Do imports create decent jobs in Africa? Evidence from firm-level data

Transcription

1 Do imports create decent jobs in Africa? Evidence from firm-level data Marta Duda-Nyczak Christian Viegelahn 10 May, 2016 Abstract This paper evaluates whether importing is related to the quantity and quality of jobs in African firms. Providing firm-level evidence from 46 African countries in on the basis of World Bank Enterprise Survey data, this paper shows that importers employ on average more full-time permanent workers than firms that only source domestically. The difference between importers and non-importers is stronger for women than for men. There is also evidence of a positive wage premium of importing, but this premium turns negative after controlling for firm size. We also find evidence that the average skill level of a firm s production workforce is significantly larger for firms that import. Using country-level data, we finally investigate the role that economic development plays in explaining the magnitudes of the impacts estimated for different countries. Keywords: Africa; Employment; Firm-level data; Importers; Intermediate inputs; Job quality; Labour Market JEL classification: F14; F15; F16 All views expressed in this paper are those of the authors and do not reflect those of the institutions they are affiliated with. This paper is a preliminary and yet incomplete draft. United Nations Economic Commission for Africa (UNECA). mdudanyczak@uneca.org. International Labour Organization (ILO), Research Department. viegelahn@ilo.org. 1

2 1 Introduction The international trade literature on firms has traditionally focused on analyzing the causes and consequences of exporting, while importing has become prominent as a topic only more recently. There is however no reason to assume that importing is less relevant to firms than exporting. Importing in a Melitz (2003) type of model is likely to come along with fixed costs that only the most productive firms can afford to pay, which implies that only firms whose productivity exceeds a certain threshold import some of their inputs (Kasahara and Lapham, 2013). But it is also likely that firms can derive productivity gains from importing once they have started to import. Trade theory suggests that these gains can be generated through various channels, including learning from new technologies embedded in foreign inputs, access to a better quality of inputs, or access to a larger variety of inputs (Ethier, 1982; Markusen, 1989; Grossman and Helpman, 1991). There is a stock of empirical evidence that confirms such a positive impact of increased access to foreign inputs on firm productivity (Amiti and Konings, 2007; Stone and Shepherd, 2011; Halpern, Koren and Szeidl, 2015). Considerably less work has been done on analyzing the relationship between importing and labour market outcomes. For manufacturing firms in the United States, it has been documented that importers tend to be larger firms in terms of employment, paying higher wages on average and employing higher skilled workers (Bernard, Jensen, Redding and Schott, 2007). Firm-level evidence from Columbia suggests that increased access to foreign inputs through trade liberalization has led to higher wages (Amiti and Davis, 2011). Indeed, standard bargaining models suggest that there is some rent sharing, where workers can at least partially participate in the gains from importing, where the degree of participation however depends on workers bargaining power. If new technology is embedded in imported inputs, firms also likely need to employ higher skilled workers that are able to work with these inputs. This paper uses African firm-level data and systematically explores the relationship between importing and labour market outcomes in African manufacturing firms. We use data from the World Bank Enterprise Surveys, comprising 65 surveys conducted in 47 African countries in This rich dataset is unique in the sense that it provides data that are comparable across all countries included, which allows us to analyze the relationship of importing with labour market outcomes by sector and country, and its drivers. In the paper, we engage in a two step approach. In a first step, we quantify the firm-level employment premium of importing both in terms of total employment, employment by sex, non-production employment and temporary employment. We also quantify the firm-level wage premium of importing in terms of average wage, and investigate whether there is a skill intensity premium of importing in terms of the average education level of a firm s workforce. In a second step, we make an attempt to explain the heterogeneity of wage, employment and skill intensity premia across countries. 2

