Market Institutions, Labor Market Dynamics, Growth and Productivity: An Analysis of
Latin America and the Caribbean
Research proposal: Productivity Dynamics of the Colombian Manufacturing Sector
Section I − Introduction
What allows some industries to become more productive over time? A possible explanation is
technological improvement. Firms learn by doing or by watching competitors that employ
better methods of production, and as firms become more productive, industry productivity
increases. An alternative explanation is that in open markets, some firms thrive while others
disappear. In this case, industry productivity increases as the more productive firms survive
and grow while the less productive contract or exit. An implication of this second explanation
is that policies directed at sheltering the less efficient firms from the forces of the market have
a negative effect on aggregate industry productivity.
These two explanations are complementary, rather than mutually exclusive, determinants of
productivity improvements. Separating the degree to which each of them characterizes
productivity growth will promote a better understanding of the driving forces behind industry
productivity changes and will consequently facilitate appropriate policy making directed at
promoting economic growth. Furthermore, both explanations have potentially serious
implications for factor markets. Productivity growth derived from the contraction and exit of
less productive firms will entail the displacement of the entire workforce of a fraction of the
industry’s plants. Similarly, industry productivity growth that stems from general
improvements across firms may have serious, but rather different, implications for the
displacement of labor, in particular if it is motivated by skill-biased or labor augmenting
technological progress. Such technological progress would entail the partial displacement of
the industry’s workforce across all firms in the industry, rather than exclusively the workforce
of exiting firms.
From an analytical point of view, the second source of productivity evolution requires the
explicit modeling of firm heterogeneity in order to capture the process by which some firms
thrive while others lag. In order to determine the role played by each source, an economic
model consistent with both homogeneous and heterogeneous firms must be taken to the data.
Olley and Pakes (1996) develop such a firm-level model of productivity. They provide an
estimation framework that takes into account both the simultaneity bias induced by the
contemporaneous correlation between input levels and the productivity shock, and the selection
bias induced by ignoring exit. Levinsohn and Petrin (1999) suggest some methodological
extensions to Olley and Pakes (1996) of particular importance for developing country data and
develop a more unified framework that encompasses several commonly used approaches to
estimating productivity.
We propose to apply the estimation framework developed Olley and Pakes (1996) and
Levinsohn and Petrin (1999) to the Colombian case to provide evidence on the distribution and
evolution of plant level productivity during the last two decades. As a first stage of our
45
research, we will document the changes in plant level productivity and analyze shifts in its
distribution over the period of study, without explicitly incorporating the changing economic
and policy environment. Employment reallocation statistics can provide some preliminary
indications of the extent to which entry and exit has played a significant role in job flows. If
less productive firms exit over time, while more productive firms enter, we would expect these
firms to contribute significantly to the job turnover within an industry. Similarly, an analysis of
trends in the evolution of labor and total factor productivity as related to entry and exit will
provide us with some early evidence of how important the two sources of productivity growth
may be in the case of Colombian manufacturing.
The focus of our in-depth econometric analysis will be to provide empirical evidence about the
degree to which recent policy changes, both in the national tax system and in trade instruments,
have contributed to the characterization of productivity growth in various Colombian
manufacturing sectors. We will focus on the effects of tax exemptions put in place over the
past two decades that may have affected plant-level efficiency, and in particular we will
analyze the effects of trade policy through tariffs and other foreign trade taxes. As an aside, it
is worth noting that governmental reform packages in Colombia have been implemented for a
variety of reasons, providing microeconomic incentives being of potentially minor importance.
The Colombian government has used modifications in the tax code effectively to achieve
revenue targets and fiscal prudence. Beyond exploring the effect of targeted subsidies and tax
breaks on industrial productivity, this study will try to assess how this arbitrariness in tax
reform in general has impacted on performance in the manufacturing sector.
To relate productivity changes to such policy reforms, we will start from the plant-level
measures of productivity obtained in the first stage of our study. The policy measures that are
available to us are sector-wide, time-varying tax rates and measures of trade policy such as
tariff and quota rates. Furthermore, the Colombian government has used tax exemptions
effectively for certain sectors in specific geographic regions. To investigate the impact of these
policy measures on productivity, we will, in a second stage, aggregate the first-stage plant-level
productivity residuals to the corresponding sector or geographic level. This aggregation will
result in an index of sector-level productivity measures, subdivided by geographic region,
where applicable. This productivity index will serve as our dependent variable for the secondstage regressions, relating it to the available policy measures.
This analysis is subject to a potential endogeneity problem as the tax level for each sector may
have been endogenously determined in response to the particular sector's productivity. Part of
this endogeneity may arise through, for example, lobbying by certain sectors, that is the
political-economy side of policy-making. Endogeneity would be a serious problem if the
lobbying powers of the sector were systematically related to its productivity. A possibly more
severe important endogeneity problem would arise if the government explicitly sets taxes and
trade policy instruments to promote growth in certain, lagging sectors only. The potential for
this kind of problem will be addressed in estimation through instrumental variable methods and
the /or exploitation of the panel nature of our data to control for sector-level fixed effects.
Our main interest in relating productivity to tax policy measures lies in exploring in how far
observed and unobserved plant and industry heterogeneity play a role in mitigating the
46
effectiveness of policy reforms as a stimulant for productivity. Rather than focusing on the
quantitative impact of policy reforms on productivity only, we hope to answer questions such
as which sectors benefited most, as measured in terms of productivity improvements, from tax
and trade policy reforms; is the effect more pronounced for larger than for smaller firms; are
these productivity gains generated primarily through plant exit or through within-plant
productivity improvements.
As a by-product of our analysis we propose to shed light on the benefits and costs of the
alternative methodologies by which plant level productivity may be estimated, by replicating
all our estimations for one manufacturing sector of our choice using several of the alternative
methods. Time permitting this exercise will include estimation of plant-level productivity using
a fully specified dynamic -blown structural model of firm investment.
Section II briefly presents our research team. Section III expands on the detail of our work
proposal. Section IV reviews the related literature. Section V briefly reviews the alternative
methods to estimate productivity and presents a version of the model that will be taken to the
data. Section VI presents the data. Section VII describes the dissemination activities that will
be undertaken to promote the discussion of the lessons from our study, as well as the channels
we will employ to make our results available for further research.
