Externalities, Location, and Regional Development: Evidence from German District Data

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Büttner, Thiess

Working Paper Externalities, location, and regional development: Evidence from German district data

Diskussionspapier, No. 43

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Suggested Citation: Büttner, Thiess (1997) : Externalities, location, and regional development: Evidence from German district data, Diskussionspapier, No. 43, Universität Konstanz, Forschungsschwerpunkt Internationale Arbeitsmarktforschung, Konstanz

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Center for International Labor Economics ( CILE )

Fakultat fiir Wirtschaftswissenschaften und Statistik Universitat Konstanz

Thiess Biittner

Externalities, Location, and Regional Development: Evidence from German District Data y 752 (43)

1 3. SEP. 1397

Postfach 5560 D 139 Diskussionspapier 78434 Konstanz 43 - 1997 Deutschland / Germany Externalities, Location, and Regional Development: Evidence from German District Data

Thiess Buttner

Diskussionspapier

Nr. 43

September 1997 Externalities, Location, and Regional Development: Evidence from German District Data

Thiess Biittner l

Faculty of Economics and Statistics Center for International Labor Economics University of Konstanz P.O.Box 5560 D 139 78434 Konstanz Germany email: Thiess.Buettner@uni-konsta'nz.de

Abstract Local productivity externalities play an important role in the recent literature on economic geography and regional growth. But apart from case studies there is only weak empirical evidence. Using a simple theoretic framework the paper derives two empirical implications of those externalities which are applied to district level data for German manufacturing industries. Although important regional concentration is found, significant correlation with local demand renders it difficult to draw conclusions. Yet, the importance of local externalities is supported by the long-run development of single industries, as the extent of general manufacturing activities represented by employment and the number of establishments has positive effects on growth.This result is confirmed from an analysis of regional establishment growth, and is finally shown to be consistent with the aggregate development in manufacturing employment.

11 would like to thank B. FitzenborRPr. \V. Franz, and P. Winker for comments and criticism. 1 Introduction

Recently, there has been a revival of interest into the concepts of localization and urbanization economies and their effects on regional productivity differences. Large potential effects of these local externalities on the regional allocation of activities have received broad attention in the literature on economic integration.2 Dynamic equivalents of localization economies, sometimes referred to as Marshall-Arrow-Romer externalities,3 also play a major role in the literature on economic growth. But compared to the importance of issues raised, empirical evidence abstracting from case studies is hard to obtain. Not only are regions quite heterogeneous, showing differences in their characteristics as well as in their interrelationship, but by stressing the endogeneity and the interdependence of agents location decisions the concepts of localization and urbanization economies are hard to identify empirically. In order to evaluate empirical content and relevance of local externalities basically two approaches can be distinguished: A descrip- , tive approach concerned with the study of the observed regional pattern of economic activities, and a more analytic approach concerned with its development over time. This paper presents an empirical study for German manufacturing employment along these lines. The focus on manufacturing is primarily due to the availability of data. But it is also justified by the fact that, compared to service industries, manufacturing industries are to a larger extent concerned with the production of tradeable goods, and therefore fit better into the interregional goods market assumption which is behind the agglomeration result. There exist only few corresponding studies for the German case: Lau (1996) is concerned with externalities and regional employment concentration, Brocker (1989) and Reimers (1984) study local externalities as determinants of sectoral employment growth, and Harhoff (1995a,1995b) investigates the role of externalities in the formation of firms. As the present study derives the empirical implications from the theory it shows how regional employment concentration, regional employment growth, and regional firm formation are related. Moreover, it explicitly distinguishes different types of externalities, thereby offering additional opportunities for identification. Furthermore, it critically examines and robustifies the finding of local productivity externalities in several di- rections. In the following section the two strategies of empirical research on the role of agglomeration economies are derived from the theory. Section 3 follows the more descriptive approach concerned with the hypothesis, that if agglomeration economies matter for manufacturing industries, their spatial employment distribution is pulled away from the general employment

vSw. for instance Venables (1995). 3rf Claesrr et al. (1992) distribution. The emphasis lies on a general discussion of this descriptive approach and the problems involved. As a by-product some features of selected manufacturing sectors are discussed, which form the basis of the more analytic approach of section 4. Here the focus is on the regional development of employment in major manufacturing sectors and total manufacturing. The main purpose is to test whether positive local externalities can be identified in a regional dataset but results on the persistence of the employment pattern are also obtained.

2 Empirical implications of agglomeration economies

Dating from Weber (1909,1929) agglomeration economies have played an important role in location theory. Whereas broader definitions exist4 they are commonly explained by localized external economies of scale. Therefore, formal analysis can be carried out using the concept of increasing returns external to the firm by Chipman (1970) and Helpman (1984). Consider a set of regions each assembled in a single point in space. Assume a traded good is produced.in each of these regions with an industry production function where the externalities are captured in a separable productivity term:

Xr = G{Lr\Lr)F(Sr,Lr,Kr) (1) where Xr is the output of the local industry and G is the productivity shift function with the regional labor input in the industry under consideration (Lr) and the total employment in the region (LT) as arguments. F is a linear homogeneous production function using three factors: labor (Lr), capital (Kr), and a specific factor (Sr). It is assumed that each firm in the local industry treats the shift function G parametrically, thus there are economies of scale external to the firm but internal to the local industry (first argument) or internal to the location (second argument). Whereas the former represent localization economies, the latter represent urbanization economies.

Suppose the output of the local industry Xr is traded on the large interregional market such that the output price (P) is given. Under the assumption of zero profits the goods price equals the unit cost, formally:

l P = C{Vr,Wr, R) Gr(Lr,Lr)~ (2) where C denotes the cost function, VT is the rental rate of the region-specific factor, WT the wage rate of mobile labor, and R the nationwide rate of 4According to Goldstein / Gronberg (1984, p. 97) agglomeration economies exist, "..'. when it is less costly to combine two or more product lines in one (urban) area." This definition not only applies to agglomeration due to nonconvexities but also to the case of an interindustry linkage with transport costs and a specific factor. return on capital. For the empirical application a log-linear specification of the productivity-shift term is assumed:

G{Lr,Lr) = GrU^lf 7i>0 t = l,2 (3) where Gf captures exogenous productivity differences. By the requirement that 7,- > Oi = 1,2 negative externalities from the level of employment are prevented. Assuming a Cobb-Douglas form of the basic production function (F) the labor input consistent with profit maximization of individual firms and with the given output price can be determined from the price-cost equality and the factor demand equation using Shepard's lemma. After taking logs, one obtains:

(4) -7i

-7i

logGr + log,Zr ai —71 oil — 7i where LQ is a constant, ai is the production elasticity of the specific factor, and 0:2 is the production elasticity of labor. By the requirement 71 < «i constellations are excluded where the scale effect on production dominates the effect of,the specific factor on production. Otherwise the specific factor would be no longer relevant for the equilibrium since any decrease in the specific factor could be balanced by an increase in employment in order to hold productivity constant. The condition imposed, therefore guarantees the determinateness of the interregional equilibrium.5 Summing up all regions, and applying a Taylor approximation, a corresponding equality holds for the national aggregate.6 Variables, which are equal throughout the regions, can then be removed by subtraction of the national average from each variable:

lr ~ sr ;WT (5) en -71 c*i - 71

-71 Qi -71

ssee Henderson (1985) for a similar condition for the solution of the equilibrium city size. 6 . For a simple illustration, suppose: Lr = St- If aT and a, are some weights which sum up to unity, the average employment is: L — arLr + a,L,. Similarly, S denotes the average supply of the specific factor. Taking logs yields, after some manipulation: b log L = log (5^ + (arSr + a,S% - S )) Expansion of this expression around S* yields: log L s: logS6 + S~b (arSt + a,Sb, - Sb) Now. by'expansion of Sb and St. each around Sk. it can be shown that the second term vanishes, such that: log L a: log 5* Since total employment is held fixed, employment location in a single industry is considered conditional on the location of other industries. For convenience, the notation has been simplified by introducing lower case let- ters for the logarithmic difference between the local value and the national 7 average. Assuming full interregional labor market integration (wr = 0) and the absence of productivity differences except pure localization economies (72 = 0, gr = 0), the employment difference is a function of the relative specific factor input:

^ (7) • 0:1 ~ 71 Taking squares and summing up all regions, this allows us to state a relationship between the sample variance of logarithmic specific factor supply and the sample variance of logarithmic employment:

(8) -71 Accordingly, the presence of localization economies (ai > 71 > 0) causes the variance of the logarithms of employment to be larger than the variance of the logarithms of the local conditions. Yet, it is generally difficult to find a measure for the vague concept of local conditions. In the literature it is pro- ceeded with the assumption that the distribution of total employment (Lr) is a reasonable proxy for the distribution of the other determinants of location, as represented by a^ in equation (8). For instance, Krugman (1991b) and Ellison / Glaeser (1994) compare single industries' regional employment distribution with the distribution of total employment to reveal the importance of localization economies for single industries.8 This approach can be regarded as an inspection of the distribution of the location quotient.9 It seems adaequate in the case where the specific factor is land available for location given the location decisions of all other industries. But it might

-log(i^L3 j (6) where m represents the number of regions. 8 lf the specific factor supply is approximated by total employment lr, a simple test is to compute the variance of the employment share of the industry (log (Lr/£,•))• The distance from zero reveals the importance of agglomeration economies, since after replacing S with L from equation 7:

The left hand side Is zero, if •> is zero. * Following Isard (1960). the location quotient (LQ) Is defined as the industry's regional be questioned in case of factors which are specific to single industries. Yet, the difficulty to observe the specific conditions prevents other less coarser measures. Leaving aside the difficulty of measuring the exogenous determinants of industry location, the basic employment equation (4) might be unsuited for testing implications of agglomeration economies for a number of reasons. First, the specification of the productivity shift function precludes any instability and indeterminacy of the interregional equilibrium. If instead, agglomeration economies are based on the variety of local intermedi- O ate inputs as in the monopolistic competition approach to the interregional equilibrium10 the productivity-shift function is primarily a function of the number of firms, and there might be no unique solution to the number of firms.11 Therefore the productivity-shift term needs to be augmented by the size of existing firms, which has the additional advantage of putting less emphasis on the endogeneity of productivity. Consider the following general function of the productivity-shift term:

where jfc , ~F- denote the local firm size in the industry under consideration and the aggregate industry respectively. The coefficient of the two firm size variables represents the productivity effect from the variety of firms as suggested by the monopolistic competition framework. As a larger firm size implies a smaller number of firms, the effect of firm sizes should be negative. If the number of firms is proportional to employment, as in the stylized theoretical industry equilibrium, the firm size variables are constants. If, on the other hand, the number of firms is not related to the employment level and employment has no influence per se, two parameter restrictions apply: 7i = 73 and 72 =74- Consequently, the two employment terms drop out and only the number of firms affect productivity. The more realistic case will lie in between, where the first two arguments of the productivity-shift term capture the externalities arising directly from the size of employment, and the two firm size variables reflect the variety not implied by the employment size. After separating the number of firms and the employment in the own

employment share divided by its national employment share, formally:

