The World Bank Economic Review, 38(2), 2024, 251–273 https://doi.org10.1093/wber/lhad034 Article Allocative Efficiency between and within the Formal Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 and Informal Manufacturing Sectors in Zimbabwe Godfrey Kamutando and Lawrence Edwards Abstract Resource misallocation has the potential to reduce aggregate total factor productivity and undermine indus- trial development. Aggregate productivity losses are found to be particularly pronounced in emerging economies where large market frictions impede efficient resource allocation. Available estimates, however, almost entirely exclude firms in the informal sector that in some countries, such as Zimbabwe, make up a high share of overall production and employment. The exclusion of informal firms can result in either an over- or under-estimate of the aggregate productivity losses from misallocation. This paper, therefore, uses firm-level survey data to analyze how market distortions contribute to the misallocation of resources within and between the formal and infor- mal manufacturing sectors in Zimbabwe. Applying the approach developed by Hsieh and Klenow (2009) to firm-level microdata, the results reveal extensive resource misallocation in both the formal and informal manu- facturing sector. Market shares of informal firms are found to be low relative to their productivity—an outcome associated with relatively large capital market distortions. Misallocation is also more pronounced among rela- tively productive firms, thus exacerbating aggregate losses in total factor productivity (TFP). Estimates indicate that aggregated gains in TFP of 151.4 percent can be realized through efficient resource allocation. JEL classification: C83, D24, L11, L60, O12, O17 Keywords: misallocation, total factor productivity, formal and informal sector, manufacturing, Zimbabwe 1. Introduction Differences in aggregate total factor productivity (TFP) have been shown to be a key explanatory fac- tor behind the large differences in incomes and development across countries (Hall and Jones 1999; Hsieh and Klenow 2009; Asker, Collard-Wexler, and De Loecker 2014; Gopinath et al. 2017; David and Venkateswaran 2019). Traditionally, these income gaps have been attributed to differences in tech- nologies and factor input accumulation (such as labor and capital) (Hall and Jones 1999; Howitt 2000). More recently, however, the contribution of resource misallocation in explaining the observed disparities in cross-country aggregate TFP has been emphasized (Hsieh and Klenow 2009; David and Venkateswaran Godfrey Kamutando (corresponding author) is a Carnegie Developing Emerging Academic Leaders post-doctoral research fellow at the Policy Research in International Services and Manufacturing (PRISM), School of Economics, University of Cape Town, South Africa; his email is godfrey.kamutando@uct.ac.za. Lawrence Edwards is a professor at the University of Cape Town, South Africa, and the director of PRISM; his email is lawrence.edwards@uct.ac.za. The research for this article was financed by the Growth and Labour Markets in Low-Income Countries Programme (“GLM | LIC”) (GA-C3-RA6-345) managed by the Institute of Labor Economics (IZA) and administered by the Southern Africa Labour and Development Research Unit (SALDRU). The authors thank the editor Nina Pavcnik and three anonymous reviewers for their constructive comments. A supplementary online appendix for this article can be found at The World Bank Economic Review website. C The Author(s) 2023. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by- nc- nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 252 Kamutando and Edwards 2019; Restuccia and Rogerson 2017). In efficient markets, resources are allocated across firms such that more productive firms control a larger share of the market. When output and factor market distortions impede this reallocation, aggregate productivity falls. These misallocation effects can be large. Hsieh and Klenow (2009) calculate that China and India could experience aggregate TFP gains of between 50 per- cent and 60 percent should resource allocation become as efficient as in the United States. Consequently, Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 reducing the misallocation of resources is one channel through which substantial increases in aggregate productivity and incomes of emerging economies can be achieved, despite the constraints they face in accessing technology, capital, and other productive resources. This paper analyses how market distortions contribute to the misallocation of resources within and between the formal and informal manufacturing sectors in Zimbabwe.1 Research literature on misallo- cation and aggregate productivity has expanded rapidly, covering both advanced economies (e.g., Hsieh and Klenow 2009; Bartelsman, Haltiwanger, and Scarpetta 2013; Restuccia and Rogerson 2017) and emerging economies (e.g., Nguyen, Taskin, and Yilmaz 2016; Chuah, Loayza, and Nguyen 2020; Cirera, Fattal-Jaef, and Maemir 2020). One shortcoming of the field, however, is the lack of research on the con- tributions of the informal sector to resource misallocation. In many emerging economies, the informal sector is a substantive contributor towards overall employment and aggregate production (La Porta and Shleifer 2008). This also holds in the case of the Zimbabwean manufacturing sector, where Medina and Schneider (2018) estimate the average size of the informal sector, or shadow economy as they term it, to be 61 percent of gross domestic product (GDP)—the highest among a sample of 158 countries. Ignor- ing the informal sector in the analysis of misallocation can bias estimates of its impact on total factor productivity (TFP) (Fossati, Rachinger, and Stivali 2021). The direction of the bias, however, is ambiguous and is dependent on two different positions regarding productivity within the informal sector. In the dualist model, the informal production sector is seen as a backward traditional sector with high market frictions, low productivity, a highly segmented labor market and limited scope to drive aggregate productivity growth. On the other hand, the structuralist model portrays the formal and informal sectors as two competitive and integrated economic systems where the informal sector is able to trigger aggregate productivity and growth (Mcpherson 1996; Maloney 1999; Fields 2011; Benjamin and Mbaye 2012). These positions give rise to very different implications of the informal sector for resource misallocation, with very different policy recommendations. Assessing the impact of the informal sector on misallocation and aggregate productivity in an economy is, therefore, an empirical matter. This paper, as noted, analyses misallocation of resources within and between the formal and informal manufacturing sectors in Zimbabwe. There are several reasons for focusing on Zimbabwe. The country has experienced widespread interventions by the state in the operation of product and factor markets, including controls over prices and access to foreign exchange; periods of hyperinflation; restrictive labor regulations; severe constraints to access finance; and weak economic infrastructure related to the provi- sion of water and electricity (Velenchik 1997; Gunning and Oostendorp 1999). Such market frictions are expected to negatively affect allocative efficiency, firm performance and aggregate TFP in the economy. Further, the economy has undergone a process of de-industrialization and informalization with the share of formal manufacturing in nonagricultural formal employment falling from 22 percent in 1992 to 8 per- cent in 2019, and the informal sector share in total manufacturing employment rising from 29 percent in 2011 to 69 percent in 2019.2 Finally, existing studies in Zimbabwe predominantly present descriptive 1 Informal manufacturing firms are defined as unincorporated or unregistered enterprises engaged in the production of goods for employment or income (ILO 2002). In Zimbabwe a firm becomes formal once it gets registered with the Registrar of Company’s office. Therefore, all unregistered firms are informal firms. 2 Formal employment data for 1992 is obtained from the Employment and Earnings series provided by Zimbabwe Na- tional Statistics Agency (ZIMSTAT). The informal and formal sector employment data for 2011 and 2019 are drawn from the 2011 and 2019 Labor Force Surveys (ZIMSTATS 2012; 2020). The World Bank Economic Review 253 overviews of the characteristics of the formal and informal sectors without in-depth analysis of produc- tivity and misallocation (McPherson 1996; Velenchik 1997; Luebker 2008). Thus, Zimbabwe provides a suitable context to study the link between market frictions, informality and misallocation in emerging economies. The analysis focuses on manufacturing, as it is seen as a key driver of industrialization and economic Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 development in Africa, given its level and capacity for productivity growth. However, the contribution of the manufacturing sector to aggregate output and employment in Africa has been stagnant or declining (Söderbom and Teal 2004; Söderbom, Teal, and Harding 2006; Bigsten and Söderbom 2011; McMillan and Rodrik 2011; Diao et al. 2021; Ndung’u, Shimeles, and Ngui 2022), thereby diminishing aggregate productivity (McMillan, Rodrik, and Verduzco-Gallo 2014; Diao, Kweka, and McMillan 2018; Kouamé and Tapsoba 2019). Whereas these studies have generally focused on sectoral shifts in the composition of employment and output, the present study provides an additional