Section II – Research Team
Under the coordination of Roberto Steiner, Director of CEDE, we have teamed up two
empirical industrial organization economists.
Roberto Steiner is a macroeconomist, interested in the analysis and design of economic policy.
His experience as a researcher in these areas is well known both at national and international
levels. Most relevant for this study, he recently published a book co-authored by Carolina Soto,
which contains five essays on the topic of tax policy in Colombia (Steiner and Soto, 1999). He
has also written several papers on liberalization and foreign trade in Colombia. To this project
Roberto Steiner will contribute not only his skills as research director, but also his knowledge
and insight about the Colombian tax policy history. He will be working with Marcela
Meléndez and Katja Seim. Marcela Meléndez and Katja Seim are both economists trained in
the Yale tradition of empirical industrial organization.
Marcela Meléndez is an Assistant Professor at the Department of Economics of Universidad de
los Andes, and a researcher at CEDE. Her line of research is empirical, and she is interested in
unveiling how productive units make choices in response to public policy, regulation, or other
endogenous and exogenous forces. Her expertise as econometrician is in the area of dynamic
models and handling of panel data.
Katja Seim is an Assistant Professor at the Stanford Graduate School of Business, where she
belongs to the Economic Analysis and Policy group. Part of her research considers the
implications for productivity and allocation of workers of organizational changes at the firm
level. She has agreed to team up with the economists from CEDE for the purpose of this
47
project to extend this research to the estimation of plant-level productivity and its implications
for policy design. Her expertise as econometrician is in the area of semi-parametric estimation,
simulation, and panel data.
Section III – Detail of our research proposal
Using the database described in Section V below, we will develop the items marked as required
for topics 1 through 5 in the terms of reference following the suggested methodological
approaches for the calculation of statistics when applicable, and the minimum requirements
posed by IADB after the project’s initial seminar, to ensure comparability across country
projects. We will focus, however, primarily on the evolution of firm-level productivity over
our sample period.
Productivity growth is a goal of any development policy, to the extent that it promotes
economic growth. An understanding of the causes that underlie changes in productivity in an
industry is thus essential for the appropriate design of policy. We will, as mentioned above,
focus on two possible explanations for the characterization of an industry’s productivity
growth over time. On the one hand, technological change, either in the form of learning by
doing or through technological progress, may induce firm and, consequently, industry-level
productivity increases. On the other hand, in open markets some productive units thrive while
others disappear through selection effects. Industry-level productivity increases to the extent
that the more productive units survive and grow, while the less productive contract or exit. An
important implication of the second explanation is that policies directed at protecting the least
efficient productive units can have a negative effect on aggregate industry productivity.
These alternative explanations have different implications for the factor markets. If
productivity growth comes from increased individual productivity of the firms or productive
units, then labor displacement arises to the extent that technological progress may either be
skill biased or labor augmenting. In the case of skill-biased technological change, workers
across firms with a particular skill either benefit or lose by the technological change where the
groups of interest are oftentimes low skilled versus high-skilled workers. Labor augmenting
technological change is a more general phenomenon that augments labor productivity without
explicitly differentiating by skill. Under the second explanation of productivity growth
induced by selection effects, the exit of the least productive units will imply the labor
displacement of those firms’ work force.
We plan to frame the in-depth part of our analysis in the methodology developed by Olley and
Pakes (1996), adapted to investigate the connection between factor reallocation and industry
productivity in Colombia as thoroughly as possible. The Olley and Pakes (1996) methodology
allows us to directly use entry, exit, and factor usage choices at the plant level to learn about
unobserved plant-level productivity and aggregate the resulting plant-level productivity to
derive measures of aggregate industry productivity. This detailed analysis will allow for an
assessment of the importance of technological change as well as turnover of firms as drivers of
industrial productivity growth. While the Olley and Pakes (1996) methodology effectively
controls for selection and simultaneity biases, the applicability of the methodology is limited in
48
certain empirical settings. To evaluate the seriousness of such biases, we will contrast the
resulting productivity measures with productivity measures based on alternative approaches to
estimating productivity put forth in the literature. Further details are given in section V.
The resulting measures of plant-level productivity will be used to assess the impact of tax
policy and trade policy reforms over the past two decades on industry efficiency. Trade policy
can affect industrial productivity through a variety of channels. Productivity may increase due
to firm shake out through selection since inefficient producers stand in greater competition
with (presumably) more efficient foreign producers. Productivity may also increase due to
increased technology diffusion from abroad, or due to increased availability of inputs.
Similarly, tax policy reforms can act through a variety of ways in affecting industrial
productivity, including causing productivity increases due to tax incentives geared at
investment in more efficient technologies. Furthermore, productivity may also be caused to
increase due to tax incentives that are more beneficial to larger, more productive firms, causing
the less efficient ones to exit. These various channels will be difficult to separate empirically.
However, since the focus of our work centers primarily on firm turnover, entry and exit, we
will concentrate on the effect of policy reforms on productivity through selection channels.
One way to do so, would be to investigate in how far policy reforms were not only correlated
with productivity changes, but also with pronounced entry and exit by plants in general. Policy
indicators can be related to both productivity measures and entry and exit measures in a
regression framework, exploiting cross-industry and temporal variation in protection to assess
the impact of foreign trade taxes on producers’ efficiency.
Tax reforms in Colombia
Tax reforms in Colombia have by and large neglected the impact on microeconomic incentives.
Revenue targets dictated by fiscal prudence have very often been achieved through ad hoc
changes in the tax code. Beyond exploring the effect of targeted subsidies and tax breaks on
industrial productivity, our study will try to assess how the arbitrariness in tax reform in
general has impacted on performance in the manufacturing sector.
