Chapter 4 The Effect of Regulatory Policy on Firm Growth in Sub-Saharan Africa

International Monetary Fund

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Keywords:: firm growth; firm size; allocation index; panel data; firm-ownership dummy; Employment; Minimum wages; Labor markets; Productivity; Sub-Saharan Africa; Africa; Asia and Pacific

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Abstract

The Global Informal Workforce is a fresh look at the informal economy around the world and its impact on the macroeconomy. The book covers interactions between the informal economy, labor and product markets, gender equality, fiscal institutions and outcomes, social protection, and financial inclusion. Informality is a widespread and persistent phenomenon that affects how fast economies can grow, develop, and provide decent economic opportunities for their populations. The COVID-19 pandemic has helped to uncover the vulnerabilities of the informal workforce.

Introduction

After a decade of strong growth, buoyed by a supercycle of global commodity prices and surging investments in natural resources, economic growth in sub-Saharan Africa has recently faltered. The sustained decline in global commodity prices and a prolonged slowdown of growth among the region’s main trading partners (China, in particular) have weighed on Africa’s convergence toward higher incomes.

The recent slowdown in economic performance reminds us that sub-Saharan Africa continues to lack a competitive industrial base. Despite rapid urbanization driven by fast population growth, Africa has in fact deindustrialized since the mid-1970s (Jedwab, Christiaensen, and Gindelsky 2017). Africa’s growth contributed little to formal manufacturing industries, leaving the sector dominated by small and informal firms. The manufacturing share of employment stands well below the global average at 8 percent, and manufacturing output, measured by a percentage of GDP, has declined from 15 percent in the mid-1970s to around 10 percent in 2020. On the basis of data from the Groningen Growth and Development Centre, Rodrik (2016) finds that African countries are underindustrialized at all income levels, whereas industrialization in Asia has progressed as income has grown.

Another reason Africa did not grow in conformity with a neoclassical growth convergence model is the large informal economy. Average productivity of the informal sector in sub-Saharan Africa is only about 20 percent of the formal sector (IMF 2017). Few informal firms grow out of informality, dragging on the region’s productivity.

At the same time, sub-Saharan Africa’s population is expected to more than double over the next three decades, growing from 1.2 billion in 2020 to nearly 2.5 billion in 2050. With an ample young population entering the labor force, firms will realize significant productivity dividends if they manage to reallocate factor inputs more efficiently (Hsieh and Klenow 2009; IMF 2017; Restuccia and Rogerson 2017).

Whereas stronger labor and business regulations should induce firms to formally register, recent studies have shown that developing countries achieve little formalization by regulating small and informal firms (de Andrade, Bruhn, and McKenzie 2014; Benhassine and others 2018). In fact, labor market regulation and taxation may be a cause of informality. As De Soto (1989) describes, informal firms’ choice to operate informally is rational when the cost of formal registration outweighs the benefits (see also Maloney 2004). If informality in sub-Saharan Africa’s economies is the equilibrium outcome of firms weighing costs versus benefits, factor market reforms should improve market efficiency. However, product and factor markets in the region are still at the infancy stage because of weak state capacity (Besley and Persson 2011), wage rigidities, or market domination by state- and foreign-owned enterprises (Bai, Hsieh, and Song 2016).

How efficient are factor markets in sub-Saharan Africa compared to those in developing Asia? How can policymakers in the region design formal and informal sector regulations to support firm growth and reduce informality?

Using data on firm balance sheets from a World Bank Enterprise Survey, this chapter quantifies the degree and determinants of factor allocative efficiency in labor and land for 40 sub-Saharan African countries. Next, it estimates the effects of factor allocative efficiency on firm age, size, and productivity using a pooled country sample, then verifies findings through a single country case (Nigeria) using firm panel data. In identifying the effect, the endogeneity of factor allocations is addressed by the instrumental variable (IV) regression using subnational institutions as the exogenous variation.

The empirical findings on labor allocation can be summarized as follows. Efficiency of labor allocation is significantly explained by the strength of formal and informal sector regulations, but the correlation can be either positive or negative depending on the country’s legal capacity. The inspection of informal activities often depends on firm size, with larger firms attracting more attention from regulators (size-dependent regulatory policy); such an imbalance is detrimental for productive firms’ growth in weak institutional context. For this reason, when legal capacity is weak, the formalization of labor contracts with social insurance benefits is found to be more effective than regulation in supporting firm growth. In contrast, as legal capacity develops, stronger informal sector regulation becomes more effective in accelerating the reallocation of workers to productive formal activities. This finding underscores that regulatory design needs to match local legal capacity to improve factor allocative efficiency in sub-Saharan Africa.

As for land allocation efficiency, ethnic fractionalization in sub-Saharan Africa is associated with conflict and thus less optimal land allocation across firms, as typically found in development literature. Moreover, land use is often dictated by custom or local authority rather than by legally binding contract. Regression results confirm that improvement in factor allocative efficiency through reallocation of land and workers would allow firms to grow and survive longer in the formal sector. The IV regression result highlights that marginal firms operating in a weak institutional environment can reap especially large benefits from the introduction of formal labor contracts, which allows productive firms to attract workers.

Last, the chapter further explores whether factor reallocation contributes to firm growth through credit and taxation channels. The result indicates that sub-Saharan Africa could increase access to credit and corporate tax revenues by addressing factor misallocation, which may further support firm growth.

Stylized Facts

Firm Size, Age, and Productivity

As typically found in the literature (for example, Hsieh and Klenow 2014), balance sheet data from a World Bank Enterprise Survey show that enterprises in sub-Saharan Africa remain small during their life cycles. Midsized and large firms are conspicuously absent in Africa compared to developing Asia, making firm-size distribution highly skewed. On average, African firms are only one-third of the size of Asian firms, and most are young and unproductive (Annex Table 4.1). A Kolmogorov-Smirnov test supports that the distributions of firm age, size, and productivity in sub-Saharan Africa and Asia are statistically distinct (Figure 4.1).

Land and Labor Markets

Lack of access to land can prevent firms from scaling up business operations and using land as collateral to obtain loans. As land size increases, firm size also significantly increases in both sub-Saharan Africa and developing Asia (Figure 4.2). Whereas large parts of sub-Saharan Africa are land abundant, land with access to utilities and transport to markets is scarce. Land title systems are weak, and land rental markets are severely underdeveloped. Secure property rights and removal of restrictions on land markets are critical as the population grows and land use intensifies (Dihn and others 2012; Holden and Otsuka 2014).

In many parts of sub-Saharan Africa, land use is governed by custom and land-use rights are allocated locally by village chiefs. Land ownership is often inherited, and land transfers are severely restricted (Restuccia and Santaeulalia-Llopis 2017). Given weak institutions and pervasive corruption, political connections also affect access to land. Such weak regulatory and institutional situations raise a concern that land is not allocated to the most productive firms.

Labor markets in sub-Saharan Africa are dominated by subsistence agriculture and informal employment. The share of wage earners in the region’s labor force is small, which leads to stagnant productivity growth. Pervasive informality in sub-Saharan Africa complicates effective regulation design because many small firms do not comply with law. Governments have weak capacity to enforce regulations and seek informal payments (that is, bribes) from small firms for granting business licenses, land, or utility access. Business owners may thus remain informal to avoid costly regulatory requirements. From a worker’s perspective, formal employment is attractive only if labor regulation is enforced, mandating that firms pay social benefits such as pension, insurance, or severance payments under a contract (Almeida and Carneiro 2012).

Minimum Wage and Firm Productivity

In addition to the provision of social insurance, labor codes often stipulate a wage floor for formal sector jobs. Considering that a minimum wage prevents downward wage adjustments below the statutory floor, workers with marginal productivity below the minimum wage are induced to participate in the formal sector. International Labour Organization data show that statutory minimum wages in sub-Saharan Africa are generally lower than in developing Asia, and that noncom-pliance with minimum wage law is common (Bhorat, Kanbur, and Stanwix 2017). How does minimum wage affect firm size and productivity? The law of supply and demand suggests that firms will cut labor if the minimum wage is set above the market-clearing wage. If the minimum wage is instead below the equilibrium level, raising the minimum wage could increase formal employment. The evidence on employment effects in developing countries is quite mixed (Neumark and Corella 2019). When a country’s minimum wage law is not enforced, previous studies found a small or statistically nonsignificant disemployment effect of higher minimum wage. When informal employment is large, the enforcement of the minimum wage could benefit workers by providing higher wages and mandated social benefits under formal labor contracts (see Dinkelman and Ranchhod [2012] for the case of South Africa). If social insurance benefits for workers outweigh the cost of employment for employers, formal sector employment will expand. Our data show a positive relationship between minimum wages (defined in level or as a ratio of GDP per capita) and firm productivity in sub-Saharan Africa, whereas the correlation is not clear in Asia.¹

Theoretical Framework

Based on these stylized facts, a dual-economy model with two margins of informality (at the extensive and intensive margins) explains how formal versus informal labor regulations determine employment size in equilibrium (Galiani and Weinschelbaum 2012; Ulyssea 2018). This section summarizes the key hypotheses and discusses the possible effect of heterogeneity by state legal capacity.²

The theoretical framework describes how formal versus informal taxes distort labor allocation that distinguishes formal and informal employment. This distinction affects policy prioritization: for which conditions should the government either provide an incentive to formalize labor contracts with social insurance or restrict informal contracts through stricter inspections.

In this framework, the optimization of a firm and its workers determines the equilibrium outcome. A payroll tax is levied on firms as mandated benefits to hire workers, while firms pay penalties for informal activities (if detected). Enforcement of formal regulation (payroll tax) and intensity of informal sector inspections (tax collection by the authority) are size dependent. That is, formal regulations are rigorously applied to large firms that face pressure from a labor union to offer competitive social security benefits. Likewise, informal sector inspection would be stricter for large businesses that are more visible to tax regulators and have more difficulty avoiding inspections. Size-dependent regulatory policies lead to factor misallocation: when large (productive) firms are taxed more strictly, they use fewer workers than less-productive firms.