firm-level perspective of structural changes within manufacturing and the aggregate TFP gains that can be realized through more efficient allocations of resources. To measure misallocation, the paper adopts the well-known Hsieh and Klenow (2009) framework (mostly referred to as HK in the rest of the paper). Misallocation is measured as the dispersion of total factor revenue productivity (TFPR). TFPR is then decomposed to isolate the relative importance of factor and product market distortions in driving misallocation, and how this differs across the formal and infor- mal sectors. Finally, the correlation between distortions and firm productivity is examined to determine whether aggregate TFP losses are exacerbated (attenuated) by distortions that penalize relatively efficient (inefficient) firms, as emphasized by Restuccia and Rogerson (2008). To conduct the analysis, the paper draws on firm-level data collected as part of the Zimbabwe Man- ufacturing Firm Survey 2015–2016.3 The survey was conducted with formal and informal firm owners or managers and contains detailed information on firm sales, raw materials, indirect costs, employment, capital stock, and so forth. It further contains information on factor and product market constraints to the operation of the firm. This survey allows for an empirical analysis of misallocation that extends to include informal manufacturing firms, unlike other available sources of informal firm data where capi- tal stock is lacking, including, for example, the Zimbabwe 2016 World Bank Informal Business Sector Survey. The data reveals substantial resource misallocation between firms in both the formal and informal manufacturing sectors. Misallocation particularly affects informal-sector firms that are found to be rela- tively small given their productivity levels. While both output and capital market distortions contribute to resource misallocation, the capital market distortions are strikingly large for informal firms. Frictions in the capital market are therefore found to be a key constraint to growth of informal manufacturing firms. Furthermore, misallocation disproportionately constrains production of relatively productive firms, thus exacerbating aggregate losses in total factor productivity (TFP). Estimates indicate that aggregated TFP losses from misallocation are very high in Zimbabwe, with possible gains from more efficient resource allocation of up to 151.4 percent. Excluding the informal sector from the analysis leads to an underesti- mate of potential productivity gains of nearly 22 percentage points. These results are found to be robust to the use of alternative methods of measuring misallocation and alternative survey data on formal firms (e.g., the 2016 World Bank Enterprise Survey for Zimbabwe). The remainder of the paper is structured as follows: Section 2 presents the brief background context of the Zimbabwe manufacturing sector. Section 3 reviews the theoretical and empirical literature. Section 4 presents the methodology and data. The data are discussed in section 5. The results are presented in section 6. Section 7 presents the robustness checks, and the conclusion is presented in section 8. 3 For access to the data, see “Zimbabwe Manufacturing Firm Survey 2015–2016,” DataFirst, https://www.datafirst.uct. ac.za/dataportal/index.php/catalog/702/study-description. 254 Kamutando and Edwards 2. The Zimbabwe Manufacturing Sector Zimbabwe is a low-income economy emerging from over a decade-long economic crisis. From 2000 to 2009, the Zimbabwean economy collapsed in the face of hyperinflation and severe macroeconomic imbalances. While growth initially recovered in response to the stabilization and reduction of inflation following the dollarization of the economy in 2009 (averaging close to 8 percent per annum from 2009 Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 to 2011), it remained fragile and susceptible to continued external (e.g., lower commodity prices) and internal (government deficit, trade deficit) pressures, and an uncertain political environment. The Zimbabwean economic crisis had a profound impact on production, industrialization, employ- ment and human development in the country (Confederation of Zimbabwe Industries [CZI] 2012; World Bank 2012; World Economic Forum 2017). According to the 2011–2012 Poverty, Income, Consumption and Expenditure Survey (PICES), 62.6 percent of Zimbabwean households were poor with 16.2 percent in extreme poverty (ZIMSTAT 2012). During the early 1990s, Zimbabwe had one of the most advanced and diversified industrial sectors in Africa (Gunning and Oostendorp 1999). In 1993, the manufacturing sector produced 24 percent of gross domestic product (GDP), provided 21 percent of nonagricultural formal employment, and accounted for 42 percent of total export earnings. By 2009, when hyperinflation ended, the share of manufacturing in GDP and nonagricultural employment had fallen to 15.5 percent and 17.7 percent, respectively, as manufacturing firms contracted, exited, and shed employment.4 De- spite a raft of economic policies by the government to enhance economic growth, employment, industrial development, and international trade, manufacturing employment continued to decline in subsequent years, and by 2019, manufacturing’s share of formal employment had fallen to 8 percent (ZIMSTAT 2020).5 Associated with the deindustrialization of formal employment, was a rise in the level and share of the informal sector in production and employment, including in manufacturing. In contrast to the for- mal manufacturing sector, total employment in informal manufacturing is estimated to have risen from roughly 77,000 in 2011 to 151,000 in 2019, thus surpassing the number of employees in formal manu- facturing (67,000 in 2019) (ZIMSTAT 2012, 2020). The informal manufacturing firms in Zimbabwe largely produce in designated areas in the urban centers and compete with formal firms in the pro- duction and sale of goods, mainly in the textile, metal and wood industries. Furthermore, the two sec- tors appear integrated with informal firms purchasing intermediates from the formal sector, while some formal-sector firms outsource production to informal producers (e.g., in the clothing industry) (Luebker 2008). The implications of these structural changes on aggregate productivity of the Zimbabwean manufactur- ing sector are uncertain. The relatively strong growth of informal production may have reduced aggregate productivity through increased misallocation of resources or may reflect a dynamic efficient adjustment in response to relatively severe distortions within the formal economy. For example, informal firms may be less constrained by government regulations, including labor laws that impose rigidities on changes to employment in the formal firms. To provide further insight into this matter, the remainder of the paper presents an analysis of misallocation between and within the formal and informal manufacturing industry in Zimbabwe. 4 The 2009 employment shares are based on Employment and Earnings data provided by ZIMSTAT. The national accounts data are drawn from the revised GDP 2009–2012 data provided by ZIMSTAT. 5 Policies include the government’s Medium-Term Plan (2011–2015), its Industrial Development Policy (2011–2015), and the National Trade Policy (2012–2016). Some of the earlier economic policies include the 1995 Economic Struc- tural Adjustment Program (ESAP), the Zimbabwe Programme for Economic and Social Transformation, ZIMPREST (1996–2000), the 2001 Millennium Economic Recovery Programme (MERP), the 2003 National Economic Revival Plan (NERP), and the Zimbabwe Agenda for Sustainable Socio-Economic Transformation—ZIMASSET (2013–2018). See Mhone and Bond (2001) and Makina (2010) for more details on these policies. The World Bank Economic Review 255 3. Theoretical and Empirical Literature This study is closely related to the strand of literature that has applied the Hsieh and Klenow (2009) conceptual framework to examine the extent and impact of firm-level resource misallocation on aggre- gate productivity. This section first discusses the concept behind misallocation as presented by Hsieh and Klenow (2009), and then reviews the relevant empirical literature. Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 As argued by Hsieh and Klenow (2009), in competitive markets with no frictions, firms pay common factor prices, resulting in the equalization of the marginal revenue product (MRP) of factor inputs across firms with similar production functions. Should MRP for a particular factor differ across firms in a com- petitive market, then the higher MRP firms will bid for these factors, leading to a re-allocation of factors from low to high marginal revenue product firms. This results in a convergence in MRP across firms and an increase in aggregate output. A further consequence of this adjustment (see in the next second para- graphfor formal derivation) is that firms within the same industry will converge on equivalent levels of total factor productivity revenue (TFPR). Factor and product market distortions, however, impede the (re)allocation of given production re- sources across heterogeneous firms. This will happen, for example, if the outputs of firms within the same industry are taxed differently or when distortions affect the cost of inputs across firms differently. These distortions impede the equalization of marginal revenue products of capital and labor across all firms, thereby generating misallocation (Hsieh and Klenow 2009). Further, they give rise to dispersion in TFPR across firms, with high TFPR firms being inefficiently small and those with TFPR below the industrial average inefficiently large. Empirically, therefore, the dispersion of TFPR across firms within the same in- dustry has been used to determine the presence and extent of resource misallocation (Hsieh and Klenow 2009). Formally, Hsieh and Klenow (2009) illustrate these concepts by assuming an economy with hetero- geneous (in total factor productivity) manufacturing firms, where each firm i in industry s produces a differentiated product using the same Cobb-Douglas production technology.6 In optimizing profits, firms face firm-specific output distortions (e.g., tax), τY si , and firm-specific capital distortions, τKsi , that affect the cost of capital relative to labor (e.g., access to credit, credit rationing, government subsidies).7 For both these distortion measures, a positive value is equivalent to a tax, and a negative value is equivalent to a subsidy. From these conditions, Hsieh and Klenow (2009) derive marginal revenue product of capital (MRPK) and labor (MRPL) as follows: σ − 1 PsiYsi 1 + τKsi MRPKsi = αs =R (1) σ Ksi 1 − τY si σ − 1 PsiYsi 1 MRPLsi = (1 − αs ) =w (2) σ Lsi 1 − τY si where PsiYsi is the firm’s value-added (firm’s revenue less cost of raw materials), and w and R are, respec- tively, the unit cost of labor and capital, αs is the industry level of capital share, and σ is the elasticity of substitution.8 Thus firm-specific capital and output distortions cause the marginal revenue product as 1−αs 6 The firm’s Cobb-Douglas production is given by Ysi = Asi Ksi Lsi , where Asi is firm-specific productivity (TFP), and Ksi and Lsi are capital and labour inputs respectively. Industry output is the total of individual firm’s production, aggregated according to a constant elasticity of substitution technology. 7 The profit function is given by πsi = (1 − τY si )PsiYsi − w Lsi − (1 + τKsi )RKsi where PsiYsi is the firm’s value-added (firm’s revenue less cost of raw materials), Ksi and Lsi are capital and labor inputs respectively, w and R are the unit cost of labor and capital respectively. 8 If the elasticity of substitution between factor inputs differs from 1, then the dispersion of the marginal product of capital and hence the gains from reallocation can change substantially. The more substitutes factor inputs are, the more technologically similar they are and the less important will relatively factor market distortions be. Intuitively, when σ 256 Kamutando and Edwards of capital and labor to deviate from the market wage and cost of capital. For example, distortions that raise the cost of capital to a firm, result in the firm’s under-utilizing capital relative to labor in production leading to higher MRPK relative to firms facing no distortions. Similarly, output distortions that reduce the price received on sales, reduce firm’s profit-maximizing output and raise MRPK and MRPL relative to non-distorted firms. Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Hsieh and Klenow (2009) further derive total factor product revenue (TFPR) as a weighted average of marginal revenue products, (1 + τKsi )as T F PRsi = ϕs (3) 1 − τY si where ϕs is a constant.9 In the absence of distortions (τKsi = 0 and τY si =0), TFPR for all firms converges on the constant ϕs , implying no variation in TFPR across firms within the same industry. This implies that in the absence of distortions, more capital and labor resources will be allocated to firms with relatively high total physical productivity (TFPQ) compared to those with lower TFPQ. TFPR is equilibrated across these firms through product price adjustments: the low-productivity firms produce less output and charge higher prices while high-productivity firms produce more and charge lower prices. The equation also shows how firm-specific output and capital-labor distortions cause deviations in TFPR across firms. For example, firm-specific increases in the cost of capital and taxes on output, distort production and factor usage decisions leading to a reduction in the firm’s TFPR relative to other firms. Hence, Hsieh and Klenow (2009) use the dispersion of TFPR across firms to represent aggregate resource misallocation and allocative inefficiency. This approach to measuring resource misallocation has been widely applied in the empirical litera- ture.10 Hsieh and Klenow (2009) apply their method to manufacturing firm data for China (1998–2005) and India (1987–1994) and find that the removal of capital and output distortions to mimic those of the United States (US), would increase aggregate manufacturing TFP by 30 percent to 50 percent in China and 40 percent to 60 percent in India. Additional studies on emerging economies, where factor and out- put market distortions are expected to be relatively large, include Busso, Madrigal, and Pagés (2013) for 10 Latin American countries, León-Ledesma (2016) for 62 developing countries; Nguyen, Taskin, and Yilmaz (2016) for Turkey; Chuah, Loayza, and Nguyen (2020) for Malaysia; De Nicola, Nguyen, and Loayza (2020) for Indonesia, Malaysia, Philippines, and Vietnam; and David et al. (2021) and Fattal- Jaef (2022) for additional groups of developing countries. Notable contributions to Sub-Saharan African (SSA) economies include Cirera, Fattal-Jaef, and Maemir (2020) for Ivory Coast, Ethiopia, Ghana, and Kenya; Newman, Rand, and Tsebe (2019) for South Africa, Chauffour and Diaz-Sanchez (2017) for Morocco; Kalemli-Ozcan and Sørensen (2016) for 10 African countries; and Dennis et al. (2016) for Uganda. The general finding from these studies is that market frictions lead to large aggregate TFP losses via the misallocation channel. For example, Cirera, Fattal-Jaef, and Maemir (2020) apply the HK approach to formal manufacturing firms using census data and World Bank Enterprise Surveys (WBES) and estimate TFP gains of 66.6 percent, 75.7 percent, and 162.6 percent in Ethiopia, Ghana, and Kenya respectively if misallocation is corrected. They also find that more productive firms are “taxed” by distortions. Chauf- four and Diaz-Sanchez (2017) also estimate TFP gains of 56 percent in Morocco using firm-level data for manufacturing from the WBES. These losses for SSA countries are similar to those of other emerging is larger, TFP gaps are closed more slowly in response to the reallocation of inputs, and, in this case, gains are higher (Hsieh and Klenow 2009). a σ R as w 1−as (1+τKsi ) s 9 More precisely, T F PRsi = 1− σ ( as ) ( 1−as ) 1−τY si , where as is the input elasticity and σ is the elasticity of substi- tution. 10 Additional details on misallocation studies on emerging economies are provided in table S1.1 in the supplementary online appendix. The World Bank Economic Review 257 economies. For example, Nguyen, Taskin, and Yilmaz (2016) find that resource misallocation in Turkey is substantial with hypothetical gains of 24.5 percent of manufacturing total factor productivity from moving to “U.S. efficiency.” Chuah, Loayza, and Nguyen (2020) find evidence of large capital and output distortions in Malaysia with productivity gains of 36 percent if misallocation is corrected. Similar conclu- sions were reached by De Nicola, Nguyen, and Loayza (2020), Zaourak (2020) for Argentina and Busso, Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Madrigal, and Pagés (2013) for several Latin American countries. What appears missing from this empirical literature is the contribution of informal firms to misallo- cation. Several studies compare productivity levels across formal and informal firms. For example, La Porta and Shleifer (2008) analyze productivity differences across formal and informal firms in 17 African countries and 8 other countries using the World Bank Informal, Micro and enterprise surveys. They find substantially higher productivity levels in the formal sector and conclude that aggregate TFP would rise with a reallocation of resources from the informal to the formal sector. Similarly, Kathuria, Raj, and Sen (2013) apply a stochastic frontier analysis to Indian manufacturing firms and find significantly higher levels of technical efficiency in formal compared to informal firms. Other studies similarly providing sup- port of the “dualist” theory include Fajnzylber, Maloney, and Montes-Rojas (2011); Benjamin and Mbaye (2012); La Porta and Shleifer (2014); and Maloney (2004). However, productivity differences across formal and informal firms are not necessarily indicators of resource misallocation. Lower value added per worker may reflect constraints to access to capital, a com- mon problem faced by small and informal firms (Rand and Torm 2012; Siba 2015). While informal firms may be relatively inefficient, in heterogeneous firm models what matters for misallocation is whether these firms account for a disproportionate share of the market given their lower productivities. Other studies have therefore directly measured misallocation using data that include informal firms. For example, Busso, Fazio, and Algazi (2012) apply the HK approach to firm data in Mexico and find that informal firms com- mand a disproportionate share of resources given their (lower) productivity status. Lopez-Martin (2019) comes to a similar conclusion using firm-level data for Mexico, Egypt, and Turkey. The paucity of avail- able studies, however, prevents a generalization of these findings. By focusing on Zimbabwe, this study, therefore, provides an additional data point on the association between misallocation and the informal economy. 