1 Introduction
Latin American countries have shown slow economic growth and low productivity in recent decades. In this context, it is important to study productivity to implement policies to boost growth and to improve economic conditions (Busso et al.
2013).
Latin America and the Caribbean (LAC) countries stagnated in per capita income compared to the USA in the last 50 years (Pagés
2010). The low economic performance can be explained to a large extent by the slow growth of productivity in the region according to multiple studies (Crespi et al.
2014; Daude and Fernández-Arias
2010; Pagés
2010).
GDP per capita growth between 1960 and 2011 was sustained by the accumulation of factors, not by the growth of aggregate TFP (Grazzi and Pietrobelli
2016). In fact, while US aggregate TFP grew by 1.2%, the TFP of the region remained stagnant in such period. The productivity of LAC compared to that of the USA decreased from 73% in 1960 to 51% in 2013. This generated a productivity gap between LAC and the USA (Grazzi and Pietrobelli
2016).
Conventional investigations such as Howitt (
2000) and Klenow and Rodríguez-Clare (
2005) argue that increases in productivity are generally obtained by improvements in the production processes, machinery and/or products due to the firms’ investments in human capital and in research and development (R & D). Slow growth of TFP was considered to be caused by barriers that prevent the diffusion and implementation of new technologies.
However, recent studies by Banerjee and Duflo (
2005), Restuccia and Rogerson (
2008) and Hsieh and Klenow (
2009) argue that the slow growth of TFP may also be a consequence of policies and market failures. These determine the existence of the firms and the allocation of resources among them. When the allocation of resources is inefficient, the aggregate TFP is affected because productive firms are smaller than they would be in an economy with adequate allocation of resources.
Developing countries are characterized by large differences in productivity between formal and informal firms, which indicates the existence of inefficiencies in the allocation of resources (Busso et al.
2013). The misallocation of resources arises when the most productive firms have insufficient resources to increase their production, while the less productive firms continue to use a certain amount of resources instead of reducing their use of capital and labor, and eventually stop operating (Busso et al.
2013). Therefore, the correction of inefficiencies between formal and informal businesses is a determining factor to increase the productivity of an economy and, at the same time, boost its growth.
In this paper, we use micro-data on manufacturing establishments for the Ecuadorian manufacturing sector from 2002 to 2015 to determine whether there are differences in productivity between firms, and an inefficient allocation of resources between them. We use the model proposed by Hsieh and Klenow (
2009) to calculate the differences in productivity and the potential gains of the reallocation of resources in the presence of distortions. We find informal firms have lower physical productivity than formal firms, and formal firms have a restricted growth due to the distortions
1 they face. These results indicate the existence of an inefficient allocation of resources among firms. By reallocating resources to equalize marginal productivity in the manufacturing sector, gains of 80% in 2002 and 69% in 2015 would be obtained.
Informality is a characteristic that underdeveloped and developing countries present and can cause further economic retardation, taking into consideration that the misallocation of resources has a negative impact on productivity. Loayza et al. (
2009) find that there is a large heterogeneity in the extent of informality in Latin America. However, informality rates are higher than in the USA, and some countries of the region are among the countries with the higher informality rate in the world. They find that the typical country in Latin America produces about 40% of GDP and employs 70% of the labor force informally.
Enste and Schneider (
2000) attribute the existence of high taxes and social security contributions combined with a high density and intensity of regulations in the official economy (especially on labor markets) as one of the most important causes of a high level of informality. Loayza (
1999) shows that informality in Latin America and Caribbean countries is positively associated with levels of taxation and labor market regulations and negatively correlated with the strength and efficiency of government institutions. Ulyssea (
2010) explains that a common feature of most Latin American countries is the restrictive labor market institutions and strict regulation of entry. In consequence, operating formally can be extremely costly for a firm and reduce the incentives for firms to operate formally; that is why Latin American countries face high informality rates.
Enste and Schneider (
2003) find that the informal sector represents more than a third of the global output of developing countries and around 10 to 20% of global output in developed countries. The ILO
2002 find similar results: 51% of non-agricultural employment in Latin America is informal, 48% in North Africa, 65% in Asia and 72% in Sub-Saharan Africa. Benjamin and Mbaye (
2014) explain that the urban informal sector tends to absorb the rural workforce and that the informal sector has a strong female presence (60% of female workers in the developing world are in the informal sector).