Lr L where L and L denote national industry emplyoment and total national employment. - respectively. "'Seee.g. Fujita (1989), Abdel-Rahman (1988), Krugnian (1991a), Rivera-Batiz (1988). "See Ciccone / Matsuyama (1996). industry, one obtains an alternative employment equation:

ai 7* OiX+Oi2 . . lr ~ • sT • wr (10) 0:1 - 7i + 73 oil - 7i + 73 1 , 73 ;9r H; + ~ 7i + 73 9r H oil ~ 7i +; 73 nT 74 oi 72-77 +4 7 7 Oil -71 +73 «i-7i+7+ 3 Additional problems arise from the assumption that the final good is per- fectly tradable. In particular, if there are input-output relations between local industries because some produce non-tradables, the interindustry externality measured by the last term might rather reflect the pecuniary externality from local demand. However, this ambiguity does not apply to effects of the establishment size, captured by the coefficient of (nT). Another ambiguity arises, if the supply of the region-specific factor of the considered industry depends on the other industries' demand for region-specific factors as the industries compete for that factor. Therefore, an increase in total employment (lr) might have adverse employment effects. But, again, this does not apply to the total establishment size, captured by the coefficient of'(nr). . Equation (10) presupposes that actual employment coincides with equilibrium. Even if this is the case, most of the right hand side variables have to be regarded as endogenous. As the emphasis on endogeneity in the interregional equilibrium renders it difficult to find instruments, this suggest estimation of some dynamic equation. This is also warranted as the equilibrium location of employment may well be changing in time. Basically there are two reasons for this. The first one is the existence of region-specific events: local employment, firm numbers, and perhaps-wages are affected by sectoral shocks in demand or productivity which are transformed into regional shocks by their different sectoral composition. Region-specific events also arise from regional integration, as for instance German reunification or European integration, since impediments for mobility are removed and transport costs change. The other reason for changes in the observed pattern of employment is the existence of dynamic externalities which are localized to some extent. Even if an existing state is an interregional equilibrium, differential growth might change the equilibrium over time. Therefore, an analysis of the relationship between a set of lagged local conditions and the development of employment by means of multivariate regression would not only circumvent the endogeneity problem, but also reveal the long-run effects of the local conditons. Following this line of research, recently siudies by Glaeser et al. (1992), and Henderson et al. (1995) have found significant effects of externalities on growth. To see liow such an approach is related to the theoretical framework, suppose there are adjustment costs giving rise to a partial adjustment in discrete time:

A log Lrjt = A (log L*r>t - log Lr,t-i) 0 > A < 1 (11)

A is the difference operator, L* t is the optimal employment, A is determined by the parameters of the adjustment costs, and t is a time index. A = 1 indicates full adjustment within the time period, whereas A = 0 depicts no adjustment at all. Note that the speed of adjustment is equal for all regions. The implied assumption is that the speed of adjustment constitutes an industry characteristic. Summing up all regions, weighted with their lagged employment shares, and employing Taylor approximations as above, we can again find a corresponding relation at the aggregate level. By subtraction an equation for the differential employment growth results:

•AJr,t = AA/r*it - A-(jr>t_i - /* t-1) (12)

Accordingly, the actual employment growth differential is caused by the differential growth in the optimal value and the adjustment if the optimal value has not been met in the previous period. Whereas we can observe the lagged employment as well as the actual employment growth and whereas the lagged equilibrium employment /* is determined by equation (10), the differential growth in the optimal employment needs to be specified. If the region-specific factor is constant, if wages are equalized in the interregional equilibrium, and if the level effect of the productivity-shift function evolves equally in all regions {Agr,t = 0), it follows from equation (10) that differential employment growth is caused by differential productivity growth, formally:

Al r,t = + (73Anr,f + 74Anr,t + (72 - 74) Alrit) (13)

As the growth rates on the right hand side must be regarded as probably highly endogenous to the employment growth in the considered industry, this relation should not be used directly for the purpose of empirical analysis. Rather, the notion of dynamic externalities suggests relating them to the lagged conditions in the locality. Consider the growth of the industry's own number of firms. If dynamic externalities are analogous to localization economies, the growth rates should be related to past employment, lr,t-i or to the past number of firms nrj-\. If dynamic externalities are related to the general level of industrial activities, the growth of the industry's own aggregate number of firms should be a function of the aggregate employment level lr,t-\, and the total number of firms nr.(-i- Denoting the dynamic external effects from past conditions with (i, the productivity growth equation may be rewritten as:

A/;( = - (/Mr.f-1 + -Vr.l-1 + /V ft) (N) r», - 7i +7:t Inserting equations (14) and (10) into equation (12) yields:

A/r,t = - A ^ lr,t-i (15) V -.-71 + 73/ A (ai + 0:2) + Sr,t-1 -IWr.t-l ai - 71 + 73 «i - 7i + 73 . X , A73 + r% - + ; 9r,t-i H ; n ,t-\ Oil - 71 + 73 011 - 71 + 73r A74 + A _ A (72 - 74) 4- & + ; rirt-i H ; lr ,t-l ai - 71 + 73 e*i - 71 +' 73 r All terms measuring the external effects now contain some expression of /? and.^7 or A coefficients, such that each term can be different from zero, irrespective whether the corresponding 0 term or, alternatively, the corresponding 7 term is zero. This indicates that without further assumptions the difference between dynamic and static externalities cannot be identified. Putting it more general, the process of economic growth cannot be distinguished from changes in the regional location of activities.12 Nevertheless, Glaeser et al. (1992), as well as Henderson et al. (1995) assume that adjustment is fast relative to the period of their analysis, such that A = 1. Moreover, they assume that the lagged employment in the base period was at the equilibrium level. This amounts to direct estimation of equation (14) after replacing the equilibrium employment growth with actual employment growth. Because adjustment in the spatial context is slow, and in Ger- many compared to the U.S. to which these studies refer is much slower, taking decades rather than years,13 I do not want to follow this assumption. Rather, I suggest interpreting the coefficients of total employment and of the firm numbers as localization or urbanization economies, without decid- ing whether they belong to the class of static or dynamic externalities.14 A remark should be made with respect to the firm size variables in the dynamic setting. Referring to the well-known article of Vernon (1966), Miracky (1.995) argues that the firm size variables also capture the product cycle, if younger products are offered by new firms and new firms are smaller. Yet, research on innovation is far from having yielded a conclusion if the firm size can be equated with the age of a firms products. Moreover, the recent literature on job creation and destruction raises doubts on the hypothesis that small firms contribute more than proportionally to employment growth.15 However, the empirical analysis will also include tests for the existence of product-cycle effects.

uCf., for instance, Siebert (1969). 13Cf. Moller (1995). MCf. Stahl (1995). 15See Davis / Haltiwanger / Schuh (1996) and Ulanchflowcr / Burgess (1996). 3 The regional distribution of employment

As has been suggested above, evidence on the relevance of agglomeration economies might be found by considering the distribution of single compo- nents of manufacturing employment. To be able to identify regional effects despite the heterogeneity of regions, a dataset with deep regional differentiation has been collected for the empirical analysis from a number of different sources (exact definitions and sources are given in the appendix). The observations are collected at the level of administrative districts, more precisely the units of observation are the 327 "Kreise und kreisfreie Stadte" of west Germany, which in the following are simply denoted as districts. The data used for the inspection of the employment distribution are taken from the employment statistics (ES) based on the social security accounts. It covers all employees which are obliged to contribute to the social security system. However, it does not take into account employees with earnings below a certain threshold. But, this may be ignored since in 1990 approximately as few as 2.3 %.of employees in the manufacturing industries were below this threshold.16. As the treatment only points to some general aspects and as the later analysis of the long-run evolution can only be carried out for larger sectors, let me focus here on the ten largest sectors, comprising roughly 73 % of all manufacturing employment. Table 1 displays some of their characteristics. The first five industries (31-38) belong to the class of industries producing capital goods, the others are producing consumer nondurables (54-58), basic materials (40), and food (68). The industries differ strongly in employment shares and in establishment shares. The comparison between the share of manufacturing employment according to the employment statistics (ES) and the statistics of manufacturing industry (SMI) reveals some differences, which might be caused by the neglect of small firms in the SMI but may also be due to different sectoral classification.17 Note that the employment share seems to be a weak predictor of the establishment share pointing to strong differences in the firm size among industries. According to the figures for the exports share there are also large differences in the tradability of the goods produced. The last column displays the frequency that no employment is reported in a district for a given industry.18 It has been outlined in the theoretical section that the basic difficulty in drawing conclusions from the distribution of employment is controlling for the distribution of other local conditions. As these are quite difficult to measure, a rough method would be to control at least for the size of total manufacturing employment, as suggested by Krugman (1991b). The disper-

16Cf. Poschl (1992). I7See the appendix for the matching of different classifications. 18Due to data protection rules, the exact cut off point for no reporting is not zero but between zero and five. Therefore, these cases were treated as an employment of three. Table 1: Selected manufacturing industries

manufacturing share of non activity manuf. empl. establ. exports rep. No. in percentage distr. ES SMI SMI SMI ES (1) (2) (3) (4) (6) (7) (8) 31 Structural Metal Products 2.59 2.84 3.84 11.09 4 32 Machinery and Equipment 12.23 13.85 13.42 43.61 0 33 Vehicles and Repairs 12.73 11.76 5.68 44.64 0 36 Electrical Appliances 12.53 13.75 8.84 32.39 1 38 Ironware . 5.19 4.70 5.69 23.81 0 40 Chemical Products 7.38 8.35 3.69 41.54 19 54 Wood Products 4.90 3.22 . 5.00 9.41 0 57 Printing and Publishing 2.14 2.72 4.95 7.10 0 58 Plastic Products 4.03 4.30 5.68 21.63 3 68., Food and Beverages 9.17 7.26 9.84 9.72 0 sum 72.75 72.89 66.65 28.72

Source: Columns (3)-(6) are own computations based on the data for 327 districts from SMI (employment, establishments), and ES (employment). The export shares in column (7) are own computations based on national industry data of the SMI (see appendix). Note: Column (1) contains the Sypro classification number. sion measures for the subset of industries displayed in table 2 are therefore based on the share of the industries in total manufacturing employment. Column (1) reveals districts with an employment share above 50 % for some industries. For example, consider the manufacturing of vehicles (33), where a maximum employment share with a value 0.961 is reported for the city of Wolfsburg, where Volkswagen, one of Europe's largest car manufacturers, is located. Besides maximum, minimum, and mean, also inequality measures are displayed. Since inequality measures have different properties, and different measures are used in the literature, the table gives the common ones: starting with the fourth column, the variance of the logarithmic population numbers, the coefficient of variation, the Gini-coefficient and Theil's entropy measure are displayed.19 Since the observed values in each region can be regarded as random variables, for means of comparison, standard errors have been computed without specification of the underlying distribution by application of a standard bootstrap. In the case of the Gini-coefficient,

