This document summarizes a presentation on quantifying the effects of economic distortions on firm-level productivity using data from the World Bank Enterprise Surveys. The presentation includes the following key points in 3 sentences: The presentation aims to estimate the marginal effects of observable variables capturing firms' choices and policies on total factor productivity, and the contribution of these observables to variations in productivity. It uses a balanced panel of survey data from over 9,000 firms across 68 countries to estimate an extended Cobb-Douglas production function. Next steps include deepening the current analysis and relating results to previous work quantifying distortions using the Hsieh and Klenow methodology.

1. Quantifying the Effects of Economic Distortions
on Firm Level Productivity:
What can WBES data tell us?
Work In Progress
Paulo Correa, World Bank
Ana P. Cusolito, World Bank
Jorge Pena, Instituto Empresa

2. Plan of Presentation
1. Motivation
2. Enterprise Survey
3. Methodology
4. Key results
5. Next steps
2

3. ECA is the top DB reformer
3

4. Yet, effect on productivity is unclear
4

5. Literature
• Hsie and Klenow (2009) and the frictionless economy
• Two approaches in the literature (Restuccia and Rogerson
2017) -- direct and indirect – to identify distortions.
• Neither approaches help identify a dominant effect or
provide unequivocal guidance on reform priorities.
• Moreover, measurement issues: several factors may
explain TFPR distortions
5

6. Motivation
• How best advise governments (technically sound and
of practical relevance)?
• Current efforts IMF-OECD-WB : What indicators of
structural reforms? How do they affect productivity?
– PMR, labor, capital (bankruptcy)
– DB indicators
• WB: Enterprise Survey?
6

7. This presentation
• What do data tell us when potential sources of
distortions become observable variables?
– Observables: Policy and ‘firm choices’
– Preliminary evidence
• We estimate:
– Observables and TFPR: marginal effects
– Contribution of observables to TFPR variance
7

8. Enterprise Surveys
• Firm-level survey data collected via face to face interviews with business
owners or managers: 139 countries (131,000 firms)
• Data collection: survey representative samples of an standardly defined
universe: non-agricultural, non-extracting formal private sector of 5+ employees
• Data collected based on a global methodology (fully comparable)
• Stratified random sampling
• Global questionnaire (plus 1/3 customized questions)
• Standardized fieldwork supervision
• Over 500 studies have been produced using Enterprise Survey data

9. Enterprise Surveys: Coverage Map

10. Basic Structure of the Survey
10
• Legal status
• Age
• Size
• Sector
• Location
• Ownership
structure
• Ownership and
management
by women
• Use of quality
certification or
license
• Sales
• Employment
• Capital use
• Fixed capital
investment
• Exports
• Innovation
• Capacity
utilization
• Management
practices
• Infrastructure
• Regulations:
permit, license,
taxes,
government
contracts, etc.
• Customs and
transport
• Crime and
corruption
• Use and
applications for
financial
services
• Ranking of
obstacles
• Individual
assessment of
each element
on its degree
of obstacle
BUSINESS
ENVIRONMENT
(factual)
BUSINESS
ENVIRONMENT
(perception)
FIRM
OUTCOMES
FIRM
CHARACTERISTICS

11. Illustration: red tape and credit
Philippines 2015 Philippines 2015
11

12. Enterprise Survey (Final Sample)
• Since some of the variables present missing values, in order to maximize sample
size, we imputed the missing values using a pseudo-Gibbs sampler, van Buuren et
al. (1999) and Raghunathan et al. (2001).
12
• We end up using a balanced panel that covers 9,497 firms from 68 countries
grouped in 4 income groups, 6 WB ‘regions’; Panel: Two years.

13. Methodology
• We estimate an extended Cobb-Douglas production function using De Locker (2013)
under the following assumptions:
a. Equal input-output elasticities at the country group-sector level.
– Countries were grouped in four clusters according to their income level and following the
World Bank Country Classification (e.g., High, Upper-middle, Lower-high, and Low).
– ISIC Industries were aggregated at the 2-digit level.
b. We proxy output at the firm-level with deflated sales at the firm-level.
yit
= bL
lit
+ bK
kit
+ bM
mit
+wit
+eit
(1)
13

14. Methodology
• Productivity is, thus, assumed to evolve according to:
• i.e productivity shocks depends on lagged productivity and
also on the set xk of firms’ choice and policy variables.
• Z is a vector of other control variables: age, capacity
utilization.
• To approximate the unknown functions g0, g1, …, gK we use
third degree polynomials in ωit-1.
wit
= g0
(wit-1
)+ gk
(xk,it-1
,wit-1
)
k=1
K
å + Zit
g +xit
(2)
14

15. Methodology
• Estimation of the initial specification may be affected by
multicollinearity
• The selection of the final set of significant variables in the
productivity equation is done using the general to specific
procedure (GETS) method (Hendry and Krolzig (2001), Hoover
and Perez (1999))
• The final list of firms’ choice (G1), policy (G2) and other
control (Z) variables are listed in Table 1.
15

16. Table 1: Descriptive Statistics

17. Table 2: Percentage contributions of G1 and G2 variables to the
variation of TFPR by productivity decile
17
Key results: IC –Marginal effects

18. Table 3: Percentage contributions of G1 and G2 variables to the
variation of TFPR by groups of firms
18
Key results: IC –Marginal effects

19. Table 4: Contribution of observables to TFPR var.
19
Key results: Observables and TFPR Var

20. Figure 1: TFPR distributions (red density) and TFPR distributions without the negative
effects of POWER, WATER, RED TAPE, INF, INF PAYM (green dashed density).
20

21. Next Steps
• Deepen the current analysis
• Relate to HK (2009)
– Correlation between HK distortions measures and
our observables
– Compare two counterfactuals of TFP gains from
removing distortions (with/without observables):
HK and ours
21

22. ANNEXES
22

23. Hsieh & Klenow (2009) distortions Table 11: Country level regressions of contributions of policy variables and HK distortions
Robust standard errors in parentheses, all the regressions controls for income and region
Sales missalocation Capital missalocation
(1) (2) (3) (1) (2) (3)
EXP 0.027 0.020 0.063 0.117
(0.018) (0.019) (0.149) (0.168)
OWN 0.023*** 0.022** -0.158* -0.173*
(0.007) (0.007) (0.076) (0.077)
CREDIT 0.009 0.003 -0.095 -0.070
(0.005) (0.006) (0.050) (0.055)
TRAIN 0.005 0.007 -0.083 -0.092
(0.006) (0.006) (0.071) (0.067)
EDUC 0.001 0.002 0.061 0.075
(0.005) (0.005) (0.041) (0.045)
COMP -0.021* -0.016 -0.308** -0.257
(0.011) (0.014) (0.111) (0.132)
POWER -0.007 -0.004 0.056 0.015
(0.004) (0.004) (0.030) (0.032)
WATER 0.011 0.005 -0.115 -0.030
(0.011) (0.011) (0.089) (0.094)
RED TAPE -0.000 0.000 -0.042* -0.037
(0.002) (0.003) (0.017) (0.021)
DOPLIC 0.043** 0.022 -0.131 -0.069
(0.014) (0.018) (0.123) (0.148)
INF 0.005 0.003 -0.049 -0.009
(0.005) (0.005) (0.034) (0.035)
INF PAYM 0.003 0.005 -0.077 -0.090
(0.021) (0.019) (0.119) (0.116)
R-squared 0.212 0.136 0.201 0.180 0.088 0.169
N 157 157 157 162 162 162

24. 24

25. Table 1: List of variables used in the analysis
25

26. 26