Effect on firm performance of support from Innovation Norway

Transcription

1 Rapporter Reports 2015/35 Ådne Cappelen, Erk Fjærl, Dana Iancu and Arvd Raknerud Effect on frm performance of support from Innovaton Norway

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3 Reports 2015/35 Ådne Cappelen, Erk Fjærl, Dana Iancu and Arvd Raknerud Effect on frm performance of support from Innovaton Norway Statstsk sentralbyrå Statstcs Norway Oslo Kongsvnger

4 Reports In ths seres, analyses and annotated statstcal results are publshed from varous surveys. Surveys nclude sample surveys, censuses and regster-based surveys. Statstcs Norway When usng materal from ths publcaton, Statstcs Norway shall be quoted as the source. Publshed August 2015 ISBN (prnted) ISBN (electronc) ISSN Prnt: Statstcs Norway Symbols n tables Symbol Category not applcable. Data not avalable.. Data not yet avalable Not for publcaton : Nl - Less than 0.5 of unt employed 0 Less than 0.05 of unt employed 0.0 Provsonal or prelmnary fgure * Break n the homogenety of a vertcal seres Break n the homogenety of a horzontal seres Decmal punctuaton mark.

5 Reports 2015/35 Effect on frm performance of support from Innovaton Norway Preface Innovaton Norway (IN) s a government agency that ams to promote frm growth through nnovaton programs, regonal support and other ndustral polces. In ths report we analyse effects of government support to frms from Innovaton Norway. The ndcators and results presented here are used by IN as a tool n ther process of goal settng and decson makng. The project s fnanced by Innovaton Norway. We would lke to thank Pål Aslak Hungnes and Sgrd Gåsednes, n partcular, for a wealth of detaled nformaton about IN and many valuable suggestons for mprovng our analyses. We also acknowledege many useful comments from Kjetl Telle and Øvnd Ant Nlsen. Statstsk sentralbyrå, 13. august Torbjørn Hægeland Statstcs Norway 3

6 Effect on frm performance of support from Innovaton Norway Reports 2015/35 Abstract In ths report we study possble effects of government support to frms from Innovaton Norway (IN) a government agency that ams to promote frm growth through nnovaton programs, regonal support and other ndustral polces. We document a tool for management by objectves/management by results (MBO/MBR) for IN, ntended for repeated use n the process of goal settng and decson makng. We compare frms that receved support from IN durng (the treated frms) wth a comparson group of non-treated frms that we have matched accordng to a set of ndvdual frm characterstcs. We estmate average treatment effects of partcpaton (average treatment effects on the treated) n four types of IN-programs (assgnments): nnovaton, regonal, lendng and nnovaton cluster. Our evaluaton context s not an expermental one and one should therefore not conclude that our fndngs necessarly represent causal effects. Nonetheless, we have taken nto account selecton effects due to fxed frm effects, whch are elmnated through dfferencng, and observable frm characterstcs, whch are controlled for through propensty score matchng. Average treatment effects on the treated frms are measured as dfferences n average annual growth rates between treated and matchng frms (matched dfference-n-dfferences) n the frst 3-year perod followng the year of assgnment to treatment by IN. For the nnovaton and regonal development assgnments we fnd sgnfcant postve effects wth regard to the performance ndcator varables number of employees, sales revenues and value added, but much smaller effects wth regard to labor productvty and returns to total assets. On the other hand, we fnd no evdence that the commercal and low-rsk lendng assgnment enhances frm performance. Moreover, we fnd no evdence that fnancal support to start-up frms mproves survval probabltes of the clent frms compared to the matchng frms, measured fve and ten years after start-up. Partcpaton n IN-supported clusters leads to hgher sales and employment n frms durng the mmedate perod after enrollment. 4 Statstcs Norway

7 Reports 2015/35 Effect on frm performance of support from Innovaton Norway Sammendrag I denne rapporten studerer v potenselle vrknnger av offentlg støtte tl enkeltbedrfter gjennom Innovasjon Norge (IN) - en offentlg etat som har som mål å bdra tl vekst bedrfter gjennom nnovasjonsprogrammer, regonal støtte og annen nærngsutvklngspoltkk. V dokumenterer vårt verktøy for mål- og resultatstyrng (MRS) for IN, ment for gjentatt bruk nnen deres målstyrngsprosesser. V sammenlgner foretak som fkk støtte fra IN løpet av («behandlngsgruppen») med en sammenlgnngsgruppe av foretak som kke fkk slk støtte, og som v har matchet henhold tl et sett av ndvduelle kjennetegn målt det første hele drftsåret v observerer foretaket. V beregner gjennomsnttlge effekter av deltakelse IN-programmer basert på fast-effekt modellerng (faste bedrftsspesfkke effekter) og propensty-score matchng. Gjennomsnttlg effekt er målt som forskjeller gjennomsnttlg årlg vekstrater mellom behandlede og matchende foretak den første tre-års peroden etter tldelngsdato for IN-støtte (årlg mervekst forhold tl kontrollgruppen). Mht. støtte fra nnovasjonsprogrammet og det regonale utvklngsprogrammet fnner v sgnfkante postve effekter på ndkatorene antall ansatte, omsetnng og verdskapng, men mye svakere effekter på arbedsproduktvtet og totalkaptalrentabltet. For programmet for kommerselle, kke-subsderte lavrskolån fnner v generelt små effekter, og ngen av de estmerte effektene er robuste overfor fjernng av ekstreme datapunkter eller endrng matchng-prosedyre. V fnner ngen støtte for hypotesen at programmene rettet mot nyetablerte foretak/gründere øker overlevelsessannsynlghetene for IN klenter som får slk støtte sammenlgnet med kontrollgruppen, målt fem og t år etter etablerng. For cluster-programmet fnner v postve effekter på vekst omsetnng og antall ansatte den første peroden etter at foretaket er bltt med clusteret. Statstcs Norway 5