'"The coefficient of variation is obtained by dividing the standard deviation by the mean. The variance of the logarithm simply measures the variance of the transformed observations. Following Cowell (1995) the other two measures of inequality are defined as follows: the Gini-coefficient [GC) of a variable y with u observations measures the average difference between all possible pairs of observations in the sample expressed as a

10 Table 2: Industries' employment dispersion among districts, 1994

no. max min mean Var. Coeff. Theil Gini Rank of log. of Var. (1) (2) (3) (4) (5) (6) (7) (8) Share of manufacturing employment in districts 31 0.201 0.000 0.026 1.111 1.079 0.409 0.480 4 ( -127) ( .064) ( .038) 32 0.697 0.001 0.123 0.658 0.739 0.232 0.370 9 ( .077) ( .031) ( .022) 33 0.961 0.010 0.108 0.526 1.143 0.387 0.439 5 ( .052) ( .070) ( .044) 36 0.848 0.000 0.110 0.971 0.873 0.311 0.428- 8 ( -117) ( -041) ( .028) 38 0.375 0.001 0.049 1.421 1.082 0.456 0.515 2 ( -US) ( .067) ( .034) 40 0.881 0.000 0.061 3.170 1.686 0.831 0.663 1 ( -255) ( .155) ( .069) o 54 0.341 0.001 0.058 0.780 0.906 0.324 0.436 6 ( .067) ( .044) ( .027) 57 0.184 0.001 0.029 0.770 0.910 0.324 0.435 7 ( .067) ( .046) ( .028) 58 0.272 0.000 0.046 1.274 0.977 0.393 0.482 3 ( .135) ( .051) ( .027) 68 0.400 0.009 0.113 0.359 0.607 0.164 0.317 10 ( .032) ( .022) (.012)

Source: Own computations based on data from ES (employment) (see appendix). Notes: Column (8) gives a rank based on (7). Numbers in parentheses are bootstrap standard errors based 500 resamples.

the bootstrap will lead to a biased measure of inequality and therefore no

proportion of the total sum of observations:

Theil's entropy measure (TE) subtracts the actual value of the entropy from the maximum value of the entropy, which yields: •

TE = — > — log —

Both measures have a minimum of zero, but whereas a maximum Gini-coefficient approaches unity asymptotically, Theil's measure ha* a maximum of log n.

11 standard errors are computed. Generally, the inequality measures show a large variety in the distribution of industries, with the chemical industry (40) being most concentrated and machinery (32) and the manufacture of food (68) ranking last, irrespective of which dispersion measure is used. Using a critical value of three times the standard error20 the difference between the two industries ranked first is significant. But other inequality differences between industries are generally not significant for small differences in the ranking. To test whether the inequality measures depend on the definition of spatial units of observation, they have been computed also for planning regions (see table 10 in the appendix). Whereas the inequality measures are much lower, the differences of inequality between industries is quite similar. This is confirmed by a value of 0.855 for the Spearman's coefficient of rank correlation between the rankings based on districts and planning regions. Whereas the data clearly support the notion of agglomeration of activities, it is difficult to draw conclusions on the forces behind. One major difficulty in the spatial context is the traded goods assumption. Namely, some industries are localized not because there are agglomeration economies in the production of goods exchanged freely on interregional markets, but simply because demand is localized. A first hint is provided by the large differences in the export shares (see table 1.) A more formal testing has been carried out by Justman (1994) using U.S. data. He uses a national input-output table and local employment shares of the industries to compute local demand for each industry, and compares it with the localization pattern. Local demand is separated with respect to interindustry demand and consumption. Interindustry demand is computed from the local sectoral composition of employment, the national input-output table, and the national labor productivities. Consumer demand is computed from the population share and the private consumption share from the input-output table. The local demand is then compared to the local activity in each sector implied by its local employment and national labor productivities. An examination of the ten selected industries has been carried out by making use of the 1988 pre-unification national input-output table. In order to match the input-output table with the classification used by the ES, 51 sectors are distinguished, of which 30 are manufacturing industries, including the selected 10 industries which are under consideration here (see appendix for a description of classification). Since it is hard to see why demand from public sectors should be located where the offices are located they have been neglected.21 In order not to explain local activity with itself, the own input demand is neglected.

20Cf. Cowell (1995). "This applies to sectors 56 (public sen-ices) and 57 (social security services), according to the classification of the input-output table.

12 tfibliothek d@s Institute fur Weltwirtschaft Kiel

Table 3: Local demand and supply in planning regions, 1988

Correlation of local activity with manuf. ind. all ind. total No. demand demand demand (1) (2) (3) (4) (5) (6) 31 .749 .393 .797 .445 .797 .445 32 .695 -.029 .708 -.088 .710 -.091 33 .609 -.045 .647 -.191 .679 -.142 36 .765 .160 .803 .176 .809 .123 38 .745 .344 .734 .301 .727 .254 40 .600 -.014 .672 -.029 .688 -.053 54 .613 .282 .633 .497 .612 .433 57 .650 .312 .644 .248 .601 -.002 58 .718 .324 .718 .311 .718 .307 68 .646 -.233 .861 .407 .879 .573

Source: Own calculations based on OS, BfLR, and ES (see appendix). Notes: Demand and activity values in logarithms. (2), (4), and (6) are weighted by total employment.

Table 3 presents the coefficients of correlation obtained from a corresponding procecure with 1988 data for 74 planning regions. The same computation has also been carried out for the districts, giving weaker cor- relations but largely comparable results, indicating that demand linkages are not confined within districts. The first column reports the correlation between local sectoral activity and manufacturing industries' intermediate demand. Because the regions differ strongly in their total employment, the simple correlation might overstate the relation between demand and supply if the driving variable behind them is total employment. Therefore, the second column reports coefficients obtained after dividing the local activity and the local demand by the local employment. Correspondingly, columns (3) and (4) report correlation coefficients with total intermediate goods demand. The last two columns display correlation with total demand, which also includes consumer demand. Whereas column (1) indicates significant correlation with manufacturing intermediate demand for all industries, it is revealed to be spurious for some industries after dividing by total employment. For sectors 32 (machinery and equipment), 33 (vehicles and repair), 36 (electrical appliances), 40 (chemical products) and G8 (food) the dependence of local demand is rejected.22 If considering the correlation with total

"Provided the variables are bivariate normal, if the coefficient of correlation is larger than 0.193, it is significantly larger than zero at 5 */i level of significance

13 Table 4: Firm concentration by gross output, 1994

No. number gross Theil Gini Rank of output firms (Bill.) (1) (2) (3) (4) (5) 31 1468 "36.7 4.695 0.69 6 32 5025 194.6 5.334 0.72 5 33 , 1724 255.3 5.897 0.92 1 36 2904 228.8 5.699 0.84 2 38 2085 64.3 4.778 0.67 7 40 1210 203.4 4.928 0.82 3 54 2000 44.2 4.697 0.65 9 57 1926 29.7 4.665 0.60 10 58 2144 61.4 4.776 0.66 8 68 3567 196.7 5.265 0.78 4

Source: SMI, column (3) shows own calculations based on SMI, and column (5) shows own calculations based on (4). interindustry demand (columns (3) and (4)), there is significant correlation for industry 68 (food). According to the last two columns, no differences arise from additionally taking consumer demand into account. By noting that local demand is important for a majority of sectors there is a clear indication of agglomeration economies only in the cases of machinery (32), the chemical (40), and the vehicles industry (33). As the latter two are common examples of industries subject to economies of scale this result is not very suprising. Ellison / Glaeser (1994) have suggested testing for the presence of localization economies against internalized increasing returns to scale by comparing the observed inequality in location shares to within-industry firm concentration measures. Unfortunately the dataset available to this study does not provide this information. Moreover, even the official statistic does not give adaequate concentration measures based on employment. For a rough comparison, consider table 4 which contains some measures based on gross output, and firms rather than employment and establishments.23 Although there is no clear matching with the previous table one finds quite substantial firm concentration. Note that the chemical industry (40) and vehicles (33) are again found to be among the first three. This provides an indication that scale economies within firms are of importance for the observed agglomeration.

23The official statistic distinguishes between firms as organizational units and establishments as localized units of production. The conclusion of the descriptive approach of testing for agglomeration economies is quite pessimistic. Although the empirical relevance of the concentration has been supported, which may be a result of own interest, it is not possible to discover the forces behind.

4 The regional development of employment

The formal basis to the study of the long-run development of industrial activity on a district level is the employment growth equation (15) discussed above. Starting with selected sectors basically the employment growth and the growth of establishment numbers over a long period is regressed on a number of determinants suggested by the theory. Before starting into the investigation let me summarize the results of some existing studies relevant to the analysis. Employing data for U.S. metropolitan areas, Glaeser et al. (1992) as well as Miracky (1995) report negative growth effects from the average establishment size, which, however, in both studies is used relative to the national establishment size. Miracky (1995) distinguishes between intraindustry and interindustry effects, and finds negative effects for both. The U.S. studies thus imply positive coefficients of the firm numbers in the above setting. For the German case there is a comparable study by Brocker (1989) also reporting negative effects of the own establishment size for single industries. He is using employment data mainly from the employment statistics (ES) at the level of the planning regions for the two periods 1970 to 1978 and 1978 to 1982. Yet, there is an earlier study by Reimers (1984) which reports positive effects of the own establishment size for regional employment growth in Germany in the sixties. Besides a negative coefficient of lagged own employment, Henderson et al. (1995) also using data for U.S. metropolitan areas find positive effects of the ratio of lagged employment and total employment. On the other hand Glaeser et al. (1992) and Miracky (1995) find negative effects of the related location quotient.24 Also Reimers (1984) and Brocker (1989) find negative effects of the location quotient, although no additional lagged employment variable is employed in their estimations. However, in the log-linear setting of equation (15) effects from the location coefficient and related measures of relative employment are not identifiable. Correspondingly, the finding of . those effects strongly depends on specification.25

24See footnote 9 in section 2. "in a study of regional firm formation Harhoff (1995a, 1995b) uses a quadratic specification of relative own employment and finds a decreasing positive effect which becomes negative at a higher level of own relative employment.

15 4.1 Employment data

The official data available to this study refer to the years 1978 to 1994 such that the period length is 16 years. Anyway a longer period is difficult to obtain on a district level, since major reforms of district territories were going on in the seventies. Generally the data refer to 322 West German districts,26 providing a total of 11,270 regional employment growth observations. How- ever, there are major shortcomings of the data, since even if establishments are reported for a given district at a given period, some employment data are kept secret. The specific selection process here involves different steps:

1. Only firms with more than 20 employees are present in the dataset. Establishments with less than 20 employees are reported if they belong to larger firms. . 2. Five districts are excluded because total manufacturing employment data are missing. 3. Districts are excluded if the number of establishments is zero. 4. Employment data are missing if there are less than three establishments. 5. Additional employment values are kept secret to disable (re)computation of missing values.27 The extent depends on the publication strategy of the statistical offices involved.

