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Demand for labour and unemployment : Canada's Maritime Provinces Glyde, Gerald Patrick 1969

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DEMAND FOR LABOUR AND UNEMPLOYMENT: CANADA'S MARITIME PROVINCES by GERALD PATRICK GLYDE B . S c , Colorado State U n i v e r s i t y , 1963 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of A r t s i n the Department of Economics We accept t h i s t h e s i s as conforming t o the r e q u i r e d standard THE UNIVERSITY OF BRITISH COLUMBIA A p r i l , 1969 In p r e s e n t i n g t h i s t h e s i s i n p a r t i a l f u l f i l m e n t of the r e q u i r e m e n t s f o r an advanced degree a t the U n i v e r s i t y o f B r i t i s h Columbia, I a g r e e t h a t the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and Study. I f u r t h e r agree t h a t p e r m i s s i o n f o r e x t e n s i v e c o p y i n g o f t h i s t h e s i s f o r s c h o l a r l y purposes may be g r a n t e d by the Head o f my Department or by h i s r e p r e s e n t a t i v e s . I t i s u n d e r s t o o d t h a t c o p y i n g o r p u b l i c a t i o n of t h i s thes,is f o r f i n a n c i a l g a i n s h a l l not be a l l o w e d w i t h o u t my w r i t t e n p e r m i s s i o n . Department o f Economics The U n i v e r s i t y o f B r i t i s h Columbia Vancouver 8, Canada Date A p r i l , 1969. ABSTRACT In Canada, as i n most other i n d u s t r i a l c o u n t r i e s , concern i s expressed over the existence of r e g i o n a l unemployment imbalances. I f these imbalances were q u i c k l y a l l e v i a t e d , by a c t i o n of l a b o r and c a p i t a l markets, there would be no r e g i o n a l problem. U n f o r t u n a t e l y , t h i s i s not the case. Not only do r e g i o n a l unemployment d i f f e r e n t i a l s e x i s t , b u t , more i m p o r t a n t l y , they tend t o e x i s t i n s p i t e of m i g r a t i o n . This phenomenon suggests t h a t , i n the depressed r e g i o n , there i s some exogenous change, d e c l i n i n g export demand, t a k i n g place c o n c u r r e n t l y w i t h net out-migration. In a d d i t i o n , net out-migration i t s e l f reduces employment l e v e l s t o some extent i n the l o s e r r e g i o n . Changes i n the demand f o r la b o r i n a r e g i o n are r e l a t e d t o : changes i n the export demand f o r i t s commodities which form the employment base; and changes i n p o p u l a t i o n s i z e r e s u l t i n g from r e g i o n a l m o b i l i t y . This r e l a t i o n s h i p i s e s s e n t i a l l y founded i n a type of m u l t i p l i e r r e l a t i o n between the employment base of a r e g i o n and i t s t o t a l employment. In t h i s paper a model i s developed from which the above t h e o r e t i c a l r e l a t i o n s h i p can be e m p i r i c a l l y i n v e s t i g a t e d . Debate on p o l i c y measures f o r reducing r e g i o n a l unemployment, i n d u s t r i a l l o c a -t i o n and m o b i l i t y p o l i c i e s , have proceeded l a r g e l y without knowledge of r e l a t i v e magnitudes. For a more o b j e c t i v e approach we need estimates of r e g i o n a l m u l t i p l i e r s . With t h i s i n f o r m a t i o n we would be b e t t e r equipped t o judge the employment e f f e c t s of out-migration and changes i n export demand on depressed re g i o n s . The estimation technique used in this paper is cross-sectional multiple regression analysis. The counties of the Maritime Region serve as the population sample for the analysis. Data comes mainly from the Censuses of 1951 and 1961; both provide considerable informa-tion for such series as employment by industry and changes in popula-tion due to migration. It is concluded from the analysis carried out in this paper that emigration does indeed contain a de-stabilizing element for the loser region, in the form of income depression. We cannot expect out-migration of the.unemployed to reduce unemployment on a one for one basis. Also, the regional employment multiplier may be larger in the case of declines in the employment base than for increases in i t . The results suggest that mobility policy and industrial location policy may not reduce regional unemployment as quickly as we might suppose, a p r i o r i . TABLE OF CONTENTS Page ABSTRACT i i LIST OF TABLES v ACKNOWLEDGMENT v i CHAPTER I . INTRODUCTION 1 CHAPTER I I . THE RELATIONSHIP OF EXPORT ORIENTED EMPLOYMENT TO TOTAL EMPLOYMENT 5 CHAPTER I I I . THE RANGE OF EXPECTED COEFFICIENT VALUES 19 CHAPTER IV. THE CALCULATION OF BASIC EMPLOYMENT 33 CHAPTER V. THE RESULTS 51 CHAPTER V I . IMPLICATIONS FOR POLICY AND CONCLUSIONS ' 60 SOURCES CONSULTED 72 APPENDIX 75 V LIST OF TABLES Table Page I. Gross Domestic Product and Total Labour Input by Industry, Canada, 1955....... 75 II. Characteristics of Emigrants from the Maritime Provinces (1956 - 1961 Period).... 76 III. Derivation of Factor for Adjusting 1951 Labour Force to 1961 Productivity level.... 77 IV. Industry Labour Force by County (1951 and 1961) 78 V. Adjustment to 1951 and 1961 Agricultural Labour Force, by county.. 81 VI. The Sum of Agriculture (1), Forestry (2), Fishing (3), Mining (4) and Manufacturing (5) Labour Force, by County, 1951 and 1961 83 VII. Location Quotient Calculations by County and Industry, 1951 and 1961 84 VIII. Basic Labour Force Residual, by County and Industry, 1951 and 1961 85 IX, Basic Labour Force in the Construction and Transportation, Communication and U t i l i t i e s Sectors by County, 1951 and 1961 87 X. Basic Labour Force Totals by Industry Groups and County, 1951 and 1961 88 XI. Total Labour Force, Basic Labour Force, and Average Multiplier Estimates, by County, 1951 and 1961 89 XII. Basic Labour Force, 1951 and 1961, and Changes in Basic Labour Force, by County............ 90 XIII. Estimates of Changes i n Total Employment (N), Basic Employment (& x), and Population Due to Emigration (M) , 1951 to 1961, by County 91 v i ACKNOWLEDGMENT I am deeply indebted to those people who assisted in the preparation of this study. Professor John VanderKamp, of the Economics Department of the University of British Columbia, who was project advisor, i s thanked for his valuable criticisms, suggestions and guidance. A special vote of thanks also goes to Mary Holbrook who was most helpful and conscientious in typing the f i n a l text. Beverly Sielski i s to be commended for her typing of the tables for this paper. Last, but not least, thanks go to my wife, Karen Glyde, who typed the rough copy and helped proof-read; in addition to providing me with moral and financial support during the preparation of this paper. I alone remain responsible for any errors that are lef t in the paper. CHAPTER I INTRODUCTION In Canada, as in most industrial countries, concern i s expressed over the existence of regional unemployment imbalances. If these dispa-r i t i e s were quickly alleviated, by action of the labour and capital markets, there would be no regional problem. Unfortunately, this i s not the case. Not only do regional unemployment differentials exist, but, more importantly, they tend to persist in spite of migration. This phenomenon suggests that in the depressed region there is some exogenous change, declining export demand, taking place concurrently with net out-migration. In addition net out-migration i t s e l f reduces employment levels in the loser region. The classical theory suggests that migration w i l l be wholly stabilizing, in response to relative prices. Indeed, i t may be s t a b i l i z -ing in a "net" sense. However, there are definite de-stabilizing effects involved in migration. When people move from one area to another they transfer their expenditures to the receiver region. Reduced expenditures in the loser region have multiplier effects. One expects, after a "certain" number of people withdraw their expenditures, that more unemploy-ment w i l l occur i n the loser region. This aspect of migration i s de-stabilizing. The net effect depends upon the relative magnitudes in-volved. In order to go beyond a p r i o r i reasoning of this sort, i t is necessary to quantify the variables involved. Debate on policy measures, vi z . mobility and industrial location policies, designed to alleviate 2 regional unemployment disparities have proceeded largely without knowl-edge of relevant magnitudes. For a more objective approach, one needs estimates of regional multipliers or coefficients of variables related to total employment. With this information, we would be better equipped to judge the employment effects of emigration and export demand on depressed regions. The objectives of this thesis are: f i r s t , to develop an hypothesis which purports to explain variation in regional employment; second, to empirically test the hypothesis, using cross-sectional multiple regression analysis; and f i n a l l y , to analyze the derived coefficients, or regional multiplier estimates. The end result, i t is hoped, w i l l be a contribu-tion to our quantitative knowledge of factors influencing regional employ-ment levels. Counties of the Maritime Provinces of Canada, experiencing net out-migration, serve as:the sample population for the analysis. The Maritimes has typically exhibited a higher than national average unemploy-ment rate. However, with the exception of a few counties net out-migration has taken place with no apparent narrowing of the unemployment dif f e r e n t i a l . There are 36 counties in the Maritimes and 29 of these counties, of approximately the same size, showed net emigration between 1951 and 1961. Eighty per cent of the net emigration from the counties was also out of the Maritimes. Therefore, although we are interested in the Maritimes as a whole, county analysis provides us with 29 represen-tative cross-sectional observations, and hence with a useful s t a t i s t i c a l population, which would not be available at the aggregate level. Using county information i t is possible to empirically investigate the relation-ship between emigration and total employment. 3 The s t a t i s t i c a l information used in this paper comes from the Census of Canada, 1951 and 1961. Analysis of county employment data by industry allows us to construct a measure of the change in a regions employment base. The employment or export base of a region provides i t with exogenous expenditures which stimulates regional income, thus total employment i s sympathetic to changes in the base. Chapter II of this study contains the basic theoretical frame-work. A model is developed which explains variation in regional employ-ment levels. It begins in the form of a Keynesian income-expenditure relationship. From this position the model is transformed into measureable employment and population units. In words, the f i n a l equation of the model avers that changes in total regional employment are related to: a) changes in basic or export oriented employment, reflecting changes i n export demand for a region's commodities; and b) changes in population due to emigration. This relationship i s essentially founded in a type of multiplier relation between the employment base of a region and i t s total employment. In addition, however, the importance of emigration i s recognized because of i t s effect on the amount of income generated within the region. Both of the above independent variables can be expected to have an effect on regional expenditures, thus on regional employment levels. Chapter III continues in a theoretical vein. By using some casual empiricism and bri e f l y reviewing other studies' results, we discuss co-efficient values that might be expected to result from our analysis. The purpose of making these rough advanced estimates is to provide a basis of comparison for the actual coefficients to be derived later in 4 f the paper. Chapter IV deals with the thorny problem of identifying the basic employment component of each of the counties involved. Eleven broad industry groups are available for analysis in 1951 and 1961 at the county level. No simple method exists for isolating basic employment across a l l industries; therefore, i t i s necessary to use a variety of means to accomplish our purpose. This chapter, then, deals with the empirical problems which must be solved before an estimate of the employment multiplier i s possible. At the conclusion of this chapter we w i l l have observations on changes in basic employment between 1951 and 1961 for each county involved. In Chapter V, having derived the required basic employment informa-tion we bring in the emigration variable and total employment variable from which coefficient estimates can be derived using cross-sectional multiple regression analysis. The results presented are analyzed and interpreted. Finally, coefficient values thought to be representative for the Maritime Region are chosen for further discussion in Chapter VI. Chapter VI, in the form of a conclusion, provides a discussion of the usefulness of our results and their implications for policy. This paper i s mainly concerned with contributing to our quantitative knowledge of variables influencing regional employment. However, in this xhap'ter" we conclude by i l l u s t r a t i n g the implications of our results as related to the problem of regional unemployment i n the Maritimes. Although a detailed examination of location policy and mobility policy i s beyond the scope of this paper, the policies are discussed within the context of our results. CHAPTER II THE RELATIONSHIP OF EXPORT ORIENTED EMPLOYMENT AND EMIGRATION TO TOTAL EMPLOYMENT - A MODEL The objective of the present chapter is to set out in a precise way the relationships to be examined. This paper i s concerned with de-veloping quantitative estimates of coefficients of variables affecting levels of employment in the Maritime Provinces of Canada. In order to accomplish this, i t i s necessary to construct a model which can be investigated in a systematic manner. Given a satisfactory model, we can proceed to quantify the variables involved. Changes in the demand for exports from a region affect the level of expenditures in that area. Regional expenditures are also influenced by emigration and i t s composition. Any structural s h i f t , that results in expenditure fluctuations, sets in motion the multiplier process with consequences for regional income. The level of employment in an area i s functionally related to i t s level of income. Therefore, i t is pertinent to pose questions such as the following: a) What is.the employment effect in a region when the demand for i t s exports changes? b) How does emigration affect total employment in a loser region? Quantitative answers to questions such as the above w i l l not be forthcoming u n t i l estimates of regional multipliers can be made. The questions serve to i l l u s t r a t e the need for empirical research in the f i e l d of regional economics. The r e l a t i o n s h i p of changes i n export demand and emigration t o changes i n t o t a l employment i s not a d i r e c t one. The l i n k i n a l l cases has t o do w i t h income-expenditure e f f e c t s and the m u l t i p l i e r process. Therefore, i t i s i n s t r u c t i v e t o begin the a n a l y s i s w i t h i n the framework of the Keynesian d o c t r i n e . * Although the model t o be developed here w i l l not end up i n terms of monetary u n i t s , the Keynesian approach f a c i l i t a t e s the development of the c e n t r a l ideas i n v o l v e d . To b e g i n , we can w r i t e a simple income determination equation t o r a r e g i o n j u s t as f o r a country as i n equation ( 1 ) . (1) Y o E + E c a Symbol Y represents c u r r e n t r e g i o n a l income; E- stands f o r r e g i o n a l value added consumption expenditures and E^ stands f o r autonomous r e -g i o n a l expenditures. Assume that a l l other components of aggregate 2 demand can be p l a c e d i n e i t h e r E or E . c a In the simple Keynesian model, consumption expenditures are assumed t o be a l i n e a r f u n c t i o n of income, t h a t i s , induced expenditures. An autonomous constant term i s i n c l u d e d i n the consumption equation i n d i c a t i n g t h a t consumption w i l l not f a l l below an assumed f i x e d l e v e l no matter how low income might f a l l . This constant term, i n g e n e r a l , i s not considered s i g n i f i c a n t i n n a t i o n a l income a n a l y s i s . However, i n 1 John Maynard Keynes, The General Theory of Employment, I r i t e r e s t and Money, (London: MacMillan and Co. L t d . , 1964). 2 S p e c i f i c a t i o n of a l l components of aggregate demand i s not c r u c i a l f o r t h i s model as long as the marginal r e l a t i o n s h i p s of i n t e r e s t can be analyzed. 3 Keynes, op. c i t . , p. 115. 7 the present paper i t i s desirable to investigate this autonomous element of consumption expenditures in more detail. Assume, then,-that the autonomous portion of consumption is not constant. We can write: (2) E c = f(Y) + A «s A + bY b = constant (mpa)4 Aj£ constant Two different types of consumption expenditures can be specified. Further, different conditions w i l l bring about changes in the magnitude of each. Regional residents can consume current regional production from income earned regionally. Clearly, changes in income affect this type of consumption expenditure; hence, the term bY in equation (2). But, res-idents can also consume current regional output from funds not earned regionally, or not earned currently; hence, the term A in equation (2), which is not directly influenced by income. What is the source of consumption expenditures not tied to current income? At the regional level there are many, such as: unemployment insurance, family allowance, old age pensions, welfare and other transfer payments. In addition, running down assets or increasing l i a b i l i t i e s are other autonomous consumption expenditures. By substituting equation (2) into equation (1), the familiar equa-tion (3) can be derived, which isolates the multiplier and relates expenditures to income. Equation (4) i s the marginal form of (3) i l l u s t r a t i n g the effect of a change in A or E a on Y.5 4 Let mpa stand for the marginal propensity to add value locally, that i s , marginal propensity to consume minus the marginal propensity to import. 5 • .. - .. Bradress over a variable represents a "change i n " the quantity. 