International Trade and California s Economy: Summary of the Data

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1 Internatonal Trade and Calforna s Economy: Summary of the Data by Professor Dwght M. Jaffee Fsher Center for Real Estate and Urban Economcs Haas School of Busness Unversty of Calforna Berkeley CA Tel: Emal: jaffee@haas.berkeley.edu (Second n a seres of workng papers on the general topcs of Foregn Trade and Calforna s Growth, by Dwght M. Jaffee, Cyntha A. Kroll, Ashok Deo Bardhan, Josh Krschenbaum, and Davd Howe) Workng Paper: February 1998

2 Acknowledgment I want to express thanks to my colleagues on ths project, Cyntha A. Kroll, Ashok Deo Bardhan, Josh Krschenbaum, and Davd Howe, for ther wllngness to share wth me ther detaled knowledge of the trade, employment, and related data for the Unted States and the state of Calforna.

3 Internatonal Trade and Calforna s Economy: Summary of the Data Abstract Ths paper presents most of the prmary data sets that have been assembled for use n the project, Foregn Trade and Calforna s Growth. The data cover the employment, sales, and nternatonal trade (mports and exports) actvtes of all ndustres for both Calforna and the US as a whole. The data are avalable prmarly at the 3-dgt SIC code level, although some 4-dgt SIC code data are also used. Hstorcal data are avalable for at least fve prevous years. The dscusson summarzes the key features of the Calforna economy that are llustrated by the data and descrbes the statstcal algorthms that were developed and appled n order to create a complete and consstent data set. These data are used to carry out statstcal tests of varous hypotheses n other papers of the project.

4 1 Introducton A large data set s needed to determne the effects of nternatonal trade on the Calforna economy. The data should cover at least the employment, sales, and nternatonal trade (mports and exports) of all ndustres for both Calforna and the US as a whole. The data should be avalable at a detaled ndustry level, preferably to the level of 4-dgt SIC codes. The ndustry defntons should be consstent across the employment, sales, and trade data. Hstorcal data should be avalable for fve years or more. Ths paper presents most of the man data sets we have assembled for ths purpose. The dscusson summarzes the key features of the Calforna economy that are llustrated by the data and descrbes the statstcal algorthms that were developed and appled n order to create a complete and consstent data set. These data are used n other papers assocated wth ths project to carry out statstcal tests of varous hypotheses. 2 Employment Data for Calforna and the US To understand the lnks between nternatonal trade and the state economy, we frst need accurate data on employment, sales, and trade flows. Ths s not as smple as t mght frst appear. There are ssues regardng the tmelness, consstency, and completeness of the data even at the US level and especally for Calforna (or other states). In the followng dscusson, we use the computer ndustry as an example to llustrate some of these ssues. The employment data we have assembled cover Calforna and the US as a whole, for manufacturng and servce ndustres (to 3-dgt, and n some cases to 4-dgt, SIC codes), gong back annually to at least We have used three prmary sources of employment data, each wth ts own advantages and dsadvantages. 1

5 The Annual Survey of Manufacturers (ASM) (from the Bureau of the Census) Ths source covers only the manufacturng sector (SIC codes 20 to 39, to the 3-dgt level), but s also the only source to provde consstent data on sales for the same SIC codes. In addton, every 5 years, a complete Census of Manufacturers s taken of all manufacturng frms, the most recent year beng The Census s a prmary source of 4-dgt SIC code data for employment and sales. Although the annual survey uses only a partal sample, the data are calbrated wth the most recent census data n order to provde estmates for the unverse of all manufacturng frms. In partcular, data for Calforna and the US from 1987 to 1994 are now avalable on a data CD, and new and revsed data for 1994 and 1995 have just been released by the Department of Commerce. Some ASM data are not released at the Calforna state level, n order to protect the confdentalty of ndvdual frms. However, we have developed a statstcal methodology to fll n the gaps (see Appendx 1). We use the ASM data to analyze employment n Calforna s manufacturng sector. The Calforna ASM data are shown n Tables 1A and 1.B for 2-dgt and 3-dgt SIC codes respectvely. The cells shown n bold represent the cases where our algorthm has been used to compute mssng values, as descrbed n Appendx 1. The lne labeled auxlares, near the bottom of each table, refers to manufacturng employment n auxlary servce centers (such as research labs or garages) that cannot be dentfed wth a sngle SIC code. Country Busness Patterns (CBP) (from the Bureau of the Census) Ths source provdes the most complete set of employment data for both the manufacturng and servce sectors, often to the level of 4-dgt SIC codes, for the US and Calforna. The data are smlar to the Annual Survey of Manufacturers for the manufacturng 2

