Do firms learn by exporting or learn to export? Evidence from Senegalese manufacturing firms

Transcription

1 Journal of African Development 2017, 19(1): Do firms learn by exporting or learn to export? Evidence from Senegalese manufacturing firms Fatou Cisse Abstract This paper examines the causal relationship between exporting and productivity in the manufacturing firms in Senegal using a unique firm-level panel data for the period We control for endogeneity and sample selection by jointly estimating the productivity and the export-participation equations. Our results indicate strong evidence of both self-selection of the most efficient firms enter into the export market and effect of Learning in the export market. Findings show that firms with better financial health are likely to exports. Furthermore, the ownership of intangible assets like brevet and the quality of labour positively affect the probability to export of the manufacturing firms. We investigate the sectoral heterogeneity of the Learning-by exporting effect (LBE) and find evidence of a weak heterogeneity of the learning-by-exporting effect between the sectors. From a policy relevance, the evidence of learning-by-exporting suggests Senegal has much to gain from encouraging exports by helping domestic firms to overcome the barriers to enter into foreign markets by promoting access to intangible assets like brevet. Particularly, export promotion policies could be helpful, reducing the level of financial constraints faced by firms and indirectly enhancing their investment spending and productivity. As a driver of manufacturing exports, labour quality must be carefully considered in the perspectives of industrial development. Considerable efforts are required in the Senegalese educational system in order to match the training to the requirements of the labour market. Keywords: Exporting, Total Factor Productivity, Learning by exporting, Self-selection JEL Classification : F14 D24- C33 I thank Ji Eun Choi, Ousmane Faye and Abdoulaye Diagne for their valuable comments on an earlier version of this paper. I am grateful to Akim-Al Mouksit for his helpful assistant on the data arrangement. This work was supported by the African Development Bank through the project Learning to Compute (L2C). Fatou Cisse, Department of Economics, Universite Cheikh Anta Diop de Dakar, Senegal and Consortium pour la Recherche Economique et Sociale, Lots 1 et 2, Cite Iba Ndiaye Diadji, Sacre Coeur Pyrotechnie, Rue 10 prolongée -Dakar. BP 7988, Dakar, Senegal, fatcis2@gmail.com

2 134 Journal of African Development 2017, 19(1): Introduction Trade openness increases firm s productivity and stimulates growth by exposing countries to the knowledge stocks of their trading partners (Grossman and Helpman 1991; Love and al. 2010). Then, many development economists viewed participation in export markets as a prerequisite for economic growth in developing countries and believe that an export-led development strategy would improve the efficiency at the level of the firm. However, the empirical literature did not show a systematic evidence on the relation between productivity and participation to export market. Firm performance is heterogeneous and subject to many influences unrelated to exporting. In addition, firms learn from many external as well as internal sources and thus it is not always easy to separate out the learning by exporting effect. Indeed, causality may run in the both directions: efficient firms may self-select into the export market as well exporting can cause efficiency gains. In the self-selection (SS) mechanism, only the more productive firms can afford the higher cost of exporting. This implies that future exporters have significantly higher productivity than non-exporters before they start exporting (Clerides et al. 1998; Melitz 2003). In the Learning by exporting (LBE) mechanism, firms improve their productivity after entering a foreign market (Clerides et al. 1998). Therefore, exporting results in productivity gains because, exporters are exposed to knowledge flows from international buyers and competitors and to more intense competition in international markets. This leads to larger opportunities and incentives to improve productivity for exporters, than those experienced by firms which sell only on the domestic market. Since the seminal paper of Bernard and Jensen (1995) on the US economy, there is a large literature in developing countries that examines the relationship between productivity and exporting, but the overall evidence is inconclusive. For example, Clerides et al. (1998) find that efficient firms self-select to become exporters but do not experience any efficiency gains as a result of being exporters in Columbia, Mexico and Morocco. In contrast, using firm level data for the manufacturing sector in four African countries Bigsten et al. (2004) find significant efficiency gains from exporting, which they interpret as the effect of learning-by-exporting. Recently, Bigsten and Gebeeyesus (2008) find productivity improvements for exporting firms in Ethiopia post-participation in foreign markets. In Africa the potential gains from exporting are large (Bigsten and al. 2004). The competitiveness gap and the narrowness of the market for manufactures can be reduce under learning-by-exporting through international trade. From a policy perspective, investigation the link between productivity and exporting is an important issue. In this paper, we provide evidence on the link between exporting and productivity in the manufacturing sector using a unique panel data on manufacturing firms in Senegal for the period Numerous methodological issues arise when testing the effect of exporting on productivity. One of the most common problems is the endogeneity due to self-selection of the firms in export market. The nature of this issue bias suggests that exporting firms might possess some observable and unobservable characteristics that make them more productive than their domestic counterparts, thus allowing them to overcome sunk cost to enter into the export markets. Hence, estimating the learning-by-exporting effect using conventional econometric methods would lead to biased and spurious results. To control for endogeneity and sample selection, we follow Bigsten and al. (2004) approach by jointly estimating the productivity and the export-participation equations. This paper contributes to the literature in three ways. First, while the small literature on the link between productivity and exporting in sub Saharan Africa has by now focused on four countries as Cameroun, Ghana, Kenya and Zimbabwe (Bigsten and al. 2004; Bigsten and Gebeeyenus 2008), its findings can hardly be generalized to Africa. These countries can differ

