metadata: authors: 0: author: Patino Pena,Fausto Andres
1: author: Ferro,Esteban
count: Ecuador
docna: 0: docna: The Role of Firm Dynamics in Aggregate
            Productivity, Job Flows, and Wage Inequality in Ecuador
docty: Policy Research Working Paper
theme: Inclusive Growth,Job Creation,Human Development and Gender,Economic Policy,Green Growth,Economic Growth and Planning,Private Sector Development,Regulation and Competition Policy,Labor Market Policy and Programs,Active Labor Market Programs,Jobs,Business Enabling Environment,Structural Transformation and Economic Diversification
abstracts: cdata!: This paper examines the role of firm
            dynamics in aggregate total factor productivity, job flows,
            and wage inequality in Ecuador. Utilizing a comprehensive
            employer-employee dataset, the paper documents firm dynamics
            and job flow patterns that are consistent with the presence
            of market distortions. Also, the paper identifies factor
            misallocation as the main contributor to Ecuador's
            total factor productivity deceleration. Given these trends,
            the paper explores allocative inefficiency drivers through
            firm- and industry-level regressions. Firms in the top
            productivity quintile face distortive non-wage labor costs
            that are 3.7 times higher than the bottom quintile, after
            controlling for firm size and age. The findings also provide
            evidence of credit misallocation across firms. Additionally,
            industries with higher job mobility, credit access, and
            competition and lower non-wage labor costs, minimum wage
            incidence, and zombie firms demonstrate higher allocative
            efficiency. Moreover, worker-level regressions indicate that
            misallocation drivers explain up to 41 percent of wage
            inequality, with non-wage labor costs and product market
            frictions as distortions driving this inequality.
display_title: The Role of Firm Dynamics in
            Aggregate Productivity, Job Flows, and Wage Inequality in Ecuador
listing_relative_url: /research/2024/03/34289093/role-firm-dynamics-aggregate-productivity-job-flows-wage-inequality-ecuador
topicv3: Income Inequality m1503891 2668,Job Reallocations m1504194 2863,Firm Dynamics and Productivity Growth
            m1504195 2864,Firm Behavior and Jobs m1504204 2873
publishtoextweb_dt: 2024-03-27T00:00:00Z
dois: 10.1596/1813-9450-10739
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 50
author_keys: 0: author_key: 1816473
1: author_key: 1078137
model_extractions: Policy Research Working Paper 10739 The Role of Firm Dynamics in Aggregate Productivity, Job Flows, and Wage Inequality in Ecuador _Fausto Patiño Peña Esteban Ferro_ Macroeconomics, Trade and Investment Global Practice & Finance, Competitiveness and Innovation Global Practice March 2024
Policy Research Working Paper 10739

# **Abstract**

This paper examines the role of firm dynamics in aggregate total factor productivity, job flows, and wage inequality in Ecuador. Utilizing a comprehensive employer-employee dataset, the paper documents firm dynamics and job flow patterns that are consistent with the presence of market distortions. Also, the paper identifies factor misallocation as the main contributor to Ecuador’s total factor productivity deceleration. Given these trends, the paper explores allocative inefficiency drivers through firm- and industry-level regressions. Firms in the top productivity quintile face distortive non-wage labor costs that are 3.7 times higher than the bottom quintile, after controlling for firm size and age. The findings also provide evidence of credit misallocation across firms. Additionally, industries with higher job mobility, credit access, and competition and lower nonwage labor costs, minimum wage incidence, and zombie firms demonstrate higher allocative efficiency. Moreover, worker-level regressions indicate that misallocation drivers explain up to 41 percent of wage inequality, with non-wage labor costs and product market frictions as distortions driving this inequality.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice and the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fpatinopena@worldbank.org and eferro@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Role of Firm Dynamics in Aggregate Productivity, Job Flows, and Wage Inequality in Ecuador**<sup>*****</sup>

Fausto Patiño Peña<sup></sup> and Esteban Ferro<sup>++</sup> JEL Codes: D2, J21, J3, L11, L25, O47 Keywords: Firm Performance, Aggregate Productivity, Job Flows, Wage Inequality

> * For helpful comments we thank Besart Avdiu, Elwyn Davies, Christian Gonzalez, Mariana De La Paz Pereira Lopez, and Julio Velasco.

>  fpatinopena@worldbank.org.

> ++ eferro@worldbank.org.
# **1 Introduction**

This paper studies the role of firm dynamics in aggregate total factor productivity (TFP), job flows, and wage inequality in Ecuador. A large strand of literature has documented that policy-related drivers and structural frictions impact firm performance with significant effects for aggregate TFP (Restuccia and Rogerson [2008, 2013], Hsieh and Klenow [2009], Bartelsman et al. [2013], among others). Recent literature has found that distortions that impact firm-level dynamics are also important for worker-level outcomes, including employment flows and wage inequality (Haltiwanger et al. [2013, 2016], Decker et al. [2014], Barth et al. [2016], Manning [2021], Criscuolo [2020, 2021, 2022], among others). To examine how firm performance drivers affect aggregate TFP and, simultaneously, impact job flows and wage inequality, the paper leverages a comprehensive employer-employee panel dataset encompassing formal firms in Ecuador. At the firm level, this paper documents a substantial overlap in the productivity distributions of incumbent firms versus entrants and survivor firms versus exiters. Additionally, firm productivity growth is negligible across firms’ life cycle. These firm dynamics suggest underlying market frictions, patterns that are corroborated by macro-level empirical trends. By decomposing aggregate TFP growth, the paper identifies factor misallocation across firms as the predominant channel contributing to Ecuador's aggregate TFP deceleration. Moreover, the distributions of firms by size and age and job flow trends are consistent with persistent allocative frictions, as larger and more productive firms are underrepresented in the economy, new jobs are predominantly created by small, unproductive entrants, and existing jobs are primarily destroyed by old, unproductive firms that continue operating instead of exiting. To reconcile these firm-level and aggregate trends, the paper investigates structural and policy drivers of allocative inefficiencies. Regression analysis at the firm level shows that firms in the top quintile of the productivity distribution face distortive non-wage labor costs that are 3.7 times higher than the bottom productivity quintile, after controlling for firm size and age. Also, the paper finds that credit is allocated to both productive and unproductive large firms, while productive small firms face higher credit constraints. Moreover, industry-level regressions indicate that sectors with higher job mobility, lower non-wage labor costs, less exposure to the minimum wage policy, improved credit access, fewer zombie firms, and greater market competition exhibit higher allocative efficiency. Furthermore, the paper finds that drivers contributing to resource misallocation account for up to 41 percent of aggregate wage inequality. Workerlevel regressions indicate that non-wage labor costs and product market frictions are allocative distortions driving aggregate wage inequality, while the minimum wage policy dissipates this inequality despite generating misallocation.

Between 2012 and 2020, the sample period studied in this paper, Ecuador witnessed economic stagnation, as growth driven by factor accumulation was offset by a significant decline in aggregate TFP, starting in 2014. This suggests that aggregate TFP growth was constrained by the presence of institutional and regulatory distortions that kept factor inputs from being efficiently distributed across firms and sectors. These distortions also slowed down firm-level productivity growth and impeded healthy firm entry and exit dynamics. This decline in aggregate TFP coincided with a change in the trend of income inequality, whose previous decreasing pattern plateaued after 2014 and slowly began increasing in 2018. By 2020, Ecuador was the third-most unequal country in South America. These patterns posit the question of whether the same drivers that constrain aggregate TFP performance are also linked to labor market outcomes and aggregate inequality trends.

Market distortions deter aggregate TFP performance by constraining firm outcomes and dynamics. In particular, these distortions diminish aggregate TFP, impeding the efficient allocation of capital and labor across firms (i.e., the allocative efficiency channel) and sectors (i.e., the structural transformation channel), restricting the growth of firm-level technical efficiency (i.e., the technical efficiency channel), and yielding unfavorable dynamics in firm entry and exit (i.e., the market selection channel). These market frictions, which restrain firm outcomes and depress aggregate TFP, also have effects on workers by distorting labor market quantities and prices. In an economy without distortions, heterogeneity in firm-level productivity results in differences in firm-level employment, with more productive firms employing a larger workforce 2
compared to less productive firms. As markets are efficient, marginal products of labor equalize across firms, resulting in equal wages for workers with similar characteristics. However, when input and output markets face structural or regulatory distortions, marginal products of labor no longer equalize across firms, which leads to wage inequality among similar workers for two reasons. First, market distortions generate labor market rents that can be shared between firms and workers. Differences in workers' bargaining power across firms determine the sharing of these rents, leading to wage differences. Second, market frictions distort firms’ marginal costs, generating dispersion in marginal revenue products. As a result, similar workers will receive different wages due to the distorted performance of firms. Moreover, labor markets adjust through wages as they cannot fully adjust through quantities. Hence, market distortions impact job flows and wage inequality by affecting firm dynamics.