6 sector and to the Current Establshment Survey (see below) for the servce sectors. The major drawback s that data are currently avalable only to Table 2 shows the US and Calforna CBP employment data to the 4-dgt level for computer manufacturng (SIC code 357) and computer servces (SIC code 737). It s noteworthy that employment n the computer servces ndustry exceeds employment n the computer manufacturng ndustry by a rato of more than 4 to 1 at the US level and almost 3 to 1 at the Calforna level. The Current Establshment Survey (CES) (from the Bureau of Labor Statstcs and Calforna s Economc Development Department) For the US, ths source covers both the manufacturng sector and servce sector (SIC codes 70 to 89, to at least the 3-dgt level). However, at the Calforna state level, only a few seres are avalable at the 3-dgt level. Annual data are currently avalable through Table 3 provdes a comparson of the CES, CBP, and ASM data for the manufacturng and servce ndustres, ncludng computer manufacturng (SIC code 357) and computer servces (SIC code 737). In most cases (total US manufacturng s an example), the values from the 3 sources are very smlar. The dfferences that do exst are due to alternatve defntons, collecton methods, and revsons. A very large dfference occurs, however, n the computer manufacturng sector (SIC code 357), where the CES data are substantally hgher for both the US and Calforna. Ths s due to dfferent SIC code defntons, and to the fact that the CES data nclude auxlary employment n the SIC code. For the computer servce ndustry (SIC code 737), there are no data from the Annual Survey of Manufacturers by defnton; and no Calforna data are avalable from the CES. 3

7 Locaton Quotents for Calforna Employment The employment structure of Calforna and the US can be compared wth locaton quotents for each ndustry (SIC code). The locaton quotent s the rato between the proporton of Calforna s employment n SIC code and the proporton of US employment n the same SIC code. The locaton quotent for ndustry s thus defned as: (1) LQ = E E CA US E E CA T US T where: E = employment, CA = Calforna, US = Unted States = SIC code, T = total for all SIC codes. The locaton quotents for Calforna s manufacturng employment (based on the ASM data) are shown n Tables 4.A and 4.B for 2-dgt and 3-dgt SIC codes respectvely. Startng wth the 2-dgt data n Table 4.A, most of the values are relatvely stable over the years, although some trends are evdent (such as the growth of Apparel (code 23) and the declne n Transportaton equpment, (code 37)). The rank order based on the 1995 value s shown n column 2. Scentfc nstruments (code 38), Apparel (code 23), and Electroncs (code 36) are the most mportant ndustres, each wth an employment share more than 50% above the US share. Tobacco (code 21), Textle mlls (code 22), and Prmary metals (code 33) are the least mportant ndustres, each wth an employment share less than 50% of the US share. In partcular, there appears to be no tobacco manufacturng (code 21) actvty n Calforna. 4

