi-

ness groups, in particular, may possess considerable qualitative and

quantitative data on the value chain in question and knowledge and

expertise on the topic. They may have suggestions and contact details

for firm representatives and experts to include in a participatory

exercise.

Understand enabling factors. Although understanding linkages among

firms in the value chain is the main focus, it is also critical to identify

how firm and sector performance is supported (or hindered) by the

wider enabling environment. This includes factors such as customs

and transport policies and business support services, which must be

aligned to improve sector performance.

Make sure the analysis is appropriate for the purpose. Although the

value chain tool is useful, it is important to recognise that value chains

differ significantly from one industry to the next. As a result, value

chain analysis is unlikely to be a relevant tool for analysing the local

economy in its entirety, but rather only for analysing individual sectors

of the economy.

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ple, flow of goods and services, employment by firmand/or activity, regional imports and exports by sector,added value by activity.

� An understanding of value chains theories and the role ofvalue chains in defining regional and firm competitiveness.

This analysis is moderately complex and requires capac-ity in econometric analysis if it is based on quantitative analy-sis. In any case, it will require some understanding of theoriesof competitive advantage, of basic business strategy, and ofthe specific industries in question.

This analysis does not require any particular resourcesother than data and human resources. If data at the local level

are available and additional data collection is not required,the analysis has a moderate resource intensity.

Asset Mapping

What Issues Are Addressed by Asset Mapping?

The following questions can be addressed by asset mapping:

� What are the assets that might make the city morecompetitive?

� What are critical tangible assets?� What are critical intangible assets?

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Further Information

For a discussion of value chain analysis and theory in Value Chain Analysis for Policy-Makers and Practitioners by Hubert Schmitz, see:http://www.ilo.org/dyn/empent/docs/F204969253/VCA_book_final.pdf.

For a methodological and theoretical discussion of the value chain analysis, see “A Handbook for Value Chain Research” at: http://www.ids.ac.uk/ids/global/manuals&handbooks.html.

To view documents on value chain theory and analysis posted on the Institute of Development Studies Web site, see: http://www.ids.ac.uk/ids/global/valchn.html.

For the “Participatory Value Chain Analysis” toolkit developed by the Enterprise Development Impact Assessment Information Service,see: http://www.enterprise-impact.org.uk/informationresources/toolbox/valuechainsanalysis.shtml.

CASE STUDYADEN (Yemen)

Asset mapping was an important analytical tool in the city developmentstrategy process in Aden. In 2002, Aden’s city strategy process was builtaround taking advantage of what were seen as the city’s key infrastructureassets. The CORE, as they were defined in Aden’s CDS process, included: ThePort of Aden, The Aden Free Zone, and Aden International Airport. One com-ponent of the local economy assessment in this city involved undertakinga specific competitiveness assessment of the CORE, with regards to compet-ing transport hub locations in the wider urban region.

More broadly, Aden undertook a basic asset mapping exercise within thecompetitiveness assessment stage. As part of the Economic and BusinessOpinion Survey that was undertaken in the city during 2002, respondentswere asked to name what they viewed as the city’s three most importantassets (tangible or intangible). These assets were then further discussedand prioritised at a subsequent competitiveness seminar.

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How Is Asset Mapping Used?

Asset mapping is a relatively quick and simple tool for under-standing local assets and how they can facilitate local eco-nomic competitiveness. The tool is used to document themain tangible assets (physical infrastructure and buildingssuch as transport, property, utilities, cultural amenities, andso forth) and intangible assets (knowledge, trust, coopera-tion, and so forth) available to the local economy. In the eco-nomic development context, it is most often applied at thecommunity or small-city level.

Asset mapping is often conducted by using a participatoryapproach in which local stakeholders help identify, rank, andprioritise the different local assets. In some cases, it alsoinvolves assessing factors that may threaten and/or strengthenthese assets and developing strategies to support and enhancethem. The type and number of participants will depend on thepurpose of the exercise. If the tool is used as an exclusively ana-lytical exercise, it may be best to choose participants based ontheir expertise. However, if the aim of the asset mapping exer-cise is also to sensitise stakeholders to local economic develop-ment and strategic planning, then it may be best to include abroader stakeholder group.

Asset mapping can also be conducted through a more for-mal survey (a household survey or investment climate sur-vey), but typically, a survey would only supplement a moreparticipatory process. A main strength of asset mapping isthat it generally supports a positive outlook as it focuses onopportunities rather than problems (which is often a draw-back with participatory issues identification and other par-ticipatory approaches). Therefore, it can be a more unifyingand motivating tool for engaging stakeholders.