The policy measures that are available to us are sector-wide, time-varying tax rates and
measures of trade policy such as tariff and quota rates. Furthermore, the Colombian
government has used tax exemptions effectively for certain sectors in specific geographic
regions. To investigate the impact of these policy measures on productivity, we will, in the
second stage, aggregate the first-stage plant-level productivity residuals to the corresponding
sector or geographic level. This aggregation will result in an index of sector-level productivity
measures, subdivided by geographic region, where applicable. This productivity index will
serve as our dependent variable for the second-stage regressions, relating it to the available
policy measures.
To identify systematic variation in productivity in response to policy reforms, two possible
approaches can be identified. Sector effects and their interactions can be used to represent a
one-time, fixed shift in a policy regime, such as the once-and-for-all trade policy reform in
Chile studied by Pavcnik (1999). Pavcnik, for example, isolates the effect of trade reforms by
identifying systematic shifts in productivity between import-competing and non-traded goods
49
sectors. The time and sector interaction effects capture the dynamic effect of trade policy
through potential delays in firms' responses to policy reforms.
This approach is problematic when the time period of the data covers more than one explicit
shift in policy regimes, as is the case for some of the trade and tax instruments used by the
Colombian government during our sample. As an alternative then, we will explicitly include
the actual tax rates in a regression framework, setting them to zero for those sectors that are
tax-exempt. This approach employs a closer link to the actual policy instrument than the
previous and can more easily allow for policy reversals over the sample period. One downside
of this approach is, however, that it will be more difficult to identify persistence in response to
policy reforms since time-sector interactions are no longer feasible.
Both approaches are subject to a potential endogeneity problem as the tax level for each sector
may well have been endogenously determined in response to the particular sector's
productivity. Part of this endogeneity may arise through, for example, lobbying by certain
sectors, that is the political-economy side of policymaking. Endogeneity would then be a
serious problem if the lobbying powers of the sector were systematically related to its
productivity. A more serious endogeneity problem would arise if the government explicitly sets
taxes and trade policy instruments to promote growth in certain lagging sectors only. We will
address this issue in estimation through instrumental variable methods and/or exploitation of
the panel form of our data, which will allow us to estimate a model with fixed effects.
Our proposed methodology will allow us to evaluate the impact of both tax exemptions and
foreign trade taxes on industry efficiency, through their effect on output allocation among
plants with different efficiencies and heterogeneous response patterns. We plan to evaluate the
policies both conditional on the distribution of fixed factors such as plant age, capital, and
productivity, and unconditionally, regardless of firm characteristics. To motivate our choice of
topic for the in-depth study, in what follows we briefly expand on the recent history of
exemptions, and other tax benefits in Colombia. We close this section with a brief discussion
of the recent history of trade policy through taxes.
Tax exemptions
The tax structure in Colombia includes an important number of “special treatments” which
include exemptions, discounts, deductions, and differential tax rates. These are all distorting
elements that not only make the tax system complex and costly, but also erode important
sources of fiscal income. Although in principle the diverse tax incentives have been introduced
in order to aid the development of specific sectors or regions with the goal of promoting social
and regional equality, their adoption has been in many cases related to the existence of pressure
groups, and has not always obeyed principles of efficiency and welfare. For year 1999, tax
incentives in Colombia (“exemptions”) amounted to about ten percentage points of GDP.
The adoption of tax incentives has been a tool of public policy frequently and widely used with
the justification of correcting or reducing market imperfections. In theory, an exemption to a
general tax rate may be granted if it renders the tax structure more equal or if it promotes (or
discourages) the production of goods with positive (negative) externalities. However, also
50
according to theory, although fiscal incentives may generate a short-term positive impact, in
the longer run they induce distortions by directing resources away from their more efficient
allocation, benefiting investments that would have taken place in their absence, or by favoring
non-eligible firms that may capture rents by mimicking eligible firms with the only purpose of
enjoying the tax incentives. Empirical evidence points in the same direction: the fiscal cost of
tax exemptions is often greater than the investment it generates and, in addition, the level of
rent transfers is hard to quantify.
According to Bird and Chen (1999), in Latin America tax incentives have been adopted as
tools of “industrial policy” more than as a policy oriented towards economic growth as in the
case of the Asian countries. Consequently, recent Latin American policy reforms lean towards
an elimination of tax incentives as shown by the experiences in Argentina, Bolivia, Mexico,
and Colombia (Bird, 1992). Table 1 shows the tax benefits that prevailed as of 1999 for a
selection of Latin American countries.
In Colombia, regardless of the efforts exerted over the last decade towards expanding it, the
taxable base continues to be small as a proportion of GDP, especially compared to other Latin
American countries. According to the IMF, from a sample of 20 countries for 1994, Colombia
was the country with the smallest VAT coverage as a percent of GDP (33%, compared to an
average of 50% for the sample). This is to a large extent the result of the various forms of
exemptions incorporated into the country’s tax code.
51
Table 1 – Fiscal Incentives in Latin America, 1999
Type of incentive
Country
Objective
Beneficiary
Foreign
Rent VAT
Other
Trade
Investors
Regional
X
X
development
Firms
X
X
X
Argentina Impulse to
regions through
impulse to sectors
Export promotion Exporters
X
X
X
Investment firms
X
Industry, agriculture,
Development of
X
construction in
regions and
specific regions
sectors
X
X
Auto producers
Brazil
X
X
Promotion of
research and
development in
capital goods
Development of
regions
Industry
Investments in duty
free areas, Isla de
Pascua and south
X
Development of
sectors
Agriculture, mining,
tourism,
transportation,
industry and energy
transmission projects
X
Incentives for
investment
X
X
Chile
Social
development
Health and education
X
Export promotion
Sector promotion
X
Manufacturing
Industry
X
X
X
X
X
Industry in dutyfree areas
Investors in the area
52
Tourism
Hotels, airlines
53
Table 2 – Fiscal Incentives in Latin America - continued
Law Páez:
Regional development
Firms
X
Investors
X
Firms of late yield
X
Coffee axel: new
enterprises
X
X
X
X
X X
X
Services: Geographic
risk insurance
X
X X
Borders: new enterprises
Law of Culture
X
Law of the book
Producers of goods of
interest to culture
Firemen Law
Paper industry
X
X
Import and acquisition
of fire extinction
equipment
Colombia
X
Development of sectors
X
X
Tourism: firms
X
X
X
Science and Technology:
Institutions
Donors and Investors
X
Duty-free industrial areas
Environment:
environmental
investments
X
Special economic areas for
exports
Investors and exporters
in the areas
54
Investment in general
Investment in free border
areas
México
Perú
X
X X
Investors and exporters
in general
Export promotion
Investment in research and
development
Amazon
Border regions
X
X
X
Entrepreneurs in the
region
X
X
X
X
Source: Hernandez, G. et al, 2001
Several authors have analyzed and compared the many tax reforms of the last two decades
(Steiner and Soto, 1999), and there has been an effort to quantify the fiscal cost of current tax
exemptions in the Colombian case (Hernández, G. et al., 2001). However, the response from
industry to such tax incentives remains uncertain because it is difficult to separate the role that
tax incentives play in inducing investment from other determinants of firm investment
decisions. Instead of focusing on the investment response, we propose to concentrate on output
reallocation and productivity growth in manufacturing, in order to assess whether systematic
changes in productivity within and across industries coincide with periods of reforms to the
system of industrial policy tax incentives.
