REGIONAL DEVELOPMENT

1. REGIONAL DEVELOPMENT WEEK 2

2. Recap • Lastweekwehavementionedtheevolutionpath of regionaleconomicmodels. • Traditionaltradetheory • New tradetheory • New EconomicGeographyModels • Thisweekwewill talk aboutsomeimportantmodelsthatarequiteimportant in regionaleconomictheoryandpolicy in detail.

3. Traditional Tools for Measuring and EvaluatingRegional Economic Performance • Regional economic development policy is basically about the allocation orreallocation of resources to enhance the economic performance of industries.Planners and policy makers need to be able to measure and evaluate thatperformance.

4. Thus it is necessary to: • Measure the degree to which economic activity and employment in a region isrelated to serving local demand as against serving demand external to the region(i.e. exports). • Assess a region’s overall performance relative to that of other regions. • Assess which industry sectors are performing better in the region. • Assess a given sector’s efficiency relative to other industry sectors’ performancein the region.

5. Measures of Concentration • There are several measures used in empirical studies to investigate geographical and industrial concentration within and across countries/regions. • Wewillonlyconsidersomebasicmeasures.

6. Herfindahl Index • H= • Where; sijc denotes share of employment in industry i in region j in total employment of industry i:

7. The Herfindahl index is a measure of industrial concentration. Its main advantage is the computational simplicity. On the other hand Herfindahl index does not take the areas of the region into account, it assumes they all have same sizes and it is also sensitive to the number of firms in each industry

8. The Dissimilarity IndexforRegionalSpecialization • DSRj= • Where; sijs denotes share of employment in industry i in region j in total employment of region j and si denotes share of total employment in industry i in total employment and calculated as follows:

9. The Dissimilarity IndexforIndustrialConcentration • DCRi= • Where; sj denotes share of total employment in region j in total employment and calculated as follows;

10. KrugmanSpecilization Index • KSI=where k and l are two different regions. • The Krugman specialization index, compares two regions and identifies how specialized or despecialized these regions are.

11. Gini CoefficientforRegionalSpecialization • GINIjs= • where, Ri= • λi indicates the position of the industry i in the ranking of Ri in descending order.

12. Gini CoefficientforIndustrial Concentration • GINIic= • where, Cj= • λj indicates the position of the region in the ranking of Cj in descending order

13. Economic Base Theory • Economic base theory (EBT) is an easily understood traditional body of thought inthe field of regional economic development. • EBTviews an economic system as composed of two parts: • one, called non-basic, is viewed as producing for local consumption; • the other, called basic, is viewed as producing goods and services primarilyfor external consumption, (that is for export from the region).

14. Economic development theorists believe that the critical cornerstone of aregional economic system is its basic economic activities. • Thus byexpanding (export) base activities, the local regional economy not only expandsemployment and earnings in the region directly, but also expands employment andearnings indirectly. • As a consequence, theprimary focus in sectoral targeting analysis is on basic economic sectors andactivities.

15. A complimentary approach is entitled import substitution, whereby goods andservices are imported to support basic and, in some cases, non-basic production. • With this approach, industry sectors that are insufficiently developed to supportlocal basic activity are targeted for investment and development. By expandingthese sectors, the relative importance of basic sectors often can be increased.

16. Measuring the Economic Base of a Region • A variety of techniques have been developed to separate economic systems intotheir basic and their non-basic parts. The simplest method is to sort industrysectors into those that are primarily basic and those that are primarily non-basic. • Subsequent efforts have relied on location quotients, which are measuresestimating the importance of industry sectors to the local economy relative to theirimportance in a larger reference economy

17. Twotypes of location quotients: • One is measured the minimum requirements approach, where that localitywhich has the least employment (earnings or some other indicator of scale) in asector becomes the base against which the same sector in all other regions iscompared. • Alternatively, location quotients can be computed in terms of some referencearea, (e.g. the nation), whereby the contribution to the basic part of the economy ismeasured as the part that is greater than the proportional amount found in thereference area.

18. Calculating Location Quotients • Location quotients are well known measures of the relative importance ofsectors compared to their importance in a larger frame of reference as describedabove. Location quotients (LQ) are computed as follows: • LQir = (Eir/Er)/(EiN/EN) • Measures of scale other than employment can be used; for example earningsand gross regional product GRP

19. LQ>1, means a higher concentration in the region than in the country and LQ>1.25 considered as an initial indicator of regional specialization.

20. An Example: Industrial Targeting in Northern Virginia • A case study application of industrial targeting analysis is a study of the NorthernVirginia region in the United States. • The time period for this study was 1988–1993. • One objective of the study was to identify and evaluate the performance of theprimary technology intensive industry sectors.

21. An Example: SoutheastAnatoliaRegion (1980-2000)

23. MediterraneanRegion

25. Shift-ShareAnalysis • A simple descriptive, quick and relatively inexpensive technique for analyzingregional growth and decline over time is shift-share analysis. • This techniqueenables the assessment of a region’s overall performance relative to other regions.