Therefore, the dataset is plagued by a double truncation problem: whether there are establishments reported, and whether employment is reported. Since the focus is on the growth of employment, it can be further distinguished whether the truncation occurs in 1978 or 1994. Table 5 reveals quite important truncation. Column (1) displays the number of districts where the respective industry is to be found, column (2) reports the numbers after the omission of five districts where general manufacturing data are missing.28 Whereas the largest industries (32,33, and 36) (see table 1) are to be found in almost every district a significant fraction (up to 15 %) of districts contains no establishments for other industries. Using the subset of districts where establishments exist, column (5) displays the numbers where employment is also reported. Accordingly the extent of truncation due to data protection is much stronger. In some cases less than 50 % (industries 31 and 40) of the districts in which industries are prevailing in other cases.

26Today there are 327 districts. Five districts are excluded due to reforms of district territories (see appendix). 27 The reason here is that sums of employment are given for the districts 1-digit industries on district level and for each 2-digit industry for various sets of districts (soe appendix). •""See appendix for a list.

16 Table 5: Truncation of official employment data

number of districts where establishments exist empl. is reported 6 c 1978a> 19786' A+ M A- M) 19786) A+ - ) A- M) (1) (2) (3) (4) , (5) (6) (7) 31 275 271 26 22 116 68 20 32 320 315 2 0 279 19 36 33 316 311 3 5 214 54 52 36 313 308 6, 6 230 44 25 38 289 288 18 7 166 48 25 40 280- 277 16 15 120 49 19 54 310 305 3 16 229 19 54 57 297 293 10 7 181 36 .26 58 293 291 19 6 . 188' 51 28 68 322 317 0 36 265 18 68

Notes: Own computations based on OS. a) Numbers refer to 322 West- German districts existing 1978 and 1994. b' Numbers refer to a subset of 317 districts (see text). c) Newly reported districts. d) No longer reported districts. more than 80 % (industries 32 and 68) are reported. In order to obtain data for the evolution over 16 years, the available sample size reduces further, since establishments are newly reported in 1994 in some districts (cf. column (3)) and no longer reported in others (cf. column (4)). Accordingly there are only minor changes in the existence pattern. More important are changes with respect to the reporting of employment (cf. columns (6) and (7)). The final number of observations available for employment growth can be found by subtracting column (7) from column (5), leading to samples sizes between 243 and 94. Besides the reduction in sample sizes the truncation raises questions whether results are representative and whether the empirical relations found are driven by the sample selection. For example, suppose there are urban externalities causing employment growth to vary positively with population density. If districts with growing employment have a larger probability to be selected in the sample, the effect of density may be underestimated since the adverse effect of low density is not observed in the selected subsample. More generally, the selection process may well be endogenous to the interregional location equilibrium, in the sense that both the observed employment and the selection process have determinants in common. After estimation of the basic employment growth equation, therefore, some testing on whether sample selectivity can be ignored will be conducted.

17 4.2 Sectoral employment growth

In addition to the respective industry's employment growth a number of variables describing the local conditions in 1978 are employed. Two variables describe the conditions within the respective industries. The first is the industry's lagged own employment describing the extent of past concentration. The different effects reflected in this coefficients have already been mentioned. On the one hand, dynamic externalities causing localization to have positive effects on employment growth may exist, such that large locations may grow faster due to concentration. On the other hand, the coefficient of past employment may simply be the parameter of adjustment. If the coefficient is minus unity there is full adjustment within the 16 years, and if the coefficient approaches zero no adjustment takes place. Moreover, the coefficient of lagged own employment captures region-specific effects and the effects of omitted variables, which may introduce a bias.29 Thus, the reasons behind a slow measured adjustment are not identifiable. Therefore, Glaeser et al. (1992), Henderson et al. (1995), and Miracky (1995) use a second variable, the lagged share of the own employment in total employment, to test for externalities. However, the present data reject the additional use of this variable. Districts with large employment in a single industry, with low area, and with many or large establishments act as influential observations (see table 11 in the appendix for descriptive statistics of the data), as revealed by an analysis of the leverage of the raw data.30 Therefore, a logarithmic specification has been found to be more appropriate. Conse- quently, with total employment and total population used in other variables (see below) the share does not add meaningful variation. The second variable concerning the own industry as proposed in the employment growth equation is the lagged number of the industry's establishments. As there is a close relationship to the lagged employment rather a related variable is used, namely the share of the industry's establishments among all local manufacturing establishments. This variable represents the specialization of the location towards the considered industry. It will show a positive coefficient, if externalities arising from firm numbers are more important within industries, and will be negative, if externalities act between them. Two other variables are proposed in equation (15) describing the presence and extent of general manufacturing activities, namely the lagged employment and the lagged number of establishments in total manufacturing. As the own employment in the industry is already used as a regressor, total employment except the industry under consideration is used for the first. Moreover, to distinguish effects from the density this variable is used in

Bernard / Durlauf (1996). the concept of leverage see. for instance. Davidson / McKinnon (1993).

18 relation to the total employment. The resulting variable is the other manufacturing industries' employment share. Here different hypotheses apply: important manufacturing activities might foster employment growth due to static or dynamic productivity externalities or indicate favorable locations due to the existence of interindustry demand. With respect to the second variable, instead of the total number of establishments in manufacturing suggested in equation (15) the number of establishments without the industry under consideration is used. To control for the level of manufacturing activities, the average establishment size in other manufacturing industries is used. This variable indicates whether the other manufacturing activities are conducted by a few large employers or by a large variety of small employers. In conjunction with the specialization variable defined above, this variable allows testing for one of the central propositions of the monopolistic competition approach, namely that a larger variety of activities in the own as well as in related industries, favors productivity and growth. If this holds empirically, a negative coefficient will be found. Finally, there are four variables of the local conditions in general: population, districts' area, a dummy whether the location is a core city, and the average wage in total manufacturing.31 To take the size of regions as well as their density into account, population and area are jointly included. The additional inclusion of a core-city dummy shall capture suburbanization effects. In contrast to the changes in the interregional employment pattern, which are the object of this study, suburbanization is seen as a change in the intraregional allocation of activities.32 Estimation is carried out by regressing the logarithmic employment difference between 1994 and 1978 on the logarithmic variables referring to 1978. Table 6 reports the regression results. The fit of the regressions varies con- siderably between industries, but nevertheless the results seem to be fairly consistent, at least qualitatively. Lagged employment always shows a negative coefficient, significant in most cases. With values between -.036 and -.330, that points to a clear correlation between current employment on lagged employment. For comparison, this correlation is much larger than that implied in industry employment regressions based on US state data by Henderson et al. (1995) using a period of 17 years.33 Note that a coefficient of zero is compatible with stationarity in the employment levels, and in that sense with full effect of historical employment. For industry 33 (vehicles and repair), full dependency on own historical employment cannot be rejected. This supports the results obtained from the above inspection of the spatial distribution of employment,

31 See appendix for description of data and sources. 32See Seitz (1996) for a recent study of suburbanization in German core-cities. MHenderson et al. (1995) report coefficients for a regression of current on lagged (log) employment between 0.365 and 0.647. cf. ibid. (1994), p. 1073.

19 Table 6: Employment growth regressions

Industry 31 32 33 36 38 constant 6.989*** 2.459 ** .471 .240 5.294 *** (3.107) (1.246) (1.602) (1.468) (1.906) own employ- -.330*** -.127 *** -.036 -.128 *** -.252 *** ment (.088) (.041) (.037) (.036) (.081) own share .049 .002 -.302*** -.031 .195 * of establ. (.168) (.098) (.113) (.085) (.118) other manuf. .700 ** .464 *** -.091 .199 * .492 * empl. share (.319) (.107), (.159) (.111) (.256) other manuf. -.489 * -.365 *** .077 -.273 ** -.281 establ. size (.267) (.127) (-171) (.115) (.214) core city .301 -.010 -.014 -.128 -.350 * (.232) (.104) (.184) (.136) (.175) population .206 .102 .015 .118 .251 * (.179) (.101) (.110) (.073) (.136) area -.014 .008 -.024 .033 -.023 (.102) (.055) (.065) (.056) (.074) wage -.782. .057 -.442 .423 * -.692 rate (.798) (.358) (.541) (.445) (.582) R2 (obs.) .415 ( 94) .242 (243) .080 (162) .207 (205) .284 (141) Industry 40 54 57 58 68 constant -2.041 2.125 -1.088 1.934 -.383 (1.622) (1.292), (1.169) (1.375) (1.022) own employ- -.111 ** -.122 ** -.094 * -.305 *** -.166 ** ment (.050) (.054) (.053) (.061) (.070) own share -.104 .066 -.266*** -.059 .027 of establ. (.112) (.089) (.086) (.096) (.102) other manuf. ' -.172 -.135 -.047 .253 ** .052 empl. share (.185) (.169) (.115) (.129) (.127) other manuf. -.271 .138 -.067 -.029 -.125 establ. size (.166) (.162) (.134) (.138) (.148) core city -.356 ** -.349 * .135 -.384 *** -.214 * (.163) (.184) (.143) (.147) (.130) population .010 -.030 -.076 .037 .112 (.092) (.094) (.094) (.113) (.113) area -.031 .032 .129 ** .093 .015 (.067) (.063) (.063) (.061) (.053) wage 1.188 ** -.649 .236 -.046 .515 rate (.527) (.433) (.414) (.491) (.353) /?'-' (obs.) .300 (101) .172 (175) .374 (155) .367 (160) .140 (197)

Notes: OLS estimates. Standard errors in parentheses are heteroskedastic-consistent es- timate!* suggested by White (1980). Significant a^fficients are marked with one. two or three stars for levels of 10%, 5%. and l'/S. 20 where this industry showed concentration but no dependence on local demand. Yet, as mentioned already, it is not possible to ascertain the reason behind the persistence, as it may be caused by adjustment, by unobserved region-specific conditions, or by localization economies. A part of the effect of past employment may be picked up by the specialization variable, defined as the own share of the number of establishments, because it shows a significant negative effect on two of the three industries with strongest dependency on past concentration. As regions with a high specialization towards the industry do not have advantages in employment growth and even disadvantages in two cases, the hypothesis of positive externalities within industries is not supported.34 If significant, the total manufacturing employment share shows a positive effect on employment growth. This is in line with the finding of Henderson et al. (1995) .35 Although the manufacturing milieu seems to be favorable, the reasons behind may be productivity as well as demand externalities. The latter is indicated by the loose accordance to the results on manufacturing interindustry demand (see column (4) in table 3.) since no significant correlation with demand has been found for industries 32, 33, and 40. Therefore, only in the case of machinery (32) the other manufacturing employment is significant, and, also, no correlation with local demand has been found above. Most industries show negative effects from the total establishment size, which is however significant only in few cases. Putting it inversely, in those cases, a larger number of local firms in manufacturing has positive effects on employment growth. This is in line with the productivity-externality hypothesis in the monopolistic competition approach to the interregional equilibrium. The finding of positive effects of the overall number of establishments per employee on sectoral employment growth is in line with Glaeser et al. (1992) and Miracky (1995). Whereas suburbanization is confirmed, since the core-city dummy is negative in most cases, area and population show almost no significance. The wage rate is significant only in some cases. In these cases it shows a positive sign, which might be explained by higher labor productivity. As already mentioned in the previous section the finding of positive growth effects of the firm number is probably not challenged by the product- cycle hypothesis, since recent studies reject a more than proportional contri- bution of small firms to employment growth. However, one may test for the product-cycle hypothesis, suggesting that the smaller establishments in the, sample have experienced relatively large growth since their products experi- 34Those cases are in line with Brocker (1989) who reports negative effects of the establishment size in the respective industries. However, the study of Reimers (1984) for German and Scandinavian regions reports positive effects of the own establishment size in the sixties. "Henderson et al. (1995) use the log of all other manufacturing employment and report significant coefficients between 0.223 and 0.98G. cf ibid., tables H3 and B4

