Identifying the Effects of Female Ownership on Firm Performance: Evidence from Ghana

Transcription

1 Identifying the Effects of Female Ownership on Firm Performance: Evidence from Ghana Patricia "Tracy" Jones Vassar College March 2012 Abstract How do female-owned firms perform relative to male-owned firms in developing countries? In this paper, I explore this question using a twelve-year panel of manufacturing firms from Ghana. The length of this panel permits a more extensive analysis of firm dynamics than previous studies based on randomized experiments. Addressing the potential simultaneity bias arising from unobserved productivity shocks and entrepreneurs' input choices, I estimate plant-level total factor productivity (TFP) and then test whether female-owned firms have lower TFP than their male counterparts. The results indicate that, on average, female-owned firms are about 25% less productive than male-owned firms, controlling for time-invariant characteristics like sector, location, and union status. In addition, female-owned firms have lower survival prospects and their reduced survival is statistically linked to lower levels of productivity. This last result suggests that markets in Sub-Saharan Africa are fairly effective in forcing the least efficient firms out of business. Keywords: Productivity; Entrepreneurship; sub-saharan Africa JEL Codes: D24; L26; 012 Address for correspondence: Department of Economics, Vassar College, Poughkeepsie, NY patjones@vassar.edu.

2 I. Introduction A well-known stylized fact uncovered by the empirical growth literature is that differences in aggregate productivity are the primary source of income variation across countries (Klenow and Rodriguez-Clare, 1997; Hall and Jones, 1999; Easterly and Levine, 2001). What is less well understood among economists is why some countries are able to achieve higher levels of aggregate productivity than others. A relatively new literature investigates this issue by examining how productivity gaps across countries are related to the within-industry dispersion of productivity across firms operating in the same country (Restuccia and Rogerson, 2008; Hsieh and Klenow, 2009; Bartelsman et al, 2009). Understanding the source of these productivity gaps is important from a policy perspective since aggregate productivity in a country can be increased if production is switched away from low-productivity firms and toward high-productivity firms. Large differences in productivity exist between firms, even those operating in the same narrowly defined industry (Syverson, 2011; Bartelsman and Doms, 2000; Tybout, 2000). Several potential correlates of firm productivity have been proposed and measured using data from both developed and developing countries level. Empirical studies, for example, reveal that productivity increases when firms are exposed to greater competition resulting from policy changes like deregulation or trade liberalization (Melitz, 2003; van Biesebroeck, 2005a; Dollar et al, 2005; Bernard et al, 2006; Verhoogen, 2008). Productivity increases after entry into export markets because international trade facilitates the transfer of knowledge and best-practice techniques (Blalock and Gertler, 2004; Fafchamps, 2009). Technological differences across firms-- particularly those in rich and poor countries-- have long been viewed as a potential source of productivity differentials. Firms in developing countries are assumed to be at a disadvantage due to the slow transfer of knowledge from rich to poor countries and their smaller stock of human 2

3 capital. Several studies have shown that firms with more educated workers in developing countries have higher levels of productivity (Bigsten et al, 2000; Jones, 2001; van Biesebroeck, 2011). Less human capital--including management ability--has been shown to reduce the productivity of firms in developing countries (Bloom and van Reenen, 2007, Bloom and van Reenen 2010; Bloom et al, 2010). This paper adds to the literature on firm heterogeneity and its sources by examining the role of female ownership as a potential correlate of firm productivity. Women own and operate a large percentage of firms in developing countries. More than 50% of all firms in East Asia and the Pacific, for example, have some level of female ownership while the percentage is slightly lower in Latin America and the Caribbean and Sub-Saharan Africa at 38% and 32%, respectively (World Bank Enterprise Survey Database, 2011). Identifying whether female-owned firms have lower productivity than male-owned firms and what constraints they face is obviously important for understanding the underlying sources of firm heterogeneity. Only a few studies examine the productivity of female-owned firms relative to male-owned firms in developing countries. There is some recent work by Sabarwarl and Terrel (2008) and Bruhn (2009) who measure the productivity of female-owned firms in Eastern Europe and Latin America, respectively. Both of these studies, however, rely on ordinary least squares (OLS) estimation techniques. Several econometric issues arise when estimating firm productivity and few of these problems can be addressed when using OLS methods. In addition, there is a related strand of work which uses randomized experiments to measure the returns to capital in femaleowned enterprises (De Mel et al, 2008, 2009; Banerjee et al, 2010; Fafchamps et al, 2011). A 3