26. The Traditional Shift-Share Model • The traditional shift-share model measures regional growth or decline bydecomposing it into three components: • Nationalshare (NS): that is, that part of change attributable to overall nationaltrends • Industrialmix (IM): that is, that part of change attributable to the industrialcomposition or mix of the region • Regionalshift (RS): that is, that part of change attributable to regional advantageor competitiveness.

27. Early shift-share models outlined in Perloff et al. (1960) focused on totalregional employment and had only two components: • Total shift (TS), expressed as:

28. DifferentialShift (DS), expressed as: • where: • ei and Ei respectively are regional and national employment in industry i; • e and E respectively are regional and national total employment in allindustries; and • t-1 is the initial period and t the end period (e.g. inter-censal dates) of theanalysis.

29. Dunn (1960) introduced to the model differential rates of growth in individualindustries, to give what is known as the ‘proportionality effect,’ which isequivalent to the industry composition or mix (IM) effect referred to above. • Ashby(1967) introduced a three-component model of regional change, incorporatingnational share (NS), industry mix (IM), and regional shift (RS)

31. This classical shift-share model—which has been used extensively byeconomists, geographers, regional scientists and planners in regional analysis—thus emphasizes not only the role of regional change for a region-specificindustry, but also the regional shift or competitive component as a measure of therelative performance of the region for a specific industry.

32. A position shift isinterpreted as being associated with the comparative or competitive advantage ofthe region for that industry, or vice versa. The partition of regional change into thethree components—NS, IM and RS—was intended to enable researchers to studythe sources of change separately.

33. An Example: Regional and Axial Shifts in the United StatesSpatial Economy • In the early 1980s the United States, the Northeast and Midwest regions wereloosing out to growth in the Southeast, South, Southwest and West. This wasevidenced by population movements as well as shifts in industrial location andemployment. The Northeast always was a net out-migration region. What was ofsignificance was the sudden change in pace and destination of populationmovement beginning in the 1970s. While the West and Southwest showed gains inpopulation, the dramatic growth appeared in the South. At least some observersinterpreted this as a direct transfer from the (old) North to the (new) South

34. Shift-share analyses were computed for all States and for seven primarytransportation industries in order to compare the rates of growth among thedifferent states and in particular to compare the regional competitiveness of thestates in these industries.

35. Total Transportation, Communications and Public Utilities • Local and Interurban Passenger Transit • Trucking and Warehousing • Water Transportation • Air Transportation • Pipelines • Transportation Services

36. The general pattern among the results was that in all transportation sectors jobloss was occurring due to industry mix and further loss in the Northeast andMidwest due to a loss of State competitive share (see Table 3.4). • Transportationservices were the highest growth part of the transport sector (see Table 3.5). Thegeneral trend shows a loss in competitive share from North to South and Westeven though, in absolute terms, employment remained highest for most transportsectors in the North, because of the heavy industrial concentration there.

37. Twocriteria were used to identify high performer States: • the competitive share had to at least equal the national growth component; • the absolute growth in employment must be at least 2 percent of absolutegrowth for the industry nationally.

38. Critiques and Extensions of Traditional Shift-ShareAnalysis • Despite its continued widespread use, shift-share analysis has been heavilycriticized for having temporal, spatial, industrial aggregation, theoretical contentand predictive capability deficiencies

39. Shortcomings • Dawson (1982) lists six shortcomings of the traditional shift-share model: • Changes in the industry mix in the national economy are not taken into account.This is a weighting problem as changes from the beginning to the endof the period of time over which change is being measured may have quitedifferent weights or opposite signs for the industrial mix and the competitiveeffects. • Results are sensitive to the degree of industrial and regional disaggregation. • The differential industry component is unstable over time, and the degree ofinstability varies among industries.

40. Growth resulting from inter-industry linkage and secondary multi-sector effectsare not explicitly isolated but are included in the competitive component(RS), whereas they should be included in the industry mix component (IM). • The differential component (RS) may be influenced by relatively spuriouscauses, including the incorrect classification of firms, product heterogeneitywithin firms, and transfers of production between separate sites of individualfirms. • The technique provides no information on the capacity of a region to retaingrowing industries or on how to attract them in the first place (Richardson1978).

41. Modifications and Extensions • Researchers have incorporated the shift-share model into other statisticalforecasting methods, including: • (a) Analysis of Variance (ANOVA) models (Berzeg and Koran 1984, 1978). • (b) A multiplicative model of shift-share (Theil and Gosh 1980; Kurre and Weller1989). • (c) Univariate autoregressive integrated moving average (ARIMA) time seriesmodels. • (d) A linear model of shift-share analysis (Knudsen and Barff 1991).

42. Another trend in shift-share analysis is the use of econometric modelsdeveloped by Emmerson et al. (1975) and by Berzeg and Koran (1978). These areearly forms of the information-theoretic approach developed by Theil and Gosh(1980).

44. NextWeek • Total FactorProductivityApproach (3.4.3)