21 ence expansion. The resulting growth in the other manufacturing industries could simply spillover by demand effects to the dependent variable. If this were the driving force behind the firm-size effects, one would observe, that regions with high employment growth also experience an increase in establishment size. Accordingly, by replacing the lagged establishment size in other manufacturing industries with the growth rate of the establishment size between 1978 and 1994 a positive coefficient should be observed. As revealed by table (12) in the appendix this is not the case: the rate of growth of the establishment size in other manufacturing industries never shows a significant effect on employment growth. As already pointed out, the results may suffer from selectivity bias, since some districts in the sample show missing values in 1978 or 1994. As usual, the first step in the testing for sample selectivity is to specify a set of equations which determine the probability for a district to be selected into the sample. Due to the multistep-selection process the joint probability of four events might have to be specified: existence of establishments and reporting of employment in 1978 and 1994. They are correlated, in particular the reporting of employment in 1978 is an important predictor of the reporting in 1994. In face of the identification problems arising from this and the small data set used, only a single selection equation is specified. However, since the number of establishments in a district in 1978 is regarded as an essential piece of information on the specific local conditions, we might reduce the complexity and specify the propability conditional on the existence of establishments in 1978. In other words, endogenous sample selection is assumed to be confined to the reporting vs. nonreporting of employment, given the location has contained, an establishment in 1978. According to column (3) in table 5 only a few districts are neglected (in four cases less than 1% is omitted). Testing for the sample selection is carried out using the simple two-step procedure, suggested by Heckman (1979). In the probit estimations with the reporting vs. nonreporting in 1994 as the dependent variable two additional instruments are used, intending to mirror in particular the last step in the selection process, concerned with the prevention of a recomputation of missing values. The first instrument is the total number of industries reporting establishments in the district in 1994. The second is the number of missing values in other industries in 1994. The hypothesis behind is that if many industries exist in the district, the data protection rules allow the publication of more employment data, but if many other industries data are kept secret, missings are more likely to occur since publishers try to avoid recomputation. For the probit estimations, two modifications of the set of the explanatory variables are necessary. Since the joint reporting of employment in 1978 and 1991 is the endogenous variable, the lagged employment level cannot be used as an explanatory variable, and average establishment, size as well as the local importance of manufacturing refer to total manufacturing.

22 The probit estimations displayed in the appendix (see table 13) show quite similar patterns for different industries. Many of the explanatory variables contribute significantly to the prediction of being in the sample. The two instruments in the selection equation always show the right sign and are significant in many cases, thus, the identification of the sample selection rule by the two instruments is acceptable. The most important predictor of the observation of employment in 1978 and 1994 is the own share of establishments. The results of the corresponding second-step employment growth regressions are also displayed in the appendix (see table 14). The results are quite similar to the basic estimates. In particular, they support the finding of positive effects from the local weight of other manufacturing employment and of negative effects of the establishment size. A significant sample selection bias is supported only for industries (31) and (38). In these cases both the positive effect of other manufacturing employment as well as the negative effect of other manufacturing establishment size have gained in significance. The sample selection can be ignored in eight of ten cases. However, one might employ more efficient estimators provided a correct specification of the econometric model is found. But there is another more appealing way to examine the indication of productivity externalities by focusing on establishment numbers, which are reported without missing values.

4.3 Sectoral growth of establishment numbers

Theoretical discussions of dynamic externalities based on the variety of goods36 suggest that the firm-formation process can be described analogous to the equilibrium employment growth equation (14). Basically, it is assumed that the number of firms evolves according to the following equation:

LfiD,r,t-i stands for the local employment in research and development activities, aT^-i reflects the local stock of knowledge not affected by the local number of firms,an d 6 is a constant. The analogyfis obvious: not only is the change in the number of firms related to the past number of firms,bu t arjt_i may also be positively related to the local number of firms or the local employment in total manufacturing, if there are positive dynamic externalities. m As with the employment growth the lagged level of NT.t-\ ^y differ from its equilibrium value. Then, part of the change in the firm numbers will reflect adjustment, and again effects from productivity growth are no longer discernible from adjustment, by relocation. Hence, static externalities might also cause positive effects of the number of firms and the total employment

"'See. for instance. Grossman / Helpman (1991) and Romer (1990) in manufacturing. Also the other local conditions, namely the supply of specific factors and the wage rate will have qualitatively the same effect as in the employment growth equation, because they affect the profit of the new firms in the same direction as they affect the equilibrium employment. Therefore, except for the measurement of employment in research and development, it seems viable in the present context to circumvent the problem of missing employment data, and focus on the growth of firm numbers. However, since the dataset only applies to establishments belonging to firms with more than 20 employees, the interpretation as a firm-formation equation is not straightforward. First, most entry and exit involves smaller firms. Moreover, the change in the number of establishments might result from relocation and therefore does not necessarily indicate the formation of a firm as an enterprise. And, the dataset only reports the net change in the establishment number. Hence, when some establishments entry and others exit the market, a constant number may be reported. Therefore, the relation to the literature on regional variations of firm formation is rather weak.37 Nev- ertheless each reported change in establishment numbers reflects a location decision. With the establishments there is still the truncation problem that some districts contain no establishments. It is however less dramatic than with employment (see table 5). As with employment, truncation can be distinguished with respect to the existence of establishments in 1978 or 1994. Again, estimation is carried out conditional on the existence of establishments in 1978 since it makes the analysis easier and only few observations have to be neglected (see ibid.). The set of variables used is similar to the employment growth regressions with the respective industries' employment replaced by the industries' establishment number. The dependent variable is the percentage change in establishment numbers. As with the probit estimation for sample selection, the variables for establishment size and the local weight of manufacturing refer to total manufacturing since the industry's employment data are not used. Without employment data the difference between the past localization and specialization measures is no longer relevant, therefore the own share of etablishments constituting the specialization variable is supressed. Instead, a measure of specialization within all manufacturing industries based on establishment shares is constructed. Since industries are not always present in each district, a Herfindahl-index allowing for zero observations is used. For each district r the index contrasts the share of an industry s of the total number of establishments with the national share of the respective industry. By summation the following Herfindahl-index is obtained:

37.See Reynolds / Storey / Westhead (1994).

21 where Nr>s denotes the number of industry s's establishments in regions r. The lowercase letter n denotes the number of industries, and a dot denotes summation over the respective index. An increase indicates larger inequality or less diversity. The computed inequality measure for districts has a strongly skewed distribution (see table 11 in the appendix). Since also problems with influential observations occured as above, all explanatory variables have been transformed logarithmically, which solved those problems. Table 7 presents the results. The regressions for different industries being quite similar, they support the results from the employment growth regressions. The lagged establishment numbers are highly significant in most cases. The coefficients are much higher than for employment growth, but as the dependent variable is the percentage change and the lagged establishment number is in logs the results are not fully comparable. Yet, as with the lagged employment in the above employment growth regressions, the coefficient is difficult to explain as the variable may pick up unobserved regional characteristics. In most cases^the weight of manufacturing employment has also significant effects. Similar to the employment growth regressions no effect is.found for industries 33 (vehicles), 40 (chemical Industry), and 68 (food). The average establishment size shows negative effects in all of the investment goods industries (31-38) and some of the others. No industry shows a significant positive effect. Therefore, the results confirm the above finding of positive externalities from the total manufacturing activities. The inequality index is significant only for industry 36 (electrical appliances) sup- porting the hypothesis that larger diversity has positive effects on growth. This is in accordance with Harhoff (1995a, 1995b). He reports a large number of high-technology oriented firm-birth rates in this industry, and finds positive effects of the diversity of manufacturing on firm-birth rates. According to the core-city dummy, there is some suburbanization going on, but population always has strong positive effects. Since there are no corresponding negative effects in area this does not simply represent density effects or urban externalities. With coefficients roughly similar in order of magnitude to the coefficient of the manufacturing employment share, it rather indicates that the absolute scale of manufacturing employment, but not its relative importance, matters for the growth in establishment numbers.

4.4 Total manufacturing employment growth

As it is reported almost without any missing values, it is also instructive to consider the total manufacturing employment. Moreover, the better coverage of regions makes it possible to test for two other determinants, namely region-specific trends, such as the north-south divide, and the regional investment-promotion policy. Another appealing feature of an anal-

25 Table 7: Number of establishments' growth regressions Industry 31 32 33 36 38 constant -5.780 * -.579 -.887 .917 1.168 (2.816) (2.157) (3.088) (2.434) (3.063) own number -.696*** -.572*** -.703 *** -.586 *** -.347 *** (.112) (.091) (.123) (.114) (.075) manuf. .731*** .782*** .182 .632 *** .707 *** empl. share (.232) (.150) (.141) (.173) (.187) manuf. -.456*** -.274 * -.354 ** -.523 ** -.509 *** establ. size (.176) (.165) (.169) (.224) (.174) core .227 -.035 -.153 -.335 ** .038 city (.169) (.100) (.145) (.147) (.154) population • .580*** .457*** .620 *** .819 *** .427 *** (.137) (.117) (.161) (.183) (.139) area .100 .025 -.142 *** -.270 *** -.044 (.072) (.045) (.050) (.089) (.064) wage 1.866 * .546 .488 .236 .311 rate (.962) (.761) (1.095). (.858) (.968) Herfindahl -.029 -.017 -.099 -.169 ** .073 index (.086) (.043) (.063) (.075) ,(.055) R2 (obs.) .290 (271) .372 (315) .338 (311) .230 (308) .163 (288) Industry 40 54 57 58 68 constant -2.278 3.419 * 1.835 4.493 ** -.052 (2.531) (2.003) (2.591) (5.651) (1.618) own number -.314*** -.276*** -.333 *** -.798 *** -.112 (.087) (.059) (.072) (.147) (.086) manuf. -.013 .359*** .293 ** 1.177 *** .158 empl. share (.170) (.130) (.129) (.262) (.112) manuf. .148 -.199 -.330 * -.625 ** -.065 establ. size (.139) (.126) (.178) (.260) (.103) core -.311 ** .002 -.191 -.176 .026 city (.141) (.143) (.152) (.228) (.110) population .314 ** .252*** .545 *** .579 *** ,210 *** (.116) (.089) (.104) . (.154) (.080) area -.034 .073 -.167 *** . .068 .047 (.067) (.054) (.056) (.077) (.035) wage .224 -1.043 -.288 -.680 -.218 rate (.827) (.655) (.857) (1.990) (.577) Herfindahl .001 -.005 -.015 -.109 -.009 . index (.052) (.049) (.056) (.076) (.039) R2 (obs.) .111 (277) .162 (305) .145 (293) .243 (291) .087 (317) f Notes: OLS estimates. Standard errors in parentheses are heteroskedastic-consistent estimates suggested by White (1980). Significant coefficients are marked with one, two or three stars for levels of 10%.5%,and 1%.

