metadata: authors: 0: author: Amin,Mohammad
1: author: Jolevski,Filip
2: author: Islam,Asif Mohammed
count: World
docna: 0: docna: The Resilience of SMEs and Large Firms in
            the COVID-19 Pandemic : A Decomposition Analysis
docty: Policy Research Working Paper
abstracts: cdata!: This study analyzes the difference in
            the decline in sales between small and medium-size
            enterprises and large firms (the
            &#x201c;gap&#x201d;) following the outbreak of
            COVID-19 in 19 developing countries. The decline in sales as
            a percentage of the pre-pandemic level was bigger for small
            and medium-size enterprises by 12.2 percentage points. The
            paper uses the Kitagawa-Oaxaca-Blinder and quantile
            decomposition methods to estimate individual
            factors&#x2019; contributions to the gap at the mean and
            across the sales decline distribution. Several important
            results emerge. First, relative to large firms, small and
            medium-size enterprises faced greater incidence of input
            supply disruptions during the pandemic, had lower initial
            labor productivity levels, and were concentrated in
            country-industry cells with a bigger sales declines. These
            differences in the level of factors widened the gap. Small
            and medium-size enterprises also suffered more than large
            firms from a given level of financial constraints, input
            supply disruptions, and country-industry-specific factors,
            and benefitted less from a given level of initial labor
            productivity. These differences in the returns to factors
            also widened the gap. Second, the gap was much larger at the
            relatively high quantiles of sales decline distribution,
            indicating that relative to large firms, small and
            medium-size enterprises were much less resilient to large
            shocks than small shocks. Third, individual
            factors&#x2019; contribution to the gap varied across
            the sales decline distribution. Thus, the optimal policy mix
            depends on the size of the shock. Fourth, there were some
            important differences between geographical regions in what
            drove the gap. Thus, an eclectic policy approach is needed
            that duly accounts for the prevailing local conditions.
display_title: The Resilience of SMEs and Large
            Firms in the COVID-19 Pandemic : A Decomposition Analysis
listing_relative_url: /research/2023/09/34158035/resilience-smes-large-firms-covid-19-pandemic-decomposition-analysis
topicv3: Economic Shocks and Vulnerability m1326506 702,Financial Crisis Preparedness m1326513 321,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2023-09-08T00:00:00Z
dois: 10.1596/1813-9450-10562
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 717499
1: author_key: 1429199
2: author_key: 950662
model_extractions: Policy Research Working Paper 10562

# The Resilience of SMEs and Large Firms in the COVID-19 Pandemic

## A Decomposition Analysis

_Mohammad Amin Filip Jolevski Asif M. Islam_ Global Indicators Group & Middle East and North Africa Region September 2023
Policy Research Working Paper 10562

# **Abstract**

This study analyzes the difference in the decline in sales between small and medium-size enterprises and large firms (the “gap”) following the outbreak of COVID-19 in 19 developing countries. The decline in sales as a percentage of the pre-pandemic level was bigger for small and medium-size enterprises by 12.2 percentage points. The paper uses the Kitagawa-Oaxaca-Blinder and quantile decomposition methods to estimate individual factors’ contributions to the gap at the mean and across the sales decline distribution. Several important results emerge. First, relative to large firms, small and medium-size enterprises faced greater incidence of input supply disruptions during the pandemic, had lower initial labor productivity levels, and were concentrated in country-industry cells with a bigger sales declines. These differences in the level of factors widened the gap. Small and medium-size enterprises also suffered more than large firms from a given level of financial constraints, input supply disruptions, and country-industry-specific factors, and benefitted less from a given level of initial labor productivity. These differences in the returns to factors also widened the gap. Second, the gap was much larger at the relatively high quantiles of sales decline distribution, indicating that relative to large firms, small and medium-size enterprises were much less resilient to large shocks than small shocks. Third, individual factors’ contribution to the gap varied across the sales decline distribution. Thus, the optimal policy mix depends on the size of the shock. Fourth, there were some important differences between geographical regions in what drove the gap. Thus, an eclectic policy approach is needed that duly accounts for the prevailing local conditions.

This paper is a product of the Global Indicators Group, Development Economics and the Office of the Chief Economist, Middle East and North Africa Region. 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 mamin@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 Resilience of SMEs and Large Firms in the COVID-19 Pandemic: A Decomposition Analysis**

## August 2023

By: Mohammad Amin*, Filip Jolevski**, and Asif M. Islam*** Keywords: SMEs, crisis, COVID-19, resilience, firm size, decomposition JEL Codes: D21, D22, E32, L25

* Corresponding author. Senior Economist, Enterprise Analysis Unit, DECIG, World Bank, Washington, DC. Email: mamin@worldbank.org.

** Economist, Enterprise Analysis Unit, DECIG, World Bank. E-mail: fjolevski@worldbank.org

*** Senior Economist, Middle East and North Africa region Chief Economist Office of the World Bank. E-mail: aislam@worldbank.org

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the data available. We would also like to thank Jorge Luis Rodriguez <mark>Meza, Norman Loayza, and participants at a seminar organized by DECIG, World Bank for useful</mark> <mark>comments. All remaining errors are our own.</mark>
# **1. Introduction**

Small and medium-size enterprises (SMEs) provide jobs and incomes to millions of individuals in the developing countries (see ILO 2019, ILO and GIZ 2013, Ayyagari et al. 2011, 2014). According to the World Bank’s Enterprise Surveys data on formal firms in 125 developing countries, SMEs (5-99 workers) account for about 47 percent of the total formal sector employment and 52 percent of the total output on average across countries. The rest is accounted for by large firms (>99 workers). Ayyagari et al. (2014) report that over 95 percent of the new formal sector jobs are created by SMEs in the developing world and only 5 percent by large firms. SMEs are also expected to promote inclusive growth (see OECD 2019). The contribution of SMEs is even higher if we include the self-employed, micro firms (fewer than 5 workers), and the unregistered or informal firms (see ILO 2019). Thus, it is not surprising that most countries have implemented policies to promote SMEs.

However, a lingering concern with SMEs is that they may be unduly affected by economic downturns and crises, such as the COVID-19 pandemic. That is, SMEs are less resilient to crises than large firms. This may happen for several reasons that are discussed in detail in Section 2. The relative underperformance of SMEs in periods of crises implies that SMEs act as destabilizers, magnifying the adverse impact of crises. Furthermore, while it may not be a surprise that SMEs were considerably affected by the COVID-19 pandemic, what is interesting is uncovering the potential contributors that favored large firms while debilitating SMEs. A rigorous comparison and quantification of SMEs’ and large firms’ performance during a crisis in the context of developing countries is lacking (see Section 2). This study attempts to fill this gap in the literature. It analyzes the difference in the decline in sales due to the COVID-19 pandemic between SMEs and large firms (henceforth, the “sales decline gap” or the “gap”) in 19 developing countries spread 2
across several regions of the world. At the mean and various quantiles of the sales decline distribution, the size of the sales decline gap and the contribution of individual factors to the gap are estimated. This is achieved using the traditional or mean Kitagawa-Oaxaca-Blinder (KOB) decomposition methodology (Kitagawa 1955; Oaxaca 1973; Blinder 1973) and the quantile decomposition methodology (Firpo et al. 2007, 2009). The decomposition exercise yielded several important and policy-relevant insights.

The COVID-19 pandemic resulted in a decline in sales of 34.8 percent from the prepandemic level for SMEs and 22.6 percent for large firms. Thus, the decline in sales was bigger for SMEs than for large firms by 12.2 percentage points. This is the unconditional total gap. The decline in sales is larger for SMEs in all the countries except Chad and Mongolia (see figure 1). One reason for the sales decline gap is that the level or endowment of factors that influence the decline in sales is different for SMEs and large firms (the “endowment” effect). Another reason is that the determinants of sales decline have a different impact on SMEs and large firms (the “structural” effect). Both the “endowment” and “structural” effects contribute significantly to our sample’s sales decline gap at the mean. Of the total gap of 12.2 percentage points at the mean in the full sample, the aggregate (of all the individual factors’) “endowment” effect, accounts for about 7.96 percentage points, or about 65.1 percent of the total gap. The remaining gap is due to the “structural” effects.

Regarding the statistically significant individual “endowment” effects at the mean, SMEs have higher levels of input supply disruptions and lower initial labor productivity than large firms, which widen the sales decline gap. This is not surprising as large firms have stronger connections to supply chains (see Bak et al. 2023 for a literature review) and higher levels of productivity that allow them to buffer against shocks (confirmed below). Another factor widening the gap is that, 3
relative to large firms, SMEs are disproportionately located in country-industry pairs that experienced a larger decline in sales. SMEs have less foreign ownership and are less constrained by skills shortages, which help to narrow the gap in some of the specifications. For the “structural” effects, SMEs are more adversely affected than large firms by input supply disruptions, financial constraints, high share of women workers, higher share of exports in total sales, and countryindustry specific factors. These differences widen the gap. However, SMEs are less adversely affected by high tax rates than large firms, which narrows the gap.

The quantile decomposition methodology developed by Firpo et al. (2007; 2009) is used to examine the sales decline gap at various quantiles of the sales decline distribution. One motivation for the exercise is that the decline in sales varies substantially across firms. About 74.6 percent of the firms in our sample experienced a decline in sales, 17.3 percent no change, and 8.1 percent an increase. An increase in sales can result from very different factors than a decrease in sales. The same applies to a small vs. large decline in sales. Thus, the factors that influence the performance of SMEs and large firms may also vary at low vs. high levels of sales decline. Another motivation is that the sales decline gap in our sample varies significantly across the sales decline distribution. Figure 2 illustrates the point. The indication here is that the factors that drive the gap are also different at low vs. high levels of the sales decline.

The quantile decomposition exercise provides several interesting results. We preview some of the findings based on the full sample. First, the sales decline gap is statistically significant at all the quantiles, but it is much smaller at the lower quantiles (Figure 2). This indicates that SMEs underperform large firms much more when the crisis (sales decline) is more severe. Second, the aggregate “endowment” effect plays a much bigger role in driving the total gap at higher quantiles than at lower quantiles of the sales decline distribution. The opposite holds for the aggregate 4
“structural” effects. Thus, for relatively large shocks, the weaker resilience of SMEs is mainly because SMEs have fewer resources than large firms. However, for the relatively smaller shocks, it is mainly because SMEs benefit less than large firms from a given level of resources. Third, the individual determinants of the sales decline gap contribute differently across the sales decline distribution. For instance, differences in the levels of labor productivity between SMEs and large firms widen the gap significantly via the “endowment” effect at the 40<sup>th</sup> to 90<sup>th</sup> quantiles but not at lower quantiles. Thus, efforts to reduce the sales decline gap by improving SMEs’ labor productivity are more effective if targeted to SMEs at the relatively high quantiles of the sales decline distribution. However, differences in the incidence of input supply disruption matter across all quantiles, emphasizing their importance in closing the gap.

We explore the role of regional factors. To this end, we perform the mean and quantile decomposition analysis separately for countries in Sub-Saharan Africa (SSA), Eastern Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), and the Middle East and North Africa (MENA). Some commonalities and differences emerge. Regarding commonalities, we find the following statistically significant results hold in all the four regions: the decline in sales at the mean is much bigger for SMEs than large firms; country-industry specific factors contribute to the gap at several quantiles; supply disruptions widen the gap at the mean via the “endowment” effect; the sales decline gap is positive and large at most quantiles; 10 of the 17 explanatory variables that we consider contribute to the total gap at one or more quantiles. Further, we find that the aggregate “endowment” effect at the mean widens the gap by a large 37 to 124 percent of the total gap across the four regions, which is statistically significant in three regions. Also, 16 of the 17 variables that we consider contribute significantly to the gap at one or more quantiles in at least three out of the four regions. These commonalities suggest that these factors may be prominent and are likely to 5
matter for a global sample of economies beyond those included in our sample. Regarding some of the differences, the sales decline gap is only 6 percentage points in MENA and about 11 percentage points in the other regions; government support significantly narrows the gap in MENA at some of the quantiles but widens it in all the other regions; a shortage of skilled workers widens the gap in SSA and MENA at some of the quantiles and narrows it in ECA and LAC. Several other differences are discussed in detail below. Thus, an eclectic approach is required that incorporates the common findings in this study and the broader literature while appropriately accounting for the prevailing regional or local conditions.

We contribute to the literature in several ways. First, we use cross-country comparable survey data on formal SMEs and large firms in 19 developing countries. Most previous studies on the impact of COVID-19 or other crises on small vs. large firms are restricted to rich countries or a handful of developing countries (see Section 2). Second, we identify several factors that contribute to the sales decline gap between SMEs and large firms. An estimate of their contributions is provided. Third, we use the mean and quantile decomposition methodology to decompose the contribution of the individual factors to the sales decline gap into “endowment” and “structural” effects. To the best of our knowledge, ours is the first such study. Fourth, we shed light on how the size of the gap and the contributions of the individual factors to the gap via the “endowment” and “structural” effects vary along the sales decline distribution. We are not aware of any previous work that does so. Fifth, we explore heterogeneities between geographical regions in the size and nature of the sales decline gap. This is important for assessing the relevance of regional factors. Sixth, the breadth of the surveys we use enables specific policy recommendations regarding the importance of salient firm factors, such as initial labor productivity, input supply 6
disruptions during the crisis, financial constraints, international trade and foreign ownership, skills shortages, and the presence of women workers.

