WPS3621
Assessing the Impact of the Investment Climate on Productivity
Using Firm-Level Data: Methodology and the Cases of
Guatemala, Honduras, and Nicaragua1
Alvaro Escribano2
Universidad Carlos III, Madrid and Georgetown University, Washington, D.C.
and
J. Luis Guasch
World Bank and University of California, San Diego
World Bank Policy Research Working Paper 3621, June 2005
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 view of the World Bank, its Executive Directors, or the countries
they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.
1We are indebted to Heisnam Singh for his excellent research assistance. The empirical section, which illustrates the
implementation of this econometric methodology, is an extension of initial work with Ana M. Fernandes and Heisnam
Singh. We have benefited from the suggestions and questions from Juan Miguel Crivelli, Pablo Fajnzylber, Luke
Haggarty, Danny Leipziger, Marialisa Motta, Isabel Sánchez and Stefka Slavova, and from participants at a World
Bank seminar. Escribano gratefully acknowledges financial support from the Foundation BBVA (Spain).
2Telefonica-UCIIIM Chair on Economics of Telecommunications.
Econometric Methodologies for Investment Climate Assessments on Productivity
Abstract
Developing countries are increasingly concerned about improving country competitiveness and
productivity, as they face the increasing pressures of globalization and attempt to improve
economic growth and reduce poverty. Among such countries, Investment Climate Assessments
(ICA) have become a standard instrument for identifying key obstacles to country competitiveness
and imputing their impact on productivity, in order to prioritize policy reforms for enhancing
competitiveness. Given the survey objectives and the nature and limitations of the data collected,
this report discusses the advantages and disadvantages of using different productivity measures
based on data at the firm level. The main objective is to develop a methodology to appropriately
estimate, in a robust manner, the productivity impact of the investment climate variables. To
illustrate the use of this methodology, the report applies it to the data collected for ICAs in three
countries: Guatemala, Honduras and Nicaragua. Observations in logarithms (logs) of the
variables, and not in rates of growth, are pooled from all three countries. The econometric analysis
is done with variables in logs to reduce the impact of measurement errors and allow inclusion of
as many observations as possible since the "panel" data set is very unbalanced. Endogeneity of the
production function inputs and of the investment climate variables is addressed by using a variant
of the control function approach, based on individual firm information, and by aggregating
investment climate variables by industry and region.
It is shown that it is possible to get robust results for 10 different productivity measures, if one
follows a consistent econometric methodology of specification and estimation. For policy
analysis, the report strongly recommends using those results of investment climate variables on
productivity that are robust for most of the productivity measures. Efficiency aspects of firms in
each country are also analyzed. Finally, the results are decomposed to obtain country-specific
impacts and establish corresponding priorities for policy reform. The actual estimates for the three
countries show the level of significance of the impact of investment climate variables on
productivity. Variables in several categories, red tape and infrastructure in particular, appear to
account for over 30 percent of productivity. The policy implications are clear: investment climate
matters enormously and the relative impact of the various investment climate variables indicates
where reform efforts should be directed. Given the robustness of the results, it is argued that the
econometric methodology of productivity analysis developed here ought to be used as a
benchmark to assess productivity effects for other ICAs or surveys with firm-level data of similar
characteristics.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Assessing the Impact of the Investment Climate on Productivity Using
Firm-Level Data: Methodology and the Cases of Guatemala,
Honduras, and Nicaragua
Contents
1. Introduction ..........................................................................................................................1
2. Productivity (P) Measures and Total Factor Productivity (TFP)....................................5
3. Productivity Measures Based on the Rates of Growth. ....................................................7
4. Alternative Productivity Measures Based on Variables in Levels.................................15
5. Proposed Econometric Methodology to Estimate the Productivity Impact of
Investment Climate (IC) Variables in Guatemala, Honduras and
Nicaragua 23
6. Step-by-Step Implementation of this Productivity Methodology ..................................34
6.1 Restricted Coefficient Estimates ...........................................................................36
6.2 Unrestricted Production Function Coefficients Estimated by Industry.................39
6.3 Further Productivity Analysis of IC Determinants by Age and by Size
of the Firms............................................................................................................42
7. Production Allocation Efficiency by Country, by Size of the Firms
and by Industry................................................................................................................43