4 key result from this work is that capital alone is not sufficient to stimulate firm growth in femaleowned microenterprises. If capital is not the binding constraint, it is important to identify which factors are important in determining the productivity and survival prospects of female-owned enterprises. This paper contributes to the growing literature that attempts to identify these factors. Using a twelve-year panel of manufacturing firms in Ghana, it measures plant-level total factor productivity (TFP) and then tests whether female-owned firms have lower TFP compared to their male counterparts. In addition, it examines the effectiveness of market forces in determining whether female-owned firms survive or exit the market. In doing so, the study addresses two important questions. First, are female-owned firms as productive as male-owned firms in sub- Saharan Africa (as represented by Ghana)? And, second, how important is productivity in determining whether a female-owned firm will exit the market? The answers to these questions are central to understanding firm dynamics in developing countries. A major contribution of this study is that it uses a twelve-year panel of manufacturing firms which permits a more extensive analysis of firm dynamics than previous studies based on randomized experiments. The rest of the article is organized as follows. Section II reviews the literature on firm heterogeneity and its impact of firm survival. Section III provides discusses the data and estimation methods used for analysis. Section IV presents the evidence on how female-owned firms in Ghana perform (in terms of productivity) relative to their male counterparts. In addition, this section identifies the factors which lower productivity in female-owned firms and compares these factors to the constraints to firm growth identified by male-owned firms. Section V 4

5 examines the role of productivity in determining the survival rates of female-owned firms. Section VI provides some concluding remarks and possible avenues for future research. II. Literature on Firm Productivity & Market Exit There is now a great deal of empirical evidence revealing that large and persistent productivity differentials exist across firms, even those operating within the same narrowly defined industry. These productivity gaps can be very large, particularly in developing countries, and are indicative of allocative inefficiencies which lower aggregate productivity. Shiferaw (2007), for example, finds that in Ethiopia a firm at the 10 th percentile of the productivity distribution is about 5% as productive as a firm at the 90 th percentile when classified by 3-digit SIC industries. Similarly, Syverson (2004) measures productivity differentials across firms within 4-digit SIC industries in the US manufacturing sector and finds that a firm at the 10 th percentile of the productivity distribution produces about half as much output with the same inputs as a firm at the 90 th percentile of the productivity distribution. Understanding the underlying sources of such productivity differentials is important for several reasons. First, empirical evidence has shown that a firm's current level of productivity is a good predictor of its future productivity and likelihood of survival. In competitive markets, competition shifts market share toward more productive firms (because they have lower costs and generally lower prices), reducing the market share of less-productive firms and possibly forcing them to exit the market. If a large number of low-productivity firms go out of business, it follows that aggregate productivity at the industry level will rise. The reason is simple: all remaining firms have higher productivity than those which exited the market and any potential 5

6 entrant must match this new productivity level in order to enter the market. This process is known as the "between" component of aggregate productivity growth. In addition, heightened competition can induce firms to make productivity-enhancing investments which they might not do otherwise. Such investments can lead to increased aggregate productivity at the industry level by raising the productivity of existing firms. This process is known as the "within" component of aggregate productivity growth. There is now a growing body of research which examines whether competitive forces in Africa are effective at forcing the most inefficient firms out of the market. Frazer (2005) uses the first four rounds of the same data set to identify the determinants of firm survival and exit. He finds strong evidence that exiting firms in Ghana are less productive than surviving firms. Shiferaw (2007) finds similar evidence for Ethiopia. Using a census-based panel of manufacturing firms, he reveals that markets in Ethiopia are as effective as markets in developed countries in forcing inefficient firms to close down. Söderbom et al (2006) extend this analysis by examining the association between productivity and firm survival for three African countries: Ghana, Kenya, and Tanzania. They find evidence that market selection based on efficiency occurs only among firms which are larger than 10 employees. "At 10 employees the exit rate is insensitive to lagged TFP; however at 50 and 200 employees there is a negative relationship that is both statistically and economically efficient" (Söderbom et al, 2006, p. 549). This last result casts some doubt on the efficacy of market forces in sub-saharan Africa in driving the least-efficient firms out of the market given the fact that a large proportion of firms in African countries employee less than 10 workers. 6