3 Journal of African Development 2017, 19(1): widely in term of composition of GDP, composition of exports, exports constraints (access to markets, lack of skill labour, inadequately educated workforce, access to financing, foreign currency regulations, energy and water access, taxes system, external trade policy environments, ect) 1. Second, the literature on credit constraints and exports that emerged since the pioneering study by Greenaway et al. (2007) suggests that financial constraints are important for the export decisions of firms. Exporting firms are less financially constrained than non-exporting firms and that less constrained firms self-select into exporting, but that exporting does not improve financial health of firms. Thanks to the data availability, our paper adds the liquidity constraints in the learning by exporting model. Third, to our knowledge no empirical paper investigates yet the evidence on how exporting is linked to firm productivity in Senegal. Our research make the first estimation of the learning by exporting effect in the Senegalese manufacturing firms. Our findings indicate strong evidence of both self-selection of the most efficient firms enter into the export market and effect of Learning in the export market. They highlight also a strong persistence of the previous exporters firms in the export decision. This suggests that firm s current involvement in exporting activity may well lower the fixed costs of engaging in exporting in the next period. As expected, liquidity ratio affects positively and significantly the probability to export supporting the idea that firms with better financial health are likely to exports. Furthermore, the ownership of intangible assets like brevet has positive effect on the probability to export as in Harris and al. (2005). Another interesting finding is that skill workers affect positively the probability to export of the manufacturing firms in Senegal. These findings may have policy implications. The evidence of learning-by-exporting suggests Senegal has much to gain from encouraging exports by helping domestic firms to overcome the barriers to enter into foreign markets by promoting access to intangible assets like brevet. Particularly, they suggest that export promotion policies could be helpful, reducing the level of financial constraints faced by firms, and indirectly enhancing their investment spending and productivity. The latter effect could be particularly relevant for small and medium sized enterprises (SMEs), whose investment is often constrained by lack of finance. Another key issue is the labour quality as a driver of manufacturing exporting. The Senegalese educational system is suboptimal as it offers training that does not meet the requirements of the labour market. Considerable efforts are required in order to improve Senegalese human capital, especially in the areas of secondary and university education, in order to rapidly achieve a quality of human capital comparable to that existing in emerging countries. The remainder of this paper is organised as follows. Section 2 summarizes the related literature. Section 3 is devoted to the background on the Senegalese industrial policies. Section 4 provides an overview of the data and presents some relevant descriptive statistics, while section 5 presents our empirical framework and the econometric methods to test the relationship between the firm-level efficiency and export experience. In section 6, we discuss the main results from our analysis. Finally, Section 7 concludes and discusses the policy implications. 2. Related literature on exporting and productivity 2.1. Self-selection or Learning by exporting? Since the first empirical paper of Bernard and Jensen (1995) on exporting and firm performance on the US economy, there is a growing number of empirical papers investigate the relationship 1 see the Global Competitiveness Report2015 for a global indictor of competition and those of the constraints of exporting in the countries in the world.

4 136 Journal of African Development 2017, 19(1): between exporting and productivity using data at the level of the firm. But there is still little systematic evidence that efficiency firms may self-select into the export market or exporting causes efficiency gains. A survey of Wagner 2007 summarises that, on average, firms that export are more productive than firms that do not export and that there is evidence of self-selection in the exporting process, while the evidence on the learning effect is mixed and unclear. Some studies do not find any evidence of post entry productivity changes (Wagner 2002 ; Arnold and Hussinger 2005 ; Hansson and Lundin 2004; and others find evidence (Greenaway and Kneller 2004, 2007, 2008; Girma et al. 2004; Biesebroeck 2005; Damijan and Kostevc 2006; De Loecker 2007, 2010; Serti and Tomassi 2008; Máñez et al. 2010; and Dai and Yu 2013). An abundance of evidence indicates that firms entering export markets grow substantially faster in employment and output than non-exporters, especially for countries like Columbia, several sub- Saharan African countries, Slovenia and Canada (Beisebroeck 2005, De Loecker 2007, Lileeva and Trefler 2010). Martins and Yang (2009) conduct a meta-analysis of more than 30 published papers that study the causal relationship between exporting and firm productivity. They find that there is no evidence of publication bias in the literature about the effects of exporting upon firm performance. Their findings also highlight that the impact of exporting upon productivity is higher for developing than developed economies, and the export effect is likely to be influenced by different performance measurements and estimation methods What have we learned from different applications of the learning-by-exporting model? While the question on the causal relations between export and productivity growth is still debatable, the recent literatures go beyond the on-going discussion and investigate specifically through which channels those impacts occur. Innovation and technological upgrading are widely studied mechanisms with related to trade and productivity increase. In industrial organizations, standard approach is that firms invest in intangible assets, such as R&D and advertising, to overcome existing barriers to entry into new markets. However, recent empirical studies in trade demonstrate that exporting leads to productivity improvements by influencing process and product innovations, increasing labour productivity and inducing firms to upgrade technology for the most productive firms (Damijan et al ; Lileeva and Trefler 2010). Changes in technology not only affect productivity but also can have implications for factor markets. For instance, the technology investment requires skilled labour and relative demand for skill increased in developing countries during the trade liberalization period (Goldberg and Pavcnik 2007). Market size may be another channel which motivate firms to innovate and hence being more productive. Wagner (2002) is the first paper to investigate the causal effect of exporting on firm size and labour productivity. Market size and trade affect the competition across markets, in terms of the number and average productivity of competing firms. Business managers are well aware of the fact that credit constraints can hamper or even prevent exporting. The reason is that exporting involves extra costs to enter foreign markets (e.g. for the acquisition of information about a target market, for the adaption of products to foreign legal rules, etc) that often have to be paid up front and that to a large extent are sunk costs. Firms need sufficient liquidity to pay for these costs, and constraints in the credit market may be binding. Empirical papers on learning by exporting looked at the links between financial constraints and export activities using data at the level of the firm 2. The findings show that less constrained firms self-select into exporting, but that exporting does not improve financial health of firms 2 Wagner J. (2014) provides a survey of empirical studies using firm-level, data.