8 The 3-dgt locaton quotents, n Table 4.B, follow the same pattern as the 2-dgt aggregates, although there can be substantal varaton wthn each group of 3-dgt codes. For example, the 2-dgt locaton quotent for food products (code 20) n 1995 s just slghtly above 1.0, whle the 3-dgt locaton quotent for Meat products (code 201) s only 0.40 and the locaton quotent for Canned food products (code 203) s Smlarly, the 2-dgt locaton quotent for transportaton equpment (code 37) s about 1.0, but the quotent for Mssles and space vehcles (code 376) s over 4.7, whle the quotent for motor vehcles (code 371) s Table 4.C provdes a separate lstng of the 3-dgt locaton quotent data based on the 1995 rank. Ths shows, for example, that Mssles and space vehcles (code 376) has the hghest locaton quotent. It s nstructve and perhaps surprsng that Luggage (code 316) and Women s outerwear (code 233) also have very hgh locaton quotents. At the other end, several Tobacco and Fabrc mll codes show lttle or no Calforna manufacturng employment. The locaton quotents for Calforna s computer manufacturng employment and servce employment at the 4-dgt level (based on the CBP data) are shown n Table 5. The overall locaton quotent n 1994 for Calforna s computer manufacturng (SIC 357) s 2.63, and Calforna s most ntensve n computers, storage devces, and termnals. For computer servces (SIC code 737), the overall locaton quotent n 1994 for Calforna s 1.36, and Calforna s most ntensve n prepackaged software. 3 Sales Data Sales data are avalable for manufacturng frms from the Annual Survey of Manufacturers (ASM), the same source just descrbed for employment. We have constructed an algorthm that allocates values for mssng Calforna data, n the same manner descrbed for the 5

9 employment data (see Appendx 1). The assocated Census of Manufacturers, taken every 5 years, s a source of 4-dgt SIC code sales data that are otherwse not avalable. The Calforna data for sales are shown for Tables 6.A and 6.B for 2-dgt and 3-dgt SIC codes respectvely. These data are formally referred to as the value of shpments n the ASM reports and are measured n mllon of current dollars. These shpments nclude reshpments of the same goods between dfferent producers, and therefore wll exceed, to an unknown amount, the ndustry-level data for value added that are provded n the state-level natonal ncome and product accounts. The cells shown n bold represent the cases where our algorthm has been used to compute mssng values, as descrbed n Appendx 1. Sales data are avalable for servce frms, but only at the US level, from the Annual Survey of Servces (ASS), a new survey from the Department of Commerce. The data are llustrated n Table 7. Ths table shows that natonwde sales n 1995 for computer servces (SIC code 737) were over $152 bllon. Ths s almost 5 tmes as large as the sales n 1995 for computer manufacturng (SIC code 357), shown n Table 5.B. 4 Internatonal Trade Data For the Unted States, detaled nternatonal trade data (mports and exports) are avalable for manufactured goods, whle some aggregated data are avalable for traded servces. For Calforna, only derved data are avalable for goods exports and no data are avalable for ether mported goods or for traded servces. In ths secton, we frst descrbe the avalable nternatonal trade data and then descrbe a methodology that could be appled to generate estmates of Calforna s mported goods. 6

10 Unted States Internatonal Trade n Manufactured Goods US nternatonal trade data for 2-dgt and 3-dgt SIC codes are currently beng revsed by the Department of Commerce, but prelmnary estmates are avalable. Custom s Reports are the basc source of all US trade data for goods. These reports, however, do not classfy US nternatonal trade data by ndustry wth the same SIC codes that are used for the Annual Survey of Manufacturers. Therefore, there s the need to harmonze the ndustry codng. Ths s beng carred out separately by the Internatonal Trade Admnstraton and by a project at the Natonal Bureau of Economc Research headed by Professor Rob Feenstra. We focus here on the more recent data, released by the Internatonal Trade Admnstraton, whch classfes mports and exports by 2-dgt and 3-dgt SIC codes from 1989 to Internatonal Trade Shares for the US The trade share for a SIC code s defned as the amount of nternatonal trade (exports, mports, or net exports) relatve to the net domestc supply of the same good. In partcular, the gross export (GXS), mport (GIS), and net export (NXS) shares for ndustry are wrtten as: (2a) (2b) (2c) X GXS = S + I X I GIS = S + I X X I NXS = S + I X where: GXS = export share for SIC code, GIS = mport share for SIC code, NXS = net export share for SIC code, I = mports for code, X = exports for code, S = domestc shpments for code. 7