As in Olley and Pakes (1996), a less-than-perfect correlation between productivity and factor use indicates a misallocation of factors across firms. Later in the empirical analysis section, the allocative efficiency index is estimated as the correlation between firm productivity and factor use.

The model analysis derives comparative statics to interpret the effect of labor regulations on employment size. First, an increase in formal labor regulation increases the cost of hiring formal labor, and firms may substitute with informal labor. Regulation simultaneously creates workers’ incentive for formal employment by securing mandated benefits. The equilibrium outcome is ambiguous, depending on the elasticity of labor demand and supply, to an increase in payroll tax. If labor demand elasticity is high, stronger regulation may decrease formal sector employment in the equilibrium; yet if labor demand elasticity is low and labor supply elasticity is high, formal sector employment will increase. Labor supply would be more elastic in countries with a large informal sector.

Stricter informal sector inspections and penalties increase the cost of hiring informal labor, thereby creating an incentive to formalize labor contracts. Workers may also prefer to shift to the formal sector after an increase in informal tax. The efficiency of informal sector regulation depends on the enforcement of penalties. If state legal capacity is weak, informal sector regulation would be applied ad hoc, which may distort labor allocation and could be negative for firm growth.

The model also analyzes labor demand and supply responses to the minimum wage hike. When the minimum wage is initially set below the market wage, there is an excess demand for labor. As the minimum wage increases closer to the market-clearing wage, labor supply increases and the formal sector expands. If the minimum wage is raised above the market wage, firms may retrench formal labor demand because of higher labor costs, contracting formal employment.

Data

This chapter uses a World Bank Enterprise Survey that samples formal manufacturing and service firms with at least five employees in low- and middle-income countries.³ The sample uses 23,000 firms in 40 sub-Saharan African countries and about 29,000 firms in 14 developing Asian countries (see Annex 4.2 for the country sample).

The Enterprise Survey is conducted using stratified sampling procedures based on industry group (using the two-digit International Standard Industrial Classification), average sales, firm size, and geographical location. The aggregate-level analysis uses cross-sectional firm balance sheet data for countries surveyed from 2006 until 2017. Later in the case study, Enterprise Survey panel data for Nigeria are used to examine the role of factor markets in determining firm productivity at the micro level.

Summary statistics are provided in Annex Table 4.1. Spatial distributions of firm size and productivity are also provided in Annex 4.3. For the pooled firm sample in the 40 sub-Saharan African countries, the average firm size is 58 employees, only one-third of the average size in Asia. Annex 4.3 shows that the average firm size is less than 31 in many sub-Saharan African countries. Value added per employee and land value show larger variance and spatial variations in sub-Saharan Africa, with wide disparity in the region’s firm productivity and land values. Land ownership and access to credit are about 20 percent and 12 percent smaller in sub-Saharan Africa than in Asia, respectively. More foreign-owned firms exist in the sub-Saharan Africa sample, with 81 percent being manufacturing firms and the rest in the service sector.

Empirical Analysis

The empirical analysis tests the hypotheses related to labor regulations but also considers the role of land allocation in reducing informality and promoting firm growth.

Aggregate-Level Analysis

The aggregate-level analysis estimates factor market efficiency for sub-Saharan Africa and examines its effect on firm growth.

Degree of Factor Misallocations

As the first step, the efficiency of labor and land allocations are estimated based on the correlation between firm productivity and factor allocation, following Olley and Pakes (1996). Models of heterogeneous firms predict that productive firms yield more output by using larger factor inputs. The correlation between labor and land allocations and firm productivity is computed for each district in a country.

Following the approach of Hsieh and Klenow (2009), real output total factor productivity (TFPQ) is used as firms’ productivity measure. The allocative efficiency index is the correlation between the TFPQ and factor use ( s_isj) for firm i within sector s in district j . The correlation is weighted by firm i’s share of production in each sector-district group to define the firm-level measure of misal-location, M_isj:

\begin{matrix} M_{i s j} = w_{i s j, t - 3} \times ρ (T F P Q_{i s j}, s_{i s j}) . & (1) \end{matrix}

The weight w_isj is firm i’s past market share at three years before each survey year t. The allocation measure is transformed to the standardized z-score. As M_isj gets larger in positive values, factors are allocated more efficiency to productive firms. Smaller positive or negative values of M_isj indicate factor misallocation that results in less output compared to the output under an efficient allocation.

Annex 4.4 shows the spatial distribution of the land allocation and labor allocation indices. Land allocation is negative in most sub-Saharan African countries, but worse in southern Africa. Labor allocation has more variation across sub-Saharan African countries, showing that some countries have a more efficient labor market.

Determinants of Factor Allocative Efficiency

Which policies or institutions determine the variation in factor allocative efficiency? Since the seminal contribution by Easterly and Levine (1997), ethnic fraction-alization has been found to shape bad policies, conflicts, and inefficient resource allocation in sub-Saharan Africa. Local socioeconomic hierarchies in the region define who gets access to land. Ethnic fractionalization often creates land-related disputes, making land allocation inefficient.

The subnational ethnic fractionalization index developed by Alesina and Zhuravskaya (2011) is used here as the measurement. For each district j of country c, the fractionalization index captures the probability that two randomly drawn individuals belong to different groups: $F_{j c} = Σ_{m = 1}^{M_{c}} π_{m j c} (1 - π_{m j c})$ where π_mjc stands for the fraction of group m in district j of country c.

Figure 4.3 examines how ethnic fractionalization and land allocative efficiency are correlated in sub-Saharan Africa compared to developing Asia. The figure uses the sampling weight to compute average land allocative efficiency and land-ownership variables at the country level. The figure shows that land allocation tends to be less efficient in countries with more ethnic fractionalization.

Figure 4.4 shows the relationship between labor allocative efficiency and the country’s regulatory quality. The variable is constructed based on firms’ responses to a World Bank Enterprise Survey on business environment, defined by the intensity of regulatory action to enforce labor codes (r_isj) and the frequency of tax inspections to regulate informal activities (t_isj), in each sector-district pair where firms operate. The regulatory environment is district specific, thus the average regulatory action in each district j is computed by taking its average for firms within the same sector: R_j = E_s(r_-isj). Tax administration varies by sector, thus the average tax inspection is computed similarly but for firms within the same district j: T_s = E_j(t_-isj). In both expressions, -i indexes peer firms (all except own firm) that operate in the same sector (to compute R_j) or in the same district (to compute T_s).

The analysis accounts for heterogeneity in the legal capacity to design proper regulatory and tax inspection frameworks. Firms are grouped into those operating in districts with weak or strong state capacity.⁴ Weak state capacity is defined as ineffective contract enforcement accompanied by widespread informal activity. In an area with weak legal capacity, workers would perceive less value in mandated social security, considering the benefits may not be legally enforced. In such a context, stricter formal regulatory action is needed to enforce the labor code, which may improve the efficiency of labor allocation. In contrast, if strong legal capacity is already in place, additional formal regulation may be too burdensome for businesses. In that context, stricter monitoring of “off the books” informal labor may create an efficiency gain.

As expected, panels 1 and 2 in Figure 4.4 indicate that stricter formal labor regulation improves the efficiency of labor allocation only in the weak state-capacity group, whereas intensive regulatory actions create a burden for private businesses in the strong state-capacity group.

Panels 3 and 4 give the opposite picture: stricter inspection efforts decrease labor allocative efficiency for the weak state-capacity group, presumably because inspection agencies under weak institutional environments demand informal payments from small firms. The correlation is slightly positive for the strong state-capacity group for which inspection efforts improve compliance and reduce informality, making the labor market more efficient.

These descriptive patterns imply that ethnic fragmentation and weak regulatory capacity, as typically observed in sub-Saharan Africa, drive factor misallocation in the region. This descriptive pattern is confirmed by the linear regression that estimates the determinants of factor allocative efficiency in Table 4.1:

\begin{matrix} M_{i s j c k} = δ_{0} + δ_{1} Z_{s j c k} + δ_{2} x_{i s j c k} + κ_{s} + μ_{k} + ε_{i s j c k} \\ Z_{s j c k} = {\begin{matrix} F_{j c k} & for land allocation index \\ (R_{j c k}, T_{s c k}), & for labor allocation index \end{matrix} & (2) \end{matrix}

^{Table 4.1.}

Determinants of Factor Allocative Efficiency in Sub-Saharan Africa

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < 0.10; **p < 0.05; ***p < 0.01.

^{Table 4.1.}

Determinants of Factor Allocative Efficiency in Sub-Saharan Africa

	(1) Land Allocation Index
	Owned	Owned or Rented	(2) Labor Allocation Index
Ethnic diversity index	-0.660*** [0.102]	-0.437*** [0.047]
Stronger regulatory action			0.000 [0.002]	0.006** [0.002]	-0.008*** [0.002]
Stronger inspection efforts			0.009 [0.008]	-0.011 [0.011]	0.030*** [0.009]
Ln(capital investment)	0.022*** [0.004]	0.011*** [0.001]	0.004*** [0.001]	0.005*** [0.001]	0.004*** [0.001]
Ln(manager experience)	0.074*** [0.019]	0.068*** [0.007]	0.038*** [0.005]	0.028*** [0.009]	0.044*** [0.008]
Foreign ownership	0.059** [0.029]	0.126*** [0.018]	0.123*** [0.014]	0.138*** [0.020]	0.116*** [0.021]
State ownership	0.315** [0.134]	0.138** [0.056]	0.023 [0.033]	0.022 [0.044]	0.061 [0.055]
GDP per capita growth	-0.035*** [0.008]	-0.020*** [0.003]	-0.012*** [0.003]	-0.017*** [0.004]	-0.005 [0.004]
Private credit/GDP	0.110 [0.139]	0.236*** [0.075]	0.134** [0.053]	0.218** [0.100]	0.062 [0.074]
Judicial efficiency	0.260 [0.188]	0.511*** [0.113]	0.011 [0.091]	0.250* [0.147]	-0.252** [0.115]
Trade openness	-0.283** [0.116]	-0.105** [0.041]	0.092* [0.048]	0.165** [0.070]	0.095* [0.048]
Constant	0.047 [0.171]	-0.190*** [0.072]	-0.211*** [0.080]	-0.373*** [0.116]	-0.082 [0.083]
No. of observations	6,707	19,962	15,169	6,927	8,242
R²	0.059	0.056	0.040	0.071	0.044
Sample	All	All	All	Weak state capacity group	Strong state capacity group
Sectoral and regional fixed effects	Yes	Yes	Yes	Yes	Yes

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < 0.10; **p < 0.05; ***p < 0.01.