5 Journal of African Development 2017, 19(1): Other literature made sectorial analysis to compare the export s impact on productivity across sectors. There is evidence that service sector firms are able to reap the benefits of exposure to export markets at an earlier (entry) stage of the internationalization process than are manufacturing firms (Love and al 2010). From UK firm data, Harris and Li (2005) found that productivity gains from entering and exiting export markets, however such productivity effects are larger in the services, in particular, financial and business services than in production including agriculture, manufacturing and construction. 3. Background on the Senegal s Industrial Policy Nearly all of the newly independent countries in the region started with an import substitution (IS) approach to industrialization, where nascent industries received protection. Senegal applied these policy instruments during the 1960s primarily to preserve the industrial base inherited from the colonial era From Import Substitution to Economic Liberalization Senegal s first two development plans which were implemented during the period 1961 to 1969 focused primarily on promoting industrial development to substitute for manufacturing imports. Tariff and non-tariff barriers protected large enterprises which were created through this strategy. In the 1970s, an alternative policy emphasized the development of small and medium businesses through the creation of the National Company for Industrial Research and Development (SONEPI), followed by the Dakar Industrial Free Trade Zone (ZFID), in 1969 and 1974 respectively. SONEPI is primarily responsible for technical support for private initiatives and gives substance to state industrial policy, particularly by developing industrial areas in regional capitals Tariff protection Prior to the 1979 tax reform, taxes on imports included customs duties, statistical taxes, and flat-rate taxes, making the tax system complex, benefiting firms that process primary products such as peanuts, imported wheat, textile, and fish for consumption. This tariff system is intended to procure resources for the state and protect domestic firms Quantitative restrictions Quotas, prior authorizations, and prohibitions against the import of certain goods provided quasi-monopoly status to some firms. Moreover, domestic producers in certain sectors benefited from additional protection that restricted imports through special conventions and memoranda of understanding and administrative pricing. However, exporting firms had to pay a statutory tax, a transaction tax, a statistical tax, and a market research and packaging tax (only for peanuts), thus increasing the anti-export bias in import protection measures Investment code Senegal introduced its first investment code in 1962 to help domestic firms that were set up to promote the objectives of the strongly IS-oriented development plan. Beneficiary firms