We emphasize that the decomposition analysis is descriptive and not causal. The descriptive approach is consistent with previous research (see, for example, Nix et al. 2016, Essers et al. 2021, and Islam and Amin 2023).

# **2. Literature review**

In this section, we review the literature on the impact of the COVID-19 pandemic and earlier shocks on firms. The focus is on studies that compare the impact on small vs. large firms. There are several other studies that deal with the impact of recessions and crises on small firms alone. Some of these studies are discussed in Section 3, where we motivate our main explanatory variables.

# 2.1 _Small vs. large firm performance during crises_

Recent studies have explored how firms of different sizes performed during the COVID-19 pandemic. Using firm-level survey data on 23 economies in ECA collected by the World Bank’s Enterprise Surveys, Bruhn et al. (2023) found that small firms suffered larger declines in output and employment due to the COVID-19 pandemic than large firms. Using data from the US, Bloom et al. (2021) showed that the smallest offline firms experienced sales drops of over 40 percent due to the pandemic, compared with a 10 percent drop for the largest offline firms. Ding et al. (2021) used data on 61 mostly developed economies and found that the stock market response to the COVID-19 shock was less negative for large corporations than for small firms. Dörr et al. (2022) found that in Germany, small firms experienced stronger financial distress than large firms due to 7
the pandemic and were more likely to have gone bankrupt without policy assistance. There are other studies, such as Liu et al. (2022) for China, that found a large adverse effect of the pandemic on small firms. However, these studies do not provide a comparison with large firms. We discuss some of these studies in Section 3, where we motivate our main explanatory variables.

<mark>There is also evidence based on other types of shocks. For instance, Chen and Lee (2023) reported that in 18 developed economies in Europe, the global financial crisis of 2008–2009 resulted in a much larger decline in the TFP growth rate for SMEs (including micro firms) than for large firms. Peric and Vitezic (2016) found that during the recession of 2008–2013 in Croatia, larger firms performed much better in terms of turnover growth. Gertler and Gilchrist (1994) found that during the 1981–82 recession in the US, large firms performed better than small firms in terms of total production. The study by Ozar et al. (2008) for Türkiye found that during the 2001 Turkish financial crisis, large firms performed better in terms of employment growth rate. While most studies confirm less resilience to crises among relatively small firms, there are some that show the opposite. For example, Varum and Rocha (2013) found that periods of economic downturn or crisis between 1988 and 2007 in Portugal led to a larger decline in firm growth for large firms than SMEs.</mark> <mark>T</mark> he studies discussed above are, at best, a starting point for understanding the resilience of small vs. large firms during a crisis. These studies do not explore the mechanisms or contributions of the underlying factors to the differential impact by firm size. Another limitation is that many of these studies focus on developed countries. A few studies, such as Ozar et al. (2008) and Liu et al. (2022), are based on developing countries. However, these studies are restricted to a single country, with no indication if the results apply more broadly to the developing world.

8
# 2.2 _Reasons for differential effects by firm size_

There are several reasons for the differential impact of a crisis on small and large firms. First, access to finance and cash reserves plays a key role in allowing firms to tide over a crisis (see Amin and Viganola 2023 and Ding et al. 2021). Smaller firms have fewer cash reserves and poorer access to formal finance. Thus, the liquidity crunch that followed the COVID-19 pandemic and the earlier financial crises is likely to adversely impact small firms more than large firms (see Iyer et al. 2014). Second, small firms’ relative shortcomings in terms of technological, managerial, and human capabilities may reduce their capacity to deal with a crisis (see United Nations 2022, Varum and Rocha 2013). Third, small firms’ greater dependence on a small set of customers, suppliers (Herskovic et al. 2022, Marconatto et al. 2022), and markets (see United Nations 2022) may lead to increased difficulties in maintaining their activity in the face of the crisis. Fourth, one argument in favor of smaller firms is that smaller firms may suffer less from inertia, rigidity, and sunk costs (see Varum and Rocha 2013, Tan and See 2004, and Dean et al. 1998). This allows smaller firms to be more flexible when adapting to economic downturns. However, Belghitar et al. (2022) argue that despite their lean organizations, smaller firms are quite often characterized by a cost structure that is more rigid than that of larger organizations. As a result, even a small drop in sales can have an important impact on how efficiently the assets are used. Further, smaller firms tend to have fewer resources than large firms, which makes it more difficult for smaller firms to detect and respond to ever-changing environments (Mueller and Jungworth 2022, Chan et al. 2019). Fifth, another factor favoring smaller firms is that such firms tend to concentrate on activities characterized by economies of agglomeration rather than scale. Thus, smaller firms are less affected by a reduction in demand and the subsequent production cuts (Varum and Rocha 2013). Sixth, there are several factors, such as management quality, ownership structure, the presence of 9
women in the workforce, and innovation capacity, that may affect firms’ ability to deal with the COVID-19 pandemic and other such crises. Although there is no formal study, the possibility that these factors have a differential impact on small and large firms cannot be ruled out. Section 3 discusses several of these factors in detail.

# **3. Data and Main Variables**

# 3.1 _Data description_

Our data sources are the World Bank’s Enterprise Surveys (ES) and the World Bank’s Enterprise Surveys COVID-19 follow-up (COV-ES). The ES are firm-level surveys representative of a country’s registered or formal private sector employing 5 or more employees in most manufacturing and select service industries. Informal firms and businesses operating in agriculture, the extractive sector, and service sectors such as banking and finance, health care, and education are excluded from the sampling universe. A standard sampling procedure based on location, sector, and firm size stratifications ensures representativeness at the country, sector, and firm size level, while the use of a common questionnaire guarantees comparability across countries. The ES included in this paper were conducted between 2016 and 2020, covering the pre-pandemic period defined as before March 2020. After the pandemic was declared, the same firms interviewed in the ES were re-contacted with the aim of measuring the effects of the pandemic (COV-ES). These interviews were administered over the phone. Our baseline sample includes all the firms in the developing countries (low- and middle-income countries) that were operational at the time of COV-ES, were successfully interviewed in COV-ES between May and December 2020, and for which data on the main variables are available. There are 5,197 firms (SMEs and large) spread across 19 developing countries in this sample. In Appendix A, Table A1 contains the list of 10
countries, the number of firms, and the period the ES and COV-ES cover; Table A2 contains a formal definition of all the variables; and Table A3 contains the summary statistics of the variables.

# _3.2 Main variables_

The outcome or dependent variable is the decline in sales in the last month (after the outbreak of COVID-19) compared to sales in the same month one year ago (pre-pandemic), expressed as a percentage of the latter (Sales decline). The mean decline in sales equals 32.2 percent, the median decline is 30, and the standard deviation is 32.7. The decline ranges between 100 and -300 percent. A positive value of Sales decline implies that sales were lower after the outbreak of the pandemic than before, and a negative value implies an increase in sales following the pandemic. About 74.6 percent of the firms experienced a decline in sales, 17.3 percent no change, and 8.1 percent an increase. By country, the mean decline was smallest in Belarus (16 percent) and highest in Niger (53.8 percent).

# _3.2.1 Main explanatory variables_

The main explanatory variable is a dummy variable equal to 1 if the firm is an SME and 0 otherwise (SME). Following the definition used by the ES for survey stratification, SMEs are all firms that had 5-99 full-time workers employed in the pre-pandemic period. The rest (>99 full-time workers) are large firms. There are 4,097 SMEs in our baseline sample, which is about 78.8 percent of all firms. We consider several determinants of the firm’s performance during the pandemic. Many of these determinants are for the pre-pandemic period (taken from ES), and some are for the period after the outbreak of the pandemic (taken from COV-ES). We focus more on the pre-pandemic variables because information is available on a much larger set of variables before the outbreak of 11
the pandemic than after. This provides a sense of whether pre-existing conditions of businesses play a part in their resilience. Also, it helps avoid the simultaneity problem.

We begin with a set of dummy variables indicating the country times industry to which the firm belongs (Country-Industry fixed effects). Industry is defined at the 2-digit level (ISIC Revision 3.1). These dummy variables account for all industry-wide factors, specific to each country, that influence the decline in sales. By construction, they also account for all country-wide factors (country fixed effects) and industry-wide factors (industry fixed effects). Several studies have documented the importance of country-specific factors in the incidence, spread, and intensity of COVID-19 infections. Bargain and Aminjonov (2021) show that COVID-19 infections per capita were higher in countries with a higher poverty rate. Pitterle and Niermann (2021) show that a country’s GDP growth performance during the pandemic was affected by its stringency of containment measures, good governance, provision of fiscal support to private agents, and the strength of the macroeconomic fundamentals. Chang et al. (2022) identify 21 country-level factors, some of which include population density, level of urbanization, education, democracy, corruption, quality of institutions, and health care infrastructure. Although limited, direct evidence on the impact on firms is also available. For instance, Hu and Zhang (2021) show that better health care, financial development, and the quality of institutions at the country level significantly improve firms’ ability to deal with the pandemic.

Industry-specific differences may arise because some “essential” industries were not affected or were less affected by the mandatory lockdown. Also, the scope of remote work and online sales varies across industries depending on the need for face-to-face interaction with customers and physical proximity to other workers (see Bloom et al. 2023, Fairlie and Fossen 2022, del Rio-Chanona et al. 2020, Stemmler 2022).

12
Disruptions in the supply of inputs due to the pandemic are well documented, although their impact on firms remains to be thoroughly analyzed (see Butt 2021, Pitschner 2022, Meier and Pinto 2020). We capture supply disruptions with a dummy variable equal to 1 if the firm’s supply of inputs, raw materials, or finished goods and materials purchased to resell decreased in the last month (after the outbreak of the pandemic) compared to the same month a year ago (prepandemic). The data source is COV-ES.

Voluntary dropouts from the labor market due to health or family reasons may have resulted in firms facing a labor shortage (see Pitschner 2022, Forsythe et al. 2022). For instance, Forsythe et al. (2022) find that for the US economy, the labor market remained surprisingly tight throughout the entire pandemic, despite the dramatic job losses. A shortage of hard-to-replace skilled workers may be especially worrisome. The COV-ES did not ask firms about skilled shortages. We use a question in the ES that asks firms how severe (on a 0–4 scale) inadequately educated workers is as an obstacle to their current operations (Skills obstacle).

Next, we account for the labor productivity of the firm prior to the pandemic using ES. It equals the log of total sales of the firm (in 2009 USD) in the last fiscal year divided by the number of full-time workers at the end of the fiscal year covered by the ES. Several studies have found a positive link between a firm’s pre-pandemic productivity and performance during the pandemic in terms of sales, employment, and survival (see Bloom et al. 2023, Bruhn et al. 2023, Muzi et al. 2022, Fernández-Cerezo et al. 2022, Kozeniauskas et al. 2020). Unlike earlier recessions, COVID19 resulted in a larger drop in women’s relative to men’s employment (Bundervoet et al. 2022, Adams-Prassl et al. 2020, Russell and Sun 2020, Bluedorn et al. 2023). One reason for this could be the mandatory closure of schools and the collapse of childcare facilities due to the pandemic. As a result, firms that are more dependent on women workers are likely to be more adversely 13
affected. We capture this effect using a dummy variable equal to 1 if the share of women workers in the firm prior to the pandemic was more than 25 percent and 0 otherwise (High share of women workers). The data source is ES.

We also consider the financial condition of the firm. Studies find that a stronger financial condition (more cash, less debt, and better access to finance) prior to the pandemic helped firms perform better during the pandemic (Amin and Viganola 2023, Ding et al. 2021). Since many SMEs may obtain finance from non-bank financial institutions, our main measure of a firm’s financial constraints is a broad one. This equals the pre-pandemic severity level (on a 0–4 scale) reported by the firms of access to finance as an obstacle to their operations (Finance obstacle). The second measure focuses on bank finance. It is equal to 1 if the firm had an overdraft facility prior to the pandemic and 0 otherwise (Overdraft). The data source for the two finance variables is ES. The age of the firm and location (urban vs. rural) have been found to impact the firm’s performance during the pandemic. Using data on over 4,000 firms in Spain, Fernández-Cerezo et al. (2022) report a much larger decline in sales due to the pandemic among younger firms and firms located in urban vs. rural areas. Age may matter because younger firms are less experienced and have fewer resources to deal with the pandemic. Younger firms are also disadvantaged because they have a less developed supplier network and a less dedicated customer base. Urban areas may be more susceptible to the virus because of greater contact between people due to higher population density, more crowded housing, and more interconnected public transport. Thus, we account for a dummy variable equal to 1 if the firm is 10 years old or younger (in 2020) and 0 otherwise (Young firm), and a dummy variable equal to 1 if the firm is in the country’s capital city and 0 otherwise (Capital city). The data source for both variables is ES.

14
We account for the proportion of the firm’s sales made directly abroad (Exports) and the proportion of the firm owned by foreign entities (Foreign ownership). Both the variables are prepandemic and taken from the ES. Studies have found that exporters (see Demir and Javorcik 2020) and firms with foreign ownership (see Liu et al. 2021, Gu et al. 2020) may be more at risk from the pandemic. This could be because exporters and foreign-affiliated firms tend to rely more on global supply chains, which have undergone tremendous shocks during the pandemic. Lack of proper international law means that an unexpected adverse economic shock, such as COVID-19, may increase the risks of non-payment or non-delivery of prepaid goods across borders.