3 The paper also explains the heterogeneity in gender gaps across countries, measured through the difference between the male and female employment premia. The analysis in the second step exploits the comparability of data across different surveys, which provides an opportunity not only to quantify the relation between importing and labour market outcomes for different countries but also to explain why this relation differs across countries. This is different from other studies which use firm-level data from only one or at most a few countries and hence provides not much scope for a comparison of estimated coefficients across countries. The number of firm-level studies that look into the firm-level causes and consequences of trade in the African context is relatively limited, given the scarcity of firm-level databases from this region. Moreover, most existing studies on African firms do not study firms importing behaviour and its impact, but focus on exporting. Based on firm-level data from Cameroon, Ghana, Kenya and Zimbabwe, there is evidence for both firm self-selection into exporting and learning-by-exporting (Bigsten et al., 2004). For firms decision to export, firm size has been identified as a relevant characteristic which holds even after controlling for firm productivity, using firm-level data from Kenya, Ghana, Tanzania, South Africa and Nigeria (Soederbom and Teal, 2003; Rankin et al., 2006). With regards to the labour market, exporting is found to have positive spinoffs on employment and wages across a wide range of developing countries including countries on the African continent (Brambilla et al., 2014). Only very recently, some evidence has been provided on the impact of increased access to foreign inputs. Bigsten et al. (Forthcoming) analyze firm-level data for Ethiopian manufacturing firms and show that a reduction on output tariffs does not have any impact on firms productivity, while reductions in input tariffs increase firms productivity. This result suggests that importing is an important channel through which firms can benefit, also in the African context. From a policy perspective, Africa is a very relevant region to look at the question how importing is related to labour market indicators and what is driving this relationship. The economic and social development of the African continent has been on the agenda of policy makers and the international community for a number of decades. With over a billion inhabitants and the fastest growing population, African market presents an enormous potential. Despite remarkable economic growth rates, many countries on the continent struggle to translate those into higher scoring on socio-economic indicators. The international development frameworks have set up ambitious goals. It is however crucial to conduct more research in order to identify the exact transfer mechanisms so that decision-makers can address the potential bottlenecks and implement evidence-based policies effectively. International trade in general and regional integration in particular is viewed by a large number of policy makers in the region as a powerful driver of sustainable economic and social development. The last decades have witnessed a rapid shift towards sub-regional and regional preferential trade agreements in order to boost the economies on the continent. The Regional Economic Communities have played a fundamental role in curtailing trade barriers and fostering free trade. Current trade 3

4 policy focuses on connecting some of the already existing free trade areas in order to create even larger internal markets, with the ultimate objective to form a customs union that integrates all countries in Africa. In 2015, negotiations for the Tripartite Free Trade Area were concluded, which is a free trade agreement between the Common Market for Eastern and Southern Africa, the East African Community and the Southern Africa Development Community and consists of 27 African countries. In 2017, negotiations for the Continental Free Trade Area are expected to be finalized, which would form a single market for goods and services, encompassing all 54 members of the African Union. The establishment of a Continental Customs Union by 2022 and an African Economic Community by 2028 are further key benchmarks set up by the Abuja Treaty signed in Fostering regional integration will likely result in a better access for firms to foreign inputs. It is important to determine the conditions under which such a better access can translate into more employment without discrimination of a particular sex, and generate higher wages. Factors such as good governance or the quality of trade-related transport infrastructure are likely to play a role in this respect. The attempt of this paper is to shed light into the role of such factors to translate importing into more and better jobs. Our results indicate that importers tend to employ more fixed-term permanent employees than non-importers. Even though much of the difference between importers and non-importers can be explained through firm size as measured by sales, a significant importer premium on employment remains, even after controlling for sales. Importers in particular employ more female workers on average, while the importer premium on male employment is quantitatively smaller. The importing status of a firm in contrast does not explain the number of temporary workers, at least when considering the results for the whole African continent. The findings on the average wage are in contrast not robust. While there is a positive importer premium on wages, when comparing importers with non-importers, this premium turns negative as soon as firms sales are controlled for. In the case that firms sales are equal and firms only differ in the importer status, it will be on average the firm that is non-importer that pays a higher wage to its workers. This result indicates that economic gains of importing are not always translated into gains in terms of increased wages for workers. This is the case, even though importers employ production workers that on average have more years of education. This importer premium on skill intensity is significantly positive not only in continent-wide regressions, but also in a large number of regressions by survey. We also examine the role that the level of economic development has for explaining some of the estimated coefficients. There is no evidence that economic development explains neither the importer premium on employment and on female vis-à-vis male employment, nor the importer premium on skills intensity. However, we find some weak evidence that import premia on wages are positive in particular for countries at a lower level of economic development. 4