6 138 Journal of African Development 2017, 19(1): had major tariff and tax exemptions that remained unchanged for a full twenty-five years. The code was amended in 1965, lowering the required investment from 1 billion FCFA to 500 million FCFA and expanding tariff and tax exemptions on replacement parts, in addition to deferred taxes on imported inputs. In 1977, a new law came into force with the goal of broadening the industrial base through promotion of small and medium enterprises (SME). With this law, there were now two tax codes: the grand code for large firms and the petit code for businessmen with initial investments under 20 million CFA francs (FCFA) Dakar industrial free trade zone The Dakar Industrial Free Trade Zone (ZFID) was established in 1974 to accommodate firms engaged in manufacturing, assemblies, and processing for export, as well as complementary service firms. Authorized firms were exempted from taxes on corporate profits, wages, and all indirect domestic taxes on production. In terms of tariffs, they did not pay tariffs and taxes on imports of capital goods, equipment, primary materials, and other semi-finished inputs. Free trade zones were introduced in 1991, and firms benefited from all the advantages conferred upon those in the ZFID without having to set up there. Exporting firms benefiting from the free trade zone or ZFID frameworks had to face a corporate tax of only 15 per cent, starting in Industrial zones The idea of industrial zones appeared in 1965, but was not the object of any financing scheme until the 1975 agreement between the governments of Senegal and Germany, which led to creation of the Dakar Industrial Zone Corporation in The government also created industrial zones in other regional capitals, albeit with varying success. SMEs operating in these industrial zones received assistance from management firms in addition to major tax advantages. In particular, they benefited from a five-year exemption from corporate taxes in Dakar and a seven-year exemption in other regions, and from other taxes on equipment and materials not manufactured or otherwise produced in Senegal, as well as from taxes on replacement parts for this equipment, activities relating to training or the expansion of firms, and services offered by management firms. The industry zones typically provided technical, legal, administrative, and marketing assistance to firms operating in the area. By the early 1980s, the process of industrialization had begun to show several weaknesses: (1) the IS policy had reached its limits in a small and overprotected market; (2) the process weakened benefits from natural resources and the export crisis left traditional export sectors (peanuts, phosphates, fish) with no prospects for recovery; (3) highly effective tariffs ( per cent) had perverse effects through lost competitiveness and high rigidities against adaptation; (4) state intervention proved excessive and costly. Industrial growth after 1980 generally ranged from 0 2 per cent, as opposed to 4 5 per cent in the preceding decade Adjustment Policies and Liberalization of the Economy The 1970s world economic crisis arrived in Senegal with the collapse of macrofinancial stability, exacerbated by the return of the drought cycle. Faced with a growing deficit of resources, the state appealed to Bretton Woods institutions at the cost of more orthodox policy based on fiscal consolidation and the use of market forces to govern access to resources and their use. In 1979, the year of the first stabilization program, the state was to simplify and

7 Journal of African Development 2017, 19(1): reduce import tariffs and taxes, while export taxes were eliminated except on peanuts and phosphates. By 1986, it was the transformation sector s turn, with a New Industrial Policy (NPI) aiming to dismantle tariff barriers. Another major decision was taken in 1986 to abandon administrative pricing (used to address undervaluation of declared imports) to calculate tariffs on imports. The NPI action plan was comprised of four axes: revised protections for domestic industrial sectors, export promotion, revival of investments and improvement of the environment for industrial activities. According to the first, the tariff code was revised, with rates cut from 65 percent to percent over two years and by reducing the number and range of applicable rates. The resulting reduction in the anti-export bias was supplemented by introducing an export subsidy which totalled 10% of the FOB value of exports over This rose to 15 percent of the FOB value over and 25 percent of value added in export activities after Peanut products and phosphate exports, which did not benefit from the subsidy, saw tax levies eliminated in Reform of the export subsidy system was supplemented by establishing an integrated credit insurance and financing system for the export of manufactured goods. According to the third and fourth axes of the NPI action plan, the investment code was also revised, an industrial restructuring fund was created, and assistance and advice provided to investors was expanded. To improve the business environment in the industrial sector, measures aimed to liberalize prices and marketing channels, reduce production factor prices and simplify administrative formalities. However, these measures were adopted in a context of persistent domestic currency appreciation and declining competitiveness of Senegalese firms. In 1989, being pressured by firms and faced with declining tax receipts, the state had to postpone implementation of the second phase of the NPI, referred to as the recovery phase, committed to in The 1989 reform plan could not be implemented until 1994, as part of the overall adjustment initiated by devaluation of the CFA franc. The NPI action remains a painful failure in the history of economic reform in Senegal, with the closure of under- or uncompetitive firms causing significant job losses (7 percent of permanent staff between mid-1987 and mid-1988) Implementation of a Common External Tariff (CET) The period preceding the 1994 devaluation saw a rich debate on the future of the WAMU. The perspective which prevailed was preservation of the Union, and ultimately provided the inspiration to transform it into an economic union with the goal of accelerating integration and convergence among economies in the CFA franc zone. This having been done, the goal of not creating a WAEMU fortress was upheld and the union proceeded with tariff reductions and established a common external tariff (CET). The 50% reduction in the FCFA exchange rate had already made possible the substantial 1994 reduction in tariff rates and simplification of import taxes which remained up to the initiative of each state. Implementation of the CET is considered as a productivity shock comparable to the NIP except that it came in the wake of the major productivity gains associated with devaluation of the CFA franc. Over , industrial activities grew by an annual average of 3.8% (Rapport du FMI n 12/337, Novembre 2012) Deepening economic liberalization and other measures to promote the private sector Given the benefits from devaluation, special conventions and protocols were renegotiated, in that many benefits were eliminated or reduced. Similarly, price control regimes were made more flexible and the privatization program initiated during the 1980s was extended to sectors previously considered as strategic, such as infrastructure services and the financial sector.