7 One aspect of firm dynamics which has not been examined is the association between productivity and firm survival for female-owned firms. In both developed and developing countries, female-owned firms tend to be smaller than male-owned firms. For the Ghanaian panel, more than half of all female-owned firms employee 10 or fewer employees. This study examines the relationship between productivity in female-owned firms and their likelihood of survival and exit. To do this, it is important to have a good measure of total factor productivity (TFP) that is, the variation in output across firms which is not explained by differences in observable inputs. Conceptually, it is easiest to think of TFP in terms of a standard production function ( ) (1) where is the firm's output and ( ) is a function of the firm's observable inputs: capital,, labor,, and intermediate inputs,. In terms of the production function, TFP is represented by the shift-factor,. For the Cobb-Douglas case, equation (1) can be estimated by taking the logarithms of each of variable and then using a simple estimation method like OLS. The estimated equation is (2) In this case, the TFP estimate is + where the first term is constant across firms in the sample and the second term corresponds to the particular 'shift' effect particular to an individual firm. This 'shift' effect arises due to factors such as superior technology or better management practices. There are two terms in equation (2) that are unobservable to the researcher, and. These two terms have distinct economic interpretations. The term represents productivity 7

8 shocks that are not anticipated by the firm at time whereas the term represents productivity shocks that are potentially predicted by the firm but cannot be observed by the researcher. With cross-section data, it is impossible to separate the residual, from the productivity effect, without making strong parametric assumptions about the distribution of both terms which is not appealing. For this reason, cross-sectional data and OLS methods are generally not used to estimate TFP. Several econometric issues arise when trying to estimate TFP. First, a firm's choice of inputs is likely to be correlated with its level of productivity, Ceteris paribus, more productive firms are expected to use more inputs. Profit-maximizing firms are expected to respond to positive productivity shocks by expanding output and hiring more inputs. Similarly, they respond to negative productivity shocks by lowering output and reducing their input usage. These productivity shocks affect not only the firm's input choices but also its decision to stay in business. The problem for the researcher is that is not observable. When productivity shocks are present, OLS estimates of input coefficients are biased which can lead to biased estimates of TFP. Several estimators have been developed to address these issues with some more appropriate for panel data from developing countries. III. Data and Estimation Methods A. Data The analysis in this study is based on a panel of 200 manufacturing firms in Ghana covering the period 1991 to The data were collected in seven rounds. The first three rounds (1992 to 1994) were implemented under the World Bank's 'Regional Programme on Enterprise 8

9 Development' (RPED). During these rounds, annual data were collected based on the firms' previous year of operation. The next four rounds (1996, 1998, 2000, 2003) were collected as a joint effort by the following organizations: the Centre for the Study of African Economies (CSAE), the University of Oxford, the University of Ghana, Legon and the Ghana Statistical Office. The first three rounds collected two years of data, corresponding to the firms' previous two years of operation. The last round collected three years of firm data. In total, twelve years of establishment data were collected. The original sample consists of 200 firms drawn on a random basis from firms contained in the 1987 Census of Manufacturing Activities. This sample was intended to be broadly representative of the size distribution of firms across Ghana's major manufacturing sectors. Firms in ten threedigit manufacturing sectors were interviewed. These sectors were: food processing ( ), beverages (313), textiles (321), garments (322), wood processing (331), furniture (332), chemicals (351), metal products (381), and machinery (382). About half of the original sample survived through all twelve rounds of the survey. Detailed information was collected on the firms' production, employment, capital stock, ownership, and general characteristics (e.g., sector and location) during each wave. Most of the variables used in the production analysis are quite standard and their definitions can be found in Appendix A. The Ghanaian data set, however, is unusual in several ways. First, it includes firmspecific data on the prices of both outputs and material inputs. Such data are essential for differentiating real changes in firm revenue from changes which result from shifts in market power across firms. Second, the length of the panel is unusual for a survey of manufacturing 9