11 In each equaton, the denomnator s the net domestc supply n ndustry, consstng of domestc shpments plus mports mnus exports. The export (GXS) and mport (GIS) shares represent the gross level of trade, whereas the net export share (NXS) shows the net effect of exports mnus mports. In many cases, the postve mpacts of exports are exactly offset by the negatve effects of mports, so that the net export share s the best (and smplest) measure to use when evaluatng the effects of nternatonal trade on the economy. When ths s not true, however, t wll be preferable to use the gross trade shares. Tables 8.A and 8.B show gross export shares at the 2-dgt and 3-dgt SIC code levels respectvely. Tables 9.A and 9.B show gross mport shares at the 2-dgt and 3-dgt SIC code levels respectvely. Table 8.A shows that SIC codes 35 to 38 represent 4 of the 5 hghest export shares for the US. At the same tme, Table 9.A shows that SIC codes 35 to 38 also represent 4 of the 7 hghest mport shares for the US. Ths reflects the ntensve nternatonal trade patterns on both the export and mport sdes for the hgh tech sectors, whch s why the net export shares are often a more useful measure for evaluatng the effect of nternatonal trade on the economy. Net export shares for the US from 1989 to 1995 are shown n Tables 10.A and 10.B for 2- dgt and 3-dgt SIC codes respectvely. Startng wth the 2-dgt codes, the negatve values n the last row of Table 10.A show that, for all US manufacturng, there has been a contnung trade defct (mports exceedng exports). In 1995, the trade defct for manufactured goods represented about 3.4% of US domestc demand for all manufactured goods. Among the 2-dgt ndustres, only 5 (food, tobacco, prntng, chemcals, and nstruments) had a postve trade balance n Not surprsngly, the US had the largest trade defcts for apparel (SIC 23) and leather (SIC 31). Perhaps more surprsngly, the trade defct n Electroncs (SIC 36) represented almost 8% of US demand for these products. 8

12 Table 10.C shows the net export shares for 3-dgt SIC codes ranked from the hghest (net exports) to the lowest (net mports). The top US export ndustres are Ordnance, Arcraft engnes, and Cgarettes. The major US mport ndustres, wth mports exceedng 25% of US demand, are apparel, audo and vdeo equpment, footwear, leather, and luggage. Unted States Internatonal Trade n Servces The US trade statstcs for servces are much less detaled than for manufacturng. The top half of Table 11 shows the most detaled trade statstcs that are avalable for servces. The secton wth net exports shows that the US mantans a large trade surplus (postve net exports) n all the shown categores of servces. Ths secton also shows data for the net ncome derved on foregn drect nvestment n the computer manufacturng and computer servces ndustres. The mportance for the Calforna economy of the large trade surplus n travel servces has been dscussed n an earler study of the Fsher Center for Real Estate and Urban Economcs. 1 The net export secton of Table 11 shows that the trade surplus n computer servces s lkely to be equally mportant. Furthermore, the large trade surplus n foregn drect nvestment ncome, and n that part of royaltes that could be attrbuted to computer software and servce frms, are of the same order of magntude as the drect net exports of computer servces. The bottom part of Table 11 shows the export, mport, and net trade shares for busness servces and the computer servces. These shares are based on the same prncple used above to compute the trade shares of manufactured goods (see equaton 2 above). Although the values for the net export shares are all postve, they represent a relatvely small share of total US domestc sales for servces n the busness and computer felds. 1 See Ashok Bardhan, Cyntha Kroll and Dwght Jaffee, The Growng Role of Foregn Trade n Calforna s Economy, Fsher Center for Real Estate and Urban Economcs, Workng Paper No