^{Table 4.1.}

Determinants of Factor Allocative Efficiency in Sub-Saharan Africa

	(1) Land Allocation Index
	Owned	Owned or Rented	(2) Labor Allocation Index
Ethnic diversity index	-0.660*** [0.102]	-0.437*** [0.047]
Stronger regulatory action			0.000 [0.002]	0.006** [0.002]	-0.008*** [0.002]
Stronger inspection efforts			0.009 [0.008]	-0.011 [0.011]	0.030*** [0.009]
Ln(capital investment)	0.022*** [0.004]	0.011*** [0.001]	0.004*** [0.001]	0.005*** [0.001]	0.004*** [0.001]
Ln(manager experience)	0.074*** [0.019]	0.068*** [0.007]	0.038*** [0.005]	0.028*** [0.009]	0.044*** [0.008]
Foreign ownership	0.059** [0.029]	0.126*** [0.018]	0.123*** [0.014]	0.138*** [0.020]	0.116*** [0.021]
State ownership	0.315** [0.134]	0.138** [0.056]	0.023 [0.033]	0.022 [0.044]	0.061 [0.055]
GDP per capita growth	-0.035*** [0.008]	-0.020*** [0.003]	-0.012*** [0.003]	-0.017*** [0.004]	-0.005 [0.004]
Private credit/GDP	0.110 [0.139]	0.236*** [0.075]	0.134** [0.053]	0.218** [0.100]	0.062 [0.074]
Judicial efficiency	0.260 [0.188]	0.511*** [0.113]	0.011 [0.091]	0.250* [0.147]	-0.252** [0.115]
Trade openness	-0.283** [0.116]	-0.105** [0.041]	0.092* [0.048]	0.165** [0.070]	0.095* [0.048]
Constant	0.047 [0.171]	-0.190*** [0.072]	-0.211*** [0.080]	-0.373*** [0.116]	-0.082 [0.083]
No. of observations	6,707	19,962	15,169	6,927	8,242
R²	0.059	0.056	0.040	0.071	0.044
Sample	All	All	All	Weak state capacity group	Strong state capacity group
Sectoral and regional fixed effects	Yes	Yes	Yes	Yes	Yes

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < 0.10; **p < 0.05; ***p < 0.01.

where i indexes firms, s indexes sector, j indexes district, c indexes country, and k indexes region (western, central, eastern, and southern sub-Saharan Africa dummies). M stands for either the land or the labor allocation index, and x controls for firm-level variables such as machines and equipment investments, managers’ work experience, and firm-ownership dummies (for example, state-owned enterprise or foreign enterprise). Country-level variables such as real GDP per capita growth, the ratio of private credit to GDP, judicial efficiency (the quality of the legal system, including judicial administration, processing time, and court regulations to enforce contracts), and trade openness are also included.⁵ K and u, are sector and region fixed effects.

In column 1, the negative coefficient of the ethnic fractionalization index confirms lower land-allocative efficiency (for both owned and rented land) in districts where ethnic fractionalization is higher. In column 2, a labor allocation index is regressed on two institutional variables (stronger formal regulations and inspection efforts) along with other controls, separately for weak and strong state-capacity groups. As found in Figure 4.4, stronger regulatory action improves the efficiency of labor allocation in the weak state-capacity group, whereas it worsens labor allocative efficiency in the strong state-capacity group. Also, stronger tax inspection efforts worsen (or statistically have no effect on) labor allocative efficiency in the weak state-capacity group, whereas it improves the allocative efficiency in the strong state-capacity group.

Coefficients of other covariates indicate that land tends to be more efficiently allocated for firms with more capital, more experienced managers, and foreign or state ownership. For country-level variables, financial deepening and judicial efficiency support efficient factor allocations, whereas fast GDP growth does not necessarily improve factor allocation. Trade openness is also associated with better labor allocation.

Table 4.1 is used as the first stage for the IV-Tobit regression to identify the effect of factor allocative efficiency on firm performance in Table 4.4.

^{Table 4.2.}

Direct Effect of Land and Labor Regulations in Sub-Saharan Africa (Reduced Form)

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < .10; **p < .05; ***p < .01.

^{Table 4.2.}

Direct Effect of Land and Labor Regulations in Sub-Saharan Africa (Reduced Form)

	Ln(Firm Size)
	(1)	(2)	(3)	(4)	(5)	(6)
Ln(land value)	0.039*** [0.002]	0.031*** [0.003]	0.033*** [0.004]
Stronger regulatory action				0.015*** [0.004]	0.014*** [0.005]	0.011** [0.004]
Stronger inspection efforts				0.023 [0.020]	-0.003 [0.023]	0.014 [0.023]
Ln(capital investment)	0.062*** [0.008]	0.083*** [0.009]	0.079*** [0.008]	0.045*** [0.004]	0.045*** [0.004]	0.046*** [0.004]
Ln(manager experience)	0.222*** [0.024]	0.253*** [0.026]	0.236*** [0.030]	0.260*** [0.016]	0.262*** [0.018]	0.254*** [0.019]
Foreign ownership	0.786*** [0.068]	0.747*** [0.060]	0.795*** [0.067]	0.772*** [0.040]	0.767*** [0.039]	0.804*** [0.042]
State ownership	1.075*** [0.154]	0.968*** [0.175]	0.944*** [0.193]	0.450*** [0.104]	0.393*** [0.110]	0.368*** [0.112]
Minimum wage/GDP per c	apita	-0.014*** [0.002]	3.027** [1.443]		-0.007*** [0.002]	2.443** [0.970]
GDP per capita growth	0.007 [0.011]	0.009 [0.019]	0.016 [0.018]	0.028*** [0.008]	0.024* [0.014]	0.026** [0.013]
Private credit/GDP	0.897*** [0.254]	0.825*** [0.246]	0.738*** [0.237]	0.626*** [0.179]	0.274 [0.210]	0.245 [0.202]
Judicial efficiency	-0.632* [0.345]	-0.25 [0.603]	0.007 [0.644]	-0.266 [0.311]	-0.236 [0.403]	-0.02 [0.384]
Trade openness	-0.519*** [0.175]	-0.568** [0.252]	-0.580** [0.239]	-0.465*** [0.122]	-0.376*** [0.133]	-0.403*** [0.123]
Constant	2.077*** [0.243]	1.797*** [0.426]	1.512*** [0.458]	2.152*** [0.187]	2.338*** [0.216]	2.058*** [0.226]
No. of observations	7,997	6,023	5,598	19,237	15,414	14,417
Sample	All	All	Minimum wage below market wage	All	All	Minimum wage below market wage
Sectoral and regional fixed effects	Yes	Yes	Yes	Yes	Yes	Yes

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < .10; **p < .05; ***p < .01.

^{Table 4.2.}

Direct Effect of Land and Labor Regulations in Sub-Saharan Africa (Reduced Form)

	Ln(Firm Size)
	(1)	(2)	(3)	(4)	(5)	(6)
Ln(land value)	0.039*** [0.002]	0.031*** [0.003]	0.033*** [0.004]
Stronger regulatory action				0.015*** [0.004]	0.014*** [0.005]	0.011** [0.004]
Stronger inspection efforts				0.023 [0.020]	-0.003 [0.023]	0.014 [0.023]
Ln(capital investment)	0.062*** [0.008]	0.083*** [0.009]	0.079*** [0.008]	0.045*** [0.004]	0.045*** [0.004]	0.046*** [0.004]
Ln(manager experience)	0.222*** [0.024]	0.253*** [0.026]	0.236*** [0.030]	0.260*** [0.016]	0.262*** [0.018]	0.254*** [0.019]
Foreign ownership	0.786*** [0.068]	0.747*** [0.060]	0.795*** [0.067]	0.772*** [0.040]	0.767*** [0.039]	0.804*** [0.042]
State ownership	1.075*** [0.154]	0.968*** [0.175]	0.944*** [0.193]	0.450*** [0.104]	0.393*** [0.110]	0.368*** [0.112]
Minimum wage/GDP per c	apita	-0.014*** [0.002]	3.027** [1.443]		-0.007*** [0.002]	2.443** [0.970]
GDP per capita growth	0.007 [0.011]	0.009 [0.019]	0.016 [0.018]	0.028*** [0.008]	0.024* [0.014]	0.026** [0.013]
Private credit/GDP	0.897*** [0.254]	0.825*** [0.246]	0.738*** [0.237]	0.626*** [0.179]	0.274 [0.210]	0.245 [0.202]
Judicial efficiency	-0.632* [0.345]	-0.25 [0.603]	0.007 [0.644]	-0.266 [0.311]	-0.236 [0.403]	-0.02 [0.384]
Trade openness	-0.519*** [0.175]	-0.568** [0.252]	-0.580** [0.239]	-0.465*** [0.122]	-0.376*** [0.133]	-0.403*** [0.123]
Constant	2.077*** [0.243]	1.797*** [0.426]	1.512*** [0.458]	2.152*** [0.187]	2.338*** [0.216]	2.058*** [0.226]
No. of observations	7,997	6,023	5,598	19,237	15,414	14,417
Sample	All	All	Minimum wage below market wage	All	All	Minimum wage below market wage
Sectoral and regional fixed effects	Yes	Yes	Yes	Yes	Yes	Yes

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < .10; **p < .05; ***p < .01.

