Using firm-level micro data for evidence based policy making

Using Firm-level micro data for evidence-based

policy making

OECD Productivity Network

Juan Rebolledo

October, 20161

Research based on ENAPROCE’s policy-relevant data.

Understanding the Productivity gaps across

firms, sectors, and economic regions in Mexico.

Mexico’s National Micro, Small and Medium Enterprise

Productivity and Competitiveness Survey, the first effort to

measure “hard-to-observe” skills at the Firm-level.

INDEX

2

Productivity and low economic growth in Mexico

Total Factor Productivity decline in Mexico

(1990-2014)***

(***) Source: INEGI.

90

92

94

96

98

100

102

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Average Growth in GDP and Productivity

(Annual growth rate, 1980-2014)

(*) Source: Penn World Table 8.1 (1980-2011) and The Conference Board (2011-2014).

(**) Source: Penn World Table 8.1 (1980-1990) and INEGI (1990-2015), based on KLEMS methodology.

6.4%

4.0%4.6%

2.5%

1.2%0.6%

-0.2%-0.8%

Korea* Ireland* Chile* Mexico**

GDP Productivity

Total Factor Productivity (TFP) has declined

since 1990. In 2014 it was 7.8% lower than in 1990, an

annual fall of 0.4%, on average.

3

National

AGS

BCBCS

CHI

CH

COA

COL

DF

DURGUA

GUE

HID

JAL

MEX MICMOR

NAY

NL

OAX

PUE

QRO

QR

SLPSIN

SON

TAB

TAM

TLAX

VERYUCZAC

70

170

270

370

470

570

670

0 5 10 15 20 25 30 35L

abo

ur

pro

duct

ivit

y (G

DP

/E

mp

loye

d p

op

ula

tio

n)

% of population living in extreme poverty

Labour productivity and poverty

Productivity gaps and poverty by state

In 2014,

11/32states were above the national

average.

The 5 most productive states were

3xmore productive than the

5 least productive states.

National Average

Source: INEGI and CONAPO. 4

0.5 times the national average


0.7-0.8 times the national average


Above the national average

In Mexico, the Northern border is 2.5x more productive than the

South and has 2x more formal employment.

Labour productivity by state

Source: INEGI.

57.20%

40.43%

80.07%

National Average Northern Border South

Informality (% of working population)

2x

The inefficient allocation of factors of

production results in wide productivity gaps In Mexico,

employment in

manufactures

along the northern

border (20.4%) is

more than twice

that of the south

(9.4%).

5

Firm-endogenous factors may also

explain low productivityManagerial practices

Innovation

Production chain integration

TrainingMSME

productivity

Financing

However, recent literature

suggests that other non-given

firm-level characteristics,

such as:

managerial practices,

training,

use of ICTs,

financial access,

better capital or innovation

also influence firm-level

productivity2.

1For example: La Porta and Shleifer (2008), Gennaioli (2013) and Hurst and Pugsley (2011).2 Bloom et al (2010).

(*) Informality could be considered either endogenous or exogenous, or both.

Most of the empirical

evidence that explains the

low productivity of firms

across countries, especially

among MSMEs, has

studied exogenous factors

that influence firm

productivity, such as the

quality of infrastructure,

informality*, government

regulations, trade policies,

and levels of human

capital1.

6

The productivity of Firms in developing countries

appears to be extremely low and disperse

Management

PracticesPoor management practices

have a substantial negative

impact on productivity in

developing countries. Latin

American countries have

significantly lower average

management scores. In

India, training on basic

management practices led

to a 50% reduction in

quality defects in 3 months1.

Delegation of

Decision MakingThe lack of delegation of

decision making negatively

impacts the productivity of

firms in developing

countries. In some, a sign-

off from owners is required

for every purchase, while

managers in Japan and the

US can make investments

of up to $50,000 directly1.

Human Capital

Firms in cities with small

growth rates in their college

graduate populations miss

out on potential human

capital externalities and have

lower productivity rates after

controlling for firms’ actual

levels of human capital2.

1Bloom et al. (2010)2Moretti (2004) 3Suresh De Mel, McKenzie, and Woodruff (2007a); Banerjee and Duflo (2008)4De Fuentes et al. (2004)

*KIBS refers to Knowledge-intensive based services.

ENAPROCE is a recent effort by the Mexican government to provide firm-level micro

data precisely on these factors that may explain productivity.