13 Calforna Trade Data Custom s Reports can be the source for Calforna export data, snce the custom s forms requre the pont of orgn of US exports. However, the data must be adjusted for varous techncal problems, ncludng mssng entres and trans-shpment. The federal government has not done ths, but adjusted values are avalable from the Mser Project at the Unversty of Massachusetts, although only at the level of 2-dgt SIC codes. In contrast, Custom s Reports cannot readly be used to determne Calforna mports because pont of destnaton nformaton s not collected on US mports. Port data provde another possble source for Calforna trade data (both mports and exports). 2 The state of Calforna has 3 customs dstrcts: Los Angeles-Long Beach, Oakland- San Francsco, and San Dego, whch together cover all ponts of entry and ext to and from the state, connectng t to foregn destnatons. Several ponts n the state of Nevada are also ncluded n these dstrcts. Snce foregn trade data are not avalable by state n any great detal, the port data for a state are often used as a proxy for the state s actual trade fgures. However, ths approach obvously overestmates both the mports and the exports of a coastal/lttoral state such as Calforna. For example, a good part of the trade wth Asa of the 18 states wth no customs dstrcts s lkely to pass through Calforna ports. For the regresson tests n Bardhan and Howe (1998) 3, whch requre only the changes n varables (not the levels), we use the port data as proxy for Calforna s trade data. We ntend, however, to use a modfed verson of Calforna s port data n our future work, based on the followng steps: 2 Ashok Bardhan had the prmary responsblty for the port data dscussed n ths and the followng paragraph. 3 See Bardhan, Ashok Deo and Davd K. Howe, Transnatonal Socal Networks, Transportaton Costs, and the Geographc Dstrbuton of Calforna s Exports, Workng Paper Fsher Center for Real Estate and Urban Economcs, Unversty of Calforna, Berkele y,

14 1) We wll use the Mser data for Calforna exports (X m ), and the port data for Calforna exports (X c ), to create the separatng rato SR, SR = X m /X c. 2) We wll then multply the mports through Calforna customs dstrcts by SR to provde estmates of Calforna mports. Ths assumes that the rato between the Mser and the port export estmates apples as well on the mport sde. Ths requres that the geographc pattern of exports and mports be smlar, and that the trade of other states not be growng at a rate dramatcally dfferently from that of Calforna. We may also adjust the mport fgures for changes n the US share of mports from Asa. A Proposed Method for Drectly Estmatng Internatonal Goods Imports for Calforna Our project has developed another methodology that could construct Calforna mport (and export) data from the hghly detaled nput/output data that are produced by the Census of Manufacturers. We conclude ths paper wth a summary descrpton of ths methodology. The basc prncple of the proposed method s that Calforna s share of total US mports for each commodty should reflect Calforna s share of the actvtes that create the demand for that commodty. In partcular, goods mported to the Unted States can be separated nto two broad classes: goods mported to meet fnal demand (for consumpton, captal nvestment, or government use) and goods mported as ntermedate products n a producton process. The followng descrbes some of the detals of ths proposed estmaton process. A Basc Identty We start wth a basc dentty for each ndustry (SIC code ) at the natonal level: ( 3) F + U = P + M X, = 1, 2... I, where F = Fnal demand (C+I+G) for good (from natonal ncome accounts), U = Input demand (use of good as ntermedate nput n all ndustres (ncludng )). P = Producton of good, M = Imports of good, X = Exports of good. 11