^{Table 4.3.}

Firm Performance and Factor Allocative Efficiency in Sub-Saharan Africa (Ordinary Least Squares Tobit)

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < .10; **p < .05; ***p < .01.

^{Table 4.3.}

Firm Performance and Factor Allocative Efficiency in Sub-Saharan Africa (Ordinary Least Squares Tobit)

	LnfFirm Age)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Land allocation index (owned)	0.574*** [0.066]				0.205*** [0.035]
Land allocation index (owned or rented)		0.315*** [0.065]				0.113*** [0.022]
Labor allocation index			1.380*** [0.132]	1.479*** [0.139]			0.243*** [0.046]	0.202*** [0.045]
Labor allocation index squared			-0.295*** [0.052]	-0.303*** [0.050]			-0.074*** [0.022]	-0.058*** [0.022]
Ln(capital investments)	0.074*** [0.010]	0.047*** [0.004]	0.051*** [0.004]	0.047*** [0.004]	0.009*** [0.003]	0.006*** [0.001]	0.005*** [0.002]	0.006*** [0.002]
Ln(manager experience)	0.217*** [0.023]	0.267*** [0.015]	0.176*** [0.023]	0.201*** [0.023]	0.558*** [0.016]	0.561*** [0.010]	0.523*** [0.017]	0.527*** [0.015]
Foreign ownership	0.776*** [0.068]	0.683*** [0.040]	0.590*** [0.052]	0.683*** [0.041]	0.083*** [0.028]	0.061*** [0.019]	0.067** [0.027]	0.062*** [0.022]
State ownership	1.060*** [0.157]	0.506*** [0.097]	0.222** [0.088]	0.445*** [0.114]	0.482*** [0.083]	0.252*** [0.043]	0.116** [0.051]	0.303*** [0.070]
GDP per capita growth	0.017 [0.012]	0.017** [0.007]	0.030*** [0.012]	0.032** [0.013]	0.014** [0.007]	0.009** [0.004]	0.036*** [0.006]	-0.002 [0.006]
Private credit/GDP	0.666** [0.259]	0.615*** [0.159]	0.132 [0.185]	1.098*** [0.267]	0.348*** [0.094]	0.184*** [0.062]	0.115 [0.102]	0.364*** [0.086]
Judicial efficiency	-0.345 [0.403]	-0.367 [0.258]	-1.657*** [0.395]	0.741** [0.351]	-0.326 [0.207]	-0.598*** [0.111]	-1.055*** [0.198]	-0.268* [0.156]
Trade openness	-0.424** [0.194]	-0.337*** [0.106]	-0.451** [0.190]	-0.815*** [0.155]	-0.119 [0.084]	-0.294*** [0.043]	-0.615*** [0.073]	-0.131** [0.057]
Constant	2.069*** [0.259]	2.228*** [0.144]	3.387*** [0.225]	2.007*** [0.216]	1.214*** [0.126]	1.504*** [0.076]	2.158*** [0.132]	1.305*** [0.091]
No. of observations	7,687	22,295	6,927	8,242	7,539	21,770	7,253	8,861
Sample	All	All	Weak state capacity group	Strong state capacity group	All	All	Weak state capacity group	Strong state capacity group
Sectoral and regional fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < .10; **p < .05; ***p < .01.

^{Table 4.3.}

Firm Performance and Factor Allocative Efficiency in Sub-Saharan Africa (Ordinary Least Squares Tobit)

	LnfFirm Age)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Land allocation index (owned)	0.574*** [0.066]				0.205*** [0.035]
Land allocation index (owned or rented)		0.315*** [0.065]				0.113*** [0.022]
Labor allocation index			1.380*** [0.132]	1.479*** [0.139]			0.243*** [0.046]	0.202*** [0.045]
Labor allocation index squared			-0.295*** [0.052]	-0.303*** [0.050]			-0.074*** [0.022]	-0.058*** [0.022]
Ln(capital investments)	0.074*** [0.010]	0.047*** [0.004]	0.051*** [0.004]	0.047*** [0.004]	0.009*** [0.003]	0.006*** [0.001]	0.005*** [0.002]	0.006*** [0.002]
Ln(manager experience)	0.217*** [0.023]	0.267*** [0.015]	0.176*** [0.023]	0.201*** [0.023]	0.558*** [0.016]	0.561*** [0.010]	0.523*** [0.017]	0.527*** [0.015]
Foreign ownership	0.776*** [0.068]	0.683*** [0.040]	0.590*** [0.052]	0.683*** [0.041]	0.083*** [0.028]	0.061*** [0.019]	0.067** [0.027]	0.062*** [0.022]
State ownership	1.060*** [0.157]	0.506*** [0.097]	0.222** [0.088]	0.445*** [0.114]	0.482*** [0.083]	0.252*** [0.043]	0.116** [0.051]	0.303*** [0.070]
GDP per capita growth	0.017 [0.012]	0.017** [0.007]	0.030*** [0.012]	0.032** [0.013]	0.014** [0.007]	0.009** [0.004]	0.036*** [0.006]	-0.002 [0.006]
Private credit/GDP	0.666** [0.259]	0.615*** [0.159]	0.132 [0.185]	1.098*** [0.267]	0.348*** [0.094]	0.184*** [0.062]	0.115 [0.102]	0.364*** [0.086]
Judicial efficiency	-0.345 [0.403]	-0.367 [0.258]	-1.657*** [0.395]	0.741** [0.351]	-0.326 [0.207]	-0.598*** [0.111]	-1.055*** [0.198]	-0.268* [0.156]
Trade openness	-0.424** [0.194]	-0.337*** [0.106]	-0.451** [0.190]	-0.815*** [0.155]	-0.119 [0.084]	-0.294*** [0.043]	-0.615*** [0.073]	-0.131** [0.057]
Constant	2.069*** [0.259]	2.228*** [0.144]	3.387*** [0.225]	2.007*** [0.216]	1.214*** [0.126]	1.504*** [0.076]	2.158*** [0.132]	1.305*** [0.091]
No. of observations	7,687	22,295	6,927	8,242	7,539	21,770	7,253	8,861
Sample	All	All	Weak state capacity group	Strong state capacity group	All	All	Weak state capacity group	Strong state capacity group
Sectoral and regional fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. *p < .10; **p < .05; ***p < .01.

^{Table 4.4.}

Firm Performance and Factor Allocative Efficiency in Sub-Saharan Africa (IVTobit)

Source: Author. Note: Standard errors clustered at the sampling strata level appear in brackets. Basic controls include country and firm characteristics as included in the ordinary least squaresTobit regression in Table 4.3. *p <. 10; **p < .05; ***p < .01.

^{Table 4.4.}

Firm Performance and Factor Allocative Efficiency in Sub-Saharan Africa (IVTobit)

	Ln( Firm Age)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Land allocation index (owned)	0.408*** [0.114]				0.416*** [0.072]
Land allocation index (owned or rented)		0.288*** [0.089]				0.569*** [0.062]
Labor allocation index			-0.708 [0.790]	-3.856*** [0.805]			-1.412*** [0.435]	-0.935* [0.561]
Labor allocation index squared			1.684*** [0.618]	1.722** [0.715]			0.124 [0.347]	1.312** [0.531]
Constant	2.213*** [0.125]	2.354*** [0.069]	3.091*** [0.247]	1.366*** [0.211]	1.375*** [0.077]	1.729*** [0.047]	1.824*** [0.135]	1.252*** [0.156]
First Stage: F-Statistics for Excluded Instrumental Variables (p value)
Land allocation index (owned)	174.5 [0.00]				165.8 [0.00]
Land allocation index (owned or rented)		298.2 [0.00]				287.9 [0.00]
Labor allocation index			24.8 [0.00]	49.5 [0.00]			22.4 [0.00]	46.8 [0.00]
Labor allocation index²			8.2 [0.00]	13.3 [0.00]			7.1 [0.00]	11.0 [0.00]
No. of observations	6,707	19,962	6,927	8,242	6,564	19,462	6,687	8,050
Sample	All	All	Weak state capacity group	Strong state capacity group	All	All	Weak state capacity group	Strong state capacity group
Sectoral and regional fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Basic controls included	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

^{Table 4.4.}

Firm Performance and Factor Allocative Efficiency in Sub-Saharan Africa (IVTobit)

	Ln( Firm Age)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Land allocation index (owned)	0.408*** [0.114]				0.416*** [0.072]
Land allocation index (owned or rented)		0.288*** [0.089]				0.569*** [0.062]
Labor allocation index			-0.708 [0.790]	-3.856*** [0.805]			-1.412*** [0.435]	-0.935* [0.561]
Labor allocation index squared			1.684*** [0.618]	1.722** [0.715]			0.124 [0.347]	1.312** [0.531]
Constant	2.213*** [0.125]	2.354*** [0.069]	3.091*** [0.247]	1.366*** [0.211]	1.375*** [0.077]	1.729*** [0.047]	1.824*** [0.135]	1.252*** [0.156]
First Stage: F-Statistics for Excluded Instrumental Variables (p value)
Land allocation index (owned)	174.5 [0.00]				165.8 [0.00]
Land allocation index (owned or rented)		298.2 [0.00]				287.9 [0.00]
Labor allocation index			24.8 [0.00]	49.5 [0.00]			22.4 [0.00]	46.8 [0.00]
Labor allocation index²			8.2 [0.00]	13.3 [0.00]			7.1 [0.00]	11.0 [0.00]
No. of observations	6,707	19,962	6,927	8,242	6,564	19,462	6,687	8,050
Sample	All	All	Weak state capacity group	Strong state capacity group	All	All	Weak state capacity group	Strong state capacity group
Sectoral and regional fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Basic controls included	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Effect of Land Market and Regulations on Firm Size

Table 4.2 shows reduced-form estimates that regress firm size on land value, labor regulation, and inspection actions to test our theoretical hypotheses. Columns 3 and 6 restrict the sample to places where the minimum wage is set below the market-clearing wage.⁶

Columns 1 to 3 estimate the effect of higher land value on firm size. The ownership of higher-value land significantly increases firm size, considering firms can reap scale benefits by relaxing collateral constraints. The effect is positive regardless of the minimum wage.⁷

Estimates in columns 4 and 5 show that formal regulations increase firm size whereas stronger inspections have an insignificant effect. Column 6 tests the effect of stronger regulation on firm size when the sample only covers countries where the minimum wage is set below market-clearing wage. The effect is ambiguous in theory, depending on labor demand and supply elasticity to regulation changes. The result shows that stronger regulation has a positive effect on firm size, suggesting that labor demand shrinks less in response to stronger regulations, whereas formalization of workers increases more.