Relevant evidence points to the following factors:

Financial Constraints

Micro and small enterprises in

developing countries have

difficulty accessing formal

financial resources, despite

their marginal returns being

higher than average interest

rates. An experiment in Sri

Lanka found a causal

relationship between firms’

access to finance and growth

in their profits or sales3.

Innovation

An IDB discussion paper

finds that only 13.7% of

Mexican firms surveyed

reported having innovated.

This was lower for firms in

the services (6.6%) and KIBS

(10.5%) sectors*. It also

found intensive investment in

innovation has a positive

effect on labour productivity

and is unrelated to firm size4.

7

96% Response RateStratification by sector,

region, and firm size25,456 firms in sample

ENAPROCE characteristics — 2015 baseline for panel dataThe information provided by ENAPROCE enables researchers to study the determining factors

behind the lagging productivity of MSMEs in the country.

managerial practices financing

government support

innovation

training

Main themes

use of ICTs

Source: ENAPROCE 2015.

production chain integration

business atmosphere

Productivity measurement: Törnqvist changing-weight index

A Törnqvist formula expresses the

change in multifactor productivity as

the difference between the rate of

change in output and the weighted

average of the rates of change in the

shares and prices of inputs.

human capital

8

ENAPROCE representativeness

Source: ENAPROCE and World Bank.

• Large sectors

(industry, retail and

services)

• Size

• Strategic sectors*

**Regions are defined as follows:

North: Baja California, Baja California Sur, Sinaloa, Sonora, Coahuila, Chihuahua, Durango, Nuevo León and Tamaulipas West:

Aguascalientes, Colima, Guanajuato, Jalisco, Michoacán, Nayarit, San Luis Potosí and Zacatecas

Center: Distrito Federal, Hidalgo, México, Morelos, Querétaro and Tlaxcala;

South: Campeche, Chiapas, Guerrero, Oaxaca, Puebla, Quintana Roo, Tabasco, Veracruz and Yucatán

State

Level

National

Level

Regional

Level**

• SMEs


• Micro enterprises


Observation unit:

Firm

Some firms have one or more

establishments under the same

business name. To account for

location, the parent company or

the establishment with the biggest

number of employees is

considered.

*Strategic sectors:

• Electrical appliances

• Communication and electronics equipment

• Ground transportation equipment

• Medical equipment

• Textile industry

• Plastics and rubber

• Wood products

• Construction products

• Business support services

• Tourism services

• Pharmaceutical products

• Chemical industry

• R&D services

9

Firm heterogeneity by size

305%

200%

274%

Retail Manufactures Services

Total Factor Productivity heterogeneity by sector

(Gap between percentile 10 and percentile 90)Total Factor Productivity Kernel density by firm size

Micro

Small

Medium

Large

Source: ENAPROCE and World Bank.

10

Preliminary data analysis

- Relatives as owners of businesses

- Relatives as firm decision-makers

Family business practices

- Target-based planning

- Incentives and salaries

- Employee monitoring

Managerial practices

- Share of uneducated workers

- Share of workers with various levels of

educational attainment

Higher education

Innovation

- Patent registration

- Use of ICTs

- Presence of innovation spending

- Innovation spending per worker

- Presence of certifications

- Presence of financing

- Sources of financing

- Costs of financing

Financing

11

Firm characteristics prevalent in Mexico are

associated with higher productivity

Family

business

These practices are negatively

correlated with higher firm

productivity. Their presence

reduces firm productivity by almost

Dependent variable: ln(TFP) (1) (2) (3) (4) (5)

Firm characteristics

Share of workers with HE0.0004

(0.0000)***

0.0004

(0.0000)***

0.0005

(0.0001)***

.0004

(.0000)***

.0004

(.0000)***

Family practices-0.15

(0.0230)***

-0.19

(0.0473)***

-.15

(.0230)***

-.14

(.0235)***

Survey indices

Managerial practices z-score0.11

(0.0166)***

Financing z-score.04

(.0081)***

Innovation z-score.09

(.0089)***

Note: all regressions control for K, L, and the state in which the firm is established.15%The various indices are constructed using factor analysis. Firm weights

were utilized, and in all cases, only the first factor was considered. The

factor analysis strongly suggests that the indices are accurate

representations of the underlying variables.