5 The next section describes in more detail the data source that is used in this paper. Section 3 presents the underlying empirical methodology to in a first step quantify employment, wage and skill intensity premia of importing, and in a second step determine their respective drivers. Section 4 discusses the results. The final section concludes. 2 Data This paper uses firm-level data for manufacturing firms from the World Bank Enterprise Surveys. The database consists in total of over 15,391 observations for manufacturing firms, comprising data from 65 surveys conducted in 47 African countries between 2006 and For one country, the Democratic Republic of Congo, data from three surveys are available. For 16 countries, we have data from two surveys. For the remaining 30 countries, data have only been collected once. For those countries for which more than one survey has been conducted, the database is in principle an overlapping panel database. However, the number of firms that are observed more than once is very small, allowing us to only exploit the cross-section dimension of the dataset. The employment, wage and skill intensity premia of importing that we will derive should hence not be interpreted as causal to importing. For different surveys, the sample size varies between 21 observations for a survey conducted in 2009 in Liberia, and 2,015 observations for a survey conducted in 2013 in Egypt. The average sample size across surveys is 237 observations. In order to ensure that results are based on sufficient data, we do not consider any regression results, for which the number of observations is less than 30. In country-specific regressions, results for some of the individual countries are dropped due to this restriction. The firm-level data that are included into the database are representative of formally registered firms that employ at least 5 workers and are not state-owned. On the basis of the information provided in the survey, firms can be assigned to the manufacturing sectors in which they operate. We distinguish between 8 manufacturing industries, namely food and beverages, textiles and garments, wood and paper, chemicals, non-metals and plastics, metals and machinery, furniture and all other manufacturing not included in the preceding categories. Table 1 shows basic descriptive statistics for the firm-level database that is used in this paper. The table shows that 53% of firms are importers, while only 23% are exporters. The average number of full-time permanent employees reported by firms is 82 of which 22 are women. There are on average 19 non-production employees, while the average number of temporary workers across all firms is 15. Monetary values such as sales, electricity costs and the average wage cannot be directly interpreted, given that they are reported in national currencies. The regressions, however, will control for differences in reported currencies across different surveys. 5

6 Table 1: Decriptives statistics on African manufacturing firms Variable Mean Sd. Min Max N Importer dummy (1=importer) Exporter dummy (1=exporter) Log(Sales) Log(Electricity costs) No of full-time permanent employees No of full-time permanent female employees No of full-time permanent male employees No of full-time permanent non-production employees No of temporary employees Log(Average wage) Average length of education (categorical variable) Source: Authors calculation based on the World Bank Enterprise Surveys. Production workers average years of education in a firm is a categorical variable that can take on discrete values between 1 and 5. It is 1 if production workers had between 0 and 3 years of education on average, 2 if they had between 4 and 6 years of education, 3 if they had between 7 and 9 years of education, 4 if they had between 10 and 12 years of education and, finally, 5 if they had 13 or more years of education. The average value in the sample is about 3. Data are comparable across different surveys. This advantage is exploited when relating estimated coefficients at the country-level to country characteristics. We use data on GDP per capita in PPP international dollars from the World Bank World Development Indicators database to investigate the role of economic development in shaping the relationship between importing and labour market outcomes. 3 Methodology The empirical analysis is conducted in two steps. In the first step, we estimate the employment, wage and skill intensity premia of importing, using different specifications. The second step then consists in explaining these premia at the country-level using different country characteristics as explanatory variables. In order to quantify the employment and wage premia of importing, we run regressions on obser- 6