Table 2 below presents a summary of the recent Colombian tax history from 1979 to 1999. It
includes both the history of exemptions and tax benefits, as well as the history of foreign trade
taxes, on which we briefly expand next.
Table 2 – Summary of tax exemptions in the Colombian tax structure 1979-1999
Income
Reform
Single and
Joint Filers
Corporate tax
1983
Laws 9
and 14
Rate:
Tax rate
decrease; the top
marginal tax rate
went from 56%
to 49%.
Rate:
Tax rate
decrease for
firms of limited
ownership, from
20% to 18%.
Taxable base:
Elimination of
Exclusion of
double taxation
population in the sources.
lower-income
VAT tax
Foreign
Trade
Other
Rate:
General tax
rate set at
10% for most
goods.
Luxury goods
to be taxed at
35%. Some
services to be
taxed at 6%
55
Table 2 – Summary of tax exemptions in the Colombian tax structure 1979-1999
Income
Reform
Single and
Joint Filers
brackets from
the taxable base.
Increase in
personal tax
exemptions
1986
Law 75
Rate:
Tax rate
decrease; 4
marginal tax
rates were
determined. The
top marginal
rate was set at
35%.
Corporate tax
VAT tax
Foreign
Trade
Other
Oil products
Inflation
exempt from
adjustments to
financial income. increased
VAT rate
Taxable base:
Taxable base:
Extension of
Extension of
presumption
VAT to retail
income tax to
sales.
trade and
financial
Extension of
intermediary
base to
sectors.
include
service
Exemption to
sectors.
public
enterprises in
charge of
providing
electricity or
education
services.
Exemption to the
first 8% points
of monetary
adjustment
perceived by
associations with
savings in the
UPAC system.
Rate:
Unification of
tax rate for
associations of
all types of
ownership, at
30%
Elimination of
Simplification
of tax forms.
Authorization
to pay taxes at
banks.
Creation of
larger and more
56
Table 2 – Summary of tax exemptions in the Colombian tax structure 1979-1999
Income
Reform
Single and
Joint Filers
Taxable base:
90% of the
population was
exempt from
payroll
deductions due
to income tax.
Exemption to
payments to
workers for
sickness or
accidents at
work.
1990
Law 49
Corporate tax
VAT tax
Foreign
Trade
Other
efficient tax
offices.
double taxation
sources to firms.
Taxable base:
Extension of
base to include
investment
funds, capital
funds, firms of
mixed
public/private
ownership and
other public
enterprises.
Tax exemption
to equity
markets.
Exemption to
investment and
capital funds.
Reduction
of “overtax” rates
on imports
from
Taxable base: 16.5% to
Extension of 13%.
the base.
Gradual
reduction
of average
import
tariff from
16.5% to
7%
Rate:
Increase of
general rate
to 12%
Reduction to
rate of retention
on foreign
capital
incoming cash
flows.
861 tariff
positions
set duty
free.
1991
Ruling
Decree
2912
Establishes
system of
adjustments for
inflation
57
Table 2 – Summary of tax exemptions in the Colombian tax structure 1979-1999
Income
Reform
1992
Law 6
Single and
Joint Filers
“Over-tax” rate
of 25% on
income tax.
Elimination of
“net-worth” tax.
Corporate tax
VAT tax
Taxable base:
Public and
mixed
enterprises,
public funds and
cooperatives of
the financial
sector included
in the taxable
base.
Rate:
Gradual
increase of
rate to 14%
Exemption to
foreign capital
funds.
Taxable base:
Extension of
the base to
more service
sectors.
Differential
rates from
14% to 45%
for luxury
goods.
Foreign
Trade
Other
Elimination Further
reduction to
of import
restrictions rate of retention
of foreign
incoming cash
flows.
Foreign capital
funds not
required to file
taxes.
Exemptions
granted to
basic
consumption
goods and
imported
agricultural
capital
equipment.
1995
Law
223
Rate:
Increase in tax
rates and
elimination of
the “over-tax”
rate created in
1992.
Rate:
Tax rate increase
from 30% to
35%
VAT on
capital goods
deductible
from income
tax.
Rate:
Increase of
rate to 16%.
Established
presumption
income.
Taxable base:
Increases to
the list of
Granted tax
amnesty for tax
debtors.
58
Table 2 – Summary of tax exemptions in the Colombian tax structure 1979-1999
Income
Reform
Single and
Joint Filers
Corporate tax
Foreign
Trade
Other
exemptions
and decrease
of the taxable
base.
Taxable base:
Exemption to
30% of the total
labor income
received by a
worker, and
exemption of
100% to lowincome brackets.
1995
Law
218
VAT tax
Reduction in
penalties for
tax evasion.
Large
contributors
to retain
taxes
upfront from
buyers and
sellers.
Exemption to
new economic
developments in
agriculture,
cattle breeding,
mining of
products other
than oil,
manufacturing,
tourism, or
exports in the
area of influence
of river Páez.