Another important issue is the support provided by the government to help private firms deal with the pandemic. Cirera et al. (2021) find that policies such as credit and cash transfers appear to be helping firms address liquidity constraints, while receiving wage subsidies seems to be associated with a lower probability of firing workers. Guerrero-Amezaga et al. (2022) find that across eight Latin American countries, government support led to improved outcomes at the firm level. Interestingly, both of these studies find that the smallest firms benefit less from government programs. Thus, we account for a dummy variable equal to 1 if the firm received any national or local government support in response to the pandemic and 0 otherwise (Government support). The data source for the variable is COV-ES.

A high tax burden squeezes profits and drains the firm of much-needed cash reserves. As a result, high taxes may adversely affect firm performance during the pandemic. Our countryindustry fixed effects capture the tax burden that is common to all firms within a country or country-industry cell, but not the difference between SMEs and large firms. We account for such effects using a dummy variable equal to 1 if the firm in the pre-pandemic period reported the tax rates as a major or very severe obstacle to its current operations and 0 if it reported them as a no 15
obstacle, minor obstacle, or moderate obstacle (Tax rates are a major obstacle). Note that even through SMEs and large firms may face similar tax rates, the burden they impose to SMEs vs large firms may be different. The data source is ES.

Apart from taxes, there may be differences in enforcement or in the ability of the firms to deal with corruption, power outages, and other such factors. We account for the overall quality of the business environment by using the average severity level (on a 0–4 scale) of the following as obstacles for the firms’ operations in the pre-pandemic period: electricity, tax administration, obtaining licenses and permits, corruption, and labor laws (Obstacles severity). The data source is ES.

The capacity of the firms to adapt existing products, delivery methods, and operating methods (such as work from home) or introduce new ones may impact their performance during the pandemic (see Clampit et al. 2022). We capture such effects through firms’ innovation and R&D activity prior to the pandemic. That is, we use a dummy variable equal to 1 if the firm spent on R&D activity during the three years prior to the year the ES was administered and 0 otherwise, and a dummy variable equal to 1 if the firm introduced a new product during the three years prior to the year the ES was administered and 0 otherwise. Both variables are for the pre-pandemic period and taken from the ES. We show that our results do not change if we use adjustments made by the firm after the outbreak of the pandemic in product, process, and delivery.

<mark>We consider the firm’s legal status as a possible factor influencing its performance. A few studies have shown that business legal forms may differ with respect to risk-sharing and risklimiting possibilities, which in turn impact firms’ ability to cope with external shocks (see Doś et al. 2022). To this end, we use two dummy variables for the pre-pandemic period: one for sole</mark> 16
<mark>proprietorship firms vs. the rest, and another for partnerships or shareholding companies with nontraded shares vs. the rest. The data source is ES.</mark>

# 3.2.2 _Additional explanatory variables_

For robustness, we experimented with a few additional variables. These variables did not have any significant effects on the sales decline gap or on the results for the main explanatory variables listed above. The additional variables for the pre-pandemic period and taken from the ES include: a dummy variable equal to 1 if the firm has a woman owner and 0 otherwise; a dummy variable equal to 1 if the firm suffered losses due to crime during the year and 0 otherwise; and the growth rate of employment over the last three years. Additional variables for the period after the outbreak of the pandemic taken from COV-ES include the percentage of sales made online and the percentage of the workforce working remotely.

# **4. Empirical results**

# _4.1 Mean differences between SMEs and large firms_

Table 1 provides descriptive statistics and means tests of the main variables between SMEs and large firms. The decline in sales for SMEs is higher than for large firms by 12.2 percentage points (34.8 vs. 22.6 percentage points). The difference is significant at the 1 percent level. This is the (unconditional) total gap in sales decline at the mean between SMEs and large firms (henceforth “gap” or “total gap”). Figure 3 provides the kernel density plot of the sales decline for SMEs and large firms.

The differences in the level of the explanatory variables between SMEs and large firms help explain the “endowment” effects in the decomposition analysis. Unless stated otherwise, all 17
the differences between SMEs and large firms discussed in this paragraph are statistically significant at the 1 percent level. The labor productivity of SMEs is significantly lower by 0.51 log points, or about 40 percent, than that of large firms. About 67 percent of the SMEs, compared to a significantly lower 52 percent of large firms, experienced input supply disruptions. As expected, large firms have significantly better access to finance than small firms. That is, 48 percent of large firms have an overdraft facility, compared to 29 percent of SMEs. The severity of the finance obstacle equals 1.45 for SMEs and a significantly lower 1.24 for large firms. In contrast, large firms report a significantly higher level of the skills obstacle than SMEs (1.54 vs. 1.28). A significantly higher proportion of SMEs are young than large firms (21 percent vs. 11.3 percent). Compared to SMEs, large firms export significantly more of their output (19.3 percent vs. 5.2 percent) and have significantly higher foreign ownership (18.8 percent vs. 6.2 percent). Similarly, a significantly higher share of large firms spent on R&D than SMEs (20.5 vs. 9.3 percent) and introduced a new product or service (36.6 vs. 30.4 percent, a difference significant at the 5 percent level).

The levels of the remaining baseline variables are not significantly different between SMEs and large firms. These include proportion of firms that receive government support, proportion of firms that have a high share of women workers, proportion of firms located in the capital city, proportion of firms that report tax rates are a major obstacle, overall obstacle severity level, and the proportion of sole proprietorship and shareholding or partnership firms.

# _4.2 Differences in returns to drivers of sales decline_

In addition to the level of factors, the gap also depends on the differences in the returns to the factors between SMEs and large firms (“structural” effect). A difference in returns could be 18
because of differences in the ability to benefit from a given level of endowments or because of unobserved or unaccounted-for determinants of sales decline, such as management quality.

To uncover the differences in the returns to factors, we regress sales decline on the baseline explanatory variables discussed above. Regression results are provided in Table 2 for the full sample (column 1) and the subsamples of SMEs (column 2) and large firms (column 3). The main findings are summarized below.

An increase in labor productivity is associated with a smaller decline in sales, significant at the 1 percent level, in all three samples. Large firms benefit more from higher productivity than SMEs. That is, a 1 log point increase in labor productivity is associated with a 2.8 percentage point smaller decline in sales for large firms and 2.1 percentage points for SMEs. Disruptions in the supply of inputs are associated with a much bigger decline in sales in all three samples, significant at the 1 percent level. SMEs tend to suffer more. That is, input supply disruptions lead to a 27.9 percentage point bigger decline in sales for large firms and 33.1 percentage points for SMEs.

The relationship between the severity level of the finance obstacle and sales decline is positive and significant at the 5 percent level for SMEs. It is positive for the full sample too, but much smaller and statistically insignificant. For the large firm sample, the relationship is negative and statistically insignificant. Thus, SMEs are adversely affected by a more severe finance obstacle but not the large firms. However, having an overdraft facility reveals a different picture. Overdraft facility helps arrest some of the decline in sales due to the pandemic, but the effect is small and statistically insignificant in all three samples. Nevertheless, overdraft facility benefits large firms more than SMEs. It lowers sales decline by 2.9 percentage points for large firms compared to 0.72 percentage points for SMEs. One reason for this difference could be that banks typically favor large firms and may grant them bigger overdraft limits.

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An increase in the severity level of the skills obstacle is associated with a larger decline in sales for large firms, which is significant at the 5 percent level. The impact on SME and in the full sample is also positive, but much smaller and statistically insignificant. The decline in sales due to a unit increase in the skills obstacle is higher by a mere 0.26 percentage points for SMEs compared to 1.65 percentage points for large firms.

Higher exports are associated with a significantly larger decline in sales in the full sample and among SMEs. For large firms, the opposite result holds, although it is not significant. In short, exporting hurts SMEs in dealing with the pandemic but not large firms. The opposite result is obtained for foreign ownership. That is, higher foreign ownership has no significant impact on the decline in sales in the full sample or the SME sample but has a significant positive impact on large firms.

Like foreign ownership, large firms are adversely affected when tax rates are a major obstacle, and this effect is significant at the 5 percent level. However, the impact on SMEs and the full sample is close to zero and statistically insignificant. Firms that introduce new products or services experience a smaller decline in sales during the pandemic. However, this effect is statistically significant only in the full sample. The impact is also quantitatively small, at around 1.5 to 2 percentage points across the three samples.

The remaining variables do not have any significant or quantitatively large impact on the performance of firms in any of the three samples. These variables include a high share of women workers, young vs. old firms, government support, overall obstacles severity level, legal organization of the firm, location in a capital city, and R&D spending. However, there are some noticeable differences between the impact on SMEs and large firms. That is, a higher share of women workers leads to a higher decline in sales for SMEs by 2.1 percentage points but reduces 20
it by 1.6 percentage points for large firms. Being a young firm is associated with a larger decline in sales for SMEs by 1.9 percentage points, and a smaller decline in sales for large firms by 1.72 percentage points. Location in the capital city is associated with a smaller decline in sales for SMEs by 0.46 percentage points and by 2.65 percentage points for large firms. Last, spending on R&D is associated with a smaller decline in sales for SMEs by 1.46 percentage points and a larger decline in sales by 0.21 percentage points for large firms.

# **5. Mean Decomposition Results**

This section analyzes whether the differences in the levels and returns to the determinants of sales decline discussed above contribute to the sales decline gap at the mean. The traditional KOB decomposition methodology is used (see Appendix B for a summary). The main or baseline results in this section are based on the twofold mean decomposition with the large firms as the reference group (see equation 4 in Appendix B). Twofold decomposition results with SMEs and the pooled sample as the reference groups and threefold decomposition results are also provided as robustness checks in Sections 5.2 and 5.3, respectively.<sup>1</sup> One issue is that the estimated impact of countryindustry dummies depends on which country-industry pair is “omitted”. To overcome this indeterminacy, we follow the “normalization” strategy of Yun (2005), which involves using deviations of the country-industry dummies from their grand mean. This is equivalent to averaging all possible estimates of the impact of country-industry dummies with permuting reference or omitted group.

# 5.1 _Baseline results_

> 1 Without much loss of generality, throughout the remaining sections, only the combined effect of R&D and new product or process development is reported.

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The baseline mean KOB decomposition results are provided in Table 3. The “endowment” effects are provided in column 2 and the “structural” effects in column 3. Note that a positive contribution of a variable to the gap in sales decline means that the variable widens the gap, and a negative contribution narrows the gap.

As stated above, the decline in sales among SMEs is on average 12.2 percentage points higher than that of large firms. This is the total gap. The aggregate “endowment” effect, defined as the sum of all the individual “endowment” effects, is positive and significant at the 1 percent level. Thus, “endowment” effects widen the gap by 7.96 percentage points, or about 65.1 percent (7.96*100/12.22) of the total gap. Likewise, the aggregate “structural” effect is positive and significant at the 1 percent level. Thus, structural effects widen the total gap by 4.26 percentage points, or about 34.9 percent of the total gap. We now analyze the contributions of the individual factors to the total gap. Figure 4 shows the main results that are discussed below.

# _5.1.1 Endowment effects_

The biggest individual contribution via the “endowment” effect comes from the input supply disruptions. We previously documented that input supply disruptions are more prevalent among SMEs than large firms and that such disruptions lead to a significantly larger decline in sales. As a result, the difference in the incidence of input supply disruptions between SMEs and large firms widens the gap by 4.2 percentage points, or about 34.7 percent of the total gap. This effect is significant at the 1 percent level. In other words, about one-third of the sales decline gap between SMEs and large firms can be eliminated by improving the supply of inputs for the SMEs.

We find that the differences in the distribution of SMEs and large firms across countryindustry pairs widen the gap by about 2.7 percentage points, or about 22.4 percent of the total gap.

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This effect is significant at the 5 percent level. Thus, relative to large firms, SMEs tend to be disproportionately located in country-industry pairs that suffered a larger decline in sales due to the pandemic.

Recall that large firms have higher labor productivity than SMEs, and a higher labor productivity leads to a smaller decline in sales. It follows that the difference in the level of labor productivity between SMEs and large firms widens the gap by 1.4 percentage points, or about 11.7 percent of the total gap, which is significant at the 1 percent level.

We also found that large firms have more foreign ownership than SMEs. Further, the impact of foreign ownership on sales decline for large firms, which is used for evaluating the “endowment” effect, is positive. Likewise, large firms report a higher severity of the skills obstacle, and a higher skills obstacle is associated with a significant positive impact on sales decline for large firms. Thus, the difference in the level of foreign ownership between SMEs and large firms narrows the gap by 0.55 percentage points, or about 4.5 percent of the total gap. This effect is significant at the 5 percent level. Similarly, the difference in the level of skills obstacle between SMEs and large firms narrows the gap by about 0.44 percentage points, or about 3.6 percent of the total gap. This effect is significant at the 5 percent level.

The remaining variables do not contribute significantly to the total gap via the “endowment” effect.