To empirically assess the role of firm dynamics in aggregate TFP, job flows, and wage inequality in Ecuador, this paper utilizes a comprehensive employer-employee matched panel dataset constructed from two firmlevel datasets and one worker-level database. The firm-level datasets provide variables, including firms’ value-added, capital, labor, and materials, which are relevant for estimating firm-level (revenue) productivity using the control function approach to correct for endogeneity (Ackerberg et al. [2015]). The employee database contributes information on wages and human capital characteristics, essential for identifying the determinants that drive aggregate wage inequality.

This paper first documents firm-level patterns, which are indicative of underlying market distortions. As is standard in the literature, the paper finds an increasing relationship between different measures of productivity (i.e., revenue TFP, labor productivity, and sales per worker) and size. However, the paper also finds that older firms are not necessarily more productive than younger ones, signaling constraints on firmlevel productivity growth. Comparing the productivity of surviving firms relative to exiters, the former group is, on average, more productive than the latter. Still, a substantial overlap in their productivity distributions implies inefficiencies in exit dynamics (Levy, 2018). Similarly, incumbent firms exhibit higher productivity levels than entrants, but again, an overlap in their productivity distributions suggests inefficient entry patterns. Furthermore, examining firms' life cycle patterns reveals additional evidence of market distortions. The median firm in Ecuador experiences initial modest growth in technical efficiency during the early years of operation but encounters stagnation and decline in later stages, underscoring the inability of firms to catalyze and sustain productivity growth as they age.

At an aggregate level, the paper documents a decline in aggregate TFP over the sample period. Employing the Melitz and Polanec (2015) approach to decompose aggregate TFP growth, the paper identifies the allocative efficiency channel as the component driving aggregate TFP performance, stemming from persistent factor misallocation across firms. Sectoral factor reallocation is also inefficient, especially during economic downturns. Following adverse external shocks, the combined effect of market distortions with broader macro-structural challenges proves notably detrimental, as it amplifies these economic downturns by reallocating resources towards less productive firms (allocative efficiency channel) and sectors (structural transformation channel), rather than serving as cleansing mechanisms that shift resources more efficiently. Technical efficiency growth in Ecuador has not been able to offset the negative impact of the allocation channels on aggregate TFP trends. The modest contributions of the technical efficiency channel to aggregate TFP growth align with the poor life cycle patterns observed in firm-level productivity growth. Last, the market selection channel (entry plus exit) contributes marginally to aggregate productivity changes due to the entrance of many unproductive firms and the low survival of high-productivity entities.

The paper finds that the poor contributions of the various productivity growth channels are linked to persisting distortions in factor and product markets. To formalize the relationship between allocative efficiency and the aforementioned market distortions, the paper conducts firm- and industry-level regressions, examining various measures of policy-related distortions. At the firm level, distortions in labor and capital markets are studied. The fluidity of Ecuador's labor market is hindered by regulatory frameworks that impose distortionary regulations, leading to escalated labor costs for firms. In comparison to other South American countries, Ecuador has higher firing costs. Also, its labor laws impose additional 3
employment costs on firms, such as compulsory worker participation in firm dividends. As a result, larger and more productive firms face higher non-wage labor costs, leading to an inefficient allocation of labor towards less productive enterprises. The paper finds that firms in the top productivity quintile have nonwage labor costs per worker that are 3.7 times larger than firms in the bottom productivity quintile, after controlling for firm size and age. Credit access patterns indicate misallocation of capital in Ecuador. Banking credit is inefficiently allocated among larger firms, as those with access to credit from banks are 3 percent less productive than their credit-constrained large counterparts, although this negative differential is not significant. Conditional on having access to credit, smaller firms are more productive than larger firms, contrasting the productivity-size relationship of the entire firm sample. Micro enterprises with access to credit are 20 percent more productive than large firms with credit access. These findings suggest an inefficient allocation of capital between firm size groups, with credit being allocated to both productive and unproductive large firms, while productive small firms face limited access to capital due to higher interest rates and collateral constraints. These constraints are also less binding for older, unproductive firms, leading to inefficient hoarding of capital. In addition to generating resource misallocation, these labor and credit market distortions dampen firm productivity growth as fewer resources are available for innovation. Furthermore, these distortions in factor markets, combined with weak institutional frameworks, such as inadequate insolvency regimes, create disincentives for low-productivity firms to exit while promoting the entry of poor performing new firms. At the industry level, the paper finds that sectors exhibiting higher job mobility, lower non-wage labor costs, less exposure to the minimum wage policy, improved credit access, fewer zombie firms, and greater market competition exhibit higher levels of allocative efficiency.

Beyond yielding underwhelming aggregate TFP outcomes, market distortions have also generated firm distributional patterns and job flow trends that further underscore Ecuador's subpar economic performance. Large firms (those with more than 100 workers) represent less than 0.5 percent of formal firms but employ over 40 percent of the workforce. In contrast, micro firms (those with fewer than five workers) constitute over 90 percent of firms and contribute around 30 percent of total employment. Compared to the United States, Ecuador exhibits a substantially lower share of large firms and a higher share of micro firms, a characteristic pattern in developing economies attributed to market inefficiencies (Hsieh and Olken, 2014; Krueger, 2013; Banerjee and Duflo, 2005, 2011; among others). Moreover, there has been a considerable shift in economic activity from younger to older firms during the sample period. While this shift could be positive for aggregate TFP if firms experienced productivity growth as they aged, this is not the case in Ecuador. Market frictions have deterred the entry of productive firms and hindered the exit of unproductive older enterprises, resulting in an aging population of firms without accompanying firm productivity growth. These market frictions also drive aggregate job flow trends, with small, unproductive entrants creating most new jobs, while existing jobs are destroyed by old, unproductive firms that persist in the market.

The underlying structural and policy drivers that influence Ecuador’s poor firm-level outcomes and resource misallocation also play a crucial role in explaining aggregate wage inequality. In Ecuador, variations in aggregate wage inequality are primarily driven by differences in wages across firms for similar workers (between-firm wage inequality) rather than differences in wages within firms for different workers (within-firm wage inequality). Between-firm wage inequality primarily stems from misallocation-related rents generated by policy-driven distortions (i.e., non-wage labor costs, firing costs, and institutional constraints on labor mobility). Specifically, distortionary policies and constraints affecting firms’ performance potentially account for up to 41 percent of aggregate wage inequality in Ecuador. Additionally, estimates of the passthrough of revenue productivity to wages suggest that formal workers have low bargaining power and that the distortions leading to labor misallocation are also associated with wage inequality. As explained by Criscuolo et al. (2020, 2021, 2022), higher levels of pass-through are an indication of both the higher bargaining power of workers and resource misallocation at the firm level. In Ecuador, a 10 percent increase in revenue productivity yields a 0.8 percent increase in wages, which is lower than most OECD countries (Criscuolo, 2021). This can be attributed to the limited availability of formal sector jobs, which reduces workers' bargaining power. Moreover, this paper finds that non-wage labor costs and product market competition not only contribute to misallocation and aggregate productivity 4
losses but also to higher wage inequality. In contrast, minimum wage policies reduce wage inequality in Ecuador, as observed in other countries, while also generating allocative inefficiencies.

This paper contributes to three strands of literature. First, it adds to the work on aggregate TFP and its relation to firm outcomes and dynamics. Seminal work by Restuccia and Rogerson (2008, 2013), Hsieh and Klenow (2009), Bartelsman et al. (2013), among others, have attributed differences in aggregate TFP to resource misallocation across firms. Papers including Hsieh and Klenow (2009,2014), Bartelsman et al. (2013), Midrigan and Xu (2014), Gopinath et al. (2017), and Restuccia and Rogerson (2017) have connected resource misallocation to specific market distortions constraining firm dynamics. Additionally, studies by Krueger (2013) and Hsieh and Olken (2014) have highlighted that these distortions not only deplete aggregate TFP but also yield anemic firm distributional patterns. This paper provides additional empirical evidence on drivers of resource misallocation and their contribution to poor aggregate TFP outcomes and lackluster firm distributional patterns in the case of Ecuador. Second, this paper contributes to the literature on firm dynamics and job flows. Papers, such as Haltiwanger et al. (2013, 2016) and Decker et al. (2014), have underscored the significance of young firms in accounting for most of the job flows and employment reallocation in the U.S. Their work complements Dunne et al. (1989), Davis and Haltiwanger (1990, 1992), Davis et al. (1996), Foster et al. (2001), and Becker et al. (2007), which find that employment reallocation is accompanied by output growth and capital reallocation. This paper finds that, in the context of a developing economy characterized by highly distorted markets, job creation by entrants is inefficient, and job destruction is largely explained by unproductive, older firms that continue to operate. Lastly, this paper contributes to the literature on the pivotal role of firm outcomes in explaining wage inequality. Papers such as Faggio et al. (2010), Barth et al. (2016), Berlingieri et al. (2017), Manning (2021), and Criscuolo (2020, 2021, 2022), among others, have documented that firms are important for explaining wage variation among workers. Furthermore, Criscuolo (2020, 2021, 2022) have studied potential drivers of misallocation that are linked to wage inequality outcomes for OECD countries. This paper builds upon this work by simultaneously linking drivers of misallocation to wage inequality in a developing economy, underscoring the relevance of non-wage labor costs and product market distortions for wage dispersion.