8 140 Journal of African Development 2017, 19(1): Concerning the labour market, the reforms carried out over allowed firms to resort to economic layoffs and also reduced restrictions on fixed term labour contracts. The creation of the Investment Promotion and Major Projects Agency (APIX), and the Agency for the Development and Supervision of SMEs (ADEPME) was followed by the 2002 transformation of the Senegalese Standards Institute to an association in order to encourage professionals to be more accountable in product quality certification. Previously, the interest of the state in improving the quality of its intervention in the economy and services provided to firms led to the January 2005 launch of a process to prepare the Accelerated Growth Strategy (SCA) by building upon the benefits linked to and orientations of the private sector development strategy (SDSP) adopted in The SCA offers a common framework to establish a business environment with international standards which benefits all sectors including: transformation activities, the promotion of promising sectors such as horticulture, agro industry, aquaculture, telecommunications and tourism or the improvement of sectors such a fisheries and textiles through a competitiveness cluster approach. 4. Model specification and estimation procedure 4.1. Model specification The literature proceeds mainly in two ways to test learning by exporting. The first is the two steps methodology which consists to compute total factor productivity (TFP) from a production function before to test the learning-by-exporting on TFP. Yang and Mallick (2010) used the same approach in the context of China. However, the potential correlation between the factors in the production function and unobserved variables that includes productivity or export status can yield to biased estimates of TFP. As a result, all conclusions drawn from learning by exporting test rely on biased TFP and can be inconsistent (Van Biesebroeck 2005; Bigsten and Gebreyesus 2008). Another approach which might reduce biases is the one-step approach suggested by Bigsten and al. (2004) who followed Clerides and al. (1998). This approach consists to test the learning by exporting directly on the production function by including export status in this equation. We adopt this approach. We assume the production function (measured as deflated gross output) to be a Cobb- Douglas function of inputs, such as labour, capital, raw materials and productivity of the firm specified as follow: y it λy it 1 (1 λ){α k k it α ls skilled workers it α lu unskilled workers it α m m it } loga it η it (1) where y it, k it, skilled workers it, unskilled workers it, m it and A it refer to respectively the logarithms of output, capital stock, skilled workers, unskilled worker, raw materials and total factor productivity (TFP) ; i=1,,n and t=1,,t are firm and time indices respectively; α k, α l, α m are estimated elasticities of output with respect to inputs and is a residual term which captures productivity shocks assumed serially uncorrelated. The dynamic form of this model relies on the assumption that it may take time for output to reach its new long-run level after a change of factors of production. The inclusion of lagged dependant variable also makes serial correlation of the residual less likely. In line with learning-by-exporting hypothesis, the firm-level productivity (TFP) or efficiency is supposed to be a function of past export status Export it 1. We allow for heterogeneity in A it, by including dummy variables for timeand industry denoted Sector it.we hence write the efficiency equation A it in logarithmic form as :

9 Journal of African Development 2017, 19(1): log A it β 1 Export it 1 β 2 Sector it β 3 Year i μ i (2) where Export is a dummy variable equal to one if there is some exporting and zero if there is not ; β 1, β 2, β 3, denote parameters to be estimated, μ i. is an unobserved heterogeneity in the form of firm specific effects. The production equation is then written as follow: y it λy it 1 (1 λ){α k k it α l l it α m m it } β 1 Export it 1 β 2 Sector it β 3 Year i μ i η it (3) Although results from standard estimation techniques of equation (3) like instance OLS or the standard panel GLS ( random effects estimator) to test the relation between exporting and efficiency will tell us a lot about the main patterns in the data in term of existence of LBE, they are likely to yield misleading estimates in presence of unobserved heterogeneity as the firm s management capacities, or endogeneity between export status and productivity due to self-selection. Indeed, exporting firms might possess some characteristics that make them more productive than their domestic counterparts, thus allowing them to overcome sunk cost and enter the export markets Bigsten and al. (2004). As discussed in Harris and Li (2005), there are three standard approaches in the literature that attempt to eliminate the bias that arises from self-selection. The first one is the Propensity Score Matching which involves matching every exporting firm with another firm that has very similar characteristics but does not export based on the score of the propensity to exports. The main limit of the PSM method is that in presence of unobservable characteristics, estimates are likely to be biased (Rosenbaum 2002). The second one is the instrumental variable (IV) estimations like GMM or Heckman model whose main concerns is to find good instrument to eliminate the endogeneity. We are unable to implement this method because of lack of suitable instruments. The third approach is that of Clerides et al. (1998) which is a joint estimation of the productivity-equation and export-participation equation. Our paper adopts a similar approach to deal with self-selection to exports. We assume that export participation depends on previous export participation, Export it 1, labour productivity, L it 1, firm characteristics X it 1 We rely on the existing literature for selecting the variables which are included in X it. The literature recognizes that learning-by-exporting is conditional on firm characteristics such as firm log age in order to control the firm s experience (Fernandes and Isgut 2005), logarithm of firm size, quality of workers measured by the number of skilled workers (Söderbom. and Teal 2000), the duration in export market measured by the number of exports year. Another strand of the literature argues that learning-by-exporting is conditional on the existence of intangibles assets 3 as Brevets, Patents and Licences (Harris and Li 2005). Hence, we include dummy variable indicating the ownership of brevet and another one showing if the firm invest in Research and development. Furthermore, we add a financial dimension in the export equation in order to take into account the financial constraint of the firm (Wagner 2014). Financial dimension is important because firms face sunk entry costs into the export market. We expect that firms which exhibit better financial health are more likely to exports. Based on the financing constraints literature, we capture financial health by the liquidity ratio defined as the firm s current assets less current liabilities over total assets (Fazzari and Petersen 1993; Cleary and al. 2007; Greenaway and al. 2007). The higher its liquidity ratio, the better the firm s financial health. These characteristics are included in the model to capture a potential self-selection process by which certain firms choose to export because they are relatively efficient. 3 Assets refer to corporate intellectual property (e.g. patents, copyrights, trademarks, etc.)

