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Learning to export and learning from exporting: The case of Ethiopian manufacturing

In this study, we use a long panel dataset of Ethiopian manufacturing firms for the period 1996-2009 to test certain hypotheses regarding firm performance and exporting, namely export hysteresis, self-selection, and learning-by-exporting. We find evidence for export hysteresis and the presence of high sunk costs associated with exporting. However, the magnitude of this effect has become lower when controlling for unobserved firm heterogeneity using a dynamic random effect probit model suggesting that not addressing the unobserved heterogeneity can lead to an overestimation of the coefficient of the sunk costs. Moreover, we find no evidence of self-selection into exporting when controlling unobserved firm heterogeneity and unlike to most previous studies. On the other hand, we find strong robust evidence in support of the learning-by-exporting. Our results support the view that export promotion efforts might have long-term effects in terms of sustaining exports and industrial competitiveness.


Introduction
The development thinking on the relationship between export orientation and economic performance has long been optimistic. Exports are expected to promote economic growth through increased earnings of foreign exchange (thus relaxing balance of payments constraints), economies of scale and access to new technologies and knowledge (Helpman and Krugman, 1985;Melitz, 2003). There is considerable cross-country empirical evidence of a positive association between growth in aggregate exports and real output growth (see Greenaway and Sapsford, 1994 for a review). The miracle of industrialization in East Asia is also often explained by the export orientation of these countries (World Bank, 1993).
Several microlevel studies have also documented a positive association between exporting and firm performance (Aw and Hwang, 1995;Roberts and Tybouts, 1997;Clerides et al., 1998;Bernard and Jensen, 1999;Bigsten et al., 2004;Bigsten and Gebreeyesus 2009). The direction of causality, however, has been a point of debate between two competing (but not necessarily exclusive) hypotheses: self-selection and learning-by-exporting. The self-selection hypothesis points out that exporting involves a large sunk cost that include modifying domestic products for foreign consumption and searching for markets, new distribution networks and transportation. Thus, firms that enter into export markets need sufficiently high profits to cover the fixed costs; consequently, the more productive firms may self-select into the export market (Roberts and Tybout, 1997). The learning-by-exporting hypothesis, on the other hand, emphasises the productivity improvement of firms following their entry into export markets. This occurs because entry into export markets improves access to information on best practices of managerial and marketing techniques, new technologies and exposure to competition (Clerides et al., 1998). Self-selection into exporting is well supported by most of the existing empirical studies for both developed and developing countries. However, the evidence for learning-by-exporting is mixed. For example, Bernard and Jensen (1999), Clerides et al., (1998) and Delgado et al., (2002) find evidence in support of self-selection but not of learningby-exporting, whereas Hahn (2004), Fernandes and Isgut (2005), Kraay (1999) and Girma et al., (2004) find evidence for both self-selection into and learning from exporting.
Despite the continuing engagement of several sub-Saharan African (SSA) countries in some type of export promotion activities, only a few studies explicitly investigate the relationship between export and firm-level productivity, partly due to lack of suitable data. Bigsten et al., (2004) find some evidence of learning-by-exporting using firm-level data collected as part of the Regional Program on Enterprise Development (RPED) in the early 1990s from four SSA countries. Van Biesebroeck (2005), based on the same data source but covering three more SSA countries, finds both self-selection and learning-by-exporting. Both of these studies rely on a dataset that covers a very short time period, a maximum of three years, which makes it hard to investigate any learning effect when learning takes a longer period of adjustment in technology and productivity. Rankin et al., (2006) use relatively longer panel data (although not collected annually) with a focus on the determinants of export participation. They found a strong and robust firm size effect on export participation, while the evidence for self-selection is weak.
Using annual 10-year (1996-2005) firm-level panel data for Ethiopian manufacturing firms, Bigsten and Gebreeysus (2009) provide evidence for both self-selection into exporting and learning-by-exporting. The current study extends their work in the following ways. First, it extends the study period by four more years. This will obviously improve the precision of the estimation results and enable us to identify any long-term effect of learning by exporting. Second and more importantly, a closer look at the recent period is warranted given the Ethiopian government's intensive export promotion efforts and the recent increase in the number of manufacturing firms entering into the international market. For example, in the period 2005/06-2008/09 alone, the number of manufacturing firms with 10 or more employees almost doubled and the number of exporting firms grew by about 40% (Table 3). By comparing the propensity to export of young firms (i.e., those born within the last few years), the current article examines whether the recent export promotion efforts reduced the sunk cost of exporting and resulted in increased entry into the export market.
Third, the post-2005 period has also seen a substantial increase in the share of Ethiopian manufacturing exports destined for the developing country markets (Table 2). By controlling for sector-level share of manufacturing exports directed to the developing world, we investigate whether and how the rise in the developing countries' share in total exports affects the scope of learning from exporting during the post-2005 period.