The paper is organized as follows: Section 2 describes the dataset leveraged for the different empirical analyses. Section 3 provides aggregate motivating facts about aggregate productivity and inequality trends in Ecuador. Section 4 documents productivity, market selection, and life cycle patterns of Ecuadorian firms. Section 5 studies the main channels and drivers of aggregate productivity growth in Ecuador. Sections 6 and 7 characterize firm distributional and job flow patterns, respectively. Section 8 assesses the role of firm dynamics on wage inequality in Ecuador. Last, Section 9 concludes.

# **2 Data**

This paper utilizes two firm-level datasets and one employee-level database to construct a comprehensive employer-employee matched dataset, facilitating a simultaneous examination of firm and labor outcomes. The first source is the Directory of Companies and Establishments (Directorio de Empresas y Establecimientos [DIEE]), a public database created by the Ecuadorian Statistics Institute (Instituto Nacional de Estadística y Censos [INEC]), which incorporates firm administrative data from the national revenue service (Servicio de Rentas Internas [SRI]) and firms’ employment data from the social security institution (Instituto Ecuatoriano de Seguridad Social [IESS]). The DIEE covers information for all formal firms registered with the SRI since 2012, totaling around 800,000 firms. However, it has limitations, providing only limited firm performance variables (sales and employment) and lacking data necessary for estimating value added and productivity, such as firms' costs.

The DIEE data is complemented with non-public firm administrative data obtained from the SRI, which encompasses the entire universe of formal firms in Ecuador. This second source includes key variables for estimating firm TFP, such as sales, gross production, costs, fixed assets, investment, employment, and materials at the firm level. The dataset also comprises relevant firm characteristic variables, including economic sector of activity, age, and geographical location, among others. The third source, the Labor and 5
Business Dynamics Laboratory (Laboratorio de Dinámica Laboral y Empresarial [LDLE]), is constructed by INEC using information from IESS, SRI, and other government institutions. It contains worker-level data for over 70,000 firms that meet two criteria: reported sales to SRI and registered employment with IESS (formal employment). Combining these three data sources yields a rich employer-employee database useful for analyzing firm dynamics, aggregate productivity, and labor demand patterns for the 2012–2020 period.<sup>1</sup> The main firm performance variable of interest in this paper is firm revenue based TFP. The control function approach developed by Ackerberg et al. (2015) is used to estimate revenue TFP. The variables necessary for estimating revenue TFP are value-added, capital, labor, and materials (which is used as the proxy variable for the control function approach) at the firm-level. The paper follows the methodology of Avellan and Ferro (2017) to construct these variables using the Ecuadorian firm administrative data, adhering to standard measurements in the literature. Value-added is calculated as firm gross output (sales adjusted for product inventories) minus intermediate consumption of materials and services. Intermediate consumption comprises operational expenditures and production costs. Capital is measured based on the value of fixed assets reported in firms' financial statements, lagged by one year. Labor is measured as the number of workers employed by a firm. Materials are computed as the sum of raw materials, transport costs, gasoline costs, and utilities costs. Monetary variables are transformed into constant prices using sector deflators from the Ecuadorian input-output matrix. Consequently, firm gross production, intermediate consumption, and value-added are deflated using their respective sector deflator indexes. Similarly, materials are deflated using the sector deflators for intermediate consumption, while capital is deflated using the sector deflators for gross fixed capital formation. Lastly, there are two significant firm characteristic variables relevant in the analyses of the paper: sector and age. The firm's sector of activity corresponds to the International Standard Industrial Classification (ISIC) sector classification. The age of a firm is calculated as the difference between the observation year and the year of the firm's commencement of operations.

Variables pertaining to workers are measured in a standard manner to facilitate the wage inequality analyses. Worker-level information is available at a monthly frequency in the LDLE. Following relevant papers in the literature (Barth et. al., Criscuolo et al. [2020, 2021, 2022]), only workers that are fully employed for a calendar year are kept in the estimation sample. Wages are constructed as a worker’s daily wage rate from reported monthly wages and the number of days worked in a month. These are deflated by the consumer price index to obtain real wages. To match to the firm-level data, which is at an annual frequency, yearly real wage rates are estimated as the average across months of the real daily wage rate. The paper also utilizes the following variables associated with workers’ job characteristics and human capital: part-time employment, education, age, gender, and ethnicity. A dummy for part-time employment is constructed using the number of days worked in a month. Workers with less than 15 days of employment per month are classified as having part-time employment.<sup>2</sup> For education, a bivariate variable is created to identify if a worker has obtained at least a high school diploma. Last, workers’ age, gender, and ethnicity are provided by the LDLE.

# **3 Motivation Facts: Aggregate Productivity and Inequality Trends in Ecuador**

A concern in Latin American economies is that growth has been unsatisfactory and driven by poor productivity performance, as documented by Busso et al. (2013). Ecuador is no exception to this trend. As depicted in Figure 1, between 2003 and 2014, Ecuador's GDP grew at an average annual rate of 4.7 percent, largely driven by the growth of physical capital stock. However, economic growth has been slightly

> 1 It is important to acknowledge certain limitations of the data sources employed in this paper. Firstly, the universe of firms and workers considered only pertains to formal economic activity. Additionally, due to variations in regulations governing the submission of tax returns by firms, many entities do not report information necessary for measuring relevant variables for the paper. Given this, the sample size can vary across the exercises carried out in this paper, as the number of observations is maximized in relation to the variable of interest in each exercise. To mitigate sample attrition, values are imputed for observations with missing data for the variables used in the productivity estimation method.

> 2 To transform number of days worked per month to an annual measure, the paper considers a worker's most frequently reported number of days worked per month within a year as their annualized measure.

6
<!-- Start of picture text --> 6.0%<br>4.0%<br>orbs —SS<br>- =<br>4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>2003-2014 2014-2020 2012-2014 2014-2019 2019-2020<br>TM Capital Stock @ labor Human Capital TM Total factor productivity © GDP growth<br><!-- End of picture text -->
<!-- Start of picture text --> 11s 7 == ~ ~ 5<br>2<br>0.90<br>0.80<br>—TTre —— Gini index -seee* Mov. Avg. TEP Trend Mov. Avg. Gini index Trend<br><!-- End of picture text --> ij,t ijt ij,t ij,t ij,t k t,t lL i,t ij,t ij,t t,t. lj kj it it ijt ijt
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<!-- Start of picture text --> 2013 2016<br>5<br>> 0<br>7)<br>5 2019<br>a 5<br>250 In<br>4 6 8 10 12 84 6 8 Oe10 12ae<br>LN TFP<br>= exiter ==" survivor<br>Top and bottom 1% and CIIU sectors O, P, Q are omitted Active firms are those reporting sales or labor differnte than zero.<br><!-- End of picture text -->
<!-- Start of picture text --> 2013 2016<br>Bs)<br>> te)<br>a7)<br>e 2019 2020<br>Q5<br>0<br>TY<br>4 6 8 10 12 4 6 8 10 12<br>LN TFP<br>—— entrant == incumbent<br>Top and bottom 1% of distribution,and CIIU sectors O.P.Q are cropped. Active firms are those reporting sales or labor different than zero.<br><!-- End of picture text -->
<!-- Start of picture text --> Revenue TFP Labor Productivity<br>1.06 1.12<br>11<br>1.04<br>1.08<br>1.02<br>1.06<br>1.04<br>1<br>1,02<br>0.98<br>1<br>0.96 0.98<br><5 5-9 10-14 15-19 >=20 1 2 3 4 5<br>———Mean =—=——=—Median =—=——=P10 -—-==P90 ——Mean <———Median -——=P10 --=—=—=P90<br>Sales Capital<br>14 13<br>125<br>13<br>12<br>12 115<br>11<br>11<br>1.05<br>1 1<br><5 S-9 10-14 15-19 >=20 <s 5-9 10-14 15-19 >=20<br>———Mean =—=—=Median <—=<P10 --=—=P90 ——Mean ——=—Median ——=P10 <<<P90<br>Employment Wage Bill<br>6 1.2<br>118<br>5<br>1.16<br>114<br>4<br>112<br>3 11<br>1.08<br>2<br>1.06<br>1.04<br>1<br>1.02<br>0 1<br><s 5-9 10-14 15-19 >=20 < 5-9 10-14 15-19 >=20<br>————Mean =—=Median ==—=P10 —=—=P90 ———Mean =——=Median =——=P10 -=—=—=P90<br><!-- End of picture text -->
opposite occurs, suggesting that factor input growth is driven by market imperfections that encourage inefficient factor hoarding and hinder the efficient reallocation of resources from older, less productive firms to younger, more productive ones. This further implies that labor productivity growth stems from factor accumulation (i.e., capital deepening) rather than improvements in firm capabilities.