8 1 (4) Y = KA • K E a; K = — C5) N = g (Y) Changes in income are assumed to influence or be reflected in changes in employment, N. This relationship i s set out in equation (5). The model suggests that the size of the multiplier has much to do with any resultant change in income, and employment, transpiring from A or E^. Multiplier values w i l l vary i n magnitude between regions. This i s primarily due to the fact that regions* marginal propensities to add value locally w i l l d i f f e r . Although marginal propensities ' to consume may be much alike, marginal propensities to import are likely to d i f f e r , generally with the size of the region.** More specifically, small regions tend to specialize in production; demand, however, is relatively diversified. Small areas are not able to support a wide range of produc-tion for obvious reasons. Therefore, they must depend on extra-regional sources to a large extent to satisfy internal needs. In contrast, larger regions are better able to take advantage of economies of scale over a wider spectrum of output. With this broader base, the area has less expenditure leakage to extra-regional points. It seems safe to say that, ceteris paribus, smaller regions* propensities to trade are greater than those of larger regions. 7 Therefore, the multiplier i s expected to 6 Size in this context is population size. ^Gerald Sirkin, "The Theory of the Regional Base", Review of Economics and Sta t i s t i c s , XLI (June, 1959), 427. 9 be of a higher magnitude for the Maritimes as a whole than for an average county within i t . With this simple model, a firmer basis exists for discussion of the relevant reaction paths mentioned at the outset of the chapter. It is easier to visualize the effect that changes in export demand, changes in population due to emigration, have on a region's total employment. It i s generally agreed that exports play a major role in income determination, especially for regional economies within a country. With relative freedom of factor mobility, regional dependence is encouraged for the sake of efficiency. The propensity to trade, as mentioned above, is generally related to the size of the region. The smaller a region i s the more important i t s export sector becomes, relative to i t s total out-8 put. Demand for i t s output, then, is in large measure autonomously determined and fluctuations in this exogenous element w i l l , therefore, affect induced or domestic demand for the region's production. It can be assumed that exports, for the regional economy, play a large role in 9 i t s growth and development. At the provincial level in Canada, for 8 In the extreme the individual in society exports a l l his output in the form of his employment; in return he imports the goods he needs. On the other end of the spectrum, the world community has no exports or imports. 9 For a discussion of the importance of exports or the economic base of a region related to i t s growth see: Douglass C, North, "Loca-tion Theory and Economic Growth", Journal of P o l i t i c a l Economy, LXIII (June, 1955), 243-58. Charles M. Tiebout, "Exports and Regional Economic Growth", Journal of  P o l i t i c a l Economy, LXIV (April, 1956), 160-64. Douglass C. North, "A Reply", Journal of P o l i t i c a l Economy, LXIV (April, 1956), 165-68. Walter Isard, Methods of Regional Analysis: An Introduction to Regional  Science. (Cambridge, Massachusetts: M.I.T. Press, 1964), p. 190. ) 10 example, this appears to be true: o i l and wheat in Alberta and Sask-atchewan respectively, forestry and mining in British Columbia, and less so manufacturing and mining in Ontario, have provided much of the secondary growth. Areas smaller than provinces depend to an even greater extent on specialization of output. Consider the effects of a change in the demand for regional * exports. If demand advances, increased receipts flow into the exporting area. In terms of equation (4), autonomous expenditure takes place by some amount E . The new autonomous expenditure stimulates activity in a the region; not only in the export sector, but i n the local service sectors as well. Income rises by some amount, Y = K E & , and induced expenditures rise as equation (2) implies. In the f i n a l analysis income w i l l have risen by an amount greater than the i n i t i a l change in expen-10 diture. This is what the multiplier theory suggests. The increase in income and regional activity supports more employment. Therefore, an advance in export demand can generally be expected to result in increased employment. The reverse situation i s also true. As exports decline, a dampening effect i s spread throughout the region. The multiplier process works in reverse and employment is expected to f a l l . The reaction path resulting from emigration can be analyzed in a similar manner. When people migrate from one region to another, their expenditures move with them. In terms of equation (4), one expects a negative change in A for the loser region when emigration occurs. The 10 The multiplier must be greater than unity in this case. In Chapter III the possible values a regional multiplier might take are discussed. 11 i n i t i a l decline in expenditures has multiplier effects. The loss of income to the loser region i s some amount Y = K A. The income loss for the region w i l l be greater than the i n i t i a l withdrawal. Government spending on infra-structure tends to "follow the men". This w i l l add to the depressing effect of emigration in the loser region. By the same reasoning as before; i f income f a l l s , employment w i l l also. There-fore, any substantial emigration w i l l cause further unemployment in the loser region v i a income-expenditure depression.* 1 Suppose one labor force member emigrates from a region, and further, assume he was unemployed prior to his departure; the loser region i n this case w i l l lose at a minimum an average unemployment in-surance benefit plus any other expenditures the unemployed person was 12 able to make. If the labor force member who leaves was employed prior to leaving, the loss to the loser region , with considerable un-employment, w i l l be the same. This i s because an unemployed person is l i k e l y to f i l l the migrant's position. The net loss w i l l thus s t i l l 13 be an average unemployment insurance benefit. This net loss must be multiplied by K to arrive at the loss i n regional income. In the form of a summary to this point, i t i s reasonable to posit that changes in export demand and emigration both have an impact 11 This i s not to suggest that out-migration i s not good, i t a l l depends on relative magnitudes. 12 It is reasonable to assume that the marginal propensity to consume unemployment benefits i s equal to unity. 13 G. C. Archibald, "Regional Multiplier Effects in the U.K.", Oxford Economic Papers, XVIV (March, 1967), 25. 12 on regional employment levels via expenditure and income effects. In a l l cases the multiplier has to be applied to the i n i t i a l s h i f t in autonomous expenditures. The foregoing simple model has illustrated the reaction paths involved. The objective of this chapter, as mentioned at the outset, i s to construct a model that can be empirically investigated to reveal causal factors affecting total regional employment levels; as set out in equation (4) this i s not possible. In i t s present form we would have to have extensive data on local money flows, i.e. local incomes, expenditures and investments. Also, the complex interrelationships involving income, investment and regional balance of payments would make an examination of causal factors affecting employment extremely precarious. In short, i t is not possible to make reliable estimates . . . of K and hence to calculate Y resulting from A or E . I f this were possible, i t would s t i l l be necessary to translate the results into employment units. As i t is not possible to quantify the relationship as set out in equation (4), i t i s necessary to substitute variables for Y, A and E . a The income expenditure relationship is best translated into 14 employment and population units. F i r s t , these data are easier to 14 Other studies have taken this approach although they were only dealing with one variable; that being an employment substitute for the export variable. See: George H. Hildebrand, and Arthur Mace, Jr., "The Employment Multiplier i n an Expanding Industrial Market: Los Angeles County, 1940-47", Review of Economics and  Statistics, XXXII, (August, 1950), 241-49. Gerald Everett Thompson, "An Investigation of the Local Employment Multiplier", Review of  Economics and Statistics, XLI, (February, 1959), 61-7. 13 obtain and are available at the county level. County analysis allows 29 observations which strengthens the analysis. Also, as our interest is in terms of employment, there is no need to become involved in the complexities of moving from monetary units to employment. Tracing causal effects through income-expenditure analysis would perhaps be ideal, but i t is not possible. Therefore, dealing directly with employ-ment data is the best alternative. In order to get quantitative re-sults, then, we w i l l be looking at the "end points" of the relationship. We can observe changes in employment in the export or basic sector and changes in total employment. Also, we can observe changes in popula-tion due to emigration and relate this to changes in total employment. However, we cannot observe the intermediate steps which directly re-sult i n shifts i n total employment. It i s important that the variables which cannot be quantified were discussed f i r s t . The empirical work deals with variables that are not directly linked. The preceding discussion serves to put the variables to be investigated into proper perspective. Consider, equation (4) again. (4) Y = K A + K E v a It can be used to il l u s t r a t e the substitutes involved. As a proxy for changes in income, Y, we can measure changes in total regional employ-* ment, N. Changes in employment can be assumed to reflect changes in income as equation (5) illustrated. Changes in the demand for exports were represented by & in a equation (4). When exports r i s e , the economic base of the exporting region expands. Expenditures are made for plant, equipment and labor. In the case of a small region, such as a county, expenditures 14 that accrue to the region are those made for the labor input. Most other expenditures flow to points external to the region. I f one was able to trace expenditures of new export oriented employees, resulting from an expanded base, i t would provide a satisfactory indication of the regional effect of changing export demand. We previously noted that expenditure data are not available. Employment data, by industry, are available and i t i s possible to isolate that amount of employment tied to the export sector or economic base of a region (Chapter IV discusses the method for calculating export oriented employment). When regional exports rise we recognize that the relationship of the change in autonomous expenditures to changes i n regional income i s founded in a multiplier relation. A multiplier relation also exists between changes in export oriented employment, which reflects export demand, and total employment. By observing changes in regional basic employment and total employment, i t i s possible to estimate the employment effect of changes in export demand. Equation (7) expresses the relationship between employments. (7) N a K' N N — basic employment 9 The variable A in equation (4) represented changes in autonomous consumption expenditures. Our interest is in tracing expenditure fluctuations due to emigration, but again this information i s not avail-able for analysis. In li e u of the consumption variable. A, we can substitute changes in population due to emigration, M, and measure i t s effect on total employment. This relationship i s set out in equation (8) N = K" (M) Equations (7) and (8) can now be combined and the result i s an adjusted form of equation (4). Instead of a marginal income-expenditure 15 relationship, equation (9) expresses a marginal relationship between total employment, basic or export oriented employment, and emigration. • • • (9) N = K» N + K" M x It i s now appropriate to discuss the difference between K of equation (4), and K' and K" of equation (9). A satisfactory definition of a multiplier i s as follows: "The multiplier i s the marginal effect of a change of one economic variable upon another economic variable, of which the f i r s t variable i s a 15 component." K, in equation (4), qualifies under this definition as does K' in equation (8). In the former, the multiplier, K, i s a co-efficient relating an increment in expenditure, to an increment in income. In the latter, the multiplier i s a coefficient relating an increment in basic employment to an increment in total regional employ-ment. K, then, i s an expenditure multiplier, while K' i s an employ-ment multiplier. The employment and expenditure multipliers can be thought of as being essentially the same. "For i n given circumstances a definite ratio, to be called the multiplier, can be established between income and investment and, subject to certain specifications, between the total employment and employment directly employed on investment (which 16 we shall c a l l the primary employment)." The "specifications" referred 15 Oscar Lange, "The Theory of the Multiplier", Econometrica, XI (January, 1943), 227. 16 Keynes, op. c i t . , p. 113. The words 'multiplier' and 'primary employment' are i t a l i c i z e d i n the original passage. Basic employment, as defined earlier in this paper may be substituted for 'primary employ-ment' in the quote. 16 to would include the following: that wages and price levels remain un-changed; that there is no redistribution of income; and that there are no increasing or decreasing returns which would cause output to i n -crease more than (less than) employment. Alvin H. Hansen has gone as far as stating: "Thus, while not s t r i c t l y correct, for practical purposes we do no great violence to the facts i f we assume that the 17 employment multiplier K' equals the investment multiplier K." The employment multiplier, then, may be thought of as a proxy for the income 18 multiplier. This requires, as the above assumptions imply, that income, employment, and product rise and f a l l together. In actuality the employment multiplier and expenditure multiplier are expected to d i f f e r in value. This is because output, or value added, per worker is not constant across a l l industries. The difference between K and K' can be illustrated by a simple example. Suppose that autonomous expenditure in an area rose by an amount E = $100,000 and 3. the result was that income or output increased by Y = $300,000. The expenditure multiplier in this case is 3. Assume further that value added per worker in the primary or investment goods sector is $5,000, and in the secondary or consumption goods sector i t is only $4,000 per employee. On this basis N = 20 and N = 70 resulting in an employment x multiplier of 3,5. This example illustrates how the result might d i f f e r . In Chapter III this point of difference w i l l be raised again 1 7 A l v i n H. Hansen, A Guide to Keynes, (New York: McGraw-Hill Book Company, Inc., 1953), p. 87. 18 The employment multiplier's f i r s t significant appearance came in a 1931 article by R. F. Kahn, "The Relation of Home Investment to Unemployment", The Economic Journal, XLI (June, 1931), 173-192. 17 when discussing the range of expected results. An attempt w i l l be made then to estimate the likely numerical difference between the two multipliers. Consider now what has happened to K'* of equation (9), which re-placed the coefficient of A in equation (4). K" is not a multiplier in the sense that "multiplier" was defined earlier. Changes in popula-tion due to emigration are not a component of changes in total employ-ment. The two variables are expressed in different units. If we expressed M i n employment units or their equivalent, which we could do by calculating the proportion of labor force in the emigrant popula-tion, we s t i l l could not relate K" to K quantitatively because we don't know what the expenditures of the unemployed are. However, expenditures associated with the unemployed are less than those associated with the average employee. Therefore, we expect K"<K and K"< K*, even i f M were in employment units. The fact that M is expressed in population units suggests that K" w i l l be even smaller relative to K" and K, than i f M were in employment units. In i t s present form K" i s simply a coefficient relating M to N. The relationship as stated in equation (9) can be written in standard linear difference form with slightly different coefficient notation to f a c i l i t a t e the regression analysis, which i s carried out in Chapter V. Equation (10) summarizes the model we can quantify, and therefore, derive coefficient estimates. (10) N = a + a N - a M J o i x 2 The coefficients a and a have been substituted for K' and K" 1 2 respectively. The minus sign before the a coefficient w i l l result 18 because a l l observations of M w i l l be negative changes in population due to emigration. Within the context of this model we can now suggest a two-stage reaction to any decline i n regional basic employment. F i r s t , we recognize that a reduction in basic employment w i l l reduce autonomous expenditures in the region. However, this reduction w i l l be partly offset by autonomous expenditures of the unemployed, providing they remain in the region of decline. But i f the unemployed emigrate, autonomous expenditures are further reduced and so i s regional income. Before investigating the county data needed to carry out the cross-sectional regression analysis, for estimation of the foregoing relationships, Chapter III discusses the range of coefficient values we might expect to derive. CHAPTER III THE RANGE OF EXPECTED COEFFICIENT VALUES Before actually calculating the employment multiplier and emigra-tion coefficients, using regression analysis, i t is useful to develop some idea of the values one expects to obtain. Having done this, the derived results w i l l hopefully be more meaningful. Estimates in .this chapter are made by using some casual empiricism and br i e f l y reviewing results of other relevant studies. The discussion w i l l of necessity move back and forth between expenditure and employment multipliers. This i s because i t i s d i f f i c u l t to make an a p r i o r i estimate of the employment multiplier in isolation. The employment multiplier coefficient w i l l be reviewed f i r s t followed by discussion on the probable value of the emigra-tion coefficient. Archibald, referring to the regional multiplier in Britain, sug-gests that: " I f one had to guess at possible values for regional 1 multipliers, one could obviously say 'somewhere between one and two'." This seems a reasonable assertion with regard to the lower limit, how-2 ever, an upper limit of two does not appear so patently clear. Consider f i r s t the minimum value. Archibald, op. c i t . , p. 27. He narrows the range to (1.2-1.7) by making assumptions about the marginal propensity to consume and marginal propensity to import. A more sophisticated technique he employs does not improve this original estimate. 