10 142 Journal of African Development 2017, 19(1): Because our exports variable is a binary we employ a latent variable formulation and write the exports decision probability equation as below: Prob(Export it 1) (α 1 L it 1 α 2 Export it 1 α 3 X it 1 i it ) (4) Where is a probit function, Export is a dummy variable equal to one if there is some exporting and zero if there is not. α 1, α 2, α 3 denote parameters to be estimated, i is an unobserved firm specific time invariant effect affecting the decision to export and is it a homoscedastic, serially uncorrelated and normally distributed residual. The equations (3) and (4) form the basis for the econometric test for learning effects. y it λy it 1 (1 λ){α k k it α l l it α m m it } β 1 Export it 1 β 2 Sector it β 3 Year i μ i η it (3) Prob(Export it 1) (α 1 L it 1 α 2 Export it 1 α 3 X t 1 i it ) (4) The equation (3) is a linear specification of the production to test the learning by exporting (LBE). The coefficient β 1 captures the learning by exporting effect. If β 1 0, then this is evidence of learning, i.e. exporting results in higher productivity. The equation (4) is a probit model of the decision to export. If there is support for self-selection-into-exporting, i.e. that efficient firms become exporters, α 1 would be positive. If there are fixed costs associated with exporting, so that firms tend to continue exporting once they have entered the international market, α 2 would be positive Estimation procedures The econometric model (3)-(4) is estimated by maximum likelihood (ML) methods. The model ignores the panel nature of the data and assumes that there is no unobserved heterogeneity in the forms of firm specific effects. The likelihood function for each observation i is defined as follow: L i Π T t 1 F((2Exportit 1).(z 2it b 2 (ρ ηω /σ η ).(y it z 1it ).(1 ρ 2 ηω) 0.5 ) σ η 1. f((y it z 1it b 1 )/σ η ) (5) Where z 2it forms the group of independent variables in the export equation, z 1it represents the explicative variables of production equation, F(.) and f(.) are the standard normal distribution and density functions respectively, ρ ηω is the correlation between η and ω, σ η is the standard deviation of η, b 1 and b 2 are the parameters to be estimated. We use ml model command in stata to estimate simultaneously the different parameters. 5. Data set and basic statistics 5.1. Data The data used in this paper was collected by the CUCI (Centre Unique de Collecte d Information), department of the Senegalese National Agency for Statistics and Demography (NASD) over the period The data contain information on the annual accounts of representatives companies registered in Senegal. The main business sectors of the firms include business

11 Journal of African Development 2017, 19(1): agriculture, mining, energy, food products, leather, wood, textiles, chemicals products, other industries, trade and several other services sectors. The data was collected through two table Sheets. The first one which is filled by medium and large companies 4 gives information on firm-level business activities, turnover, sales, capital stock, staff costs, intermediates materials, tangibles and intangibles assets, exports and various financial data such as, profits, debts, subsidies and investment. The second table concerns all the firms currently operated and gives information on the number of employees, the employers s qualification and the salaries. The two sources of information were merged using the firm identification number, which allow us to follow the firms over time. For the purpose of our research, we selected those firms operating in the manufacturing sector and that provided information for at least three years during the period 1998 to This criterion was selected in order to be able to check the consistency of time-invariant characteristics (Cruz and al. 2014). We drop from the data set those firms with missing information on critical variables for the analysis, such as firm turnover, value added, capital, labor, salaries, age, size, among others. We build a balanced panel for our estimation to avoid bias coming with the presence of attrition. The resulting balanced panel includes 232 manufacturing firms which is present in the data all the period. It includes 3016 observations for 527 firms exporters and 2489 non-exporters. The data set reports information on whatever the firm has export or not or the amount of the exports or the both information. Owing to missing data on export amount, we define, exporters in a given year as plants that reported a positive amount of exports or declared having an export activity. The information in the data set are in monetary values. Output is defined as production of the firm. The Intermediate input-cost includes raw materials and related supplies (consumables, energy and other goods). The labour cost includes salaries and wages. Capital is defined as total fixed tangible assets. Investment is calculated on the basis of firm-level capital, using a standard depreciation rate of 10 percent with is the official rate calculated by the National Statistical Agencies (Cabral and al. 2014). Real values are obtained by deflating monetary values using inputs and output deflators calculated from the reported volume and value of the national account data. Production was deflated by value added deflator. In the absence of a sectoral input deflator, staff costs and intermediate input-cost was deflated by value added deflator; for capital costs, we constructed a Gross Fixed Capital Formation deflator. Of course, this is unlikely to yield perfect deflators but the difficulty is the lack of appropriate data Descriptive Statistics Table 1 exhibits the number of firms, exporters and non-exporters by year. In the sample, between 11 percent and 24 percent of the firms each year report positive earnings from exports or had export activities. The share of exporting plants is around 15 percent since 1998 but decreased between 2005 and 2006 then start to grow slightly until 2010 and decrease again in 2011.The rise in the share of exporting plants since 2007 can be attributed mostly to the improving export competitiveness policies and the growth clusters approach starting in 2005 with the creation of the ASEPEX in the same year. There are exporters in all manufacturing sectors, but there is considerable variation in the internationalization of each sector (table A3). 4 It means firm that have a turnover of at least 30millions CFA francs.