Fourth, unlike most previous studies on export participation (including Bigsten and Gebreeysus 2009), we control for unobserved firm heterogeneity using the dynamic random effect model proposed by Wooldridge (2005).
The remainder of the paper is organized as follows. Section 2 gives some background information on the policy and export performance of Ethiopia. Section 3 sets the empirical framework. Section 4 describes the data and gives some descriptive statistics. Section 5 discusses the econometric results and Section 6 concludes.

Some background: Ethiopia's policy and export performance
Agricultural Development Led Industrialization (ADLI) was formulated in the mid-1990s as Ethiopia's broad development vision. The philosophy of ADLI is that agricultural development plays a leading role in industrialization; thus, the policy focuses on improving productivity of smallholder agriculture and creating linkages with the industrial sector. Based on this broad development vision, the country formulated an industrial development strategy (IDS) in 2002/03. One of the guiding principles of the IDS was that sustainable and fast industrial development can only be ensured if the sector is competitive in international markets. To this end, the strategy identifies and gives preferential treatment for labor-intensive and export-oriented sectors, including textiles and garments; meat, leather and leather products; agro-processing; construction; and micro and small-scale enterprises.
The IDS was put into action by subsequent development plans, by way of setting targets, particularly on export performance. 1 In order to meet the targets, the government has played an important role through improving the investment climate and providing direct support to the selected sectors. Government support to the selected sectors has included providing economic incentives; promoting export-oriented cluster developments; and capacity building in terms of increasing the supply of skilled manpower for the sectors. As part of the economic incentives, exporters/investors were granted cheaper credit, easy access to land at lower lease prices with longer tenure periods, and duty and tax exemptions. The government also has increasingly engaged in developing industrial zones around major cities and in providing the export-oriented sectors with the necessary infrastructure, such as roads, power and telecommunications. For instance, the Ministry of Trade and Industry has allocated industrial zones for export sectors, including garment and footwear industries, in the vicinity of Addis and elsewhere for local and foreign investors (e.g., China, India, Turkey and Egypt) promising to engage in export sectors.
The Ethiopian economy has shown spectacular growth since 2003/04, registering an average annual growth of 10.6% between then and 2010/11. This is about double the average growth rate (5.2%) recorded for Sub-Saharan Africa over the same period. Ethiopian merchandise exports have almost quadrupled during this period (i.e., increased from less than half a billion USD in 2004 to above 2 billion USD in 2011). Both agriculture and 1 These plans include the Plan of Action for Sustainable Development and Eradication of Poverty, which covers the period 2005/06-2009/10, and the recent five-year plan, called the Growth and Transformation Plan (2010/11-2014/15).
manufacturing exports grew, but the growth of the former is much faster than the latter. For example, between 2001 and 2011, agricultural exports grew by about eight-fold, while manufacturing exports grew by threefold (Gebreeyesus, 2013). However, these achievements have resulted in little structural transformation. The Ethiopian economy remains agrarian, with the share of the industrial and manufacturing sector value added not exceeding 14 and 5%, respectively, in 2010 (World Bank, World Development Indicators). Both merchandise exports per capita and manufacturing exports per capita have also remained far lower than the average for sub-Saharan Africa (Table 1). Another recent trend in Ethiopia's manufacturing exports is a substantial increase in the share of exports destined to the developing country markets. For example, in 2000, the developed and developing countries' markets share was respectively 60 and 40%. In 2010, this was reversed and the developed and developing country markets accounted for 42 and 58%, respectively (Table 2). This shift might limit the scope for technological learning and spillover of best practices.

Empirical framework
3.1 Self-selection into export markets Roberts and Tybout (1997) developed a dynamic discrete choice model of export participation with sunk costs of a profit-maximizing firm. 2 The existence of sunk costs in export markets has two interrelated implications. First, it creates barriers to entry. As discussed above, firms that enter into export markets need sufficient profits to cover the fixed costs of entry; thus, the more productive firms self-select into export markets. Second, high sunk 2 The theory of sunk costs and exporting was developed by Dixit (1989), Baldwin (1989) and Baldwin and Krugman (1989).