26 ysis of total employment growth is that it yields further results about the long-term effect of the past employment pattern. In the above industry regressions the national trend is implicit in the constant. With aggregate manufacturing employment the trend can be made more explicit. By making use of the local employment shares of manufacturing industries and the national employment growth of these industries, a local average of national employment trends is constructed. As it reports an employment growth which would be predicted given the knowledge of the past regional industry composition and the national trends in employment growth, it can be referred to as the predicted employment growth. It is a central variable in the shift-share analysis.38 Provided any district's share of an industry's employment is low, or the district is large such that the industry is not a dominant employer, the local average captures an employment trend, exogenous to the district. Table 15 in the appendix displays the largest shares of the industries' employment located in a single district and the corresponding local share. Although there are some industries where more than a third of the employment is located in a single district, the weight in the district's employment in these cases is low. Therefore it seems difficult to reject the exogeneity of the employment growth averages. Table 8 displays the regression results. In column (1) without the average of sectoral employment growth there are significant negative effects of lagged employment and positive effects of the number of local establishments. This implies significant negative effects of average establishment size but no effects of lagged employment. The implied coefficent of establishment size being -0.154 is in accordance with the industry employment growth regressions. However, the wage rate shows strong positive effects. According to column (2), employment growth is nearly proportional to the local average of sectoral employment growth, as the coefficient is not significantly different from unity but is highly significantly different from zero. Accordingly, the national employment growth is a good predictor of the regional employment growth.39 Note that the effect of wages is significantly reduced after the inclusion of the average employment growth. Therefore, the local wage rate tends to reflect the industry composition rather than the wage level. As an alternative wage variable, therefore, the log differ-

' ^See Armstrong / Taylor (1993). 39This result is quite different to Peschel / Brocker (1988) and Brocker (1989). They find no significant correlation between regional employment growth and industry-mix effects for the planning regions in west Germany for an even smaller time period. For comparison, the basic correlation between the actual and the predicted rate of employment growth in the present study is 0.27. When using the actual employment growth in percentage rather than in log differences the correlation reduces to 0.26. Provided the variables are bivariate normal, in the given case the critical value at 5 % level of significance is 0.094. Besides differences in the industry classification used the difference to the present study might arise from the focus on firms with at least 20 employees. Table 8: Total manufacturing employment growth

(1) (2) (3) (4) (5) (6) constant -.942 ** -.226 .354 * .309 .541*** 1.294 * (.380) (.339) (.183) (.181) (.187) (.401) predicted .831*** .925*** .844*** .879*** .917*** growth (.170) (.170) (.159) (.151) (.150) lev. of -.134*** -.079 ** -.058 ** -.067 ** -.096*** -.107*** empl. (.032) (.031) (.029) (.029) (.028) (.030) num. of .154*** .119*** .093*** .114*** .111*** .110*** establ. (.037) . (-034) (.032) (.031) (.033) (.033) Herf. -.039*** -.045*** -.043*** index (.015) (.015) (.014) core city -.039 -.036 -.036 -.022 -.024 -.026 (.034) (.034) (.034) (.035) (.034) (.033) popu- -.162*** -.156*** -.139*** -.162*** -.137*** -.114*** lation (.033) (.031) (.030) (.030) (.033) (.033) area .083*** .085*** .083*** .081*** .082*** .076*** (.014) (.014) (.014) (.014) (.014) (.014)- wage rate .515*** .214 * (.144) (.126) wage diff. .099 .084 .216 .302 ** (.135) (.135) (.148) (.153) user cost -.332 ** of cap. (.153) north -.130*** -.159*** (.033) (.033) north -.032 ,-.037 -west (.034) (.034) west -.122*** -.128*** (.035) (.035) , south -.072 ** -.073 ** -west (.029) (.029) east -.047 -.060 ** (.029) (.029) south '.541*** .608*** (.141) (.140) R2 .396 .451 .447 .459 .511 .518

Notes: OLS estimates. Standard errors in parentheses are heteroskedastic-consistent estimates suggested by White (1980). Significant coefficients are marked with one, two or three stars for levels of 10%, 5%, and 1%.

28 ence of local manufacturing wages to the local averages of national wages in the manufacturing industries is calculated from the local employment shares (see appendix for further description of this variable).40 In analogy to the employment growth variable, it may be referred to as the predicted wage. Its use has the advantage of removing the effect of contract wages which are set rather uniformly across regions for manufacturing industries.41 The results of a regression using this alternative wage variable are displayed in column (3). Note that the coefficient of the average employment growth is increased which supports the hypothesis of an industry composition effect in the local wage rate. However, the wage difference shows no significant effect on employment growth. The regression reported in column (4) additionally contains the inequality index. As a significant negative coefficient is obtained, the diversity hypothesis is again supported. Interestingly, no significant core-city effect is found, but population density shows strong negative effects, which differs from the above industry regressions. This supports the earlier finding of Bade (1984) who finds a negative relation of total manufacturing employment growth with density for west Germany in the seventies. According to column (5), which displays the regression results using regional dummies relative to the center (see appendix for a description of region dummies), the results are not driven by regional trends, although the common north-south trend clearly shows up in the dummies. Therefore, the results support the notion of interindustry externalities, but positive externalities from density cannot be revealed. Throughout the theoretical and empirical analysis so far an important set of location factors hve been neglected, namely those created by an active regional policy. Although the current study does not aim at analyzing the effects of regional policy measures, one might question the implied assumption that regional policy is negligible. In the base year 1978, a set of policy instruments consisting of investment grants, tax credits, tax rates, and additional capital allowances induced regional differences in the user cost of capital.42 Estimates of the regional user cost of capital in the manufacturing industry in 1978 by Deitmer (1993) range from 6.37 % to 8'.87 % for the 317 districts contained in our subsample. Therefore, one might dispense with the assumption of equalized returns to capital made in the previous chapter, and use the districts' user cost of capital as an additional variable. Column (6) presents results of a corresponding regression. As the estimated coefficient is significantly negative, districts with a lower user cost of capital seem to have experienced larger- employment growth, which indicates an effectiveness of the regional policy. A reduction of the user cost of capital

40Cf. Brocker (1989). 41See Biittner (1995). 42 For a discussion of regional investment-promotion policy in Germany, see Franz / Schalk (1995) and the references listed there.

© . ' 29 increases employment relative to its level by 33%. This is exactly twice the total employment effect found by Deitmer (1993) and Franz / Schalk (1994). Whereas the set of districts subject to investment-promotion did not change much during the period 1977-1989 on which their estimation result are based, the difference might well be explained by the relatively low variation of the user cost of capital in 1978 compared to later years.43 Note that the inclusion of region dummies has been found necessary for the significance of the user cost of capital variable, indicating that districts' employment is affected by the regional policy only relatively to the regional level. The other results of the estimation are not affected qualitatively, with the exception of a weakly significant positive effect of the wage difference. Using district data for manufacturing employment from the same source but controlling for regional productivity differences Deitmer (1993) and Franz / Schalk (1995) do find wage effects on employment. Thus, the positive wage coefficient in the employment growth regression might still indicate productivity differences.

5 Conclusions

Following the literature, as a deficient but available indicator of the region- specific conditions selected industries' employment has been compared with total manufacturing employment. It has been shown that the industries' employment shares tend to cluster spatially. Notably, the ranking of industries with respect to spatial concentration is similar for districts and planning regions. However, for the majority of industries, it seems difficult to conclude from this that agglomeration economies apply, in particular since significant correlation with interindustry demand computed with the input-output table has been found. And, some correspondence was found to the ranking of the concentration of employment among firms. The regional industry specialization pattern is quite stable over the 16 years, as today's local share of an industry's employment is.almost identical to its past value. This finding does not allow the deduction that this is caused by agglomeration economies. However, some sectors indicate that locations with a stronger manufacturing employment relative to total employment gain in employment relative to the average district. Since no industry exhibits the contrary result, this supports interindustry externalities within manufacturing. Still, this finding does not allow a determination of the nature of the externalities, i.e. whether they are simply demand linkages on factor or product markets, or spillovers in productivity. But, in some

"The coefficient of variation of the user cost of capital increases from 5.8 % in 1977, and 7.1 % in 1978 to a value of 15.8 % in 1989. (Own computations from the user cast of capital as published by Deitmer (1993).

30 cases, the number of establishments in total manufacturing is revealed to have strong positive effects on the development of the location's relative employment position. As the product cycle hypothesis could be rejected, this points to the existence of broad interindustry productivity spillovers. On the other hand, specialization of location towards the industry under consideration measured by the establishment numbers shows insignificant or negative effects, such that there is no support for intraindustry or narrow localization economies. These results find clear support from regressions with the local growth of establishment numbers as the dependent variable. Again, a larger weight of manufacturing employment and a larger number of establishments in manufacturing show positive effects on the development of the location's relative establishment position. The empirical analysis therefore adds support from German data to the conclusion of Glaeser et al. (1992) and Miracky (1995) that positive local interindustry externalities are a relevant determinant of regional employment change. The analysis of the districts' total manufacturing employment growth shows that these findings are consistent with the development of the manufacturing employment position. The diversity of manufacturing activities and the number of establishments show positive effects on growth, even after controlling for the sectoral composition of manufacturing employment. However, the sectoral composition shows strong effects, indicating that employment growth at industry level is a good predictor for local employment growth. Furthermore, the results are not affected from taking regional employment trends and the regional investment-promotion policy into account. With respect to the role of agglomeration economies in bringing about dif- ferentials in productivity growth, no clear result is obtained. This is due to a theoretical inseparability of the effects of regional productivity growth and regional relocation due to adjustment. Therefore, it is not known whether the empirical support for local productivity externalities in fact indicates static or dynamic externalities. Whereas there are significant effects from density, such as negative growth in core cities, almost no significant effects of wages have been found. There are different possible explanations for this. Besides deficiencies in the wage index used, there might be a problem with the endogeneity of wages, if wages gain from the productivity advantage of favorable locations. Another explanation is that there are no revelant regional wage differences. If there is high mobility between regions, only cost of living differences are relevant for regional wage differences, which might be already captured in the population density. Alternatively, wages could be set at a national level, and therefore do not show regional variation. The persistence of the industries' regional employment, pattern suggests strong region-specific effects from sectoral shocks. This has been docu- mented by the finding that extrapolation of local manufacturing employment from the past sectoral employment composition and the national trends

31 yields a good predictor of today's employment. Therefore, structural change manifested in long-run changes in sectoral employment is shifted into specific regions. Note that this result is not called into question by the north-south divide in employment growth, which is, however, present in the period under consideration.