4. Methodology and Data This paper draws on the Hsieh and Klenow (2009) framework to measure misallocation across Zimbab- wean manufacturing firms. Of particular interest is the dispersion of TFPR as a measure of aggregate misallocation in the economy and how this varies between sectors (formal vs. informal) and by firm size within these groups. For example, informal firms in the “dualist” model will be located to the right of the TFPR distribution reflecting inefficient misallocation of resources, whereas in the structuralist framework there should be no systematic differences across formal and informal firms. While informative of misallocation, the HK approach, nevertheless, faces several challenges (Bartelsman, Haltiwanger, and Scarpetta 2013; Haltiwanger, Kulick, and Syverson 2018; David and Venkateswaran 2019; Bils, Klenow, and Ruane 2021; Restuccia and Rogerson 2017; Wu 2018). Restuccia and Rogerson (2017) argue that deviations in capital-labor ratios across firms in the same industry may reflect the heterogeneity of the production function, rather than the effect of factor market distortions. Similarly, Haltiwanger, Kulick, and Syverson (2018) and Bartelsman, Haltiwanger, and Scarpetta (2013) argue that the dispersion in marginal revenue products of capital and labor may simply reflect differences in adjustment costs across producers rather than misallocation. Measurement error in the data can also drive dispersion in marginal revenue products (Cirera, Fattal-Jaef, and Maemir 2020; Bils, Klenow, and Ruane 2021). Restuccia and Rogerson (2008) also show that the aggregate TFP losses will be exacerbated if negative distortions penalize more efficient firms relative to less efficient ones. In this case, production of 258 Kamutando and Edwards the efficient firms is constrained, while the production of less efficient firms is stimulated beyond efficient levels, further reducing aggregate TFP. The robustness of findings using the HK method is therefore tested using several approaches. First, the study follows Inklaar, Lashitew, and Timmer (2017), Chauffour and Diaz-Sanchez (2017), and Cirera, Fattal-Jaef, and Maemir (2020) and applies the Olley and Pakes (1996) (OP) decomposition technique as Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 first used by Bartelsman, Haltiwanger, and Scarpetta (2013). This approach is extended by decomposing the OP covariance indicator of misallocation into the within-sector and between formal and informal sector contributions. Second, an alternative measure of capital misallocation developed by Wu (2018) is applied to the data.11 Third, to control for potential measurement error in the data, the sensitivity of the results is tested with the use of alternative firm survey data, namely the 2016 WBES for Zimbabwe. 5. Data The empirical analysis draws on firm-level data collected as part of the Zimbabwe Manufacturing Firm Survey 2015–2016. The data were collected via structured interviews with owners or managers of manu- facturing firms.12 The sample of formal manufacturing firms was selected using stratified random sampling with three levels of stratification: firm size (5–19, 20–99,100+), industry (6 industry strata based on 2-digit ISIC Rev.2), and main industrial location (Harare and surrounds, Bulawayo, Gweru, Kwekwe/Redcliff, and Mutare). In total, 195 interviews were completed out of an estimated universe of 973 firms. The sur- vey, therefore, covers around 18 percent of formal manufacturing firms in Zimbabwe. Weights for each firm were generated as the number of interviewed firms divided by estimates of the universe of firms for each strata obtained from ZIMSTAT. The informal-sector survey only covered the metal, wood and furniture, and the textile and leather industries as these industries make up the bulk of informal manufacturing. Data are also only collected in Harare and Bulawayo, the two largest urban cities in Zimbabwe where most informal manufacturing activity takes place. A two-stage sampling process was followed, with random draws of blocks of roughly equal numbers of firms for each industry within each region, and then random draws of firms within each of these blocks. This process was made easier by the tendency for informal manufacturing industries to cluster in specific locations (e.g., manufacturers of metal products are located in Mbare Magaba in Harare). Further details on the sampling procedure for formal and informal firms are provided in section S2 in the supplementary online appendix. For comparability across formal and informal sectors, the sample of firms is restricted to industries common in both sectors, that is: metal, wood, and textiles. Further, because misallocation is an aggregate national measure, the measures of misallocation are constructed using weighted data. The survey data contains information on sales and production, raw material costs, indirect costs, capital stock and labor inputs, among other important information. Following HK, labor input is measured by the wage bill (sum of wages, bonuses, and benefits) rather than employment, to account for differences in human capital and hours worked. The capital stock is measured by the market value of fixed assets (vehicles, machinery and equipment, and land and buildings). Value-added is computed as the difference between sales and cost of raw materials, overhead expenses, and energy costs (electricity, fuel, gas). All observations where value-added could not be calculated because of either missing or negative values (14 firms) are dropped. The calculation of the HK measures of misallocation requires information on the elasticity of substi- tution (σ ), interest rate (R), and industry labor and capital shares (αs ). Following HK, the elasticity of 11 David and Venkateswaran (2019) propose an alternative measure of misallocation based on a structural general equi- librium model of firm dynamics. Unfortunately, the application of this approach is not possible given the data available. 12 Full details on the survey, including data and documentation on sampling methods can be obtained from “Zimbabwe Manufacturing Firm Survey, 2015–2025,” DataFirst, https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/702/. The World Bank Economic Review 259 Table 1. Summary Statistics for Key Variables Formal sector Informal sector Obs Mean Std. dev. Obs Mean Std. dev. Value added per worker (ln) 92 8.4 1.4 105 7.9 0.8 Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Capital/Labor ratio (ln) 92 8.6 1.4 105 5.6 1.5 Capital (ln) 92 11.6 1.6 105 6.7 1.4 Labor costs (ln) 92 11.1 1.8 105 8.5 0.9 Firm size (employment (ln)) 92 3.0 1.2 105 1.1 0.5 Firm age (years) 92 28.0 20.6 105 9.7 7.2 Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: For the formal sector, the summary statistics are only for overlapping industries with the informal sector (Metal, Textile and Wood) for plausible comparisons. substitution is set to 3.13 The interest rate (R) is set at 12.5 percent drawing from the average interest rate reported in the data for the formal and informal firms. The labor share in the production function is wLsi calculated as the mean firm share of labor expenditure in value-added ( P si Ysi ) for each industry. The capital share (as ) is 1 minus this value. Table 1 presents summary statistics on the key variables. The final sample covers 92 formal manufac- turing firms (18 percent of the population of formal firms in these industries) and 105 informal manu- facturing firms. Compared to firms in the informal sector, firms in the formal sector are larger (average employment of 3 compared to 1.1 in logs), older (28 vs. 9.7 years), and more productive, as measured by the value-added per worker (a difference in logs of 0.5), reflecting the substantially higher capital-labor ratio in the formal-sector firms (difference in logs of 3). As expected, the average wage bill and capital are also substantially higher for formal firms (difference in logs of 3.3 of 4.9, respectively). 6. Results This section presents the results of the HK model applied to the Zimbabwe manufacturing sector data. The analysis is structured in three parts. The results on the dispersion of productivity (TFPQ) and the measure of misallocation (TFPR) are first presented. As argued in the earlier sections, the presence of misallocation leads to the survival of many low productivity firms that would otherwise exit operations and release resources to more productive firms (Restuccia and Rogerson 2008). The existence of many low-productive firms is the first evidence indicating the prevalence of misallocation. Likewise, high TFPR denotes firms that produce too little relative to the efficient benchmark. This implies that too few resources have been allocated towards production in the firm, therefore giving rise to misallocation. Second, the paper presents the results on the correlation between indicators of misallocation and pro- ductivity (TFPQ). Theoretically, a positive correlation implies that misallocation disproportionately af- fects relatively productive firms compared to less productive firms, thus leading to higher aggregate TFP losses. Finally, the paper calculates the aggregate TFP gains that can be achieved if misallocation is elimi- nated. Productivity and Misallocation Figure 1 shows the distribution of TFPQ (in panel A) and TFPR (in panel B) across firms. To facilitate analysis, plant-level measures are demeaned by the industry average using the pooled data for formal and informal firms, and the natural logs of the demeaned indicators are presented (e.g., ln(T F PRsi /T F PRs ) 13 The elasticity of substitution of 3 was chosen based on previous literature (e.g., Nguyen, Taskin, and Yilmaz 2016; Chuah, Loayza, and Nguyen 2020; Cirera, Fattal-Jaef, and Maemir. 