8. Empirical Results on Investment Climate (IC) Determinants of Productivity
in Guatemala, Honduras and Nicaragua..........................................................................45
8.1 Productivity Elasticities or Semi-Elasticities of Each IC Variable...... ...............46
A. Red Tape, Corruption and Crime .................................................................47
B. Infrastructure ................................................................................................47
C. Quality, Innovation and Labor Skills ...........................................................48
D. Finance and Corporate Governance .............................................................49
E. Other Control Variables ............................................................................49
8.2 Country Specific Productivity Impact of Each IC Variable..................................51
A. Red Tape, Corruption and Crime .................................................................51
B. Infrastructure ................................................................................................52
C. Quality, Innovation and Labor Skills ...........................................................53
D. Finance and Corporate Governance .............................................................55
E. Other Control Variables ...............................................................................55
8.3 Olley and Pakes (1996) Productivity Decomposition for Each Country ..............62
9. Conclusion...........................................................................................................................63
Appendix .......................................................................................................................................66
References .....................................................................................................................................91
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Econometric Methodologies for Investment Climate Assessments on Productivity
List of Figures and Tables
Figure 1: Productivity Elasticities or Semi-elasticities with Respect to IC Variables.....................50
Figure 2: Guatemala: Productivity Impact (gains and losses) of Investment Climate
Variables (in %) ................................................................................................................57
Figure 3: Honduras: Productivity Impact (gains and losses) of Investment Climate
Variables (in %) ................................................................................................................58
Figure 4: Nicaragua: Productivity Impact (gains and losses) of Investment Climate
Variables (in %) ...............................................................................................................59
Figure 5: Productivity Impact in Guatemala, Honduras and Nicaragua due to
Investment Climate Variables (in %) (Cumulative Contribution) ....................................60
Figure 6: Productivity Impact in Guatemala, Honduras and Nicaragua due to
Investment Climate Variables (in %) (Cumulative Absolute Contribution).....................61
Figure 7: Olley-Pakes Decomposition of Solow Residual ...............................................................89
Figure 8: Olley-Pakes Decomposition of Solow Residual ...............................................................89
Figure 9: Olley-Pakes Decomposition of Industry Solow Residual
Guatemala, Honduras, Nicaragua......................................................................................90
Table A.1: General Information at Plant Level and Production Function Variables ..........................66
Table A.2: Investment Climate (IC) Variables ...................................................................................67
Table A.3: Other IC and Plant Characteristics (C) Variables .............................................................68
Table A.4: Investment Climate Perception (PE) Variables..............................................................68
Table B.1: Number of Firms that Enter into the IC Regressions by Industry and by
Country..............................................................................................................................69
Table B.2: Number of Firms that Enter into the IC Regressions by Industry and by
Year...................................................................................................................................69
Table B.3: List of Significant ICA Variables and their Measurement Units ......................................70
Table C.1: Correlation between Solow Residuals in Levels and Estimated
Productivity.......................................................................................................................71
Table C.2: Two-Step Restricted Estimation........................................................................................72
Table C.3.1: Single Step Restricted Estimation: Cobb Douglas Specification.....................................73
Table C.3.2: Single-Step Restricted Estimation: Translog Specification .............................................74
Table C.3.3: Production Function Parameters from the Restricted Estimation .....................................75
Table C.4.1: Two-Step Unrestricted by Industry Estimation.................................................................76
Table C.4.2: Two-Step Unrestricted by Industry Estimation with Perception Variables ......................77
Table C.5.1: Single-Step Unrestricted by Industry Estimation: Cobb Douglas