10 firms in sub-saharan Africa. While data on the industrial sector in sub-saharan Africa is growing, few data sets contain panels of more than three or four observations. 1 Third, the Ghanaian sample includes firms which represent all size classes. It includes data on microenterprises (< 5 employees), small enterprises (5-19 employees), medium enterprises (20-99 employees) and large enterprises (>100 employees). Most census-based surveys do not interview microenterprises which comprise a large proportion of enterprises in developing countries. And finally, the survey asks detailed questions about entrepreneurs' background, including questions on their age, education, and prior job experience. These data were collected by face-to-face interviews with the firm's entrepreneur or, if the entrepreneur was not available, a member of the firm's management team. In total, data on 233 entrepreneurs were collected. Approximately 25 percent of these entrepreneurs are women (58 firms) while the remainder are men (175 firms). Table 1 presents the descriptive statistics on firms divided by gender of owner. On average, female-owned firms are smaller than maleowned firms both in terms of employment (30 employees versus 77 employees) and value-added (573,414 million Cedis versus 990,592 million Cedis). In addition, workers in female-owned firms work with about half as much capital as workers in male-owned firms (993,697 million Cedis versus 2,423,829 million Cedis). It does not follow automatically that women entrepreneurs are capital-constrained. It could be the case that women entrepreneurs simply choose to operate in industries with low capital requirements. Indeed, the women entrepreneurs in our sample are concentrated in two sectors--food processing (52%) and garments (30%)-- which have lower capital-labor ratios than other sectors. Finally, the majority of women 1 Since 2005 the World Bank has collected establishment-level data on 39 countries in sub-saharan Africa as part of the World Bank Enterprise Surveys (WBES) program. Panel data is available for twelve of these countries. See 10

11 entrepreneurs manage their firms as sole proprietorships (80%) while male entrepreneurs choose more varied ownership structures. For males, 50% choose to manage their firms as limited liability enterprises, 39% as sole proprietorships, and 10% as partnerships. 2 As can be seen in Table 2, similar differences arise when looking at the descriptive statistics of female and male entrepreneurs. On average, female entrepreneurs have completed about two years less education than males (10 years versus 12 years). Perhaps most significant, a full 20 percent of female entrepreneurs report that they have no education at all. This percentage is much higher than that reported by male entrepreneurs (7%). Smaller differences between the sexes arise for higher levels of education. Roughly the same percentage of male and female entrepreneurs has completed primary school (6% versus 5%), middle school (33% versus 29%) and secondary school (14% versus 11%). Surprisingly, a similar percentage ( 25%) report that they have completed some form vocational and professional training. Some interesting differences arise when we look at what entrepreneurs were doing before they started their own business. About 50% of males were employed by another firm immediately prior to starting their own business whereas the percentage was lower for females (35%). A higher percentage of females, however, had industry experience in the same sector which they started their business (74% versus 66%) and a higher percentage of women were native to the town where they choose to locate their firm (48% versus 26%). Surprisingly, gender made little difference with respect to the size of the firm at start-up. On average, female entrepreneurs employed eleven employees at start-up while male entrepreneurs employed twelve employees. 2 The numbers do not add up exactly to 100% due to rounding errors. 11

12 Both sexes employed about the same number of apprentices-- female entrepreneurs employed two apprentices while male entrepreneurs employed three apprentices. B. Estimation of Total Factor Productivity Economists have long understood the inherent difficulties in obtaining reliable estimates of firmlevel productivity. A key issue is that profit-maximizing firms respond to positive (negative) productivity shocks by increasing (decreasing) input demand. When OLS is used, input coefficients are biased and, by implication, estimates of firm-level productivity are biased as well. If panel data are available, one method for solving this problem is to use firm fixed effects when estimating production functions. While this method minimizes the simultaneity bias, it is only appropriate when it is reasonable to assume that the firm-specific effect is time-invariant. A related approach is to regress the firm's fixed effects on some function of time. This approach allows the productivity term to vary over time but it does not capture the correlation between and the firm's input levels and therefore does not fully address the simultaneity problem. To address these issues, Olley and Pakes (OP) (1996) proposed a new estimator which uses investment as a proxy for a firm's unobserved productivity effect. To do this, they make the assumption that a firm's optimal investment is a strictly increasing function of its current productivity,. Given this assumption, they show the conditions under which a firm's investment function can be inverted and used to control for in its production function. The OP estimator is generally viewed as superior to both the fixed-effects and general methods of moments estimators because it leaves more identifying variance in the variables (Mairasse, 1998). One weakness of the OP estimator, however, is that it requires firms to have non-zero 12