2 It appears that Archibald bases the upper value estimate on a calculation of the national multiplier, implicitly assuming that a regional value could not be greater. This point i s not explained in this paper. 20 For the expenditure multiplier, an assumption of a lower limit of unity implies a concomitant assumption regarding the marginal propensity to add value locally (mpa). I f o< mpa < 1, and positive, then 1-mpa The county economies, which serve as the sample for this paper, are r e l -atively small and are thus expected to have high propensities to trade. Therefore, their marginal propensities to import are likely to be high. If this is in fact the case the multiplier w i l l not be large. If half of a region*s goods are imported the multiplier w i l l not be greater than 4 two. When dealing with the employment multiplier we can be sure of a lower limit of unity. This i s clearly true because one additional basic employee resulting from some exogenous injection w i l l show up as at least one addition to total employment. However, the exogenous increase in employment may not provide much secondary employment leverage due to high import leakages. It i s safe, though, to aver that the lower constraint for the regional employment multiplier w i l l be one. It i s not so easy to set an hypothetical upper constraint on a regional multiplier. Consider again the expenditure form. Unfortunately, i t i s not possible to calculate values for the marginal propensity to 3 As mpa approaches 1, K approaches <*> As mpa approaches o, K approaches 1, 21 consume or to import; however, i t i s possible to make rough estimates by calculating average values and using them as proxies for the marginal values. Czamanski calculated the average propensity to consume for Canada to be between .63 and .65 for the 1961-1964 period, while the 5 same s t a t i s t i c for Nova Scotia was .73 and .75. Assuming that these values reflect actual marginal values, for comparison purposes, and ignoring the role of imports temporarily, we would expect the multiplier for Nova Scotia to be greater than the national one. However, due to the relative openness of the provincial economy, the larger marginal propensity to consume in Nova Scotia is more than offset by i t s higher import leakage. Thus, when Czamanski calculated an investment multiplier including import considerations, the Nova Scotia value derived was less than the national one. If one assumes that the propensity to trade at the county level i s greater than at the provincial level, then one would expect a county multiplier to be less than the national figure and, in a l l likelihood, to be less than the provincial multiplier, Czamanski calculated an average investment multiplier for Nova Scotia by assuming his derived average propensity to consume and import figures to be marginal values, and by dividing government expenditures into autonomous federal and derived local amounts. The result was an investment 6 multiplier of 1.6, based on the 1961-1964 period. It should be remembered that the employment multiplier that w i l l be calculated in this paper does not represent a value for the Maritimes 5 Stanislaw Czamanski, Regional Income and Product Accounts of North-Eastern Nova Scotia, Regional Studies Series Number 1, " ~ Institute of Public A f f a i r s , Dalhousie University (Halifax: 1968), p, 71. 6 i b i d , p. 72. 22 as a whole, or any particular county within i t . Rather, i t i s a value for an average county (experiencing net out-migration) within the Maritime region. Assuming that counties have propensities to import higher than a province, then a derived multiplier, for counties in 7 terms of income, should be less than 1.6. In the previous chapter i t has already been mentioned that the employment multiplier i s not likely to have the same value as the expenditure multiplier. A simple example illustrated how the two re-sults might d i f f e r . It i s useful to return to this point for a moment to decide i f indeed the employment is greater than the investment multiplier, or vice versa. It i s expected, a p r i o r i , that the expenditure multiplier w i l l be somewhat less than the employment multiplier. This i s likely to be true because value added per employee in basic industries i s on the average expected to be higher than in the service or non-basic sector. Table I of the Appendix shows output per man-hour for a rough grouping of industries in Canada. By averaging output per man-hour for the group representative of basic and service industries separately we see that in 1955 output per man-hour in the basic sector is $1.87, while in the service sector i t is only $1.45. A study done recently in British Columbia supports the conclu-sion that output per worker in basic industries exceeds that i n the service sectors. For British Columbia, 1961 average annual product 7 Czamanski estimated the average propensity; to import for Nova Scotia to be .45; a county value is expected to exceed this. 23 per worker in the export sector was found to be $8,869, while in the 8 domestic sector, i t was only $7,659 per worker. If these rough estimates are representative, then one expects the employment multiplier to be larger than the expenditure multiplier. If i t had been observed that basic output per unit of effort was less than that of the service sector then we would expect the employment multiplier to be smaller than the expenditure multiplier. The point to keep in mind i s that when dealing with the latter, the units of cause and effect are in monetary units, thus, they are directly com-parable. When the relationship i s in terms of employment, however, each employee across a l l industries does not necessarily represent the same expenditure power. It i s now reasonable to ask - "by how much w i l l the two multiplier values differ?" We can make an estimate of this difference by using employment information contained in a Canadian study (by Rosenbluth) on the economics of disarmament, in combination with our 9 estimates of output per man-hour. A portion of the Rosenbluth study was devoted to estimating the relationship between basic and non-basic employment in Canadian c i t i e s , towns, and metropolitan areas with populations in excess of 10 10,000 in 1951 and 1961. This cross-sectional analysis revealed a The Growth and Impact of the Mining Industry in British  Columbia, Mining Association of British Columbia, Appendix VI, (Vancouver: 1968), p. 2. 9 Gideon Rosenbluth, The Canadian Economy and Disarmament, (Toronto: MacMillan of Canada Ltd., 1967). 10 i b i d . , p. 127-32. 24 rather stable relationship between the two types of employment; a basic, non-basic ratio of 1:1, or an average employment multiplier of 2. 1 1 Rosenbluth found that in the smaller population centers observed in his sample the basic-service ratio worked out to about .7:1, imply-ing an employment multiplier of 1.7. These findings support an earlier suggestion in this paper that the proportion of export oriented activity to total activity becomes greater as the areas observed be-come smaller. It should be noted that Rosenbluth's estimates are made from urban areas, whereas in the present study the sample areas are counties, which are l i k e l y to contain relatively more basic employment in sectors such as mining, forestry, and fishing not captured in his analysis. To the extent that this i s true one would expect a lower em-12 ployment multiplier to result from county analysis. Given Rosenbluth's ratio of basic to non-basic employment and the implied simple employment multiplier, together with our earlier estimates of output per man-hour in the two sectors, a rough estimate of the expenditure multiplier i s possible. F i r s t , we define the simplified form of the employment multiplier in (1). ,• N ^ K Nx ~ Nx * N s ~ 1 + 1 B 2 Where: N = non-basic N x 1 or service employment. 11 The employment multiplier in this case i s assumed to be total employment divided by basic employment. Inferring an employment multiplier from the basic, non-basic ratio i s a standard technique (See: Isard, op. c i t . , p. 192). 12 . Rosenbluth's division of employment into the two categories i s , of course, also somewhat arbitrary and differs from the present study. For example, he places a l l construction into the non-basic sector, whereas a portion of the construction employment in this paper i s allocated to the basic sector. This would tend to inflate a multiplier derived by his method relative to one based on our divison. 25 Then we weight the respective employments by t h e i r relevant output per man-hour figures, from which an estimate of the adjusted m u l t i p l i e r i s derived i n (2). ( 2 ) N (1.87) + N (1.45) 3 > 3 2 K ' = — _____ = x • / / (1.87) 1.87 The adjusted m u l t i p l i e r i n terms of expenditure, then, i s 1.8, and less than the employment m u l t i p l i e r , as was expected. I f we s t a r t with an employment m u l t i p l i e r of 1.7, a more l i k e l y value f o r small areas, the adjusted form turns out to be 1.5, as i l l u s t r a t e d i n (3) and (4). ^ 1 + 7 (3) K = 1 * = 1.7 1 (4) K - 1.87 * (1.45) ( .7). 1 # 5 4 1.87 The above exercise, then, gives us some idea of the difference between the two m u l t i p l i e r s . In the B r i t i s h Columbia study referred to e a r l i e r the m u l t i p l i e r calculated, using value added by industry, yielded a m u l t i p l i e r of 1.83, whereas the employment m u l t i p l i e r was 2.1. This 13 d i f f e r e n t i a l i s s i m i l a r to the one we have estimated. I t i s now useful to review b r i e f l y results of other studies that have dealt with the employment m u l t i p l i e r . Hildebrand and Mace calculated 14 an employment m u l t i p l i e r for Los Angeles County, C a l i f o r n i a . Using 13 Mining Industry i n B r i t i s h Columbia, op. c i t . , Appendix VI, p. 2 14 Hildebrand and Mace, op. c i t . , p. 241-49. 26 1940 U. S. Census data and California State Employment Service monthly employment estimates, they divided employment into basic and non-basic categories for 37 monthly observations (1940, 1941, 1946 and September, 1947). A regression of localized employment, N g, on basic employment, N , for the time series data resulted in the following equation: x 15 „„„ , „. „ ,r (coefficient of . _ , „, N = 222,000 + 1.248 N \ n ^ a l 9 l . i n n _ Q n n . In our framework the s * x correlation = .ybj employment multiplier i s about 2.25. One would expect the employment multiplier to be calculated in the present study to be less than 2.25 as our areas are much smaller, or less diversified, than Los Angeles County. Thompson calculated an employment multiplier for Lancaster County, Nebraska, using similar data to the above study, for the period 16 1953 - 1955. In this case an employment multiplier of 2.3 resulted. One would have expected, a p r i o r i , a lower value in Thompson's study than the preceding example considering that Los Angeles county has a much larger population than Lancaster County. However, the time period 17 of the studies differed as did the methods for calculating N and N . x s For these reasons i t is d i f f i c u l t to make a comparison. Sasaki calculated an employment multiplier in a study of military 15 ib i d . , p. 247. 16 Thompson, op. c i t . p. 61-7. 17 Both studies used a mechanical method, employing a location quotient, to derive N g and N x« This method is referred to in Chapter IV of the present paper. 27 18 expenditures of the economy of Hawaii. His method of calculating N 19 X was again different from the above two studies. Also, the region under analysis was substantially different from the other two (an island economy). In the time series analysis the observations were years rather than months (1947 - 1955, a minimal number of observations). A regression of total employment on basic employment resulted in the following 20 equation: N = 89,398 + 1.279 N . The comparatively low employment multiplier derived likely reflects the very high import content of the economy. Increased income from exports to the mainland may simply flow out again in return for imports, without much significant secondary effect on the island. Based on the earlier discussion of probable values for the employ-ment multiplier and a brief look at the other studies i t is safe to con-clude that the employment multiplier should be greater than one. This implies that an exogenous change i n employment has some secondary lever-age. Considering Czamanski*s estimate of 1.6 for the average income mul-t i p l i e r in Nova Scotia (perhaps 1.7 or 1.8 in terms of an employment mul-t i p l i e r ) and Rosenbluth1s estimate implying an average employment multiplier of 1.7 for smaller urban areas in his sample, an upper value estimate of 1.5.for a county employment multiplier does not seem unreasonable. Kyohei Sasaki, "Military Expenditures and the Employment Mul-t i p l i e r in Hawaii", Review of Economics and Statistics, XLV (May, 1963), p. 298-304. 19 Only 10% of the industries in this particular study were categorized by using the location quotient. 20 Sasaki, op. c i t . , p. 301. 28 It was mentioned at the end of Chapter II that we should recognize a two-stage reaction to any given decline in basic employment, the end result depending on whether the stock of unemployed emigrate or remain in the region of decline. Therefore, we would like to know the effect of emigration on total employment. If a l l the unemployed emigrated we would expect a higher multiplier; i f they remain, a lower multiplier i s expected due to the associated increase in transfer pay-ments. We can think of our rough multiplier estimate as lying between the two limits, assuming that some unemployed leave and others remain. In the following section we attempt to make a rough estimate of the emigration coefficient. It i s not expected that the emigration coefficient w i l l be as large as that of the basic employment variable. This is so because an emigrating unemployed person does not represent as large an expenditure 21 loss to a region as a unit decline in basic employment. Therefore, the absolute secondary effect of the emigration would not be as great as the shift in basic employment. In this paper, the emigration variable is made up not only of labor force members, but of a l l segments of the population. For this reason the measured emigration coefficient w i l l be even smaller; each out-migrant does not represent one worker. Following Archibald's approach, i t is possible to roughly estimate how many workers need emigrate from a region to cause one more person to 21 Recall that i t does not matter i f the emigrant i s an employed or unemployed, the regional loss i s the same. 29 22 become unemployed in that area. Extending this to suit our model, i t i s possible to estimate how much out-migration, in population units, would be associated with the loss of one job in the loser region. With this s t a t i s t i c an estimate of the size of the emigration coefficient is possible. In order to make this rough calculation i t i s necessary to assume values for: the multiplier, K; an expenditure coefficient for the un-u employed, X; a ratio of unemployed to employed receipts, S = _ ; and a f i n a l l y , the ratio of labor force emigrants to total emigrants. With this information we can calculate: 23 1 W = K.XS where W represents the number of emigrant workers whose leaving results i n one job lost to the loser region. 22 Archibald, op. c i t . , p. 35. There i s no real precedent for estimating the emigration co-efficient so Archibald's approach, adjusted for our purposes, was use-f u l . 23 Derived from: (a) a = KXuW a _ a v e r a g e weekly wage a K • the regional multiplier ^ W = KXu X = expenditure coefficient of the unemployed (u) 1 u _ » « (c) W = __ , S = _ u = unemployed receipts (a) KX a W • the number of increments j of u to satisfy the (d) W = equation KXS S = ratio of unemployed to employed receipts. 30 • i Let K = 1.3, an expenditure multiplier, consistent with K = 1.5, the rough estimate made earlier. The expenditure coefficient of the un-employed is assumed to be .5. This i s reasonable because the unemployed are expected to spend a l l they receive, that i s , a marginal propensity to consume of unity. Given a marginal propensity to import of .5, a value of .5 for X does not seem unsound. The ratio of unemployed receipts or expenditures to average wages is assumed to be just over one-half (S = .6). This may seem rather high, but in Canada the unemployed receive substantial assistance. When one considers allowable earnings under the Unemployment Insurance Act, un-employed workers can receive a large proportion of their normal income. For workers with dependents the benefit payments range from around 50 per cent of earnings to around 65 per cent for the low income groups. If allowable earn-ings are added to the weekly benefits, the ratio is raised substantially, reaching 100 per cent for the low-income workers with dependents. 24 In addition, unemployed persons can incur l i a b i l i t i e s or draw on assets 25 they might have. Given the values K= 1.3, X= .5 and S = .6, then 1 W = = 2.56 (1.3) (.5) (.6) That i s , for every two to three unemployed workers who emigrate, one job is lost in the loser region. If the emigration variable in this paper referred s t r i c t l y to workers, the above estimate would imply a coefficient of .39. However, the data to be used in this case are in population 24 H. D. Woods and Sylvia Ostry, Labour Policy and Labour  Economics In Canada,(Toronto: MacMillan of Canada, 1967) p. 389. 25 People w i l l try to maintain their normal expenditure levels even though income declines. 