7 vations at the firm-level. We estimate the following equations: L ctmi = α + β IM ctmi + γx ctmi + ɛ ct + ɛ m + ɛ ctmi (1) L ctmi = α m + β m IM ctmi + γ m X ctmi + ɛ ct + ɛ ctmi (2) L ctmi = α ct + β ct IM ctmi + γ ct X ctmi + ɛ ctmi (3) where equation (1) is estimated on the full sample of manufacturing firms, equation (2) is estimated by manufacturing sector m and equation (3) is estimated by survey conducted in country c and year t. Index i stands for a particular firm that belongs to a manufacturing sector m and is observed in the survey conducted in country c and year t. The dependent variable L stands for different labour market indicators. In this paper, we report results for the logarithm of the total number of full-time permanent employmees, the number of male and female full-time permanent employees, the number of non-production full-time permanent employees, the number of temporary employees, the average wage, and non-production employees average years of education as dependent variables. Importer dummy IM takes a value of one if the firm imports at least some of its inputs. β, β m and β ct are the main coefficients of interest and measure the overall, sector-specific and survey-specific premia of importing for the above dependent variables. X is a vector of control variables, ɛ ct is a survey fixed effect, ɛ m is a sector fixed effect and ɛ ctmi is the error term. The regression is estimated with OLS. Only for the case where non-production employees average years of education is the dependent variable, we estimate an ordered logit model, as this variable is a categorical variable with values varying between 0 and 5, with a higher number indicating more years of education. All regressions are run using four different sets of control variables. For the first specification (labelled in the regression tables as spec 1 ), we set γ ct = 0 and do not include any further control variables. The second specification (spec 2 ) includes an exporter dummy as control variable, so that we control for the exporter status of the firm. The third specification (spec 3 ) controls, in addition, for firm size as measured through the logarithm of firms sales. Finally, we also include a specification (spec 4 ), where we add the logarithm of electricity costs as control variable. As data on firms capital stock is missing for around one third of our sample, we prefer to use electricity costs, for which data availability is better, as a proxy. For the existing observations, electricity costs and firms capital stock are highly correlated, suggesting that the quality of the proxy is sufficiently good. Reported standard errors are robust. Next, we make an attempt to explain the heterogeneity in the relation between importing and different labour market indicators across different countries. In some countries, the employment, wage and skill intensity premiums are higher than in others and we make an attempt to identify the sources of this variation. Focusing more narrowly on the employment premium, we also undertake an analyis that aims at explaining the difference between male and female employment premiums, 7

8 and between total and temporary employment premiums. More specifically, we use OLS to estimate the following equation: β ct = a + b C ct + u ct (4) where β ct is the survey-specific estimate of the employment and wage premium of importing respectively. C ct stands for the economic development of a country as measured through GDP per capita. In future versions of this paper, more explanatory variables will be included, including variables that are directly related to policies. 4 Results 4.1 Employment Table 2 shows results on the importer premium on the total number of full-time permanent employees, using the four different specification. For the full sample, we can always identify a significant premium of importing on employment. Without any control variables, we find that importers employ on average 91.9% more full-time permanent employees than non-importers. 1 The difference is statistically significant in all manufacturing sectors. After controlling for exporter status, the premium reduces to 54.8%. The inclusion of firm size measures through sales into the regression reduces the import premium to just above 11.5%. Considering the costs of eletricity, the importer premium diminishes to 10.0% and remains significant in just three sectors: food and beverages, textiles and garments and metals and machinery. Without any control variables, we find significantly higher employment in importers compared with non-importers in 42 out of 58 surveys for which we have a sufficiently large number of observations to run the regression separately. There is no case in which non-importers employ more full-time permanent employees. In the specification that includes exporter status, sales and electricity cost as control variables, we still find significantly more employees employed in importers compared with non-importers in 11 surveys. For two surveys, Burundi in 2014 and Tanzania in 2013, we find a weakly significant negative importer premium. However, all in all, we tend to find a positive and significant correlation between importing and the number of full-time permanent employees in our sample. tables. 1 The percentage premium of importing is 100 (exp(β) 1), where β is the estimated coefficient reported in the 8

9 Table 2: Importing premium on the total number of permanent fixed-term employees Sample N Spec 1 Spec 2 Spec 3 Spec 4 Full sample *** 0.437*** 0.109*** 0.095*** Food & beverages *** 0.582*** 0.203*** 0.186*** Textiles & garments *** 0.344*** 0.129*** 0.100*** Wood & paper *** 0.369*** Chemicals *** 0.410*** Non-metals & plastics *** 0.338*** Metals & machinery *** 0.506*** 0.116** 0.108** Furniture *** 0.219*** Other manufacturing *** 0.474*** Angola * 0.153* Angola Benin *** 1.056*** Botswana *** 0.515** Botswana * Burkina Faso *** 0.657*** Burundi *** 0.685*** Burundi ** ** Cameroon *** 0.657** Cape Verde * Chad Cote d Ivoire *** 0.517** DRC ** 0.332** DRC *** 0.833*** 0.546** 0.418** DRC ** Egypt *** 0.473*** Eritrea *** 0.604*** Ethiopia *** 1.214*** 0.672*** 0.521** Gambia * Ghana ** Ghana *** 0.345** Guinea Guinea-Bissau Kenya *** 0.489*** Kenya *** 0.828*** 0.350** 0.315** Madagascar *** 0.655*** Madagascar *** 1.209*** 0.480** 0.459** Malawi *** 1.153*** Malawi *** 0.603* Mali *** 0.241*** Mali *** 0.950*** Mauritania * 0.273** 0.253** Mauritania Continued on next page... 9