# _5.1.2 Structural effects_

The biggest significant individual contribution via the “structural” effect comes from the countryindustry dummies. Differences in the returns to country-industry dummies widen the gap by about 4.9 percentage points, or about 39.7 percent of the total gap. This effect is significant at the 1 23
percent level. Thus, SMEs benefit less or suffer more from the country-industry specific factors than large firms.

Recall that large firms benefit more from an increase in labor productivity than SMEs. As a result, we find that “structurally,” labor productivity widens the gap by about 7.4 percentage points, or about 60.2 percent of the total gap. While this is a quantitatively large effect, it is not statistically significant. In the next sections, we show that there are several cases (regions and quantiles) where the impact of labor productivity is large and significant.

The next biggest contribution via the “structural” effect comes from input supply disruptions. Recall that the impact of input supply disruptions on sales decline is bigger for SMEs than for large firms. This difference widens the gap by about 3.5 percentage points, or about 28.7 percent of the total gap, which is significant at the 5 percent level.

Recall from our results that a more severe finance obstacle leads to a significantly bigger decline in sales for SMEs but not for large firms. This difference in returns to the finance obstacle widens the gap by 2.6 percentage points, or about 20.9 percent of the total gap. This effect is significant at the 5 percent level.

As previously noted, SMEs with a high share of women workers suffered a larger decline in sales during the pandemic. The opposite result was found for large firms. While neither of these effects was statistically significant, their net impact on the total gap is large and significant. That is, the difference in the returns to high share of women workers between SMEs and large firms widens the gap by 2.2 percentage points, or about 18.3 percent of the total gap. This effect is significant at the 10 percent level.

As previously documented, a more severe skills obstacle led to a much larger decline in sales for large firms than SMEs. This difference in the return to skills obstacle narrows the gap by 24
about 1.8 percentage points, or about 14.5 percent of the total gap. However, this effect is not statistically significant.

We also found that among large firms, the decline in sales was significantly larger for firms that reported tax rates as a major or higher obstacle. No effect of the obstacle was found on SMEs. Thus, the difference in returns to tax rates as a major constraint between SMEs and large firms narrows the total gap by 1.5 percentage points, or about 12.2 percent of the total gap. This effect is significant at the 5 percent level.

The last statistically significant “structural” effect is due to exports. We documented that a higher level of exports leads to a larger decline in sales for SMEs but not for large firms. Thus, “structurally,” exports widen the gap by 0.39 percentage points, or about 3.2 percent of the total gap, which is significant at the 10 percent level.

The remaining variables do not contribute significantly “structurally” to the gap. However, like labor productivity, a few of them do make a quantitatively noticeable contribution. That is, as a percentage of the total gap, being a shareholding company or partnership narrows the gap by 10.5 percent; being a young firm widens the gap by 6.3 percent; having a location in the capital city widens the gap by 5.7 percent; and having an overdraft facility widens the gap by 5.1 percent.

# _5.2 SME and pooled samples as the reference group_

For robustness, we repeat the mean decomposition results above using SMEs as the reference group (see equation 5 in Appendix B) and the full or pooled sample (see equation 6 in Appendix B). These decomposition results are provided in Table A4 in Appendix A. Columns 1 to 3 contain the baseline decomposition results discussed above for an easy comparison, columns 4-6 contain the decomposition results with SMEs as the reference group, and columns 7-9 contain the results 25
with the pooled sample as the reference group. As is evident from Table A4, most of the baseline results discussed remain intact qualitatively when we use SME and pooled samples as the reference groups. A few noticeable changes are as follows.

First, the contribution of input supply disruptions to the total gap via the “endowment” effect is larger when the reference group is SMEs (5.0 percentage points) and the pooled sample (4.8 percentage points) compared to the baseline results above (4.2 percentage points). In contrast, its contribution via the “structural” effect is lower but statistically significant with SME and pooled samples as the reference group (2.7 and 2.9 percentage points, respectively) than the baseline results above (3.5 percentage points). Second, exporting makes a much larger and statistically significant contribution to the total gap via the “endowment” and “structural” effects with SMEs and pooled samples as the reference groups than in the baseline model. Third, foreign ownership and skills obstacle contributed significantly to the total gap via the “endowment” effect in the baseline results. Both effects are much smaller and statistically insignificant with the SME and the pooled samples as the reference groups.

# _5.3 Threefold mean decomposition results_

The threefold decomposition (see equation 7 in Appendix B) results are provided in Table A5 in Appendix A. Columns 1 to 3 contain the baseline decomposition results discussed above for an easy comparison. Columns 4–8 contain the threefold decomposition results. In the threefold decomposition, the aggregate “interaction” effect is small and statistically insignificant. Results for the individual factors are roughly the same as for the baseline model above qualitatively.

# _5.4 Additional controls_

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The mean decomposition results with the additional controls are provided in Table A6 in Appendix A. None of the additional controls contribute significantly to the total gap. The baseline results discussed above are roughly unchanged except that the contribution of country-industry fixed effects “structurally” is much smaller and statistically insignificant. However, these fixed effects continue to contribute via the “endowment” effect significantly.

# **6. Quantile decomposition results**

In this section, we provide the quantile decomposition results based on the approach of Firpo et al. (2007, 2009). A summary of the methodology is provided in Appendix B.

# _6.1 Aggregate quantile decomposition results_

The results for the total gap, aggregate “endowment” effects, and aggregate “structural” effects at the 10<sup>th</sup> to 90<sup>th</sup> quantiles are provided in Table 4. As can be seen in Panel A of the table, the (unconditional) total gap is positive and significant (at the 1 percent level) at all the quantiles. That is, SMEs suffered a significantly larger decline in sales during the pandemic than large firms at all the quantiles. However, the gap is much larger at higher quantiles (above 30<sup>th</sup> quantile) than lower quantiles. The mean gap at the 10<sup>th</sup> , 20<sup>th</sup> , and 30<sup>th</sup> quantiles is 7 percentage points, and 16.6 percentage points between the 40<sup>th</sup> and 90<sup>th</sup> quantiles. In short, SMEs underperform large firms much more when the decline in sales is relatively high or the crisis is more severe (also see figure 2).

Panel B in Table 4 provides the aggregate “endowment” effect and the “structural” effect. Panel C expresses these aggregate effects as a percentage of the total gap at the quantile. The main findings are as follows. First, as with the mean decomposition (Table 3), the aggregate 27
“endowment” effect is positive and statistically significant at all the quantiles. The aggregate “structural” effect is positive and significant at all the quantiles except the 50<sup>th</sup> and 80<sup>th</sup> (positive but insignificant) and the 90<sup>th</sup> quantile (negative but insignificant). Thus, overall, the difference in both the endowments and returns to factors widens the gap at most quantiles.

Second, as for the total gap, both the aggregate “endowment” and “structural” effects increase in value as we move from lower to higher quantiles, although not monotonically. However, the aggregate “endowment” effect rises proportionately more than the aggregate “structural” effect (see Panel C in Table 4). Thus, the aggregate “endowment” effect accounts for a smaller share of the total gap at lower quantiles (below the 50<sup>th</sup> quantile) than at higher quantiles.

The opposite holds for the aggregate “structural” effect. For instance, from Panel C in Table 4, we see that between the 10<sup>th</sup> and 40<sup>th</sup> quantiles, the aggregate “structural” effect accounts for on average about 62 percent of the total gap, and only 41 percent between the 50<sup>th</sup> and 90<sup>th</sup> quantiles. The remaining part of the gap is accounted for by the aggregate “endowment” effect. In other words, for small shocks or sales declines, SMEs underperformed large firms, mainly due to the latter’s better use of the existing endowments. However, for a large shock, SMEs underperformed large firms, mostly because the latter had more endowments or resources.

# _6.2 Decomposition results for individual factors_

Quantile decomposition results for the individual factors are provided in Table 5. Table 6 expresses the estimates in Table 5 as a percentage of the total gap at each quantile. In both these tables, Part A contains the “endowment” effects and Part B the “structural” effects.

# _6.2.1 Individual endowment effects_

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From Part A in tables 5 and 6, the main individual “endowment” effects are as follows. First, input supply disruptions widen the gap significantly at all the quantiles. However, the impact, both absolutely and relative to the total gap, is much larger at the intermediate quantiles. That is, on average, input supply disruptions widen the gap by 6.2 percentage points, or about 41.3 percent of the total gap, at the 40<sup>th</sup> , 50<sup>th</sup> , and 60<sup>th</sup> quantiles. They widen the gap by 1.6 percentage points, or 22.6 percent of the total gap, below the 40<sup>th</sup> quantile and by 4.2 percentage points, or 23.5 percent of the total gap, above the 60<sup>th</sup> quantile.

Second, in the baseline mean decomposition results, we found that differences in labor productivity between SMEs and large firms widened the gap by about 1.4 percentage points, or 11.7 percent of the mean total gap. This effect rises, absolutely and relative to the total gap, almost monotonically with the quantiles. It ranges between 0.127 percentage points, or 1.7 percent of the total gap (30<sup>th</sup> quantile), and 3.8 percentage points, or 20.9 percent of the total gap (90<sup>th</sup> quantile). These contributions to the gap are statistically insignificant at the 30<sup>th</sup> quantile and below and significant above it.

Third, the impact of the country-industry fixed effects on the total gap via the “endowment” effect is mostly confined to the higher quantiles. From the 10<sup>th</sup> to the 40<sup>th</sup> quantiles, countryindustry fixed effects widen the gap by 3.5 to 9.3 percent of the total gap. However, these effects are statistically insignificant. For the remaining quantiles (50<sup>th</sup> to 90<sup>th</sup> ), country-industry fixed effects widen the gap significantly by 23.5 to 73.6 percent of the total gap.

Fourth, the baseline mean decomposition results above showed no significant contribution of the overdraft facility via the “endowment” effect. The quantile decomposition results show that having an overdraft facility widens the gap via the “endowment” effect at the 10<sup>th</sup> , 20<sup>th</sup> , and 30<sup>th</sup> quantiles significantly. It does so by about 7.9 percent of the total gap on average. Above the 30<sup>th</sup> 29
quantile, overdraft facility has a much smaller and statistically insignificant impact on the total gap (averaging 2.8 percent of the total gap).

In the baseline mean decomposition results, the skills obstacle narrowed the gap via the “endowment” effect by 3.6 percent of the total gap. Across quantiles, we find that the skills obstacle narrows the gap, and significantly so, at low and high quantiles (20<sup>th</sup> , 30<sup>th</sup> , 70<sup>th</sup> , 80<sup>th</sup> , and 90<sup>th</sup> ). The impact at intermediate quantiles (40<sup>th</sup> , 50<sup>th</sup> , and 60<sup>th</sup> ) is much smaller and statistically insignificant.

# _6.2.2 Individual structural effects_

Regarding the “structural” effect, the country-industry fixed effects were the biggest contributors to the total gap in the baseline mean decomposition results. The quantile decomposition results reveal that country-industry fixed effects make a significant contribution “structurally” to the total gap at all the quantiles. Further, as a percentage of the total gap, these fixed effects narrow the total gap at the 10<sup>th</sup> and 20<sup>th</sup> quantiles by 172 percent and 231 percent, respectively; widen the gap at the 30<sup>th</sup> to 90<sup>th</sup> quantiles by 210 to 1071 percent. Thus, there is a large variation in how the countryfixed effects impact the total gap across the different quantiles.

Differences in the returns to labor productivity have varied effects at different quantiles. They narrow the gap at lower quantiles (10<sup>th</sup> to 30<sup>th</sup> quantiles) and widen it thereafter. Except at the 40<sup>th</sup> quantile, the “structural” contribution of the variable as a percentage of the total gap at the various quantiles is large and above 48 percent. However, these contributions are statistically significant only at the 30<sup>th</sup> and 80<sup>th</sup> quantiles.

As a percentage of the total gap, the difference in returns to input supply disruptions significantly widens the gap by 40 and 57 percent, respectively, at the 10<sup>th</sup> and 20<sup>th</sup> quantiles and 30
by 414 percent at the 30<sup>th</sup> quantile. It narrows the gap significantly by 23.6 percent and 37 percent at the 40<sup>th</sup> and 50<sup>th</sup> quantiles, respectively. Above the 50<sup>th</sup> quantile, the impact is statistically insignificant.

Having an overdraft facility “structurally” widens the gap significantly by 15.2 and 14.2 percent of the total gap at the 10<sup>th</sup> and 20<sup>th</sup> quantiles, respectively. However, above the 20<sup>th</sup> quantile, the contribution of the overdraft facility “structurally” is statistically insignificant and ranges between close to 0 percent and 7 percent of the total gap. The finance obstacle “structurally” widens the gap by 14 to 44 percent of the total gap at the 70<sup>th</sup> to 90<sup>th</sup> quantiles. These effects are statistically significant at 10 percent or 5 percent levels. There is no significant impact of the variable “structurally” at the remaining quantiles. Thus, “structurally,” the finance obstacle widens the gap between SMEs and large firms when the sales decline shock is relatively severe.