Informal employment, which refers primarily to employment in enterprises that lack registration and social security coverage for their employees (OECD
2009), is high in Latin America. Gasparini and Tornarolli (
2009) identify that the informal labor workforce is characterized by being unskilled and operating in low-productivity jobs, in marginal, small-scale and usually in family-based activities.
Informality is also a characteristic present at firm level in Latin American countries. Maloney (
2004) explain that small-scale, semi-legal, often low-productivity, frequently family-based enterprises employ between 30 and 70% of the urban workforce in the region. He argues that small firms face higher costs, are likely to be informal and have high failure rates.
In Ecuador, 67% of firms indicate to face competition with informal firms which is higher than in Latin America (63%) and higher than in upper middle-income countries (51%) according to the World Bank (2017). These figures show that informal firms have an unfair advantage over formal firms in the country because they can avoid costs form complying with regulations. Also, 88% of Ecuadorian firms affirm being registered when starting operations, which is slightly higher than in the region (87%) and lower than in upper middle-income countries (91%).
Having this background, we characterize the Ecuadorian informal firms for the manufacturing sector in the period analyzed. We find that 20% of the firms in the Amazon are informal, 27% in the Andes, and 32% in the Coastline. We also find that from the main subsectors of manufacturing, the leather manufacturing subsector has the highest rate of informality (40%), followed by print and reproduction of recordings subsector (35%). On the other hand, we find that 32% of the small firms, 25% of medium A firms, 24% of medium B firms and 24% of large firms are informal.
4 Empirical method
Hsieh and Klenow (
2009) propose a model of monopolistic competition with heterogeneous firms facing distortions in the prices they observe. The misallocation of resources is caused by the distortions that produce differences in the marginal productivity of capital and labor, affecting the aggregate factor productivity (Hsieh and Klenow
2009). In the model,
Y is a final good of a set of goods
\(Y_{s}\) produced by a representative firm in a perfectly competitive market, with constant returns to scale (
\(\sum Y_{s} = 1\)) and a Cobb–Douglas production function:
$$Y = \mathop \prod \limits_{s = 1}^{S} Y_{s}^{{\theta_{s} }}$$
(1)
where the output of each sector
\(Y_{s}\) is produced by combining
\(M_{s}\) differentiated goods, produced by the individual firms using a CES technology. It is assumed that the elasticity of substitution is same for all industries:
$$Y_{s} = \left[ {\mathop \sum \limits_{i = 1}^{{M_{s} }} Y_{si}^{{\frac{\sigma - 1}{\sigma }}} } \right]^{{\frac{\sigma - 1}{\sigma }}}$$
(2)
In the model, the production function for each differentiated product is given by a Cobb–Douglas production function, consisting of productivity (A), and inputs of capital (K) and work (L):
$$Y_{si} = A_{si} K_{si}^{{\alpha_{s} }} L_{si}^{{1 - \alpha_{s} }}$$
(3)
where s is sector, i is firm,
\(\alpha_{s}\) is the capital share
2, and labor share is 1 −
\(\alpha_{s}\). The individual return of a firm is expressed as follows:
$$\pi_{si} = \left( {1 - \tau_{Ysi} } \right)P_{si} Y_{si} - wL_{si} - \left( {1 + \tau_{Ksi} } \right)RK_{si}$$
(4)
where w refers to wages and R denotes the cost of capital. Two types of distortions affect the decisions of firms: output distortions (
\(\tau_{Ysi}\)) and capital distortions (
\(\tau_{Ksi}\)). The former by affecting production affects both capital and labor. These include high transport costs, bribes, operating costs of firms, costs of government restrictions due to the firms’ size, among others. On the other hand, capital distortions increase the cost of capital. These include credit restrictions, credit conditions which differ between firms (credit history, evasion patterns), among others.