13 investment. Firms with zero investment in any given period are dropped from the sample which can result in a severely truncated sample. This is a severe limitation when estimating production functions using data from developing countries where it is common for firms to have zero investment in some years. To avoid this weakness, Levinsohn and Petrin (2003) (LP) propose an estimator which uses intermediate inputs as a proxy for unobserved productivity shocks. Levinsohn and Petrin consider a firm which operates through discrete time and seeks to maximize the present discounted value of both its current and future profits. They start with the following production function: (3) where is the logarithm of firm 's value-added (gross output net of intermediate inputs, ), is the logarithm of firm 's capital input, and is the logarithm of firm 's labor input, is firm 's productivity shock, and is an error term which is uncorrelated with the firm 's choice of inputs. The basic idea behind the LP estimator is that a firm's demand for intermediate inputs is less lumpy than its demand for investment. Therefore, the strict monotonicity condition is more likely to hold for intermediate inputs than for investments. LP assume the intermediate demand function takes the following form: ( ) (4) 13

14 Notice that is indexed by in equation (4). This permits input prices or demand conditions to vary across time but not across firms. From equation (4) it is clear that intermediate inputs at time are chosen as a function of This means that intermediate inputs are chosen when production takes place. Similarly, labor inputs are also chosen simultaneously with production. If this were not the case, the firm's choice of would affect its optimal choice of Given these conditions as well as the assumption that intermediate inputs are monotonically increasing in enables the demand function to be inverted to ( ) (5) The unobserved productivity term,, is now expressed in term of two observable variables: and. The LP approach takes a structural approach to identification of the production function. Specifically, they assume that the productivity term, evolves according to a first-order Markov process. That is, ] (6) where is a productivity shock that is uncorrelated with but not necessarily with The intuition behind the LP estimation strategy is that a firm's current capital stock is determined before the surprise in its current productivity. Substituting (5) back into (3) yields: ( ) (7) This equation can be rewritten as ( ) (8) 14

15 where ( ) ( ) (9) Substituting a third-order polynomial approximation in and in place of ( ) makes it possible to estimate equation (8) using OLS. The following equation is estimated (10) where is not separately identified from the intercept of ( ). This completes the first stage of the LP estimation procedure and provides estimates of both and (up to the intercept). The second stage derives estimates of It begins by estimating using (11) (12) For any candidate value, a predicted value of can be computed for all periods using As stated by Petrin et al (2004, p. 116) 3 : "using these values, a consistent (nonparametric) approximation to ] is given by the predicted values from the regression (13) 3 For this quotation, I change the notation use by Petrin et al (2004) and use my own notation in order to retain consistency with the rest of the paper. 15

16 which LP call ]." Given and ], the sample residual of the production function can be written as ] (14) The estimate is defined by Levinsohn and Petrin (2003) as the solution to the following minimization problem: ( [ ]) (15) A bootstrap method is used to construct standard errors for both and For a more detailed discussion of the alogorithm and estimation of the LP estimator using STATA, see Levinsohn and Petrin (2003) and Petrin et al (2004). 4 Once consistent and unbiased estimates of the input coefficients are obtained, the next step is to construct a measure of plant-level measure TFP. To do this, I simply subtract predicted valueadded from actual value-added for each firm in the sample. These measures of TFP are used to assess how female-owned firms perform relative to male-owned firms in terms of productivity. This is done by regressing the TFP estimates on female-ownership. In addition, the TFP estimates are included as regressors in a model of firm exit. The results from both set of analysis are discussed in the next section. 4 Caves and Ackerberg (2003) (AC) critique the LP estimator for not completely solving the collinearity problem. They argue that, if is a function of and, it follows that should be a function of and as well. A comparison of the coefficients estimated by each estimator on earlier rounds of the Ghanaian data yields nearly identical results. See Frazer (2005). 16