Small and Medium Firms

12

Family

business

Their presence reduces

firm total factor

productivity by almost

The presence of family business practices is

associated with lower productivity levels

Low human

capital

33% lower share of

HE workers

Indebtedness

and credit application

TFP

-10%

=15%

Indebted

Family business practices

reduce scores by

0.09 SD

Managerial

practices

-10%Loan denied

13

0

0.2

0.4

0.6

0.8

1 2 3 4 5 6 7 8 9 10

Pro

duct

ivit

y

Managerial practices decile

Productivity by managerial practices score decile

Relationship between managerial practices, innovation

and productivity

Firms that score higher on the managerial practices index tend to be more

productive and spend more on innovation per worker.

Source: ENAPROCE and World Bank.Source: World Management Survey, Management Matters, Manufacturing Report (2014).

Mexico

0%

5%

10%

15%

20%

1 2 3 4 5 6 7 8 9 10

Sh

are

of

firm

s th

at e

xpo

rt


Share of firms that export by managerial practicesscore decile

14

Better Managerial Practices lead to improvements

in productivity, and higher investment in

innovation and trainingBigger firms,

better managers

Managerial practices improve

as firm size increases:

Large - Medium = ∆ 0.4 SD

Medium - Small = ∆ 0.2 SD

Large - Small = ∆ 0.6 SD

Skills

formation

Firms with higher MP scores have

more training.

Firms with training have a score

0.4 SD higher

Managerial

practices

A 1 SD standard deviation

rise in the MP score is

associated with a

productivity increase of

10%

=

Youngerfirms

have lower

scores

0.2

0.6 0.6 0.6

0.5 0.5

0.6 0.6

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Score

years

15

Firms with higher innovation scores train their workers

more and experience improvements in productivity

Investment in

innovation increases

dramatically after year 1

=

Innovation

A higher score in the innovation

index is also positively correlated

with increases in productivity.

A 1 SD rise is associated with a

productivity increase of

9%Skills

formation

Firms with higher innovation

scores have more training.

Firms with training have a score

0.5 SD higher

Bigger firms

score higher

Larger firms invest

2x more on innovation

than other firms

0.3

1.11.4

1 0.9

1.9

4.2

0

1

2

3

4

5

1 3 4 5 6 7-10 >10In

novation e

xpenditure

s

(mpp)

Years

16

Financial capacity has mixed results on the

mechanisms that lead to higher productivity

Financing

A 1 SD increase in the financing

index score results in a

productivity rise of

4%

FDI

Financing score is

0.4 SD higher if the firm has FDI

Supplierof

foreignfirms

Supplierof

exporters

Financing score is

0.4 SD lowerif the firm is a supplier to firms that

are exporters

Financing score is

0.4 SD lowerif the firm is a supplier to

foreign firms

=

17

What do we learn from these preliminary results?

1. Family Business

Align incentives for firms to hire professional managers instead of family members to improve corporate governance.

2. Managerial practices

In order to meet the best international managerial practices, design policies aimed to support firms when they find

themselves at their weakest: around the time they are established or when they take out loans.

3. Financing

Given the analysis’s mixed results on financial practices and being a part of the global market, more research should

be done in order to clarify the mechanisms that underlie MSME financing.

According to the scores analyzed, in terms of productivity gains, policies that target family business and

managerial practices offer the highest benefits.

18

Research agenda: moving forward

Exporters and FDI

Look at the characteristics

specific to foreign firms,

firms that export or firms

that have shares of foreign

capital.

Leading and lagging

firms:

Analyze leading and lagging

firms’ productivity and

salaries by sector. Look at

leading and lagging firms’

convergence rate across

sectors and regions.

Stock of Knowledge

and RoR

Observe differing patterns in

terms of technology

adoption and innovation

practices.

Firm Survival

Identify the factors behind

firm survival and the changes

in financing, management

practices and decision-making

over time.

1. 2. 3. 4.

19

OECD Productivity Network

Juan Rebolledo

October, 201620

Relationship between managerial practices, innovation

and productivity

Firms that score higher on the managerial practices

index are more likely to export and have FDI.

Source: ENAPROCE and World Bank.Source: World Management Survey, Management Matters, Manufacturing Report (2014)..

0%

2%

4%

6%

8%

10%

12%

14%

1 2 3 4 5 6 7 8 9 10

Sh

are

of

firm

s w

ith

FD

I


Share of firms with FDI by managerial practices score decile

0

1000

2000

3000

4000

1 2 3 4 5 6 7 8 9 10

Inn

ova

toin

spen

din

gp

er

wo

rker


Innovation spending per worker by managerial practices score decile

22

Using firm-level micro data for evidence based policy making

Economy & Finance

Transcript of Using firm-level micro data for evidence based policy making