12 144 Journal of African Development 2017, 19(1): Table 1. Export patterns of Senegalese manufacturing firms. year Non-exporters Exporters Total firms Exporters (%) , , , , , , , , , , , , ,84 Total ,47 Source: Computations from CUCI firm data set. Following Gupta and al. (2013), we estimate the transition probability of firms between exporter and non-exporter status from year t to t + 1. In Table 2, 0 depicts non-export status and 1 depicts export status. As we can see in the transition probability, the probability that a firm exports knowing that it had not exported the previous year is very low (6.15 percent) while when a firms starts out as a status 0, there is a 93,85 percent probability of non moving into status 1 ie exporter. This result suggests that since entering export markets is costly, firms do not easily switch from domestic to export market over a 1-year horizon. However, firms are likely to continue to export the following year once they enter export market because the probability of exiting from export markets is 26.58% over a 1-year horizon while the probability to continue to export knowing that we exported previous year is higher (73.42 percent). Table 2. Transition probability from t to t 1. Non exporter 0 Exporter 1 Total Non exporter % 6.15% 100% Exporter % 73.42% 100% Total Source: Computations from CUCI firm data set % 17.60% 100% We account on the movement of exit and entry of the firm. By studying the firms exporting status over the whole period, we distinguish five different types of firms: never exporters,

13 Journal of African Development 2017, 19(1): always exporters, starters, stoppers and switchers. Never exporters are firms which never export during all the period Always exporters are firms that export during all the period. Starters are firms which becomes an exporter during the period and does not reswitch through the end of the period. Stoppers are firms which cease exporting during the period and does not reswitch. Finally, the switcher firms reswitch export status more than once in the period of data set. Table 3 summarizes the distribution of firms by export status over the period Of the manufacturing firms in the dataset, an average 56.9 percent never supplied in the external market during the period At the same time, nearly 4.74 percent were starters, 4.74 percent stopped exporting. Almost, percent of all the firms are switchers. The minority, 0.86 percent were always exporters over the thirteen-year period. Table 3. Distribution of type of firms by year (%). Year Always Switcher Starter Stopper Never Total ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, ,86 32,76 4,74 4,74 56, Total 0,86 32,76 4,74 4,74 56, Source: Computations from CUCI firm data set. The literature has well-established that exporters are better than non-exporters by various performance standards (Bernard and Jensen 1995 ; Jamal 2012 ; Yang and Mallick 2010). Table 4 compares various plant attributes between exporters and non-exporters over the period It shows that, on average, exporters tend to have higher production; spend more on capital; pay higher wages; have more sales and are willing to hire more workers. Consistent with earlier studies comparing (see, for instance, Bernard and Jensen 1995; Blalock and Gertler 2004), it is clear that exporters tend to be larger (in terms of number of employees) and more capital intensive than non-exporters. Exporting firms have higher share of qualified workers and pay higher wages per employee, reflecting their higher labor productivity. The percentage of plants with foreign ownership was higher for exporters than non-exporters: 27 percent for exporters and only 12 percent for non-exporters. As a consequence, levels of total factor productivity (TFP calculated with three methods - OP, LP and GMM) of exporting plants

