Bocci, Chiara

Marco, Mariani

English

Florence Research

An evaluation of export promotion programmeswith repeated multiple treatmentsValutazione di programmi di promozione dell’export contrattamenti multipli ripetutiChiara Bocci and Marco MarianiAbstract Export promotion programmes usually consist of the provision of multi-ple services and aids, including consultancy, trade missions and international fairsand business-to-business meetings, of which firms can take advantage either simul-taneously or at different moments in time. This constitutes an unusually complexsetting for programme evaluation. Relying on assumptions of sequential ignorabil-ity extended to the multiple-treatment framework, and exploiting the programmeparticipation data of an Italian region (Tuscany), we estimate the treatment effectof different services and aids on multiple aspects of the firms export performanceusing a marginal structural model that adjusts for dynamic confounding by meansof inverse-probability-of-treatment weights.Abstract I programmi di promozione dell’export consistono tipicamente nella for-nitura di vari servizi e aiuti, tra cui consulenze specialistiche, partecipazioni a fiereinternazionali e incontri business-to-business, che le imprese possono utilizzare siasimultaneamente che in diversi momenti temporali. Questo ambito costituisce unsetting complesso per la programme evaluation. Basandosi sull’assunzione di ig-norabilita´ sequenziale estesa all’ambito dei trattamenti multipli, si utilizzano i datidi un programma regionale di promozione dell’export per stimare, attraverso unmodello strutturale marginale, gli effetti causali delle principali forme di supportosui diversi aspetti della performance esportativa delle imprese, Tale modello af-fronta il problema del confondimento dinamico attraverso l’utilizzo di pesi inverse-probability-of-treatment.Key words: causal inference, multiple treatments, sequential ignorability, marginalstructural modelsChiara BocciIRPET - Regional Institute for Economic Planning of Tuscany, Via Dazzi, 1 - 50141 Firenze, Italye-mail: chiara.bocci@irpet.itMarco MarianiIRPET - Regional Institute for Economic Planning of Tuscany, Via Dazzi, 1 - 50141 Firenze, Italye-mail: marco.mariani@irpet.it12 Chiara Bocci and Marco Mariani1 IntroductionMarginal structural models are a popular tool used for causal inference in epidemi-ology, where treatments are often taken in sequences, which raises issues of time-dependent, or dynamic, confounding [3, 7]. Surprisingly, their use is not commonyet for the evaluation of economic and social programmes: for example, marginalstructural models are not covered by the review by Imbens and Wooldridge [4]on the “econometrics of programme evaluation”, a review that is popular witheconomists and social scientists.In longitudinal observational settings, dynamic confounding arises when vari-ables that can be affected by past treatments can, in their turn, affect future treatmentreceipt. Under these circumstances, a possible identification assumption is that ofsequential conditional ignorability. This assumption represents an extension to thelongitudinal setting of the conditional ignorability assumption (or unconfounded-ness) of cross-sectional settings [8]. Ignorability is now invoked at each point intime, conditional on the past histories of treatment, outcome and covariates.Under sequential ignorability, several approaches have been thought in order toestimate treatment effects. Marginal structural models are one of these approaches.Although treatments in micro-economic policy hardly resemble a medical ther-apy that involves the repeated intake of a drug over time, there is a wide range ofsituations in which individuals or firms take one, or more, treatment at differenttime points. A typical example are unemployment subsidies and active labour mar-ket policies, which support the income of individuals and promote their job searchevery time they are outside of regular employment spells (e.g. [6]).With respect to enterprise or innovation policy, one might think of a repeatedreceipt of a particular type of aid (e.g. R&D or investment grant) or also to the morecomplex situation where firms take different supports over time.An area of policy where the latter situation is quite common is that of exportpromotion programmes. These usually consist of the provision to firms of a vastarray of services and aids, including specialised consultancy, participation in trademissions and international fairs, organisation of business-to-business meetings orthe set-up of temporary selling outlets, of which firms can take advantage eithersimultaneously or at different moments in time.Note that, in the presence of different types of treatment, the sequential ignora-bility assumption needs to be extended to a multiple-treatment setting. This goal iseasily achieved by transposing the generalisation of unconfoundedness put forwardby [5] in a sequential setting.Focusing on the programme participation data of an Italian region (Tuscany), wetry to establish what services and aids are more effective in promoting the firmsperformance in subsequent years in terms of foreign sales, expansion of the numberof markets served and of the range of products sold abroad. Particular attention inthe analysis is devoted to establish if effects are heterogeneous across interestingsub-populations of participating firms, such as first-time