8 Effect on frm performance of support from Innovaton Norway Reports 2015/35 Contents Preface... 3 Abstract... 4 Sammendrag Introducton Innovaton Norway polcy areas and actvtes MBO/MBR n Innovaton Norway How does IN affect frms? Measurng effects: Lmtatons and challenges Data Choce of evaluaton method The classcal methods: DID and matchng Implementng Matched DID (MDID) to analyze the effect of support from IN Econometrc model Estmaton of ATT Results The balancng propertes of matchng Estmaton results Concluson References Appendx: Supplementary results Lst of fgures Lst of tables Statstcs Norway

9 Reports 2015/35 Effect on frm performance of support from Innovaton Norway 1. Introducton In 2013 Statstcs Norway s Research Department receved a commsson from the publc agency Innovaton Norway (IN) to develop a tool for management by objectves/management by results (MBO/MBR), ntended for repeated use n the current process of goal settng and decson makng n ther organzaton. Ths report documents the procedures we have developed and provdes some emprcal results. IN s prmary task s to promote ndustral development. Our commsson ncludes the operatonalzaton of effect ndcators, choce of emprcal methodology, establshng datasets and carryng out emprcal effect analyses. The new MBO/MBR system ncludes estmaton of publc beneft effects, whch shall serve as a bass for owners and clents n the management of IN. Our task nvolves some degree of research and nnovaton Innovaton Norway polcy areas and actvtes IN s a government agency that ams at promotng nnovaton and proftable busness development n Norway. The Mnstry of Trade, Industry and Fsheres owns 51 per cent of IN and the 19 countes own the remanng 49 per cent. IN s the Norwegan government's offcal trade representatve abroad. Besdes havng an offce n every county n Norway, the organzaton also runs 35 offces abroad. IN provdes Norwegan frms wth an extensve set of busness support systems. IN offers loans, grants, guarantees and equty to frms. IN also provdes advsory servces, promotonal servces and network servces that stmulate nteracton between enterprses and varous knowledge nsttutons. The marketng of Norway as a tourst destnaton s also consdered an mportant task. After a reorganzaton of several government enttes more than a decade ago, IN also has the task of securng development n rural areas usng funds from (manly) the Mnstry of Local Government and Modernsaton. IN has a wde varety of polcy nstruments at ts dsposal. They nclude grants and advsory servces to entrepreneurs. More establshed frms may also receve advsory servces from IN regardng market orentaton, for example n order to mprove ther export performance. IN operates busness networks where enterprses that wsh to enter nto strategc cooperaton wth other frms can receve support to establsh and admnster meetng places and other collaboratve measures. Three dfferent cluster programs are supported by IN: The ARENA program, the Norwegan Centres of Expertse (NCE), and the Global Centres of Expertse (GCE). IN has a number of nnovaton programs where frms may apply for grants or loans for busness developments. These programs can be of a farly general nature, or can be desgned for a specfc ndustry or branch but may also have a specfc regonal focus. IN-fnanced actvtes totaled nearly seven bllon NOK n A lttle more than half of ths amount conssted of low rsk loans manly gong to agrculture and fsheres, of whch the Mnstry of Trade, Industry and Fsheres fnanced 2.5 bllon NOK. Funds fnancng nnovaton actvtes ncludng hgh rsk loans fnanced by the Mnstry of Trade, Industry and Fsheres (NFD) amounted to 1.2 bllon NOK n The Mnstry of Local Government and Modernsaton (KMD) fnances a number of polcy nstruments targetng regonal development admnstered by IN. In 2013 these polcy nstruments amounted to around one bllon NOK. IN also handles some nnovaton programs relatng to forestry fnanced by the Mnstry of Agrculture and Food, whch amounted to nearly 40 mllon NOK n All n all the polces admnstered by IN that we wll study n ths report amounted to 4.7 bllon NOK n These polcy areas are 1 1 USD 8.2 NOK (August 2015). Statstcs Norway 7

10 Effect on frm performance of support from Innovaton Norway Reports 2015/35 specfed as four separate polcy assgnments from the government to IN. These are the followng: The nnovaton assgnment (NFD) The regonal assgnment (KMD) The lendng assgnment (NFD, ncludng low-rsk loans to the agrcultural sector) The agrcultural assgnment (Mnstry of Agrculture and Food) The agrcultural assgnment s not ncluded n ths study because few of the recpents are lmted lablty companes wth publcly avalable accounts. In addton we study effects of three ndustry cluster programs, fnanced by both NFD and KMD. The cluster programs are defned as a ffth assgnment and analyzed n a smlar way as the other assgnments MBO/MBR n Innovaton Norway All publc agences are requred by law to mplement some form of MBO/MBR. An MBO/MBR process nvolves defnton of objectves, montorng of results and evaluatng performance. Our work deals wth the latter two, as the objectves of IN are predefned by the organzaton tself and ts owners (NHD, 2013). IN and ts owners have also consdered relevant effect ndcators for each IN objectve. Our operatonalzaton of effect ndcators s based on these suggested ndcators, wth some necessary modfcatons. Innovaton Norway s msson s to be the Natonal and the Regonal Governments polcy nstrument for value-creatng busness development across Norway. The man goal s that IN shall trgger off ndustral and commercal development that s proftable from both a prvate and socoeconomc perspectve, and to release the busness opportuntes of all regons of Norway. The secondary goals are: 1. More successful entrepreneurs 2. More enterprses wth a capacty for growth 3. More nnovatve busness clusters. The suggested effect ndcators for each of the secondary goals are, respectvely: 1. Survval rates for new frms and turnover growth (share of new frms wth growth n turnover) 2. Turnover growth, productvty growth (share of frms wth productvty growth) and growth n proftablty (share of proftable frms) 3. Turnover growth and growth n proftablty. Snce dentcal ndcators are assgned to dfferent objectves, the suggested ndcators cannot dentfy each specfc goal. Of course, ths may also reflect that the dstncton between the three secondary goals s dffcult: What do we mean by successful entrepreneurs? Shouldn t successful entrepreneurshp mean more enterprses wth a capacty for growth? And wouldn t more nnovatve busness clusters help entrepreneurs and stmulate growth? The three secondary goals reflect three target groups, whch may be treated somewhat dfferently. Dfferent theoretcally approaches, such as theores of entrepreneural behavor, market falure due to asymmetrc nformaton between the busness and the fnancer and new nnovaton theores and system falure, are all relevant. Dfferent needs for resources such as fnance, knowledge and networks, requre dfferent nstruments and proportonng to release dfferent behavor. 8 Statstcs Norway