What Key Inputs Are Required for Asset Mapping?

The data required for this analysis are readily available fromparticipants in the local economy. Asset mapping is a relatively

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Coastal view of Aden, Yemen

Tips

Use visual aids. It is often a good idea to visualise assets, problems, andplanning ideas by using photos and other visual tools. This is particu-larly important in processes with wide stakeholder participation,where not all participants may have sufficient literacy skills.

Be cautious in interpreting the results. Highly participatory processessuch as asset-mapping are sometimes overly optimistic (or pessi-mistic) in interpreting sources of strength and weakness in the localeconomy. Information on local assets should be taken as a startingpoint for analysis and should be complemented with comparativeanalysis (with other cities and regions) and other objective analyticaltools.

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easy tool to use and does not require any specific econometricor analytical expertise. Using the tool effectively does requirean experienced and appropriately trained facilitator, if using aparticipatory approach. Other than access to facilities to host aparticipatory session, the tool does not require any other spe-cific resources. Overall, the tool has a low resource intensity.

Skills Audit

What Issues Are Addressed by a Skills Audit?

The following questions can be addressed by a skills audit:

� To what extent are the skill requirements of industry inthe city covered? Are there any skill gaps or shortages inthe local economy?

● What impact does this have on local firms?● What impact does this have on the local economy?● Is this a barrier to employment growth?

� What are future skill requirements? What are futuretrends in workforce skills?

� Is the city’s skills base competitive? Does its skills basematch the strategic vision for the city?

How Is a Skills Audit Used?

A skills audit is used to document the skills base and map-ping against the identified needs of the local economy. Thistool is typically used to: 1) determine current and/or futureskill requirements of the local economy (overall or for spe-cific industry sectors), and 2) determine the skill base neededto fulfil the city’s strategic vision.

There are several possible ways to conduct a skills audit,including participatory assessments and more quantitativeapproaches that differ in methodology and the degree of expert-ise required. A first step is often to review the local economicstructure and identify any immediate changes to local employ-ment (such as a planned opening or closing by a major localemployer). This is of interest because the sectoral compositioncan indicate what skills are needed. For example, rapid expan-sion in the construction sector could indicate a need for moreengineers and manual labourers, whereas growth in businessservices could indicate a need for more lawyers and accountants.

A quantitative skills audit could simply involve using afirm survey to collect data on the perceptions of firms’ man-

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Further Information

To view the step-by-step guide for asset mapping developed by The Canadian Rural Partnership, see: http://www.rural.gc.ca/conference/documents/mapping_e.phtml.

To view the step-by step guide for asset mapping developed by Community Builder, see: http://www.communitybuilders.nsw.gov.au/download/Making_Headway_ToolKit.pdf.

For a discussion of an asset mapping exercise with a focus on local residents, organisations, and community leaders, see: http://faculty.salisbury.edu/~mdforaker/IDIS/Mapping%20assets%20of%20community.pdf.

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agement (on the current and future skills demand) and tocompare these data with the actual skill and education levelof both the economically active population and studentsentering higher education. However, it could also involveconstructing and analysing a matrix in which the number offilled posts and vacancies in terms of sector (usually standardindustrial code [SIC] or harmonised system [HS] code clas-sification) and occupation (generally according to a nationalstandard occupational classification system). This is thentypically complemented with an analysis of the supply ofskills (for example, the level of education of the economicallyactive population).

To plan for future skills shortages, forecasting or sce-nario planning methods are often used along with a skillsaudit to identify future skills needs (see scenario planning inchapter 9 of this Resource Guide for more information onthese tools).

Note: If the aim of the analysis is to map skills needs in relation to reach-ing a strategic vision, this may entail comparison with a reference economy(by using benchmarking analysis). For example, if a city wants to establishitself as a location for research and development firms in information tech-nology, it can be useful to benchmark the city’s skill base against a city thathas already established itself as a location for research and developmentfirms.

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CASE STUDYTORONTO (Canada)

The most recent Economic Development Strategy conducted in Toronto in2000 places substantial emphasis on competing in a knowledge-intensiveeconomy through the human capital of its residents. To further assess itshuman capital competitiveness and needs and to develop a strategy for thefuture, the city undertook a detailed skills audit and skills mapping assess-ment, which culminated in the 2003 Toronto Labour Force Readiness Plan.