$$1 + \tau_{Ksi} = \frac{{\alpha_{s} }}{{1 - \alpha_{s} }}\frac{{wL_{si} }}{{RK_{si} }}$$
(5)
$$1 - \tau_{Ysi} = \frac{\sigma }{\sigma - 1}\frac{{RK_{si} }}{{\alpha_{s} P_{si} Y_{si} }}$$
(6)
In the presence of distortions, the marginal productivity of labor and capital is determined as:
$${\text{MRPK}}_{si} = \frac{{R \left( {1 + \tau_{Ksi} } \right)}}{{1 - \tau_{Ysi} }}$$
(7)
$${\text{MRPL}}_{si} = \frac{w }{{1 - \tau_{Ysi} }}$$
(8)
There are two types of productivity: physical productivity (
\({\text{TFPQ}})\) measured by
\(A_{si}\), and revenue productivity (
\({\text{TFPR}}\)) measured by
\(P_{si} Y_{si}\). In the absence of distortions, more resources would be allocated to firms with greater physical productivity. That is, in the absence of distortions, TFPR should be same among all firms in the same industry or sector. Deviations from this benchmark determine the magnitude of the distortions, which are measured through the dispersion of TFPR. TFPR is determined as:
$${\text{TFPR}}_{si} \overset{\wedge}{=}P_{si} A_{si} = \frac{{P_{si} Y_{si} }}{{K_{si}^{{\alpha_{s} }} L_{si}^{{1 - \alpha_{s} }} }}$$
(9)
TFPR can be expressed as the geometric mean of the marginal productivity of labor and capital:
$${\text{TFPR}}_{si} \propto \left( {{\text{MRPK}}_{si} } \right)^{{\alpha_{s} }} \left( {{\text{MRPL}}_{si} } \right)^{{1 - \alpha_{s} }} \propto \frac{{\left( {1 + \tau_{Ksi} } \right)^{{1 - \alpha_{s} }} }}{{\left( {1 - \tau_{Ysi} } \right)}}$$
(10)
A high TFPR means firms face barriers that increase products marginal of capital and work of the plant, making the plant smaller than optimal. Similarly, the aggregate TFPQ should be high in the absence of distortions, which would imply a reallocation of resources from the least productive firms to the most productive firms. However, with the presence of inefficiencies in the allocation of resources, there will be some dispersion in the distribution of the firms’ physical productivities. TFPQ is determined as:
$${\text{TFPQ}}_{si} \overset{\wedge}{=}A_{si} = \frac{{Y_{si} }}{{K_{si}^{{\alpha_{s} }} \left( {L_{si} } \right)^{{1 - \alpha_{s} }} }}$$
(11)
To assess productivity gaps, the efficient level of TFP is calculated to compare it with the actual level of TFP. If the marginal products of capital and labor of all the firms in a given sector are equalized, the TFP of the industry (also called the TFPQ* because it is the geometric average of
\(A_{si}\)) would be:
$$\bar{A}_{s} = {\text{TFPQ}}^{*} = \left( {\mathop \sum \limits_{i = 1}^{Ms} A_{si}^{\sigma - 1} } \right)^{{\frac{1}{\sigma - 1}}}$$
(12)
For each industry, the TFP ratio is calculated with its efficient level, and it is added for all industries using the Cobb–Douglas method:
$$\frac{Y}{{Y_{\text{efficient}} }} = \mathop \prod \limits_{s = 1}^{S} \left[ {\mathop \sum \limits_{i = 1}^{Ms} \left( {\frac{{A_{si} }}{{\bar{A}_{s} }} \frac{{\overline{\text{TFPR}}_{s} }}{{{\text{TFPR}}_{si} }}} \right)^{\sigma - 1} } \right]^{{\frac{{\theta_{s} }}{{\left( {\sigma - 1} \right)}}}}$$
(13)
This expression allows estimating counterfactual productivity gains in the absence of distortions, where:
$${\text{TFPR}}_{si} = \frac{\sigma }{1 - \sigma }\left( {\frac{{{\text{MRPK}}_{si} }}{{\alpha_{s} }}} \right)^{{\alpha_{s} }} \left( {\frac{{{\text{MRPL}}_{si} }}{{1 - \alpha_{s} }}} \right)^{{1 - \alpha_{s} }} = \frac{\sigma }{1 - \sigma }\left( {\frac{R}{{\alpha_{s} }}} \right)^{{\alpha_{s} }} \left( {\frac{w}{{1 - \alpha_{s} }}} \right)^{{1 - \alpha_{s} }} \frac{{\left( {1 + \tau_{Ksi} } \right)^{{\alpha_{s} }} }}{{\left( {1 - \tau_{Ysi} } \right)}}$$