Exemption to
ore-existing
economics
activities in the
area able to
demonstrate
damages from
1996
Law
Investments
made in the
59
Table 2 – Summary of tax exemptions in the Colombian tax structure 1979-1999
Income
Reform
Single and
Joint Filers
Corporate tax
VAT tax
Foreign
Trade
345
Other
area of
influence of
river Páez were
declared tax
deductible.
1997
Law
383
Tax on financial
income
Exemptions to
firms that invest
in their own net
worth
1998
Law
488
Taxable base:
Increase in the
taxable income.
Exemptions to
contributions to
pension funds.
Public debt
exemption
eliminated.
Exemptions to
foreign debt.
Exemptions
granted for job
creation.
Elimination of
presumption
income tax on
gross net worth.
Limit to
exemptions
on imports
of goods.
Rate: general
tax rate
reduced to
15%.
10-year
exemption
from import
tariffs and VAT
for capital
equipment and
inputs in the
area.
New
mechanisms to
deal with tax
evasion and
smuggling.
Regional gas
taxes
authorized.
Taxable base:
Basic foods,
antibiotics,
Exemptions to
electricity
the acquisition
and
of fixed assets.
agricultural
Financial interest capital goods
excluded
payments
deductible from from the
taxable base.
taxable income.
Exemption to
firms that
provide public
services to
Exemption to
books,
periodicals,
cultural
60
Table 2 – Summary of tax exemptions in the Colombian tax structure 1979-1999
Income
Reform
Single and
Joint Filers
Corporate tax
homes.
VAT tax
Foreign
Trade
Other
magazines
and school
materials.
Exemption
to hotel
services.
Service
sectors, such
as public
transportation
excluded
from taxable
base.
Tax extended
to futures
currency
operations.
Source: Steiner and Soto (1999), Hernández et al (2000) and tax legislation
Trade policy
Before 1990, Colombian trade policy was directed at protecting the economy to promote
economic growth through import substitution and to diversify exports away form exclusively
primary goods. The evolution of the implicit tariff, implicit costs of importing − in the form of
security deposits with the Colombian Central Bank − and excessive prices generated by the
quantitative import restrictions peak in the beginning of the 1970s and towards the end of the
1980s. The current restrictions are at the lowest values of the last twenty-five years.
Colombia’s tariff policy, and trade policy in general, has evolved towards liberalization over
the last decade (see Table 2).
Our assessment of the evolution of productivity is most relevant from the point of view of
protective policy design. As mentioned above, if contraction and exit of the less efficient plants
prove to be of greater empirical importance for productivity growth than technological
advances, protective measures would hamper the economic goals of policy making, namely
economic growth.
61
Section IV – Review of Related Literature
The economic growth literature, both theoretical and empirical, has increasingly focused on
firm-level analyses to explain the determinants of aggregate productivity growth. This explicit
modeling of firms’ decision making facilitates the incorporation of factors such as the
reallocation of resources and output, turnover among firms, and heterogeneity between firms;
factors that are important drivers of productivity dynamics. After a brief review of the most
relevant theoretical literature on heterogeneity and growth, we will focus on empirical studies
of productivity dynamics and firm turnover.
Theoretical Literature
Models of endogenous growth focus on factors that are common to all firms in an industry as
drivers of firm-level productivity. Productivity improvements are attributed to learning-bydoing (Arrow (1962), Romer (1986)), investment in research and development (Griliches
(1998)), or international trade (Grossman and Helpman (1991)).
Models of economic growth based on homogeneous firms, however, are hard pressed to
explain the concurrent entry and exit of firms that is observed in productive and growing
industries over time. One possible explanation for this phenomenon is that in open markets,
some firms thrive while others disappear. As the less productive firms are weeded out, industry
productivity will grow. Most theoretical work focuses on uncertainties as the explanation for
differences in firms’ growth experiences. The firm may be, for instance, uncertain about its
own chance of success in a market.
Jovanovic (1982) provides a theory of industry evolution based on firm heterogeneity and selfselection. Each firm has some true underlying production cost, c, which is a draw from a
normal distribution with mean c and variance σ c2 . The firm knows the cost distribution, but
not its own cost parameter. Each period the firm’s unit cost of production fluctuates randomly
around the mean. From observing it over time, the firm learns about its underlying cost and is
able to estimate it consistently as the time average of its observations. Each period the firm
decides whether to exit or stay in operation based upon its current cost information. The
evolution of the economy is then driven by the learning and selection decisions of these
optimizing agents.
Viewing productivity as the dual of costs would allow us to work with this model. However,
the notion that a firm’s decision is based on its entire history of productivity draws would
hardly be empirically tractable. The length of the dependence period would need to be
restricted to that of the observed data.
Hopenhayn (1992) proposes a model in which firms are subject to a random productivity shock
every period. This productivity shock follows a first-order Markov process that is independent
across firms. The distribution of future productivity is assumed to be stochastically increasing
in this period’s productivity. Surviving firms pay a fixed cost each period, then observe their
productivity shock, and decide on a level of output for that period. Entrants pay an entry fee,
62
and then draw from a common underlying distribution of productivity shocks, and choose
output. Exiting firms earn no profits, and pay no costs. This framework allows Hopenhayn to
derive equilibrium conditions that imply predictions about the productivities of entrants,
incumbents, and exiting firms.
Hopenhayn and Rogerson (1993) propose a variant of this model and use it to evaluate the
aggregate implications of government policies that make it costly for firms to adjust their use
of labor. Using a value function that explicitly includes an adjustment cost for labor, they
develop an equilibrium model of the reallocation process of labor across firms. They prove the
existence of an equilibrium that has entry, exit, and the growth and decline of firms over time.
Ericson and Pakes (1995) develop a dynamic model of a small, imperfectly competitive
industry in which, rather than allowing firms to passively observe their uncertain productivity
over time, they allow for learning externalities. Firms can invest in technology or quality
upgrading to improve upon their productivity over time. It is now the outcome of this
investment that is uncertain. Olley and Pakes (1996) combine features of this model with
features of the Hopenhayn and Rogerson (1993) model to investigate the impact of
deregulation on productivity in the telecommunication equipment industry.