Toronto made use of the Canadian Occupational Projection System (COPS)to assess the future surplus/deficits on occupational requirements based onforecasted economic and sectoral growth. Against this demand-side assess-ment, Toronto assessed the current skills and education base of its workersusing secondary data and an employer’s survey. The telephone-based sur-

vey covered 1,000 firms in greater Toronto, and captured both data and per-ceptions on existing labour skills, job requirements, and strengths andweaknesses in the city’s labour pool.

What was seen as particularly valuable was combining data and informa-tion about the existing workforce in the city with forward-looking demand-side assessments, in order to be in a position to understand the positioningof the city for future competitiveness.

For more details on the methodology used and the development of Toronto’s Labour Force Readiness Plan, see: http://www.toronto.ca/business_publications/labour_force_readiness_plan.htm.

Tips

Use local resources. To avoid duplication, possibly reduce costs, and

take advantage of existing expertise, it is wise to make use of local

business networks and educational institutions. These groups some-

times have substantial internal analytical capacity and data, so they

may be able to help in the dissemination of results and provide infor-

mation on future economic events that affect the availability of skills

(such as the opening or closure of a local factory).

Assess both supply and demand. A skills audit of the local economy

requires a strong understanding of the supply side (existing skills in

the local economy and those being developed for the future) and the

demand side (how employers evaluate the local economy’s skills base

and what skills they need for the future). It is important that the local

economy analysis balances the two.

Mix data and participatory inputs. Understanding the existing skills

base and strengths and weaknesses of a local economy requires objec-

tive data and qualitative inputs from local stakeholders. A lack of data

increases the risk of making assessments based on perception only; a

lack of qualitative input from local stakeholders increases the risk of

not understanding the nature and scope of issues relevant to effective

strategic planning.

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A qualitative approach will generally take the form of aparticipatory workshop (see participatory issues identificationin chapter 7 for more information). In these cases, stakehold-ers, typically from the private sector and educational institu-tions but often including community organisations and skillsexperts, identify skills requirements and compare them withexisting skills.

What Key Inputs Are Required for a Skills Audit?

The data requirements for a skills audit largely depend onwhether a participatory or quantitative analysis approach isused. A minimum data requirement typically includes edu-cational attainment levels of the population, number of stu-dents by career, and some type of data on the demand forskills by industry (qualitative or quantitative). For a morecomplex analysis, time series data on the above data on thesectoral composition of the economy, and data on the vacan-cies and jobs filled by occupation and sector, are needed.

Data on future and current skill demand can be collectedusing an industrial structure survey or a business outlook sur-vey. Similarly, the requirements for analytical capacity willvary. The most basic analysis is relatively simple, whereas themore complex analyses involving forecasting and scenarioplanning require substantially more analytical capacity andeconometric skills. A trained facilitator is required for theparticipatory approach to conducting a skills audit.

Stakeholder Analysis / Institutional Mapping

What Issues Are Addressed by a Stakeholder Analysis?

The following questions can be addressed by a stakeholderanalysis:

� Who are the key stakeholders in the local economy?� What are the specific interests and roles of these stake-

holders?� What is their influence on (and interests in) the strategic

planning process?� Who are the most important stakeholders to involve in

the local economy assessment and/or strategy develop-ment process?

How Is Stakeholder Analysis Used?

In stakeholder analysis, also called institutional mapping,important stakeholders are identified, and the relationshipsamong stakeholders (along with their interests and influencesin the local economy) are analysed. A stakeholder analysiscan be conducted before the stakeholder consultations beginas part of a local economy assessment or strategy develop-ment process. The analysis can also be a valuable way toidentify potential roadblocks and/or catalysts for moving for-ward on a chosen strategy. Stakeholder analysis is thus oftenthe starting point for most participatory work.

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Further Information

For a more in-depth discussion of skills audit, see: http://www.eprc.strath.ac.uk/eprc/Documents/PDF_files/R28TrainNeedsAssessinScot.pdf.

For one possible approach to a participatory skills audit, see: http://unauthorised.org/ronni/comdev/skillsaudit.html.

For an example of a comprehensive skills audit that includes the use of scenario planning, see: http://www.yorkshirefutures.net/siteassets/documents/YorkshireFutures/6/6/6645A51B-EB50-475E-ADFE-A6F9AB7E5DE3/FRESA%20labour%20market%20analysis.pdf.