14 146 Journal of African Development 2017, 19(1): are, on average, higher than those plants producing for domestic markets only. Some of the differences in the TFP levels may be attributed to the differences in research and development (R&D) intensity and the ownership to Brevets. However, the share of expenses in term of R&D and Brevets is very low in the Senegalese context. These results are similar to those reported for sub-saharan Africa in Biesebroeck 2005; for Ethiopia in Bigsten and Gebreeyesus 2008 ; for India in Jamal 2012 and for China in Yang and Mallick Table 4. Firm characteristics. All Non-exporters Exporters Variables Mean Std Dev Mean Std. Dev Mean Std. Dev Log Turnover 20,27 2,02 19,86 1,86 22,20 1,53 Log Production 20,19 2,03 19,77 1,89 22,14 1,49 Log staff cost 20,85 1,85 20,67 2,08 21,19 1,29 Log Salaries 11,38 1,76 10,99 1,69 12,75 1,24 Log Capital 18,52 2,56 18,10 2,51 20,49 1,76 Log Size 3,70 1,54 3,44 1,48 4,93 1,22 Age 20,39 14,15 19,77 13,81 23,31 15,32 Skilled workers 37,24 143,78 28,73 142,18 77,40 144,61 Non skilled workers 50,02 134,62 40,68 127,38 94,10 157,41 Log Turnover per worker Log Production per worker Log Cost staff per worker Log Wage per worker Log Capital-labor ratio Skilled workerssize ratio 16,55 1,19 16,40 1,15 17,27 1,09 16,49 1,25 16,34 1,23 17,21 1,04 14,47 0,86 14,37 0,84 14,97 0,76 7,39 0,85 7,27 0,83 7,81 0,75 14,80 1,80 14,64 1,86 15,55 1,23 0,29 0,26 0,28 0,27 0,31 0,21 R&D-turnover ratio 0,0004 0,0043 0,0003 0,0043 0,0004 0,0044 Brevet-turnover ratio Foreign capital (dummy) 0,0012 0,0109 0,0013 0,0120 0,0010 0,0018 0,14 0,35 0,12 0,32 0,27 0,44 R&D dummy 0,04 0,19 0,03 0,18 0,06 0,24 Brevet dummy 0,36 0,48 0,29 0,45 0,66 0,47 Source: Computations from CUCI firm data set. The table A2 shows the previous characteristics by type of firms. On average, Switcher have higher turnover and production, spend more on capital, pay higher wages and have higher proportion of skilled workers than other type of firms. In addition, their average productivity labour and their average capital-labor ratio are the highest among the categories of firms.

15 Journal of African Development 2017, 19(1): Given this characteristics, Switchers are followed by Always, then Starter and Stopper. The proportion of firm which invest in R&D and spend in brevet is higher among switcher and Always in relation to other firms. While the Always firms are bigger in term of number of employers, the switcher are younger. Finally, the Total Productivity Factor is largely higher for Always and for switcher than other categories of firms Productivity before and after entry (and exit) In this section, we consider the relationship between productivity paths and exporting taking the entire exporting history of plants into account. Following Bernard and Jensen (1999) approach, we test the self-selection and learning by exporting hypotheses using a regression of the following form : Ln Y it Σ gεg Σ kεk β gk D gi D ki γ Controls ε it (10) Where Ln Y it is the log level of various firm performance, G is the set of plant groups, and K is the set of locations in the year window. D g and D k are dummy variables denoting plant group and location in the year window, respectively. Thus, the coefficient β gk denotes mean values of each plant group g at each location k, controlling for firm individual fixed effect, year effects and sector fixed effects. The regression allows us to calculate a productivity trajectory over time for different types of firms within an industry while controlling for sector and size effects. We allow five firm export types, D gi, which are defined as in the preceding : always, starter, stopper, switchers, and never exporting. We consider five years intervals so that K = { 2, 1, 0, 1, 2}. Then, we are able to track firms from two years before entry (or exit), ie D ki 2, through entry (or exit), ie D ki 0 to two years after entry (or exit), D ki 2. The interaction of the indicator variables will give us a picture of the relative productivity levels of all the types of firms as they move in and out of exporting. We consider Total Factor Productivity as measure of firm performance and compute three TFP - Olley and Pakes (1996), Levinshon and Petrin (2003) and GMM - Blundell, and Bond (1999) - as dependent variable in separate estimations. The estimates of the production function following these three approaches are presented in Annex (Table A5). Equation (1) was estimated with the never exporting at period (-2) as control category. Table 5, Figure 1 and Figures A1 and A2 in appendix shows movements of TFP for the different types of firms (grouping the two categories switchers and always which have very little observations in the group always). Table 5 contains the Total Factor Productivity level across year and plant types relative to that of never exporters in the first period (-2). In general, the group always exporting and starters performed better than those never exporting on all three measures of TFP. Even before they began exporting, starters exporters were better than those never exporting and they continued to widen the gap while converging those always exporting. Figure 1 shows that the always exporters plants are more productive than the never exporters. The productivity of starters was also found to increase in the post-export period, and to remain higher, with a widening gap from those never exporting. This is evidence of learningby-exporting. However, this learning effect is rather short lived and pronounced immediately after entry into the export market. A large part of the TFP gap between starters group and always group decreases after they start exporting. Figure 1 confirms also the existence of self-selection in the entry into the export market. Plants starters have higher TFP levels compared to those that never export several years before they enter the export market. Our results are consistent with the pattern found in Bernard and Jensen (1999), Hahn (2004), Van Biesebroeck (2005), Fernandes and Isgut (2005), Greenaway and Kneller (2008), Bigsten and Gebeeyesus (2008), Serti and Tomasi (2008), De Loecker (2010) and Dai and Yu (2013).