Columns 2, 3, 5, and 6 show that an increase in the minimum wage (relative to per capita income) reduces firm size on average, but with a small magnitude, showing the limited disemployment effect of the minimum wage; however, a higher minimum wage significantly increases firm size when the sample is restricted only to firms where the minimum wage is set below market-clearing wage. This implies that the initial level of the minimum wage is significantly lower than market-clearing wage, thus, raising the minimum wage simply attracts more labor.

Other covariates show that firms with more capital and experienced managers tend to be larger. Foreign-owned firms or state-owned enterprises are also larger than local private firms. Income growth and financial deepening also support firm growth, whereas judicial efficiency has no direct effect on firm size. Larger trade openness appears to adversely affect small firms for surviving market competition.

Effect of Factor Allocative Efficiency on Firm Size and Survival

What are the consequences of land and labor misallocations? Figure 4.1 and Annex Table 4.1 show that sub-Saharan African firms are significantly smaller (in employees), less productive, and less long-lived than Asian firms. Here we use the established factor allocation index and analyze its effect on firm size and age in sub-Saharan Africa.

To study factors that affect firm size and age, we run the following Tobit regression:

\begin{matrix} y_{i s j c k} = \max (0, α_{0} + α_{1} M_{i s j c k} + α_{2} x_{i s j c k} + κ_{s} + μ_{k} + ε_{i s j c k}) . & (3) \end{matrix}

The same explanatory variables as used in equation (2) are included as control variables. Table 4.3 shows the effect of factor allocative efficiency on firm size (in columns 1 to 4) and firm age (in columns 5 to 8). Columns 1 and 5 show the effect of the allocative efficiency of owned land, whereas columns 2 and 6 show the result for the allocative efficiency of both owned and rented land. The results in columns 2 and 6 show that an increase in land allocative efficiency by 1 standard deviation significantly increases firm size (by 32 percent) and survival (by 11 percent), both significant at the 1 percent level.

Higher labor allocative efficiency also significantly increases firm size for both strong and weak state capacity groups (columns 3, 4, 7, and 8). The negative and significant square term of the labor allocation index indicates that the effect of labor allocative efficiency on firm size and age is concave, that is, the positive effect is particularly large when initial labor allocation is inefficient. As a labor market develops to achieve efficient labor allocation, the marginal effect gets smaller.

Other covariates show similar results as found in the reduced-form regres-sion in Table 4.2.

Instrumental Variable Results: Heterogeneity in Effects of Factor Allocative Efficiency on Firm Performance

In identifying an allocative efficiency index, one faces an endogeneity problem for potential reverse causality. That is, firm performance could affect allocative efficiency. Firms whose land and labor allocations are affected by regulation and ethnic fractionalization are marginally productive in the local market (Imbens 2010). The decision to reallocate factors of production varies with firm productivity. An IV-Tobit regression estimates the local average treatment effect (LATE) of factor allocative efficiency on firm performance.

As defined in equation (2), different IVs are used in the first-stage regression. The subnational-level ethnic diversity index is the only IV for the land allocation index that influences firms’ access to land. The ethnic fractionalization index comes from census data near 2000, thus it offers predetermined ethnic diversity for each district before the World Bank conducted the Enterprise Survey.

The model is overidentified by using two IVs for the labor allocation index: the average level of regulatory action taken by subnational governments toward peer firms in the same district (R_j) and the average inspection efforts taken toward peer firms to regulate tax evasion in the same sector (T_s). The first variable captures the average level of formal regulatory measures to formalize labor contracts, whereas the second variable is the intensity of informal sector monitoring, that is, the penalty or cost of noncompliance (informal tax) (Olken and Singhal 2011).

The identifying assumption is that average regulatory situations for a peer group affect own-firm performance only through the factor allocation. The rationale for the exclusion restriction is that when looking at the same district across sectors (for R_j) or the same sector across districts (for T_s), government regulatory actions toward peer firms affect factor allocation in the same labor market but have limited effect on own firms’ production.

Table 4.4 shows IV Tobit estimates that are the LATE of the land and labor allocation index for marginally productive firms. In the lower panel of Table 4.4, the first-stage F-statistics are sufficiently high for all specifications (p value = 0.00), showing that ethnic fractionalization and regulations are valid instruments for the land and labor allocation indices.

Columns 1, 2, 5, and 6 show that land allocation matters for marginal firms to grow and survive longer (significant at 1 percent). In columns 2, 3, 5, and 6, the positive square term and negative linear term of the labor allocation index suggest that the effect of labor allocative efficiency is convex: the effect exponentially increases as a labor market develops and labor allocation gets more efficient. The convex LATE of labor allocative efficiency is different from the concave effect found by the OLS Tobit regression in Table 4.3 for the average firm. This suggests large heterogeneity in the effect of labor allocative efficiency. The marginal effect of labor allocation index on firm size is much larger for firms operating in countries with weak state capacity. Both OLS and IV-Tobit estimates find that overall, efficient labor allocation contributes to firm growth and longer survival.

Effect of Factor Allocations on Credit Access and Tax Contributions

We further investigate whether better factor allocation helps firm growth through credit access and tax contributions. Better land allocation, when land acts as a collateral, as well as more efficient labor allocation, may help firms obtain credit and grow faster. With stronger labor and tax regulations, labor contracts would be more formalized and firms’ tax contributions to the government may increase. Figure 4.5 reports the effects of land and labor allocative efficiency on firms’ access to credit (loans from banks or other financial institutions) and on tax contributions (percentage of sales reported for tax payments).

The result suggests that firms perform better as factor allocation becomes more efficient. The increase in land and labor allocation indices by 1 standard deviation increases the probability of obtaining credit by about 1 and 6 percentage points, respectively. The effect is larger at about 8 percentage points where state capacity is strong.

The estimate also shows that improvement in factor allocative efficiency would accelerate the formalization of industries through higher tax contributions. In weak institutional regions, an improvement in labor allocative efficiency through stronger regulation increases tax contributions by about 10 percentage points, which is larger than the same effect in strong-institution regions by about 2 percentage points. If land is allocated more to productive firms, they can expand for longer periods with more chance to obtain a credit line from a bank, increasing their tax contributions.

Panel Data Analysis: Case Study for Nigeria

Here we examine whether the aggregate-level findings on firm growth can be confirmed for a single country, using firm panel data from Nigeria. Nigeria panel data from the World Bank Enterprise Survey include basic financial accounts; we may therefore estimate TFPQ using Levinsohn and Petrin’s (2003) method. Firm-level analysis within Nigeria better identifies policy effects, because time-invariant factors can be removed using panel data structure.

Nigeria is the most populous country in sub-Saharan Africa, composed of more than 250 ethnic groups and endowed with the 10th largest oil reserves in the world.⁸ However, the GDP per capita (in constant 2011 international dollars based on purchasing power parity) is ranked 133 of 191 countries (IMF 2018b), with the poverty rate increasing in recent years. Poverty is most prevalent in the northern part of the country, with the state of Jigawa’s headcount poverty rate the highest at 78 percent (Nwude 2013; World Bank 2015), whereas the southern part near the Niger Delta is wealthy from its oil endowment. The quality of governance has been low: in the Transparency International Corruption Perceptions Index, Nigeria scores as one of the most corrupt countries in the world (ranked 148 of 180 countries). Furthermore, underdeveloped areas in the north are plagued by conflicts (for example, Islamist extremist insurgency by Boko Haram), again leading to weak state capacity.

The land tenure system and land rental market are underdeveloped in Nigeria. The land allocation index for Nigerian states computed using equation (1) is mapped in Annex Figure 4.4.3. Many states in the northwest and the southeast are scored at negative or small positive values, suggesting inefficiency in land allocation.

Annex Figure 4.4.4 similarly shows that the labor allocation index is negative or close to zero in the northwestern Nigerian states. Figure 4.6 shows that labor allocative efficiency is negatively correlated with tax inspection by the local government (World Bank 2014). Despite slight improvements in business conditions, the World Bank’s Ease of Doing Business Index scores most Nigerian states lower than the sub-Saharan African average. Multiple layers of regulatory requirements lead to high start-up costs.

Nigeria’s fiscal regime also entails the extensive use of tax incentives and exemptions, eroding the fairness of tax treatments and leading to tax evasion. Nigeria was the first sub-Saharan African country to explore a contributory social insurance system, but social security coverage has been limited with weak regulatory capacity, leading to high tax noncompliance and the accumulation of tax arrears (IMF 2005, 2018a). As a result, the current regulatory system is little trusted by the private sector, making firms operate informally and distorting the labor allocation.

In such a context, the following panel regression estimates the effect of land and labor allocative efficiency on firm size in Nigeria:

\begin{matrix} y_{i s j t} = α_{0} + α_{1} M_{i s j t} + α_{2} X_{i s j t} + λ_{i} + κ_{s} + μ_{t} + ε_{i s j t}, & (4) \end{matrix}

where M_isjt is the factor market efficiency index for firm i in sector s and state j at time t (t = 2007/09 or 2014). X_isjt controls for firm and state characteristics, such as the distance to the capital city Abuja, urbanization rate, and the suitability of land for agriculture from Gershman and Rivera (2018). λ_i, κ_i, and μ_i are firm, sector, and year fixed effects.