(14)
$$\overline{\text{TFPR}}_{s} = \frac{\sigma }{1 - \sigma }\left( {\frac{{\overline{\text{MRPK}}_{s} }}{{\alpha_{s} }}} \right)^{{\alpha_{s} }} \left( {\frac{{\overline{\text{MRPL}}_{s} }}{{1 - \alpha_{s} }}} \right)^{{1 - \alpha_{s} }} = \frac{\sigma }{1 - \sigma }\left[ {\frac{R}{{\left( {\alpha_{s} \mathop \sum \nolimits_{i = 1}^{{M_{s} }} \frac{{1 - \tau_{Ysi} }}{{1 + \tau_{Ksi} }} \frac{{P_{si} Y_{si} }}{{P_{s} Y_{s} }}} \right)}}} \right]^{{\alpha_{s} }} \left[ {\frac{w}{{\left( {1 - \alpha_{s} } \right)\mathop \sum \nolimits_{i = 1}^{{M_{s} }} \left( {1 - \tau_{Ysi} } \right)\frac{{P_{si} Y_{si} }}{{P_{s} Y_{s} }}}}} \right]^{{1 - \alpha_{s} }}$$
(15)
The gains from reallocation of factors are given by:
$$\left( {\frac{{Y_{efficient} }}{Y}} \right) - 1$$
(16)
5 Data
The micro-data used on Ecuadorian manufacturing establishments range from 2002 to 2015.
3 We use the data from the “Encuestas de Manufactura y Minería” (Manufacturing and Mining Surveys
4), conducted by the Instituto Nacional de Estadística y Censos (INEC). These data cover a representative sample of firms with 10 or more employees each year, and the collection unit is the firm. The variables used are International Standard Industrial Classification of All Economic Activities (three-digit ISIC), labor compensations, gross production by the establishment and capital stock. Specifically, employees’ compensations consist of wages, salaries and benefits (made up of other remunerations, bonuses, profit sharing, family allowance, employer’s contribution to social security and contribution to the reserve fund). The stock of capital refers to the historical value of fixed assets and their corresponding accumulated revaluation value.
We use the US capital and labor shares as a benchmark because their economy is considered to be comparatively undistorted. To obtain the US labor share,
5 we use the NBER-CES Manufacturing Industry database presented by the National Bureau of Economic Research (NBER) and the Center for Economic Studies (CES). Labor share is used as an annual average. For the years 2012–2015 where data are not available, we calculate the average growth
6 of labor, share is calculated and we project the values.
Once the productivity (both physical and revenue) of the firms has been calculated, we trim the 1% tails of plant productivity and distortions to make the results robust to outliers.
5.1 Parameters
To calculate the effects of resource misallocation will establish our key parameters. The capital rental price (R) is set at 10% (without considering the possible distortions), 5% real interest rate and 5% depreciation rate, according to the model proposed by Hsieh and Klenow (
2009). The true cost of capital is
\(\left( {1 + \tau_{Ksi} } \right)R\), where
\(\tau_{Ksi}\) are the capital distortions. In case these differ from 0, the cost of capital would be different from the 10% established. However, we must consider efficiency gains due to the reallocation of resources do not depend on R. Therefore, incorrect values for R only affect the average capital distortions, but not the reallocation gains. Because our hypothetical reforms collapse
\(\tau_{Ksi}\) to their average in each industry, potential efficiency gains do not depend on R. If R has been incorrectly established, it would only affect the average capital distortions but not the liberalization experiment. The elasticity of substitution is established at
\(\sigma = 3\), according to the model proposed by Hsieh and Klenow (
2009).