11 Reports 2015/35 Effect on frm performance of support from Innovaton Norway Our framework mples testng of the estmated treatment effects of three dfferent assgnments lsted above, and the nnovaton cluster program as the fourth usng the same set of ndcators as measures of success for all assgnments and program. These assgnments are defned by ther source of fnancng (whch s collected from the owners and prncpals of IN the government and the 19 county authortes wth ther own objectves) and are therefore classfed accordng to the prncpal objectve of the polcy assgnment. Our analyss s restrcted to partcpaton n a scheme or a program (means); we do not consder the amount of grants or loans gven. Correspondng to the polcy assgnments mentoned above, we study the effects of four assgnments wth dfferent but overlappng objectves and selecton crtera: The nnovaton assgnment (henceforth I-assgnment) The nnovaton assgnment comprses means such as grants, nnovaton loans/venture captal loans/rsk loans and advsory servces ntended to release nnovaton, nternatonalzaton and proflng as tools for an ncrease n compettveness and growth. The expected effects n the MBO-system are hgher turnover, proftablty and productvty. These are the drect effects n the treated busnesses. In a soco-economc perspectve, there s more to t. In addton we must add externaltes, both pecunary and technologcal spllovers and external effects such as learnng and technology transfer and adoptons from the nnovaton and nternatonalzaton processes. These mportant effects are not measured n the MBO-system, and must be evaluated separately. The nnovaton task nvolves a substantal degree of rsk-takng and thus resembles the role of nvestors. Selecton crtera: Projects should contan a new product or new technology, a new combnaton of exstng technologes not yet present n the market n producton processes, or organzatonal changes/nnovatng ther busness model. The nnovaton assgnment s consdered to be demandng and ntended for competent applcants. The lendng assgnment (henceforth L-assgnments) IN s lendng assgnment resembles ordnary bank actvtes lke provson of lowrsk loans. The man task s to encourage growth by supplementng the prvate banks where they do not perform adequately well. Ths mght be both n some rural areas and n specfc ndustres where local banks need some rsk sharng, for nstance due to the sze of the loans (for example fshng boats). The lendng assgnment s a non-proft actvty where costs of operaton and losses are covered by the nterest margn. Smlar to the I-assgnment, the expected effects are hgher turnover, proftablty and productvty n the frms. Selecton crtera by IN: The lendng assgnment ncludes low rsk loans to enterprses all over Norway and across all ndustres. In order to qualfy, projects should have growth potental n natonal or nternatonal markets and have proftable prospects, and the loans requre collateral. Fnancal support s granted to projects wthn a broad scope, such as nvestments n buldngs, machnery and nformaton and communcatons technology (ICT), restructurng and readjustment to changng market condtons, nnovaton and nternatonalzaton, renewal and modernzaton, busness establshment, generatonal change etc. The regonal development assgnment (henceforth R-assgnment) The regonal development assgnment s an addtonal effort to ncrease growth and durable employment n rural areas. The means ncludes busness development grants, nvestment grants and venture captal/rsk loans as top fnancng tool, beng lent on condtons wthout full securty (collateral) and at a hgher nterest rate. Here, the success crtera mght nvolve some trade-off between hgher turnover, proftablty and productvty aganst employment and value added n the regons of Norway. The trade off between multple goals s expected to reduce the performance on each goal. Statstcs Norway 9