costs imply high exit costs. When a firm stops exporting, its knowledge about the export market depreciates rapidly and it loses the expertise acquired over the years of exporting. Hence, those who have already incurred startup costs are more likely to continue exporting in the current period. The combination of sunk costs and uncertainty should induce persistence (hysteresis) in exporting status. We employ a simplified version of the Roberts and Tybout (1997) model of export decision below: where, y it is export participation dummy equals to one if firm i has any export in year t and zero otherwise, y it−1 is one-year lagged export participation dummy, Q/L denotes output per employee, K/L capital per employee, RM/L raw material per employee, EN/L energy costs per employee all of which entered with one-year lag. z it , on the other hand represents the set of control variables including firm age, number of years of previous exporting, ownership type, industry and year specific effects, ω i is firm fixed effects. The output per employee (Q/L) is used as a measure of productivity where factor inputs per employee are used as additional control variables. 3 In this specification, we infer self-selection from a positive and statistically significant γ 1 and persistence in exporting from β > 0. 3 Alternatively, we used value added per employee as a measure of productivity, in which case we exclude intermediate inputs from set of control variables. Results are largely similar and are not reported here for brevity. Recent studies (e.g., Van Biesebroeck, 2008;Syverson, 2011) have shown that productivity estimates are relatively insensitive to the choice of estimation method.
We use the pooled probit model to estimate the export decision equation. However, unobserved firm heterogeneity such as product attributes or managerial ability might affect firm's export decision. As these are permanent (or slowly changing) attributes they might lead to persistence in export behaviour and hence failure to account for them leads to the overestimation of the importance of entry costs in equation (1) (Bernard and Wagner, 1997). Under strict exogeneity assumption, the fixed effect method eliminates the unobserved time-invariant firm-specific effects (ω i ). However, the strict exogeneity assumption does not hold in models with lagged dependent variable (Wooldridge, 2002).
The treatment of the initial conditions is another important challenge in a dynamic panel data models with unobserved individual effects, particularly in non-linear models. Heckman (1981) suggested modeling the initial dependent variable jointly with the subsequent dependent variable. Wooldridge (2005), on the other hand, proposed an auxiliary distribution of the unobserved individual effects which is conditioned on the initial values of the dependent variable and exogenous variables. This method leads to very simple and tractable standard likelihood for static random-effect probit model with lagged and initial dependent variable as well as within-means of the time-varying explanatory variables as additional regressors. Rabe-Hesketh and Skrondal (2013) argue that the auxiliary model is overly constrained if it includes within-means of time varying explanatory variables across all periods, including the initial period, and can lead to severe bias for short panels. Akay (2012), on the other hand, argues the Wooldridge method works well for long durations (5-8 periods). Given the long panel period of our data, we adopt the Wooldridge method and re-estimate our model by including initial period export status and within-means of all time varying variables in our model. But also in addressing the concern by Rabe-Hesketh and Skrondal (2013) and following their suggestion we exclude the initial period values from calculating the within-means of time varying explanatory variables. Clerides et al., (1998) argue that productivity of firms might be boosted after entry into export markets due to reduction of inefficiency through increased competition, access to new technology, or economies of scale arising from access to larger markets. They extend the dynamic choice model of export decisions by Roberts and Tybout (1997) to allow for learning effects, i.e., causality to run not only from productivity to the decision to export but also from exporting to improvement in productivity.

Learning-by-exporting
The empirical strategy adopted by Clerides et al., (1998) and other subsequent studies on learning-by-exporting is to include export history in a regression of firm productivity.
where A it denotes productivity of firm i at time t, y it is an export participation dummy, and z it and e it represent a set of control variables and error term, respectively. If there is any learning effect due to exporting, then prior exporting experience should raise productivity (or reduce cost). We infer learning-by-exporting effect from a positive and statistically significant β 1 . If some of the learning effect spills over to domestic firms, then non-exporting firms belonging to export intensive sectors may also indirectly benefit due to local spillovers. We test the presence of local spillovers by including sector-level exports (at the level of 3-digit sectoral classification) and its interaction with previous export status in equation (2). Different measures of plant-level productivity used in the empirical literature include labor productivity, average variable costs, and total factor productivity (TFP), derived from a factor-share approach or from econometric estimation of a production function. The standard practice of linking productivity and export status, using the econometric approach, is the two-step approach, whereby at the first step TFP is derived from production function estimation and at the second step TFP becomes a dependent variable with export status as one of the main explanatory variables. However, this approach has been criticised on the grounds that, if export status is correlated with inputs, then omitting the export dummy from the production function regression could yield inconsistent input coefficients and productivity estimates (Van Biesebroeck, 2005). In this study, we use a one-step econometric approach whereby the export status is included in the estimation of the Cobb-Douglas production function.
where Q it and Q it−1 denote current and lagged output respectively, X it is an array of inputs used in production, and y it−1 is an indicator variable of previous year export status. Z it denotes other control variables such as firm age, number of years of previous exporting, and ownership type. λ t , r ᶞ and ω i represent year, sector and firm fixed effects. e it is a pure random error term.
The variable of interest here is prior export status. A positive and significant φ indicates a learning effect from export participation. The lagged dependent variable is introduced to control for the high persistence in the productivity series (output). In light of the evidence that more productive firms self-select into exporting, a lagged export dummy can also pick up a previous productivity effect if the latter is not controlled for. Firm age and number of years in exporting are introduced to capture the firm's production and export experience, respectively (Fernandes and Isgut, 2005).