15 up, C = A,1,2. I j j j The left hand sde of equaton [3] s the net domestc uses of good and the rght hand sde s ts net domestc sources. Data for the US are avalable for all the seres n equaton [3], although the fnal demand data avalable from the Natonal Income Accounts (NIA) are more aggregated than the SIC code data avalable for the other varables n equaton [3]. It s not feasble to apply equaton [3] to Calforna, because the trade varables M and X nvolve trade between Calforna and other states, as well as nternatonal trade. We can compute Calforna mports, however, usng Calforna data for producton and exports, f we assume that the rato of Calforna mports to US mports for a partcular good equals the rato of Calforna demand to US demand for that good. The steps are descrbed as follows. Steps to Compute Calforna Imports Frst, compute the amount of ntermedate nputs used n Calforna by applyng an nput/output (I/O) table to Calforna s producton levels. 4 Let u j be the amount of nput used n the producton of $1 of good j, based on the I/O table. The total amount of nput requred for Calforna producton s then: CA CA ( 4) U = ujpj, = 1, 2,... I. j Next, compute the amount of fnal demand n Calforna by usng the Natonal Income Accounts (NIA) fnal demand data for Calforna and the US to allocate Calforna s share of US fnal demand across SIC codes. Specfcally, let CA US N / j be Calforna s share of US fnal demand for group j based on the NIA data for Calforna and the Unted States: CA N CA/ US j ( 5) N j =, j = 1,2,... J. US N j 4 It would be best, of course, to apply the Calforna I/O table, except t s released much later than the US table. 12

16 Calforna s fnal demand can be translated from the J NIA categores to the larger number of I SIC codes by applyng Calforna s NIA code shares to the US SIC code aggregates. Specfcally, for each NIA group j (j = 1, 2, J), determne all the assocated SIC codes that are components of group j. We can then compute the fnal demand by SIC codes: F CA F US N N CA j ( 6) = *, foreachsic code assocatedwth NIA, j = 1,2... J US j The last step s to compute the Calforna mports. To do ths, for each SIC code, we would apply the Calforna share of total demand (fnal demand plus ntermedate nputs) to US mports: ( 7) M CA = F F CA US + + U U CA US M US Extensons of the Procedure The above procedure assumes that the amount of Calforna mports depends only on total demand, and, n partcular, nether on the nearness of Calforna to the exportng country nor on the level of Calforna producton of the good. In fact, one mght expect Calforna to mport more goods, all else the same, when the foregn producer s relatvely close. Smlarly, Calforna mght mport less of a gven good, all else the same, f Calforna s tself a major producer of the good. To mplement these refnements, we would have to measure and calbrate the lkely magntude of the effects. Ths would requre that we combne our data set of US demand, producton, mports, and exports by SIC code wth worldwde producton of the same goods. Ths could be based on a seres of statstcal tests, ncludng the followng two. 13

17 Frst, we could run a cross-secton regresson that determnes the rato of US net trade to US demand for each good as a functon of the US share of total world producton of that good. Presumably, we would fnd that US net mports relatve to demand are less for those goods where the US s an mportant nternatonal producer. Ths relatonshp could then be appled to Calforna s net mports as a functon of ts share of world or US trade n each good. Second, we could run a cross-secton regresson that determnes the rato of US net trade to US demand for each good as a functon of the proxmty of the world s largest producer of that good. Presumably, we would fnd that US net mports relatve to demand are greater for those goods where the world s largest producer s nearby. Ths relatonshp could then be appled to Calforna net mports as a functon of the proxmty of world producers. Data lmtatons and tme constrants wthn the present study have precluded carryng out the computatons of Calforna mports descrbed n ths secton. We hope to do so, however, n future work. 14