Empirical Literature
The empirical literature on firm level productivity separates three contributions to productivity
growth: improvements within plants, reallocation of output and inputs to establishments with
high productivity growth, and exit of plants with low levels of productivity.
Using U.S. data, Bailey, Hulten and Campbell (1992) investigate what underlies the changes in
industry-level productivity, and find that the relative importance of these factors varies over the
business cycle. Their results suggest the effect of increased firm-level productivity to be
quantitatively most important. Griliches and Regev (1995) conduct a similar analysis using
plant-level data for Israel.
Aw, Chen and Roberts (1998) investigate the role of firm turnover in industry-level
productivity changes, using data from Taiwan, and find that the productivity differential
between entering and exiting firms is an important source of industry-level productivity growth
in Taiwan’s manufacturing sector. They compute productivity using index number methods
rather than estimating a production function.
Roberts and Tybout (1996) is a collection of plant-level productivity studies resulting from a
large research project. The papers in this volume address productivity issues close to what we
intend, but different from our proposal in terms of the empirical approach. Among them, Lui
and Tybout (1996) is the most interesting for the purposes of this study. It examines plant-level
productivity for firms in Colombia and Chile. They find that exiting plants are, on average,
significantly less productive than incumbents, mirroring results obtained for developed
countries. In addition, the productivity of an exiting firm is found to deteriorate several years
before the firm actually does exit.
63
The time period covered by their study extends from 1981 to 1989. Most of the important labor
market, financial, tax and trade reforms that were undertaken by Colombia in recent years are
therefore not covered by their data. Studies of productivity dynamics that examine productivity
developments in times of deregulation, such as Olley and Pakes (1996) and Pavcnik (2001),
point to a significant reallocation of resources and output from less to more efficient producers
that arise primarily as a result of liberalization. One of the goals of this project will be to
quantify how far aggregate productivity improvements by Colombian firms can be attributed to
such a reallocation of resources within the industry in response to the easing of measures of
protection for potentially less efficient firms over the course of the 1990s.
Olley and Pakes (1996) examine productivity dynamics in the U.S. telecommunications
equipment industry. They develop the empirical methodology that we are interested in
applying to Colombian data. To investigate the contemporaneous covariance between output
and productivity, they use a dynamic model of firm behavior that allows for firm-specific
sources of change and for both entry and exit. This model provides a framework for analyzing
the biases in traditional estimators that result from selection and simultaneity, and for building
alternative algorithms that circumvent these biases. Section V describes their methodology
more thoroughly.
Based on Chilean data, Levinsohn and Petrin (1999) investigate the empirical relevance of the
“real productivity case” (increasing firm-level productivity leading to increasing industry
productivity) and the “rationalization case” (constant firm productivity, but productive firms
expanding while less productive firms contract and exit). They find that the rationalization case
explains much of the measured increase in industry productivity. They also show that the
value-added production function is well suited to a simple extension of the Olley and Pakes
(1996) methodology in cases where firm investment data is not available.
Pavcnik (2001) uses a similar framework to Olley and Pakes (1996) to investigate whether
some of these productivity improvements among Chilean firms can be attributed to the widereaching trade liberalization measures put in place during the late 1970s. She allows
productivity improvements to differ systematically between export-oriented, importcompeting, and non-traded goods sectors. Her findings indicate significant productivity
improvements in import-competing sectors by up to 10.4% in response to liberalized trade.
Furthermore, these productivity differentials become more pronounced over time, suggesting
persistent consequences for liberalization programs.
For the case of Colombia, the impact of the wide-ranging reform packages of the 1990s on firm
productivity and reallocation of output and inputs has, so far, received less attention. A series
of papers by Kugler and Kugler (1999, 2001) presents a notable exception. Based on a rich
panel data set over the 1982-1996 period, the authors are able, for example, to investigate the
effect of both gradual and sudden increases in pay-roll taxation during the sample period on the
composition of firms’ labor forces and wages. Their findings indicate that payroll taxes were
only partially shifted to workers in the form of lower wages. The balanced nature of their panel
hinders the extension of their analysis to evaluate the impact of the Colombian reform package
to aggregate and sector-level productivity. The use of an unbalanced panel, such as the one
suggested here, allows for a more thorough correction of the selection bias that may arise in
64
balanced panels since the analysis is no longer restricted to the more successful, surviving
plants only.
Section V – Estimation of Productivity
Since productivity is not directly observable, studying productivity implies coming to terms
with a way of measuring it. In this subsection we will discuss the traditional way to measure
productivity and the current state-of-the-art methodology, as proposed by Olley and Pakes
(1996).
The traditional method of measuring productivity at the plant level is to compute value-added
per worker. Both value-added and the number of workers are usually reported in plant-level
data, so measurement becomes a trivial exercise. The measure obtained is money-based so it is
both intuitive and easy to interpret, and it is not dependent on functional form choices that can
be arbitrary. Furthermore, this is the measure most often used by government statistical offices,
so it makes comparisons straightforward. However, the use of output per worker as a measure
of productivity creates a bias towards finding a trade-off between productivity changes and
employment changes, fostering a complicated political dynamic. Also, it can often be
misleading since other inputs in the production process need to be accounted for. This leads us
quickly to measures of total factor productivity.
If one is going to estimate rather than compute total factor productivity, the simplest way to do
it is to estimate a production function using OLS and use the residual from such regression as
the measure of productivity. The problem with this approach was pointed out long ago, by
Maarshak and Andrews (1944): input choices are likely to be correlated with unobserved
productivity. To the extent that this happens, the OLS estimates will be biased and will yield a
biased measure of productivity. The usual approach to deal with this simultaneity problem is to
use Instrumental Variables estimators. However, with plant-level data (as opposed to industrylevel data) it is very hard to find valid instruments because most variables that are correlated
with input choices are correlated with productivity. A solution to the unavailability of
appropriate instruments has often been to adopt a fixed effects estimator, but this estimator
assumes that firm-level productivity is constant over time.