2020; Fossati, Rachinger, and Stivali 2021). This allows for comparisons of the results across other countries. 260 Kamutando and Edwards Figure 1. Distribution of TFPQ and TFPR. Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: The left-side panel plots the distribution of ln(TFPQsi /TFPQs ) for the formal and informal manufacturing sector; the right-side panel plots the distribution of ln(TFPRsi /TFPRs ) for the formal and informal manufacturing sector. The distributions are estimated using sampling weights. and ln(T F PQsi /T F PQs ), where the overscore represents the industry mean). For a better comparison of firms of similar size, formal-sector firms are split into small and large size categories. The results in fig. 1 in panel (A) illustrate several interesting characteristics regarding the distribution of firm productivity. While large formal sector firms are on average more productive compared to informal and small formal-sector firms, there is wide variation in firm productivity within each category of firms, with a substantial overlap in the kernel densities. Firms of different sizes thus co-exist in the market, despite similar productivity levels. Comparing small formal and informal firms, there is a high degree of overlap in the productivity distributions. Further, several informal firms have productivity levels comparable to the more efficient large formal firms. The co-existence of the formal and informal manufacturing firms, together with the wide overlap in productivity, is suggestive of a structuralist as opposed to the dualist characterization of the informal economy. See also the thick tail to the left for small formal firms compared to informal firms indicating that a significant proportion of small formal firms survive, potentially due to distortionary policies, despite extremely low productivity levels. Figure 1 panel (B) shows the distribution of TFPR, the measure of allocative inefficiency. In efficient economies with no resource misallocation, the distribution of demeaned TFPR is expected to be spiked at zero. Contrary to this outcome, the figure reveals a wide dispersion of TFPR suggestive of widespread al- locative inefficiency in both the formal and informal manufacturing sector. As found with the TFPQ distributions, there is a large left tail in the TFPR distribution of small formal firms. These small formal firms are far larger in terms of market share given their productivity than they would otherwise be in a market with no distortions. Although informal firms tend to have low levels of productivity, at least compared to large formal firms, they have relatively high measures of TFPR indicating that their output is constrained relative to an optimal allocation of resources. To analyze this further, table 2 presents the standard deviation of TFPQ, TFPR and of output and capital distortions. The variance of TFPR, the primary indicator of misallocation, is higher for formal manufacturing firms (1.11 standard deviations) compared to informal firms (0.99 standard deviations). The higher variance is indicative of greater frictions to resource allocation within the formal sector. The variation of TFPR in Zimbabwe also exceeds estimates for most other emerging economies. For example, Cirera, Fattal-Jaef, and Maemir (2020) calculate standard deviations of TFPR in manufacturing The World Bank Economic Review 261 Table 2. Dispersion of TFPR, TFPQ and Other Indicators of Misallocation Formal ln (TFPQ) ln (TFPR) ln (MRPK) ln (1+ τ ksi ) ln (1- τ ysi ) sd 1.76 1.11 1.78 1.61 0.68 Corr. with TFPQ 1.00 0.87 0.86 0.70 −0.59 N 92 92 92 92 92 Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Informal sd 1.07 0.99 1.33 1.46 0.71 Corr. with TFPQ 1.00 0.90 0.78 0.45 −0.55 N 105 105 105 105 105 Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. σ P Y ( P Y ) σ −1 as wL σ wL Note: For each firm i, in industry s TFPRsi= a si 1 si si si −αs , TFPQsi = as si si 1−αs , 1 + τksi = 1−as Rksi and 1 − τysi = 1−σ (1−as )Psi Ysi . The statistics for ln(TFPQ) K s (wL ) K (wL ) si si si si and ln(TFPR) are deviations from respective industry means. sd is the standard deviation and N is the number of firms. Table 3. Variance Decomposition of TFPR by Between- and Within-Sector Components (1) (2) TFPR variance % contribution Informal Within 0.66 67% Between 0.32 33% Total 0.98 100% Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: Column (1) shows the decomposition of aggregate TFPR variance into within- and between-sector components while column (2) presents the percentage contribution. that range from 0.63 to 0.78 for Côte d’Ivoire, Ethiopia, India and China. Only Ghana (0.95) and Kenya (1.52) have similar or higher variances than these results for Zimbabwe. Table 2 also presents the standard deviation of MRPK and the measures of capital and output distor- tions. As with TFPR, there is substantial variation in the misallocation indicators across firms, and, with the exception of output market distortions, relatively high variation within the formal sector. In addition, the dispersion of the capital market distortion exceeds that of the output market distortion, suggesting relatively strong factor market constraints to the reallocation of resources. The indicators in table 2 only present information on resource misallocation within the formal and informal sectors. To decompose the within and between sector contribution to aggregate variation in TFPR, the approach by Chen and Irarrazabal (2015) and Calligaris et al. (2016) is used.14 The findings are presented in table 3 and indicate that the main source of TFPR variance is the within-sector component which accounts for 67 percent of the overall variance of TFPR. Nevertheless, misallocation of resources between formal and informal firms is still sizeable and accounts for a third of the overall variance of TFPR. Overall, the results in table 2 and table 3 are consistent with the prevalence of high distortions that impede efficient allocation of resources within and between the formal and informal sectors. G S Ngs 2 14 The decomposition is given by: Var(lnT F PRsi ) = θg θgs θgsiVar(l nT F PRgsi − l nT F PRgs ) + G=1 s=1 i=1 G S 2 θg θgsVar(lnT F PRgs − lnT F PR ) where the first term of RHS is within-group component and the second g=1 s=1 one is between-group component. lnT F PRgsi is natural log of TFPR of firm i, which belongs to industry s within sector (formal or informal) g; lnT F PRgs – is the average natural log of TFPR in industry s in sector g; lnT F PR –is the average natural log of TFPR for all firms. θgsi , is the value-added share of firm i in industry s in sector g. θgs is valued added share of industry s in sector g and θg is share of valued added of sector g. 262 Kamutando and Edwards Correlation Between Misallocation and Productivity As articulated by HK, the extent of misallocation is worse, and aggregate TFP is lower the greater the dis- persion of the natural log of TFPR. One possibility is that the variance in the TFPR is driven by randomly allocated output and factor market distortions across firms. An alternative, as argued by Restuccia and Rogerson (2008), is that distortions may affect particular types of firms and this can amplify aggregate Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 TFP losses. This would occur, for example, when efficient firms face high negative distortions relative to less efficient ones. To assess this, fig. 2 plots the local polynomial regression of TFPQ against TFPR (demeaned and natural logged). In an economy with no distortions, the dispersion of log(T F PRsi /T F PRs ) should be zero and all firms would be placed along the zero TFPR line. Along this line, firms would only differ in their TFPQ. With distortions affecting firms randomly, TFPR would deviate from zero but would be evenly scattered around the zero line. A high positive correlation between productivity and indicators of misallocation would signify that negative (positive) market distortions affect relatively productive (inefficient) firms. In this case, the distortions act as a tax on relatively productive firms, thereby constraining them from growing to their optimal size while promoting the growth of less productive firms beyond their optimal size. The consequence is an accentuated reduction in aggregate TFP. Looking at panel (A), the left-side figure, of fig. 2, the plots show a strong positive correlation between TFPR and productivity, as shown by the local polynomial regression (see also table 2). Further, the scatter plots in the figure illustrate a positive association for both the formal and informal-sector firms (see also the positive correlation coefficients presented in table 2), but in most cases, the scatter plots for informal firms are above those of formal firms and lie in the positive TFPR territory (above the zero line). In panel (B), the right-side figure, separate local polynomial regressions are presented for informal firms, small formal firms and large formal firms. One needs to be cautious about comparing the firms at the end ranges of the TFPQ spectrum, as the number and overlap of observations across size categories drop rapidly. Looking over the mid-range of TFPQ, where the bulk of firms in each category are situated, positive slopes are found for all firm categories, with a stacking of regression lines where informal firms are at the top and large formal firms are at the bottom. This stacking order suggests that informal-sector firms face more restrictive distortions compared to formal-sector firms. However, in all cases relatively productive firms are taxed, an outcome that Cirera, Fattal-Jaef, and Maemir (2020) refer to as “taxing the good.” Figure 2. TFPR against Firm Productivity. Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: The plots show the relationship between productivity ln(TFPQ) measured as ln(TFPQsi /TFPQs ) and TFPR measured as ln(TFPRsi /TFPRs ). The left-side panel shows relationship between ln(TFPR) and ln(TFPQ) for the aggregate manufacturing sector. The right-side panel shows a comparison between the large and small formal firms and the informal sector. The polynomial is estimated using sampling weights. The World Bank Economic Review 263 Figure 3. Output Distortions against Firm Productivity. Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: The plots show the relationship between productivity TFPQ measured as ln(TFPQsi /TFPQs ) and output distortions, ln ( 1−1 τysi ). The left-side figure (panel A) shows the correlation between output distortions and productivity for the aggregate manufacturing sector. The right-side figure (panel B) shows a comparison between the large and small formal firms and the informal sector. The polynomial is estimated using sampling weights. Output and Capital Distortions vs. Productivity To unpack the distortions that drive the relationships in fig. 2, a separate analyze of the productivity asso- ciation with capital distortions ln(1 + τksi ) and output distortions measured as ln ( 1−1τysi ) is conducted.15 These values are positive for the firm when its output and capital (relative to labor) are taxed, and negative when they are subsided. Figures 3 and 4 present the relationships. A positive association between firm TFPQ and output market distortions is observed. Highly productive firms face output distortions close to zero, whereas less productive firms experience a negative tax (i.e., an implicit subsidy), thus boosting their output higher than would otherwise be the case. Unlike the TFPR relationship in fig. 2, there are no substantial differences in the level and slope of the curves for output distortions across formal and informal firms—see the intermingled scatter plots in panel (A), the left-side figure, of fig. 3. Output distortions, therefore, do not appear to explain the level differences in TFPR across formal and informal-sector firms. This outcome is consistent with the view that formal and informal firms compete for the sale of their products in the same market. Within both sectors, however, output of less productive firms appears to be subsidized relative to more productive firms. In contrast to these outcomes are the relationships shown in fig. 4 (panel B, the right-side figure) for capital market distortions. Productive firms face large taxes on capital relative to less productive firms. The implication is that productive firms use less capital relative to labor than is optimal given the rental/wage ratio. Figure 4, panel B, also shows that the curve for informal firms lies above those for small and large formal firms, whose curves broadly overlap with each other. Informal firms, therefore, face more adverse capital market distortions compared to their formal-sector peers irrespective of their productivity level. This disaggregation of the TFPR-productivity relationship provides several insights. Firstly, the output and capital distortions reinforce each other to reduce output of productive firms relative to less productive firms. Whereas distortions in the output market subsidize low-productive firms, distortions in the factor market tax high-productive firms. Secondly, the relatively high TFPR of informal firms can largely be attributed to the challenges they face in accessing capital (relative to labor) and is unrelated to distortions in the output market. 15 Following Hsieh and Klenow (2009), the first-order condition from profit maximisation can be used to derive firm σ wLsi as wLsi output and capital distortions respectively as; 1 − τysi = 1− σ (1−as )P Y and 1 + τksi = 1−as RK . si si i 264 Kamutando and Edwards Figure 4. Capital Distortions against Firm Productivity. Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: The plots show the relationship between TFPQ measured as ln(TFPQsi /TFPQs ) and capital distortions, ln (1+ τksi ). The left-side figure (panel A) shows the relationship between capital distortions and productvity for the aggregate manufacturing sector. The right-side figure (panel B) shows a comparison between the large and small formal firms and the informal sector. The polynomial is estimated using sampling weights. Firm Estimates of Sources of Misallocation The firm survey provides additional information on firm characteristics and operational obstacles. In this section, this information is used to further explain some of the cross-firm variation in indicators of misallocation. Drawing on León-Ledesma and Christopoulos (2016), Fossati, Rachinger, and Stivali (2021) and Kalemli-Ozcan and Sørensen (2016) the following model is estimated using OLS: ln(Dis ) = β0 + β1 T F PQis + β2 In fis + Xis δ + Zis δ + εis (4) where ln(Dis ) represents the firm-level measure of misallocation (TFPR, capital distortions, and output distortions, in log form), T F PQis is firm physical productivity, In fis is a dummy variable for informal- ity (equals 1 for informal firm, 0 otherwise), Xis is a vector of firm responses that may be associated with resource misallocation (lack of finance, shortage of electricity, and import competition), Zis are firm characteristics such as firm size (measured by the number of employees), firm age, firm industry and location, and εis is a white noise error term. It is key to note that the firm responses may them- selves be endogenous to misallocation, so these regressions only establish correlations rather than causal relationships. Table 4 presents the results of the estimates. Looking first at the TFPR regressions in column (1), the coefficient estimate on firm productivity (TFPQ) is positive and significant, corroborating the relationships shown in fig. 2. More productive firms have higher levels of TFPR, indicating that they are smaller relative to less productive firms than would be expected in an efficient market, even after including other firm controls. The second column (2) and third column (3) provide results for capital and output distortions, respec- tively. In both estimates, the coefficient on TFPQ is positive and significant, implying that higher levels of firm productivity are associated with higher taxes or lower subsidies on output and capital. The con- sequence is that very productive firms are smaller than expected, as they use fewer factors of production (capital and labor) than one would expect based on market prices and they use less capital than is optimal given the rental/wage ratio. Looking at the other firm controls, informality is positively and significantly correlated with TFPR and the measure of capital distortions, but only weakly (at 10 percent level of significance) associated with output distortions. This corroborates the graphical findings presented earlier. Even after accounting for productivity, informal firms are smaller than expected, as they face higher costs in accessing capital The World Bank Economic Review 265 Table 4. Correlation between Obstacles and Indicators of Misallocation (1) (2) (3) VARIABLES TFPR Capital distortion Output distortions TFPQ 0.66∗∗∗ 0.59∗∗∗ 0.41∗∗∗ (0.02) (0.09) (0.03) Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Informality 0.65∗∗∗ 2.22∗∗∗ −0.27∗ (0.09) (0.39) (0.15) Financial inaccessibility 0.16∗∗∗ 0.63∗∗ 0.09 (0.05) (0.25) (0.09) Shortage of power −0.08 −0.73∗ 0.24∗∗ (0.10) (0.37) (0.10) Raw materials inaccessibility 0.05 −0.63∗ 0.29∗∗ (0.06) (0.32) (0.12) Unfair import competition 0.04 −0.50∗∗ 0.27∗∗∗ (0.05) (0.24) (0.09) Firm size (ln employment) 0.31∗∗∗ 0.18 0.38∗∗∗ (0.03) (0.14) (0.06) Firm age −0.00 −0.02∗∗ 0.01∗ (0.00) (0.01) (0.00) Constant 1.60∗∗∗ −0.10 −0.20 (0.14) (0.66) (0.26) Observations 197 197 197 R-squared 0.95 0.64 0.65 Location control Yes Yes Yes Industry control Yes Yes Yes Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: The dependent variables are measures of misallocation, namely ln(TFRPsi) in column 1, ln (1 − τksi ) in column 2, and ln ( 1−1 τ ). In column 3. In the regressions, ysi demeaned values of the dependent are not used. Industry fixed effects are controlled for. The key obstacle variables are binary variables that take a value of 1 if the firm reports that it suffers from such constraints and 0 otherwise. Firms were directly asked if they suffer from such constraints. Standard errors in parentheses ∗ ∗ ∗ p < 0.01, ∗ ∗ p < 0.05, ∗ p < 0.1 (relative to labor) compared to formal firms. This distorts their production decisions, leading to the use of less capital than is optimal given the rental/wage ratio. Access to finance is an additional explanatory factor. According to the regression results, firms that report they suffer from financial inaccessibility have higher TFPR values (column 1). This association is primarily driven by capital distortion values (column 2). Difficulties in accessing finance, therefore, appear to raise the cost of capital or constrain firms from accessing capital, thereby reducing their market share. Difficulties in accessing finance are widespread in Zimbabwe. The survey responses reveal that 78 percent of informal firms and 57 percent of formal firms identify access to finance as a major constraint to their operations. This is a considerably higher share than the 39 percent average for manufacturing firms in Sub-Saharan Africa (SSA) based on World Bank Enterprise data (World Bank 2016). The World Bank Enterprise