There are some econometric issues surrounding the estimation of the relationship between productivity and exports, namely unobserved firm-level heterogeneity and endogeneity. A popular approach to deal with the problem of unobserved heterogeneity and endogeneity, which we follow in this study, is to adopt a dynamic panel model with instruments-the Generalized Method of Moments (GMM), where the firm-specific effects are differenced out. When individual series (inputs and outputs in our case) are persistent, Blundell and Bond (1998) propose a system-GMM (SYS-GMM) estimator which is derived from the estimation of a system of two simultaneous equations, one in levels (with lagged first differences as instruments) and the other in first differences (with lagged levels as instruments).
Selection bias is another source of concern in estimating the learning-by-exporting model. The estimate of the causal effect obtained by comparing the treatment group (exporters) with non-experimental control group (non-exporters) might be biased. The reason is that it is impossible to precisely observe the effect of treatment (exporting in our case) because we do not know the outcome of the treated (exporters) when it is not under treatment (exporting). Thus, in non-experimental studies such as this, one needs to compare productivity of firms with similar predicted propensity to export. The counterfactual control group is then constructed based on some observable variables that predict the probability of being under treatment (exporting). The matching method enables selection of a control group from the non-treated pool in which the distribution of observed variables is as similar as possible to the distribution in the treated group. Following previous studies (Wagner 2002;Girma et al., 2004;Bigsten and Gebreeysus 2009), we apply the propensity score matching (PSM) method, which involves pairing each treatment unit (exporter) with a comparison unit (non-exporter) that is similar in terms of observable characteristics. Selection on unobservables, however, remains as one of the caveats of the PSM method as it only controls for observed variables.
An additional source of selection effect is the use of unbalanced panel dataset. This is because participating in the export markets will help firms improve not only their productivity and competitiveness internationally; it also helps improve their survival in the domestic market since the knowledge acquired from the export markets can easily be transferred in their operation domestically. We discuss robustness of the baseline results when using the balanced panel sample are discussed in Section 5.

Data and descriptive statistics
A firm-level census dataset for the period 1996-2009 is used for this analysis. This data is collected by the Central Statistical Agency (CSA) of Ethiopia on an annual basis. It contains information on firms' output, export status, capital, labor, raw material and energy inputs, investment and other industrial costs for establishments employing at least 10 employees. This means that the dataset covers large-and medium-scale formal manufacturing firms, providing a representative sample of exporters of manufactured goods in Ethiopia. Table 3 shows the number of exporting firms and their share in the total number of firms in the Ethiopian manufacturing sector. The number of firms in the manufacturing sector more than tripled in the sample period (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009), amounting to an average annual growth rate of 15%. The number of exporters also increased over this period, from 24 to 78, with average annual growth of about 16%. But the share of exporters remained small due to equivalent growth in the total number of firms in the sector. For example, in 2008/09 only 4% of the firms sold their products in the international market, which is almost equal to the share of exporters in beginning of the study period, i.e., 1995/96. Export is a significant source of income for firms engaged in exports, accounting for about 43% of their annual sales on average. However, the share of exports in total sales of the manufacturing sector at large has never exceeded 10% and has in fact exhibited a declining trend over the sample period. Figure 1 provides the pattern of entry into and exit from the export market over the period 1997-2009. 4 As shown in the figure, the number of new entrants has seen a substantial rise, particularly post-2005. Similarly, this period has seen enhanced survival of firms in export markets, as shown by the rising bar representing incumbent exporters. In other words, the recent years are characterised by not only high entry but also increased survival in the export market, partly reflecting the intensive export promotions led by the government.
Table A1 (in the appendix) gives further evidence of the growing pattern of number of exporters in recent years by disaggregating the number of exporters in 2009 by the number of years in business before entering into the export market. It shows that most of the exporters are either born exporters or operated domestically for 10 or more years prior to entering the export market. For example, 28 out of the 78 exporters in 2009 (36%) had no prior domestic production history, suggesting that a large number of exporters were set up for exports. On the other hand, 30 out of 78 exporters (38%) had been in production for more than 10 years prior to exporting. Table 4 reports the sectoral distribution of exporters. Exporting is concentrated in a few sectors, such as Food, Beverage, Textiles, Apparel, Leather and Footwear, which account for about 90% of the exporters in the manufacturing sector. About 31% of exporting firms are in the leather sector, followed by Textiles (17%), Food (16.8%), Beverage (10%), Apparel (7.75%), and Footwear (6%). Similarly, there is a skewed distribution among the sectors when it comes to the share of exports in total sales and production. The empirical analysis will distinguish between these six major exporting sectors and the remaining sectors as a robustness check. Table 5 compares the average performance of exporters and non-exporters over the sample period. Output is measured as gross value of production, whereas value added is measured as a difference between gross value of production (GVP) and industrial costs such as raw material and energy expenditures. Labor is measured as the number of workers, including permanent and seasonal or temporary workers (year equivalent when available), and capital stock is measured as average of capital stock at the beginning and end of the year. All financial values are expressed in constant terms using a GDP deflator due to the absence of a sectoral deflator. Exporters produce more outputs and use more inputs (i.e., they employ a larger number of workers, are more capital intensive, and use more raw material and energy). They have also higher labor productivity, as measured by value added per labor and output per labor. The differences in output, input usage, and labor productivity between exporters and non-exporters are statistically significant at 1%.

