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

Plan of Presentation 1. Motivation

2. Enterprise Survey

3. Methodology

4. Key results

5. Next steps

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ECA is the top DB reformer

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Yet, effect on productivity is unclear

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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

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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?

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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

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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

Enterprise Surveys: Coverage Map

Basic Structure of the Survey

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• 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

Illustration: red tape and credit

Philippines 2015 Philippines 2015

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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).

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• 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.

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.

y

it= b

Llit

+ bKk

it+ b

Mm

it+w

it+ e

it (1)

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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(w

it-1) + g

k(x

k ,it-1,w

it-1)

k=1

K

å + Zitg +x

it (2)

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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.

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Table 1: Descriptive Statistics

Table 2: Percentage contributions of G1 and G2 variables to the variation of TFPR by productivity decile

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Key results: IC –Marginal effects

Table 3: Percentage contributions of G1 and G2 variables to the variation of TFPR by groups of firms

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Key results: IC –Marginal effects

Table 4: Contribution of observables to TFPR var.

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Key results: Observables and TFPR Var

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

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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

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ANNEXES

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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

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Table 1: List of variables used in the analysis

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