10 ... table 2 continued Sample N Spec 1 Spec 2 Spec 3 Spec 4 Mauritius *** 0.495** Morocco Mozambique *** 0.494*** 0.156* 0.146* Namibia * Namibia *** 0.798** Niger Nigeria *** *** Nigeria *** 0.330*** * Rwanda Rwanda Senegal *** 0.290** Senegal *** 0.546*** South Africa *** 0.348*** Swaziland *** 1.287*** 0.520* Tanzania *** 0.742*** Tanzania Tunisia *** 0.588*** 0.379*** 0.418*** Uganda *** 0.755*** 0.235** 0.235** Uganda *** 0.397* Zambia *** 0.765*** 0.205** 0.204** Zambia *** 0.376*** Zimbabwe *** 0.745*** 0.388*** 0.326*** Note: This table shows the estimated premium of importing on total employment. Spec 1 does not include any control variables. Spec 2 includes an exporter dummy as a control variable. Spec 3 includes in addition the logarithm of sales as control variable. Spec 4 in addition controls for the costs of electricity. *, ** and *** indicate significance at the 10%, 5% and 1% level, based on robust standard errors. In Table 3, we report results for different employment subcategories, using only the specification that includes exporter status, sales and electricity costs as control variables. For the full sample, we find that the number of female employees is by 14.1% higher in importers compared with nonimporters. The estimated importer premium on male employment is 8.2% and hence quantitatively smaller. This result indicates that importing may contribute to mitigating gender differences with respect to employment that prevail in many African countries. While the employment premium is quantitatively similar for both sexes in the food and beverages industry, the impact is larger for women in the textiles and garment industry. For wood and paper, and metals and machinery, we can only find a significant coefficient for women, but not for men. Still we find a significant positive importer premium on female employment only in 8 out of 53 surveys, while male employment is significantly higher in importing firms in 11 out of 57 surveys. When focusing on non-production employees, we find a significantly positive importer premium 10

11 for the full sample that is larger than the premium on the total number of fixed-term permanent employees. It is hence non-production employees that appear to particularly benefit from the presence of importers, suggesting on average more developed firm structures of importing firms. The sectors in which importers employ more non-production employees than non-importers are food and beverages, chemicals, metals and machinery, and furniture. The above result is also visible in 11 out of 44 surveys, for which we find positive significant importer premium on non-production employment. In contrast, regressions on 3 surveys, namely Burundi in 2014, Guinea-Bissau in 2006 and Rwanda in 2006, yield significant negative coefficients. Overall, however, the correlation between importing and non-production employment appears to be positive in our sample. On the contrary, temporary employment is not correlated to importing at the aggregate continental level. In addition, the premium is significantly positive for the wood and paper sector, whereas it is negative for non-metals and plastics. At the same time, we find for 3 out of 42 surveys a positive premium, while a negative premium is also estimated for 3 surveys. The results are thus inconclusive, not indicating any clear correlation between being an importer and using temporary workforce. Table 3: Importing premium on subcategories of employees Female Male Non-production Temporary Sample N Spec 4 N Spec 4 N Spec 4 N Spec 4 Full sample *** *** *** Food & beverages *** *** *** Textiles & garments *** * Wood & paper *** ** Chemicals ** Non-metals & plastics * Metals & machinery ** *** Furniture *** Other manufacturing Angola ** ** Angola Benin Botswana ** * Botswana Burkina Faso Burundi * * - - Burundi *** ** - - Cameroon Cape Verde Chad * Continued on next page... 11