The difference in the returns to being young widens the gap significantly by 7.9 to 15.2 percent of the total gap at the 60<sup>th</sup> to 80<sup>th</sup> quantiles. The contribution of the variable at the remaining quantiles is statistically insignificant and typically much smaller. The tax obstacle “structurally” narrows the gap significantly at all the quantiles except the 40<sup>th</sup> , 80<sup>th</sup> , and 90<sup>th</sup> quantiles. This effect, however, is much stronger at lower quantiles than at higher quantiles. It ranges from 22.6 to 27.4 percent of the total gap between the 10<sup>th</sup> and 30<sup>th</sup> quantiles and from about 0 to 15.7 percent at the remaining quantiles. Like the tax obstacle, the skills obstacle “structurally” significantly narrows the gap by 21.6 to 25.5 percent of the total gap at the 10<sup>th</sup> to 30<sup>th</sup> quantiles. It narrows the gap by 17.7 percent of the total gap at the 70<sup>th</sup> quantile and 20 percent at the 80<sup>th</sup> quantile, but only the former is statistically significant.

Some other variables have a significant impact “structurally” at select quantiles. That is, government support widens the gap significantly by 14.3 percent of the total gap at the 30<sup>th</sup> 31
quantile, and R&D and innovation activity (combined) narrow the gap by 9.8 percent of the total gap at the 40<sup>th</sup> quantile.

To summarize, the quantile decomposition results reveal substantial variation in the contribution of the variables to the sales decline gap via the “endowment” and “structural” effects. Most variables contribute significantly across the sales decline quantiles. The few variables that do not contribute significantly either “structurally” or via the “endowment” effect at any quantile are having a higher share of women workers, being in the capital city, overall obstacles severity level, and the legal form of the firm (sole proprietorship and shareholding/partnership firms). In the next section, we will show that the effect of these variables is restricted to specific regions.

# **7. Regional results**

This section analyzes the sales decline gap across groups of countries. Unless stated otherwise, the discussion below relates to the mean decomposition. All “percentage” contributions are as a percentage of the total gap at the mean or the relevant quantile.

# _7.1 Total sales decline gap_

A significantly poorer resilience of SMEs relative to large firms is observed in all the four regions (Table 7). The mean gap is the smallest in MENA, equal to 6.2 percentage points. It is roughly the same in all the other regions, equaling 11.4 percentage points in LAC and SSA, and 11.2 percentage points in ECA.<sup>2</sup> All these gaps are significant at the 1 percent level. Likewise, the gap

> 2 However, do note that the formal private sector in the MENA region has a limited role in the presence of an overbearing public sector (see Islam et al. 2022).

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is positive, large, and significant at most of the quantiles in each region (see Table A7 in Appendix A).

In the full sample, we found that the gap was larger at higher quantiles. This result holds for ECA and LAC. In MENA and SSA, there is no noticeable and robust increase or decrease in the total gap as we move from lower to higher quantiles. Thus, relative to large firms, SMEs are less resilient to large downturns than small ones in ECA and LAC, but not in MENA and SSA.

# _7.2 Aggregate endowment vs. structural effects_

Figure 5 shows the contributions of the aggregate “endowment” and “structural” effects to the total gap. Interesting differences emerge between the regions. Both effects are significant in SSA, widening the gap by roughly equal amounts. In LAC and MENA, the aggregate “endowment” effect is dominant, widening the gap significantly. While noticeable, the aggregate “structural” effect is statistically insignificant. In contrast, the opposite holds for the ECA region. See Table 7 for more details. Within each region, there are substantial differences across the sales decline distribution (see Table A7 in Appendix A). For instance, in SSA, the aggregate “endowment” effect is significant at 4 of the 9 quantiles, and it ranges between -6.7 percent of the total gap and 159.4 percent. The differences within and across regions highlight the need for a proper targeting of policies aimed at improving SMEs’ resilience to economic shocks.

# _7.3 Individuals factors_

Next, we consider the individual factors in the different regions. These are provided in Table 8 (mean decomposition) and Tables A8 to A11 in Appendix A (quantile decomposition). Due to space limitations, the discussion is restricted to a few important results.

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Input supply disruptions significantly widen the gap via the “endowment” effect at the mean and at most quantiles in each region. However, as a percentage of the total gap, the impact at the mean and on average across quantiles is much larger in LAC and MENA (93 percent of the total gap on average when significant) than in SSA (31 percent) and ECA (25.5 percent). “Structurally,” input supply disruptions significantly affect the gap at one or more quantiles in all the regions. In SSA and LAC, the “structural” effect of the variable is to significantly widen the gap at some of the relatively low quantiles and narrow it at some of the higher quantiles. A similar result holds for MENA, except that the narrowing of the gap at the higher quantiles is not significant. In ECA, “structurally,” input supply disruptions widen the gap significantly at the lowest as well as the highest quantiles and narrow it at intermediate quantiles. Several important policy implications follow from these results. For example, closing the gap can be more effectively achieved in ECA by improving the “returns” to supply chains for firms at the low and high end of the sales decline distribution. In contrast, in SSA, it is important to focus on firms at the low end of the distribution rather than at the high end.

Differences in the level of the finance obstacle between SMEs and large firms (“endowment” effect) contribute significantly to the gap only in LAC. They do so by widening the gap at the 20<sup>th</sup> , 60<sup>th</sup> , and 70<sup>th</sup> quantiles by 17.3 to 21 percent of the total gap. “Structurally,” the finance obstacle contributes significantly to the total gap in all the regions, but only at select quantiles. It narrows the gap in LAC (by 43.4 percent of the total gap at the 20<sup>th</sup> quantile) and widens it in all the other regions. MENA stands out with the very large “structural” effects of the finance obstacle, which widen the gap at the mean by 146 percent and at the 60<sup>th</sup> to 90<sup>th</sup> quantiles by 209 to 793 percent.

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Differences in the level of labor productivity between SMEs and large firms widen the gap significantly at the mean and/or some of the quantiles in SSA (by 14.5 to 49.4 percent of the total gap) and LAC (by 20 to 236.5 percent), but not in ECA and MENA. “Structurally”, labor productivity significantly widens the gap in all the regions at one or more quantiles. While the impact varies substantially across the quantiles within a region, when significant, it is quite large as a proportion of the total gap. For instance, in SSA, the significant “structural” effect occurs at the 40<sup>th</sup> to 70<sup>th</sup> quantiles, which widen the gap by 261 to 355 percent, and in MENA by 635 to 2,826 percent between the 60<sup>th</sup> and 90<sup>th</sup> quantiles.

Recall that in the full sample, the skills obstacle significantly narrowed the gap “structurally” by at most 26 percent of the total gap at some of the quantiles. This overall impact is somewhat muted likely because of contrasting effects at the regional level. Focusing on the statistically significant “structural” effects at the various quantiles, the skills obstacle narrows the gap in SSA by 33 to 34 percent of the total gap, in LAC by 41 percent or more, and in ECA by 20 to 136 percent. In contrast, in MENA, it significantly widens the gap by 685 percent at the 90<sup>th</sup> quantile.

In the full sample, we found that government support did not reduce the gap between SMEs and large firms. In fact, government support widened the gap significantly at some quantiles (by 14.3 percent “structurally” at the 30<sup>th</sup> quantile). A qualitatively similar result obtains in ECA, LAC, and SSA. However, in MENA, government support significantly narrows the gap “structurally” and via the “endowment” effect. For example, “structurally”, the gap is smaller at the mean by 21.3 percent of the total gap and at several quantiles by 40.5 to 67.1 percent. Thus, MENA stands out as the only region where government support helps SMEs more than large firms. This is an 35
important result from a policy-targeting point of view if we assume that SMEs are more in need of government support.

There are several other interesting results. We provide a few examples focusing on the statistically significant results. First, in MENA, R&D and innovation activity significantly widen the mean gap by 14.9 percent via the “endowment” effect and 15.3 percent “structurally.” There is no significant impact of the variable on the mean gap in any of the other regions. By quantiles, R&D and innovation significantly widen the gap in MENA “structurally” at 5 out of the 9 quantiles. In all the other regions, the variable significantly widens (in SSA) or narrows (in LAC and ECA) the gap at one or two quantiles. In short, R&D activity plays a much bigger role in MENA in widening the gap between SMEs and large firms than in the other regions. Second, in the full sample, we did not find any significant effects of the legal form of the firm on the total gap. This changes at the regional level. For instance, being a sole proprietorship has a significant impact on the total gap at the mean or one or more quantiles in all the regions. To provide an example, in SSA, sole proprietorship narrows the mean gap by 40 percent via the “endowment” effect and widens it by 41 percent “structurally.” Both effects are significant at the 5 percent level. Third, as in the full sample, country-industry fixed effects contribute significantly to the total gap at the mean and/or at one or more quantiles in each region. Fourth, looking at all the explanatory variables one-by-one, we find that most of them contribute significantly to the total gap either at the mean or at one or more quantiles in all the regions. The exceptions are as follows: high share of women workers in LAC, foreign ownership in ECA, overdraft facility in LAC, capital city in ECA, tax obstacle in SSA, obstacles severity in ECA, and shareholding company or partnerships in LAC and ECA. Last, MENA stands out with a much larger and more widespread (across quantiles) impact of foreign ownership on the total gap than the other regions. Foreign ownership 36
significantly narrows the gap in MENA at the 20<sup>th</sup> to 50<sup>th</sup> quantiles by 24.8 to 36.1 percent of the total gap via the “endowment” effect and 10.7 to 16.5 percent “structurally”. In contrast, foreign ownership significantly narrows the gap in SSA and LAC only at the 50<sup>th</sup> quantile by about 8.8 and 11.1 percent, respectively. There is no significant impact of the variable in ECA.

# **8. Conclusion**

The key questions that this study investigates are whether SMEs suffered a larger decline in sales due to the COVID-19 pandemic than large firms, and if so, by how much and what are the contributors that exacerbate or alleviate the gap between the two. We ascertain a significantly larger decline in sales for SMEs than large firms. This gap is widespread, as it exists across the entire sample, within geographical regions, and in most individual countries. Factors that contribute to the gap are identified. This is done at the mean and various quantiles of the sales decline distribution. We identified 10 explanatory variables that contribute significantly to the gap at either the mean or one or more quantiles in all the regions, and another six variables that contribute significantly in three out of four regions.

Our results are important to policy makers in several ways. First, they suggest that SMEs are more vulnerable to crisis situations. As a result, additional policy measures may be required to help SMEs overcome economic downturns, natural disasters, armed conflicts, infectious disease outbreaks, and climate change. Second, because of their potential for creating jobs and inclusive growth, several countries have implemented national policies to promote SMEs. By showing that SMEs may suffer more during a crisis, our findings indicate that the development of SMEs comes at a cost in terms of stability. Thus, support for SMEs must note the potential tradeoffs.

37
Third, we identified and estimated the contribution of several factors to the decline in sales for SMEs relative to large firms during the pandemic. These factors highlight the specific challenges faced by SMEs during the pandemic and possibly in future shocks. Thus, they are prime candidates for policy intervention following a crisis. For instance, we found that lower labor productivity and a high incidence of input supply disruption result in poorer performance by SMEs relative to large firms. Thus, policies that improve SMEs’ labor productivity and input supply networks can help them close the gap with large firms currently and in future crises. Fourth, our results indicate that a substantial part of the gap between SMEs and large firms can be closed by providing more resources to SMEs. This is encouraging, as it is typically easier to provide more resources than improve the returns to factors for a particular group. Of course, where possible, policies can also help SMEs by enabling them to use their resources better.

Fifth, our results show that policies that are effective in one region may be ineffective or less effective in other regions. As a result, an eclectic approach is needed that combines the general findings in the literature with the prevailing regional or local factors. This poses a significant challenge to policy makers and researchers to identify the relevant local conditions and their implications for optimal policies. Sixth, there are important differences in the size of the gap and its drivers across the quantiles of the sales decline distribution. Thus, policy effectiveness can be improved through proper targeting of the relevant quantiles. Last, governments around the globe provided support to the private sector during the pandemic. Our results show that government support helped SMEs significantly more than large firms only in MENA. The opposite holds for ECA, LAC, and SSA. This is a worrying result if we assume that SMEs suffered more and are more in need of government support. Thus, a review of existing policies, their targeting, and their delivery to SMEs may be helpful.

38
There are several avenues for future research. First, the analysis can be extended to the impact of the pandemic on other performance measures, such as employment, product and process innovation, use of online platforms, and exports. Second, extending our results to countries not in our sample will be a fruitful exercise. Third, identifying the mechanisms through which the identified factors affect the performance of SMEs and large firms could further help in the design and targeting of policies for SMEs. Fourth, heterogeneous effects by various groups, such as young vs. old firms and countries with weak vs. strong rule of law, are another avenue for future research. Finally, future researchers can explore the causal effects of the factors we identified as important.

39
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**Figure 1: Sales decline gap (mean values)**

<!-- Start of picture text --> 20.0<br>17.0<br>16.3 16.4<br>14.7<br>15.0<br>13.0<br>12.2<br>11.3 11.4 11.4<br>10.0<br>9.7<br>10.0<br>7.9<br>6.5<br>6.2<br>5.8<br>5.3<br>5.0 3.9<br>0.0<br>-2.0<br>-5.0 -4.1<br>-10.0<br>sales of large firms (percentage points)<br>Percentage decline in sales of SMEs minus percentage decline in<br><!-- End of picture text --> Note: Sales decline gap is the decline in sales in the last month (post COVID-19 outbreak) compared to the same month last year (pre-COVID-19 outbreak) and expressed as a percentage of the latter.