7
In the NBER and CES databases, labor compensations omit the additional benefits and social security contributions made by the employer. According to the Income and National Products Accounts, the labor share presented in the database is 2/3 of the total labor share, which includes other forms of compensation apart from the employees’ salary. So, we multiply the labor share of the NBER and CES databases by 3/2 to obtain the elasticity of the labor that will be used for the calculations. The capital share is established as 1 minus the US labor share.
5.2 Model assumptions
-
The aggregate labor and capital shares in each industry are not affected by the magnitude of the misallocation when the average marginal revenue products remain constant. This property is due to the Cobb–Douglas aggregator (unit elastic demand
8).
-
We consider an aggregate stock of fixed capital.
9
-
We assume that the number of firms in each industry is not affected by the scope of the inefficient allocation of resources.
10
7 Discussion: misallocation and policies
These results indicate formal firms are more productive than informal firms in the Ecuadorian manufacturing sector. Formal firms also face greater distortions than informal firms. Differences in basic conditions in which firms exist are access to credits and financial markets, taxes and obligations, social policy and labor obligations. These differences result in distinct productivity levels between firms. These elements generate distortions that prevent an efficient allocation of resources between firms.
One of the most obvious causes of resource misallocation is the difference in accessibility to credit and financial markets. The difficulty to access loans prevents productive firms from expanding, prevents less productive firms to access technological improvements and/or the necessary investments that would allow them to increase their productivity (Pagés
2010). This usually happens with small or informal firms, which have difficulty accessing the financial market because they do not have a credit history or the necessary guarantees to obtain a loan (Pagés
2010).
The difficulty to access financial markets reduces the incentives of informal firms to comply with regulations (related to taxes or to labor obligations) (Eslava et al.
2010). This allows certain informal firms with lower productivity to survive because they face lower costs compared to their formal counterparts (Eslava et al.
2010). In this sense, the expansion of credit and access to financial markets would represent a contribution to the formalization of firms (Pagés
2010).
On the other hand, the expansion of credit is beneficial only if it is correctly formulated and directed (Arizala et al.
2009). However, a higher credit supply does not necessarily have a positive impact on productivity (Pagés
2010). Inefficiency in the allocation of capital and labor can be accentuated if the loans are directed toward firms with low levels of productivity (loans that may have been made by national development banks or subsidies to public credit) (Pagés
2010) For this reason, it is essential for loans to be channeled toward more productive firms or with a high productive potential (Pagés
2010). The distinction is complicated but crucial to correct the inefficient allocation of resources between firms (Arizala et al.
2009).
The second suspect in resource misallocation is the tax regime. One of the ways in which the structure of tax systems affects productivity is through the tax distinction between firms and industries (Pagés
2010). Certain industries and firms enjoy greater tax deductions or they evade tax payment, causing them to grow in greater quantity independently of their productivity, and that the allocation of productive resources is distorted (Pagés
2010).
Simplified regimes and tax exemption for small businesses also affect productivity because certain firms avoid growth not to exceed the limit that allows them to benefit from the tax regime for small-sized firms. This represents a growth constraint growth for firms (Chong and Pagés 2010). According to Pagés (
2010), simplified tax regimes represent discrimination due to the firm’s size, greater ease of evading tax obligations, lesser control across companies and limited information to control the payment of taxes All these elements would represent an advantage to unproductive and/or informal firms.
A tax regime in which the most productive firms face higher taxes avoids the efficient allocation of resources between firms. High taxes and inefficient control induce firms to evade their tax obligations (Chong and Pagés 2010). So, certain firms survive despite their low productivity, and the growth of high-productivity firms is restricted (Chong and Pagés 2010). It works in the same way for formal and informal firms. Formal firms face greater costs (taxes), while informal firms evade these costs.
The existence of incorrectly structured tax regimes affects the productivity of firms because it promotes the survival of unproductive firms and obstructs the growth of large and small firms. It also promotes inequality and segmentation between firms in terms of tributary obligations (Chong and Pagés 2010). Tax regimes differentiated by size, industry or based on other firms’ characteristics generate distortions in the allocation of resources. These taxes also represent an additional burden for the public administration and, in turn, reduce the tax revenue (Chong and Pagés 2010). A properly structured tax regime is necessary to reduce evasion and to generate incentives for the payment of taxes. Simplification, unification and compliance with tax provisions could generate productivity gains (Pagés
2010).