17 IV. The Performance of Female-owned Firms in Ghana This section presents the results from the production analysis of the panel of Ghanaian manufacturing firms. Table 3 compares the estimated input coefficients based on the LP estimator (column 1) with those obtained using OLS with a pooled sample (columns 2 to 4). As discussed above, the OLS results will be biased if unobserved productivity shocks affect entrepreneurs' choices of inputs. When OLS is used to estimate a simple Cobb-Douglas specification (column 3), the estimated coefficient on employment is 0.96 and that on lagged capital is Both estimates are significant at the 1% level and similar in size to those estimated by Söderbom and Teal (2004) using earlier waves of the same panel of Ghanaian firms. When the LP estimator is used (column 1), the input coefficients remain significant at the 1% level but are smaller in magnitude. The estimated coefficient on employment is 0.33 whereas that on lagged capital is In column 3 the estimated production function controls for several firm characteristics as well as industry and year fixed effects. Industry fixed effects are included because female-owned firms tend to be concentrated in some 3-digit industries (e.g., garments) which may have lower levels of productivity than others. This specification also controls for firm size which is measured in terms of paid employees. The inclusion of firm size stems from the theoretical literature which relates firm size to market selection through entry and exit (Jovanovic, 1982). Such models assume that small firms have higher (but more variable) growth rates than large firms which tend to be at the top of the productivity distribution. As expected, larger firms produce more than smaller firms, even after controlling for a vector of firm characteristics. The coefficient on each 17

18 firm size dummy is significant and positive and increases in magnitude for larger classes of firms. Other controls include a firm union status and whether it is located in Ghana's capital city, Accra. There is a great deal of empirical evidence which suggests that unionized firms are more productive than non-unionized firms. Unions often provide industry-specific training which raises the productivity of their members. As expected, the coefficient on union status is both positive and significant. Somewhat surprising, however, is the result that firms located in Accra are not significantly more productive than those located outside of the capital. This is a surprising result since firms located in the capital are likely to have many advantages relative to those located in smaller cities. For example, firms located in the capital are likely to have access to better infrastructure (e.g., fewer power outages) than regional firms. Markets in the capital are likely to be larger and more competitive than those in smaller cities, making it more difficult for less productive firms to stay in business. And finally, firms located in the capital are likely to benefit from a high density of closely related industries which might generate positive externalities such as "thick" labor markets and abundant suppliers. The next step taken is to add a set of human capital variables to the production function. The results from this analysis are reported in column (4). Several studies find evidence in support of the assumption that more educated workers produce more output than lesser educated workers. Jones (2001), for example, uses matched employee-employer data from earlier rounds of the same panel data to test whether wage differentials between workers with different levels of education reflect genuine productivity differentials. She finds strong evidence that education is 18

19 positively correlated with worker productivity. Most productivity studies do not control for the overall level of human capital in the firm. This omission is simply due to data constraints. Detailed information on the education and training of workers is usually not collected as part of standard industrial surveys. For the Ghana surveys, however, up to ten workers from each firm were interviewed during each round. Workers were asked, among other things, to give their age, years of completed education, usual hours worked per week, and level of earnings. These responses were then merged with the firm level data so that a measure of human capital stock could be constructed for each firm. For a detailed description of how this variable was constructed, see Teal (2011). As expected, the coefficient on education is both positive and significant. The average returns associated with a one-year increase in a firm s average stock of education is a 9 percent increase in its value-added per worker. Similarly, both age and its square are significant and have the expected signs. The production functions in columns 3 and 4 are estimated using a pooled data set. Of primary interest is the coefficient on female which indicates whether the firm is owned by a woman. In both specifications, the coefficient on female is negative and significant at the 1% level. For the full model in which all controls are added (column 4), the coefficient is -0.30, indicating that female-owned firms produce about 30% less than male-owned firms. Interestingly, this coefficient is about two to three times larger than those estimated by Bruhn (2009) for Peru and Mexico using a similar estimation strategy (i.e., a pooled data set estimated by OLS). This result suggests that the gender productivity gap is larger in sub-saharan Africa than it is in Latin America although, because it is estimated using OLS, it may biased. One avenue of future research would be to investigate whether the gender productivity gap is as large in other African 19