Source: Authors’ own calculations based on data from World Bank Enterprise Surveys.

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**Figure 2: Sales decline gap at the various quantiles**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>10th 20th 30th 40th 50th 60th 70th 80th 90th<br>Sales decline gap 95% confidence interval<br>Sales decline gap (percentage points)<br><!-- End of picture text --> Source: Authors’ own calculations based on data from World Bank’s Enterprise Surveys. Note: Sales decline gap is the decline in sales due to the pandemic as a percentage of the prepandemic sales level of SMEs minus the same for large firms. A positive value of sales decline gap implies that the percentage decline in sales is bigger for SMEs than large firms.

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**Figure 3: Kernel density plot**

<!-- Start of picture text -->
-300 -200 -100 0 100<br>Sales decline (%)<br>SMEs<br>Large firms<br>.02<br>.015<br>.01<br>Density<br>.005<br>0<br><!-- End of picture text -->

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**Figure 4: Baseline mean decomposition results**

<!-- Start of picture text --> Endowment effects (% of the total gap)<br>Skills obstacle (0-4)<br>-3.6<br>Foreign ownership (proportion)<br>-4.5<br>Labor productivity (logs) 11.7<br>Country-Industry fixed effects 22.4<br>Supply disruption Y:1 N:0 34.6<br>-10 -5 0 5 10 15 20 25 30 35 40<br>Structural effects (% of total gap)<br>Exports (proportion of sales) 3.2<br>Tax rates are a major obstacle Y:1 N:0<br>-12.2<br>High share of women workers Y:1 N:0 18.3<br>Finance obstacle (0-4) 20.9<br>Supply disruption Y:1 N:0 28.7<br>Labor productivity (logs) 60.2<br>Country-Industry fixed effects 70.6<br>-20 -10 0 10 20 30 40 50 60 70 80<br><!-- End of picture text --> Source: Authors’ own calculations based on the results in Table 3. A positive value implies that the variable widens the sales decline gap between SMEs and large firms. A negative implies that the variable narrows the gap.

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**Figure 5: Aggregate endowment and structural effects at the mean by regions**

<!-- Start of picture text --> 130<br>110<br>90<br>37.7<br>70 55.7** 63.3***<br>124.1***<br>50<br>30 62.3*<br>44.3**<br>36.7<br>10<br>-10 SSA ECA LAC -24.1 MENA<br>-30<br>-50<br>Aggregate endowment effect Aggregate structural effect<br>% of the total mean gap<br><!-- End of picture text --> Source: Authors’ own calculations based on the results in Table 7.

*** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level. See Table 7 for more details.

50
**Table 1: Mean differences**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
||Mean value<br>for SMEs<br>sample|Mean value<br>for Large<br>firms sample|Difference<br>(SME minus<br>Large firms)|
|Sales decline (%)|34.77|22.55|12.22***|
|Labor Productivity (logs)|10.4|10.91|-0.511***|
|Supply disruption Y:1 N:0|0.67|0.52|0.152***|
|Overdraft|0.29|0.48|-0.194***|
|Finance obstacle (0-4)|1.45|1.24|0.210***|
|Skills obstacle (0-4)|1.28|1.54|-0.266***|
|Young firm (<=10 years old): Y:1 N:0|0.21|0.11|0.098***|
|Exports (proportion of sales)|0.05|0.19|-0.141***|
|Foreign ownership (proportion)|0.06|0.19|-0.126***|
|Introduced new product/service Y:1 N:0|0.304|0.366|-0.062**|
|Spent on R&D Y:1 N:0|0.093|0.204|-0.111***|
|Government support Y:1 N:0|0.18|0.17|0.013|
|High share of women workers: Y:1 N:0|0.61|0.61|-0.002|
|Capital city Y:1 N:0|0.32|0.32|-0.006|
|Taxes are a major obstacle Y:1 N:0|29.88|26.36|3.512|
|Obstacles severity|1.24|1.22|0.019|
|Sole proprietorship Y:1 N:0|0.29|0.09|0.209|
|Shareholding company (non-traded) or<br>Partnership Y:1 N:0|0.57|0.63|-0.054|

Significance level is denoted by ***(1%), **(5%). Standard errors used are Huber-White robust and clustered on the country.

51
|Dependent variable: Sales decline (%)|**Table 2: OLS**<br>**(1)**|**(2)**|**(3)**|
|---|---|---|---|
||**Full sample**|**SMEs**|**Large firms**|
|SME|4.362***|||
||(0.914)|||
|Labor productivity (logs)|-2.177***|-2.090***|-2.797***|
||(0.388)|(0.473)|(0.721)|
|Supply disruption Y:1 N:0|31.954***|33.145***|27.914***|
||(1.293)|(1.467)|(2.139)|
|Overdraft Y:1 N:0|-0.819|-0.720|-2.875|
||(0.992)|(1.101)|(1.776)|
|Finance obstacle (0-4)|0.428|0.880**|-0.880|
||(0.430)|(0.433)|(0.877)|
|Skills obstacle (0-4)|0.509|0.258|1.647**|
||(0.327)|(0.348)|(0.809)|
|Young firm (<=10 years) Y:1 N:0|1.682|1.949|-1.724|
||(1.376)|(1.635)|(2.060)|
|High share of women workers Y:1 N:0|1.636|2.065|-1.614|
||(1.128)|(1.256)|(1.780)|
|Exports (proportion of sales)|3.395*|5.449**|-2.166|
||(1.759)|(2.201)|(3.164)|
|Foreign ownership (proportion)|0.633|-0.853|4.407*|
||(1.489)|(2.451)|(2.256)|
|Tax rates are a major obstacle Y:1 N:0|0.003|-0.006|0.044*|
||(0.010)|(0.009)|(0.023)|
|Government support Y:1 N:0|1.340|1.597|0.234|
||(1.880)|(2.180)|(3.330)|
|Capital city Y:1 N:0|-1.035|-0.460|-2.650|
||(1.371)|(1.663)|(1.981)|
|Obstacles severity|-0.425|-0.596|-0.621|
||(0.667)|(0.742)|(1.458)|
|Sole proprietorship Y:1 N:0|0.850|0.540|-0.681|
||(1.886)|(2.073)|(3.488)|
|Shareholding company (non-traded) or|-0.260|-1.351|0.891|
|Partnership Y:1 N:0|(1.447)|(1.806)|(2.005)|
|Introduced new product/service Y:1 N:0|-1.783**|-1.592|-1.498|
||(0.860)|(1.040)|(1.718)|
|Spent on R&D Y:1 N:0|-0.686|-1.456|0.213|
||(1.262)|(1.633)|(2.130)|
|Country-Industry fixed effects|Yes|Yes|Yes|
|Constant|29.644***|32.973***|39.691***|

52
|(4.178)<br>(5.258)|(8.267)|
|---|---|
|Number of observations<br>5,197<br>4,097|1,100|
|Huber-White robust standard errors in brackets clustered on country-industry pairs. *<br>* (10%).|** (1%), ** (5%),|

53
|**Table 3: Baseline**<br>Dependent variable: Sales decline (%)|**mean decomp**<br>**(1)**|**osition**<br>**(2)**|**(3)**|
|---|---|---|---|
|||**Endowment**<br>**effect**|**Structural**<br>**effect**|
|SMEs|34.766***|||
||(1.849)|||
|Large firms|22.548***|||
||(2.209)|||
|Difference (SME-Large firms)|12.218***<br>(1.883)|||
|Country-Industry||2.732**|4.852***|
|||(1.163)|(1.200)|
|Labor productivity (logs)||1.430***|7.354|
|||(0.512)|(8.726)|
|Supply disruption Y:1 N:0||4.233***|3.509**|
|||(0.758)|(1.515)|
|Overdraft Y:1 N:0||0.557|0.618|
|||(0.341)|(0.518)|
|Finance obstacle (0-4)||-0.185|2.554**|
|||(0.184)|(1.275)|
|Skills obstacle (0-4)||-0.439**|-1.776|
|||(0.223)|(1.100)|
|Young firm (<=10 years) Y:1 N:0||-0.168|0.773|
|||(0.193)|(0.576)|
|High share of women workers Y:1 N:0||0.003|2.237*|
|||(0.052)|(1.164)|
|Exports (proportion of sales)||0.306|0.393*|
|||(0.430)|(0.205)|
|Foreign ownership (proportion)||-0.554**|-0.329|
|||(0.280)|(0.231)|
|Tax rates are a major obstacle Y:1 N:0||0.155|-1.488**|
|||(0.129)|(0.659)|
|Government support Y:1 N:0||0.003|0.251|
|||(0.042)|(0.646)|
|Capital city Y:1 N:0||0.015|0.697|
|||(0.077)|(0.782)|
|Obstacles severity||-0.012|0.031|
|||(0.043)|(1.928)|
|Sole proprietorship Y:1 N:0||-0.142|0.359|
|||(0.690)|(0.999)|

54
|Shareholding company (non-traded) or||-0.048|-1.288|
|---|---|---|---|
|Partnership Y:1 N:0||(0.108)|(1.390)|
|R&D and New product/process||0.069|-0.183|
|||(0.243)|(0.628)|
|Constant|||-14.303|
||||(9.536)|
|**Total or Aggregate effect**||**7.956*****|**4.262*****|
|||**(1.927)**|**(1.215)**|
|Number of observations|5,197|5,197|5,197|

Huber-White robust standard errors clustered on country-industry pairs. *** (1%), ** (5%), * (10%). Without much loss of generality, only the combined contribution of R&D and new product/process development is shown.

55
|||**Table 4: Base**|**linequantil**|**e decomposi**|**tion results**|||||
|---|---|---|---|---|---|---|---|---|---|
|Dependent variable: Sales decline(%)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
||**10th**<br>**quantile**|**20th**<br>**quantile**|**30th**<br>**quantile**|**40th**<br>**quantile**|**50th**<br>**quantile**|**60th**<br>**quantile**|**70th**<br>**quantile**|**80th**<br>**quantile**|**90th**<br>**quantile**|
|||||**Panel A: T**|**otal gap (un**|**conditional)**||||
|SMEs|2.392***|11.163***|18.058***|30.776***|35.634***|47.300***|56.919***|66.328***|84.028***|
||(0.707)|(0.707)|(2.529)|(2.283)|(2.669)|(2.677)|(2.542)|(2.995)|(2.431)|
|Large firms|-4.690***|4.911***|10.368***|11.853***|23.501***|32.331***|36.359***|51.088***|66.107***|
||(0.959)|(0.891)|(0.891)|(2.407)|(2.769)|(2.836)|(3.162)|(4.380)|(5.354)|
|Difference (SME-Large firms)|7.082***|6.252***|7.690***|18.923***|12.134***|14.968***|20.561***|15.240***|17.921***|
||(0.838)|(0.804)|(2.319)|(2.283)|(2.620)|(2.702)|(2.508)|(3.435)|(4.565)|
||||**Panel B:**|**Aggregate**|**endowment**|**and structur**|**al effects**|||
|Aggregate endowment effect|2.443**|2.270**|2.270**|9.958***|10.668***|9.397***|8.713***|12.042***|19.370***|
||(1.062)|(0.905)|(0.905)|(2.370)|(2.594)|(2.686)|(2.828)|(3.763)|(4.805)|
|Aggregate structural effect|4.639***|3.983***|5.420**|8.966***|1.466|5.571**|11.847***|3.199|-1.449|
||(1.092)|(0.894)|(2.227)|(1.883)|(2.116)|(2.202)|(1.991)|(3.087)|(4.860)|
|||**Panel C: Ag**|**gregate end**|**owment and**|**structural e**|**ffects as a pe**|**rcentage of t**|**he total gap**||
|Aggregate endowment effect|34.5%|36.3%|29.5%|52.6%|87.9%|62.8%|42.4%|79.0%|108.1%|
|Aggregate structural effect|65.5%|63.7%|70.5%|47.4%|12.1%|37.2%|57.6%|21.0%|-8.1%|
|Huber-White robust standard errors clust|ered on countr|y-industry pa|irs. *** (1%)|, ** (5%), *|(10%).|||||