12 ... table 3 continued Female Male Non-production Temporary Sample N Spec 4 N Spec 4 N Spec 4 N Spec 4 Cote d Ivoire DRC DRC ** ** ** DRC Egypt Eritrea Ethiopia ** ** Gambia Ghana * * ** Ghana *** Guinea * Guinea-Bissau * * - - Kenya Kenya * Madagascar ** Madagascar *** Malawi Malawi Mali Mali Mauritania ** * Mauritius Morocco * Mozambique ** * Namibia Namibia Niger Nigeria ** Nigeria * *** Rwanda ** Rwanda ** Senegal Senegal South Africa ** Swaziland * Tanzania * Tanzania * *** Tunisia *** ** *** Uganda ** Uganda ** Zambia * Zambia Zimbabwe *** * Continued on next page... 12

13 ... table 3 continued Female Male Non-production Temporary Sample N Spec 4 N Spec 4 N Spec 4 N Spec 4 Note: This table shows the estimated premium of importing on total employment. Spec 1 does not include any control variables. Spec 2 includes an exporter dummy as a control variable. Spec 3 includes in addition the logarithm of sales as control variable. Spec 4 in addition controls for the costs of electricity. *, ** and *** indicate significance at the 10%, 5% and 1% level, based on robust standard errors. 4.2 Wages This section investigates the implications of being an importer for wages. The wage variable is derived from the total cost of labour reported in the World Bank Enterprise Surveys, which we divide by the number of full-time employees. As shown in Table 4, the Africa-wide coefficients are always significant. Interestingly, however, the sign depends on the specification. While there is a positive importer premium on wages of 21.7% and 15.5%, respectively, when including no controls or only the exporter status as a control, the sign becomes negative when including sales as a control variable. In the specification that controls for exporter status, sales and electricity costs, we find that the average wage paid by importers is 11.2% lower on average than the average wage paid by non-importers. Also the survey-level analysis points out that there are numerous negative importer premia on wages, when including all control variables. We estimate a significantly negative coefficient for 10 out of 56 surveys. This result suggests that importers in Africa tend to only pay higher wage, because they are larger in terms of sales than non-importers. When comparing two firms with identical sales and export status, the importing firm on average pays lower wages compared with the firm that sources its inputs only domestically. Table 4: Importing premium on the average wage Sample N Spec 1 Spec 2 Spec 3 Spec 4 Full sample *** 0.144*** *** *** Food & beverages ** *** *** Textiles & garments ** *** Wood & paper ** Chemicals Non-metals & plastics *** 0.266*** Metals & machinery *** 0.234*** Furniture * * Other manufacturing *** 0.286** Angola ** ** Continued on next page... 13

14 ... table 4 continued Sample N Spec 1 Spec 2 Spec 3 Spec 4 Angola Benin *** *** Botswana Botswana * ** Burkina Faso Burundi *** 0.712*** 0.229** 0.244** Burundi ** 0.675** Cameroon *** 0.840*** 0.401** 0.341* Cape Verde Chad Cote d Ivoire *** 0.946*** DRC *** 0.294*** 0.138* 0.139* DRC DRC ** 0.605** Egypt *** * Eritrea ** Ethiopia Gambia Ghana *** *** *** *** Ghana Guinea Guinea-Bissau Kenya *** 0.308*** Kenya *** 0.491*** Madagascar * Madagascar Malawi Malawi Mali *** 0.186*** Mauritania Mauritius Morocco Mozambique *** 0.260** Namibia ** Namibia Niger Nigeria *** *** *** *** Nigeria *** ** *** *** Rwanda * 0.457* Rwanda Senegal *** 0.263*** 0.118* 0.141** Senegal *** 0.522** South Africa *** 0.149** Swaziland Continued on next page... 14