and habitual exporters.An evaluation of export promotion programmes with repeated multiple treatments 32 Marginal structural modelsUnder sequential ignorability, we can rely on “longitudinal” propensity scores inorder to summarise, at each point in time, the past histories of treatment, outcomeand covariates [1, 7]. Similarly to what happens in other propensity-score-basedapproaches, marginal structural models require first to model the treatment receipt asa function of the past histories of treatment, outcome and covariates, which returnsa propensity score, and then to use the obtained propensity scores as an adjustmentdevice in a second model where the outcome of interest is a function of treatments.This adjustment occurs by inverse-probability-of-treatment weighting.Before recalling these aspects in greater detail, it is worth to clarify the assump-tion of sequential ignorability in the presence of multiple treatments. First, supposethat unit i can be assigned to one of m treatments a1, ...,am. In a cross-sectionalsetting, this unit is associated with m potential outcomes Yi(A = a1), ...,Yi(A = am),of which only one is actually observed depending on the treatment received. In thissetting, the ignorability assumption states that these potential outcomes are indepen-dent of treatment assignment mechanism, conditional on a vector of pre-treatmentobservable covariates Li [5].In a longitudinal setting, the unit i is followed for a total of T times and it canreceive some treatment A(t), e.g. a1,a2, ...,am, at multiple points t = 1, ...,T . Underthese circumstances, ignorability needs to be assumed at each point in time t, in asequential fashion. Let A¯i(t−1) and L¯i(t−1) be, respectively, the unit’s treatmenthistory and the unit’s covariates history up to moment t. The sequential ignora-bility assumption states that the m potential outcomes in t are independent of thecurrent treatment assignment mechanism, conditional on the unit’s past history oftreatments and covariates, the latter including past observed outcomes:Yi [Ai(t) = a1] , ...,Yi [Ai(t) = am]⊥ Ai(t)|A¯i(t−1), L¯i(t−1).Under this assumption, the longitudinal propensity score at each time t isPr[A(t) = a(t)|A¯(t−1) = a¯(t−1), L¯(t−1) = l¯(t−1)]and can be estimated by mean of a generalised linear model suitable for multinomialvariables.As shown in [7], stabilised inverse-probability-of-treatment weights can then beobtained as follows:swi(t) =t∏k=0Pr[Ai(k) = ai(k)|A¯i(k−1) = a¯i(k−1),Vi = vi]Pr[Ai(k) = ai(k)|A¯i(k−1) = a¯i(k−1), L¯i(k−1) = l¯i(k−1),Vi = vi]where covariates are now split in two parts: a vector of time-invariant covariates Viand a vector of time-varying covariates L¯i(t−1) detailing the unit’s history.The stabilised weights above are finally used to build a weighted estimator ofthe coefficients of a structural regression model where the outcome of interest is afunction of treatments.4 Chiara Bocci and Marco Mariani3 Application and resultsThe application focuses on a complex public strategy of export promotion imple-mented in 2006–2012 by Tuscany’s regional government, either by the direct pro-vision on subsidies or via a public agency that supplies smaller firms with freespecialised supports, including export-oriented consultancy, participation in inter-national fairs or involvement in organised business-to-business meetings. A maincharacteristic of this program is that firms were allowed to take multiple supports,also repeatedly over time. In fact the repeated intake of multiple treatments occurredrather frequently and, therefore, cannot be neglected in the analysis.We are interested in estimating the average treatment effects of all these supports,versus the non-treatment situation, on three main outcome variables: increase inforeign sales, in the number of markets served and in the number of products soldabroad. The non-treatment situation is reconstructed using a set of never-treatedfirms, selected by means of matched sampling techniques [9] so that we obtain anever-treated twin for each firm that will receive support in the future. The data onboth treated and untreated firms are derived from two important datasets, SDOE andASIA, held by the Italian Chambers of Commerce and by ISTAT respectively.We express the outcomes in terms of first differences ∆Yi,t =Yi,t−Yi,t−1 in orderto discard individual fixed effects. Therefore, the average treatment effects representdifferences in differences (and not differences in means):AT EDID,a−0 = E[∆YA=a−∆YA=0|A¯, L¯,V].Outcomes have been reconstructed for each treated or untreated firm at each timestarting from the SDOE yearly dataset, which is available for our analysis from 2005to 2012. This archive reports details on each single export flow (exporting firm, typeof good according to the NC8 classification, destination market, etc.) that originatesfrom Italy in a series of years. Since the programmes under scrutiny aimed at pro-moting export towards non-EU countries, EU markets are excluded when measuringthe three outcomes of interest. Moreover, as the export benefits of the supports arelikely to require some time in order to arise, we estimate treatment effects not onlywhen the supports are actually received (t+0), but also one year later (t+1). Lon-gitudinal firm records reconstructed from SDOE are then merged with the ASIAdataset in order to link them to a number of background firm characteristics.Using this information