31 u n i t s , therefore, i t i s necessary to know the approximate proportion of labor force members i n the emigrant population. This r a t i o can be calculated from Census data. Based on Table I I of the Appendix i t i s quite reasonable to assume that the r a t i o of labor force to population emigrants i s 1:2. Given this information our emigration c o e f f i c i e n t would be about .20. The values of K, S, and X were somewhat a r b i t r a r i l y chosen. I f any of the values were increased, W would, of course, be less and thus the c o e f f i c i e n t r e l a t i n g emigration to t o t a l employment would be higher. The reverse i s true, of course, i f the values of K, S, and X are less, Archibald estimates that not more than seven workers leaving a 26 T ' . region cause one more man to lose his job. In terms of our emigration c o e f f i c i e n t , which relates emigration, i n population units rather than employment u n i t s , to t o t a l employment, the value would be .07. Archibald admits that his values f o r K, S, and X are "bottom" estimates, and i n fact the emigration of substantially less than seven workers could re-s u l t i n one job loss. He i s implying by t h i s admission that the emigra-tion c o e f f i c i e n t i n our framework could be much higher than .07. Archibald's outside estimate i s based on K • 1.2, S » .4 and X * .3; 27 these are the lowest values that could possibly occur i n his judgment. From our previous discussion i t i s l i k e l y that the m u l t i p l i e r we are concerned with i s higher than 1.2. Also, S could e a s i l y be higher than .4, because the unemployed not only spend transfer payment benefits, 26 Archibald, op. c i t . , p. 22, 27 i b i d . , p. 37. 32 but also consume by incurring l i a b i l i t i e s and drawing on assets. In addition, X w i l l r e a l i s t i c a l l y be above .3 i f the marginal propensity to consume i s unity, and the marginal propensity to import in small 28 regions i s not below .5. Recall that Czamanski calculated an average propensity to import for Nova Scotia of .45; a county value is expected to be higher. A rough estimate of 1.5 for the employment multiplier has been made and this appears to be consistent with an emigration coefficient of .20, given the values of S and X. With the above estimates in mind we move to the next chapter, where we must deal with the empirical problem of isolating the basic employment variable. This must be done before a calculation of the employment multiplier is possible. 28 Czamanski, op. c i t . , p. 71. CHAPTER IV THE CALCULATION OF BASIC EMPLOYMENT The discussion in this paper up to this point has been of a theoretical nature. In this chapter the task i s to come to grips with the empirical problem of identifying the basic employment component of a region, given i t s total employment, by industry, as data. The identifica-tion w i l l necessitate a more explicit or working definition of the economic base concept. The data pertain to the counties of the Maritime Provinces of Canada and include information on eleven broad industry groups. Twenty-nine of the 36 counties are included in the f i n a l regression analysis; those showing net in-migration were excluded. 1 Basic employment w i l l be calculated for each county for two periods, 1951 and 1961. Only after this step i s completed can we go on to study the relationships between basic and total employment change and calculate the employment multiplier. The computation of basic employment w i l l of necessity be somewhat arbitrary and rough due to the gross industry classifications involved. However, the estimates are used in regression analysis, in which i t is not the absolute size of the change i n employment which matters, but the 1 In other preliminary calculations, a l l 36 counties are used; therefore, tables in the Appendix show data on a l l 36 counties except for the f i n a l regression data. 34 2 relative position. Further, allocation to the basic category w i l l be liber a l where doubt exists; therefore, any future refinement w i l l "re-sult in a larger derived multiplier, not the reverse. It i s obviously 3 better to underestimate the multiplier than to overestimate i t . The procedure for this chapter w i l l be: f i r s t , to discuss the economic base concept; second, to review briefly the data used in the analysis; and third, to carry out and explain the actual calculations necessary in order to derive an estimate of basic employment, N , for x 1951 and 1961 for each county. We can then estimate the change in basic employment over the period. The economic base of a region has been variously construed. The usual interpretation assumes that the base i s made up of industries, or fractions of them, engaged in the export of goods, services or 4 capital for extra-regional consumption. Strict adherence to the 2 Example showing minimization of errors made. 51 ~ 51 .i 500 N - 1000 N (actual) =500 N (est.) • 600 ) Actual K = 2 = — -51 x x 250 61 ~ fit ' ) 3 500 N = 1500 N (actual) = 750 N 0 1 (est.) = 900 ) Est. K "•• 1.7 - . 61 x x 300 Error = 100-150 Error » ,50 3 M. C. Daley, "Approximation to a Geographical Multiplier", Economic Journal, L, (June-September, 1940), 250. 4 The most extensive discussion of the economic base concept is found in a series of articles by Richard B. Andrews, "Mechanics of the Urban Economic Base", Land Economics, May, 1953, to February, 1956 (a series of 12 ar t i c l e s ) . See also: Isard, op. c i t . , p. 190, and Sirkin, op. c i t . , p. 426, and John W. Alexander, "The Basic-Non-Basic Concept of Urban Economic Functions", Economic Geography, XXX (July, 1954), p. 247-50'.; 35 above definition results in a restrictive allocation of industry or i t s employment to the basic category. Clearly, this definition w i l l not include a l l of a region's industry for which export demand i s directly responsible. For example, suppose in a small county a steel mill pro-duces for an export market.^ Assume further, that the coal input for the m i l l comes from within the county. By s t r i c t interpretation, the coal industry and i t s concomitant employment would be classified as local because i t s output is not exported. Coal production, however, is clearly linked to and reacts directly to a change in the export demand for steel. In this paper i t i s the intention to include in the basic category those industries which do actually export. In addition, however, those industries or portions of them that are linked through production to 6 export trade are included as basic. Activity in a region that might be linked to export trade via household income and expenditure and local 7 investment i s considered non-basic. With the broad industry groups used in this study no detailed linkage problems w i l l be investigated. How-ever, i t i s important to make clear our interpretation of "basic". In essence, we want to include in the basic group a l l industries, or 5 In this analysis a county market is the domestic market; a l l extra-county sales are export sales. 6 This interpretation of basic is consistent with the model as discussed in Chapter II. Local investment and expenditure are a l l endogenously determined, while export activity i s exogenously de-termined. 7 Isard, op. c i t . , p. 197, footnote 34. Isard states that in a Wichita employment study that any industry was considered basic i f i t served an export industry. However, construction, for example, was considered local. In fact, a large portion of construction is directly linked to exporting manufacturing industries. 36 portions of them, for which export demand is directly responsible. The raw data for this study comes from the 1951 and 1961 Census of Canada. From this source one can obtain the best detailed information 8 on an industry by county basis. The following broad industry groups were available for comparison of 1951 and 1961: agriculture, forestry, fishing, mining, manufacturing, construction, transportation, communica-tion and u t i l i t i e s , trade, finance, personal services, and other services 9 (community, business, government and defence). It should be noted that the data are in terms of labor force rather than employment. A good deal of time was spent attempting to adjust the labor force figures to estimates of employment. However, in the end i t was f e l t that the employ-ment estimates would be more suspect than the original labor force data, 10 so the latter were considered to be most useful. Labor force, then, 11 is assumed to reflect employment for our purposes. 8 A more detailed breakdown by county is available by occupation. 9 It would have been desirable to use 1961 breakdown to isolate the government and defense group. However, to be consistent with 1951 this was not possible. Forestry includes logging; included i n the Fisheries sector i s trapping; quarrying is included in the Mining sector. 10 The problem in transforming labor force to employment stemmed from the fact that a "looking for work" category of labor force was pro-vided in the Census for each county, but no distribution by industry was available. Provincial "looking for work" by industry was available, but the 1951 figures included a large block of "Unstated Industry""looking for work", which made the transition to county data tenuous. 11 Alfred T e l i a , The Relationship of Labour Force to Employment", International Labour Relations Review, XVII (April, 1964), 455. His analysis shows that..."changes in labour force bear a s t a t i s t i c a l l y significant relationship to changes in employment taken as an index of demand for manpower." 37 In order to calculate the employment multiplier in the next chapter an estimate of the change in basic employment, N , i s required. As the two periods for which N and N w i l l be calculated are ten years x apart, an adjustment for changes in productivity over that time span is desirable. The method of making this adjustment is dealt with below. If N x is observed to decline, we expect N to decline also, according to our theory. When dealing with changes over a decade, how-ever, productivity has likely risen, especially in the goods producing sectors. For example, between 1951 and 1961 the "value added index" for Nova Scotia secondary manufacturing rose from 147 to 207 (1947=100), whereas the employment index in that sector f e l l from 101 to 92 12 (1947=100). This means that with no allowance for productivity growth, N may f a l l , but real output or income may rise causing observed N to rise (or f a l l less rapidly than N^). It would clearly be an improvement i f this productivity bias could be removed from the analysis. To alleviate this problem, 1951 employment data were adjusted to correspond to 1961 productivity levels. The adjustment i s necessarily rough but is f e l t to be a definite improve-ment over ignoring this problem altogether;-National data were used to adjust the 1951 county employment figures. Although productivity levels in Canada di f f e r from those of the Maritimes, with the latter being lower, changes in productivity are expected to be similar. 12 Nova Scotia Voluntary Economic Planning Board, First Plan For Economic Development to 1968, (Halifax: Queen!s Printer), February, 1966^ 38 The 1961 industry indexes of production for Canada were divided by their respective 1961 employments to arrive at 1961 productivity factors. These factors were then divided into their respective 1951 indexes of production to give an estimate of 1951 employment, by industry, in terms of 1961 productivity. The employment estimates, divided by their actual 1951 employment figures, provided factors with which the 1951 county data could be adjusted. The calculations of the adjustment factors are shown in Table III of the Appendix. The resulting 1951 employment data are shown in Table IV of the Appendix, along with the 13 The method of deriving the adjustment factors can be explained in symbolic form. Let: I = index of production by national industry. N N = employment by industry at the national level. N * employment by industry at the county level. 1. I 5 1 n a Isf** (estimate of 1951 employment i n terms 61 n of 1961 productivity). I n ~61 N n ~51 2. N n 51 „S1 . ,N a N (estimate of 1951 county employment in terms of 1961 productivity). N B n 3. Can be rewritten as: 51 61 51 _51 I N N = N. n n Tl 61 N I n n 39 14 required 1961 data. With this information we can proceed to calculate basic employment. Actually, identifying basic employment presents a rather bother-some technical problem. Ideally, one vrants to pinpoint areas of specialization and direct linkages and then allocate employments accord-ingly. No single method is able to accomplish this; therefore, a combination of methods seems in order. Some industries may be wholly basic, others wholly local. But what of the mixed ones; that i s , those directly affected by export demand, but also serving the community? A problem arises; how to separate out that fraction of an industry's employment supported by exogenous demand? Once a decision is made to consider an industry wholly basic there i s no trouble allocating i t s employment. However, the mixed industries present a special problem. Much of this chapter is devoted to this particular enigma. F i r s t , how-ever, consider those industries which may be considered entirely basic in light of the industry groups available for analysis. In this paper the following industries w i l l be considered wholly basic: agriculture, forestry, fishing, mining, and manufacturing. The mining industry is probably the clearest case of a basic industry in the small counties concerned. It i s clearly not likely that local residents or localized industry consume output of the mineral sector. Rather, i t s output w i l l be either directly exported or linked via production to an 14 The sectors of finance, personal services, and other services were not adjusted. The adjustment method applied to them indicated a slight decline in productivity over the 1951-1961 period. Calculation of productivity i n service sectors is rather tenuous and as our main concern is with productivity in the goods sector, the three sectors were not adjusted. 40 industry that does. Note that i t i s not necessary for the mining industry i t s e l f to export for i t to be classified as basic. The fact that we are dealing with broad industry groups, and have limited i n -formation, leads one to include other industries as basic. These are manufacturing, forestry, and fishing; agriculture w i l l be discussed separately as i t presents some special problems.1"' There w i l l be some error in the above assumption, but as the 16 majority of counties are small, the error should not be too large. Manufacturing in small counties is likely to be of a specialized nature, rather than diversified. We do not observe each county manufacturing a l l the goods i t needs internally. Rather, the bulk of i t s internal 17 demand i s satisfied through imports. That manufacturing i t does have w i l l likely be largely destined for extra-regional use, be i t an adjoining county, the provincial market, the Canadian or international market. In the forestry sector, logging statistics are sparse; however, the same principle applied to the manufacturing case can be used for forestry. Aside from small woodlot activity, which is included within the agricultural sector, the vast majority of the remainder is tied to 15 The more refined the data are the more accurate a division i s possible. Sasaki, op. c i t . , for example, had output and export data on 45% of his industries. He divided employment into N x and N g by multiplying industry employment by value of export sales value of total sales. 16 Keep in mind the policy here is to be ultra-conservative in the implicit placement of employment in the local category so as not to overestimate the multiplier. 17 If this was not the case, one would expect to derive a high value for the multiplier in small areas. 41 pulp and paper activities or timber production, which i s basic in nature, being linked to export demand. In the case of fishing, the Census definition of labor force i s very restrictive, resulting in the exclusion of marginal fishermen from Census figures. Only full-time fishermen engaged in commercial fisheries are included, making the allocation of i t s employment to the basic category defensible. Again, i n small counties local consumption of the commercial fish catch w i l l be small. The bulk of the catch w i l l be sold extra-regionally or go into a processing plant, which in turn exports i t s 18 output. The allocation of agricultural employment presents some special problems. Clearly, some portion of i t s employment should be placed in each category. However, a representative allocation was found to be a 19 d i f f i c u l t task; there appears to be no precise method for this division. There exists a further enigma in the agricultural sector, the marginal farm. Tenants of these holdings do not contribute, in the income generating sense, to either the basic or non-basic sector of the commu-nity. There i s no multiplier process involved in home production and consumption. Suppose the agriculture sector was arbitrarily allocated to the 18 Sasaki, op. c i t . , p. 300, used this natural method of placing employment into N i n 45% of his industries. 19 Even i f labor force figures were available for the segments of agriculture ( i . e . , f i e l d crops, dairy, etc.) by county, which they are not, i t would be d i f f i c u l t to categorize employment. In working toward a f a i r method, gross revenues by agricultural segments, by county, were studied with a view to using them as a basis for employment allocation. But no method of division was f e l t to be useful. One would have to know employment by segment and further that portion of output not used for local consumption. 42 basic category. When one came to calculate the employment multiplier, the subsistence farms would create a problem. During the decade 1951 to 1961 there has been a rapid decline in this type of farm and i t s associated employment. This would be calculated as a decline in basic employment, when clearly i t should not be. The solution to the problem of isolating agricultural employment in some other studies has been to exclude that sector from the analysis 20 altogether. This obviously is not the most desirable answer; there-fore, i n this paper another approach was used. Rather than rely on employment data alone, in the case of agriculture, i t was decided to analyze the number of commercial farms 21 by county, that i s , farms with output'sold of $1200 or more. These commercial farm units can be assumed to contain the employment which should be allocated to the basic category. It i s true there is likely to be some local employment included here too. However, exclusion of the remainder of the farms w i l l not result in exclusion of any basic employment. A simple observation of total number of farms and employment revealed that these two figures were nearly the same in many cases. For example, in 1961 Nova Scotia agricultural labor force was 12,518; 20 Daley, op. c i t . , p. 249. Thompson, op. c i t . , p. 62, footnote 4. Hildebrand and Mace, op. c i t . , p. 247, footnote 17. 