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31 Appendices ' ' •

A Data sources and definitions

District classification: From the 327 districts, five had to be excluded due to administrative changes in the district structure. They are (official keycodes in parentheses) Ammerland (3451), Friesland (3455), Wittmund (3462), Giessen (6531), and Lahn-Dill (6532). Another five districts had to be excluded as general manufacturing industry data are missing: Salzgitter (3102), Wolfsburg (3103), Gifhorn (3153), Emden (3402), and Aurich (3452). Planning regions: The classification of districts into planning regions is due to the BfLR (Bundesforschungsanstalt fiir Landeskunde unde Rau- mordnung) and taken from BfLR (1992). Area: The area for districts is taken from the Eurostat database Regio. It is measured in square kilometers. Population: Numbers of population in districts 1978 and 1988 are taken from the Eurostat database Regio. They are published as yearly aver-. ages. Numbers for 1994 are taken from the Statistik-Regional of the statistical office of North Rhine-Westphalia (Landesamt fiir Daten- verarbeitung und Statistik, Nordrhein-Westfalen). They refer to the beginning of the year. Population at working age: The proportion of population at working age is defined as the share of total population between age of 15 and 65. It is computed from age-specific population data. The data source is the official population statistic. Data for 1994 are taken from the Statistik-Regional of the statistical office of North Rhine-Westphalia (Landesamt fiir Datenverabeitung und Statistik, Nordrhein-Westfalen). They refer, to the beginning of the year. Core cities: The core city dummy originates in the classification of districts from the BfLR (1992). Core cities are defined as cities with a population above 100,000. Region dummies: Regions have been defined by collecting districts mainly according to the states. The definitions are: North: Schleswig-Holstein, Hamburg, Bremen, and Lower Saxony. North-West: North Rhine- Westphalia. West: Rhineland-Palatinate and Saar. South-west: Baden- Wiirttemberg. South: Bavaria except Franconia. East: Franconia. Center: Hesse. Manufacturing employment and establishments (SMI) by district: Employment and establishments for 35 two-digit industries for the 327 west German districts in 1978 are taken from the series 4.4.1.3. of the federal statistical office (Statistisches Bundesamt). This source is referred to as the statistics of manufacturing industry (SMI) (Statistik des Produzierenden Gewerbes: Bergbau und Verarbeitendes Gewerbe). The data refer to the end of September. The corresponding 1994 data are taken from the Statistik-Regional of the statistical office of North Rhine-Westphalia (Landesamt fiir Daten- verabeitung und Statistik, Nordrhein-Westfalen). With the exception of districts in Bavaria and Hesse, which report annual averages, the 1994 data also refer to the end of September. Establishments are defined as localized units of production, and are distinguished from firms. The dataset covers those establishments which belong to firms with at least twenty employees. Two industries (Sypro no. 25 and 68; see the industry classification table 9) partially cover firms with at least ten employees. As a rule, in districts with less than three establishments in an industry the industry's employment value is not reported. In a large number of cases where values are missing, the reason is to prevent recomputation of other missing values since many subtotals are given. Subtotals report the sum of employment for. one-digit industries at district level and for each two-digit industry at the level of 26 sub- state level regions (Regierungsbezirke), for the 12 sub-sub-state level regions in Baden-Wurttemberg, for the 11 states in west Germany including west Berlin, and at the level of the former Federal Republic of Germany. However, the subtotals are often also missing. All industries' employment (ES) in the districts: Employment data for the complete set of 95 two-digit industries for the 327 west German districts in 1994 and 1988 have been obtained from the employment statistics (ES) (Beschaftigtenstatistik) of the IAB. They are based on the social security accounts. The reported district is the location of the employer. Only employees obliged to contribute to the social security system are contained in the dataset. The data are referenced on the 30th of June and report employment with respect to the districts. To match the 95 industries of the ES with the 35 industries of the SMI a classification with 33 industries has been designed by the author (see classification table 9). Districts' wage rate in manufacturing: Local wages in 1978 are defined as the annual sum of payments to employees (Bruttolohn- und gehalts- summe) divided by the annual average of total employment for the establishments in manufacturing industries. The underlying dataset

36 of establishments is the same as for the employment data (SMI) intro- duced above. The data are taken from the series E I which is published separately by the statistical offices of the German states (Statistische Landesamter). Manufacturing industries' employment (SMI) by district: Employ- ment shares of the 35 two-digit industries (see the industry classification table 9) at district level in 1978 are computed from the SMI employment data (see above). Missing data have been reestimated by the author by making use of the local establishment numbers and the employment data for the various regional aggregates: for all districts with a missing employment number in a specific industry which belong to the same regional aggregate, the residual regional employment obtained by subtracting the reported from the aggregate number was computed. This sum of nonreported employment was then allocated to the districts according to the number of local establishments. User cost of capital: The districts' user costs of capital in 1978 as used in Deitmer (1993) and Franz / Schalk (1995) have ben obtained from the latter authors. They are defined as:

where gi,g2 are rates of regional investment grants (#2 is taxed), v is the local tax rate on profits, z is the current value of additional capital allowances, i is the nominal interest rate, d denotes technical depreciation, and q is the price index of investment goods. In this definition the net interest (corrected for taxation) is used to capitalize profits.44 With the exception of i, z and q, all parameters vary at the district level. Export shares: Export shares for the 10 selected industries in 1994 are computed from yearly total sales and sales abroad of establishments without sales tax according to series 4.4.1.4. of the SMI of the federal statistical office. They refer to the pre-unification territory. Firm concentration (table 4): Data on firm concentration by gross output in 1994 are taken from the series 4.4.2.3. of the SMI of the federal statistical office. In contrast to the other manufacturing data used, they refer to firms rather than establishments. Input—Output table: The national input-output table used in the computation of interindustry demand has been obtained from the federal

44Cf. Deitmer (1993) for a formal derivation. O 37 statistical office. It refers to home production in 1988, where prices are free of sales tax and transport. To match the 58 two-digit industries with the 95 industries of the ES, a classification with 51 industries has been designed by the author (see classification table 9). Labor productivity: Numbers for labor productivity are also taken from the national input-output table. Manufacturing industries' wages (SMI): National data on annual payments to employees and on annual average employment in 1978 defined exactly as in the case of the local wages have been taken from the series 4.4.1.1. of the federal statistical office. Manufacturing industries' employment (SMI): For each of the 35 two- digit manufacturing industries annual average employment data for 1994 and 1978 are taken from the series 4.4.1.1. of the federal statistical office.

B Tables

Table 9: Industry classifications

Sypro curr. SIO SBA activity no. no. no. no. (1) (2) (3) (4) ' (5) 01 01 00-01 Agriculture 02 02 02-03 Forestry and Fisheries 03 03-5 04 Electricity, Gas and Water 21 04 06 05 Coalmining 21 05 07 06,08 Other Mining 21 06 08 07 Oil and Gas Extraction 40 07 09 09-10 Chemical Products 22 08 10 11 Petroleum Processing 58 09 11 12 Plastic Products 59 10 12 13 Rubber Products 25 11 13 14 Stone and Earth Products 51 12 14 15 Fine Ceramics 52 13 15 16 Glass Products 27 14 16 17 Iron and Steel 28 15 17 18 Non Ferrous Metals 29 16 18 19 Foundry 30 17 19 20-22 Fabricated Metal Products 31 18 20 23-24 <-• Structural Metal Products continued on next ;>a#e

38 Sypro curr. SIO SBA activity no. no. no. no. (1) (2) (3) . (4) (5) 32 19 21 26-27 Machinery and Equipment 50 20 22 33 Office and Data Proc. Machines 33 21 23 28-30 Vehicles and Repairs 34 22 24 31 Shipbuilding 35 23 25 32 Air and Space 36 24 26 34 Electrical Appliances 37 25 27 35-36 Precision and Optical Instruments 38 26 28 37 Ironware 39 27 29 38-39 Musical Instruments, Jewelry 53 28 30 40 Woodwork 54 29 31 41-42 Wood Products 55,56 30 32-33 43 Paper and Products 57 31 34 44 i Printing and Publishing 61 32 35 45-46 Leather 62 33 36 47-51 Textiles 63 34 37 52-53 Apparel 68 35 38-39 54-57 Food and Beverages 69 36 40" 58 Tobacco 37 41 59-60 Construction Proper 38 42 61 Other Construction 39 43-44 62 Trade 40 45 63 Railways 41 46 66 Ship Traffic, Waterways and Harbors 42 47 64 Postal Services 43 48 65,67-68 Other Transportation 44- 49-50 69 Banking and Insurance v 45 51-55 72-73,79-86 Other Services 46 52 70-71 Catering and Hotels . 47 53 74-77 Education, Science and Culture 48 54 78 Health and Veterinary 49 56 91-92 Government 50 57 93 Social Security 51 58 87-90,94 Priv. Households and Non-Profit Org.

Notes: The first column refers to the classification in the SMI (Sypro: "Systematik der Wirtschaftszweige. Fassung fur die Statistik im Produzierenden Gewerbe" ). The third column contains the classification used in the national input-output table (SIO: "System- atik der Produktionsbereiche in Input-Output Rechnungen, Ausgabe 1980). The fourth column contains the classification used by the employment statistic (ES) (SBA: "Verzeich- nis der Wirtschaftszweige fur die Statistik der Bundesanstalt fiir Arbeit, Ausgabe 1973).

39 Table 10: Employment inequality in Planning Regions Regions (1994)

no. max min mean Var. Coeff. Theil Gini Rank of log. of Var. (1) (2) (3) (4) (5) (6) (7) (8) Share of manufacturing employment in Planning Regions 31 0.070 0.005 0.025 0.376 0.631 0.179 0.334 4(4) ( .054) ( .046) ( .021) 32 0.279 0.037 0.122 0.161 0.395 0.075 0.218 10(9) (-.025) ( .019) (-on) 33 0.497 0.035 0.121 0.384 0.800 0.239 0.369 2(5) ( .062) ( .071) (.040) 36 0.402 0.024 0.109 0.265 0.552 0.131 0.280 7(8) ( .046) ( .035) ( .027) 38 0.261 0.003 0.048 0.456 0.826 0.239 0.357 .3(2) ( -117) ( .079) ( .054) 40 0.447 0.002 0.064 1.194 1.134 0.478 0.526 1(1) ( .182) ( -149) ( .072) 54 0.154 0.013 0.053 0.296 0.602 0.155 0.307 5 (6) (.043) ( .042) ( .022) 57 0.122 0.009 0.029 0.242 0.566 0.129 0.273 8 (7) ( .041) ( .038) ( .032) 58 ~ 0.140 0.006 0.042 0.365 0.556 0.143 0.293 6(3) ( .069) ( .037) ( .025) 68 0.308 0.033 0.106 0.191 0.501 0.106 0.247 9(10) ( .033) ( .028) ( .018)

Source: Own computations based on data from ES (employment) (see appendix). Notes: Column (8) gives a rank based on (7) Numbers in parentheses are bootstrap standard errors based on 500 resamples.