As an alternative, Cornwell et al. (1990) use a plant-specific and time-varying efficiency
estimator that can be described as a quadratic function of time. This methodology is also used
in Liu (1993) and Liu and Tybout (1996). It requires an initial estimation of the production
function by fixed effects in order to obtain the input coefficient vector. The residuals are
calculated by subtracting the actual from the predicted values of output. They then regress for
each plant this residual measure on a constant, time, and time squared. The productivity
measure is constructed using the estimates of the coefficients from the last regression. This
approach improves on the fixed effects methodology, but since it requires a parametric
specification of productivity many degrees of freedom are lost in the estimation process.
Moreover, this procedure still uses fixed effects estimation in the first step that provides the
residual for the construction of the productivity measure. Although the measure does vary over
time, it is still likely to be based on biased coefficients in the presence of simultaneity.
65
Olley and Pakes (1996) provide a methodology that deals explicitly with both the simultaneity
problem described above. In addition, their approach corrects for a selection problem due to the
fact that firms’ exit decisions depend on their perceptions of future productivity, which are
partially determined by their current productivity. Thus, firms in a balanced panel data set are
in part selected on the basis of their unobserved productivity realizations. Since we are
proposing to use a variant of the methodological framework developed by Olley and Pakes
(1996) to estimate plant-level productivity for the Colombian data, we proceed to briefly
outline their model.
The Olley-Pakes (1996) Model
The empirical goal of the authors is to estimate the parameters of a production function for the
telecommunications equipment industry, and to use those estimates to analyze the evolution of
plant-level productivity. To do so, they employ the following dynamic model of firm behavior.
At the beginning of each period a firm has three decisions to make. The first is to decide
whether to exit or to continue in operation. In order to make this decision the firm compares the
sell-off value it gets if it chooses to exit with the expected discounted present value of the
profits that will accrue to it if it stays in operation. If it exits, it receives the sell-off value and
never reappears again. If it chooses to continue, it then chooses variable factors (labor) and a
level of investment, which together with the current capital value determine the capital stock at
the beginning of the next period. Both of these choices are such that the expected discounted
present value of the firm is maximized, given the firm’s available information at time t.
Olley and Pakes assume that the firm’s current profits are a function of its own state variables,
factor prices, and a vector that lists the state variables of the other firms active in the market. In
their example the vector of firm-specific state variables consists of at, the age of the firm, kt,
the firm’s capital stock, and t, an index of the firm’s efficiency. A market structure is defined
as a list of these triples for all active firms. Factor prices are assumed to be common across
firms and to evolve according to an exogenous first order Markov process. The accumulation
equation for capital and age are given by:
k(t+1) = (1-δ) kt + it
and
a (t+1) = at + 1
The index of productivity, ω, is know to the firm and evolves over time according to an
exogenous Markov process.
The exit and investment decisions of a firm will depend on its perceptions about the future
market structures given current information, and will, in turn, generate a distribution of the
market structure for the coming years. The incumbent firm’s manager is then seen as solving
the following Bellman equation:


V t (ωt , a t , k t ) = max Φ, sup π t (ωt , a t , k t ) − c(i t ) + βE[V t +1 (ωt +1 , a t +1 , k t +1) | J t ]
 it ≥0

where Φ is the sell-off value of the firm, πt(.) is the restricted profit function giving profits as a
function of the vector of state variables, c(it) is the cost of current investment it, β is the firm’s
66
discount factor, and Jt represents the information available at time t. The solution to this
problem generates an exit rule, and an investment demand function:
1
Rt = 
0
ω t ≥ ω t (a t , k t )
otherwise
i t = i t (ωt , a t , k t )
The exit rule and the investment demand function can then be used in the estimation of a
production function to yield a measure of productivity.
Limitations of the Olley/Pakes Methodology
The methodology developed by Olley and Pakes (1996) to estimate plant level productivity faces
limitations both on empirical and theoretical grounds. On the empirical side, the most important is
the issue of how to deal with lumpy investment over time, that is, how to deal with periods on
which there is no investment at the plant level. The empirical specification used by Olley and Pakes
is derived from an investment demand function that is inverted to substitute for productivity.
Periods of zero investment break this invertibility condition by forcing the investment function to
be non-monotonically increasing in productivity and discontinuous. Three alternative solutions to
this problem have been proposed: (1) to ignore the theoretical/empirical connection and proceed
with the O/P methodology even in face of observations with zero investment, as proposed by
Pavcnik (2000); (2) to replace the investment function by a raw material demand function and
apply a “modified” O/P methodology since raw material demand does not exhibit the same
lumpiness as investment, a la Levinsohn and Petrin (2000); or (3) to estimate plant-level
productivity using a dynamic structural model of firm behavior.
As our primary estimation methodology, we choose to use the approach put forth by Levinsohn and
Petrin. However, in order to shed light on the differences that the various methodological
approaches may entail for the resulting productivity estimates, we propose to estimate plant-level
productivity measures for a sector of our choice using a variety of alternative approaches. These
will include the methodologies suggested above, including the static production function estimation
a la Levinsohn and Petrin as well as a structural dynamic model (time permitting), but also
computationally less complex productivity measures, such as labor productivity, total factor
productivity derived by growth accounting, and fixed effects estimation of the production function
controlling for simultaneity through instrumental variables.
Theoretical limitations of the O/P methodology have been raised in recent work by Syverson
(2001), who argues that the use of investment as a right-hand side variable to capture unmeasured
productivity shocks is not robust to demand shocks inducing cross-firm variation in investment
rates. Syverson proposes as an alternative local demand instruments for the production function
estimation. Relatedly, Melitz (2000) points out that productivity measures derived from sales
deflated at the industry level are spuriously pro-cyclical in differentiated product industries. He
provides a structural estimation method that assumes a constant elasticity of substitution between
67
products on the demand side, to adjust the above methodologies for the case of symmetrically
differentiated products.