Poor enforcement and incomplete coverage of social security systems is another tax downfall. The coverage of social security for employees and other labor obligations (family allowance, contribution to the reserve fund, among others) in some cases represents constraints due to the high cost it represents for employers and in many cases, also for the employees given the little use they give to this type of programs (Pagés
2010).
Some firms choose to evade the payment of social security for their employees and other labor obligations to evade the costs it represents (Kapteyn et al.
2005). This is generally related to the size and formality of the firms. Small firms and informal firms have greater incentives to evade these obligations and to incur in lower labor costs (Pagés
2010). This behavior affects the correct allocation of resources because it represents a cost for the most productive firms and a subsidy for the least productive ones (Pagés
2010).
Once we have studied the three possible drivers of misallocation, it is important to determine which of those is the main resource of misallocation in Ecuador´s manufacturing sector. Busso et al. (
2013) suggest that the relationship between misallocation and firm size is useful to identify the source of misallocation. If small firms face growth constraints, this would imply misallocation of resources is due to limited access to credit and financial market failures. In this case, the average returns to an additional unit of capital or labor would be higher in small firms than in larger ones.
On the other hand, if misallocation is due to unequal enforcement of taxes, social security regulations or labor contributions, then the returns to additional factors would be expected to be lower in smaller firms. This happens mainly because non-complying firms are usually small firms. Evasion of tax or labor obligations works as a subsidy, as it helps these firms to avoid costs. Therefore, evasion helps them expand more than they would have in case they complied with all the regulations. This lowers the marginal returns of factors relative to compliant firms.
To study the relationship between marginal products of factors and firm size, we used log (\({\text{TFPR}}_{si} /\overline{\text{TFPR}}_{s}\)) on firms’ size dummies. We established the firm’s size based on the personnel employed, and the intervals were defined based on resolution 1260 of the CAN. The control group is small firms (10–49 employees).
Table
7 shows the relationship between TFPR and the size of the firms, which indicates the returns of an extra unit of capital and labor decrease with the firm’s size. Following the heuristic interpretation of the TFPR differences presented above, the results indicate that a dollar of capital and labor allocated to small firms is worth 8.7% more than for medium-sized A firms, 10% more than for medium-sized B firms and 19% more than for large firms.
Table 7
Log regression (\({\text{TFPR}}_{si} /\overline{\text{TFPR}}_{s}\)) on size
Source: Author’s calculations using the INEC Manufacturing Surveys
Median A | − 0.087 |
| [0.016]*** |
Median B | − 0.100 |
| [0.018]*** |
Large | − 0.198 |
| [0.016]*** |
Constant | 0.263 |
| [0.009]*** |
Observations | 16,517 |
R-squared | 0.008 |
The results show that the returns to an extra unit of capital and labor tend to decline with firm’s size, which indicates that smaller firms tend to have size constraints, while larger firms appear to be subsidized. This could imply small firms have greater difficulties in accessing financing for not having a credit history or the necessary guarantees to obtain credit. Also, it could mean small firms have difficulties in compensating for the lack of credit, with evasion of taxes and labor regulations. The results indicate that in the Ecuadorian manufacturing sector, credit market constraints seem to be the most likely source of misallocation.
The literature identifies two underlying factors that cause the misallocation of resources: policy distortions and market imperfections. Policy distortions can prevent an efficient allocation of resources at levy plant-level taxes or subsidies to output or the use of production factors such as capital and labor. Idiosyncratic distortions can be generated by multiple policies which create heterogeneity in the benefits or costs faced by individual producers.
In this context, Restuccia and Rogerson (
2017) identify that misallocation may be related to policy distortions that reflect statutory provisions such as tax code and regulations. These include labor, land and market regulations, as well as differentiated regulations which are applied according to the firms’ characteristics. They also identify discretionary provisions as a source of misallocation. These regulations formulated by public or private institutions favor or penalize specific firms through tax breaks, subsidies, financial credit with interest rates, selective enforcement of the law, preferential market access, between others.