At the bottom of Table 4.5, the Breusch and Pagan Lagrange multiplier (LM) test supports the random effect specification rather than the pooled OLS regression, whereas the Hausman test supports the fixed effect rather than the random effect model.

^{Table 4.5.}

Determinants of Firm Size in Nigeria (Fixed versus random effect regressions)

Source: Author. Note: Robust standard errors appear in brackets. FE = fixed effect; RE = random effect. *p < .10; **p < .05; ***p < .01.

^{Table 4.5.}

Determinants of Firm Size in Nigeria (Fixed versus random effect regressions)

	Ln (Firm Size)
	(1)		(2)		(3)		(4)
	RE Model	FE Model	RE Model	FE Model	RE Model	FE Model	RE Model	FE Model
Ln(land value, owned or rented)	0.022*** [0.006]	0.022*** [0.007]
Stronger regulatory action			0.006** [0.003]	0.000 [0.003]
Stronger inspection efforts			-0.215*** [0.065]	-0.199*** [0.070]
Land allocation index					0.109*** [0.028]	0.057** [0.028]
Labor allocation index							0.205*** [0.044]	0.129*** [0.036]
Ln(capital investment)	0.012* [0.006]	0.001 [0.007]	0.021*** [0.005]	0.009 [0.006]	0.023*** [0.006]	0.010 [0.006]	0.018*** [0.006]	0.008 [0.006]
Ln(manager experience)	0.058 [0.039]	0.029 [0.045]	0.054 [0.039]	0.027 [0.045]	0.048 [0.042]	0.018 [0.051]	0.069* [0.040]	0.038 [0.048]
Foreign ownership	0.277** [0.117]	0.173 [0.142]	0.269** [0.119]	0.158 [0.144]	0.210* [0.119]	0.161 [0.138]	0.318*** [0.120]	0.239* [0.143]
State ownership	0.195* [0.107]	0.164 [0.123]	0.139 [0.110]	0.13 [0.126]	0.245** [0.111]	0.148 [0.125]	0.171 [0.113]	0.107 [0.125]
Ln(distance to Abuja)	0.212*** [0.066]		0.209*** [0.066]		0.209*** [0.065]		0.204*** [0.065]
Urbanization rate	0.668*** [0.188]		0.685*** [0.187]		0.687*** [0.186]		0.724*** [0.183]
Land suitability for agriculture	-0.094 [0.062]		-0.112* [0.062]		-0.097 [0.061]		-0.104* [0.060]
Constant	1.124** [0.440]	2.402*** [0.135]	2.097*** [0.521]	3.339*** [0.315]	1.217*** [0.441]	2.473*** [0.147]	1.289*** [0.436]	2.493*** [0.147]
No. of observation	1,499	1,499	1,499	1,499	1,374	1,374	1,415	1,415
R² (overall)	0.135	0.054	0.139	0.049	0.170	0.077	0.192	0.097
Sector dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Diagnostic Tests
Breusch and Pagan	220.61			212.38		130.46		158.96
Lagrange multiplier test	[0.000]			[0.000]		[0.000]		[0.000]
Hausman test	73.42 [0.000]			101.28 [0.000]		74.43 [0.000]		77.77 [0.000]

Source: Author. Note: Robust standard errors appear in brackets. FE = fixed effect; RE = random effect. *p < .10; **p < .05; ***p < .01.

^{Table 4.5.}

Determinants of Firm Size in Nigeria (Fixed versus random effect regressions)

	Ln (Firm Size)
	(1)		(2)		(3)		(4)
	RE Model	FE Model	RE Model	FE Model	RE Model	FE Model	RE Model	FE Model
Ln(land value, owned or rented)	0.022*** [0.006]	0.022*** [0.007]
Stronger regulatory action			0.006** [0.003]	0.000 [0.003]
Stronger inspection efforts			-0.215*** [0.065]	-0.199*** [0.070]
Land allocation index					0.109*** [0.028]	0.057** [0.028]
Labor allocation index							0.205*** [0.044]	0.129*** [0.036]
Ln(capital investment)	0.012* [0.006]	0.001 [0.007]	0.021*** [0.005]	0.009 [0.006]	0.023*** [0.006]	0.010 [0.006]	0.018*** [0.006]	0.008 [0.006]
Ln(manager experience)	0.058 [0.039]	0.029 [0.045]	0.054 [0.039]	0.027 [0.045]	0.048 [0.042]	0.018 [0.051]	0.069* [0.040]	0.038 [0.048]
Foreign ownership	0.277** [0.117]	0.173 [0.142]	0.269** [0.119]	0.158 [0.144]	0.210* [0.119]	0.161 [0.138]	0.318*** [0.120]	0.239* [0.143]
State ownership	0.195* [0.107]	0.164 [0.123]	0.139 [0.110]	0.13 [0.126]	0.245** [0.111]	0.148 [0.125]	0.171 [0.113]	0.107 [0.125]
Ln(distance to Abuja)	0.212*** [0.066]		0.209*** [0.066]		0.209*** [0.065]		0.204*** [0.065]
Urbanization rate	0.668*** [0.188]		0.685*** [0.187]		0.687*** [0.186]		0.724*** [0.183]
Land suitability for agriculture	-0.094 [0.062]		-0.112* [0.062]		-0.097 [0.061]		-0.104* [0.060]
Constant	1.124** [0.440]	2.402*** [0.135]	2.097*** [0.521]	3.339*** [0.315]	1.217*** [0.441]	2.473*** [0.147]	1.289*** [0.436]	2.493*** [0.147]
No. of observation	1,499	1,499	1,499	1,499	1,374	1,374	1,415	1,415
R² (overall)	0.135	0.054	0.139	0.049	0.170	0.077	0.192	0.097
Sector dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Diagnostic Tests
Breusch and Pagan	220.61			212.38		130.46		158.96
Lagrange multiplier test	[0.000]			[0.000]		[0.000]		[0.000]
Hausman test	73.42 [0.000]			101.28 [0.000]		74.43 [0.000]		77.77 [0.000]

Source: Author. Note: Robust standard errors appear in brackets. FE = fixed effect; RE = random effect. *p < .10; **p < .05; ***p < .01.

In Table 4.5, both random and fixed effect estimates confirm that landholding with higher land value and efficient factor allocations significantly increases firm size in Nigeria. The coefficients get smaller in magnitude under the fixed effect model, but the effects remain significant. Stronger inspection efforts significantly constrain firm growth, whereas the effect of formal regulation has no effect under the fixed effect model. Estimates for other control variables show that firm size gets larger as firms locate in urbanized areas with land suitable for nonagricultural activities.

Conclusions

Despite a long period of strong growth, pessimistic development prospects dominate in sub-Saharan Africa because of its heavy reliance on natural resources and low competitiveness. This chapter examined the roots of the region’s weak industrial performance by examining the efficiency of the factor market and its role in firm growth.

This chapter first estimated the allocative efficiency of land and labor in 40 sub-Saharan African countries following Olley and Pakes (1996), which suggests significant factor misallocations in the region. Factor market distortions stem primarily from fragile institutional environments, including conflict among diverse ethnic groups, customary land systems, and weakly enforced regulations. Estimated factor allocation indices suggest ample scope for improving land and labor efficiencies through factor reallocations to more productive firms.

On the basis of predictions from a dual-economy model with two margins of informality, the chapter tested whether sub-Saharan African firms could achieve significantly more scale and productivity gains by improving factor market efficiency.

IV regression confirms that African firms face significant factor misallocation from ethnic fractionalization and regulatory actions. Given that the allocation of land is often informally determined and land disputes among ethnic groups are common in sub-Saharan Africa, access to land is limited for productive firms. Under limited enforcement of labor regulations, workers are unwilling to continue formal business at a large scale. In low-income sub-Saharan Africa where corruption is widespread, stricter monitoring of small enterprises by tax inspectors increases “informal tax,” which often outweighs the benefits of running a formal business.

Based on first-stage results, the IV-Tobit regressions using pooled data of 40 sub-Saharan African countries and Nigerian panel data show that factor reallocation would allow firms to survive longer and achieve better growth, with an especially large policy effect (LATE) for marginally productive firms. The results also suggest that access to credit and tax contributions could increase if factor misallocation were addressed, which may further support firm growth in sub-Saharan Africa.

From a policy perspective, the results imply that the effect of regulatory reforms on factor market efficiency and firm growth depends on local legal capacity. There is no one size fits all, but regulation design needs to match local legal capacity. Stricter monitoring of informal activities does not always support firm growth in sub-Saharan Africa. Formalizing labor contracts with mandated benefits is more effective in supporting firm growth when legal capacity is weak. As legal capacity develops, stronger informal sector monitoring becomes more effective in accelerating the reallocation of workers to productive formal activities.

As it stands, high informality in sub-Saharan Africa could be the equilibrium outcome of informal firms’ rational choice to stay in the informal sector. This may reflect that the informal sector provides small African firms with safety nets, whereas the costs outweigh the benefits of operating formal businesses. In this regard, a natural way to reduce informality in sub-Saharan Africa is to introduce simple formal sector or informal sector regulations, as they fit the local context, to achieve more efficient land and labor allocations and support the growth of formal micro entrepreneurs.

Annex 4.1. Summary Statistics

^{Annex Table 4.1.1.}

Cross-Country Pooled Firm Data: Sub-Saharan Africa versus Developing Asia

Sources: IMF 2018b; IMF, International Financial Statistics database; IMF, African Department database; World Bank, Ease of Doing Business Index; World Bank, Enterprise Survey; and World Bank, World Development Indicators.