21 The definition of "commercial farm" is consistent between the 1951 and 1961 Censuses, whereas the definition of ''farm" changed. This is another reason for analyzing the commercial farms. 43 22 the number of farms was 12,038. If one could relate the number of com-mercial farms to their share of the labor force, this would not be an un-reasonable estimate of export oriented employment in the agricultural 23 sector. To obtain an estimate of labor force associated with commercial farms, total agricultural employment was regressed on the number of com-mercial farms as one variable; the residual number of farms as the other variable. Both 1951 and 1961 data were used for 36 counties which pro-24 vided 72 observations. The equation which resulted is as follows: 2 A = -71.39 + 1.77 B + .72 C R = .95 (46.65) (.06) (.04) Where A = agricultural employment, B • the number of commercial farms, and C = the number of other farms. The terms in brackets are standard 2 error of estimates; R i s the coefficient of determination. The re-gression result confirms the simple observation made earlier about the high correlation between total number of farms and agricultural employ-ment. The derived B coefficient was used as an estimate of employ-ment by county engaged in commercial farm operations; they were further assumed to be wholly basic. The remainder of the agriculture sector was excluded from both basic employment and total employment for con-sistency. For this sector, then, the basic employment estimate was 22 See Source for Table V of the Appendix. 23 Any error in this method w i l l be biased toward the basic cat-egory and this would result in a smaller multiplier. 24 See Table V of the Appendix for the data used. 44 25 based on 1.8 "times" the number of commercial farms in each county. The sum of the basic employment calculated so far for the agriculture, forestry, fishing, mining and manufacturing sectors i s shown in Table VI of the Appendix. The next group of industries is approached in a different manner. The remaining industries to be dealt with are those which are in general of a supporting nature. F i r s t , the service sectors w i l l be looked at, to be followed by a separate discussion of the two sectors of construction and transportation, communication and u t i l i t i e s . Services, for our purposes, include four broad groups: trade; finance, insurance, and real estate; personal services; and other services. In the majority of cases these segments w i l l tend to be local in nature. Employment magnitudes in each w i l l tend to reflect average population needs in the counties. It i s true that services employment w i l l reflect to some extent the amount of basic employment in the county, but expenditures of basic employment for services i s of a secondary nature, not tied to the export sector through production. Services de-mand is a derived demand. However, in some instances a portion of the services may be basic because the county, or town within i t , may act as a service center for surrounding areas. An obvious example w i l l serve to i l l u s t r a t e this point. In Halifax county, containing the Halifax-Dartmouth center, there are a large number of public administration employees not serving the local county alone. The area i s a trade and 25 The exclusion of a portion of agricultural employment necessitated the subtraction of the same amount from total employment for consistency. 45 finance center also. Some of the employment in these services then should be categorized as basic as they serve non-local residents. In other locations such as Truro, Sidney, Pictou-New Glasgow, St. John, Fredericton and Charlottown, service employment w i l l in part be export 26 oriented. There i s no "catch a l l " method available which w i l l distinguish between basic and non-basic service employment. Again, we are dealing with rather gross classifications and this compounds the problem. How-ever, an estimate of the basic portion is desirable and in this paper a location quotient or concentration ratio i s used to deal with the 27 service industries. In symbolic form the location quotient may be written: e . Where: = employment in the i industry ' of a county, e e = total employment in a county. th. E. E. = employment in the i industry 1 of the benchmark region. E E = total employment in the bench-mark region. This ratio is used to isolate portions of county employment by industry, which is over and above that amount needed to f u l f i l l local demand. 26 It i s not likely that basic employment w i l l be a very large proportion of total services employment, but the analysis w i l l be more accurate i f we can isolate i t . 27 The following studies provide a good review of the location quotient method: John M. Mattila, and Wilbur R. Thompson, "Measurement of Economic Base: Metropolitan Areas, Land Economics, Vol. XXXI(August, 1955), 215; John W, Alexander, "The Basic-Non Basic Concept of Urban Economic Functions", Economic Geography, XXX (July, 1954), 247; Moyerman and Harris, "The Economics of Base Study", American Institute of Planners  Journal, XXI (Spring to Summer, 1955), 88; Isard, op. c i t . , p. 189. 46 Specification of this excess employment is accomplished by comparing the county (subject economy) to a benchmark economy. The subject economy may be defined as the particular region (county) under analysis, whereas the benchmark economy usually consists of a larger region used for comparison purposes. The latter serves as a norm. In essence, this method allows one to calculate by how much the subject economy deviates from some average. If the ratio for a particular industry equals unity, one assumes that the subject economy does not specialize in that industry. If the ratio i s greater than one, the subject economy is assumed to specialize in that industry under consideration. That amount of employment causing the ratio to be in excess of unity i s assumed to be basic. If the ratio i s less than one the industry i s considered non-basic. It i s important to remember that the ratio is constructed from employment data. Therefore, per se, the ratio by i t s e l f indicates only relative concentration of employment in industries. In order to translate this into a measure of regional specialization, we must assume the two regions of comparison to be homogenous in such areas as tastes, pro-ductivity levels, and consumption per capita. The demand functions of the regions should also be similar, that i s , similar incomes and relative price levels. An hypothetical example w i l l i l l u s t r a t e how an error could be made i f the productivity of the two areas was quite different. Suppose that productivity per worker in a subject economy for an industry i s one-half that of the same industry in the benchmark economy. Both areas produce the same per capita quantity and consume i t locally. In this case a location quotient f o r the subject economy would be 2. An 47 erroneous conclusion would be that half of the output of the industry in the subject economy is basic i n nature. In fact, the location quotient here is reflecting productivity differentials. Only when the above broad assumptions of homogeneity can be made, w i l l the location quotient reflect specialization in a particular industry. As there are differences in average income and productivity be-tween an average Maritime county and the rest of Canada, i t would not be wise to pick the Nation as the benchmark economy. However, the Maritime Region i s relatively homogeneous within i t s e l f . It serves rather well as a benchmark economy from which i t s individual counties can be compared. An adjusted form of location quotient i s used in this paper in order to make the benchmark economy a net figure, that i s , net of over-lap. The adjusted form may be written: E - e In this way the effect of the county under analysis i s excluded from the . . 28 area we are comparing i t with. The location quotient method is not a desirable tool to use for calculating basic employment in the aforementioned sectors of agriculture, 28 For discussion of this method see: Mattila and Thompson, op. c i t . , p. 217. Note that i f the unadjusted ratio (u) equals one, so does the adjusted ratio (a). If XK. 1, a« u; i f u> 1, a? u. 48 forestry, fishing, mining and manufacturing; for example, we have already mentioned why manufacturing is likely to be of a specialized nature in each small county. It i s quite possible, i f the location quotient were used, that a ratio of one might be derived. In that case the industry would implicitly be placed in the local category. However, i f a l l the counties, which make up the benchmark economy, specialize in their manufacture, the employment w i l l be basic even though the derived ratio might equal unity. This phenomenon could occur with the other basic 29 sectors as well. In Table VII of the Appendix, location quotients have been cal-culated for each county industry in the four service segments for 1951 and 1961. By observation one can see that most of the ratios are less than one, which is expected. Our concern is with those that show a value greater than one. The next step is to calculate that amount of employment in each c e l l which causes the ratio to exceed unity and assign i t to the basic category. Given the ratios that exceed one, let A.^ = the actual number of employees of the j county in the i industry. Let B^. = the number of employees i f the same ratios had equaled one. Then A..-B.. = where C i s then the basic employment of the j t n county in the i * j l 30 industry. This operation is carried out for each c e l l where required. 29 It is likely that more employment w i l l end up in the non-basic category by using the location quotient exclusively. The precise technique for isolating the basic employment residual in the Service Sectors is shown in the note following Table VIII of the Appendix. 49 The results are shown in Table VIII of the Appendix. Following the dis-cussion of the two remaining sectors, an estimate of the counties' total basic employment i s possible. The construction sector and the transportation, communication and u t i l i t i e s sector are not dealt with on the basis of the location quotient just discussed. The location quotient would underestimate the true basic portion of these sectors, given the interpretation of "basic" in this paper. For example, a ratio of one or less for the construc-tion industry would suggest that no construction employment was basic. This would be an erroneous conclusion. The ratio of unit might reflect that construction in a county was not a specialized industry relative to other counties, but i t would ignore the direct linkage of the industry to the manufacturing sector and other basic industry. If about one-third of construction is tied directly to basic industries in the benchmark economy and i n the county concerned, then the location quotient w i l l not account for basic employment in the county construc-tion industry. The same would be true of transportation, though not so much with communications and u t i l i t i e s . The actual method of deciding how much of these sectors' employ-ment w i l l be allocated to the basic category is somewhat arbitrary; however, any error w i l l result from putting too much employment into the basic category, not the reverse. This is consistent with the policy of being conservative in the implicit placement of employment into the non-basic group. The rule of thumb for calculating basic employment in these two sectors was to multiply the sum of their employment figures, in each 50 county, by their respective ratios of1(1-5) , where Z(l-5) equals the N calculated sum of basic employment in agriculture, forestry, fishing, mining, manufacturing; N is total county employment. The results of these calculations employing the above technique are shown in Table IX of the Appendix. Estimates of basic employment have now been calculated for a l l eleven industry groups for each county for 1951 and 1961. The totals are shown in Table X of the Appendix. If the calculation of basic employment has been reasonably accurate, a comparison of i t with total employment in each county w i l l allow observation of the average employment multiplier. Table XI of the Appendix shows the calculation of N for each county. The values derived tend to support the method which x has been used to calculate basic employment. The average appears to be between 1.5 and 2,5, which is within the range one might expect. 1951 values are below those of 1961 on the average, which is not surprising considering the tremendous growth of the service industries in a l l areas of Canada during the de-cade, 1951 - 1961. We have now calculated basic employment for each county in 1951 and 1961, and from this information we can calculate i$x» to be used in estimating the employment multiplier in the next chapter. The estimates for N are shown in Table XII of the Appendix. CHAPTER V THE RESULTS In this chapter, given the necessary data, the coefficients of the model equation: • • • (1) N = a + a N - a M o i x 2 set out in Chapter II can be quantified using cross-sectional multiple regression analysis. Given the results of this exercise an interpreta-tion and discussion of the derived coefficients i s necessary. We also need to discuss the consistency of our results, and having done this, decide on coefficient values most likely representative of the Maritime Region. In order to calculate the coefficients of the model equation, three sets of data are required for each cross-sectional observation. These are: changes in total employment, N, changes in basic employment N^ and changes in population due to net emigration, M. In Chapter IV, changes in basic employment were calculated over the 1951 to 1961 period, and this proved to be the most awkward variable to handle. Total employment was used in the calculation of basic employment; therefore, that leaves just the emigration variable to quantify. Fortunately, migration information for the 1951 to 1961 period, by county, i s available from the 1961 Census, without which rough estimates would have had to suffice. Net emigration data is shown in Table XIII 52 of the Appendix along with the other required input. From this infor-mation the coefficients of regression can be directly calculated. Our results are presented below, A regression of total employment on basic employment results in the following regression equation (2) .: (2) N = 947.49 + 1.78 N x R2 = .72 (200.47) (.21) which was calculated from 29 observations with 28 degrees of freedom. 2 The terms in brackets below the coefficients are standard errors; R i s the coefficient of determination. The inclusion of the emigration variable in the analysis results in regression equation (3); . . . 2 (3) N = 89.05 + 1.29 - .26 M R = .84 (255.48) (.20) (.06) whetfe total employment is regressed on both changes in basic employment and emigration. In this case there are 27 degrees of freedom and again 29 observations. Given regression equations (2) and (3), what can be said about them? F i r s t , in both equations the regression coefficients are significant at the .1 per cent level. Second, the coefficient of determination is improved by the explicit inclusion of emigration as a variable, i n -dicating that i t is useful along with changes in basic employment in explaining changes in total employment. Third, the coefficient is significantly reduced from equation (2) to (3) by the introduction of As mentioned earlier, 29 of 36 Maritime counties have experienced net emigration and i t is these counties with which we are concerned. 53 the emigration variable. This last point i s a most interesting one and can be explained i f we set up the relationships involved in a more precise manner. The coefficient of equation (2) is larger than i t s counter-part in equation (3) because of the correlation that exists between N and M. F i r s t , given our original model equation (1): (1) N • a +. a x N x *• a 2 ft we have e x p l i c i t l y asserted that emigration w i l l result in changes in total employment. The relationship between N and M is not restricted to this unilateral causal association, however. In addition to the particular relationship of interest to us, we recognize that emigration is in part a function of changes in total employment. When the level of employment declines, we expect increases in emigration; conversely, increases in regional employment are consistent with lower rates of emigration than otherwise would occur. In other words, emigration is a function of employment opportunity. . . . The relationship between N and M, with M specified as the de-pendent variable, is set out in equation (4): (4) M = b + b, N + b Z v 3 o 1 2 where Z represents other variables affecting emigration, such as popula-tion composition, educational and s k i l l levels and other correlates of mobility. We can now reduce structural equations (1) and (4) into equation (5), by substitution. a + a- b a. a b 15) N ° 2 0 • 1 \ * 2 2 Z l-a„ b. l-a„ b b 2 1 2 1 2 1 54 In terms of our regression results, equation (2) can be thought of as an estimate of structural equation (5), omitting the Z variable. Re-gression equation (3), shown above, i s an estimate of our model equation (1). 5 We are now in a better position to ill u s t r a t e why the co-efficients in our two regression equations are different. Although equation (2) does not expli c i t l y specify emigration as a variable, i t s effect w i l l nevertheless be present due to the relationship that exists between N and M; we can see this from the N coefficient in equation x (5). In regression equation (3), however, and M are specified separately. We can ill u s t r a t e how the differential in coefficients occurs. Consider the case in our structural equations, where bj = p, that i s , emigration does not respond to changes i n employment opportunities. I f this i s true, then the coefficient of equation (5) w i l l be the same as the coefficient of equation (1). If -l<b< o, and -1< a< o, then the coefficient of equation (5) w i l l be larger than i t s counterpart in equation (1). Our regression results indicate that b < o . 