Self-selection into the export market-baseline specification
We start our analysis by investigating firm characteristics associated with firm exporting status by estimating equation (1) using a pooled probit regression model. The dependent variable is a dummy variable equal to one if a firm has any export at year t and zero otherwise. Here, we are mainly interested in analyzing the effect of previous productivity level and export status on the likelihood of current exporting. One-period lagged capital per employee and intermediate inputs per employee, along with current ownership type, firm size, year and sector dummies, are used as control variables. 5 Table 6 reports the estimation results for selection into exporting. Column 1 gives the results of the benchmark model estimated using a pooled probit regression model. 6 Lagged export status is positive and highly significant, indicating that firms with previous exporting experience are more likely to export. This is consistent with the existence of high entry and exit costs in the export market, as argued by Roberts and Tybout (1997). Output per employee, conditional on inputs, is used as a measure of productivity. The one-period lagged output per employee gives a positive and marginally significant coefficient, providing some evidence that more productive firms self-select into exporting. 7 Other firm characteristics such as age, size and type of ownership are also found to affect the probability of exporting in the Ethiopian manufacturing sector. In all specifications, we find that larger firms, capital-intensive firms, and those owned by the state are more likely to engage in exporting than their counterparts. Firm age is associated non-linearly with the likelihood of exporting; suggesting age increases the probability of exporting only up to certain age, while thereafter the relationship is negative.
As discussed earlier, the post-2005 period has shown a dramatic increase in the number of manufacturing exporters. This development coincides with the government's intensified assistance and support programs to promote exports. One would expect export promotion programs to reduce the sunk cost of entering into the export market; if so, persistence in Note: ***p < 0.01.
5 Firm size, measured in number of employees, is included as a categorical variable to avoid high correlation with other inputs expressed as a ratio of labor input. Size4, a dummy variable equal to one for firms employing at least 100 workers, is used as a base category. 6 Coefficients reported in Table 6 are estimates of probit parameters of equation (1) and not marginal effects. 7 A 1% increase in productivity is associated with 0.23% points higher likelihood of exporting, though the marginal effect is not statistically significant.

Continued
the export market, i.e., the relationship between current exports and previous exports, might be weakened. However, when the export promotion involves export performance monitoring, it may lead to even stronger persistence in the export market. For example, many of those born exporters often enter agreements with the government to export most of their products in exchange for preferential treatment. That means that, in the post-2005 period, there are two opposing forces that can influence export persistence. The export promotion might also impact the relationship between current exports and previous productivity level, i.e., it might affect whether more productive firms self-select into exports. For example, as indicated in the descriptive statistics, a large number of exporters are set up for exports (born exporters), and thus have little or no prior production or export history. In light of this, in Column 2 we introduced a dummy variable capturing young firms that were born post-2005 and its interaction with export status and productivity to examine whether the sunk cost and self-selection effects are changed following the scaling-up of the export promotion programs. The previous year export status variable and its interaction with the young firms dummy are both positive and statistically significant. The implication is that export persistence is present in both the old and young group of exporters. But the export persistence in the young group of exporters is much stronger (2.272 + 1.047 = 3.319) in contrast to the old group of exporters (2.272). This suggests that the export promotion program has a much greater effect in increasing export commitment and persistence than simply reducing the sunk cost easing entry and exit. This is consistent with the story of a higher survival rate in the export market in recent years, as documented in the descriptive analysis above. However, neither the lagged productivity nor its interaction with the young firms dummy is statistically significant, suggesting that the evidence for self-selection into export market is weak.
In Column 3, we introduce the cumulative number of previous years of exporting as an additional explanatory variable. The export past history variable is positive and highly significant, while all other variables remain qualitatively the same, except for a weaker effect of firm age due to its high correlation with the number of years in exporting.