For an introduction to the U.K. system of Standard Occupational Classification, see: http://www.statistics.gov.uk/methods_quality/ns_sec/soc2000.asp.

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The first step in stakeholder analysis is to compile a list ofstakeholders. These can be individuals or wider groups andinstitutions, but for stakeholder groups, three broad types arerelevant: the public sector (city or municipal departments,regional or national government departments, universities,and so forth), the private sector (such as chamber of com-merce, chamber of handicrafts, banks, cooperatives, andresearch institutions), and nonprofit organisations (includ-ing minority associations, NGOs, trade unions, andwomen’s associations). Once the stakeholders have beenidentified, relationships between organisations and institu-tions are mapped out to understand roles, relations, and gapsin existing linkages among them.

Stakeholder analysis is a versatile tool that can be used atdifferent levels of formality. Although the analysis is oftenconducted as a facilitated group process, it may also involveadministering a questionnaire to stakeholder organisations(business groups and neighbourhood organisations, forexample) with questions concerning mission, geographicalarea of intervention, main types of activities, and other rele-

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CASE STUDYKARU (Nigeria)

The city of Karu used a stakeholder analysis to identify and select the par-ticipants for a series of stakeholder consultation meetings organised as partof the CDS process in 2002. The approach to the analysis was developed byUN-HABITAT.

The stakeholder analysis was conducted by a Consultation Organising Com-mittee (COC) made up of representatives from community leaders, localgovernment, and representatives of both the physical planning authoritiesand the private sector. Because the COC was comprised of people with in-depth knowledge of the town and its inhabitants, the committee was per-ceived to be in a good position to identify stakeholders.

One of the main goals of the CDS process in Karu was to develop viableapproaches to financing, operating, and maintaining public service deliv-ery and infrastructure. The COC started by compiling a full list of those witha stake in, information about, and responsibility for providing urban ser-vices. These stakeholders were then assigned to three categories: private

sector (informal and formal), public sector (local, state, and federal govern-ment), and the popular sector (civil society organisations, traditional lead-ers). The stakeholder analysis that resulted was conducted separately foreach of the three categories, using a matrix of stakeholder responsibility(low-high) and stake (low-high). The analysis of public sector stakeholdersfocused on the statuary responsibility of each (for functions related to pro-viding public services), whereas the analysis for the private sector and civilsociety focused more on the interests and resources needed in providingurban services.

The stakeholder analysis was perceived as useful to the city because it pro-vided a good overview of the stakeholders’ roles (by identifying the respon-sibilities of local versus state and federal, for example). The stakeholderanalysis also revealed that private companies and individuals were invest-ing in and providing key urban services (including water) through invest-ments in boreholes and storage facilities.

Tips

Use local resources for first cut at main groups. Although external con-sultants may be needed in some aspects of the local economy assess-ment, they are of little value in helping to identify and map localstakeholders. Local representative organisations can be useful becausethey are likely to have knowledge of and ties to the local economy.

Cast a wide net. A broad range of stakeholders and institutions willlikely play important roles in various aspects of the local economy andwill have important inputs to the assessment and strategy develop-ment process. Therefore, it is important to look beyond the most obvi-ous stakeholders.

Make use of visual tools. Links between institutions are most easilyidentified through visual processes, such as drawing maps or diagramsthat use symbols and physical distance to represent the nature andscope of relationships.

Make the process iterative. As more information and new informantsemerge, the picture of institutions and their relationships will change.This process must be allowed to evolve.

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vant issues. This may be particularly relevant for largereconomies where there are many stakeholder organisations.

A common approach to pulling together the results of thestakeholder analysis is to draw a matrix of stakeholder influ-ence (low-high) and interest (low-high stake). Based on this,it is possible to develop a strategy for involving each targetperson or group.

What Key Inputs Are Required for Stakeholder Analysis?

When a good overview of the local economy is available, andstakeholders are included in data collection, data for stake-holder analysis can be obtained. For this analysis, cities need:

� A list of main stakeholder groups.� Names and contact details for stakeholders to be included

in the analysis.

Stakeholder analysis is a relatively easy tool to use, but itrequires an experienced and trained facilitator who has goodpeople skills and is sensitive to local cultural norms.

This analysis does not require any resources other thandata and human resources. But even though stakeholderanalysis generally has a low resource intensity, it may bemoderate to high if a survey requiring more formal analysisis used (such as a survey questionnaire that involves a largeterritory or population sample).

What Other Analytical Tools Are There?