On the other hand, market imperfections such as market frictions, monopoly power and enforcement of property rights can cause misallocation. The literature (see, among others, Buera and Shin
2013, and Caselli and Gennaioli
2013) has widely related misallocation of resources to a specific market imperfection: financial frictions in the capital market. Wu (
2018) argues that financial frictions may reduce total factor productivity through two channels: preventing entry of productive firms and misallocating capital among the existing firms.
According to Busso et al. (
2012b), if distortions are due to financial market failures, then many small firms would have difficulty growing because of the lack of credit or difficulty of access. If this is the case, then on average returns to additional factors would be higher in small firms than in larger ones. On the contrary, if misallocation is due to unequal enforcement of taxes, social security contributions or labor regulations, then the returns of additional capital and labor would be expected to be lower in smaller firms mainly because non-compliant firms are usually small and use regulatory evasion as a subsidy to expand (Busso et al.
2012b).
We found that the returns to an extra unit of capital and labor tend to decline with the firm’s size. Following Busso et al. (
2012b), this implies that financial market failures seem a more likely source of distortions in Ecuador’s manufacturing sector. This implies that small firms are having trouble in accessing credit which would allow them to expand. The lack of credit history or insufficient guarantees prevents firms from acquiring credit, even if those firms are highly productive. This is especially true for small Ecuadorian manufacturing firms, which struggle to access credit because of their lack of signaling.
Although we have assessed financial market failures as one of the main causes of misallocation in the manufacturing sector by taking into consideration the returns of additional factors according to the firms, it is important to take into consideration that the causes of misallocation are a broad theme that keeps being studied by researchers. In fact, there are two main approaches that the literature has followed in its attempt to find the main causes of misallocation: the direct approach and the indirect approach. Restuccia and Rogerson (
2013) recognize there is still much work to be done, some of which faces serious challenges in assessing the potential role for misallocation and the specific channels through which misallocation occurs.
Studies that focus on specific causes of misallocation have not been done yet for Ecuador. In this sense, it is important to take into consideration that even though in this paper we made a first approach of the subject, a lot of researches are still necessary to assess the causes of misallocation in the country.
8 Conclusion
We used the method proposed by Hsieh and Klenow (
2009) to establish the relationship between informality and productivity between formal and informal firms of the Ecuadorian manufacturing sector. Informality was calculated according to the fulfillment of social security obligations.
We found differences in both physical and revenue productivity among firms in the manufacturing sector. According to our study, firms in the 90th percentile of productivity are approximately 300 times more productive than firms in the 10th percentile. In addition, we found differences in revenue productivity. These results indicate the inefficiency in the allocation of resources among firms due to the existence of distortions. Thus, a dollar of labor and capital assigned to firms in the 90th percentile is worth approximately twice as much as the same dollar assigned to firms in the 10th percentile.
In order to verify the existence of distortions in the manufacturing sector, we calculated the dispersion of capital and output distortions that affect the firms´ productive processes. The existence of both distortions showed resources are not allocated efficiently. This indicates it is possible to obtain productivity gains for manufacturing firms.
Based on these results, we calculated the potential productivity gains in the absence of distortions, in a situation of efficient resource allocation. We used the model of Hsieh and Klenow (
2009) to quantify the gains from the efficient allocation of resources within the manufacturing sector for the 3-digit ISIC industries. We found that productivity gains might have been around 80% in 2002 and 69% in 2015.
We analyzed the relationship between productivity and informality, considering the main element of study associated with informality as the differential of productivity between firms according to their situation of formality. We found that completely informal firms are on average 42% less productive than formal firms and that semi-formal firms are on average 7% less productive than formal firms. In addition, the results indicate that a dollar of capital and labor allocated to formal firms is worth 30% more than for completely informal firms and 7% more than for semi-formal firms.
These results suggest that informality represents a high loss for productivity as well as a social problem. The existence of an inefficient allocation of resources in the Ecuadorian manufacturing sector indicates productivity gains can be obtained through the reallocation of resources.