data also indicate that Zimbabwean manufacturing firms are less likely to have access to a bank loan/line of credit (10.5 percent vs. 21.9 percent for SSA), are more likely to have had recent loan applications rejected (69 percent vs. 16 percent), and when they do obtain a loan, are more often required to provide collateral (94 percent vs. 85 percent). Looking at other firm-specific characteristics, table 4 indicates that firm employment is positively and significantly correlated with TFPR and capital distortions. Output of bigger firms in terms of employment levels appears to be particularly constrained by product market distortions. Firms reporting unfair import competition have higher output distortion values. This is consistent with the effect of reductions in de- mand and thus prices associated with import competition. Lastly, counterintuitively, firms that face severe shortages of power have lower capital distortion values. Because these variables are endogenous, this may 266 Kamutando and Edwards merely reflect higher relative demand for capital and the binding electricity constraints in operating this capital by those firms with low taxes or higher subsidies on capital. Aggregate TFP Reductions To what extent does this misallocation reduce aggregate TFP? Does the exclusion of the informal sector Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 lead to underestimation of misallocation? HK calculate aggregate productivity gains that can be realized if misallocation is corrected using the ratio of actual TFP to the efficient level of TFP as shown in equation (5).16 TFPefficient s %Product iv it y Gain = − 1 ∗100 (5) TFPactual s The calculations reveal that with perfectly efficient allocation of resources, aggregate manufacturing TFP can be boosted by 151.4 percent. To assess the potential bias from not including informal firms in the analysis, the sample is restricted to formal firms, and a lower aggregate TFP gain of 129.6 percent is calculated. Excluding informal firms from the analysis, therefore, leads to underestimates of potential TFP gains by nearly 22 percentage points. These results place Zimbabwe among the top-end of countries experiencing losses in aggregate manu- facturing TFP in response to misallocation. For example, Cirera, Fattal-Jaef, and Maemir (2020) calculate that without misallocation, aggregate manufacturing productivity would have been higher by at least 31 percent in Côte d’Ivoire, 67 percent in Ethiopia, 76 percent in Ghana, and 162 percent in Kenya. Gains in aggregate manufacturing TFP, for 9 of the 10 Latin American countries studied by Busso, Madrigal, and Pagés (2013) where gains mostly range from 50 to 60 percent. The exception is Mexico where the estimated gains of 127 percent are similar to the estimates for Zimbabwe formal firms. Fossati, Rachinger, and Stivali (2021) found average TFP gains of 30.1 percent in Latin American countries and 76.9 per- cent for African countries. Interestingly, they found TFP gains of 120.91 percent for Zimbabwe, which is comparable to this study’s results for formal-sector firms. However, these studies only consider the formal-sector firms’ misallocation. Therefore, by not including the informal sector, aggregate TFP losses from misallocation can be underestimated. The results suggest that the informal sector in Zimbabwe is more closely associated with the structuralist model where formal and informal sectors compete in an in- tegrated economic system. This finding differs from the conclusions drawn by other related studies, such as Busso, Madrigal, and Pagés (2013) and Lopez-Martin (2019). 7. Robustness Checks The analysis of this study is concluded by assessing the sensitivity of the findings to the use of alternative calculations and measures of misallocation. First, the OP covariance measure is used as an alternative indicator of misallocation. Second, an alternative measure of misallocation based on Wu (2018) approach is utilized. Third, the full sample of the survey data including all industries from both sectors, not just overlapping industries, is used. Lastly, the findings for formal-sector firms are tested using an alternative source of data, namely the 2016 World Bank Enterprise Survey (WBES) for Zimbabwe. An Alternative Measure of Misallocation: The OP Covariance An alternative indicator of misallocation, as used by Bartelsman, Haltiwanger, and Scarpetta (2013) is that of Olley and Pakes (1996) (OP) who decompose aggregate labor productivity (At ) into mean firm 16 Firm-level productivity gains summed up to the industry level and then aggregated using the Cobb-Douglas aggregator θs s Ms σ −1 σ −1 T FPactual A T FPRs = [ ( ¯ si ) ] , where θ s denotes the industry share in value added. T FPe f ficient s=1 i=1 As T FPRsi The World Bank Economic Review 267 Table 5. Sectoral and Industry OP Covariance Value added per worker Value added per capital Within 0.4 −1.1 Between 0.3 −1.7 Total 0.7 −2.8 Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: Results for the covariance between capital and labor allocation and firm productivity. Value-added per worker and value-added per capital are used as measures of firm productivity. The final column uses capital stock adjusted for capacity utilization. Firm size is measured by either the number of employees, or the value of capital. All firms are included in the calculations. productivity and the covariance between market share and firm productivity as follows: K K At = ¯t + θit Ait = A ¯t θit − θ ¯t Ait − A k =1 k =1 where Ait denotes labor productivity and θit the employment share of firm i at time t. A bar over a variable denotes the arithmetic mean of that particular variable. The final term on the right measures the covariance between market share and firm productivity. Although the underlying assumptions of the OP model are different from the HK model, the intuition of the two models is the same: in efficient markets, relatively productive firms within an industry should control higher shares of productive resources. In the Olley and Pakes (1996) framework, this outcome is represented by a positive covariance term. The OP indicator is less restrictive than the HK measure, as it is not subject to restrictive assumptions regarding the production function and constant returns to scale. However, as highlighted by Berthou et al. (2020) and Brown et al. (2018), the OP covariance is not a sufficient moment of the distribution of distortions or allocative efficiency. When distortions are removed the OP can decrease or increase. Only in restrictive cases such as under perfect competition or with the CES demand function does the OP carry an informative interpretation with respect to misallocation (Haltiwanger, Kulick, and Syverson 2018). Nevertheless, the OP indicator is still useful as a consistency check of the main results based on the HK approach. This is achieved in two ways. First, the OP covariance is calculated using two measures of productivity, namely value added per worker and value-added per capital. Secondly, the total covariance is decomposed into the within and between formal and informal-sector contributions.17 This is done to highlight how distortions to re-allocation of resources between the formal and informal sectors may contribute to aggregate misallocation. Table 5 presents the results for the OP covariance. The results differ starkly according to whether productivity is measured in terms of value-added per labor or per capita. The OP covariance for labor productivity is positive, albeit low, with both the between and within components contributing positively towards allocative efficiency. While employment in the informal sector is lower than in the formal sector, so too is its labor productivity (66 percent lower according to the survey data)—hence the positive value of the between component. In contrast, capital appears to be misallocated across firms both within and between the formal and informal sector. The OP covariance term for capital is negative, and more than half (61 percent) of the neg- ative covariance can be explained by the misallocation of capital between formal and informal firms. The share of capital used by informal sector firms is far lower than their output per capital merits. This result corroborates the earlier finding of relatively high levels of misallocation in Zimbabwean manufacturing that is strongly associated with capital market rigidities. 17 Cov(X, Y) = E[Cov(X, Y|Z)] + Cov[E(X|Z), E(Y|Z)], where the first term is the within group covariance, and the second term is the between group covariance. 268 Kamutando and Edwards Figure 5. The Distribution of MRPK According to Wu’s (2018) Approach. Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Source: Authors’ analysis based on the Zimbabwe Manufacturing Firm Survey firm-level data. Note: Ln(MRPK_wu) is the indicator of misallocation measured as the residual from estimates of equation 8. The left panel plots the distribution of MRPK while the right panel shows the correlation between MRPK and firm productivity. An Alternative Measure of Capital Misallocation: The Wu (2018) Approach Further, an alternative measure of capital misallocation based on the marginal revenue product of capital (MRPKi,t ) as proposed by Wu (2018) is used.18 In this approach, capital market distortions, plus firm- specific financial frictions and policy distortions affect a firm’s optimal choice of capital giving rise to misallocation of capital across firms. Wu (2018) obtains an estimate of ln(MRPKi,t ) using the residuals from the following regression model: πi,t Ri,t ln (ARPKi,t ) = β0 + β1 ln + β2 + β3 industryi,t + β4 locationi,t + ζi,t (8) Ri,t πi,t πi,t where ln(ARPKi,t ) is the natural log of revenue-capital ratio, ln( R i,t ) is the natural log of profit-to-revenue ratio, R i,t πi,t is a revenue-to-profit ratio, and industryi,t and locationi,t are dummies for industry and location respectively. An advantage of using the Wu (2018) approach over the HK is that it does not impose a Cobb-Douglas production function and allows for heterogeneities in production functions and the presence of market power. The measure of MRPK is also easily interpretable. For example, a value of 0.15 implies that the MRPK for that particular firm is 15 percent higher than the average MRPK in the economy. Figure 5 presents the distribution of MRPKi,t for formal-sector and informal-sector firms. As found using the HK approach, the Wu (2018) measure of MRPK varies widely across firms (see panel A, the left-side figure) providing further support for widespread misallocation of capital in the Zimbabwe manu- facturing sector. Similarly, as found earlier, the positive association between MRPK and firm productivity shown in panel B, the right-side figure, accentuates the negative impact of misallocation on aggregate productivity. Finally, capital misallocation is also shown to affect informal firms most—as shown by the higher average MRPKi,t for informal firms compared to formal firms in panel B. Overall, the results based on Wu (2018) corroborate the findings based on the HK method. Expanding Sample of Industries Covered One concern with the main results is that the restriction of the sample of firms to overlapping for- mal and informal manufacturing industries (metal, wood, and textile) may result in an underesti- mate of the aggregate productivity loss from misallocation. To address this concern, measures of misallocation are re-calculated including formal firms from all industries (i.e., including chemicals, 18 For more details on this theory, see Wu (2018). The World Bank Economic Review 269 Figure 6. TFPR against Firm Productivity Using Combined Data. Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 Source: Authors’ analysis based on the 2016 World Bank Enterprise Survey (WBES) for Zimbabwe. Note: Refer to notes in fig. 2. The sample comprises overlapping industries between formal and informal firms (Textile; Wood and furniture, and Metal). It consists of 85 formal manufacturing firms obtained from the 2016 Zimbabwe World Bank Enterprise Survey and 105 informal firms from the Zimbabwe Manufacturing Firm Survey 2015–2016. Given that the WBES is stratified by Food, Textile and Garments, and Other Manufacturing, the industry subsector group variable in the WBES data is used to recategorize the industries so that they match those of formal firms in the Zimbabwe Manufacturing Firm Survey 2015–2016. food products and other manufacturing) in the survey data. This increases the number of formal firms to 161. The expansion of the sample does not materially affect the outcomes of the analysis. As shown in the summary statistics using all industries (see table S3.1 in the supplementary online appendix), in- formal firms remain less productive, less capital intensive, and smaller in terms of value-added and employment. Further, as found when using the overlapping sample of industries, informal firms have higher average TFPR values that arise from higher relative taxes on capital (see fig. S3.1 in the supplementary online appendix). Distortions also “tax” the productive firms as shown by increases in TFPR with firm productivity. Expanding the sample of industries, however, does raise the estimate of potential aggregate TFP gains to 163.3 percent (vs. 151.4 percent). Alternative Datasets–2016 World Bank Enterprise Survey One additional concern is the sensitivity of the results to the choice of firm survey. The challenges in constructing sample frames in Zimbabwe and obtaining precise estimates of the universe of firms imply that the results could be sensitive to the choice of survey and associated sample weights. An alternative survey of formal firms is the 2016 World Bank Enterprise Survey (WBES) for Zimbabwe. This survey was also conducted in 2016 and covers 344 manufacturing firms.19 The World Bank also conducted the 2016 Informal Business Sector Survey for Zimbabwe, but, unfortunately, it lacks indicators of the value of capital stock that is required for measuring misallocation.20 19 The sample was selected using stratified random sampling with industry, location, and size strata. Their sample frame consisted of listings of panel firms from the 2011 Zimbabwe WBES and fresh firms through block enumera- tion. See the implementation report available from https://microdata.worldbank.org/index.php/catalog/2829 for further details. 20 See table S3.1 and figs. S3.2 and S3.3 in the supplementary online appendix that compare different indicators across the Zimbabwe Manufacturing Firm Survey 2015–2016 and those of the World Bank. There are large overlaps in the distribution of firm indicators. There are slight differences in mean values, with the World Bank formal and informal firms tending to be slightly smaller in employment, younger, and in the case of formal firms, having higher value added per worker. 270 Kamutando and Edwards To test the sensitivity of the main findings to the use of alternative formal firm data, the informal- sector survey data from the Zimbabwe Manufacturing Firm Survey 2015–2016 is combined with the 2016 Zimbabwe WBES of formal firms. The analysis is restricted to overlapping industries resulting in a total of 105 informal firms and 85 formal firms. Figure 6 plots TFPR against physical productivity using the combined database. Downloaded from https://academic.oup.com/wber/article/38/2/251/7405388 by Sectoral Library Rm MC-C3-220 user on 01 May 2024 The results are qualitatively similar to those shown earlier in figs. 1 and 3. Distortions “tax” the rela- tively productive firms, and TFPR of informal firms exceeds those of formal firms of equivalent productiv- ity. Figure S3.4 in the supplementary online appendix plots capital and output distortions against physical productivity, and, as found earlier, informal firms face higher taxes on capital than formal firms. The possi- ble aggregate TFP gains from the removal of distortions to re-allocation are calculated to be 182.1 percent, which is 30.7 percentage points higher than the 151.4 percent gain estimated using the Zimbabwe Manu- facturing Firm Survey 2015–2016. Overall, the comparison indicates that the results are not particular to the survey used. Additional comparisons and analyses are presented in the supplementary online appendix. 8. Conclusion This paper assesses the extent of resource misallocation between and within the formal and informal manufacturing sector in Zimbabwe. The study applies the widely used Hsieh and Klenow (2009) approach to measuring resource misallocation using firm-level data for formal and informal-sector manufacturing firms collected in 2015. A key contribution of the study is the inclusion of the informal manufacturing sector in the resource misallocation analysis. The informal manufacturing sector in Zimbabwe is large and contributes significantly towards employment and GDP. The results reveal widespread distortions in output and factor markets in the formal sector and informal sector in Zimbabwe. In both the formal and informal sectors, distortions act as a tax on more efficient firms, thus exacerbating the aggregate TFP losses due to misallocation. Market frictions, mainly in the capital market, are found to be particularly detrimental to production by informal-sector firms. Informal firms are smaller and employ less capital-intensive production techniques than they would in efficient markets. The implication is that misallocation of capital between the formal and informal sector is a major contributor towards aggregate misallocation in the economy. The study reveals that by efficiently allocating resources, aggregate TFP can be boosted by about 151.4 percent. Failure to account for informal firms can bias estimates of resource misallocation. In the case of Zimbabwe, excluding the informal sector leads to underestimates of resource misallocation. The study has several relevant policy implications. The findings suggest that product and factor mar- ket frictions are high in Zimbabwe and distort the efficient allocation of resources across manufactur- ing firms. These distortions impede growth in manufacturing. Informal manufacturing in Zimbabwe is not characterized by a dualist model with limited scope to drive aggregate productivity growth. Rather, formal-sector and informal-sector firms compete in an integrated economic system. Policies that reduce barriers to growth of the informal sector, particularly through improving access to finance, can therefore play a substantive part in driving a recovery of the manufacturing sector. Conflict of Interest The authors have not conflict of interest to declare. Data Availability Statement The Zimbabwe Manufacturing Firm Survey 2015-2016 survey data is published and can be accessed at “https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/702/”. The World Bank Economic Review 271 References Asker, J., A. Collard-Wexler, and J. De Loecker. 2014. “Dynamic Inputs and Resource (Mis) Allocation.” Journal of Political Economy 122(5): 1013–63. Bartelsman, E., J. Haltiwanger, and S. Scarpetta. 2013. “Cross-Country Differences in Productivity: The Role of Allo- cation and Selection.” American Economic Review 103(1): 305–34. 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