related to 1,648 small and medium-sized firms that weretreated at least once in the observation period, as well as an equal number of never-treated twins, and following the procedure outlined in Section 2, we estimate theinverse-probability-of-treatment weights, whose distribution is plotted in Figure 1.Then, we specify for each outcome the following marginal structural model (withh = 0,1)∆Yi,t+h = β0+β1Y Marketi,0 +β2YProducti,0 +β3YSalesi,0 +β4Di,0+β5AFi,t +β6ABi,t ++β7ACi,t +β8ASi,t +β9(Di,0AFi,t)+β10(Di,0ABi,t)+β11(Di,0ACi,t)+β12(Di,0ASi,t)+ εiAn evaluation of export promotion programmes with repeated multiple treatments 5Fig. 1 Box-plots of the inverse probability-of-treatment weights for the years 2006-2012that includes baseline levels of the outcome variables (Y Marketi,0 ,YProducti,0 ,YSalesi,0 ), in-dicators for each type of treatment (AF stands for participation in a trade fair, AB forbusiness-to-business meeting, AC for specialised consultancy and AS for export sub-sidy), a dummy for the fact that the firm has some previous export experience, andinteractions between this dummy and the treatment indicators. By weighted leastsquares we obtain the coefficients and then the average treatment effects of interest,which are reported in Table 1 accompanied by p-values based on standard errorsthat are cluster-robust at the firm level [2].We find that the programmes have no statistically significant effect on foreignsales, therefore the AT Es related to this outcome are not reported in the table. In-stead, with respect to the number of non-EU markets served and to the number ofproducts sold there, the situation is much more interesting.For a first-time exporter, the receipt of a subsidy helps much more than the par-ticipation in trade fairs, business-to-business meetings or the receipt of specialisedconsultancy. This is because the subsidy provides inexperienced firms with moneythey can invest in implementing a complex attempt of entry into foreign markets, anentry that can be impracticable relying on the other services and supports alone. Onthe contrary, firms already experienced in foreign markets that have higher know-how to exploit trade opportunities can take advantage of fairs, business-to-businessmeetings or specialised consultancies, whereas it seems inappropriate to providethem with direct subsidies.4 ConclusionsOur results support the idea that export promotion programmes can be useful to letsmall and medium-sized firms attempt first exploratory approaches to new markets6 Chiara Bocci and Marco MarianiTable 1 Average treatment effects on the number of non-European markets served and on thenumber of products sold in non-European markets.Markets Productst+0 t+1 t+0 t+1Treatment AT EM p-valuea AT EM p-valuea AT EP p-valuea AT EP p-valueaHABITUAL EXPORTERS (D0 = 0)Fair 0.293 0.014 * -0.258 0.114 0.373 0.117 0.007 0.980B2B -0.047 0.701 0.346 0.008 ** -0.092 0.651 0.734 0.000 ***Consultancy 0.188 0.039 * 0.005 0.957 0.197 0.179 -0.075 0.682Subsidy 0.994 0.000 *** -0.337 0.033 * 1.027 0.000 *** 0.015 0.961FIRST-TIME EXPORTERS (D0 = 1)Fair 0.185 0.237 0.561 0.182 0.116 0.756 -0.477 0.239B2B 0.019 0.845 0.001 0.995 0.135 0.446 -0.080 0.737Consultancy 0.135 0.057 ° 0.123 0.142 0.136 0.165 0.042 0.736Subsidy 0.383 0.047 * 0.025 0.796 1.033 0.020 * -0.196 0.416a Signif. codes: ’***’ 0.1% ’**’ 1% ’*’ 5% ’°’ 10%or try the introduction of new products into new or existing foreign markets. Thatsaid, however, they do not seem to help firms in improving substantially their pene-tration in foreign markets. Therefore, we can conclude that these programmes, ratherthan fostering the volume of foreign sales, are more suitable to accompany firms thattry to implement some diversification of markets and products sold abroad.References1. Achy-Brou, A.C., Frangakis, C.E., Griswold, M.: Estimating treatment effects of longitudinaldesigns using regression models on propensity scores. Biometrics 66(3), 824–833 (2009)2. Cameron, A.C., Miller, D.L.: A practitioner’s guide to cluster-robust inference. J. Hum. Resour.50(2), 317–372 (2015)3. Gill, R., Robins, J.M.: Causal inference for complex longitudinal data: The continuous case.Ann. Stat. 29(6), 1785–1811 (2001)4. Imbens, G.W., Wooldridge, J.M.: Recent Developments in the Econometrics of Program Eval-uation. J. Econ. Lit. 47(1), 5–86 (2009)5. Lechner, M.: Identification and Estimation of Causal Effects of Multiple Treatments under theConditional Independence Assumption. In: F. Pfeiffer, M. Lechner (eds.) Econometric Evalua-tion of Labour Market Policies, pp. 43–58. Physica-Verlag, Heidelberg (2001)6. Lechner, M.: Sequential causal models for the evaluation of labor market programs. J. Bus.Econ. Stat. 27(1), 71–83 (2009)7. Robins, J.M., Herna´n, M.A., Brumback, B.: Marginal structural models and causal inference inepidemiology. Epidemiology 11, 550–560 (2000)8. Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studiesfor causal effects. Biometrika 70(1), 41–55 (1983)9. Rosenbaum, P.R., Rubin, D.B.: Constructing a control group using multivariate matched sam-pling methods that incorporate the propensity score. Am. Stat. 39(1), 33–38 (1985)

An evaluation of export promotion programmes with repeated multiple treatments.

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An evaluation of export promotion programmes with repeated multiple treatments.

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