# **5 The Role of Firm Dynamics in Aggregate Productivity**

## **5.1 Decomposition of Aggregate Productivity Growth**

This section studies the channels of Ecuadorian aggregate productivity growth between 2012 and 2020.<sup>15</sup> For this, aggregate productivity growth is decomposed into five components, as in Melitz and Polanec (2015): the within, between, entry, exit, and structural transformation components: This decomposition is valuable for identifying aggregate productivity growth drivers. For instance, if aggregate productivity growth is driven by the within component, it suggests that surviving firms have improved their technical capabilities over time through technological adoption, productive investments, or enhanced management practices. Growth driven by the between component indicates more efficient allocation of capital and labor in factor markets, potentially due to reduced market frictions or less distortive regulations. Aggregate productivity growth attributed to the entry and exit components suggests favorable market conditions that enable the entry of more productive new firms and the exit of less productive establishments. Lastly, aggregate productivity growth resulting from the structural transformation component indicates improved macroeconomic conditions, leading to the shift of economic activity from less productive sectors to more productive ones. Appendix 1 provides a further discussion on the derivation of this decomposition.

Figure 7 shows that, between 2012 and 2020, the year-to-year growth of aggregate TFP in Ecuador has been consistently poor. Only one year, 2017, exhibited a positive growth of 0.3 percent in aggregate productivity compared to the previous year. Conversely, the years 2015 (-11.8 percent), 2018 (-10.8 percent), and 2020 (-6.9 percent) witnessed the largest drops in aggregate productivity. As a result, by 2020, aggregate productivity had declined by nearly 33 percent compared to 2012. To account for the volatility in the performance of the oil industries, characterized by significant shifts in prices and quantities, the aggregate productivity growth decomposition analysis is carried out for non-oil economic formal sector activities.<sup>16</sup> Figure 7 also illustrates the decomposition of aggregate productivity growth according to Equation (2). The importance of the between component in driving the poor aggregate productivity growth outcomes suggests that market frictions have pervasive effects, hindering the efficient flow of factor inputs towards the most productive firms. The structural transformation component follows the between component, particularly during years with significant productivity losses. Despite the positive contribution of the within component, which reflects slightly higher firm technical efficiency, it has been unable to offset the negative trends caused by inefficient reallocation at the firm and sector levels. Lastly, the entry and exit components have

> 15 The measure of aggregate productivity used in this section corresponds only to formal sector activity, as the data sources used only consider formal firms.

> 16 All disaggregate 4-digit economic activities within the 1-digit industries: “O – Public Administration and Defense”, “P – Education”, and “Q – Human Health and Social Work Activities” are excluded from the aggregate productivity growth decomposition exercise, as these sectors are considered to be non-commercial. The Oil economic activity is comprised of the following disaggregate 4-digit economic activities: “B0610 – Extraction of crude petroleum”, “B0620 – Extraction of natural gas”, “B0910 – Support activities for petroleum and natural gas extraction”, and “C1920 – Manufacture of refined petroleum products”. The remaining industries make up the non-oil economic activity.

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<!-- Start of picture text --> 10.0% 10.0%<br>5.0% 5.0%<br>0.0% 0.0%<br>-5.0% -5.0%<br>-10.0% -10.0%<br>-15.0% -15.0%<br>2013 2014 2015 2016 2017 2018 2019 2020<br>@em Entry Sem Exit @e Within Sam Between mmm Structural Transformation —e=Agg. Productivity Growth<br><!-- End of picture text -->
technical efficiency for operating firms over the analyzed period. However, the bottom left panel of Figure 3 indicates that firm productivity growth in Ecuador is likely constrained, suggesting that growth in aggregate technical efficiency is primarily driven by spillovers of international technology trends rather than firm capability upgrading.

Figure 7 indicates that firm exit in Ecuador was slightly “aggregate productivity enhancing” between 2012 and 2016.<sup>19</sup> However, as shown in Figure 4, despite survivors having higher average productivity than exiters, the substantial overlap in their revenue TFP distributions suggests that aggregate productivity growth could have been higher if market selection had been efficient. From 2016 onwards, the contribution of the exit component to aggregate productivity growth became negligible (and sometimes negative) due to the reduction in the productivity gap between survivors and exiters. This was driven by an increase in high-productivity exiters and a subsequent decrease in high-productivity survivors. These trends align with the slowdown experienced by Ecuador since 2014, where the struggling economy led to higher exit probabilities for more productive firms, resulting in their increased exit over time. Furthermore, this persistent and increasing exit of high-productivity firms negatively impacted aggregate productivity through two channels. Firstly, there was a decline in the overall participation of high-productivity firms in the market, leading to a contraction in aggregate TFP through lower technical efficiency. Secondly, the exit of more productive firms resulted in a reduced allocation of resources to higher productivity enterprises, leading to lower allocative efficiency. Moreover, the inefficient exit of more productive firms was costlier as these establishments typically employed more workers and utilized more capital, resulting in larger amounts of capital and labor being destroyed upon exit.<sup>20</sup> Between 2012 and 2020, the distribution of entrants became more left-skewed, and the distribution of incumbents became slightly less left-skewed, indicating an increase in high-productivity entrants and a decline in high productivity incumbents (Figure 5). At the aggregate level, these shifts in distributions toward more productive entrants are reflected in mostly positive contributions of the entry component to aggregate productivity growth (Figure 7).<sup>21,22</sup> However, the persistent prevalence of lower TFP entrants, relative to incumbents, accounts for the modest (and sometimes negative) contribution of this channel to aggregate productivity growth. This latter trend would be less detrimental to aggregate productivity growth if entrant firms demonstrated significant potential for enhancing their capabilities. Foster et al. (2001) find that US entrants also exhibit lower productivity compared to incumbents but possess high potential for technical efficiency growth. In contrast, the invariant distribution of incumbents in Ecuador (Figure 5), the stagnation and later decline of firm productivity growth over the life cycle, and the poorer growth outcomes of more productive entrants (Figure 6) align with the presence of barriers that impede firm productivity growth. Furthermore, the entry of these less productive and growth-constrained firms exacerbates allocative inefficiencies, as lower performing firms utilize factor inputs.

> 19 Levy (2018) defines “aggregate productivity enhancing exit” as the exit of low-productivity firms from the market, which is be expected to occur in markets that function efficiently. “Aggregate productivity reducing exit” occurs when high-productivity firms exit the market, characterizing the presence of market frictions.

> 20 These trends are only for the sample of formal firms in Ecuador. Informal firms are generally less productive and smaller. Hence, the revenue productivity distributions including formal and informal enterprises would be expected to have a higher concentration of lower productivity firms for both survivors and exiters. However, the reduction in the productivity enhancement magnitude would be expected to persist, since exit trends have increased towards more productive enterprises that are generally formal.

> 21 Levy (2018) defines entry as “aggregate productivity enhancing” if higher productivity firms enter the market. “Aggregate productivity reducing” entry occurs when lower productivity enterprises enter the market.

> 22 The distributional trends of entrants and incumbents in Figure 5 only characterize formal firm distributions. One characteristic of markets in developing economies like Ecuador is that there is constant entry of informal subsistence entrepreneurs, who open businesses, not because of high and innovative entrepreneurial abilities, but because of the lack of formal employment opportunities. Hence, for the period analyzed, the “aggregate productivity enhancing” entry of formal firms may have been ameliorated by the entry of low productivity informal entrants, even more so during recessions, as levels of informality tend to increase in economic busts. This hypothesis is also supported by the productivity distribution of entrants in 2020 (Figure 5). Although the distribution is only for formal firms, the large left hump suggests the potential entry of unproductive entrepreneurs because of the negative economic context in the Ecuadorian labor market.

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# **5.2 Drivers of Aggregate Productivity Growth**

## **5.2.1 Labor Regulation**

<mark>Aggregate productivity growth in Ecuador can be partially attributed to the distortionary impact of labor market policy. Labor regulations in Ecuador impose high mandated labor costs on firms. Ecuadorian firms’ labor costs encompass not only salaries but also various other expenses, such as mandatory employment</mark> allowances<sup>23</sup> , compulsory worker participation in firm dividends<sup>24</sup> , additional obligatory services like childcare for larger firms, and high firing compensation packages<sup>25</sup> . These mandated labor costs are an <mark>excessive burden for firms. For example, severance pay and firing costs in Ecuador are higher relative to</mark> most of its South American peers, as illustrated in Figure A 4.<sup>26</sup> Additionally, as estimated by Grosh et al. <mark>(2014), mandatory employment allowances can increase costs by up to 25 percent for firms. Minimum wage policy also places a burden on firms, with Ecuador's minimum wage-to-GDP per capita ratio higher</mark> than most South American countries (Figure A 5).<sup>27</sup> Hence, the combination of these distortionary labor <mark>regulations results in a highly inflexible labor market and significantly impacts allocative efficiency.</mark> Differences in labor costs by firm size, age, and productivity indicate why the contributions of the between component negatively impact aggregate productivity growth. The top-left panel of <mark>Figure 8</mark> illustrates that larger firms report higher total labor costs per worker.<sup>28</sup> The top right panel of <mark>Figure 8</mark> indicates that this relationship persists even when examining firms’ labor costs per worker net of wages, i.e., non-wage labor costs per worker.<sup>29</sup> After controlling for firm age and revenue productivity, large firms report total labor costs per worker and non-wage labor costs per worker that are 1.6 and 1.5 times higher than micro firms, respectively. Non-wage labor costs are expenses associated with Ecuadorian regulations that impose additional costs on firms, such as compulsory allowances and workers’ participation in firm dividends. One plausible explanation for these findings is that large firms are more likely to be under scrutiny by the government to comply with the costs arising from these labor regulations. Additionally, certain obligatory services are tied to workforce size, such as the requirement for firms with 50 or more workers to provide child daycare services.