56
||**Table 5: B**|**aselinequanti**|**le decomposi**|**tion results f**|**or the indivi**|**dual factors**||||
|---|---|---|---|---|---|---|---|---|---|
|Dependent variable: Sales decline|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|<br>(%)||||||||||
||**10th**<br>**quantile**|**20th**<br>**quantile**|**30th**<br>**quantile**|**40th**<br>**quantile**|**50th**<br>**quantile**|**60th**<br>**quantile**|**70th**<br>**quantile**|**80th**<br>**quantile**|**90th**<br>**quantile**|
|||||**Panel A:**|**Endowment**|**effects**||||
|Country-Industry|0.248|0.496|0.496|1.759|2.960**|3.523**|4.834***|8.122***|13.183***|
||(0.601)|(0.520)|(0.520)|(1.074)|(1.324)|(1.508)|(1.782)|(2.271)|(3.492)|
|Labor productivity (logs)|0.163|0.127|0.127|1.083**|1.645***|2.051***|1.868**|3.115***|3.751**|
||(0.169)|(0.155)|(0.155)|(0.463)|(0.594)|(0.675)|(0.749)|(1.107)|(1.705)|
|Supply disruption Y:1 N:0|1.683***|1.517***|1.517***|7.137***|6.285***|5.148***|4.301***|3.757***|4.483***|
||(0.367)|(0.338)|(0.338)|(1.240)|(1.082)|(0.895)|(0.823)|(0.844)|(1.184)|
|Overdraft Y:1 N:0|0.632**|0.508**|0.508**|0.528|0.424|0.703|0.408|0.012|0.667|
||(0.263)|(0.216)|(0.216)|(0.410)|(0.519)|(0.444)|(0.466)|(0.628)|(1.124)|
|Finance obstacle (0-4)|0.003|-0.055|-0.055|0.016|-0.093|0.037|-0.088|-0.450|-0.759|
||(0.092)|(0.081)|(0.081)|(0.198)|(0.220)|(0.236)|(0.233)|(0.390)|(0.614)|
|Skills obstacle (0-4)|-0.257|-0.270**|-0.270**|0.045|-0.185|-0.304|-0.707*|-0.752*|-0.944**|
||(0.157)|(0.136)|(0.136)|(0.241)|(0.256)|(0.337)|(0.383)|(0.435)|(0.476)|
|Young firm (<=10 years) Y:1 N:0|0.054|0.033|0.033|-0.316|-0.113|-0.698*|-0.516*|-0.774*|-0.108|
||(0.142)|(0.108)|(0.108)|(0.361)|(0.391)|(0.389)|(0.279)|(0.406)|(0.728)|
|High share of women workers|0.002|0.002|0.002|0.003|0.007|0.004|0.004|-0.002|-0.003|
|Y:1 N:0|(0.029)|(0.034)|(0.034)|(0.046)|(0.118)|(0.065)|(0.066)|(0.037)|(0.055)|
|Exports (proportion of sales)|-0.004|-0.119|-0.119|1.113*|0.376|-0.614|-0.338|-0.396|0.866|
||(0.519)|(0.417)|(0.417)|(0.674)|(0.583)|(0.602)|(0.545)|(0.828)|(1.166)|
|Foreign ownership (proportion)|0.056|0.026|0.026|-0.728*|-0.260|-0.384|-0.942**|-0.826|-0.628|
||(0.200)|(0.176)|(0.176)|(0.407)|(0.416)|(0.431)|(0.454)|(0.593)|(0.897)|
|Tax rates are a major obstacle|0.171|0.155|0.155|0.121|0.207|0.194|0.174|0.071|0.077|
|<br>Y:1 N:0|(0.127)|(0.113)|(0.113)|(0.128)|(0.172)|(0.148)|(0.151)|(0.170)|(0.196)|
|Government support Y:1 N:0|-0.041|-0.033|-0.033|0.067|0.065|0.080|0.084|0.035|-0.093|
||(0.078)|(0.062)|(0.062)|(0.129)|(0.126)|(0.154)|(0.168)|(0.115)|(0.184)|

57
|Capital city Y:1 N:0|0.003|0.009|0.009|0.001|0.002|0.008|0.014|0.022|0.043|
|---|---|---|---|---|---|---|---|---|---|
||(0.018)|(0.045)|(0.045)|(0.021)|(0.024)|(0.045)|(0.074)|(0.113)|(0.222)|
|Obstacles severity|-0.017|-0.014|-0.014|-0.013|-0.013|0.000|-0.012|0.029|0.031|
||(0.053)|(0.044)|(0.044)|(0.049)|(0.050)|(0.031)|(0.048)|(0.095)|(0.102)|
|Sole proprietorship Y:1 N:0|-0.089|0.018|0.018|-0.503|-0.911|-0.680|-0.531|-0.622|-1.997|
||(0.478)|(0.420)|(0.420)|(0.834)|(0.931)|(0.829)|(0.836)|(1.868)|(2.197)|
|Shareholding company (non-|-0.077|-0.031|-0.031|-0.066|-0.077|0.060|0.107|0.193|0.073|
|traded) or Partnership Y:1 N:0|(0.101)|(0.076)|(0.076)|(0.152)|(0.169)|(0.155)|(0.184)|(0.240)|(0.282)|
|R&D and New|-0.085|-0.099|-0.099|-0.289|0.348|0.270|0.052|0.507|0.730|
|product/process|(0.254)|(0.214)|(0.214)|(0.338)|(0.309)|(0.316)|(0.314)|(0.378)|(0.562)|
|||||**Panel B:**|**Structural e**|**ffects**||||
||**10th**<br>**quantile**|**20th**<br>**quantile**|**30th**<br>**quantile**|**40th**<br>**quantile**|**50th**<br>**quantile**|**60th**<br>**quantile**|**70th**<br>**quantile**|**80th**<br>**quantile**|**90th**<br>**quantile**|
|Country-Industry|-12.167***|-14.472***|42.149***|39.686***|44.021***|49.094***|51.892***|95.260***|192.326***|
||(3.630)|(3.577)|(6.239)|(7.758)|(7.148)|(6.547)|(6.961)|(9.183)|(14.151)|
|Labor productivity (logs)|-3.992|-4.720|-17.272**|0.599|6.592|14.808|9.908|36.437**|42.681|
||(3.777)|(3.666)|(7.432)|(8.916)|(9.838)|(10.499)|(12.113)|(18.207)|(30.460)|
|Supply disruption Y:1 N:0|2.838**|3.570***|31.807***|-4.471**|-4.491**|0.248|-0.246|1.765|-4.461|
||(1.286)|(1.284)|(2.058)|(2.021)|(1.937)|(1.668)|(1.791)|(2.313)|(3.976)|
|Overdraft Y:1 N:0|1.073***|0.889**|0.468|0.774|0.385|1.040|0.961|-0.004|0.210|
||(0.414)|(0.356)|(0.562)|(0.699)|(0.807)|(0.680)|(0.724)|(1.061)|(2.023)|
|Finance obstacle (0-4)|-0.285|0.114|0.754|0.887|1.631|0.934|2.963*|6.450**|7.955**|
||(0.796)|(0.751)|(1.060)|(1.686)|(1.688)|(1.704)|(1.749)|(2.758)|(3.865)|
|Skills obstacle (0-4)|-1.529**|-1.595**|-1.694*|0.280|-0.644|-1.169|-3.642**|-3.048|-2.262|
||(0.759)|(0.679)|(0.932)|(1.309)|(1.474)|(1.837)|(1.810)|(2.037)|(2.156)|
|Young firm (<=10 years) Y:1 N:0|-0.129|-0.084|0.215|1.400|0.722|2.048**|1.624**|2.316**|0.580|
||(0.363)|(0.296)|(0.412)|(0.910)|(0.954)|(0.933)|(0.698)|(1.016)|(1.868)|
|High share of women workers|0.692|0.801|0.721|1.450|2.394|1.952|2.796|2.684|3.769|
|Y:1 N:0|(0.845)|(0.752)|(1.133)|(1.863)|(1.656)|(1.620)|(1.741)|(2.461)|(3.623)|
|Exports (proportion of sales)|0.038|-0.004|0.127|0.520*|0.343|0.065|0.204|0.615|1.247**|
||(0.216)|(0.186)|(0.248)|(0.266)|(0.259)|(0.242)|(0.265)|(0.383)|(0.593)|

58
|Foreign ownership (proportion)|0.031|0.017|-0.244|-0.506*|-0.306|-0.449|-0.672**|-0.468|-0.374|
|---|---|---|---|---|---|---|---|---|---|
||(0.137)|(0.130)|(0.253)|(0.265)|(0.273)|(0.304)|(0.311)|(0.432)|(0.540)|
|Tax rates are a major obstacle|-1.850***|-1.715***|-1.740***|-1.274|-1.910**|-1.706**|-1.713*|0.034|-1.111|
|Y:1 N:0|(0.530)|(0.468)|(0.479)|(0.898)|(0.970)|(0.768)|(0.949)|(1.412)|(1.823)|
|Government support Y:1 N:0|0.573|0.452|1.102**|-0.394|-0.538|-0.809|-1.244|-0.316|1.370|
||(0.398)|(0.377)|(0.502)|(0.681)|(0.667)|(0.672)|(0.964)|(1.395)|(1.063)|
|Capital city Y:1 N:0|-0.131|0.198|0.498|0.053|0.434|0.809|0.893|1.331|2.367|
||(0.584)|(0.456)|(0.614)|(1.288)|(1.287)|(1.070)|(1.289)|(1.490)|(2.112)|
|Obstacles severity|1.543|1.349|0.402|-0.173|0.645|-0.343|-0.477|-4.456|-2.099|
||(1.667)|(1.420)|(1.553)|(2.344)|(2.580)|(2.371)|(2.408)|(3.650)|(3.803)|
|Sole proprietorship Y:1 N:0|0.311|0.160|-0.261|1.244|1.415|1.135|0.353|0.709|4.323|
||(0.769)|(0.728)|(0.852)|(1.116)|(1.204)|(1.129)|(1.188)|(2.736)|(3.446)|
|Shareholding company (non-|-0.654|-0.168|-0.801|-0.538|-1.290|0.308|-0.250|0.981|1.246|
|traded) or Partnership Y:1 N:0|(1.174)|(1.107)|(1.351)|(1.818)|(1.947)|(1.734)|(1.989)|(2.478)|(3.303)|
|R&D and New product/process|0.028|-0.193|-0.688|-1.858**|-0.699|-0.835|-1.161|0.004|2.080|
||(0.630)|(0.570)|(0.706)|(0.894)|(0.825)|(0.757)|(0.827)|(1.069)|(1.332)|
|Constant|18.25***|19.38***|-50.12***|-28.71***|-47.24***|-61.56***|-50.34***|-137.1***|-251.30***|
||(6.778)|(6.691)|(10.360)|(15.075)|(16.037)|(15.960)|(14.276)|(19.296)|(38.197)|
|Observations|5,197|5,197|5,197|5,197|5,197|5,197|5,197|5,197|5,197|

Huber-White robust standard errors clustered on country-industry pairs. *** (1%), ** (5%), * (10%). Without much loss of generality, only the combined contribution of R&D and New product/process development is shown.

59
|<br>Dependent variable: Sales decline (%)|**Table 6: Base**<br>**(1)**|**linequantil**<br>**(2)**|**e decomposi**<br>**(3)**|**tion results**<br>**(4)**|**as a % of th**<br>**(5)**|**e totalgap**<br>**(6)**|**(7)**|**(8)**|**(9)**|
|---|---|---|---|---|---|---|---|---|---|
||**10th**<br>**quantile**|**20th**<br>**quantile**|**30th**<br>**quantile**|**40th**<br>**quantile**|**50th**<br>**quantile**|**60th**<br>**quantile**|**70th**<br>**quantile**|**80th**<br>**quantile**|**90th**<br>**quantile**|
|||||**Panel A**|**: Endowme**|**nt effects**||||
|Country-Industry|3.50%|7.93%|6.45%|9.30%|24.40%|23.54%|23.51%|53.29%|73.56%|
|Labor productivity (logs)|2.31%|2.04%|1.66%|5.72%|13.56%|13.70%|9.09%|20.44%|20.93%|
|Supply disruption Y:1 N:0|23.77%|24.27%|19.73%|37.72%|51.80%|34.39%|20.92%|24.65%|25.01%|
|Overdraft Y:1 N:0|8.92%|8.12%|6.60%|2.79%|3.50%|4.70%|1.99%|0.08%|3.72%|
|Finance obstacle (0-4)|0.04%|-0.88%|-0.72%|0.08%|-0.77%|0.25%|-0.43%|-2.95%|-4.23%|
|Skills obstacle (0-4)|-3.62%|-4.32%|-3.51%|0.24%|-1.52%|-2.03%|-3.44%|-4.93%|-5.27%|
|Young firm (<=10 years) Y:1 N:0|0.76%|0.53%|0.43%|-1.67%|-0.93%|-4.66%|-2.51%|-5.08%|-0.60%|
|High share of women workers Y:1 N:0|0.02%|0.03%|0.03%|0.01%|0.06%|0.03%|0.02%|-0.01%|-0.02%|
|Exports (proportion of sales)|-0.06%|-1.90%|-1.54%|5.88%|3.10%|-4.10%|-1.65%|-2.60%|4.83%|
|Foreign ownership (proportion)|0.78%|0.42%|0.34%|-3.85%|-2.14%|-2.57%|-4.58%|-5.42%|-3.50%|
|Tax rates are a major obstacle Y:1 N:0|2.41%|2.48%|2.02%|0.64%|1.70%|1.29%|0.84%|0.47%|0.43%|
|Government support Y:1 N:0|-0.58%|-0.52%|-0.43%|0.35%|0.54%|0.53%|0.41%|0.23%|-0.52%|
|Capital city Y:1 N:0|0.04%|0.14%|0.11%|0.00%|0.02%|0.05%|0.07%|0.14%|0.24%|
|Obstacles severity|-0.24%|-0.23%|-0.19%|-0.07%|-0.11%|0.00%|-0.06%|0.19%|0.17%|
|Sole proprietorship Y:1 N:0|-1.26%|0.28%|0.23%|-2.66%|-7.51%|-4.54%|-2.58%|-4.08%|-11.14%|
|Shareholding company (non-traded) or|-1.09%|-0.50%|-0.41%|-0.35%|-0.64%|0.40%|0.52%|1.27%|0.41%|
|Partnership Y:1 N:0||||||||||
|<br>R&D and Newproduct/process|-1.20%|-1.59%|-1.29%|-1.53%|2.87%|1.80%|0.25%|3.33%|4.07%|
|||||**Panel**|**B: Structura**|**l effects**||||
|Country-Industry|-171.80%|-231.47%|548.12%|209.72%|362.81%|327.98%|252.39%|625.05%|1073.18%|
|Labor productivity (logs)|-56.37%|-75.50%|-224.62%|3.16%|54.32%|98.93%|48.19%|239.08%|238.16%|
|Supply disruption Y:1 N:0|40.07%|57.11%|413.63%|-23.63%|-37.02%|1.66%|-1.20%|11.58%|-24.89%|
|Overdraft Y:1 N:0|15.15%|14.22%|6.09%|4.09%|3.18%|6.95%|4.67%|-0.03%|1.17%|
|Finance obstacle (0-4)|-4.03%|1.82%|9.81%|4.69%|13.44%|6.24%|14.41%|42.32%|44.39%|
|Skills obstacle (0-4)|-21.59%|-25.51%|-22.03%|1.48%|-5.31%|-7.81%|-17.71%|-20.00%|-12.62%|
|Young firm (<=10 years) Y:1 N:0|-1.82%|-1.35%|2.80%|7.40%|5.95%|13.68%|7.90%|15.20%|3.24%|