Self-selection into the export market-Dynamic panel model
The pooled probit model estimated above ignores the potential problem of firm-level unobserved heterogeneity, which may arise when there are correlations between the explanatory Note: the dependent variable is export dummy equal to one if firm has any export in year t and zero otherwise. Size4, dummy variable equal to one for firms with at least 100 workers, is used as a base category. Clustered standard errors at firm level in parenthesis. Unreported constant is included. ***p < 0.01. **p < 0.05. *p < 0.1.
variables and the unobserved firm-level effect. This potentially leads to an overestimation of the coefficient on lagged exports, and thus an exaggerated conclusion about the size of the entry costs. To deal with the unobserved firm-level heterogeneity we adopt the dynamic probit random effect model, which is discussed in the methodology part above. Columns 4-5 (Table 6) report the dynamic probit model estimation results. The results show that the variable measuring labor productivity and capital intensity gives statistically significant coefficients among the within-means of all time-varying variables, suggesting the presence of correlation between these variables and the unobserved heterogeneity. Despite the inclusion of initial year's export status, the dynamic random effect model gives a positive and significant coefficient of the lagged export status, confirming export persistence. However, the magnitude of the coefficient of the lagged export from the dynamic model is much lower (1.528) than the results from the pooled probit model (2.336). This confirms that not addressing the unobserved heterogeneity in the probit model can lead to an overestimation of the coefficient of the lagged exports. On the other hand, when controlling for unobserved heterogeneity the previous year, productivity becomes statistically insignificant, unlike the baseline result reported in Column 1. 8 The last column reports estimation results of the dynamic probit model with the inclusion of a dummy variable for the young firms group of exporters and its interactions with export status and lagged productivity. The results with regard to the main variable of interest are consistent with the corresponding specification reported in Column 2 when using pooled probit. The previous year export status variable and its interaction with the young firms dummy are both positive and statistically significant. This confirms the previous finding that export persistence is stronger in the young group of exporters than in the old group of exporters. However, as also shown in Column 4, and pertaining to controlling the unobserved heterogeneity effect, the magnitude of the coefficient of lagged export status becomes lower in comparison to Column 2. The estimation results in Column 5 also show that neither the lagged productivity nor the productivity interacted with the young firms dummy variable are statistically significant, again consistent with Columns 2 and 4. We conclude that the weak support for the self-selection effect found in the pooled probit model is spurious and partly driven by the presence of the unobserved firm-level heterogeneity effect.

Learning by exporting
The second objective of this study is to examine whether firms improve their productivity subsequent to their entry into export markets. To do so, we estimated equation (3), i.e., current output as a function of previous export status and one-year lagged output, current factor inputs and other firm attributes such as firm age, ownership, and number of years in export as control variables. A positive and statistically significant coefficient of the lagged export status is considered as supporting evidence for the learning-by-exporting hypothesis.
8 Estimating the relationship between export status and productivity on the balanced panel is very restrictive since we are limiting the sample to include only firms which managed to survive for the entire 14 years. Despite a significant drop in the number of observation (i.e., from over 9000 to just 1643 firm-year observations), we find more robust and stronger evidence for self-selection into exporting using pooled probit on the balanced panel sample. The self-selection effect, however, vanishes in the dynamic probit model similar to our finding in the general sample. Table 7 reports estimation results covering all the sectors, using the Ordinary Least Square (OLS) and SYS-GMM approach. 9 In the SYS-GMM estimation, we use the first and earlier lags of all the inputs and the second and earlier lags of the dependent variable and other controls such as production and exporting experience as instruments for the first difference equation; we use the lagged first difference of all explanatory variables as instruments for the equation in levels. The Hansen tests of validity of the over-identifying restrictions were not rejected and no evidence of second-order serial correlation was found. 10 The baseline specifications reported in Columns 1 and 2 provide a positive and statistically significant coefficient estimate for previous year export status. The coefficient estimates indicate that engaging in export during the previous year increases the productivity of the firm by about 8-12% (depending on the model), controlling for other firm characteristics, year and sector fixed effects. 11 In Column 3 and 4 of Table 7, we account for major developments in the Ethiopian manufacturing sector that have important implications for the scope of learning by exporting, namely, the increasing trend in the share of Ethiopia's manufacturing goods exported to destinations in the developing world and the export promotion efforts by the government of Ethiopia, which intensified during the post-2005 period. The various export promotion efforts are expected to facilitate easier entry into the exports market, which may create further opportunity for exporting firms to learn from firms in the international markets. The shift toward increased exporting to developing countries, on the other hand, might limit the scope for learning if it discourages firms' efforts to penetrate the export markets of advanced countries and learn from the best practices at the knowledge frontier.