20 countries as it is in Ghana. Furthermore, it would be useful to investigate whether the gender productivity gap falls as a country develops. A. Firm Productivity A central aim of this study is to explore the correlates of firm-level productivity and then compare how these correlates differ across firms owned by men and women. To do this, I first construct a firm-level measure of total factor productivity (TFP) using the input coefficients estimated from the LP estimator. This measure of TFP is then regressed on a dummy variable indicating whether the firm is owned by a woman and other firm characteristics. The results from this analysis are reported in Table 4. The most important result is found in column (2) which reports the coefficients for the full model which controls for both firm and worker characteristics. In this specification, the coefficient on the female dummy is indicating that, on average, female-owned firms produce about 25% less than their male counterparts. Surprisingly, the size of gender productivity gap is not significantly different from that estimated using OLS and reported in Table 3. While the LP estimation technique is preferred to OLS based on theoretical considerations, the two procedures produce similar results on the Ghanaian data. The other results which emerge from the productivity analysis are relatively standard. As seen in column (2), larger firms are more productive than smaller firms. All the coefficients on the firm size dummies are positive and significant at the 1% level. In addition, unionized firms are more productive than non-unionized firms. The union productivity differential is both positive and significant at the 1% level. The only surprising result to emerge from Table 4 is that a firm s age 20

21 does not have a significant impact on its productivity. Typically, older firms are found to be more productive than younger firms. The insignificance of firm age is likely due to a high correlation between a firm s age and its entrepreneur s age. When the entrepreneur s age is dropped from the specification, firm age then becomes significant. As expected, firm productivity is positively correlated to a firm s average level of human capital stock. The coefficient on average education in column (2) is 0.051, indicating that firm productivity rises by about 5% for each additional year of average education. The average age (and its square) of workers in the firm are also significant at the 1% level. None of the other worker variables are significant except "proportion skilled" which is negatively correlated to firm productivity but only significant at the 10% level. Understanding why female-owned firms underperform is a central question in development economics and there are few satisfactory answers. One approach to answering this question is to examine how the correlates of firm-level productivity differ across firms owned by men and women. Columns (3) through (6) report the results from the productivity equations which are estimated separately for female and male-owned firms. Several interesting results emerge from this analysis. For female-owned firms, the entrepreneur's education has a positive effect on productivity although the average education of her workforce is not significant. This result is probably due to the small size of female-owned firms. It is likely that the entrepreneur's education dominates the effect of her worker s average education. The opposite result is found for male-owned firms. In male-owned firms, the average education of the workforce is significant while entrepreneur's education is not significant. By contrast, age (and its square) of 21

22 male entrepreneurs are significant, indicating that experience matters more for male-owned firms. This is not surprising since firms owned by males tend to survive longer than femaleowned firms. None of the other human capital variables are significant for firms owned by males. Perhaps most important, firm size matters for both sexes. Small firms (those with 5 to 19 workers) are about twice as productive as micro firms (those with less than 5 employees). Medium-sized firms (those with 20 to 99 employees) are about 200% more productive than micro firms. And, large firms (those with more than 100 employees) are more than 300% more productive than micro firms. Given the large productivity differential between micro and larger firms, it is important to identify the constraints preventing micro firms from growing in terms of employment. But, in order to understand the dynamics of firm growth, it is necessary to know which firms survive and what determines the survival prospects of both male and female-owned firms. B. Firm Exit Aggregate productivity can be increased through three primary mechanisms: (1) existing firms in the market become more productive; and (2) firms with higher productivity in the market grow faster than those with less productivity; and (3) more productive firms enter the market and replace less productive firms. The first condition is known as the 'within' component of aggregate growth whereas the next two conditions comprise the 'between' component. Reallocation of market share plays a significant role in raising aggregate productivity in many countries, particularly in the United States (Baily et al 1992; Bernard and Jensen, 1999). The results are more mixed for developing countries. Aw et al (2001), for example, find that 22