15 ... table 4 continued Sample N Spec 1 Spec 2 Spec 3 Spec 4 Tanzania *** 0.426*** Tanzania *** 0.745*** 0.543*** 0.531*** Tunisia * * Uganda *** 0.445*** Uganda Zambia *** 0.290** Zambia *** 0.460*** Zimbabwe Note: This table shows the estimated premium of importing on total employment. Spec 1 does not include any control variables. Spec 2 includes an exporter dummy as a control variable. Spec 3 includes in addition the logarithm of sales as control variable. Spec 4 in addition controls for the costs of electricity. *, ** and *** indicate significance at the 10%, 5% and 1% level, based on robust standard errors. 4.3 Skill intensity Table 5 reports the results of the impact of being an importer on production workers average number of years of education within a firm. The overall impact is robust to the specification and carries a positive sign, with importers employing on average more skilled production workers. The importer premium on skill intensity is significantly positive in particular for the sectors food and beverages, wood and paper, metals and machinery, and furniture. In these sectors it appears to be crucial that imports are dealt with by workers that on average have more skills. Also at the survey-level, the impact is strongly pronounced, with a significantly positive sign for 15 out of 44 surveys in the specification that does not include any control variables. In the specification that includes exporter status, sales and electricity costs has control variables, the importer premium on skill intensity is estimated significantly positive for even 13 out of 44 surveys. This gives a strong indication that the control variables add not much explanatory power to the regression, and that importing is likely to be the most relevant channel through which workers skills is affected. Table 5: Importing premium on employees average number of education Sample N Spec 1 Spec 2 Spec 3 Spec 4 Full sample *** *** *** *** Food & beverages *** *** ** ** Textiles & garments * ** Wood & paper ** ** * * Chemicals Non-metals & plastics Metals & machinery *** *** * ** Continued on next page... 15

16 ... table 5 continued Sample N Spec 1 Spec 2 Spec 3 Spec 4 Furniture ** *** ** ** Other manufacturing * Angola *** 0.758*** 0.762*** 0.835*** Angola * 0.845* 0.821* Botswana Botswana Burkina Faso Burundi Burundi ** 1.388** 1.150* 1.129* Cameroon Cote d Ivoire *** 0.909** 0.702** 0.693* DRC DRC ** 1.558** 1.950** 1.958** DRC Egypt *** 0.424*** 0.310*** 0.315*** Ethiopia Gambia *** 2.266** 2.297*** 2.486*** Ghana Ghana Guinea Guinea-Bissau Kenya *** 0.754*** 0.536** 0.533** Madagascar * 0.668* 0.699* Madagascar * Malawi Mali *** 0.707*** 0.539** 0.595** Mauritania Mauritius Morocco Mozambique *** 0.755*** 0.660*** 0.661*** Namibia Namibia Nigeria *** 0.395** 0.420** Rwanda Senegal * Senegal *** 1.086** 1.032* 1.111* South Africa Swaziland Tanzania ** 0.443* Tanzania Tunisia Uganda ** 0.484** 0.733*** 0.749*** Uganda *** 0.898** 0.751** 0.796** Zambia *** 0.620*** 0.646*** 0.648*** Continued on next page... 16

17 ... table 5 continued Sample N Spec 1 Spec 2 Spec 3 Spec 4 Zambia Zimbabwe Note: This table shows the estimated premium of importing on total employment. Spec 1 does not include any control variables. Spec 2 includes an exporter dummy as a control variable. Spec 3 includes in addition the logarithm of sales as control variable. Spec 4 in addition controls for the costs of electricity. *, ** and *** indicate significance at the 10%, 5% and 1% level, based on robust standard errors. 4.4 What is driving importer premia? In this section, we relate the estimated importer premia to the economic development of a country. More specifically, we try to find out whether the economic development of a country as measured through its GDP per capita shapes the relationship between importing and labour market outcomes at the firm-level. We make an attempt to explain the coefficient estimated on the total number of fixed-term permanent employees, the difference in estimated coefficients for female and male employment ( gender gap ), the coefficient estimated on the average wage, and the coefficient estimated on the average number of production workers years of education. For all dependent variables, we look at coefficients estimated from two specifications: the specification that does not include any control variables and indicates simple differences between importers and non-importers (Spec 1 ), and the specification that includes exporter status, sales and electricity costs as control variables (Spec 4 ). Table 6: Impact of economic development on estimated coefficients Employment Gender gap Wage Skill intensity Spec 1 Spec 4 Spec 1 Spec 4 Spec 1 Spec 4 Spec 1 Spec 4 Log(GDP per capita) * (0.060) (0.032) (0.053) (0.057) (0.053) (0.046) (0.124) (0.178) R Number of observations Notes: *, ** and *** indicate significance at the 10%, 5% and 1% level, based on robust standard errors. Table 6 shows that the economic development does not have much explanatory power in explaining the estimated coefficients. The only (weakly) significant impact is estimated for the importer premium on the average wage with a result that indicates more positive wage premia for countries at lower levels of development. However, the result is only found for the coefficient that is estimated when not including any control variables into the wage regression. 17