12 Effect on frm performance of support from Innovaton Norway Reports 2015/35 Selecton crtera by IN: Smlar crtera as the nnovaton assgnment, but drected towards development n the regons as an addtonal effort. The nvestment grant s earmarked nvestment n non-moble nvestments n rural areas, to compensate for low second hand values. Some of the venture captal loans are used as top fnancng of the same nvestments. Busness development grants are often used to fnance soft nvestments such as knowledge buldng and technology adopton. Norwegan Innovaton Clusters (henceforth C-assgnment) In addton to the nnovaton, lendng and regonal development assgnments, we have defned a forth assgnment; the Innovaton Cluster program. Norwegan Innovaton Clusters s jontly owned by IN, the Industral Development Corporaton of Norway (SIVA) and the Research Councl of Norway. The cluster program operates on three levels: Arena, Norwegan Centres of Expertse (NCE) and Global Centres of Expertse (GCE). The C-assgnment may comprse frms that belong to one of the three man polcy assgnments mentoned above. However, we consder partcpaton n the C-assgnment as a separate treatment n our analyss. Partcpaton n the ndustry cluster program nvolves no drect subsdy to each sngle partcpatng frm, only that the organzer of the ndustry cluster receves a grant to cover the admnstraton costs and possbly some advsory servces. In our analyses, a cluster treatment starts wth the date of entry for each sngle partcpatng enterprse. Selecton crtera by IN: Applcatons are evaluated accordng to the cluster s resources, establshed relatons between the cluster partcpants and relatons wth external partners, growth potental n the cluster s market or technology, ownershp and management of the project, the project s effect and performance potental and the qualty of, and resource base for the mplementaton of the project How does IN affect frms? A frm may obtan drect publc fundng of a project from IN only by applcaton. IN decdes whether to fund the project after a screenng process. The crtera used n the screenng process wll dffer dependng on the specfc support scheme the frm has appled to. There may be ndustry specfc crtera, regonal crtera and nnovaton content ssues that to a varyng degree are emphaszed. Thus the actual projects undertaken wll reflect both frm- and IN-preferences. There are costs for the frm related to the applcaton process and there are of course costs ncurred by IN-screenng. IN fnances a range of projects, not only nnovaton projects, that we emphasze n our dscusson below. Whle there are many theoretcal models regardng the mpact of drect subsdes and tax credts on frms R&D and nnovaton actvtes (typcally extensons of the user cost of captal or Tobn s q-lterature), there are fewer theoretcal models of frms behavor regardng grants, wth Takalo et al. (2013) beng one excepton. The most commonly used framework for studyng the effects of nnovaton actvtes by frms s the so-called CDM model of Crepon et al. (1998) (see the specal ssue of Economcs of Innovaton and New Technology (2006), whch s devoted to varous studes usng ths framework). Ths framework s also used by Cappelen et al. (2012) on Norwegan data. The basc structure of the CDM approach s to model heurstcally the frm s decson to undertake an nnovaton actvty that can be classfed as (nvestments n) knowledge captal. Ths ntangble captal stock s then assumed to affect output along wth other nputs n a standard producton functon. In ther orgnal paper, Crepon et al. (1998), proposed a three-stage model. Frst they specfed a probt model of the decson to undertake an nnovaton actvty. Condtonal on a postve outcome of ths bnary choce, they estmated a lnear model of the nnovaton ntensty and then fnally a lnear outcome equaton usng a standard regresson framework. 10 Statstcs Norway

13 Reports 2015/35 Effect on frm performance of support from Innovaton Norway Compared to the three-stage CDM framework, we focus on a more reduced form framework, where we do not use (or even have access to) data on nnovaton actvtes by frms. On the other hand, we have detaled accounts data for each frm durng and can address ssues lke how quckly do nnovaton projects lead to outcomes? The causal mechansms we rely on are those of the CDM approach: The frm decdes to apply for a grant; f t s accepted by IN, the frm undertakes the project and may thereby ncrease ts knowledge stock, whch agan may have postve effects on several outcome ndcators, such as output and productvty. Support of ndustry clusters by IN dffers from support of nnovaton projects, but the operatng mechansms can be qute smlar. Partcpaton n a cluster may (or may not) ncrease access to ntangble captal or external resources that wll boost frm productvty. A frm that receves support through regonal IN support schemes may then ncrease ts nputs (and output) compared to a stuaton wthout. It may not stmulate productvty snce the am of regonal support s manly to reallocate resources. Innovaton schemes are usually thought of as havng postve productvty effects snce they enable frms to ncrease ther (tangble and ntangble) captal stock and ths latter effect may also apply to frms whch receve fnancal support Measurng effects: Lmtatons and challenges The MBO/ MBR evaluaton tool we have developed for Innovaton Norway s essentally a mcro econometrc evaluaton method. We meet n our task the same standard evaluaton problem as all other analyses of polcy nterventons based on non-expermental data: the enterprses we observe may ether be treated or nontreated and we do not observe the counterfactual outcomes for any of the frms. Our procedure falls n the category natural experments, wth the man problems beng that (1) frms self-select nto the programs by applcaton and (2) IN selects frms from the applcants based on partcular crtera (an assgnment rule). Some of the characterstcs that affect partcpaton may be observable, others not, meanng that t s dffcult to dentfy causal effects of treatment vs effects that are related to characterstcs of the frms. For example, f a growth opportunty appears to a specfc frm, for reasons not observable for the evaluator, we may observe that the frm apples for and receves a grant or loan from IN. Ths may later affect growth n turnover or employment. However, we do not observe what the outcome would have been wthout the fnancal support from IN. Such evaluaton problems are revewed n Blundell and Costas Das (2009) and wll be dscussed n Secton 3 where we present our evaluaton method. In addton to the general dffcultes often faced n evaluaton, we have consdered carefully some other dffcultes that we face n our evaluaton and whch may be of relevance to other researchers: How long does t take before partcpaton n a program has any effect? How long does the effect last? How should we treat repeated support and support that lasts more than one accountng perod? When does one treatment stop and another start? How do we classfy the type of treatment f we observe multple treatments n subsequent years? In our analyss we use a matched dfference-n-dfferences procedure to dentfy treatment effects. In order to nterpret our estmated treatment effects as causal effects, the most mportant assumpton s that n the sample of matched frms (but not necessarly all frms), frm-specfc shocks are uncorrelated wth assgnment to treatment. We elaborate on and dscuss these ssues and our operatonalzatons n Secton 3. Statstcs Norway 11