23 reallocation contributes little to productivity growth in Taiwan. Liu and Tybout (1996) find similar results for Columbia although Pavcnik (2002) shows that reallocation accounts for about 70% of productivity growth in Chile. The effectiveness of markets in transferring resources from firms with low-productivity to firms with high-productivity is critical in determining whether inefficient firms are forced out of the market. In this study two primary questions are addressed: (1) Do female-owned firms have lower survival rates than male-owned firms? (2) How important is the link between a firm's level of productivity and its exit rate? To the author's knowledge, this is the first study which examines the long-run dynamics of female-owned firms in Sub-Saharan Africa. The studies which do exist base their results on either cross-sectional data or randomized experiments. One wellknown shortcoming of randomized experiments is that they track the behavior of firms over a relatively short period of time. Given the length of the Ghanaian panel, it is possible to examine the exit rates of both male and female-owned firms over a twelve year period. To estimate the survival rates of female-owned firms, I estimate a probit model of exit where the outcome variable is whether or not the firm exists in any given year. The results of this analysis are reported in Table 5. The coefficients are the marginal effects calculated at the mean. The most important result is that female-owned firms have a much higher probability of exit in any given year than male-owned firms. In columns (1) and (2), we see that female-owned firms are more likely to exit than male-owned firms, both unconditionally and after including a number of controls. The coefficient point estimate in column (2) is 0.05 which indicates that female-owned firms are about 5% more likely to exit the market in any given year. Moreover, market selection 23

24 in Ghana does appear to weed out the least efficient firms. The coefficient on lagged TFP in column (2) is both negative and significant at the 5% level. This indicates that a firm's productivity and likelihood of exit are negatively correlated. In addition, the interaction term between the female dummy and lagged TFP is negative and significant at the 10% level, indicating that female-owned firms with higher productivity are less likely to exit. Together these two results provide fairly strong evidence that markets in Ghana are effective in forcing the least productive firms out of business. Columns (3) through (6) report the results of the probit analysis after the sample is split into male-owned and female-owned firms. The most interesting result to emerge from this analysis is that productivity is no longer significant. That is, productivity is not a significant factor in determining whether a female-owned firm will exit when compared to other female-owned firms. The same result holds for male-owned firms. Productivity gaps become an important predictor of exit rates only when the samples are combined, suggesting that there is not enough variation in TFP across firms owned by the same gender to affect exit rates. V. Conclusion This paper adds to the literature on firm heterogeneity by examining the role of femaleownership as a potential correlate of firm productivity using a twelve-year panel of manufacturing firms from Ghana. The length of this panel permits a more extensive analysis of firm dynamics than previous studies based on randomized experiments. Two primary questions are addressed: Are female-owned firms as productive as male-owned firms in developing 24

25 countries? How important is productivity in determining whether a female-owned firm will exit the market? To answer the first question, I estimate the input coefficients of a Cobb-Douglas production function using the Levinsohn and Petrin (2003) (LP) estimator. These input coefficients are used to construct measures of plant-level total factor productivity (TFP) for each firm in the sample. The TFP estimates are then used to assess how female-owned firms perform relative to maleowned firms. This is done by regressing TFP on the female-owner dummy, controlling for other inputs as well as firm and worker characteristics. The analysis reveals that female-owned firms are about 25% less productive than male-owned firms. Surprisingly, the results based on the LP estimator are not very different from those estimated using OLS, suggesting that controlling for simultaneity bias does matter much for Ghanaian firms. Future research is needed to assess the importance of controlling for simultaneity bias when estimating production functions using data from other developing countries. To answer the second question, I estimate a probit model of firm exit using the full twelve-year panel. This model assesses the likelihood of firm exit for any given year over the twelve-year period. The results from the probit analysis reveal that female-owned firms are 5% more likely than male-owned firms to exit the market. In addition, the interaction term between the femaleowner dummy and lagged TFP is negative and significant, indicating that more productive female-owned firms are less likely to exit the market. Together these two results provide fairly strong evidence that Ghanaian markets are effective in driving the least productive firms out of business. 25

26 Can these results be generalized to other African countries? Female entrepreneurship is widespread throughout Africa and Ghana is certainly typical in that respect. Whether similar productivity differentials exist between male and female-owned firms in other African countries remain to be seen. Additional research is needed which identifies both the constraints facing female entrepreneurs and how these constraints can be removed in order to raise the productivity of female-owned firms. In addition, there is a need for additional research which examines the long-run dynamics of female-owned firms. Understanding the factors which facilitate employment growth among female-owned firms is particularly important since larger firms are more productive than smaller firms. 26

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