The middle panels of <mark>Figure 8</mark> further reveal that total and non-wage labor costs per worker increase as firms age. In particular, older firms have total labor costs per worker that are 1.2 times higher than micro enterprises and non-wage labor costs per worker that are 1.1 times higher. This can be linked to distortionary labor frictions that discourage employee churning, resulting in an aging labor force within firms and higher costs associated with longer job tenure, as compulsory allowances directly increase with salary increments over a worker’s job tenure. Also, the right panel of Figure A 4 illustrates that severance payments increase with job tenure, suggesting that older firms with older labor forces would face higher severance costs if they were to lay off workers. In turn, this encourages older firms to retain their labor force despite being less productive. Furthermore, the bottom panels of <mark>Figure 8</mark> demonstrate that more productive firms face

> 23 Ecuadorian formal firms are legally obliged to pay three compulsory allowances for each worker. The “thirteenth salary” allowance is equal to a worker’s monthly salary and paid to all workers during Christmas time (December). The “fourteenth salary” allowance is equal to a minimum wage payment that is paid to all workers before the school year begins (August/September). Lastly, the “unemployment reserve funds” allowance is paid monthly to every worker and equals 8.3 percent of each worker’s salary.

> 24 According to Article 97 of Ecuador’s Employment Regulation, firms are obliged to share 15 percent of their profits with their labor force.

> 25 According to Article 188 of Ecuador’s Employment Regulation, a worker that is fired and has been employed less than three years will receive firing compensation equivalent to three months’ salary. A worker that is fired and has been employed more than three years will receive firing compensation equivalent to the number of years worked in the company times their monthly salary (up until 25 monthly salaries).

> 26 Bolivia and the República Bolivariana de Venezuela are excluded as data is not reported withing the World Bank’s _Employing Workers_ Database.

> 27 Chile and the República Bolivariana de Venezuela are excluded as data on minimum wages is not available in the ILO’s Global Wage Report 2020 – 2021.

> 28 For this estimation, firms’ total labor costs consist of wages plus social security contributions, severance pay costs, mandatory compulsory allowances (i.e., “thirteenth” salary, “fourteenth” salaries, unemployment reserve fund), compulsory worker participation in firms’ dividends, and other remunerations (i.e., transportation, commissions, bonuses, childcare).

> 29 For this estimation, firms’ non-wage labor costs include mandatory compulsory allowances (i.e., “thirteenth” salary, “fourteenth” salaries, unemployment reserve fund), compulsory worker participation in firms’ dividends, and other remunerations (i.e., transportation, commissions, bonuses, childcare). It is important to caveat that it is not possible to include firing costs into the non-wage labor cost measure as the data sources used do not allow for this.

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<!-- Start of picture text --> Total labor costs per worker by size groups Non-wage labor costs per worker by size groups<br>z 60% z 60%<br>==<br>250% 2 50%<br>3 3s<br>: 40% 2 40%<br>@@<br>><br>ka<br>ka 30% > 30%<br>2 20% 2 20%<br>La Small Medium Large al Small Medium Large<br>Size Group Size Group<br>Total labor costs per worker by age groups Non-wage labor costs per worker by age groups<br>v 25% % 15%<br><=4< =<br>eo222= — 20%15% co22 —2 10%<br>go go<br>EB 2 10% 4<br>BS BS 5%<br>a> &2><br>_—_<br>a 5-9 10-14 15-19 >=20 a 5-9 10-14 15-19 >=20<br>Age Group Age Group<br><!-- End of picture text --> <!-- Start of picture text --> Total labor costs per worker by revenue TFP Non-wage labor costs per worker by revenue<br>3 quintiles 3 TFP quintiles<br>100% E 300%<br>Ss Ss<br>F s0% oO 250%<br>22<br>8a 8 150%<br>—£ 40% «x<br>2 20% —£Z 100%<br>a 0% a 0%<br>Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Revenue Productivity Quintiles Revenue Productivity Quintiles<br><!-- End of picture text -->
<mark>exiting, in order to avoid layoff-related costs. Moreover, high labor costs that impede the productivity growth of firms across the life cycle have a second-order effect on aggregate productivity growth by encouraging the entry of unproductive firms while discouraging the entry of productive enterprises. Atkeson and Burnstein (2010) and Hsieh and Klenow (2014) explain that constraints on incumbents’ productivity growth lead to higher entry rates for unproductive firms as they face less competition from productive incumbents, thus explaining the large share of low-productivity entrants in Ecuador.</mark>

# **5.2.2 Credit Access Frictions**

The between-firm component's underperformance in Ecuador can also be attributed to credit constraints arising from distortive interest rate regulation and excessive loan collateral requirements. Ecuador’s interest rate regulation includes differential rates based on firm sales and interest rate ceilings.<sup>30</sup> Figure A 6 reveals that Ecuadorian firms with more than USD 5 million face an average effective loan interest rate of 8.5 percent, almost 5 percentage points higher than the US average. However, this complex regulatory system is very distortive. Effective interest rates are close to the ceiling for all loan categories, implying that interest rates do not accurately reflect credit risks. As a result, financial institutions demand substantial collateral, in addition to high interest rates, hindering firms' access to financial resources and efficient capital allocation (Vijil, 2021). Weak institutional frameworks and poor contractual enforcement, as noted by Iacovone and Patiño Peña (2021), further exacerbate collateral constraints in developing countries like Ecuador. Figure A 7 confirms that firm loans in Ecuador more often require collateral in comparison to peers, with the required value exceeding 200 percent of the loan value, on average. Moreover, Figure A 8 indicates that smaller firms are the most affected by these constraints, as they face higher interest rates (with those earning less than USD 100,000 in sales encountering rates above 20 percent) and more restrictive collateral constraints. Consequently, the SME segment is underserved, with private banks primarily focusing on large enterprises and only a limited number of micro firms receiving financing from microfinance institutions (Partow and Vijil, 2021).

The left panel of <mark>Figure 9</mark> reveals the average difference in revenue TFP between firms with credit access and those without, categorized by firm size. On average, firms with credit access demonstrate higher productivity compared to credit-constrained firms across all size groups, but this TFP differential diminishes for larger firms. Notably, the revenue TFP differentials of firms with credit access from financial institutions, are positive for all size groups except large firms. Large firms with access to credit are 3 percent less productive than their credit constrained large counterparts, but this difference is not significant. This implies that large enterprises with bank credit are not more productive than their credit-constrained large counterparts, indicating higher misallocation of credit, particularly bank lending, among larger firms. Furthermore, this reinforces that credit constraints become less binding as firms grow larger, allowing some unproductive large firms to access credit while limiting credit access to only highly productive small firms. The left panel of <mark>Figure 10</mark> shows that, conditional on credit access, smaller firms exhibit higher levels of TFP relative to larger firms. Large firms with access to credit are 20 percent less productive than micro enterprises with credit access. These findings suggest an inefficient allocation of capital between firm size groups, with unproductive large firms inefficiently hoarding capital and productive small firms facing limited access to additional capital due to higher credit constraints, as suggested by Figure A 8. Hence, existing credit frictions result in an inefficient allocation of banking credit among larger firms and lower access to credit for small productive firms, which in turn negatively impact aggregate productivity growth through the between component. The right panel of <mark>Figure 9</mark> demonstrates that, on average, firms with

> 30 In Ecuador, there are 23 different interest rate categories depending on the type of loan and all have a corresponding interest rate ceiling. Business loan interest rates vary according to the level of firms’ sales. The different business loan interest rate categories are the following. The Corporate interest rate applies to firms with sales above USD 5 million. The Business interest rate corresponds to firms with sales between USD 1 million and USD 5 million. The SMEs interest rate corresponds to firms with sales between USD 100,000 and USD 1 million. The Microcredit – Expanded Accumulation interest rate corresponds to firms with sales between USD 20,000 and USD 100,000. The Microcredit – Simple Accumulation interest rate corresponds to firms with sales between USD 5,000 and USD 20,000. The Microcredit – Retail interest rate corresponds to firms with sales less than USD 5,000.