1 If b. = -1 , a = 1.3 , and a «* -.5, then the N coefficient 2 of equation (5) could theoretically be as high as 2.6. But we have estimated that: 2 Assuming a • -.5 requires that we know about one of every • two emigrants i s a labor force member; then, using the .26 coefficient of equation (3), we can express i t as though ^ were in employment units. 55 a ! =1.8 r r V b l Under the assumption that our regression estimates are the true values (we relax this assumption later on), that i s , a^ = 1.3, a 2 = -.5, and the coefficient of in structural equation (5) is taken as 1,8, implies a value for bj of -.55, This suggests that .on average one of every two persons becoming unemployed in this region, due to a decline in basic employment, w i l l move away from the region of decline. We have now illustrated why the N coefficients in regression equations (2) and (3) are different. Only i f emigration is not responsive to changes in employment could we expect the two coefficients to be the same. Equation (2) may be thought of as combining the direct and pos-sible indirect effects of changes in basic employment; while equation (3) identifies the two separately. The "usual" employment multiplier assumes that when a decline in basic employment occurs, the unemployed no longer contribute to regional income. This is similar to assuming that b^ = -1 in equation (5). The model in this paper takes account of the fact that the un-employed do make a contribution to regional income, in the form of expenditures which are not earned regionally. Therefore, we can expect different changes in total employment to result from a given decline in basic employment, depending on whether the unemployed remain in , or leave, the region of decline. In any case, we must take account of two factors: the decline in autonomous expenditures of the base, and changes in expenditures of the unemployed. In the other direction, an increase in basic employment may 56 result in different magnitudes of total employment change depending on the source of labor supply. If employees are brought in from outside the region, the effect on total employment w i l l be greater than i f employees are taken from the labor surplus within the region. In the case of the former, we expect a multiplier greater than 1.3. In the latter case,where employees are drawn from within the region, we expect a multiplier effect of 1.3. When new employees come from within, the increase i n basic autonomous expenditures i s partly offset by a decline in expenditures of the unemployed. There is no similar offsetting factor involved i f workers are brought i n from outside the region. The a 2 term of regression equation (3), aside from being useful in isolating the possible indirect effect of a change in basic employ-ment, also indicates the total employment effect of emigration irrespective of changes in basic employment. Recall that the emigration variable, M, i s described in population units, not employment units. Emigration may take place for many reasons, real or imagined opportunity elsewhere, desire to see other parts of the country, following friends 3 or relatives, et cetera. Emigration, for whatever reason, w i l l affect total employment in the loser region. Our calculations suggest that the total employment effect w i l l be (M) (.26). Now that the regression estimates have been made we should check them for consistency. In Chapter III we estimated that a 1.3 expenditure 3 These variables would be included in the Z variable of structural equation (4). 57 multiplier, associated with approximately a 1.5 employment multiplier, was consistent with an emigration coefficient of about .20, The multiplier in part determines the employment effect that emigration w i l l have; therefore, the regression estimate of the employment multiplier should be consistent with the .26 regression estimate of the emigration coefficient. The method used to relate the multiplier to the emigration coefficient in Chapter III assumed an expenditure multiplier in the calculation. We can assume our 1.8 employment multiplier to be equivalent to about 1.6 in expenditure terms, using the same reasoning as before, when we related basic to non-basic industrial output per worker. Recall the equation 1 KXS where W = the number of labor force emigrants who cause one worker to be displaced in the loser region. Assuming, as before, one of every two emigrants are labor force members, i t is possible to make an estimate of the emigration coefficient, given that K = 1,6. We find that K = 1.6, or our 1,8 employment multiplier, i s consistent with a 4 .24 emigration coefficient. If we assumed that S = ,5, rather than ,6, then the calculated emigration coefficient would be .20. 4 K = 1.6" V — c A ^ , I same values as Chapter III S = ,6 ) W = * =2,9 (labor force emigrants) or 4.38 (1.6)(.5)(.6) emigrants in population units. This implies an a coefficient of .24, 58 It would appear, from the rough check above, that our emigra-tion coefficient is reasonable in view of the employment multiplier's magnitude. Taken at face value the simple test suggests that we may have overestimated the emigration coefficient slightly; especially i f we assume a value of S • .5. On i t s own, this test i s not sufficient to move us to lower our estimate of a^. However, considering the fact that in regression equation (3), the emigration coefficient, a^t might be overestimated due to the inverse two-way causal relationship of N and M, we w i l l assume a value of a^ = .20 for further discussion. This should not be an overestimate i f taken as a value applicable to the Maritime Region as a whole. If regression equation (3) overestimated the a^ term, then the • N coefficient, a , in that equation would tend to be underestimated, x l The value of a^, the minimum employment multiplier, i s 1.3. For the purposes of further discussion we w i l l assume a value for a^ of 1.4. This value should serve as a conservative multiplier estimate i f taken as being representative of the Maritimes as a whole. The value of 1.8 for the multiplier combining the basic employ-ment and emigration coefficients.estimated i n equation (2), may also be biased upward a small amount; again due to the two-way causal-relationship that exists between N and M. Again, i f 1.8 i s taken as a value for the Maritimes, i t should be a f a i r estimate. Recall that Czamanski calculated an average investment multiplier of 1.6 for Nova Scotia, and we noted that this would be higher than a county figure. Rosenbluth's calculation for cit i e s of smaller size yielded an average employment multiplier of 1.7 and we suggested a 59 county figure would be lower than this due to a relatively larger proportion of basic employment. The results we have derived are reason-able in light of these other two estimates. To conclude this chapter a direct interpretation of our estimates i s made; in the next chapter the implications of our results are discussed. The employment multiplier of 1.4 suggests that for every increase or decrease of 100 employees in the basic sector, total employment w i l l change by at least 140 employees. An emigration coefficient of .20 suggests that for every 5 persons, who emigrate from the Maritimes, one worker becomes unemployed in the loser region. This latter estimate may seem high, but recall that a high proportion of emigrants were found to be labor force members over the period studied. The higher this proportion is the more impact emigration w i l l have on the loser region. On average, a decline of 100 basic employees i n the Maritimes w i l l result in a reduction in total employ-ment of 180 persons; this estimate includes the associated emigra-tion. CHAPTER VI IMPLICATIONS FOR POLICY AND CONCLUSIONS The major purpose of this paper was to make a small contribu-tion to our quantitative knowledge about the employment effect of changes in basic or export oriented employment and emigration, related to the general problem of regional unemployment. In the last chapter the estimates were discussed and values were picked which were thought to be representative for the Maritime Region. Having derived co-effi c i e n t estimates i t i s appropriate to il l u s t r a t e the usefulness of them and their implications for policy. The results, by no means, answer policy questions regarding regional unemployment disparities, but do suggest areas of investigation not presently carried out. In this chapter, then, some specific illustrations of the usefulness of the estimates are discussed; following that, a more general review of the regional unemployment problem in the Maritimes and solution policies in light of our results. The employment multiplier can be used in regional planning. For example, employment in the mining sector of Nova Scotia dropped by 1 about 5,000 between 1952 and 1961, mostly in the coal segment. This negative change in basic employment resulted in a direct loss of 2,000 more jobs, according to our estimates; a substantial loss of employ-1 Nova Scotia Voluntary Planning Board, Plan For The Mining Sector (Halifax: Queen's Printer), June, 1966, p. 36. 61 2 ment and income to the area. But the multiplier of 1.4 used in this case assumes that the unemployed remain in the area. If a l l these people and associated families decided to leave, an additional 3 (14000) (.20) o 2800 workers could be put out of work. Even though unemployed, but remaining in the region, people have to make expenditures to li v e ; thus they contribute to regional income. However, i f they leave the region even the reduced expenditure i s withdrawn, adding an additional depressant. In reality one would not expect a l l those who become unemployed to emigrate. Therefore, the total employment effect in our example would be somewhere between 7,000 and 9,800 workers de-pending on the extent of emigration. Any regional planning program should take account of these secondary and tertiary effects of changes in basic industry. The above example illustrates the possible total employment effect of a decrease in basic employment. The reverse picture i s also useful. For example, in Nova Scotia the "F i r s t Plan" called for "an increase of 3,400 jobs annually or 13,600 new jobs over the four year 4 (1965-1968) planning period." The results in our paper indicate that 2,420 new basic industry jobs needed to be created to result in 3,400 new jobs for the province as a whole, or just over 9,700 basic industry 2 (5000) (1.4) - 5000 = 2000. 3 This assumes 7000 labor force members or 14000 total migrants leave the area. 4 Firs t Plan for Economic Development to 1968, op. c i t . , p. 9. 62 5 jobs to meet the four year target. A rough indication of the expan-sion in regional income resulting from 2,420 new basic jobs can be made. Assuming local expenditures per employed person are $3,000 per year, income w i l l rise by: (2420) (3000) (1.4) = $10,164,000. These estimates assume that new basic employees come from the labor surplus or potential labor surplus within the area. The multiplier effect of s k i l l e d workers, brought in from outside, would be larger than 1.4. However, in this case unemployment would not be reduced as much as i f the workers were hired from the labor surplus area. The employment multiplier can also be used as a rough guide in calculating how much population can be supported by an increase in basic employment. For example, suppose a new industry locates in an area creating 100 basic oriented jobs. If the participation rate of the population in the labor force is ,3, then this new injection could 6 be expected to support at least 466 people (100 + 40 _ The average employment multiplier can be used as a guide for the development of new urban centers. What mix of basic, non-basic employment should the center plan for? Rosenbluth's results suggest that on the average there is a one to one basic, non-basic employment relationship in centers across Canada, This information is useful 1.4X = 3,400, X = 2,421 1.4X = 10,460, X = 9,714 . 6 Thompson, op. c i t . , p. 67 • 63 to the planner of new development areas as a rough indication of types 7 of f a c i l i t i e s needed. We could carry on with other examples, but our central concern i s with the problem of regional unemployment. Next, then, within the context of the results derived i n this paper, we discuss the unemploy-ment problem and the policies which are designed to alleviate i t . It i s a well known fact that the Maritime Region has lagged behind the rest of the country in rate of job creation. New industries have tended to favour the industrial centers of Canada where an adequate infra-structure exists. Declining industry (or relatively slow growth in industrial development) has not been replaced by viable alternatives, as the Maritimes has not been able to compete with other areas of the country. Population due to natural increase rose 20 per cent between 1951 and 1961 in the Maritimes and an expanding labor force has not been able to find adequate employment opportunities. "A detailed examina-tion of depressed local labor markets in Canada showed such communities 8 to be concentrated in the Atlantic Region and Quebec." The classical theory suggests that regional unemployment imbalances are naturally corrected by flows of labor and capital i n response to relative prices. Labor flows to high wage areas and capital flows to low wage areas u n t i l an equilibrium i s reached. It i s true these forces 7 T. A. Wilson, "The Regional Multiplier - A Critique", Oxford  Economic Papers, XX (November, 1968), 374. 8 Ostry, op. c i t . , p. 370. 64 play a major role but not to the degree where unemployment imbalances are corrected. In Britain, Archibald has noted that the regional incidence of unemployment is unequal. More importantly, this regional disparity 9 has been rather stable in spite of migration. This finding has been stated generally as well. " I t is apparent that out-migration has not been effective in bringing the remaining labor supply into line with 10 declining demand." This observation i s probably very appropriate for the Maritime Provinces. In that area there has been a substantial net migration loss, the actual amount to be referred to shortly. In spite of the reduced supply of labour, high unemployment has been the rule in the Maritimes. The Economic Council of Canada notes that while Canada's post war average annual unemployment rate was 4.4 per cent, 11 the Atlantic Provinces have had an average rate of 7.6 per cent. These figures are averages, but the important point is that the unemploy-12 ment gap has not narrowed. Our analysis in this paper suggests that the income-expenditure approach may provide a useful theory explaining a portion of the 9 Archibald, op. c i t . , p. 23. 10 Gerald G. Somers, "The Returns to Geographical Mobility: A Symposium", The Journal of Human Resources, II ( F a l l , 1967), 428. 11 Economic Council of Canada, op. c i t . , p. 116. 12 i b i d . , p. 99; Ostry, pp. c i t . , p. 370, 65 persistent regional unemployment disparity, rather than depending on the relative price theory. We can use the income-expenditure approach to hypothesize about what may be occurring, using the Maritimes and Ontario as an example. As previously mentioned, the Maritimes have suffered from a de-cline or a relatively slow growth in basic industry with associated repercussions for employment. In the case of a decline in the basic sector, employment f a l l s and so do autonomously derived expenditures. Demand for local services w i l l drop as a result. The decline in basic industry would result in an estimated decrease in total employment of (N ) (1.4). I f the unemployed emigrate, regional expenditures decline further causing even more unemployment and loss of regional income. Both effects taken together could on average be expected to result in a decline in total employment of (Nx) (1.8). With a small increase in basic employment and a relatively small multiplier, in part due to the decline in transfer payments associated with new employment, the secondary expansion may be expected to be minimal. Between 1951 and 1961, net out-migration from the Maritimes amounted to 82,500 persons, or in terms of labor force, about 40,000 13 workers. Considering the direct withdrawal of expenditures these people made in the Maritimes and the additional negative multiplier effect, the region lost a great deal. In fact, using the results of 13 * The figure of 82,500 emigrants from the Maritimes represents 80 per cent of net out-migration from the counties. See: Source for Table XIII of the Appendix. 66 the analysis in this paper, we estimate that these 82,500 emigrants (40,000 labor force members) caused about 16,500 others to become un-14 . . . employed, or created the need for this many new jobs. It i s obvious that this exodus represents a considerable loss to the region. I f these 82,500 emigrants had remained i n the region and the associated 40,000 labor force members were gainfully employed in the basic sector, 15 they could support perhaps 24,000 non-basic employees. Now consider a prosperous region of Canada, for example, Ontario, Basic industry has expanded rapidly, resulting in large direct expenditures. This means a larger multiplicand in addition to a larger multiplier. The larger multiplier i s possible because the economy is well diversified. Also, new jobs are f i l l e d by in-migrants, rather than by labor surplus existing on welfare. Therefore, the total employ-ment expansion for a given increase in basic employment w i l l be larger than a similar increase in the Maritimes. New migrants build houses and make related expenditures and federal spending tends to "follow the; men"; these expenditures have a multiplier effect as well. Given the different opportunities i n the Maritimes and Ontario i t i s d i f f i c u l t to see any natural tendency for regional unemployment rates to equalize. Consider, then, economic policies instituted to assist in the equalization process, v i z . , mobility policies and industrial loca-tion policies. The former attempts to reduce a region's labor supply. 14 (82,500) (.2) = 16,500. 15 (40,000) (1.4) - 40,000 = 24,000. 