The most widely used tools for local economy analysis have been described in this chapter. However, other toolscan be useful as well. Some of these are briefly described intable 8.5.

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Further Information

For a step-by-step guide to the stakeholder analysis template developed by UN-HABITAT, see: http://hq.unhabitat.org/cdrom/governance/html/st.htm.

For a practical step-by-step guide for conducting a stakeholder analysis, see: http://www.scu.edu.au/schools/gcm/ar/arp/stake.html.

For a checklist of possible stakeholders to include in a local economy analysis, see: http://www.icmm.com/library_pub_detail.php?rcd=183.

For discussion of stakeholder analysis tools in the context of urban upgrading (based on GTZ and Norwegian Agency for DevelopmentCooperation), see: http://web.mit.edu/urbanupgrading/upgrading/issues-tools/tools/Ident-stakeholders.html.

For a template used to prepare a stakeholder organisation questionnaire, download the International Labour Organisation’s LED Oper-ational Guidelines in Post-Crisis Situations at: http://www.ilo.org/dyn/empent/empent.Portal?p_prog=&p_subprog=&p_category=TOOLS.

For a list of tools used by the World Bank to conduct stakeholder and institutional analysis (designed for poverty and social impact analy-sis), see: http://lnweb18.worldbank.org/ESSD/sdvext.nsf/81ByDocName/ToolsandMethodsInstitutionalanalysis.

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Analytical tools

Locational GiniIndex

Entropy indexes

Gender analysis

Systems thinking

Description

Uses the standard Gini coefficient to measure the spatial dispersion ofindustry, by replacing individuals with regions. Can be calculated for terri-tories and/or sectors.

This index has become a standard measure but is not effective for cross-regional comparisons.

Measure the spatial dispersion of industry, as an alternative to the loca-tional Gini index.

Allow for weighting within subgroups and thus have the potential todecompose data by subgroups (for example, to explain the contribution ofspecific sectors to the overall geographic concentration).

Involves a systematic analysis of the roles of women and men in the economy and the impacts of economic and social policy on women versusmen.

Can be a valuable tool for understanding human capital potential and theissues currently restricting economic participation, particularly amongwomen.

Can also be valuable for understanding the informal economy.

An approach for analysing and managing complex feedback systems.

Most appropriate for analysing interrelationships between different issuesin the economy and anticipating development outcomes by mapping pos-sible chains of causes and effects; fairly complex technique that requiresboth good understanding of economic issues and facilitation skills, and isideally aided by developing a computer simulation model in which causesand effects can be tested.

T A B L E 8 . 5 Overview of Other Data Analysis Tools

Further Information

For an example of how the locational Gini is used to measure industryconcentration in the European Union, see: http://www.hec.unil.ch/mbrulhar/papers/tep955.pdf.

For an example of how the locational Gini is used to measure industryconcentration in China, see: http://rspas.anu.edu.au/economics/publish/papers/wp2001/2001-07%20MeiWenWP2v.pdf.

For an example of how entropy indexes are used in a cluster mappingexercise in Ireland, see: http://www.tcd.ie/iiis/documents/discussion/pdfs/iiisdp89.pdf.

For discussion on use of the Theil Index to measure inequality, see: TheYoung Person’s Guide to the Theil Index: Suggesting Intuitive Interpreta-tions and Exploring Analytical Applications at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=228703.

For a set of guidelines on the use of gender analysis, see: http://www.worldbank.org/wbi/sourcebook/sba109.htm.

For a range of approaches to gender analysis, see: http://www.ilo.org/public/english/region/asro/mdtmanila/training/unit1/plngaps1.htm.

For an example of how gender analysis is applied in the U.S. state ofWest Virginia, see: http://www.polsci.wvu.edu/ipa/par/report_13_2.html.

For an introduction to various systems thinking methodologies, seethe Arizona State University Business College paper at: http://www.public.asu.edu/~kirkwood/sysdyn/SDIntro/SDIntro.htm.

For a comprehensive guide to systems thinking, see the Road Mapsseries developed by Massachusetts Institute of Technology’s SloanSchool of Management at: http://sysdyn.clexchange.org/road-maps/rm-toc.html.

The Systems Dynamics Society Web site provides many resources andpublications (for subscribers only): http://www.systemdynamics.org.

For a mini-simulation exercise and a brief introduction to dynamicsmodelling at London Business School Systems Dynamics Group, see:http://www.london.edu/sysdn.html.

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