14 Effect on frm performance of support from Innovaton Norway Reports 2015/35 2. Data In ths secton we present data that characterze the frms nvolved n programs fnanced by IN as well as data for frms that have been selected or matched as belongng to the control group. We present data for frms ncluded n the man assgnments of IN as presented earler. All our analyses are lmted to lmted lablty (AS) enterprses. We start by presentng the number of frms ncluded n the analyses from 2001 to Table 2.1 shows the number of IN-frms ncluded n our analyss as well as the number of frms matched to the IN-frms (the control group). The control group s selected from the total populaton of lmted lablty enterprses usng nearest neghbor propensty score matchng (see Secton 3). The total populaton numbered roughly n 2001, ncreasng to n IN-frms share of the total populaton of enterprses s stable around 5-6 percent. Notce that the growth n the number of matchng IN-frms has been larger than that of all IN-frms. In ths sense the relablty of our estmates may ncrease over tme snce we are able to match a larger share of IN-frms to other frms (around 78 percent n 2013 aganst 75 percent n 2001). Dstngushng between IN-frms and frms n the control groups, Table 2.2 shows the mean and medan values of number of employees, labor productvty (value added per employee) and the rate of return on total assets durng The medan s 6 employees for all three IN-assgnments compared to 3 4 employees n the control group. The mean values are hgher, n partcular among IN-frms, whch on average are about twce as large n terms of empoloyment as the frms n the control group. In general IN-frms have lower mean and medan productvty than the matchng frms, except for the lendng (L) assgnment, where the medan s hgher and the mean s lower among the IN-frms. The medan rate of return on total assets (the rato of ordnary profts before taxes plus fnancal expenses to total assets) s always postve, whereas mean proftablty s negatve across the varous assgnments and control groups, except n the control group of the lendng assgnment. Wth regard to both mean and medan values, IN-frms are less proftable than the frms n the control group across all assgnments. The least proftable frms are those belongng to the IN nnovaton assgnment (I). Table 2.1. Number of frms ncluded n the analyss vs. the populaton Year IN before matchng 1 IN after matchng 2 Control group Populaton The table shows the number of IN-frms n the sample each year they are operatve regardless of ther frst year of partcpaton. 2 The number of IN frms that were matched to at least one frm n the control group. 12 Statstcs Norway

15 Reports 2015/35 Effect on frm performance of support from Innovaton Norway Table 2.2. Effect ndcators. Mean and medan values over the perod IN Control group Indcator R I L All R I L All Frm sze (number of employees per frm) Mean Medan Labor productvty (value added n 1000 NOK per employee) Mean Medan Rates of return on total assets (n percent) Mean Medan Note: The IN-populaton conssts of all matched IN frms n all years they are observed, accordng to the frst type of INsupport receved. Lookng at the spread of productvty between IN-frms and matchng frms n the control group n 2001 and 2013, Fgure 2.1 reveals that the control group dstrbutons are more skewed towards hgh postve values wth heaver rght tals than the correspondng dstrbutons for the IN frms, who appear to be more symmetrc wth a hgher share of observatons wth negatve values. Fgure 2.1. Dstrbuton of labor productvty n 2001 (upper panel) and 2013 (lower panel) for IN-frms and frms n the control group. The lower and upper 5 percentles are excluded from the fgures Statstcs Norway 13

16 Effect on frm performance of support from Innovaton Norway Reports 2015/35 Fgure 2.2. Dstrbuton of rate of return on total assets n 2001 (upper panel) and 2013 (lower panel) for IN-frms and for frms n the control group. The lower and upper 5 percentles are excluded from the fgures Fgure 2.2 shows the dstrbutons of rates of return n 2001 and All dstrbutons are characterzed by heavy left tals (towards hgh negatve values), whch explan the large negatve mean values. However, the dsperson n proftablty s large, wth most observatons located outsde what s usually consdered a normal rate of return. From the fgure t looks lke the IN-frms are less proftable than frms n the control group wth regard to any quantle, not just the medan. Our matchng procedure s based on the characterstcs of frms n the frst full accountng year they are observed durng (more about ths n Secton 3). Of course, ths does not guarantee that IN-frms and matchng frms are equal at the tme of comparson (age at treatment). It may be of nterest to know f IN-frms are selected among enterprses that have hgher growth pror to partcpaton than the frms n the control group. To llumnate ths ssue, Tables show the growth rato from age one (.e., the frst year after establshment) to the age of frst assgnment for dfferent representatve IN-frms. The growth ratos are computed by age category at frst assgnment and depcted n the tables for turnover (sales revenues), employment and value added. Thus we compare the growth ratos of IN-frms wth correspondng measures for frms n the control groups of smlar age (and over the same agenterval). The representatve frms are aggregate frms obtaned by summng the value of the varable n queston over the frms n each age group. The tables gve 14 Statstcs Norway

17 Reports 2015/35 Effect on frm performance of support from Innovaton Norway no clear ndcaton of systematc dfferences between the IN-frms and the control groups wth respect to pre-assgnment growth. Table 2.3. Rato of turnover at treatment age to turnover at age one Age at frst IN Control assgnment R I L All R I L All 3-5 years... 1,19 1,40 1,07 1,25 1,10 1,16 1,24 1, years... 1,24 2,51 1,08 1,62 1,30 1,35 1,24 1,31 9+ years... 1,31 1,80 1,28 1,45 1,47 1,57 1,30 1,48 Table 2.4. Rato of employees at treatment age to employees at age one Age at frst IN Control assgnment R I L All R I L All 3-5 years... 0,99 1,09 0,94 1,02 1,18 1,12 1,1 1, years... 0,99 1,22 1,11 1,07 1,33 1,27 1,24 1,30 9+ years... 0,96 1,21 0,89 1,02 1,18 1,24 0,87 1,17 Table 2.5. Rato of value added at treatment age to value added at age one Age at frst IN Control assgnment R I L All R I L All 3-5 years... 1,07 1,14 1,04 1,09 1,48 1,89 0,53 1, years... 1,44 2,95 0,97 1,87 1,56 1,56 1,11 1,52 9+ years... 1,39 1,99 1,35 1,56 1,60 1,77 2,74 1,78 Table 2.6 shows the total sum of grants and loans per year for each IN-program. In 2009, there was a sharp ncrease n lendng and grants from the nnovaton assgnment, as a part of the government efforts to counteract the fnancal crss. IN-lendng was gradually reduced from However, as seen from Table 2.7, total loans as well as loans per enterprse remaned much hgher after 2009 than before 2009, ndcatng a change n demand patterns or a change n IN s lendng polcy, whereas total grants from the nnovaton assgnment quckly returned to pre-2009 levels. The number of frms that have receved support s depcted n Table 2.8. Table 2.6. Total grants and loans from IN durng , total dsbursements (n NOK) Assgnment year Lendng Regonal Innovaton Note: The IN-populaton conssts of all matched IN frms. Table 2.7. Average grants and loans from IN (per frm) durng (n NOK) Assgnment year Lendng Regonal Innovaton Note: The IN-populaton conssts of all matched IN frms. Statstcs Norway 15