18 Appendx 1 Data from the Annual Survey of Manufacturers at the state level are not publcly released when that would dsclose values for ndvdual frms. For the state of Calforna, over the perod 1987 to 1995, ths affected 2-dgt SIC codes 22 and 31 and a number of 3-dgt codes. 5 However, the Department of Commerce does nclude the concealed values n ts larger aggregates. For example, the aggregate manufacturng data for Calforna nclude the values for the SIC codes 22 and 31, even though these values are not dsclosed at the 2-dgt level. Smlarly, the 2-dgt code data nclude the concealed values for the 3-dgt sectors. Consequently, t s possble to create statstcal algorthms to estmate the concealed values. The values that had to be estmated to complete the data matrx for the Calforna manufacturng sector between 1987 and 1995 are shown n Tables 1 and 6 for employment and sales respectvely; the A tables refer to 2-dgt SIC codes and the B tables refer to 3-dgt SIC codes. All the cells shown n bold were concealed n the offcal statstcal releases. The specfc values shown n these cells have been computed by the followng statstcal algorthms (the same algorthm was appled to both the employment and sales data): 1) Values for SIC codes 22 and 31 between 1987 and In each year, the dfference between the state total (called ACT) and the sum over the dsclosed 2-dgt codes (called SUM) was computed. The dfference (ACT-SUM) for each year was then allocated between codes 22 and 31 based on the relatve sze of these two codes n 1992 (the frst year for whch full dsclosure was avalable). For example, n 1987, the employment amount to be allocated was 23,000 (=ACT-SUM), 70% of whch was placed n code 22 and 20% of whch was placed n code The 3-dgt SIC codes are 221, 222, 223, 224, 226, 228, 229, 231, 237, 261, 262, 301, 302, 311, 313, 314, 315, 316, 317, 319, 325, 328, 333, 339, 373, 374, 375, 385, 387, 391, and

19 2) 3-Dgt SIC codes n The year 1992 has the fewest dsclosure problems because that was a complete census (rather than a survey) year. In partcular, as shown n Tables 1B and 3B, there were undsclosed values n only 10 3-dgt SIC codes, whch corresponded to 6 dfferent 2-dgt SIC codes. 6 For each 2-dgt code, the dfference was computed between the complete count for the 2-dgt code (called ACT) and the sum over the correspondng 3-dgt codes for whch data were avalable (called SUM). The dfference ACT-SUM was then allocated across the 3-dgt codes for whch values had not been dsclosed: a) For the 2-dgt SIC codes 22 and 31, only a sngle 3-dgt code was mssng, so the mssng value was set drectly equal to ACT-SUM. b) For each of the other 2-dgt codes, data were not dsclosed for 2 of the 3-dgt codes, so the amount of ACT-SUM had to be allocated across the two 3-dgt codes. For SIC codes 26, 30, and 37, the allocaton was based on the nearest date for whch actual 3-dgt level data were avalable. For example, n Table 1B for code 262, there was actual employment of 2,000 n The amount of ACT- SUM n that year was 2,300. Therefore, t could be deduced that the mssng value for code 261 n 1995 had to be 300. The rato of codes 261 and 262 n 1995 was then used to allocate the amount of ACT-SUM n all other years of mssng data. c) For codes 231 and 237, data were not dsclosed for any year n the sample. Therefore, the amount ACT-SUM for code 23 was dstrbuted between 231 and 237 based on the ratos of 231 and 237 at the U.S. level for each year. 6 The SIC codes were 223, 231, 237, 261, 262, 301, 302, 315, 374, and

20 3-Dgt Codes n Years Other than The estmaton of mssng values for all the years other than 1992 was carred out n a manner smlar to that just descrbed for In partcular, for each year, the dfference was computed between the complete count for the 2-dgt code (called ACT) and the sum over the correspondng 3-dgt codes for whch data were avalable (called SUM). The dfference ACT-SUM was then allocated across the 3-dgt codes for whch values had not been dsclosed. The algorthm used the same 3 subcases, a, b, and c, just descrbed for In all cases, the allocaton among the 3-dgt codes was based on the 1992 values. For example, for codes 277 and 278, for each year 1987 to 1991, the amount ACT-SUM was allocated between 277 and 278 based on the relatve values for these codes n

International Trade and California Employment: Some Statistical Tests

International Trade and California Employment: Some Statistical Tests Internatonal Trade and Calforna Employment: Some Statstcal Tests Professor Dwght M. Jaffee Fsher Center for Real Estate and Urban Economcs Haas School of Busness Unversty of Calforna Berkeley CA 94720-1900

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