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<!-- Start of picture text --> Revenue TFP Differential of Credit Access by Firm Size Revenue TFP Differential of Credit Acces by Firm Age<br>£4 50.0% £3 40.0%<br>&Ee : ee E §£ 200%<br>ro 35<br>FS 3 30.0% = 3 25.0%<br>3232 3= £2 20.0%<br>£ 2 20.0% $ é<br>A 52°"<br>ig 10.0% reum<br>23 AG<br>83 0.0% A ee<br>2 3 Micro Small Medium oe £ E 0.0%<br>FS as fs <s 5-9 10-14 15-19 >20<br>x5 a R5 Age<br>mAccess toFinance —_TM Access to Financefrom Banks TMAccess toFinance _'TM Access to Financefrom Banks<br><!-- End of picture text --> <!-- Start of picture text --> Revenue TFP Premium of Firm Size for Firms with Revenue TFP Premium of Firm Age for Firms with<br>Access to Credit Acces to Credit<br>rT 0.0% S 10.0%<br>E fe<br>€ 5 8.0%<br>O -5.0% s<br>32<br>2-10.0% = 40%<br>Q 3<br>ee2 15.0% e[= a 0.0% - L<br>= ne |<br>5 -20.0% 2 .<br>oO<br>& -25.0% z -6.0%<br>Small Medium Large £ 5-9 10-14 15-19 >20<br>Size 2 Age<br>2<br>Access toFinance —‘TM Access to Financefrom Banks a mAccess toFinance —TM Access to Finance from Banks<br><!-- End of picture text -->
# **5.2.3 Trends of Innovation Inputs**

The slow growth in aggregate technical efficiency in Ecuador during the analyzed period can also be attributed to poor trends in firms’ innovation inputs, which are also closely linked to the previously discussed factor market distortions.<sup>31</sup> The left panel of Figure A 9 illustrates that the average years of managerial experience across firms increased marginally for all firm size groups. The right panel of Figure A 9 shows a decline in the share of medium and large firms conducting research expenditures, indicating a reduced willingness to make productive investments over the analyzed period. Labor cost distortions play a role, as larger firms facing higher labor costs allocate resources to meet these expenses instead of financing research activities for innovation. Furthermore, credit constraints for small firms limit their innovation investments, leading to a lower share of research-conducting small firms compared to medium and large firms. Moreover, factor market distortions have also reduced the sorting of higher-skilled workers into more productive firms. Figure A 10 shows that the share of high-skilled workers in high-productivity firms (top quintile of the revenue TFP distribution) decreased over the period analyzed, while it increased for low productivity firms (bottom two quintiles of the revenue TFP distribution). Hence, the complementary human capital necessary for innovation in highly productive establishments has decreased, as it is misallocated in less productive firms. This deficient performance of innovation inputs supports the hypothesis that aggregate technical efficiency growth in Ecuador is driven by international technology trends rather than by firm catch-up to the technological frontier through innovation.

# **5.2.4. Insolvency Regimes**

In Ecuador, weak insolvency regimes contribute to poor aggregate TFP outcomes primarily through inadequate market selection. The time to resolve insolvency in Ecuador exceeded five years during the analyzed period, contrasting with three years in the rest of Latin America and one year in the US.<sup>32</sup> As such, weak insolvency regimes have likely discouraged unproductive firms from leaving the market, like zombie firms.<sup>33</sup> Figure A 11 reveals that zombie firms in Ecuador are slightly less productive than non-zombie firms, consistent with the findings of Adalet McGowan et al. (2017). Figure A 12 shows that the share of firms unable to service their debt has increased over the analyzed period, particularly after 2017. Furthermore, as seen in Figure 7, the contribution of the exit component to aggregate TFP growth declined after 2016. Reconciling the trends in Figures A 11 and A 12 with those of Figure 7 indicates that firms that should be exiting the market, like zombie firms, are not doing so. As insolvent zombie firms are less productive, their continued participation in the market leads to inefficient hoarding of resources, which could have been allocated to more productive firms that are likely exiting. In turn, this inefficient exit of more productive firms is manifested through the declining contribution of the exit component to aggregate productivity growth.

# **5.2.5 Evidence of Drivers of Allocative Inefficiencies**

To provide further insight on the potential drivers of poor productivity trends in Ecuador, this paper studies the relationship between sector-level allocative efficiency and proxies of structural and policy drivers of misallocation. Formally, the following regression is estimated: where Cov�.� is sector j’s covariance between firms' revenue productivity and their value-added share in year t. This covariance measures the extent to which firms with higher productivity capture a larger share

> 31 As explained by Cirera and Maloney (2017), inputs for the innovation process of firms are: technology, equipment, R&D, intellectual property use, human capital, training, engineering and design, software and databases, and managerial and organizational capital and practices. 32 World Development Indicators.

> 33 Zombie firms are defined as in Adalet McGowan et al (2017): A firm is considered a zombie firm if it is more than ten years old, and it has an interest coverage ratio less than one for more than three years. The interest coverage ratio is the ratio of operating income (gross income or sales minus operating expenses) to interest payments.

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of economic activity, and thus, higher levels of capital and labor inputs. Higher values for this covariance reflect higher within-sector allocative efficiency.<sup>34</sup> The structural or policy variables of interest are denoted by Z�,�. The policy variables considered are job-to-job mobility, non-wage labor costs relative to total labor costs, minimum wage incidence, share of firms with access to credit from a financial institution, zombie firm presence, and product market concentration.<sup>35</sup> Last, δ� and δ� are sector and year fixed-effects, respectively.

Columns (1) – (6) of Table 1 report the regression estimates of coefficient μ� in Equation (3) for each policy variable independently. Column (7) refers to the same specification but includes all regressors simultaneously. The results of Table 1 suggest that sectors characterized by higher non-wage labor costs, increased minimum wage incidence, and lower job-to-job mobility display lower allocative efficiency, signaling distortive labor market policies. Also, sectors with more access to credit from financial institutions exhibit higher levels of allocative efficiency, indicating that firms in industries with lower credit constraints, such as less binding collateral requirements, experience more efficient resource allocation.

Weak insolvency systems enable unproductive firms to persist in the market, leading to excessive retention of labor and capital resources by these firms. As such, industries with a higher prevalence of zombie firms display lower levels of allocative efficiency. Furthermore, industries characterized by higher levels of product market concentration exhibit lower levels of allocative efficiency. This negative relationship underscores the presence of potential competition constraints and product market frictions that hinder the efficient allocation of resources across firms. Consequently, more productive firms have lower market shares of sales. For example, Ferro and Patiño Peña (2023) find a negative correlation between the presence of State-owned enterprises (SOEs) and allocative efficiency at the sector level.<sup>36</sup> Higher product market concentration also reflects barriers to entry, especially for higher-productivity firms (as most entrants are of low productivity), with detrimental effects on aggregate TFP through the market selection channel. Thus, while input market frictions play a significant role in depleting aggregate productivity, it is also important to address product market frictions to facilitate the efficient allocation of factors in Ecuador.

> 34 Regressions are carried out at the ISIC 2-digit level of sector granularity. Angrist and Pischke (2008) recommend that aggregated sectoral observations, from firm-level data, be weighted by the number of firm observations within each sector-year. Hence, regressions are weighted in this manner.

> 35 Job-to-job mobility is measured as the ratio of workers in a sector that transitioned into other jobs between t-1 and t relative to the average, between t-1 and t, of the number of workers in the same sector. The number of industry observations for specification (1) is lower given that one period is lost because of the job-to-job mobility definition. Non-wage labor costs relative to total labor costs measures how much of total labor costs are allocated towards employment costs that arise from mandated labor regulations instead of workers’ labor compensation. It is calculated as the sector average of firms’ ratio of non-wage labor costs to total labor costs. Total labor costs consist of wages plus social security contributions, severance pay costs, mandatory compulsory allowances (i.e., “thirteenth” and “fourteenth” salaries), compulsory worker participation in firms’ dividends, and other remunerations (i.e., transportation, commissions, bonuses, and childcare). Non-wage labor costs include mandatory compulsory allowances (i.e., “thirteenth” and “fourteenth” salaries), compulsory worker participation in firms’ dividends, and other remunerations (i.e., transportation, commissions, bonuses, childcare). The minimum wage incidence is measured as the ratio between the mandated minimum wage and the sector median wage. Higher values of this ratio imply that the minimum wage is more binding for firms within the sector. The share of firms with access to credit from a financial institution is measured as the ratio of firms in a sector with a loan from a financial institution relative to the total number of firms in the sector. Zombie firm presence is measured as the share of zombie firms relative to the total number of firms in a sector. The product market concentration variable was calculated using the sectors’ HHI.

> 36 Ferro and Patiño Peña (2023) find a negative and significant relationship between the SOE share of sector-level employment and the sector covariance between firms' revenue productivity and their value-added share, i.e., a measure of allocative efficiency.