18 Effect on frm performance of support from Innovaton Norway Reports 2015/35 Table 2.8. Number of frms that receved support durng Assgnment year Lendng Regonal Innovaton Note: The IN-populaton conssts of all matched IN frms. As mentoned earler, IN supports three cluster programs, the ARENA program, NCE, and GCE. There are clearly some dfferences n terms of members of the varous clusters. In recent years the number of frms n new ARENA-clusters s qute hgh compared to that n the NCE clusters and more and more frms n Norway now belong to a cluster (see Table 2.9). Frms that belong to a cluster are on average larger than the typcal IN-frm. Table 2.10 shows the sze of frms n clusters n the enrollment year. If we compare wth the frm szes n Table 2.2, we see that cluster frms are much larger than IN-frms n general. Table 2.9. Number of frms per cluster accordng to enrollment year Enrollment year Arena GCE NCE All Note: All matched IN frms n the enrollment year. Frms wth mssng enrollment year are not ncluded. Table Mean and medan number of employees for cluster members n the enrollment year Enrollment year <=3 years old >3 years old All Mean Medan Mean Medan Mean Medan ,67 7,00 6,67 7, ,00 6,00 99,17 6,00 79,11 6, ,50 46,50 93,92 43,50 87,14 43, ,00 43,00 41,25 25,50 41,83 31, ,33 325,00 380,33 325, ,00 6,00 74,43 65,00 65,88 58, ,00 106,00 63,50 26,00 67,04 26, ,75 15,50 56,62 17,00 59,89 17, ,50 6,50 102,31 29,00 109,53 24, ,69 8,00 78,77 18,00 68,17 15,50 3. Choce of evaluaton method The two most popular methods for estmatng treatment effects from panel data are dfference-n-dfferences (DID) and matchng-estmators. We wll now revew these methods n some detal and then, at the end of the secton, present our preferred method whch s a verson of Matched DID (MDID) orgnally proposed by Heckman, Ichmura and Todd (1997), and descrbed n a more general evaluaton context n Blundell and Costa Das (2009). 16 Statstcs Norway

19 Reports 2015/35 Effect on frm performance of support from Innovaton Norway 3.1. The classcal methods: DID and matchng In the classcal verson, the DID estmator uses a polcy reform takng place at some gven pont n tme and treats t as a natural experment. Formally, treatment or non-treatment of a unt () s the outcome of a stochastc assgnment ndcator, N. That s, N n wth n equal to 0 or 1. DID then compares the change (dfference) n the outcome varable before and after the reform between the treated unts (wth N 1) and the non-treated frms (wth N 0 ). Whle ths method allows treatment effects to be heterogeneous among the unts, t enables dentfcaton of average treatment effects of the treated (ATT) under very restrctve assumptons. The most mportant of these s that transtory shocks that are specfc to an ndvdual unt (e.g., frm-specfc shocks) are uncorrelated wth treatment assgnment. Ths requrement s often referred to as the common trend assumpton. As notced by Blundell and MaCurdy (1999), the DID estmator s dentcal to the famlar frst-dfference panel data estmator when only macrovarables (such as tme dummes) are allowed n the regresson. In the case of assgnment to treament from IN, there s no natural experment that excludes a subgroup of frms from treatment. The control group has to be defned through non-assgnment to treatment, whch occurs ether because a frm decdes not to apply for support, or t apples but fals n the competton for fundng. Ths assgnment process may clearly volate the requrements of DID. The second common estmator s the matchng estmator. Ths estmator seeks to establsh a control group that represents the counterfactual (non-treated) outcomes of the treated frms takng observed frm heterogenety nto account. Under certan (strct) condtons a treated frm and the correspondng matchng frms (to whch the treated frm s pared) are dentcal n all respects, except a random term that s ndependent of treatment assgnment. The most mportant condton s that there must exst a vector of matchng varables (S) such that the untreated outcome (the counterfactual outcome of a treated unt) s ndependent of treatment assgnment condtonal on S. Ths s called the Condtonal Independence Assumpton (CIA). In our context, ths means that f a frm s assgned to treatment by IN, ths assgnment per se s unnformatve about the counterfactual outcome of the dependent varable (gven S). In practce t s mpossble to fnd matchng unts wth dentcal (or smlar) S when S s of a hgh dmenson. However, Rosenbaum and Rubn (1983) effectvely reduced the mult-dmensonal matchng problem to a unvarate one, by nstead matchng on the probablty of treatment gven S : PS ( ) Pr( N 1 S). Ths probablty s called the propensty score. Under the addtonal assumpton of common support: 0 PS ( ) 1, they showed that the non-treated outcome s ndependent of N gven PS ( ). The common support assumpton s crucal, because f the propensty score s one there are no matchng unts (f t s zero there are no treated frms, whch s not a problem regardng the estmaton of ATT). Ther result facltates estmaton of ATT n the followng way: 1. Choose a treated unt from the populaton of all unts (wth N 1) 2. Par t wth a non-treated unt j wth the same propensty score, PS ( ) P(S j), but possbly a dfferent value of S. A smple matchng estmator s the average of dfferences of the outcome varable between matched pars of unts wth the same propenssty score. However, one-toone matchng s not the most effcent way of mplementng the propsensty score estmator on a fnte sample. One-to-many matchng (such as 1 to many nearest neghbor matchng) and kernel-matchng s more effcent, but nvolves weghtng Statstcs Norway 17