To account for the former, we include the share of exports destined to the developing world in total exports at the level of 3-digit sectoral classification and its interaction with previous export status in Column 3. 12 Whereas the interaction effects with previous exporting status capture learning effects from export destinations in developing countries, the export shares in levels capture local spillover effects in sectors with a high share of exports to developing countries. In line with our expectation, both exporting and non-exporting firms located in sectors with high concentrations of exports to developing countries have lower productivity, though the coefficients are not statistically significant. 13 Both lagged export status and productivity, however, remained statistically significant with larger coefficient estimates.
9 Standard errors are clustered at the firm level in all specifications that test for learning-byexporting effects. 10 The Hansen test of the over-identifying restriction is a minimized value of the two-step GMM criterion function and is robust to heteroscedasticity or autocorrelation. The null is that the overidentifying restrictions are valid. 11 The learning effect based on the balanced panel sample still holds for the OLS specification but not for the SYS-GMM specifications. This may be due to the drop in the sample size to about <1,000. 12 We thank an anonymous referee for this insightful suggestion. The developing world export destinations include countries in Africa, Asia and South America. Firm-level data on export destinations are not used due to data limitation. 13 Because all the firms within a given sector have same value for the export shares of developing countries for a given year, we excluded the sector fixed effects where the export shares are included, in order to avoid high collinearity.
In Column 4, we additionally controlled for the post-2005 dummy interacted with previous export status to examine whether there is any change in the relationship between export status and productivity in recent years, in light of the substantial export promotion efforts which intensified during the post-2005 period. Previous export status remained Note: the dependent variable is log of real value of output. The instruments for the differenced equation in the GMM estimations are first lag and earlier for the inputs and second lag and earlier for output, firm age and export history. On the other hand, only lagged first differences are used as instruments for the level equations. The standard errors of the GMM results are also robust finite sample corrected on two-step estimates derived from Windmeijer (2000). The standard errors in the OLS estimations are also clustered at firm level which makes it robust not only to within-panel (serial) correlation but also cross-sectional heteroscedasticity. Unreported constant is included. ***p < 0.01. **p < 0.05. *p < 0.1. positive and significant, while the coefficient of the interaction of previous export status with the post-2005 period is wholly insignificant. This suggests the post-2005 period is not any different from the previous periods with regard to the learning effect of exports. Lastly, we turn to the effect of other explanatory variables in the model. Lagged output is positive and highly significant in all cases, suggesting persistence in productivity. The number of years in exporting gives mostly statistically insignificant coefficients. Moreover, neither firm age nor public ownership is significant in any of the estimations.

Learning by exporting-Addressing selection bias
Next we apply the matching method to investigate whether our baseline results are robust to addressing the potential problem of selection bias discussed in the methodology section. In doing so, we return to the baseline specification because neither the share of exports to developing countries interaction nor the post-2005 dummy interaction are found to be statistically significant. We selected the counterfactual control group from the pool of nonexporters in which the distribution of observed variables is as similar as possible with the distribution of exporters. Using psmatch2 in Stata (Leuven and Sianesi, 2003), we first estimated the export participation equation, whereby the export status dummy is regressed on a 1-year lag of size (in terms of employment), capital intensity (K/L), firm age at first level and its squared term, ownership type, and industry and year dummies. The propensity score was estimated with a logistic model, k = 5 nearest-neighbors matching, and common support conditions imposed on the full sample. All but 85 of the exporting observations were matched with similar non-exporter observations, providing 1289 firm/year observations, of which 416 are exporters. The estimation results of the export participation equation are reported in Table A2 in the appendix. 14 We then formally tested the learningby-exporting hypothesis for the matched sample using OLS and system-GMM. The results are reported in Columns 1 and 2 in Table 8, respectively. Both the OLS and SYS-GMM estimations give a positive and statistically significant (at 5% or better) coefficient of the previous export status dummy variable. The magnitude of the effect in the matched sample is higher (11.8-19.3%) than those found in the unmatched estimations (8.3-11.9%), providing stronger evidence for a learning-by-exporting effect.
So far, the results on learning by exporting have been based on all the industries in the manufacturing sector. However, as indicated in Section 4, not all sub-sectors have firms meaningfully participating in the export market. Next, we limit our sample to the six export-oriented sectors (food, beverage, textiles, apparel, leather and footwear) in order to further check the robustness of our results and also compare with the findings of Bigsten and Gebreeysus (2009). Table 8 (Columns 3-6) present the estimation results for the six export-oriented sectors, using both the unmatched and matched samples. 15 14 The estimation results show that firms with larger size, firms with higher capital intensity and those owned by the state are more likely to participate in exporting. Firm age is also important but has an inverted-U relationship with export status, i.e., the probability of exporting increases with firm age only up to a certain point. This is generally consistent with our earlier finding in Section 4.1. The propensity score results also indicate that the exporters are more productive than non-exporters (Table A3). 15 Similarly, the propensity score matching for the six sectors was estimated with a logistic model, k = 5 nearest-neighbors matching and common support conditions imposed. The results of the first The estimation results of the unmatched sample (Columns 3 and 4 using OLS and the SYS-GMM, respectively) give positive coefficients of the previous year export status, but these are only significant in the OLS estimation. We then run similar regressions for the Note: the dependent variable is log of real value of output. The instruments for the differenced equation in the GMM estimations are 1st lag and earlier for the inputs and 2nd lag and earlier for output. firm age and export history. On the other hand, only lagged first differences are used as instruments for the level equations. The standard errors are of the GMM results are also robust finite sample corrected on two-step estimates derived from Windmeijer (2000). The standard errors in the OLS estimations are also clustered at firm level which makes it robust not only to within-panel (serial) correlation but also cross-sectional heteroskedasticity. Unreported constant is included. ***p < 0.01. **p < 0.05. *p < 0.1.