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## **Table 1: Drivers** **<u>of Allocative Efficiency</u>**

<!-- Start of picture text --> Dependent Variable: Sector covariance between firm revenue productivity and value-added share<br>Structural/Policy Drivers: (1) (2) (3) (4) (5) (6) (7)<br>Job-to-job mobility 5.804 3.892<br>[0.115]*** [0.111]***<br>Non-Wage Labor Costs/Total Labor Costs -0.554 -0.42<br>[0.015]*** [0.015]***<br>Minimum Wage Incidence -1.289 -0.295<br>[0.030]*** [0.032]***<br>Access to Finance from Financial Institution 3.225 3.038<br>[0.021]*** [0.024]***<br>Zombie Firm Presence -0.140 -0.270<br>[0.020]*** [0.02]***<br>Product Market Concentration -0.444 -0.037<br>[0.022]*** [0.022]*<br>N 592 666 666 666 666 666 592<br><!-- End of picture text --> Source: INEC. Authors’ Elaboration. Note: This table reports the coefficient, μ�, of Equation (3)’s specification. Regressions are carried out at the ISIC 2-digit level of sector granularity. The following sector indicators of structural and/or policy drivers of misallocation are considered: job-to-job mobility, non-wage labor costs relative to total labor costs, minimum wage incidence, share of firms with access to credit from a financial institution, zombie firm presence, and product market concentration. Job-to-job mobility is measured as the ratio of workers in a sector that transitioned into other jobs between t−1 and t relative to the average, between t−1 and t, of total the number of workers in the same sector. The number of industry observations for specification (1) is lower given that one period is lost as a result of the job-to-job mobility definition. Non-wage labor costs relative to total labor costs measures how much of total labor costs are allocated towards employment costs that arise from mandated labor regulations instead of workers’ labor compensation. It is calculated as the sector average of firms’ ratio of nonwage labor costs to total labor costs. Total labor costs consist of wages plus social security contributions, severance pay costs, mandatory compulsory allowances (i.e., “thirteenth” and “fourteenth” salaries), compulsory worker participation in firms’ dividends, and other remunerations (i.e., transportation, commissions, bonuses, childcare). Non-wage labor costs include mandatory compulsory allowances (i.e., “thirteenth” and “fourteenth” salaries), compulsory worker participation in firms’ dividends, and other remunerations (i.e., transportation, commissions, bonuses, childcare). The minimum wage incidence is measured as the ratio between the mandated minimum wage and the sector median wage. Higher values of this ratio implies that the minimum wage is more binding for firms within the sector. The share of firms with access to credit from a financial institution is measured as the ratio of firms in a sector with a loan form a financial institution relative to the total number of firms in the sector. Zombie firm presence is measured as the share of zombie firms relative to the total number of firms in a sector. The product market concentration variable calculated using the sectors’ HHI. Each regression controls for industry and year fixed effects. Columns (1) – (6) consider only one regressor at a time, while column (7) includes all regressors within the specification. As Angrist and Pischke (2008), regressions weigh aggregate sectoral observations by the number of firm observations within industry-year.

# **6 “Missing Large” and “Missing Young” Firms in Ecuador**

This section discusses supportive evidence that the previously studied market distortions have also led to anemic firm size and firm age distributions in Ecuador. The distribution of firms by size in Ecuador remained stable for the sample period, as Figure 11 illustrates. Approximately 90.9 percent of formal firms in Ecuador are classified as micro firms, while small, medium, and large firms represent around 7.0, 1.7, and 0.4 percent, respectively.<sup>37</sup> Figure A 13 (left panel) displays the distribution of firms by size in the US. Although microenterprises still dominate, their contribution is more than 30 percentage points lower than that of Ecuador. Conversely, the share of large firms in the US is more than double that of Ecuador. Consequently, relative to the US, Ecuador exhibits an underrepresentation of large firms and an overrepresentation of microenterprises.

> 37 Considering the limitations of the data, Figure 11 depicts the distribution of only formal firms. Given that smaller employment levels are typically associated with informal productive units, it is expected that the concentration of firms in smaller size groups would be even higher for the total economy (formal plus informal enterprises).

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<!-- Start of picture text --> 100.0%<br>90.0%<br>80.0%<br>70.0%<br>60.0%<br>50.0% 5 5x Ssx x& x6 ¥=<br>a a a a a a<br>40.0% S =} S s a a<br>30.0%<br>20.0%<br>10.0%<br>xx Rx xx x8 x2 zx xx xx xx<br>0.0% 2=o Lea6 Ssa6 ZSa6 eSa)—_ 2Sa6—= 2Saé eoa6 es3<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>=Micro mSmall = Medium s Large<br><!-- End of picture text -->
<!-- Start of picture text --> 45.0%<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0% 2 3 a S<br>10.0%<br>5.0%<br>0.0%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>TMMicro @Small TMMedium ® Large<br><!-- End of picture text --> <!-- Start of picture text --> 60.0%<br>50.0%<br>40.0%<br>30.0%<br>x<br>g % oe<br>20.0% = &$z<br>3<br>10.0%<br>0.0%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>m<S =5-9 =10-14 15-19 m>=20<br><!-- End of picture text -->
<!-- Start of picture text --> 50.0%<br>45.0%<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0%<br>10.0% zzxazs PezaeSay<br>5.0% Vi a t<br>0.0%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>m<5S =5-9 ©10-14 15-19 m>=20<br><!-- End of picture text --> _ SS
<!-- Start of picture text --> 208,876 Net employment variation Employment82,357 Variation Intensive vs Extensive margin<br>200000 200000<br>100000 104,002 100000. aS.A8<br>g F3 $7,336<br>2S 23520 26,063 —3 4219 767<br>=aa __ am i5 P == or<br>= 6,339 wo 2 -33,816 -20,236<br>2 -28,555 -100000<br>-100000<br>146,529<br>145,742 -200000 -158,903<br>-200000 rea 2013 «2014 «2015 2018 2017 2018 eu) 2020<br>2013 2014 2018 2016 2017 2018 2010 2020<br>Employment Variation: Entry/Exit/Expanding/Contracting<br>600,000<br>400,000<br>200,000<br>0<br>-200,000<br>-400,000<br>600,000<br>2013 2014 2015 2016 2017 2018 2019 2020<br>Bentry exit expanding «contracting<br><!-- End of picture text -->
<!-- Start of picture text -->
- 7<br>100,000 100,000<br>80,000 80,000<br>60,000 60,000<br>40,000 40,000<br>-40,000 -40,000<br>-60,000 ~60,000<br>80,000 80,000<br>-100,000 100,000<br>-120,000 -120,000<br>QSHSSSSSRSRSRSSSRSRSRSABSRSSBSRBSERRRERSRSSRSSSERSRSERSSSSSESSSRSERSESRRSRSERRRS<br>Micro ‘Small Medium Large<br>m Extensive Margin TM Intensive Margin @ Net Job Creation<br><!-- End of picture text -->
<!-- Start of picture text -->
- 7 =<br>140,000 140,000<br>120,000 120,000<br>100,000 100,000<br>80,000 80,000<br>60,000 60,000<br>40,000 40,000<br>° a | S a g & i E 0<br>-20,000 -20,000<br>-40,000 -40,000<br>~60,000 ~60,000<br>-80,000 -80,000<br>~_ RSSSSSASSRSRSESSSRSSRSSSASRSRSERSSSBSBSEBBERAERRRRRRRRRRRRARARRRARRRRRARAERRRRRARRRRR _—<br><s 59 10-14 15-19 >=20<br>m Extensive Margin Intensive Margin @ Net Job Creation<br><!-- End of picture text -->

<!-- Start of picture text --> Net Job Creation by TFP Quintiles Net Job Destruction by TFP Quintiles<br>40,000 °<br>30,000<br>-10,000<br>2Q2 22<br>=2 20,000 2a<br>-20,000<br>10,000<br>3 -30,000<br>2013 2014 2015 2016 2017 2018 2019 2020 2013 2014 2015 2016 2017 2018 2019 2020<br>GEGE quintiequintie14 MEEE quintie2quintile5 MEE quintile 3 GEEGEE quintie1quintie+ MEMEME quintie2quintie 5 ME quintile3<br><!-- End of picture text -->
hit Within—firm wage inequality Between—firm wage inequality Vinw )=V(x ~)+V(@ )- V(x “)ptV(x ~)pt+2V(x “)p + ~~Cov(xnitBS) Cov(xnitB.?)~~ é V(xnitP) Y V(xnitB) hit
dispersion in revenue productivity and labor market rents. Consequently, the firm wage premia channel represents how factor misallocation across firms can impact workers' wages.<sup>42</sup> Moreover, the impact of misallocation (i.e., policy distortions) on wage inequality can be captured through the relationship between firms' revenue TFP and workers' wages, commonly known as the revenue productivity-wage pass-through. This pass-through determines how changes in revenue TFP are translated into changes in wages. Following Criscuolo (2021) revenue productivity-wage pass-through can be as follows: where h denotes a worker, i denotes a firm, j denotes a sector, and t denotes a year. Parameter φ captures the pass-through of revenue productivity, TFPR, to wages. To identify structural or policy factors that are associated with the pass-through of revenue TFP to wages, this paper follows Criscuolo et al. (2021) by allowing the coefficient on revenue TFP to vary according to structural or policy characteristics: where Z�,� is the structural or policy variable of interest.