67 the latter encourages industrial location in order to increase a region's demand for labor. Both policies are designed to reduce un-employment. The goal of mobility policy i s to even out unemployment and income differentials between regions. Mobility policy and migration are often considered in the aggregate. However, this approach can often lead to policy errors. Migration effects should be viewed from four distinct points of view. It i s necessary to judge the effects of migration on: a) the country as a whole; b) the individual i n -volved; c) the receiver region; and d) the loser region. The interest in this paper i s in the last category, the loser region. It is a fact that we do not know too much about the costs and benefits involved in the transfer of human capital. It i s also a fact that this informa-tion i s necessary for the realization of a rational attack on regional unemployment. Therefore, attempts to quantify any aspect of this problem w i l l hopefully be of assistance. Our analysis does not t e l l us whether the net effect of out-migration from an area is positive or negative. What i t does suggest, however, i s that migration i s not totally stabilizing as the classical relative price approach implies. Removing unemployment via mobility policy cannot be expected to solve the problem of unemployment on a one for one basis. If an area has 1,000 unemployed and these people are assisted 16 See Solomon Barkin, "The Economic Costs and Benefits and Human Gains and Disadvantages of International Migration", Human Resources, II (F a l l , 1967), p. 495. 68 away or move naturally, the emigration i t s e l f w i l l cause unemployment through the decline in regional autonomous expenditures. Using our results, and again assuming one of every two emigrants are labor force members, there may s t i l l be 400 unemployed workers in the loser region looking for work after the multiplier has worked i t s e l f out. For every step forward, we move one-half a step backward. Emigration, then, may not benefit the loser region as much as one might think, a p r i o r i . " I t may be found that the only positive benefits of mobility are external to the area from which the movement originates, and yet relocation 17 subsidies are included as part of regional development programmes". At a minimum this negative aspect of migration should be con-sidered in any cost-benefit analysis of mobility policy. It is not suggested that unemployed workers should be l e f t where they are because their transfer payments and expenditures are needed in the poorer region. However, the loss by their removal should be taken into account. This i s especially true where this removal i s part of the development policy aimed at bringing the high unemployment areas into line with the rest of the country. Industrial location policy i s the alternative or complement to mobility policy for correcting regional unemployment disparities. Can we expect new industry to be effective in solving the unemployment problem in the Maritimes? This i s a very d i f f i c u l t question to answer and i t is not proposed that i t w i l l be answered here. However, from our results we can say how much secondary employment may be expected from a given increase i n basic employment. Somers, op. c i t . , p. 428. 69 If a new export oriented industry hires 100 additional employees from the labor surplus within the region, total employment i s expected to rise by (1.4) (100) • 140. We can think of the 140 employment figure as a net change in total employment. Actually, there has been an i n - t> crease in exogenous expenditure via increased export demand which in isolation would increase total employment by an amount greater than 140. But at the same time as these people become employed the region loses the associated unemployment transfer payments, and i t is estimated this loss would mean about the equivalent of 40 jobs. Therefore, the net increase in total employment is expected to be 140. The associated loss in regional income, because of reduced transfer payments, should come into the calculation of net benefits resulting from an increase in basic industry. As mentioned earlier, i f new basic employees come from outside the region the multiplier effect is larger but then the unemploy-ment problem w i l l not be cured, which in part defeats the aim of industrial policy. Our results suggest a rather important point related to industrial location policy. Declines i n basic industrial employment w i l l likely have a more substantial absolute effect on total employment than do i n -creases i n basic employment. This is the case because when declines occur they encourage out-migration, an added depressant in the loser region because of the reduction transfer payments. When basic industrial employment increases, however, expansion of total employment is offset to some extent by the decline in transfer payments. Declines in basic employment, then, are expected to have a more significant impact on total employment. This means that the problem of solving regional un-70 employment by i n d u s t r i a l location i s made even more d i f f i c u l t . I f new investment i s considered i n terms of monetary units, care must be made to distinguish between the investment and l o c a l value added. Regions can expect a high i n i t i a l leakage to occur from any "published" investment expenditure figures. The "announced" invest-ment figures usually contain a good deal of import content. This leakage i s i n addition to the consumption import leakage. Thus, when s t a t i s t i c s record investment i n plant and equipment these figures are often not the true i n j e c t i o n into the l o c a l economy. Rather, they are t o t a l investment figures including that portion which flows out with-18 out stimulating the area of investment. With a regional expenditure m u l t i p l i e r of 1.6, i t i s easy to show how high the import content of gross investment need be to resul t i n a given expansion e f f e c t , based on the o r i g i n a l investment figure, 19 of unity. Let Y = KI (1-m) where Y i s income, K i s the m u l t i p l i e r and m i s that fr a c t i o n of imports i n the gross investment, I. Letting K (1-m) = 1 gives a c r i t i c a l value f o r m. Rearranging, m = 1 -1, and K i f K - 1.6, then, m = .4. This result suggests that i f the import content of a gross investment i n j e c t i o n i s forty per cent, the t o t a l income eff e c t on a region w i l l only equal the amount of the o r i g i n a l gross investment. I f K = 1.4, then, m = .3. Investment of the public works type may have l i t t l e leverage, 18 Wilson, op. c i t . , p. 379. 19 Archibald, op. c i t . , p. 38. 71 then, as i t is' probable that forty per cent of the gross investment w i l l be made up of import content. In addition, public investment does not provide a continuous flow of funds to the region, rather, i t i s a "one shot" effort. However, private investment results in i n -creased exports which continue over time. In both cases i t should be remembered that as people become employed the region w i l l lose the transfer payments associated with the previously unemployed individuals. This w i l l tend to dampen the expansion effect of new investment. i When discussing emigration, we concluded there was a de-stabilizing factor which should be considered as a cost, not that emigration was undesirable per se. In the case of external government expenditure or private investment, the above points should be con-sidered in a cost-benefit sense. With a low multiplier i t may be found that investment in slow growth regions does not cure unemploy-ment as rapidly as i n i t i a l l y expected. The fact that the Maritime Region has remained the high un-employment, low income area of Canada, in spite of: substantial transfer payments, development programs, and heavy emigration to other areas of Canada; suggests that some of the points in this paper may be valid. Considering the expansion in other parts of Canada, that migration is moving in that direction, that the multiplier is larger in these regions, with high density industrial complexes, i t w i l l be very d i f f i c u l t to remove regional unemployment imbalances short of rather massive mobility and industrial location programs for the Maritime Region. SOURCES CONSULTED Alexander, John W. "The Basic-Non-basic Concept of Urban Economic Functions." Economic Geography, XXX (July, 1954), 247-50. Andrews, Richard B. "Mechanics of the Urban Base." Series of Articles in Land Economics, XXIX to XXXI (May, 1953 to February, 1956). Archibald, G. C. "Regional Multiplier Effects in the U.K." Oxford  Economic Papers, XX (November, 1968), 374-93. Barkin, Solomon. "The Economic Costs and Benefits and Human Gains and Disadvantages of International Migration." The Journal of Human  Resources, II ( F a l l , 1967), 495-516. Czamanski, Stanislaw. Regional Income and Product Accounts of North- Eastern Nova Scotia. Regional Studies Series Number I. Halifax: Institute of Public Affairs, Dalhousie University, 1968. Daley, M. C. "Approximation to a Geographical Multiplier." Economic  Journal, L (June to September, 1940), 248-58. Dominion Bureau of Statistics. Census of Canada: 1961. Vol. IV, Population Sample, General Characteristics of Migrant and Non- Migrant Population, Part I, Catalogue 98-509. . Vol. IV, Population Sample, Migrant and Non-Migrant Population in the Labour  Force, Part I, Catalogue 98-510. . Vol. I l l , Labour Force, Industry Division, by Sex, Part 2, Catalogue 94-522. ' . Vol. V, Agriculture, Catalogues 96-532, 96-533, 96-534. -—- . Vol. VII, General Review, Growth of Population in Canada, Part I, Catalogue 99-511. . Census of Canada: 1951. Vol. IV, Labour Force, Occupations and Industries, Part 2. . Vol. VI, Agriculture. ' . National Accounts and Balance of Payments P^ Y _ s i o n < ^ n d ^ x e s of Real Domestic Product by Industry of Origin 1935-1961, Catalogue 65-505. Economic Council of Canada. Towards Sustained and Balanced Economic Growth. Second Annual Review. Ottawa: Queen's Printer, December, 1965. 73 Hansen, Alvin H. A Guide to Keynes. McGraw-Hill Paperbacks, New York: McGraw-Hill Book Company, Inc., 1953. Hildebrand, George and Mace, Arthur, Jr. "The Employment Multiplier in an Expanding Industrial Market: Los Angeles County, 1940-47." Review of Economics and Statistics, XXXII (August, 1950), 241-49. Hood, Wm. C. and Scott, Anthony. Output, Labour arid Capital in the Canadian Economy. Royal Commission on Canada's Economic Prospects, February, 1957. Isard, Walter. Methods of Regional Analysis: An Introduction to Regional Science. Vol IV: Regional Science Studies Series, Massachusetts: the M.I.T. Press, 1960. Kahn, R. F. "The Relation of Home Investment to Unemployment." Economic  Journal XLI (June, 1931), 173-192. Keynes, John Maynard. The General Theory of Employment Interest and  Money. Paperback Books. London: MacMillan and Co. Ltd., 1964. Lange, Oscar. "The Theory of the Multiplier." Ecbriometrica XI (January, 1943), 227-245. Mattila, J. M. and Thompson, W. R. "Measurement of the Economic Base of the Metropolitan Area," Land Economics, XXXI (August, 1955), 215-228. Mining Association of British Columbia. The Growth and Impact of the Mining Industry in British Columbia. Vancouver: December, 1968. Moyerman, Sue S., and Harris, Britton. "The Economics of the Base Study." American Institute of Planners, XXI (Spring to Summer, 1955). North, Douglas C. "Exports and Regional Economic Growth, A Reply." Journal of P o l i t i c a l Economy, LXIV (April, 19563.165-167. "Location Theory and Regional Economic Growth." Journal of P o l i t i c a l  Economy, LXII (June, 1955), 243-58. Nova Scotia Voluntary Planning Board. Plan for the Mining Sector. Halifax: Queen's Printer, June, 1966. Fi r s t Plan for Economic Development to 1968. Halifax: Queen's Printer, February, 1966. Parks, Arthur C. The Economy of the Atlantic Provinces, 1940-1958. Atlantic Provinces Economic Council, Fredericton, June, 1960. Rosenbluth, Gideon, The Canadian Economy and Disarmament. Toronto: MacMillan of Canada, Ltd., 1967. 74 Sasaki, Kyohei. "Military Expenditures and the Employment Multiplier in Hawaii." Review of Economics arid Statistics, XLV (May, 1963), 298-304. Sirkin, Gerald. "The Theory of the Regional Economic Base." Review of Economics and Statistics, XLI (June, 1959), 426-429. Somers, Gerald G. "The Returns to Geographical Mobility: A Symposium." The Journal of Human Resources, II ( F a l l , 1967), 427-430. Telia, Alfred. "The Relationship of Labour Force to Employment." International Labour Relations Review, XVII (April, 1964), 454-469. Thompson, Gerald Everett. "An Investigation of the Local Employment Multiplier." Review of Economics and Statistics, XLI (February, 1959), 61-67. : Tiebout, Charles M. "Exports and Regional Economic Growth." Journal  of P o l i t i c a l Economy, LXIV (April, 1956), 160-64. Wilson, Thomas. "The Regional Multiplier - A Critique." Oxford  Economic Papers, XX (November, 1968), 374-93. Woods, H. 0., and Ostry, Sylvia. Labour Policy arid Labour Economics  in Canada. Toronto: MacMillan of Canada, 1967. APPENDIX T A B L E 1 -GROSS D O M E S T I C P R O D U C T AND T O T A L LABOUR INPUT BY I N D U S T R Y , C A N A D A , 1955 Industry Manufacturing Forestry and Fishing M i n i n g Construction a Basic Sectors Gross Domestic Product ( M i l l i o n $) 5,274.6 494.2 957.2 593.7 7 ,319.7 Man -Hours ( M i l l i o n s ) 2 ,911.6 380. 1 2 45.2 381.8 3,919.0 Output P er Man -Hour ($) 1.87 Construction a Wholesale and R e t a i l Finance Insurance and Real Estate Transportation and Communicat ion 593.7 2 ,727.8 898.5 1 ,486.0 381.8 1,787.2 840.5 857.2 Non-Basic Sectors 6 ,123.7 4,224.6 1. 45 Source: W m . C . Hood and Anthony Scott , Output Labour and C a p i t a l in the Canadian Economy, Royal Commission on Canada's Economic Prospects, (February, 1957), p . 398 . a Construction divided between basic and non -basic s e ctors . 76 TABLE II CHARACTERISTICS OF EMIGRANTS FROM THE MARITIME PROVINCES ( 1956 - 1961 Period ) a Total Emigrants From i Labour Force Emigrants From i s Prince s Prince Receiver e Edward Nova New e Edward Nova New Province x_ Island Scotia Brunswick Total X . Island Scotia Brunswick Total Quebec M 223 2,152 4,322 6,697 M 143. 1,435 2,788 4,366 F 226 2,290 4,486 7,002 F 94 524 1,367 1,985 Ontario M 1,104 9,563 6,011 16,678 M 861 6,709 4,118 11,688 F 1,306 9,269 6,388 16,963 F 568 2,870 2,019 5,457 Manitoba M 51 597 287 935 M 36 381 195 612 F 67 745 221 1,033 F 26 176 53 255 S askatchewan M SO 213 98 361 M 25 166 63 254 F 30 160 114 304 F 0 19 27 46 Alberta M 145 842 483 1,470 M 100 602 ;'.340 1,042 F 172 739 467 1,378 F 79 261 149 489 British M 118 1,645 456 2,219 M 86 1,106 89 1,281 Columbia F 85 1,596 488 2,169 F 37 262 130 429 57,209 27,904 Source : Dominion Bureau of Statistics, Census of Canada; 1961, Vol . IV, Population Sample, General Characteristics of Migrant and Non-Migrant Population, Part 1, Catelogue 98-509, Table 14, p. 14-1 j Vol IV, Population Sample, Migrant and Non-Migrant  Population in the Labour Force, Part 1, Catelogue 98-510, Table J4, p. J-4. aPopulation refers to those persons over five years of age. TABLE III DERIVATION Cf FACTOR FOR ADJUSTING 1951 LABOUR FORCE TO 1961 PRODUCTIVITY LEVEL 1. Index of Production, 1961 (1949 = 100) 2. Labour Force, 1961 3. Productivity Factor, 1961i (Row 1/2) 4. Index of Production , 1951 (1949 = 100) 5. Estimate of 1951 Labour Force in 1961 Productivity Terms (Row 4/3) 6. Actual Labour Force, 1951 7. Estimate/Actual Labour Force, 1951 .Row (5/6) a Forestry and Agriculture Logging 116.0 130.8 640,786 108,580 Fishing and Mining and Transportation Communication .00018 120.9 .81 .0012 141.5 671,667 117,917 827,030 129,832 .91 Trapping Quarrying Manufacturing Construction and Utility 115.7 266.9 36,263 121,702 .0039 111.5 34,953 50,579 .69 .0022 123.4 56,347 103,848 .54 153.0 1,404,865 .00011 115.0 1,045,455 1,360,662 .77 168.4 431,093 .00039 110.6 283,590 350,896 .81 195.4 603,286 .00032 115.7 361,562 464,521 .78 Trade 158.2 991,490 .00016 108.1 675,625 709,768 .95 Source: Dominion Bureau of Statistics, Indexes of Real Domestic Product by Industry of Origin, 1935 - 1961, National Accounts and Balance of Payments Division, Cateldgue 61-505, Table 1, p.67; Censuses of Canadat 1951 and 1961, Labour Force, by Industry, (1961, Table 8,p. 8-1) (1951, Table 18, p. 18-2). aThe factors derived in row 7 may be used to adjust 1951 county labour force by industry. The result is 1951 labour force expressed in terms of 1961 productivity levels. These adjustments when carried out result in the 1951 labour force figures shown in Table IV, this Appendix. TABLE I V INDUSTRY LABOUR FORCE BY COUNTY a (1951 and 1961) Finance County Forestry Fishing Mining Transportation Insurance and code and and and Manufac- Communication and Personal Other number Agriculture Logging Trapping Quarrying turing Construction and Utilitites Trade Real Estate S ervices Service 1 Kings 1,435 76 419 0 714 180 219 382 31 186 392 2 Prince 2,703 119 430 4 945 '562 568 1,334 98 640 1,989 3 Queens 3,523 47 291 0 1,043 777 984 2,020 282 996 2,545 4 Annapolis 805 276 153 2 548 433 318 643 75 437 3,181 5 Antigonish 426 123 146 3 200 258 259 390 50 243 774 6 Cape Breton 421 140 376 5,553 5,539 1, 365 2,566 4,745 482 1,979 4,136 7 Colchester 1,181 443 26 30 1,939 582 1,083 1,425 187 843 1,328 8 Cumberland 1,026 597 90 1,033 1,929 595 691 1,392 182 833 1,142 9 Digby 223 281 488 4 1,054 346 363 711 48 518 727 10 Guysborough 120 390 622 4 531 271 441 416 16 201 384 11 Halifax 421 448 858 21 6,158 4,790 5,756 10,823 1,763 3,960 21,190 12 Hants 926 377 4 334 973 570 437 672 82 412 757 13 Inverness 369 401 239 200 468 326 328 388 35 279 520 14 Kings 1,438 241 44 14 1,020 644 693 1,251 142 861 2,363 15 Lunenburg 646 694 625 7 1,978 852 769 1,433 128 848 846 16 Pictou 820 269 231 995 3,089 618 1,143 1,862 216 975 1,398 17 Queens 122 218 206 1 920 249 327 468 34 323 353 18 Richmond 79 168 540 69 226 316 257 216 8 119 258 19 Shelburne 62 54 1,087 1 595 256 179 584 30 278 485 20 Victoria 190 80 31 138 111 257 149 192 5 155 317 21 Yarmouth 305 171 697 1 1,157 550 494 956 76 700 770 22 Albert 315 250 6 37 517 195 272 431 26 108 387 23 Carleton 1,783 527 8 1 721 273 425 827 80 351 647 24 Charlotte 322 678 968 0 2,436 407 497 896 89 668 821 25 Gloucester 403 2,021 832 53 2,415 531 508 1,360 77 778 1,579 26 Kent 821 1,063 301 3 577 402 293 578 23 423 522 27 Kings 1,511 595 6 4 769 410 480 8849 155 435 898 28 Maoawaska 666 1,583 1 0 1,315 460 850 754 94 724 1,200 29 Northumberland 394 2,198 520 10 1,749 676 715 1,238 72 703 2,082 30 Queens 563 687 6 258 372 181 279 239 19 173 335 31 Restigouche 321 1,431 83 1 2,055 437 864 987 99 671 1,166 32 St. John 105 216 128 28 4,300 1,452 3,681 5,301 953 1,883 5,410 33 Sunbury 296 518 4 237 193 122 310 160 11 127 241 34 Victoria 933 807 9 1 548 243 559 566 44 351 482 35 Westmorland 1,378 630 189 14 4,194 1,475 3,359 1 243 4,884 699 1,722 4,298 2,791 36 York 1,394 1,468 17 3 1,846 911 2,024 245 942 a In the tables which follow, only the code number will be used, not the county name. Countiest 1-3 are Prince Edward Island, 4-21 are Nova Scotia, TABLE IV - Continued Finance County Forestry Fishing Mining Transportation Insurance and code and and and - Manufac. Communication and Personal Other number Agriculture Logging Trappinq Quarrying turing Construction and Utilities Trade Real Estate Services Services 1 Kings 1,523 85 878 0 847 239 355 569 38 211 564 2 Prince 2,923 16 848 4 858 865 922 1,746 169 687 2,654 3 Queens 3,708 41 362 0 1,309 1,111 1,502 2,472 351 1,083 3,459 4 Annapolis 709 304 190 5 644 472 546 955 - 101 579 2,827 5 Antig onish 423 79 205 34 - 257 536 450 563 85 342 1,177 6 Cape Breton 378 152 460 7,922 6,824 1,569 3,974 5,806 687 2,329 7,259 7 Colchester 1,049 321 47 21 2,145 804 1,500 2,047 275 951 1,956 8 Cumberland 916 459 241 516 1,672 740 1,032 1,847 243 952 1,758 9 Digby 194 416 508 1 1,058 379 624 822 53 512 891 10 Guysborough 124 218 710 1 684 219 568 318 21 193 445 11 Halifax 392 285 821 154 9,044 4,763 9,178 14,213 3,152 5,371 35, 693 12 Hants 824 249 4 553 1,137 844 818 1,012 121 454 1,358 13 Inverness 423 348 498 164 415 523 597 546 53 373 845 14 Kings 1,364 85 41 37 1,457 1,049 886 2,105 203 739 4,830 15 Lunenburg 691 549 468 19 2,409 1,017 1,234 1,743 178 822 1,322 16 Pictou 749 301 354 581 2,671 813 1,375 • 2,043 250 877 2,133 17 Queens 92 136 195 14 1,284 248 370 551 49 355 550 18 Richmond 83 121 370 8 556 590 425 307 25 188 362 19 Shelburne 45 29 1,268 7 979 225 332 521 29 287 779 20 Victoria 158 188 301 57 . 