60
|High share of women workers Y:1 N:0|9.77%|12.81%|9.38%|7.66%|19.73%|13.04%|13.60%|17.61%|21.03%|
|---|---|---|---|---|---|---|---|---|---|
|Exports (proportion of sales)|0.54%|-0.06%|1.65%|2.75%|2.83%|0.43%|0.99%|4.03%|6.96%|
|Foreign ownership (proportion)|0.44%|0.26%|-3.17%|-2.67%|-2.52%|-3.00%|-3.27%|-3.07%|-2.08%|
|Tax rates are a major obstacle Y:1 N:0|-26.13%|-27.44%|-22.63%|-6.73%|-15.74%|-11.40%|-8.33%|0.22%|-6.20%|
|Government support Y:1 N:0|8.09%|7.23%|14.33%|-2.08%|-4.43%|-5.40%|-6.05%|-2.08%|7.64%|
|Capital city Y:1 N:0|-1.84%|3.17%|6.48%|0.28%|3.58%|5.40%|4.35%|8.74%|13.21%|
|Obstacles severity|21.79%|21.58%|5.22%|-0.91%|5.32%|-2.29%|-2.32%|-29.24%|-11.71%|
|Sole proprietorship Y:1 N:0|4.39%|2.56%|-3.40%|6.57%|11.66%|7.58%|1.72%|4.65%|24.12%|
|Shareholding company (non-traded) or|-9.24%|-2.69%|-10.41%|-2.84%|-10.63%|2.06%|-1.22%|6.44%|6.95%|
|Partnership Y:1 N:0||||||||||
|R&D and Newproduct/process|0.40%|-3.09%|-8.94%|-9.82%|-5.76%|-5.58%|-5.65%|0.02%|11.61%|

Without much loss of generality, only the combined contribution of R&D and New product/process development is shown.

61
|**Table 7: Mean decompo **<br> <br>|**sition by regio**<br>|**ns**<br>||
|---|---|---|---|
|Dependent variable: Sales decline(%)<br>**(1)**|**(2)**|**(3)**|**(4)**|
|**SSA**|**ECA**|**LAC**|**MENA**|
|**Panel A: Aggreg**|**ate effects**|||
|SMEs<br>49.499***|28.216***|27.483***|45.674***|
|(1.153)|(1.693)|(2.003)|(2.655)|
|Large firms<br>38.097***|16.994***|16.052***|39.509***|
|(1.709)|(2.650)|(2.479)|(1.569)|
|Difference (SME-Large firms)<br>11.402***|11.222***|11.432***|6.165**|
|(1.290)|(2.676)|(2.832)|(2.420)|
|Total or Aggregate<br>5.056**|4.120|7.120*|7.651***|
|endowment effect<br>(2.445)|(2.703)|(4.010)|(1.965)|
|Total or Aggregate structural<br>6.346**|7.102***|4.311|-1.486|
|effect<br>(2.497)|(1.487)|(3.731)|(2.947)|
|Number of observations<br>1,087|2,695|726|444|
|**Panel B: Aggregate effects as a % o**<br> <br>|**f the total unc**<br>|**onditional gap**<br>||
|Total or Aggregate endowment effect<br>44.34%|36.71%|62.29%|124.11%|
|Totalor Aggregate structuraleffect<br>55.66%|63.29%|37.71%|-24.11%|
|Huber-White robust standard errors clustered on count|ry-industry pair|s. *** (1%), ** (|5%), * (10%).|

62
|||**Table**|**8: Mean decomp**|**osition by reg**|**ions**||||
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
||**SS**|**A**|**EC**|**A**|**LA**|**C**|**ME**|**NA**|
|Total unconditional gap|11.40|2***|11.22|2***|11.432|***|6.16|5**|
||(1.2|90)|(2.6|76)|(2.83|2)|(2.4|20)|
|||**P**|**anel A; Mean d**|**ecomposition**|**estimates (Perc**|**entage points**|**)**||
||**Endowment**|**Structural**|**Endowment**|**Structural**|**Endowment**|**Structural**|**Endowment**|**Structural**|
|Country-Industry|**effect**<br>-2.169|**effect**<br>-1.866|**effect**<br>1.045|**effect**<br>3.731***|**effect**<br>1.415|**effect**<br>-0.730|**effect**<br>0.531|**effect**<br>-3.066|
||(1.637)|(3.428)|(1.956)|(1.358)|(1.511)|(2.584)|(0.725)|(3.164)|
|Labor productivity (logs)|2.615**|14.327|0.502|-8.923|2.090|-4.003|0.441|28.271***|
||(1.326)|(14.629)|(0.411)|(12.581)|(1.344)|(23.750)|(1.223)|(8.768)|
|Supply disruption Y:1 N:0|3.586***|2.001|3.094***|2.703*|7.448***|2.420|3.101**|18.866***|
||(1.314)|(5.251)|(0.896)|(1.569)|(1.731)|(2.879)|(1.284)|(5.500)|
|Overdraft Y:1 N:0|1.893|2.003|0.249|0.487|-0.138|-0.641|1.667|-0.742|
||(1.337)|(1.282)|(0.317)|(0.629)|(0.895)|(2.168)|(3.227)|(2.560)|
|Finance obstacle (0-4)|<br>0.007|<br>2.066|<br>-0.012|<br>2.359*|<br>0.744|<br>-0.842|<br>-1.108|<br>8.993*|
||(0.148)|(3.109)|(0.156)|(1.233)|(0.865)|(2.768)|(1.148)|(5.043)|
|Skills obstacle (0-4)|-0.092|0.146|-0.499*|-1.690|-0.128|-6.141**|1.292|6.680|
||(0.233)|(1.973)|(0.290)|(1.318)|(0.412)|(2.883)|(1.202)|(4.126)|
|Young firm (<=10 years) Y:1|<br>0.080|<br>-1.391*|<br>0.093|<br>0.973|<br>-0.034|<br>0.227|<br>-0.032|<br>0.976|
|<br>N:0|(0.288)|(0.815)|(0.313)|(0.944)|(0.144)|(0.331)|(1.186)|(2.216)|
|High share of women|-0.030|2.376|-0.066|2.643*|-0.018|0.790|0.645|4.107|
|workers Y:1 N:0|(0.233)|(1.603)|(0.137)|(1.488)|(0.122)|(3.067)|(0.731)|(3.353)|
|Exports (proportion of sales)|1.048|0.721|-0.058|0.428|0.484|0.494**|1.307|0.107|
||(0.907)|(0.491)|(0.493)|(0.304)|(1.002)|(0.217)|(1.099)|(0.354)|
|Foreign ownership|<br>-0.144|<br>-0.640|<br>-0.185|<br>-0.049|<br>-2.227|<br>-0.463|<br>-1.217|<br>-0.040|
|<br>(proportion)|(1.048)|(0.829)|(0.214)|(0.187)|(1.475)|(0.577)|(1.176)|(0.942)|
|Tax rates are a major obstacle|-0.055|-0.182|0.261|-1.519*|-0.055|0.768|-0.041|-0.513|
|<br>Y:1 N:0|(0.142)|(1.445)|(0.244)|(0.879)|(0.380)|(2.021)|(0.220)|(1.868)|
|Government support Y:1 N:0|0.149|-0.159|-0.102|1.533|-0.675|0.508|-1.312*|-3.127***|

63
||(0.184)|(0.336)|(0.172)|(1.109)|(0.789)|(1.616)|(0.713)|(0.920)|
|---|---|---|---|---|---|---|---|---|
|Capital city Y:1 N:0|1.414|3.393|-0.030|0.478|-0.159|1.558|-0.008|0.612|
||(0.956)|(2.658)|(0.062)|(0.713)|(0.420)|(2.071)|(0.101)|(0.885)|
|Obstacles severity|-0.030|0.512|-0.001|-0.415|-0.140|0.319|0.588|-22.195*|
||(0.383)|(4.645)|(0.028)|(1.308)|(0.427)|(6.137)|(1.889)|(13.117)|
|Sole proprietorship Y:1 N:0|-4.510**|4.712**|-0.404|1.145|0.263|0.152|1.921|-2.113|
||(1.943)|(2.339)|(0.459)|(0.801)|(1.568)|(3.070)|(3.714)|(6.283)|
|Shareholding company (non-|1.209**|1.040|0.166|-1.026|0.180|-1.212|-1.042|-0.951|
|traded) or Partnership Y:1|(0.473)|(1.310)|(0.259)|(2.730)|(0.928)|(2.520)|(1.295)|(2.265)|
|N:0|||||||||
|R&D and New|0.084|-0.135|0.069|-0.033|-1.929|-4.445|0.918***|0.941**|
|product/process|(0.427)|(1.033)|(0.206)|(0.635)|(1.540)|(3.806)|(0.338)|(0.434)|
|Constant||-22.576||4.275||15.552||-38.293**|
|||(19.971)||(12.989)||(31.439)||(10.931)|
|Number of observations|1,087|1,087|2,695|2,695|726|726|444|444|
|||<br>**Panel B: M**|<br>**ean decompos**|<br>**ition estimates**|**above as a per**|**centage of th**|**e total gap**||
|Country-Industry|-19.0%|-16.4%|9.3%|33.3%|12.4%|-6.4%|8.6%|-19.7%|
|Labor productivity (logs)|22.9%|125.7%|4.5%|-79.5%|18.3%|-35.0%|7.2%|458.6%|
|Supply disruption Y:1 N:0|31.4%|17.6%|27.6%|24.1%|65.1%|21.2%|50.3%|306.0%|
|Overdraft Y:1 N:0|16.6%|17.6%|2.2%|4.3%|-1.2%|-5.6%|27.0%|-12.0%|
|Finance obstacle (0-4)|0.1%|18.1%|-0.1%|21.0%|6.5%|-7.4%|-18.0%|145.9%|
|Skills obstacle (0-4)|-0.8%|1.3%|-4.4%|-15.1%|-1.1%|-53.7%|21.0%|108.4%|
|Young firm (<=10 years) Y:1|0.7%|-12.2%|0.8%|8.7%|-0.3%|2.0%|-0.5%|15.8%|
|N:0|||||||||
|High share of women<br>workers Y:1 N:0|-0.3%|20.8%|-0.6%|23.6%|-0.2%|6.9%|10.5%|66.6%|
|Exports (proportion of sales)|9.2%|6.3%|-0.5%|3.8%|4.2%|4.3%|21.2%|1.7%|
|Foreign ownership<br>(proportion)|-1.3%|-5.6%|-1.7%|-0.4%|-19.5%|-4.1%|-19.7%|-0.6%|
|Tax rates are a major obstacle|-0.5%|-1.6%|2.3%|-13.5%|-0.5%|6.7%|-0.7%|-8.3%|
|Y:1 N:0|||||||||
|Government support Y:1 N:0|1.3%|-1.4%|-0.9%|13.7%|-5.9%|4.4%|-21.3%|-50.7%|

64
|Capital city Y:1 N:0|12.4%|29.8%|-0.3%|4.3%|-1.4%|13.6%|-0.1%|9.9%|
|---|---|---|---|---|---|---|---|---|
|Obstacles severity|-0.3%|4.5%|0.0%|-3.7%|-1.2%|2.8%|9.5%|-360.0%|
|Sole proprietorship Y:1 N:0|-39.6%|41.3%|-3.6%|10.2%|2.3%|1.3%|31.2%|-34.3%|
|Shareholding company (non-<br>traded) or Partnership Y:1<br>N:0|10.6%|9.1%|1.5%|-9.1%|1.6%|-10.6%|-16.9%|-15.4%|
|R&D and New|0.7%|-1.2%|0.6%|-0.3%|-16.9%|-38.9%|14.9%|15.3%|
|product/process|||||||||

Huber-White robust standard errors clustered on country-industry pairs. *** (1%), ** (5%), * (10%). Without much loss of generality, only the combined contribution of R&D and New product/process development is shown.

65