^{Annex Table 4.1.1.}

Cross-Country Pooled Firm Data: Sub-Saharan Africa versus Developing Asia

			Sub-Saharan Africa
Variables	Source	Definition	Number	Mean	Median	Standard Deviation	Minimum	Maximum
Firm-Level Variables
Firm size	World Bank Enterprise Survey	No. of employees	23,000	58.1	13.3	392.7	1.0	45,000.0
Firm age	World Bank Enterprise Survey	Years after starting business	22,364	14.3	11.0	11.7	0.0	65.0
Ln(value added/ employee)	World Bank Enterprise Survey, World Economic Outlook	Log of sales minus labor and input costs (including electricity, raw materials and intermediate goods, and fuels) (in 2011 international $) divided by the number of employees	22,248	9.88	9.53	2.75	-7.23	24.41
Ln(land value, owned)	World Bank Enterprise Survey, World Economic Outlook	Net book values of land and buildings (in 2011 international $)	7,307	8.56	10.51	6.33	0.00	25.60
Ln(land value, owned or rented)	World Bank Enterprise Survey, World Economic Outlook	Log of annual expenditure on purchases, repurchases, and renting of land and building (in 2011 international $)	23,000	4.79	0.00	6.37	0.00	28.91
Percent owned land	World Bank Enterprise Survey	Percent of land owned by the firm	18,594	43.75	0	48.51	0	100
Ln(capital investment)	World Bank Enterprise Survey, World Economic Outlook	Purchase or repurchase of equipment (in 2011 international $)	23,000	5	8.69	6.30	0.00	28.92
Ln(manager experience)	World Bank Enterprise Survey	Chief executive officer’s work experience in the same sector (in years)	22,604	2.49	2.48	0.69	0.00	4.33
State ownership	World Bank Enterprise Survey	Dummy: owned by government	22,702	0.02	0	0.15	0	1
Foreign ownership	World Bank Enterprise Survey	Dummy: owned by private foreign individuals or companies	22,683	0.16	0.00	0.36	0.00	1.00
Have credit access	World Bank Enterprise Survey	Dummy: firm has a line of credit or loan	22,605	0.21	0	0.41	0	1
Heavy industry	World Bank Enterprise Survey	Sector dummy	23,000	0.14	0	0.35	0	1
Wholesale and retail	World Bank Enterprise Survey	Sector dummy	23,000	0.19	0	0.39	0	1
Country-Level Variables
GDP per capita growth	WDI	Percent (in real growth)	23,000	3.44	2.94	2.78	-3.39	12.53
Private credit/GDP	International Financial Statistics	Percentage	23,000	0.20	0.16	0.16	0.02	0.83
Judiciary efficiency	Doing Business	Distance to frontier (rescaled to 0–1; larger score is closer to frontier and thus more efficient)	23,000	0.53	0.53	0.10	0.26	0.67
Trade openness	World Development Indicators	(Export+ lmport)/GDP	23,000	23,000	0.66	0.64	0.23	0.28

			Developing Asia
Variables	Source	Definition	Number	Mean	Median	Standard Deviation	Minimum	Maximum
Firm-Level Variables
Firm size	World Bank Enterprise Survey	No. of employees	29,609	180.3	31.3	2,079.4	0.0	170,666.7
Firm age	World Bank Enterprise Survey	Years after starting business	29,115	17.1	14.0	11.5	0.0	65.0
Ln(value added/ employee)	World Bank Enterprise Survey, World Economic Outlook	Log of sales minus labor and input costs (including electricity, raw materials and intermediate goods, and fuels) (in 2011 international $) divided by the number of employees	27,272	9.46	9.56	2.17	-2.60	19.80
Ln(land value, owned)	World Bank Enterprise Survey, World Economic Outlook	Net book values of land and buildings (in 2011 international $)	13,802	11.20	12.73	4.90	0.00	26.93
Ln(land value, owned or rented)	World Bank Enterprise Survey, World Economic Outlook	Log of annual expenditure on purchases, repurchases, and renting of land and building (in 2011 international $)	29,665	5.43	0.00	6.53	0.00	26.01
Percent owned land	World Bank Enterprise Survey	Percent of land owned by the firm	22,587	62.32	100.00	47.36	0	100.00
Ln(capital investment)	World Bank Enterprise Survey, World Economic Outlook	Purchase or repurchase of equipment (in 2011 international $)	29,665	7.03	9.54	6.27	0.00	23.07
Ln(manager experience)	World Bank Enterprise Survey	Chief executive officer’s work experience in the same sector (in years)	28,432	2.60	2.71	0.65	0.00	4.26
State ownership	World Bank Enterprise Survey	Dummy: owned by government	29,598	0.01	0	0.11	0	1
Foreign ownership	World Bank Enterprise Survey	Dummy: owned by private foreign individuals or companies	29,600	0.07	0.00	0.26	0.00	1.00
Have credit access	World Bank Enterprise Survey	Dummy: firm has a line of credit or loan	26,827	0.33	0	0.47	0	1
Heavy industry	World Bank Enterprise Survey	Sector dummy	29,665	0.35	0	0.48	0	1
Wholesale and retail	World Bank Enterprise Survey	Sector dummy	29,665	0.16	0	0.37	0	1
Country-Level Variables
GDP per capita growth	WDI	Percent (in real growth)	29,665	5.19	5.16	1.71	2.01	11.65
Private credit/GDP	International Financial Statistics	Percentage	29,665	0.56	0.50	0.33	0.13	1.21
Judiciary efficiency	Doing Business	Distance to frontier (rescaled to 0–1; larger score is closer to frontier and thus more efficient)	22,575	0.43	0.33	0.16	0.27	0.72
Trade openness	World Development Indicators	(Export + lmport)/GDP	29,665	0.66	0.49	0.36	0.40	1.79

^{Annex Table 4.1.1.}

Cross-Country Pooled Firm Data: Sub-Saharan Africa versus Developing Asia

			Sub-Saharan Africa
Variables	Source	Definition	Number	Mean	Median	Standard Deviation	Minimum	Maximum
Firm-Level Variables
Firm size	World Bank Enterprise Survey	No. of employees	23,000	58.1	13.3	392.7	1.0	45,000.0
Firm age	World Bank Enterprise Survey	Years after starting business	22,364	14.3	11.0	11.7	0.0	65.0
Ln(value added/ employee)	World Bank Enterprise Survey, World Economic Outlook	Log of sales minus labor and input costs (including electricity, raw materials and intermediate goods, and fuels) (in 2011 international $) divided by the number of employees	22,248	9.88	9.53	2.75	-7.23	24.41
Ln(land value, owned)	World Bank Enterprise Survey, World Economic Outlook	Net book values of land and buildings (in 2011 international $)	7,307	8.56	10.51	6.33	0.00	25.60
Ln(land value, owned or rented)	World Bank Enterprise Survey, World Economic Outlook	Log of annual expenditure on purchases, repurchases, and renting of land and building (in 2011 international $)	23,000	4.79	0.00	6.37	0.00	28.91
Percent owned land	World Bank Enterprise Survey	Percent of land owned by the firm	18,594	43.75	0	48.51	0	100
Ln(capital investment)	World Bank Enterprise Survey, World Economic Outlook	Purchase or repurchase of equipment (in 2011 international $)	23,000	5	8.69	6.30	0.00	28.92
Ln(manager experience)	World Bank Enterprise Survey	Chief executive officer’s work experience in the same sector (in years)	22,604	2.49	2.48	0.69	0.00	4.33
State ownership	World Bank Enterprise Survey	Dummy: owned by government	22,702	0.02	0	0.15	0	1
Foreign ownership	World Bank Enterprise Survey	Dummy: owned by private foreign individuals or companies	22,683	0.16	0.00	0.36	0.00	1.00
Have credit access	World Bank Enterprise Survey	Dummy: firm has a line of credit or loan	22,605	0.21	0	0.41	0	1
Heavy industry	World Bank Enterprise Survey	Sector dummy	23,000	0.14	0	0.35	0	1
Wholesale and retail	World Bank Enterprise Survey	Sector dummy	23,000	0.19	0	0.39	0	1
Country-Level Variables
GDP per capita growth	WDI	Percent (in real growth)	23,000	3.44	2.94	2.78	-3.39	12.53
Private credit/GDP	International Financial Statistics	Percentage	23,000	0.20	0.16	0.16	0.02	0.83
Judiciary efficiency	Doing Business	Distance to frontier (rescaled to 0–1; larger score is closer to frontier and thus more efficient)	23,000	0.53	0.53	0.10	0.26	0.67
Trade openness	World Development Indicators	(Export+ lmport)/GDP	23,000	23,000	0.66	0.64	0.23	0.28

			Developing Asia
Variables	Source	Definition	Number	Mean	Median	Standard Deviation	Minimum	Maximum
Firm-Level Variables
Firm size	World Bank Enterprise Survey	No. of employees	29,609	180.3	31.3	2,079.4	0.0	170,666.7
Firm age	World Bank Enterprise Survey	Years after starting business	29,115	17.1	14.0	11.5	0.0	65.0
Ln(value added/ employee)	World Bank Enterprise Survey, World Economic Outlook	Log of sales minus labor and input costs (including electricity, raw materials and intermediate goods, and fuels) (in 2011 international $) divided by the number of employees	27,272	9.46	9.56	2.17	-2.60	19.80
Ln(land value, owned)	World Bank Enterprise Survey, World Economic Outlook	Net book values of land and buildings (in 2011 international $)	13,802	11.20	12.73	4.90	0.00	26.93
Ln(land value, owned or rented)	World Bank Enterprise Survey, World Economic Outlook	Log of annual expenditure on purchases, repurchases, and renting of land and building (in 2011 international $)	29,665	5.43	0.00	6.53	0.00	26.01
Percent owned land	World Bank Enterprise Survey	Percent of land owned by the firm	22,587	62.32	100.00	47.36	0	100.00
Ln(capital investment)	World Bank Enterprise Survey, World Economic Outlook	Purchase or repurchase of equipment (in 2011 international $)	29,665	7.03	9.54	6.27	0.00	23.07
Ln(manager experience)	World Bank Enterprise Survey	Chief executive officer’s work experience in the same sector (in years)	28,432	2.60	2.71	0.65	0.00	4.26
State ownership	World Bank Enterprise Survey	Dummy: owned by government	29,598	0.01	0	0.11	0	1
Foreign ownership	World Bank Enterprise Survey	Dummy: owned by private foreign individuals or companies	29,600	0.07	0.00	0.26	0.00	1.00
Have credit access	World Bank Enterprise Survey	Dummy: firm has a line of credit or loan	26,827	0.33	0	0.47	0	1
Heavy industry	World Bank Enterprise Survey	Sector dummy	29,665	0.35	0	0.48	0	1
Wholesale and retail	World Bank Enterprise Survey	Sector dummy	29,665	0.16	0	0.37	0	1
Country-Level Variables
GDP per capita growth	WDI	Percent (in real growth)	29,665	5.19	5.16	1.71	2.01	11.65
Private credit/GDP	International Financial Statistics	Percentage	29,665	0.56	0.50	0.33	0.13	1.21
Judiciary efficiency	Doing Business	Distance to frontier (rescaled to 0–1; larger score is closer to frontier and thus more efficient)	22,575	0.43	0.33	0.16	0.27	0.72
Trade openness	World Development Indicators	(Export + lmport)/GDP	29,665	0.66	0.49	0.36	0.40	1.79

Annex 4.2. Country Sample

Sub-Saharan Africa (40 Countries)

Sub-Saharan African countries include Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Republic of Congo, Côte d’Ivoire, Eritrea, Eswatini, Ethiopia, Gabon, The Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, and Zimbabwe.