20 Effect on frm performance of support from Innovaton Norway Reports 2015/35 of the observatons. We refer to Calendo and Kopeng (2008) for dscussons and practcal detals. A man problem wth matchng s the choce of matchng varables. If S s too hghdmensonal the common support assumpton wll fal, but f S contans too few varables, CIA wll fal (.e., condtonal ndependence s not acheved f the nformaton set s too small). As demonstrated by Kvtasten (2014) on Norwegan frm-level data, the balancng propertes of the propenssty score may be poor n fnte samples even when S only contans a few contnuous varables. Another lmtaton s that P(S) s unknown and therefore must be estmated, whch ether requres auxlary assumptons about functonal form (typcally logt or probt) or the applcaton of non-parametrc methods whch are subject to the same curse of dmensonalty as covarate matchng. In any case, as stressed by Blundell and Costa Das (2009), the matchng varables must be determned before a unt potentally can be assgned to treatment (not just before t actually s). Ths s a large problem when the tme of treatment s not a fxed date, as n the case of support from IN. Then a frm may be assgned to treatment early, or late, n the lfetme and even several tmes (sequental treatment). In any case, assgnement may depend on an ncreasng or changng nformaton set over tme. The approprate nformaton set wll then be dffcult to determne. Thus ether the CIA assumpton s lkely to be volated (because S does not contan suffcent nformaton), or observatons may not be matched, n whch case only the average treatment effect over some subgroup of the treated unts s estmated by the method. The estmated parameter wll then be dffcult to nterpret. See Heckman and Lozano (2004) for a demonstraton of how dffcult and crtcal t s to choose the rght nformaton set Implementng Matched DID (MDID) to analyze the effect of support from IN Blundell and Costa Das (2009), based on Heckman, Ichmura and Todd (1997), combne DID and matchng to weaken the assumptons of both methods. In ther mplementaton the outcome equaton s decomposed nto a unt-specfc fxed effect ( ), macro-effects and dosyncratc shocks. The fxed effect wll be elmnated by dfferencng. They stll defne the propensty score as P( S) Pr( N 1 S), but N s now allowed to depend on the fxed effect. A fundamental assumpton s stll that the tme of treatment s exogenous. Thus the selecton problem s a statc one, as n the classcal matchng model. We wll now formally outlne our verson of MDID whch we use to analyse whether assgnment of treatment by IN changes the performance of frms as measured by a performance ndcator, X. Dependng on the applcaton, X wll ether denote log-employment (number of employees), log-sales (turnover), loglabor productvty (value added per employee), or return on total captal. X jrst refers to the value of the performance ndcator (dependent varable) for the th frm n the ndustry regon cohort category (j,r,s) whch we denote cell C( j, r, s ) at tme t. Our ndustry classfcaton (j) follows 2-dgt NACE, the regonal classfcaton (r) the fve zones of payroll taxes, and cohort (s ) refers to the year of establshment. As dscussed n Sectons 1 and 2, we separate between four man types of assgnments: support obtaned through the nnovaton assgnment (I), the regonal development assgnment (R), the lendng assgnment (L) and the nnovaton cluster assgnment (C). We estmate separate models and average treatment effects for all four assgnments. To smplfy, we only record the type of assgnment the frst tme a frm obtans support. Subsequent treatments are treated as sequental treatments, not a qualtatvely new category of treatment. 18 Statstcs Norway

21 Reports 2015/35 Effect on frm performance of support from Innovaton Norway The data from IN cover the perod contanng nformaton about type, tmng and amount of support gven to each IN-supported frm, hereafter referred to as treated frms. A frm may obtan repeated treatments. Each cell of treated frms are matched to a control group of non-treated frms through propensty score matchng. The frm's (ntal) characterstcs at start-up, or n 2001 for frms establshed before 2001, are used as matchng varables, as we wll descrbe below. We mplement two types of matchng: matchng wth stratfcaton (treated frms can be matched only wth non-treated frms n the same cell) and matchng wthout stratfcaton (allowng matchng across cells). A general dscusson of the pros and cons of matchng wth stratfcaton are dscussed n Calendo and Kopeng (2008). The specfc motvaton for stratfcaton n our case s that cell characterstcs are key determnants both of the probablty of obtanng support, e.g. through regonal programs and programs targetng young frms, and of the dependent varables, e.g. through ndustry-specfc market condtons or local labor market condtons. Our matchng varables, S, are measured at start-up, or n 2001 for frms establshed before 2001, and nclude the 3-dgt NACE sub-ndustry (wthn 2-dgt ndustry j) 2, sze (total assets) and owner concentraton measured by the Herfndahl ndex. The matchng procedure used s the STATA routne psmatch2 wth 1 to 5 nearest neghbor matchng wth trmmng. 3 To descrbe matchng wth stratfcaton formally, we defne the treatment group T N jrs as the group of frms that obtan a gven type of treatment (a partcular type of support) n the ndustry regon cohort cell C( j, r, s ). The correspondng control C group s denoted N jrs and s obtaned by matchng frms n the treatment group T N jrs to non-treated frms n the same cell. Let N t be the number of treatments gven to frm at t: n wth n 0,1,2,.... and defne N max N 0. Nt The matchng s done w.r.t. cell-specfc propensty scores P ( S ) Pr( N 1 S, C( j, r, s)). jrs t t Snce a frm may get repeated (sequental) treatments, N s a countng varable and CIA s not suffcent for Pjrs ( S ) to be a balancng score (see Lechner, 2001). We need the addtonal assumpton that how many treatments a frm gets gven that t obtans at least one does not depend on S. We wll turn to ths complcatng ssue below. In the case of matchng wthout stratfcaton, Pjrs( S) can be defned n the same way as above, except that C( j, r, s) are ncluded as ordnary matchng varables n addton to S (a dummy varable for each cell whch s one f C( j, r, s) ). In ths case, a treated frm can be matched wth frms belongng to another cell. The matchng wth stratfcaton approach outlned above (matchng wthn the cell only), has the practcal drawback that fewer treated frms wll be matched as the potental control group s much small for any treated frm. In our experence, the number of IN-frms that can be matched s reduced wth more than 50 percent. When we present the results n the next secton, we therefore present estmates for matchng both wth and wthout stratfcaton. 2 Ths wll exclude frms from 3-dgt NACE ndustres wthout treated frms from the matched sample through the common support condton. 3 The opton specfcaton we used s: neghbor(5) common trm(10). See Leuven and Sanes (2003) for practcal gudelnes and techncal detals regardng the algorthm. Statstcs Norway 19