While both of these papers raise important concerns, the applicability of the proposed solutions to
the Colombian case is difficult, given our data limitations and the scope of our study. Svyerson
focuses on one homogeneous-product sector which allows him to introduce differentiation in one
dimension, namely the plant’s location. In this context, local demand characteristics provide good
plant-level instruments. This is, however, difficult to extend to the case of the manufacturing
sectors that we are working with. We will, nevertheless, be able to investigate in how far firm-level
variation in investment is problematic in our data during the part of our study that will compare
various productivity estimation procedures. To be able to use similar local instruments in the form
of demand or other local variation that may be correlated with investment rates, we will attempt to
choose, for this exercise, a sector that produces a relatively homogeneous product. We are still in
the process of considering various alternative sectors.
Section VI – The Data
We propose to apply the estimation methodology outlined above to the plant-level panel data
from the Annual Manufacturing Survey of Colombia, for the period 1979-1999. The data is
available going back to 1974, but we plan to focus on the last two decades, the period in which
the most path-breaking policy reforms have taken place in Colombia.
The National Department of Statistics of Colombia (DANE) has provided us with the data.
DANE has agreed to let us install the necessary computational equipment to work with the data
in their premises since the statistical reserve regulation forbids researchers to handle it outside
of DANE. We have provided IADB with a letter from DANE certifying that we have access to
their database, under the terms of the inter-institutional cooperation agreement that exists
between DANE and Universidad de los Andes.
Each annual wave of the census is linked over time using a plant-specific identifier. This poses
no problem for the 1974 -1991 period. Up to 1991, plants were coded under a system called
NORDEN. However, the identification system changed in 1992 when all plants were assigned
a new code (NOREST2), with plants appearing in the survey in 1991 being allowed to also
keep their NORDEN code on record. The coding system was changed yet again in 1993 to a
system called NORDEST. The result is that while before 1992 each plant has a single
NORDEN identifier, from 1992 on plants that existed before 1992 keep their NORDEN code,
plants that entered in 1992 have the NOREST2 code, and plants that entered in 1993 or later
have the NORDEST code. The methodological changes in the coding for 1992 and 1993 and
the rules by which a plant is or is not included in official tables (see below) makes tracking of
each plant over time difficult. A good deal of effort has been made so far to overcome the
inconsistencies generated by the various procedures by re-checking the codes across years and
most plants have been connected to their past, if they had one. There is still a small issue with
what appears to be unnatural excess exit in 1991, paired with excess entry in 1992, which
seems to be a remaining problem stemming from the methodological changes1.
1
Of 7531 plants that appear in official tables in 1991, 794 do not appear in 1992, which is high for annual exit
(excluding 1991-1993) compared to 242 plants per year over the period 1974-1995. 1517 new plants appear in
68
Not all plants surveyed enter the data set based on which DANE reports the performance of the
manufacturing industry each year. To appear in these official tables, a manufacturing plant
must fulfill one of two conditions: it must either report an employment level at or above 10
employees or it must report a production level at or above a cutoff value set by DANE2. This
implies that in order to properly account for entry and exit, the researcher would have to go to
the “raw” database in search of the missing plants below either of these cutoffs. Alternatively,
researchers may choose to redefine entry and exit in their studies, in terms of plants falling in
or out of certain employment and production categories.
Since January of 2002, further effort has been directed to solve the remaining inconsistencies
and make all plants fully tractable over time. Under the inter-institutional cooperation
agreement that exists between both entities, Universidad de los Andes has agreed to team up
with DANE once more, this time to match-up by hand the exiting plants of 1991 with the
entrants of 1992. This exercise, which is almost completed by now, will allow us to fully
exploit the entry-exit dynamics of the Colombian manufacturing industry in our research.
Table A1 in Appendix 1 presents statistics of the number of plants by year, by 3-digit CIIU
manufacturing sector, by employment ranges and by production ranges, for the period from
1979-1999, to characterize the Colombian manufacturing industry. Table A2 presents a list of
the variables available in the panel dataset, table A3 presents a list of definitions of the “main
variables” describing each plant, and table A4 presents summary statistics of the “main
variables”3. Because of the way DANE handles the data, it is not possible to know ex-ante the
number of plants that will be contained in the panel dataset for the range of years that we have
chosen4. Before beginning the estimation, the remaining tasks include putting together a
complete file with data for all the years that we are interested in and organizing it as a panel
that can be easily worked with.
For comparability, we will follow the IADB guidelines on how to deal with specific datarelated issues. One specific issue, among others, that applies to our study is to define a fake
exit cutoff at a 15-employee level that we will adapt to adequately compute entry and exit
rates. In addition, we have made a few choices about how we will deal with some specific
problems in our data that should be mentioned here. First, we will discard observations for
plants that show up only once in the data base; second we will discard all observations of
plants that show volatility after imposing the 15 employee fake exit cutoff (i.e. plants that
disappear for one or two periods and reappear again); and third we will consider as entry and
exit the switching of a firm from a 3-digit sector code to another.
1992, also high compared to an annual average of 219 for the same period. The total number of plants in official
DANE tables for 1992 is 7888.
2
This is what we called above the “rule to be included in official tables”.
3
DANE calls “main variables” the ones that convey more general information about each manufacturing plant.
We are reporting summary statistics only for them. Until the research proposal is approved all data manipulation
has to be done by DANE personnel directly and unfortunately their time availability to prepare statistics for our
proposal is limited. For this reason they agreed to provide us with the written certification saying that the data
exists, and that it will be at our disposal to work with in their premises, if our proposal is approved.
4
For previous research DANE put together a dataset for the 1982-1998 period. In this case, only the plants that
appeared each year in tables were included and the result was a panel of 13,320 plants.
69
Section VII – Dissemination Strategy
As part of the Inter-Institutional Cooperation Agreement that exists between Universidad de
los Andes and DANE, in exchange for DANE’s permission to install our computational
equipment in their premises for the purpose of our estimations and for their support in handling
the data, our productivity measures will be let at the disposal of DANE to be disseminated and
published. This way, they will be placed at the disposal of the industry, the public in general
and, more importantly, the institutions in charge of the design of industrial and development
policy for Colombia.
In addition, the results of our work will be published as a CEDE working paper, and will be
presented in the Research Seminar of the Department of Economics of Universidad de los
Andes, the Empirical Industrial Organization Seminar at Stanford Graduate School of
Business, and other academic seminars.
70
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