Developing Asia (14 Countries)

Developing Asia includes Bangladesh, Cambodia, China, India, Indonesia, Lao P.D.R., Malaysia, Mongolia, Myanmar, Nepal, Philippines, Sri Lanka, Thailand, and Vietnam.

Annex 4.3. Spatial Distribution of Firm Performance in Sub-Saharan Africa and Developing Asia

Annex 4.4. Spatial Distribution of Factor Allocative Efficiency in Sub-Saharan Africa, Developing Asia, and Nigeria

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The previous literature shows a negative effect of minimum wage hikes on firm size in many Asian countries. See Neumark and Corella (2019) for a comprehensive survey and Nose (2020a) for a detailed study on Vietnam.

See Nose (2020b) for theoretical details (https://sites.google.com/site/econnose/).

Because the World Bank Enterprise Survey covers formal firms, labor regulations such as social insurance benefits largely determine factor allocative efficiency. A comprehensive analysis using each country’s census data is needed to represent small firms.

The World Bank Enterprise Survey asks firms’ perceptions on the severity of corruption in their business. Based on this measure, the proportion of firms facing corruption for each district is computed. Each district is categorized into the weak state-capacity group when the district’s corruption level is above the regional average corruption level.

Laeven and Woodruf (2007) show that in Mexico, improvement in the quality of the legal system supports firm growth by reducing the business risk faced by firm owners.

The market-clearing wage is defined as the employees’ average monthly earnings from the ILOSTAT. For countries where ILOSTAT does not provide data, mean wage data from Table 1 of Bhorat, Kan- bur, and Stanwix (2017) are used.

The effect of land value on firm size becomes insignificant for rented land, because it cannot serve as collateral for borrowing.

Central Intelligence Agency, “World Factbook,” https://www.cia.gov/library/publications/the-world-factbook/.

Foresee

^{Annex Table 4.1.1.}

Cross-Country Pooled Firm Data: Sub-Saharan Africa versus Developing Asia

			Sub-Saharan Africa
Variables	Source	Definition	Number	Mean	Median	Standard Deviation	Minimum	Maximum
Firm-Level Variables
Firm size	World Bank Enterprise Survey	No. of employees	23,000	58.1	13.3	392.7	1.0	45,000.0
Firm age	World Bank Enterprise Survey	Years after starting business	22,364	14.3	11.0	11.7	0.0	65.0
Ln(value added/ employee)	World Bank Enterprise Survey, World Economic Outlook	Log of sales minus labor and input costs (including electricity, raw materials and intermediate goods, and fuels) (in 2011 international $) divided by the number of employees	22,248	9.88	9.53	2.75	-7.23	24.41
Ln(land value, owned)	World Bank Enterprise Survey, World Economic Outlook	Net book values of land and buildings (in 2011 international $)	7,307	8.56	10.51	6.33	0.00	25.60
Ln(land value, owned or rented)	World Bank Enterprise Survey, World Economic Outlook	Log of annual expenditure on purchases, repurchases, and renting of land and building (in 2011 international $)	23,000	4.79	0.00	6.37	0.00	28.91
Percent owned land	World Bank Enterprise Survey	Percent of land owned by the firm	18,594	43.75	0	48.51	0	100
Ln(capital investment)	World Bank Enterprise Survey, World Economic Outlook	Purchase or repurchase of equipment (in 2011 international $)	23,000	5	8.69	6.30	0.00	28.92
Ln(manager experience)	World Bank Enterprise Survey	Chief executive officer’s work experience in the same sector (in years)	22,604	2.49	2.48	0.69	0.00	4.33
State ownership	World Bank Enterprise Survey	Dummy: owned by government	22,702	0.02	0	0.15	0	1
Foreign ownership	World Bank Enterprise Survey	Dummy: owned by private foreign individuals or companies	22,683	0.16	0.00	0.36	0.00	1.00
Have credit access	World Bank Enterprise Survey	Dummy: firm has a line of credit or loan	22,605	0.21	0	0.41	0	1
Heavy industry	World Bank Enterprise Survey	Sector dummy	23,000	0.14	0	0.35	0	1
Wholesale and retail	World Bank Enterprise Survey	Sector dummy	23,000	0.19	0	0.39	0	1
Country-Level Variables
GDP per capita growth	WDI	Percent (in real growth)	23,000	3.44	2.94	2.78	-3.39	12.53
Private credit/GDP	International Financial Statistics	Percentage	23,000	0.20	0.16	0.16	0.02	0.83
Judiciary efficiency	Doing Business	Distance to frontier (rescaled to 0–1; larger score is closer to frontier and thus more efficient)	23,000	0.53	0.53	0.10	0.26	0.67
Trade openness	World Development Indicators	(Export+ lmport)/GDP	23,000	23,000	0.66	0.64	0.23	0.28

			Developing Asia
Variables	Source	Definition	Number	Mean	Median	Standard Deviation	Minimum	Maximum
Firm-Level Variables
Firm size	World Bank Enterprise Survey	No. of employees	29,609	180.3	31.3	2,079.4	0.0	170,666.7
Firm age	World Bank Enterprise Survey	Years after starting business	29,115	17.1	14.0	11.5	0.0	65.0
Ln(value added/ employee)	World Bank Enterprise Survey, World Economic Outlook	Log of sales minus labor and input costs (including electricity, raw materials and intermediate goods, and fuels) (in 2011 international $) divided by the number of employees	27,272	9.46	9.56	2.17	-2.60	19.80
Ln(land value, owned)	World Bank Enterprise Survey, World Economic Outlook	Net book values of land and buildings (in 2011 international $)	13,802	11.20	12.73	4.90	0.00	26.93
Ln(land value, owned or rented)	World Bank Enterprise Survey, World Economic Outlook	Log of annual expenditure on purchases, repurchases, and renting of land and building (in 2011 international $)	29,665	5.43	0.00	6.53	0.00	26.01
Percent owned land	World Bank Enterprise Survey	Percent of land owned by the firm	22,587	62.32	100.00	47.36	0	100.00
Ln(capital investment)	World Bank Enterprise Survey, World Economic Outlook	Purchase or repurchase of equipment (in 2011 international $)	29,665	7.03	9.54	6.27	0.00	23.07
Ln(manager experience)	World Bank Enterprise Survey	Chief executive officer’s work experience in the same sector (in years)	28,432	2.60	2.71	0.65	0.00	4.26
State ownership	World Bank Enterprise Survey	Dummy: owned by government	29,598	0.01	0	0.11	0	1
Foreign ownership	World Bank Enterprise Survey	Dummy: owned by private foreign individuals or companies	29,600	0.07	0.00	0.26	0.00	1.00
Have credit access	World Bank Enterprise Survey	Dummy: firm has a line of credit or loan	26,827	0.33	0	0.47	0	1
Heavy industry	World Bank Enterprise Survey	Sector dummy	29,665	0.35	0	0.48	0	1
Wholesale and retail	World Bank Enterprise Survey	Sector dummy	29,665	0.16	0	0.37	0	1
Country-Level Variables
GDP per capita growth	WDI	Percent (in real growth)	29,665	5.19	5.16	1.71	2.01	11.65
Private credit/GDP	International Financial Statistics	Percentage	29,665	0.56	0.50	0.33	0.13	1.21
Judiciary efficiency	Doing Business	Distance to frontier (rescaled to 0–1; larger score is closer to frontier and thus more efficient)	22,575	0.43	0.33	0.16	0.27	0.72
Trade openness	World Development Indicators	(Export + lmport)/GDP	29,665	0.66	0.49	0.36	0.40	1.79

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Abstract

Introduction

Stylized Facts

Firm Size, Age, and Productivity

Land and Labor Markets

Minimum Wage and Firm Productivity

Theoretical Framework

Data

Empirical Analysis

Aggregate-Level Analysis

Degree of Factor Misallocations

Determinants of Factor Allocative Efficiency

Effect of Land Market and Regulations on Firm Size

Effect of Factor Allocative Efficiency on Firm Size and Survival

Instrumental Variable Results: Heterogeneity in Effects of Factor Allocative Efficiency on Firm Performance

Effect of Factor Allocations on Credit Access and Tax Contributions

Panel Data Analysis: Case Study for Nigeria

Conclusions

Annex 4.1. Summary Statistics

Annex 4.2. Country Sample

Sub-Saharan Africa (40 Countries)

Developing Asia (14 Countries)

Annex 4.3. Spatial Distribution of Firm Performance in Sub-Saharan Africa and Developing Asia

Annex 4.4. Spatial Distribution of Factor Allocative Efficiency in Sub-Saharan Africa, Developing Asia, and Nigeria

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