18 5 Conclusions The findings in this paper indicate that importing can translate into gains for workers in the form of more employment and demand for higher skills in African firms. Importing also translates into higher wages, but only as importers are larger firms in terms of sales, while importing per se appears to be negatively related to wages. These results indicate that workers can benefit from the presence of importers in the job market. The wage result that runs in the opposite direction might be driven by weak bargaining institutions in a large number of countries on the African continent, where workers are not able to participate in the gains induced by importing. Notwithstanding, caution needs to be exercised when analysing the employment impact of importing, especially with regard to the increased use of temporary contracts. An employment effect can be only deemed beneficial if importing translates into higher quality jobs with a decent level of job security. Our findings indicate that the importer premium on employment is not driven by an increased use of temporary workers. We also find that importers in particular provide more jobs for women, when compared with non-importers. The importer premium on male employment is also positive, but quantitatively smaller. The finding that importing can be associated with a higher demand for skills suggests that the liberalization of trade should go hand in hand with a higher skilled labour force, underlining the importance of policies that promote better education and training. The preliminary results are encouraging evidence that trade-inducing and trade-facilitating policies and regulations incentivizing imports can be an effective tool to foster inclusive growth in a country, provided that trade liberalization is accompanied by policies that ensure that also workers obtain their fair share of the economic gains induced by trade. This paper also looks into whether certain country characteristics can explain the size of estimated importer premia on employment, wages and skill intensity. We find that the level of economic development does not play an important role for explaining variations in the estimated coefficients across surveys. Current research focuses on indentifying variables that have more explanatory power in this respect. This also includes policy-related variables, including the role of education and the quality of infrastructure. 18

19 References Amiti, M. and D.R. Davis (2011) Trade, firms, and wages: theory and evidence, Review of Economic Studies, Vol. 79, pp Amiti, M. and J. Konings (2007) Trade liberalization, intermediate inputs, and productivity: Evidence from Indonesia, American Economic Review, Vol. 97, No. 5, pp Bernard, A. B., J. B. Jensen, S. J. Redding, and P. K. Schott (2007) Firms in international trade, Journal of Economic Perspectives, Vol. 21, No. 3, pp Bigsten, A., P. Collier, S. Dercon, M. Fafchamps, B. Gauthier, J.W. Gunning, A. Oduro, R. Oostendorp, C. Pattillo, M. Soederbom, F. Teal, and A. Zeufack (2004) Do African manufacturing firms learn from exporting? Journal of Development Studies, Vol. 40, No. 3, pp Bigsten, A., M. Gebreeyesus, and M. Soederbom (Forthcoming) Tariffs and Firm Performance in Ethiopia, Journal of Development Studies. Brambilla, I., N. Depetris-Chauvin, and G. Porto (2014) Wage and employment gains from exports: evidence from developing countries. Working Paper, retrieved from [7 March 2016]. Ethier, W. (1982) National and international returns to scale in the modern theory of international trade, American Economic Review, Vol. 72, pp Grossman, G. and E. Helpman (1991) Innovation and Growth in The Global Economy, Cambridge, Massachusetts: MIT Press. Halpern, L., M. Koren, and A. Szeidl (2015) Imported inputs and productivity, American Economic Review, Vol. 105, No. 12, pp Kasahara, H. and B. Lapham (2013) Productivity and the decision to import and export: theory and evidence, Journal of International Economics, Vol. 89, pp Markusen, J.R. (1989) Trade in producer services and in other specialized intermediate inputs, American Economic Review, Vol. 79, pp Melitz, M. J. (2003) The impact of trade on intra-industry reallocations and aggregate industry productivity, Econometrica, Vol. 71, No. 6, pp Rankin, N., M. Soederbom, and F. Teal (2006) Exporting from manufacturing firms in Sub- Saharan Africa, Journal of African Economies, Vol. 15, No. 4, pp Soederbom, M. and F. Teal (2003) Are manufacturing exports the key to economic success in Africa? Journal of African Economies, Vol. 12, No. 1, pp

20 Stone, S. and B. Shepherd (2011) Dynamic gains from trade: the role of intermediate inputs and equipment imports. OECD Trade Policy Papers No. 110, Paris: Organization for Economic Co-operation and Development. 20