step estimation (export participation) and productivity differential between exporters and nonexporters sectors are also reported in Tables A2 and A3, respectively.
matched sample in the six export-oriented sectors (see Columns 5 and 6). Unlike the unmatched results, we now find a positive and significant (at 5%) coefficient of previous export status for not only the OLS but also the SYS-GMM estimation. This shows that addressing the selection bias through matching improves the effect of export engagement on productivity. The coefficient of the previous export experience in the six sectors matched sample (0.168) is slightly lower, not only when compared with the matched sample results of all sectors in Column 2 (0.193), but also when compared with the six sectors matched sample result (0.255) reported by Bigsten and Gebreeysus (2009) based on data covering only 1996-2005. Despite the magnitude difference, all our estimates give support for the presence of a learning-by-exporting effect in the Ethiopian manufacturing sector.

Conclusions
The positive association between exporting and productivity is well documented in the literature. However, inferring causality from a positive association between exporting and firm performance has been a subject of debate. The self-selection hypothesis emphasises that more productive firms self-select into exporting due to high entry costs in the international market. The learning-by-exporting hypothesis, on the other hand, states that firms learn after entering into the export market. Using a unique and relatively long panel data over the period 1996-2009 from the Ethiopian manufacturing sector, this study tested these two hypotheses.
The estimation results of pooled probit model of the export participation equation shows that firms with previous exporting experience are more likely to export subsequently, providing evidence for export hysteresis due to high sunk cost or/and uncertainty (Roberts and Tybout, 1997). However, the magnitude of the coefficient of lagged export status becomes lower when controlling for unobserved firm heterogeneity using a dynamic random effect probit model. This suggests that ignoring firm-level heterogeneity can overestimate the size of the sunk costs.
The post-2005 period has shown a dramatic increase in the number of Ethiopian manufacturing exporters, partly driven by the increasing government export promotion support programs. Related to this, we introduced a dummy variable capturing young firms born post-2005 and its interaction with export status to examine whether the effect of sunk cost is the same for old and young exporting firms. The results show that export persistence is present for both the young and old group of exporters but the magnitude is much stronger in the former case. This is robust to controlling for unobserved firm heterogeneity. The implication is that the export promotion had a larger effect on raising the export commitment and persistence than simply reducing sunk costs that ease entry and exit.
The baseline model of export participation provides only weak evidence for selfselection into export markets. This weak relationship between export participation and previous productivity level vanishes when we (i) introduce a dummy for young firms, interacted with previous year productivity and/or (ii) control for firm-level heterogeneity using a dynamic random effect probit model. We can, thus, conclude that the self-selection effect found from the pooled probit model is spurious and partly driven by unobserved firm-level heterogeneity. This relationship have also been further weakened by the presence of intensive government export promotion programs, as a result of which many exporters are set up for exports without any prior production history.
Another objective of this study was to test the hypothesis that firms can improve their productivity following their entry into export markets, i.e., learning by exporting. The results provide evidence in support of this hypothesis. The learning-by-exporting effect is stronger when estimation is based on the matched sample that corrects for self-selection bias. Depending on the specification, previous year exporting improves firm productivity by about 8-19% points. Our results are largely consistent with the emerging evidence from other developing countries, reviewed above, and support the view that export promotion efforts might have long-term effects in terms of sustaining exports and industrial competitiveness.
Market destination of exports may affect learning, as the scope for learning might be limited if products are exported to countries with a similar level of technological advancement. In the absence of firm-level export destination data, we used a post-2005 period dummy and/or sector-level share of exports directed to developing countries; both interacted with a previous year export status variable. None of these interaction effects is significant, providing no evidence that the learning effect is any different in the post-2005 period or in sectors with a higher share of exports to developing countries. However, examining the differential impact on learning of export destination (developed versus developing countries) requires firm-level export destination information, which we do not have in our dataset. Hence, future research needs to take account of this limitation.