40 Table 11: 1Descriptive statistics on selected variables Variable nun. max. mean stddev. obs. (1) (2) (3) (4) (5) districts>' 1978 values herf.-index .004 .499 .043 .046 317 manuf. empl. 2055.0 185633.0 22743.0 24632. 317 total empl. 10188.0 752068.0 59781.0 76271. 317 establ. in manuf. 26.0 1038.0 150.1 117.7 317 establ. size in manuf. 44.7 917.2 147.1 102.5 317 population (1000) 33.9 1672.0 183.4 164.9 317 area (km2) 35.9 2880.2 759,8 534.6 317 wage rate in manuf. 18.437 40.524 26.873 3.641 317 aver, of nat. wages 22.808 35.219 28.649 1.675 317 user cost of capital 6.370 8.862 7.991 0.571 317 core city .000 1.000 .183 .387 317 manuf. empl. growth districts' development 1978-1994 actual . • -.847 .455 -.104 .212 317 predicted -.401 -.001 -.162 .067 317 Industry employment growth for industries 31 -2.324 2.159 -.076 .651 94 32 -2.124 1.397 -.058 .476 243 33 -1.548 2.421 -.021 .568 162 36 -2.103 1.385 .006 .505 205 38 -2.444 1.723 -.043 .585 141 40 -.938 1.292 .047 .437 101 54 -1.606 1.052 -.168 .486 175 57 -1.531 1.413 .076 .445 155 58 -1.235 2.301 .333 .534 160 68 -1.117 1.730 .059 .439 197 Industry establishment growth for industries 31 -1.000 6.000 .409 .957 271 32 -.800 5.000 .262 .650 315 33 -1.000 7.000 .086 .813 311 36 -1.000 7.000 .380 .826 . 308 38 -1.000 4.000 .113 .655 288 40 , -1.000 3.000 .117 .659 277 54 -1.000 3.000 -.147 .526 305 57 -1.000 4.000 .211 .649 293 58 -1.000 8.000 > .528 1.098 291 68 -1.000 3.000 -.160 .471 317 Notes: Sample consists of 317 districts in west. Germany. Districts employment growth is computed as log differetice. establLshment growth is computed as relative change.

41 Table 12: Product cycle effects in employment growth

Industry 31 32 33 36 38 constant 6.486 2.123 * .643 -.343 4.363 ** (3.501) (1.238) (1.587) (1.508) (1.745) own employ- -.332 *** -.091 ** -.043 -.101 *** -.185 ** ment (.091) (.040) (.035) (.035) (.073) own share -.056 -.033 -.281*** -.090 .124 of establ. (.156) -(.101) (.105) (.094) . (.112) other manuf. .324 .242 ** -.044 .039 .099 empl. share (.223) (.107) (.123) (.098) (.169) growth of .099 .005 .005 -.018 -.438 establ. size (.347) (.207) (.243) (.246) (.276) core city .159 -.064 .005 -.190 -.502 *** (.214) (.105) (.181) (.128) (.160) population .192 .025 .033 .049 .089 (.216) (.083) (.113) (.089) (.141) area .041 .070. -.035 .076 .031 (.104) (.045) (.067) (.050) (.071) wage -1.646 * -.556 • -.343 .064 -.980 * rate (.991) (.373) (.484) (.409) (.522) . R2 (obs.) .397 ( 94) .207 (243) .080 (162) .193 (205) .288 (141) Industry 40 54 57 58 68 constant -2.364 2.638 ** -1.334 , .1.885 -.723 (1.619) (1.224) (1.204) (1.400) (.976) own employ- -.094 ** -.135 *** -.077 * -.307 *** -.148 ** ment (.051) (.048) (.046) (.060) (.069) own share -.154 .092 -.289*** -.054 . -.020 of establ. (.109) (.080) (.084) (.096) (.091) other manuf. -.362 ** -.092 -.104 .249 ** -.031 empl. share (.157) (.131) (.090) (.116) (.089) growth of .073 -.255 -.089 .095 .074 establ. size (.198> (.208) (.167) (.189) (.163) core city -.442 *** -.299 * .111 -.393 *** -.240 * (.154) (.165) (.134) (.141) (.123) population -.017 -.039 -.113 .051 .086 (.093) (.095) (.092) (.115) (.111) area -.003 .038 .144 ** .089 .029 (.064) (.063) (.063) (.060) (.051) wage .741 ** -.539 .172 -.079 .346 rate (.514) (.361) (.364) (.420) (.283) R2 (obs.) .28694 (101) .177 (175) .374 (155) .367 (160) .138 (197)

Notes: OLS estimates. In contrast to table (6) the establishment size in other manufacturing industries is replaced by its rate of growth. Standard errors in parentheses are heteroskedastic-consistent estimates suggested by White (1980). Signifi- cant coefficients are marked with one. two or three stars for levels of 10%. 59C. and \%. 42 Table 13: Endogenous sample selection: probit

Industry 31 32 33 36 38 obs. ' 271 315 311 308 288 • pos. obs. 94 243 162 205 141 constant -.983 6.274 ' 6.390 ** 10.730*** 8.219 ** (4.326) (3.742) (3.217) (3.790) (3.855) own share 1.521*** 1.421*** 1.330*** 2.622*** 1.753*** of establ. (.219) . (.233) (.178) (.313) (.235) total manuf. .083 -.811 * .407 1.943*** 1.416*** empl. share (.534) (.479) (.401) (.502) (.513) total manuf. -.478 .909 ** -.737 ** -1.594*** -.883 * establ. size (.489) (.463) (.363) (.490) (.487) core city -.455 .310 -.124 -.689 1.064 ** (.439) (.454) , (.328) (.433) (.478) population .626 -.487 .182 1.247*** 1.025 ** (.396) (.438) (.302) (.455) (.407) area -.153 .126 -.164 -.095 .282 (.174) (.167) (.133) (.170) (.209) wage rate 1.146 -3.688 ** .089 .376 -1.313 (1.428) (1.378) (1.092) (1.273) (1.305) number of .142 ** .399*** .108 **' .089 * .067 industries (.055) (.064) (.043) (.051) (.053) number of -.048 -.084 -.074 * -.090 * -.101 ** missing val. (.047) (.053) (.040) (.049) (.051)

Notes: Maximum Likelihood estimates. Standard errors in parentheses are based on analytic second derivatives. Significant coefficients are marked with one, two or three stars for levels of 10%, 5%, and 1%.

•13 Table 13: Endogenous sample selection: probit (continued)

Industry 40 54 57 58 68 obs. 277 305 293 291 317 pos. obs. 101 175 155 160 ' 197 constant 7.143 8.325*** 13.218*** 15.586*** 6.333 ** (4.366) (3.566) (4.023) (4.099) (2.941) own share 1.696*** 1.360*** 1.895*** 2.041*** .655*** of establ. (.228) (.184) (.238) (.259) (.150) total manuf. 1.205 ** 1.493*** 1.074 ** 1.845*** .273 empl. share (.544) (.435) (.475) (.539) (.396) total manuf. -1.038 ** -1.536*** -1.102 ** -1.575*** .129 establ. size (.482) (.435) (.440) (.487) (.353) core city .126 .770 * .065 .526 .848*** (.443) (.424) (.391) (.476) (.314) population 1 .743 ** .970***. .860 ** 1.490*** -1.082*** (.374) (.352) (.375) (.414) (.304) area -.209 .064 -.346 ** -.208 .381*** (.192) (.182) (.156) (.178) (.129) wage rate .068 -.561 -1.709 -1.783 -1.250 (1.402) (1.186) (1.293) (1.360) (1.045) number .137*** .091 * .185*** .078 .196*** (.052) (.048) (.054) (.053) (.040) number -.096 * -.027 -.040 -.105 ** -.169*** (.052) (.048) (.046) (.053) (.042)

44 Table 14: Endogenous sample selection: 2nd step OLS

Industry 31 32 33 36 38 obs. 94 243 162 205 141 constant 6.936*** 1.904 .327 .429 5.899*** (3.051) (1.165) (2.087) (1.489) (1.977) own employ- -.318*** -.135*** -.037 -.123*** -.254*** ment (.085) (.042) (.038) (.036) (.080) own share .381 * -.056 -.327 .041 .378 ** of establ. (.230) (.104) (.268) (.108) (.163) other manuf. .842*** .417*** -.109 .257 ** .676 ** empl. share (.318) (.119) (.244) (.126) (.283) other manuf. -.668 ** -.341*** .098 -.317*** -.381 * establ. size (.281) (.120) (.287) (.120) (.226) core city .216 -.029 -.012 -.139 -.199 (.228) (.108) (.187) (.135) (.182) population ' .474 ** .055 .000 .145 \ .393 ** (.211) (.087) (.179) (.085) (.162) area -.047 .008 -.019 .037 .045 (.101) (.054) (.082) (.054) (.076) wage ratio -.626 .236 -.433 .437 -.919 (.807) (.364) (.535) (.443) (.603) inv. Mills .518 ** r-.201 -.040 .127 .346 * ratio (.237) (.184) (.395) (.165) (.188) R2 .434 .250 .081 .210 .298

45 Table 14: Endogenous sample selection: 2nd step OLS (continued)

Industry 40 54 57 58 68 obs. 101 175 155 160 197 constant -2.222 2.665 -1.460 2.922*** ^ -.600 (1.605) (1.539) (1.466) (1.487) (1.212) own employ- -.112** -.126** -.094 * -.304*** -.171** ment (.050) (.055) (.053) (.062) (.067) own share -.146 .167 -.311** , .071 .011 of establ. (.177) (.141) (.127) (.135) (.108) other manuf. -.228 .029 -.093 .378 ** .017 empl. share (.272) (.255) (.145) (.160) (.156) other manuf. -.228 -.003 -.031 -.138 -.111 establ. size (.181) (.235) (.147) (.154) (.154) core city -.364** -.267 .130! -.345 ** -.237 (.163) (.207) (.144) (.148) (.149) population -.020 .062 -.106 .162 .119 (•11.7) (.141) (.112) . (-143) (.110) area -.024 .048 .136** .080 .007 (.062) (.063) (.064) (.060) (.058) wage diff. 1.170** -.668 .281 -.226 .562 (.527) (.435) (.440) (.510) (.379) inv. Mills -.055 .205 -.069 .216 -.062 ratio (.210) (.200) (.133) (.200) (.194) R2 .301 .176 .375 .371 .140

46 Table 15: Maximal employment shares for 35 industries, 1978

21 .126 .383 22 .354 .058 24 .560 .028 25 .019 .212 27 .107 .420 28 .132 .118 29 .082 .111 30 .121 .253 31 .046 .130 32 :029 .363 33 .079 .477 34 .230 .175 35 .143 .118 36 .059 .368 37 .082 .305 38 .054 .201 39 .120 .291 40 .100 .872 50 .112 .037 51 .185 !.487 52 .056 .280 53 .046 .103 54 .046 .283 55 .056 .106 56 .037 .143 57 .052 .076 58 .039 .217 59 .090 .171 61 .151 .018 62 .133 .657 63 .059 .446 64 :029 .363 65 .269 .001 68 .054, .144 69 .175 .017

Notes: The first number in each box is the Sypro classification number. The second is the largest,share of the industries total employment among the 317 selected districts. The third number gives the corresponding share of regional employment.