22 Effect on frm performance of support from Innovaton Norway Reports 2015/ Econometrc model Let Xjrst ( n ) denote the dependent varable of frm (belongng to cell C,, j r s ) f t has been assgned the treatment n tmes at t, let be the (long-term) treatment (n) effect of the n th treatment and let T denote the year of the n th assgnment, 0 0 wth T 0. The average treatment effects of the treated (ATT) are: ( n) (n) ( n) E( N n). To estmate by MDID, we assume X ) m S m n t,, n 0, max(0, n 1) ( n) ( k ) (n) jrst ( n jrst ( ) T m jrst k 0 m where mjrst ( S ) s the predctable, but unknown (non-parametrc), part of X jrst ( n) ( n) gven S and jrst s the error term. The term ( / m) mn( t T, m) reflects our assumpton that the n th treatment ncurs an annual ncrease n X equal to ( n ) ( n) ( n) / m n the treatment nterval ( T, T m] and zero ncrease thereafter. The choce of m s crucal and we test n our emprcal analyses whether the last condton s volated for our chosen m (see below). A treatment effect s dentfed as a persstent effect on the level of X. The long-term change n X caused by n treatments equals n k 1 ( n ) assgnment year T : ( k ) ( n) and ths effect s realzed m years after the last n ( k ) (n) jrst ( ) jrst (0), k 1 X n X t T m. The crucal assumpton for consstent estmaton of treatment effects s the CIA assumpton: X (0) N S jrst where denotes stochastc ndependence (no selecton on the non-treated outcome), or equvalently jrst N for all t. It follows from Lechner (2001) that Pjrs ( S ) wll be a balancng score for N f the followng condton holds: P( N n S, N 1, C( j, r, s)) P( N n N 1, C( j, r, s)). That s, how many treatments a frm gets gven that t obtans at least one does not depend on S. However, t may depend on the effect of the treatments (snce ( ) X jrst (0) s ndependent of n by assumpton). As shown by Lechner (2001), gven the common support assumpton 0 P ( S ) 1, X (0) N P ( S ). jrst jrs jrs 20 Statstcs Norway

23 Reports 2015/35 Effect on frm performance of support from Innovaton Norway Note that ths s a non-trval extenson of the classscal matchng result, as count varable, not a bnary treatment ndcator. Defne N s a mjrst E P E( m ( )) P (S) jrs 1 jrst S jrs where P jrs 1 s the dstrbuton of S n the sub-populaton of treated frms n the cell C( j, r, s ) satsfyng 0 Pj rs( S) 1. By constructon of the matchng algorthm, P jrs 1 s also the dstrbuton of S n the matched subpopulaton (defned by repeatng steps 1-2 of the matchng algorthm for each treated frm n the populaton, rather than n the sample). Thus m jrst s the mean value of mjrst ( S ) n the sub-populaton of all matched frms. MDID collapses to (ordnary) DID f ether mjrst ( S ) does not depend on S (there s a common trend for all frms n the same cell), or S has the same dstrbuton among the treated and the nontreated frms wthn each cell. In ether case, there s no need to perform matchng. Let X jrst Xjrst ( Nt ) be the realzed value of the dependent varable. By dfferencng, we obtan X m e jrst m ( Nt ) ( Nt ) jrst It ( T j) jrst, j 1 m where ejrst mjrst ( S) mjrst jrst and I(A) s the ndcator functon whch s one f the statement A s true and zero else. In the matched subpopulaton, the error e has mean equal to zero gven N for all t, snce term jrst E P ( ( ) ) N,P (S ) ( ( ) ) N,P (S ) jrs 1 E mjrst S mjrst jrst jrs E Pjrs 1 E mjrst S m jrst jrs E P E( m ( ) ) P (S ) 0. jrs 1 jrst S m jrst jrs. The frst equalty follows from jrst N and the second from the balancng property of the propensty score, demonstrated by Rosenbaum and Rubn (1983): S N P( S ). The last equalty follows from the defnton of m jrst. Thus we conclude that ejrst satsfes all the condtons of a regresson error term n the matched subpopulaton Estmaton of ATT Contrary to standard DID-based estmators, we utlze panel data to estmate the autocorrelaton structure of the error term. We do so by assumng that e s a movng average process of order q (MA(q)): e L d, 2 jrst q( ) jrst, jrst...(0, ) jrst Statstcs Norway 21