# **8.2 Drivers of Wage Inequality**

As illustrated in the left panels of Figure 19 and Figure A 16, aggregate wage inequality among formal workers in Ecuador ranged from 0.3 to 0.4 between 2012 and 2021. These levels of wage inequality are consistent with the wage variation estimates reported by Criscuolo (2020) for OECD countries. Moreover, the left panel of Figure 19 highlights that more than half of the aggregate wage inequality in Ecuador can be attributed to differences in pay across firms for workers with similar characteristics (i.e., between-firm wage inequality). The right panels of Figure 19 and Figure A 16 reveal a rapid decline in aggregate wage inequality among formal workers until 2015, followed by a slower rate of reduction until it eventually stagnated and began to increase by the end of the period, mirroring the trend observed in the Gini Index as depicted in Figure 2. Furthermore, the right panel of Figure 19 highlights that over 60 percent of the changes in wage inequality stem from fluctuations in between-firm wage inequality. These findings underscore the importance of firm-level outcomes in driving wage inequality in Ecuador, which aligns with the findings of Barth et al. (2016) and Criscuolo (2020).

> 42 As highlighted by Van Biesebroeck (2014) and Criscuolo et al. (2021), under frictionless product and factor markets, differences in firm-level physical productivity result in variations in employment, with more productive firms employing a larger workforce compared to less productive firms. Additionally, marginal products of labor for workers with the same skill level equalize across firms, resulting in equal firm revenue TFP and wages for workers with similar characteristics. However, when input and output markets are characterized by technological frictions or policybased distortions, marginal products of labor no longer equalize across firms, leading to wage disparities among workers with similar characteristics for two reasons. First, market distortions generate labor market rents that can be shared between firms and workers. Differences in workers' bargaining power across firms determine the sharing of these rents, leading to wage variations between firms for similar workers. Second, market distortions generate dispersion in revenue TFP, as marginal products differ across firms. Even with the same worker-firm bargaining power or any wage-setting mechanism, similar workers will receive different wages due to the distorted performance of firms, characterized by variations in marginal products and revenue TFP. This occurs because the labor market does not solely adjust through quantities but also through wages, given that marginal products do not equalize. For instance, firms with higher physical productivity demand more labor relative to those with lower physical productivity. However, since marginal products do not equalize, revenue TFP differs across firms, causing higher physical productivity firms to pay higher wages than lower physical productivity firms for workers with similar characteristics. Consequently, market frictions contribute to variation in firms' marginal products, resulting in revenue TFP dispersion across firms and wage inequality among similar workers.

30
<!-- Start of picture text --> Wage Inequality Decomposition Contribution to Changes in Wage Inequality<br>0.45 3.00% 3.00%<br>0.4<br>035 1.00% 1.00%<br>0.3<br>0.25 -1.00% -1.00%<br>o2 -3.00% -3.00%<br>0.15<br>0.1 -5.00% -5.00%<br>0.05<br>oO -7.00% -7.00%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>TMBetween-firm & Within-firm mem Between-firm me Within-firm ——Aggregate Wage Inequality<br><!-- End of picture text --> <!-- Start of picture text --> Decomposition of Between-firm Wage Inequality Contribution to Changes in Between-firm Wage Inequality<br>100% 6.0% 6.0%<br>90% 4.0% 4.0%<br>80% 2.0% 2.0%<br>70% 0.0% 0.0%<br>60%50% -2.0% -2.0%<br>40h -4.0% -4.0%<br>30% -6.0% 6.0%<br>20% -8.0% -B.0%<br>10% -10.0% -10.0%<br>0% -12.0% “12.0%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2013’ Dots! 201s: 2ox6) 2017: zoRw 52019 i2oe0 {202%<br>=FirmPremium & Sorting mun Firm Premium smmSorting ——Between-firm Wage Inequality<br><!-- End of picture text -->
<!-- Start of picture text --> Micro Firms Small Firms<br>0.1 0.25<br>0.09<br>0.08 0.2<br>0.07<br>0.06 0.15<br>0.05<br>0.04 0.1<br>0.03<br>0.02 0.05<br>0.01<br>0 0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>=FirmPremium Sorting & Within-firm @FirmPremium Sorting & Within-firm<br>Medium Firms Large Firms<br>0.4 0.45<br>0.35 0.4<br>03 0.35<br>0.25 oS<br>0.25<br>02<br>0.2<br>0.15 0.15<br>0.1 01<br>0.05 0.05<br>0 0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>@FirmPremium Sorting & Within-firm =FirmPremium & Sorting & Within-firm<br><!-- End of picture text -->
<!-- Start of picture text --> Elasticity of Wages with respect to Firm revenue TFP<br>0.12<br>Z2% 01<br>a=<br>$ 0.08<br>é<br>5<br>2 0.06<br>8<br>3<br>oEy 0.040.<br>a=o3B 0.02<br>fra<br>0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> Elasticity of Wages with respect to Firm Revenue TFP by Gender Elasticity of Wages with respect to Firm Revenue TFP by Skill Level<br>02 g ont6<br>: Z<br>s = 0.14<br>Fs25 01 aF 0.12<br>g 2<br>2 0.08 Fy<br>=5s3<0.06 a}3 0.08O01<br>2=£8 0.04 =é32 0.06<br><gBa0.04<br>5<br>=E 0.020) reE 0.020)<br>2012 2013. «2014S 2015 2016«S 2017.—«s 2018 += 2019 2020 2012 -2013.««2014« 2015. 2016«= 2017'S 2018 += 2019-2020<br>Year Year<br>|=Men ® Women @High-skilled TMLow-skilled<br><!-- End of picture text -->
<!-- Start of picture text --> Pass-through of Revenue TFP to Wages Driven by Structural/Policy Drivers<br>20.0%<br>18.0%<br>16.0%<br>14.0%<br>& 12.0%<br>£7]32 10.0%<br>s<br>B 8.0%<br>6.0% |<br>4.0% | |<br>0.0%<br>Low High Low High Low High Low High<br>Non-wage Labor Costs Job Mobility Minimum Wage Competition in Product<br>Incidence Market<br>Structural & Policy Drivers<br><!-- End of picture text -->

2
product markets negatively affect the allocative efficiency of factor inputs. The higher pass-through in sectors with lower competition implies that increased barriers to competition result in higher wage inequality due to more allocative inefficiencies.

# **9 Conclusion**

Between 2012 and 2020, Ecuador's economic performance stagnated as growth driven by factor accumulation was offset by a significant decline in aggregate total factor productivity (TFP). This decline in aggregate TFP was driven by regulatory and institutional distortions that caused a persistent misallocation of factor inputs across firms. Furthermore, these frictions constrained firm-level technical efficiency growth and impeded healthy firm entry and exit dynamics. The decline in aggregate TFP was accompanied by a change in the trend of income inequality, whose decreasing tendency before 2014 plateaued thereafter and increased since 2018. These empirical patterns suggest that distortive policies with adverse effects on aggregate TFP also impact labor outcomes.

Factor misallocation across firms in Ecuador can be attributed to distortions in labor and capital input markets, as distortionary labor regulations (such as high firing costs and ad-hoc compulsory allowances) as well as barriers to credit (such as high collateral constraints and high interest rates) hinder the fluidity of the labor market, constrain firm access to capital, and allocate factors towards less productive enterprises. These highly distortive factor market frictions also constrain firm productivity growth, as firms must divert resources from innovation and capability upgrading to finance costly factor inputs. As a result, innovation related activities, like R&D expenditures, have reduced and firm-level TFP growth is mostly stagnated across the life cycle. Furthermore, these factor market distortions along with institutional inefficiencies (such as weak insolvency regimes) yield inefficient market selection that is characterized by a large entry and low exit of unproductive firms.

The policy-driven distortions that have led to Ecuador's underwhelming aggregate productivity outcomes have also generated distributional patterns of firms and job flows that further indicate the country's poor economic performance. Ecuador exhibits an underrepresentation of large firms in terms of numbers and economic activity (i.e., employment), while micro firms are overrepresented, also highlighting allocative inefficiencies as micro firms are less productive than large firms, on average. Additionally, these market frictions, coupled with the economic slowdown, have resulted in a shift of economic activity from younger to older firms. Aggregate job flows also portray anemic trends, with small, unproductive entrants creating most new jobs, while existing jobs are primarily destroyed by old, unproductive firms that stay in the market instead of exiting. Furthermore, these distortions that generate resource misallocation also account for up to 41 percent of aggregate wage inequality. These findings suggest that to enhance workers’ outcomes, it is also essential to address distortions that affect firm dynamics.

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