93 154 357 218 15 186 437 21 Yarmouth 274 56 812 11 1,272 574 696 1,136 112 604 1,006 22 Albert 209 65 3 6 526 261 631 924 86 173 733 23 Carleton 1,550 185 365 21 3 820 371 779 1,067 129 391 982 24 Charlotte 477 688 8 2,153 387 743 914 88 626 1,105 25 Gloucester 383 2,124 1,251 455 3,104 957 914 1,953 152 927 2,394 26 Kent 607 751 464 8 875 556 630 812 41 402 696 27 Kings 28 Madawaska 1,382 292 9 5 1,232 588 923 1,309 205 496 1,241 698 1,571 13 16 1,773 508 1,155 1,145 149 771 1,942 29 Northumberland 268 1,134 607 67 1,918 622 1,295 1,694 129 789 3,825 30 Queens 369 278 7 500 352 222 469 326 19 177 505 31 Restigouche 360 1,233 134 40 2,331 544 1,112 1,371 164 867 2,150 32 St. John 259 80 123 10 6,354 2,013 4,429 6,698 1,272 2,137 7,309 33 Sunbury 196 233 7 433 138 214 432 442 67 218 4,782 34 Victoria 877 433 12 7 506 310 528 740 70 253 881 35 Westmorland 1,087 239 294 49 4,492 2,145 5,231 6,612 916 2,168 6,630 36 York 958 1,205 22 21 1,934 1,241 2,357 3,212 388 .1,314 S.159 Source! Dominion Bureau of Statistics . Censuses of Canada! 1961, Vol. Ill, Labour Force, Industry Divisions, by Sex, Part 2, Catalogue 94-522 s Census of Canadat 1951 . Vol. IV. Labour force, Occupations and Industries , Part 2, Table 18, p. 1 8-2, See note to Table IV on next pag e. 80 Note to Table I V Note: Two adjustments have been made to the raw data as taken from the 1951 and 1961 Censuses. First, agricultural labour farce was defined to be 1.8 labour force members per commercial farm , in both periods. The remainder of the agricultural labour force was excluded from the analysis. See Table V:., this Appendix, for the method. Second, 1951 labour force data were adjusted so as to reflect 1961 productivity, except for the sectors of finance, insurance and real estate; personal services; and other services. See Table 111 of this Appen-dix for the actual calculations. TABLE V ADJUSTMENT TO 1951 AND 1961 AGRICULTURAL LABOUR FORCE, BY COUNTY •• " 1 M -9 5 1 i y 6 1 m 1.8 thJes^CoJ^B) Estimate of Basic (A) (B) (C) (D) 1.8 times Col. (B) Estimate of Basic Agricultural Commercial Other Agricultural Agricultural Commercial Other Ag ricultural County Labour Force Farms Farms Labour Force a Labour Force Farms Farms Labour Force a 1 2,933 984 1,452 1,771 1,757 846 782 1,523 2 4,755 1,854 1,848 3,337 3,601 1,624 1,016 2,923 3 5,308 2,416 1,583 4,349 3,817 2,060 1,007 3,708 4 1,675 552 1,253 994 840 394 486 709 5 1,223 292 872 526 , 514 235 447 423 6 1,090 289 796 520 443 210 208 378 7 1,975 810 888 1,458 1,276 583 585 1,049 8 2,080 704 1,356 1,267 1,155 509 723 916 9 889 153 1,150 275 338 108 451 194 10 504 82 664 148 199 69 303 124 11 861 289 464 520 539 218 221 392 12 1,699 635 744 1,143 960 458 451 824 13 2,082 253 1,920 455 921 235 811 423 14 3,321 986 1,152 1,774 2,105 758 550 1,364 IS 2,146 443 1,828 797 953 384 804 691 16 1,659 562 1,528 1,012 924 416 730 749 17 260 84 189 151 148 51 88 92 18 331 54 502 97 126 46 201 83 19 135 43 84 77 47 25 65 45 20 648 130 654 234 208 88 176 158 21 743 209 901 376 342 152 279 274 22 526 216 319 389 255 116 185 209 23 2,776 1,223 789 2,201 1,862 861 499 1,550 24 715 221 587 398 275 103 181 185 25 3,585 276 4,295 497 821 213 872 383 26 2,868 563 1,765 1,013 1,247 337 766 607 27 2,287 1,036 892 1,865 1,463 768 576 1,382 28 2,236 447 1,049 805 ' 1,136 388 470 698 29 1,691 270 2,519 486 366 149 742 268 30 1,000 386 514 695 398 205 271 369 31 1,369 220 1,237 396 640 200 308 360 32 329 72 214 130 158 44 64 259 33 577 203 546 365 195 109 167 196 34 1,550 640 557 1,152 1,217 487 218 877 35 2,834 945 2,163 1,701 1,325 604 781 1,087 36 2,491 956 1,281 1,721 1,144 532 570 958 Source: Dominion Bureau of Statistics, Census of Canada:. 1961, Vol. V, Agriculture, Catelogues 96-532 , 96-533 , 96-534; Table 14, p. 14-1, Census of Canada: 1951, Vol. VI, Agriculture, Table 25, p. 25-1. a See note on next page showing regression analysis using this data. 82 Note to Table V A regression of A on B and G yielded equation (1), where R is the co-efficient of determination and figures in brackets are standard errors of estimates. (1) A = -71.38 + 1.77 B + .72 C R 2 = .95 (46.65) (.06) (.04) From this information we assume 1.8 persons per commercial farm in both 1951 and 1961. Column B, therefore, is multipled by 1.8 to give us an estimate of basic employment in the agricultural sector. This is shown in column D of Table V . 83 TABLE V I THE SUM OF AGRICULTURE (1), FORESTRY (2), FISHING (3), MINING (4) AND MANUFACTURING (5) LABOUR FORCE, BY COUNTY, 1951 AND 1961 County £ - (1-5) in 1951 y~ (1-5) in 196 1 1 2 ,644 3,333 2 4,201 4,649 3 4, 904 5,42 0 4 1,784 1, 852 5 898 998 6 12,02 9 15,7 36 7 3,619 3,536 8 4,675 :• 3, 804 9 2,050 2,177 10 1,667 . 1,737 11 7,906 10,696 12 2,614 '2,767 13 1,677 1, 848 14 2 ,757 -2 ,9fi4 15 3, 950 4,136 16 5,404 4,656 17 1,46 7 1,721 18 1*082 1,138 19 1,799 2 , 328 20 550 797 21 2,331 2,42 5 22 1,125 809 23 3, 040 2 ,759 24 4,404 3,511 25 5,724 7,317 26 2,765 2,705 27 2,885 2 ,920 28 3,565 4,071 29 4, 871 3, 994 30 1,886 1,506 31 3,891 4,098 32 4,777 6,826 33 1,248 1,007 34 2,298 1,835 35 6 , 405 6, 16 1 36 4,728 4, 140 S ourcet T a b l e I V , this A p p e n d i x . a £ J (1-5) = sum of labour force in sections 1 to 5. 84 TABLE VII , LOCATION QUOTIENT CALCULATIONS BY COUNTY AND INDUSTRY, 1951 AND 1961 a 1 9 5 1 1 9 6 1 finance Insurance and Personal Other Finance Insurance and Personal Other County --Trade Real Estate Services Services Trade Real-Estate S ervices Services 1 0.589 0.382 0.592 0.469 0.654 0.305 0.575 0.406 2 0.884 0.516 0.876 1.029 0.913 0.615 0.852 0.871 3 1.009 1.136 1.029 0.987 0.984 0.980 1.025 0^ 861 4 0.580 0.542 0.818 2.308 0.796 0.588 1.152 1.497 5 0.847 0.872 1.093 1.312 0.830 0.880 1.201 1.093 6 1.094 0.876 0.930 0.718 0.948 0.776 0.899 0.731 7 0.981 1.035 1.207 0.705 1.133 1.066 1.253 0.672 8 0.912 0.958 1.135 0.576 1.094 1.007 1.346 0.647 9 0.932 0.501 1.412 0.738 0.922 0.415 1.372 0.626 10 0.763 0.234 0.762 0.546 0.555 0.256 0.801 0.487 11 1.255 1.779 0.893 2.201 1.061 1.919 0.928 1.958 12 0.754 0.738 0.958 0.659 0.839 0.702 0.894 0.706 13 0.680 0.491 1.014 0.708 0.697 0.474 1.136 0.678 14 0.895 0.813 1.285 1.328 1.009 0.676 0.836 1.474 15 1.015 0.721 1.248 0.459 1.023 0.728 1.149 0.481 16 1.001 0.930 1.087 0.576 1.032 0.882 1.052 0.670 17 0.907 0.527 1.298 0.530 0.878 0.546 1.348 0.549 18 0.596 0.177 0.679 0.553 0.618 0.353 0.901 0.458 19 1.010 0.414 0.994 0.650 0.707 0.275 0.927 0.664 20 0.737 0.154 1.232 0.947 0.616 0.297 1.253 0.777 21 1.016 0.644 1.552 0.632 1.064 0.732 1.349 0.588 22 1.059 0.510 0.546 0.737 1.574 1.023 0.694 0.779 23 0.914 0.707 0.800 0.552 1.010 0.855 0.877 0.580 24 0.714 0.567 1.111 0.506 0.757 0.509 1.241 0.573 25 0.800 0.358 0.950 0.720 0.814 0.439 0.921 0.623 26 0.718 0.228 1.092 0.502 0.850 0.299 1.002 0.455 27 0.866 1.277 0.917 0.709 1.045 1.151 0.939 0.618 28 0.610 0.611 1.228 0.758 0.716 0.653 1.156 0.764 29 0.741 0.341 0.873 0.975 0.837 0.442 0.928 1.200 30 0.477 0.304 0.716 0.520 0.618 0.252 0.798 0;601 31 0.755 0.606 1.06? 0.692 0.812 0.679 1.231 0.800 32 1.457 2.209 1.039 1.130 1.374 1.896 1.015 0.912 33 0.449 0.247 0.737 0.526 0.374 0.398 0.439 2.641 34 0.776 0.482 0.997 0.512 0.982 0.650 0.796 0.733 35 1.369 1.596 0.971 0.908 1.394 1.351 1.061 0.846 36 0.981 0.951 0.941 1.054 1.111 0.934 1.077 1.121 Calculated from data in Table IV, this Appendix; see formula for calculations in Chapter IV, p. 47. TABLE VU! BASIC LABOUR FORCE RESIDUAL, BY COUNTY AND INDUSTRY, 1951 AND 1961 a 1 9 5 1 1 9 6 1 s lu ' 1 11 8 9 1 10 11 Finance Finance Insurance Insurance and Personal Other and Personal Other 1 County Trade Real Estate Services S ervices N Y (8-ll) b Trade Real Estate Services Services NY(8-11)' 1 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 0.000 0 2 0.000 0.000 0.000 70.286 70 0.000 0.000 0.000 0.000 0 3 21.906 34.357 30.247 0.000 86 0.000 0.000' 27.856 0.000 28 4 0.000 0.000 0.000 2,255.063 2,255 0.000 0.000 82.151 1,264.594 1,347 5 0.000 - 0.000 22.384 231.516 254 0.000 0.000 61.560 135.109 197 6 484.888 0.000 0.000 0.000 484 0.000 0.000 0.000 0.000 0 7 0.000 6.372 156.454 0.000 162 286.418 17.398 206.237 0.000 509 8 0.000 0.000 107.250 0.000 107 188.960 1.850 262.849 0.000 453 9 0.000 0.000 163.724 0.000 164 0.000 0.000 148.918 0.000 149 10 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 0.000 0 11 2,597.441 785.682 0.000 13,953.813 17,336 977.521 1,540.349 0.000 22,377.872 24,894 12 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 0.000 0 13 0.000 0.000 4.062 0.000 4 0.000 0.000 48.016 0.000 48 14 0.000 0.000 206.964 733.621 941 21.741 0.000 0.000 2,087.681 2,110 15 24.613 0.000 182.670 0.000 208 46.551 0.000 114.242 0.000 161 16 3.087 0.000 84.182 0.000 87 75.625 0.000 46.829 0.000 123 17 0.000 0.000 80.356 0.000 80 0.000 0.000 98.439 0.000 98 18 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 0.000 0 19 7.160 0.000 0.000 0.000 7 0.000 0.000 0.000 0.000 0 20 0.000 0.000 31.687 0.000 32 0.000 0.000 40.264 0.000 40 21 18.427 0.000 269.726 0.000 288 81.327 0.000 167.563 0.000 249 22 28.497 0.000 0.000 0.000 28 402.061 ' 1.968 0.000 ' 0.000 404 23 0.000 0.000 0.000 0.000 0 12.469 0.000 0.000 0.000 13 24 0.000 0.000 72.169 0.000 72 0.000 0.000 130.323 0.000 103 25 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 0.000 0 26 0.000 0.000 38.653 0.000 39 0.000 0.000 0.657 0.000 1 27 0.000 34.338 0.000 0.000 34 67.963 27.511 0.000 0.000 96 28 0.000 0.000 145.823 0.000 146 0.000 0.000 111.692 0.000 112 29 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 858.304 858 30 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 0.000 0 31 0.000 0.000 46.919 0.000 . 47 0.000 0.000 174.692 0.000 175 32 1,968.185 531.387. 76.371 780.997 3,356 2,165.728 614.572 33.208 0.000 2,814 33 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 3,976.678 3,977 34 0.000 0.000 0.000 0.000 0 0.000 0.000 0.000 0.000 0 35 1,561.460 266.187 0.000 0.000 1,827 , 2,222.229 243.622 133.900 0.000 2,600 36 0.000 0.000 0.000 179.243 179 381.701 0.000 101.010 750.844 1,234 aSee note on following page describing precise method of calculating basic employment (labour force) residual for this table, b N x (8-11) = Basic Employment estimate for sectors 8 to 11. 86 Note to Table VUI Note: Although not shown, all county by industry employment (labour force) figures were required in the calculation of the basic employment in the four sectors concerned. This is apparent from the ratio used: E i - e i E - e Table VIJI isolates that amount of employment which makes the ratios greater than unity. The calculation were based on the following method. Given the ratio (R), we replace e^  by x ,^ where x^  is the unknown quantity which makes the ratio equal to one. The ratio can be rearranged to read: e. Ei Xi = However, the original term e^  entered into the calculation of e , Ej, E. To each term, therefore, we add x^  and subtract the relevant e^  term. With this adjustment we have ( e - e i + Xi ) (Ei - ei + x ) x _ ( E - e i + x ) which can be reduced to: e. E i + ei (ei - e - Ej) X 1 ~ ~ E + ( e i ~ T - Ei) With Xi determined we can subtract the value of xi from ej and identify the basic residual. This procedure has been used exclusively in Table Yin. 87 TABLE IX BASIC LABOUR FORCE IN THE CONSTRUCTION AND TRANSPORTATION, COMMUNICATION AND UTILITIES SECTORS BY COUNTY, 1951 AND 1961 a 1 9 5 1 1 9 6 1 County N x (6 -7) = ^ ~ 5 ) . (6-7) N X ( 6 - 7 ) = E ^ ) . (6-7) 1 263 374 2 509 714 3 688 915 4 195 255 5 160 237 6 1,730 2,328 7 666 784 8 630 656 9 305 401 10 349 393 11 1,476 1,812 12 473 632 13 307 437 14 428 445 15 729 900 16 828 831 17 265 52 18 275 . 436 19 218 290 20 138 189 21 418 470 22 206 189 23 377 495 24 515 542 25 561 936 26 382 546 27 418 574 28 616 698 29 654 613 30 281 325 31 625 6'62 32 1,026 1,417 33 242 90 34 409 335 35 1,354 1,599 36 797 828 Source : Tables IV and V I , this A p p e n d i x . a N x (6-7)= Basic employment in construction, transportation, communication and u t i l i t i e s . E T ( l - 5 ) = Sum of employment in five sectors of Table-VIII. J- (6-7) = Sum of employment in the two sectors of c o n -struct ion, transportation, communication u t i l i t i e s . N = T o t a l county employment. 88 TABLE X BASIC LABOUR FORCE TOTALS BY INDUSTRY GROUPS AND COUNTY, 1951 AND 1961 County 1 9 5 1 1 - 9 6 1 N x (l-5) Nx(6-7) N x (8-U) N x (Total) N x(l-5) Nx(6-7) N # - U ) N x (Total) 1 2,644 263 0 2,907 3,333 374 0 3,707 2 4,201 509 70 4,780 4,649 714 0 4,391 3 4,904 688 86 5,678 . 5,420 915 28 6,363 4 1,784 195 2,255 4,234 1,852 255 1,347 . 3,454 5 898 160 254 1,312 998 237 197 1,432 6 12,029 1,730 484 14,243 15,736 2,328 0 18,062 7 3,619 666 162 4,447 3,536 784 509 4,829 8 4,675 630 107 5,412 3,804 656 453 .. 4,913 9 2,050 305 164 2,519 2,177 401 149 3,727 10 1,667 349 0 2,016 1,737 393 0 2,130 11 7,906 1,476 17,336 26,718 10,696 1,812 24,894 37,402 12 2,614 473 0 3,087 2,767 632 0 3,399 13 1,677 307 4 1,988 1,848 437 48 2,333 14 2,657 428 941 4,126 2,984 445 2,110 5,539 15 3,950 729 208 4,887 4,136 900 161 5,197 16 5,404 828 87 6,319 4,656 831 123 5,610 17 1,467 265 80 1,812 1,721 52 98 1,871 18 1,082 275 0 1,357 1,138 436 0 1,574 19 1,799 218 7 2,024 2,328 290 0 2,618 20 550 138 32 720 797 189 40 1,026 21 2,331 418 288 3,037 2,425 470 249 3,144 22 1,125 206 28 1,359 809 189 404 1,402 23 3,040 377 0 3,417 2,759 495 13 3,267 24 4,404 515 72 4,991 3,511 542 103 4,156 25 5,724 561 0 6,285 7,317 936 0 8,253 26 2,765 382 39 3,186 2,705 546 1 3,251 27 2,885 418 34 3,337 2,920 574 96 3,490 28 3,565 616 146 4,327 4,071 698 112 4,881 29 4,871 654 0 5,525 3,994 613 858 5,465 30 1,886 281 0 2,167 1,506 325 0 1,831 31 3,891 625 47 4,563 4,098 662 175 4,935 32 4,777 1,026 3,356 9,159 6,826 1,417 2,814 11,057 33 1,248 252 0 1,490 1,007 90 3,977 5,074 34 2,298 409 0 2,707 1,835 335 0 2,170 35 6,405 1,354 1,827 9,586 6,161 1,599 2,600 10,360 36 4,728 797 179 5,704 4,140 828 1,234 6*202 Source: Tables VI, IV and IX, this Appendix. 89 TABLE X I TOTAL LABOUR FORCE, BASIC LABOUR FORCE, AND AVERAGE MULTIPLIER ESTIMATES, BY COUNTY, 1951 AND 1961 County N(1951) Nx(1951) N/Nx(1951) N (1961)' Nx(1961) N/N x ( l 1 4,034 2,907 1.4 5,309 3,707 " 1.4 2 9,392 4,780 2.0 11,692 4,391 2.7 3 12,508 5,678 2.2 15,398 6,363 2.4 4 6,871 4,234 1.6 7,332 3,454 2.1 5 2,872 1,312 2.2 4,15i 1,432 2.8 6 27,302 14,243 1.9 37,360 18,062 2.1 7 9,067 4,447 2.0 11,116 4,829 ' 2.3 8 9,510 5,412 1.8 10,376 4,913 2.1 9 4,763 2,519 1.9 5,458 3,727 1.5 10 3,396 2,016 1.7 3,501 2,130 1.6 11 56,188 26,718 2.1 83,066 37,402 2.2 12 5,544 3,087 1.8 7,374 3,399 2.2 13 3,553 1,988 1.8 4,785 2,333 ; 2.1 14 8,711 4,126 2.1 12,796 5,539 " 2.3 15 8,826 4,887 1.8 10,452 5,197 2.0 16 11,616 6,319 1.8 12,147 5,610 2.2 17 3,221 1,812 1.8 3,844 1,871 2.1 18 2,256 1,357 1.7 3,035 1,574 1.9 19 3,611 2,024 1.8 .4,501 2,618 1.7 20 1,625 720 2.3 2,164 1,026 2.1 21 5,877 3,037 1.9 6,553 3,144 2.1 22 2,544 1,359 1.9 3,617 1,402 2.6 23 5,643 3,417 1.7 6,478 3,267 2.0 24 7,782 4,991 1.6 7,374 4,156 1.8 25 10,557 6,285 1.7 14,614 8,253 1.8 26 5,006 3,186 1.6 5,842 3,251 1.8 27 6,112 .3,337 1.8 7,682 3,490 2.2 28 7,647 4,327 1.8 9,741 4,881 2.0 29 10,357 5,525 1.9 12,348 5,465 2.3 30 3,112 2,167 1.4 3,224 1,831 1.8 31 8,115 4,563 1.8 10,306 4,935 2.1 32 23,457 9,159 2.6 30,684 11,057 2.8 33 2,219 1,490 1.5 7,162 5,074 1.4 34 4,543 2,707 1.7 4,617 2^ 170 2.1 35 22,842 9,586 2.4 29,863 10,360 . 2.9 36 12,884 5,704 2.3 17,811 6,202 2.9 Sources: Tables IV, and X, this Appendix. 90 TABLE X I I BASIC LABOUR FORCE , 1951 AND 1961, AND CHANGES IN BASIC LABOUR FORCE, BY COUNTY County N x (1951 ) N x ( 1961 ) N x (1961 minus 1951) 1 2,907 3 ,707 800 2 4,780 4,391 -389 3 5,678 6,363 685 4 4,234 3,454 -780 5 1,312 1 ,432 120 6 14,243 18,062 3,821 7 4,447 4,829 382 8 5, 412 4,913 -499 9 2,519 3,727 1,208 10 2,016 2 ,1 30 114 11 2 6,718 37,402 10,684 12 3,087 3, 399 312 1 3 1,98 8 2,333 345 14 4, 126 5,539 1,413 15 4, 887 5 ,1 97 310 16 6,319 5,6 10 -709 17 1,812 1,871 54 18 1,357 1 ,574 217 19 2,024 2,6 18 594 20 720 1 ,026 306 21 3,037 3, 144 107 22 1,359 1 ,402 43 2 3 3, 417 3,267 -15 0 24 4, 991 4,156 -835 25 6,285 8,253 1, 908 26 3,186 3,251 65 27 3,337 3,490 153 28 4, 327 4,881 554 29 5 , 525 5 ,465 -60 30 2,167 1,831 -336 31 4,563 4,935 372 32 9,159 11,057 1,898 33 1,490 5 ,074 3,584 34 2,707 2,170 -537 35 9,586 10,360 774 36 5,704 6 ,202 498 S ourc e s Table I X , this A p p e n d i x . 91 TABLE X I I I ESTIMATES OF CHANGES IN TOTAL EMPLOYMENT (N), BASIC EMPLOYMENT (Nx) AND POPULATION DUE TO EMIGRATION (M), 1951 TO 1961, BY COUNTY County N N M 1 1,275 800 - 2 j ,5 96 2 2 , 300 -389 -5 405 3 2 , 890 685 -3j ,420 4 46 1 -780 -2 243 5 1,279 120 -391 6 10,05 8 3,821 -15 , 827 7 2,049 382 -2 , 5 46 8 866 -499 -6 812 9 695 1, 208 -2 581 10 105 114 -2 920 12 1,830 312 -1 , 401 13 1,232 345 -2 092 15 1,626 310 -1 ,771 16 531 -70 9 -6 ,221 17 623 54 -1 ,156 18 779 217 . -1 146 19 890 594 -1 534 20 539 306 -1 ,133 21 676 107 -2 ,670 23 835 -150 -2 ,870 24 -408 -835 -4 844 25 4,057 1,96 8 -8 ,789 26 836 65 -6 , 320 27 2,094 554 -5 ,161 29 1,991 -6 0 -4 ,853 30 112 -336 -3 , 326 31 2,191 372 -6 ,093 34 74 -537 -3 ,967 35 702 774 -4 ,274 Source: Tables XI and XII , this Appendix ; for emigration data see Dominion Bureau of Stat is t ics , Census of Canada : 1961, V o l . V I I , General R e v i e w , Growth of Population in Canad Part I , Catelogue 99-511, Table 2, p . 26. 


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