Specification......................................................................................................................78
Table C.5.2: Single-Step Unrestricted by Industry Estimation: Translog Specification......................79
Table C.5.3: Production Function Parameters from the Unrestricted Estimation by
Industry: Cobb Douglas Specification .............................................................................80
Table C.5.4: Production Function Parameters from the Unrestricted by Industry
Estimation: Translog Specification ..................................................................................81
Table C.6.1: Two Step Unrestricted by Industry Estimation for Young and Old Firms........................82
Table C.6.2: Two Step Unrestricted by Industry Estimation for Small and Large Firms......................83
Table C.7.1: Covariance Terms by Country of the Olley-Pakes Decomposition from 84
Different Productivity Measures .......................................................................................84
Table C.7.2: Covariance Terms by Country and Plant Size of the Olley-Pakes
Decomposition from Different Productivity Measures.....................................................84
Table D: Elasticities or Semi-elasticities and Percentage R-square Productivity
Contribution of Each Explanatory Variable, after Controlling for the Other
IC and Plant Control Variables .........................................................................................85
Table E.1: Guatemala: % Average (log) Productivity Gains and Losses in Guatemala
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Econometric Methodologies for Investment Climate Assessments on Productivity
due to Investment Climate (IC).........................................................................................86
Table E.2: Honduras: % Average (log) Productivity Gains and Losses in Honduras
due to Investment Climate (IC).........................................................................................87
Table E.3: Nicaragua: % Average (log) Productivity Gains and Losses in Nicaragua
due to Investment Climate (IC).........................................................................................88
iv
1. Introduction
As developing countries face the pressures and impacts of globalization, they are seeking
ways to stimulate growth and employment within this context of increased openness. With
most of these countries having secured a reasonable level of macroeconomic stability, they
are now focusing on issues of competitiveness and productivity through microeconomic
reform programs. From South East Asia to Latin America, countries are reformulating their
strategies and making increased competitiveness a key priority of government programs.
A significant component of country competitiveness is having a good investment climate or
business environment. The investment climate, as defined in the WDR (2005), is "the set of
location-specific factors shaping the opportunities and incentives for firms to invest
productively, create jobs and expand." It is now well accepted and documented, conceptually
and empirically, that the scope and nature of regulations on economic activity and factor
markets - the so-called investment climate and business environment - can significantly and
adversely impact productivity, growth and economic activity (see Bosworth and Collins,
2003; Dollar et al., 2004; Rodrik and Subramanian, 2004; Loayza, Oviedo and Serven, 2004;
McMillan, 1998 and 2004; OECD, 2001; Wilkinson, 2001; Alexander et al., 2004; Djankov et
al., 2002; Haltiwanger, 2002; He et al., 2003; World Bank, 2003; World Bank, 2004 a,b); and
Hall and Jones (1999). Prescott (1998) argues that to understand large international income
differences, it is necessary to explain differences in productivity (TFP). His main candidate to
explain those gaps is the resistance to the adoption of new technologies and to the efficient
use of current operating technologies, which in turn are conditioned by the institutional and
policy arrangements a society employs (investment climate variables). Recently, Cole et al.
(2004) also have argued that Latin America has not replicated Western economic success due
to the productivity (TFP) gap. They point to competitive barriers (investment climate
variables) as the promising channels for understanding the low productivity observed in Latin
American countries.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Government policies and behavior exert a strong influence on the investment climate through
their impact on costs, risks and barriers to competition. Key factors affecting the investment
climate through their impact on costs are: corruption, taxes, the regulatory burden and extent
of red tape in general, factor markets (labor and capital), the quality of infrastructure,
technological and innovation support, and the availability and cost of finance.
For example, Kasper (2002) shows that poorly understood "state paternalism" has usually
created unjustified barriers to entrepreneurial activity, resulting in poor growth and a stifling
environment. Kerr (2002) shows that a quagmire of regulation, which is all too common, is a
massive deterrent to investment and economic growth. As a case in point, McMillan (1988)
argues that obtrusive government regulation before 1984 was the key issue in New Zealand's
slide in the world per-capita income rankings. Hernando de Soto (2002) describes one key
adverse effect of significant business regulation and weak property rights: with costly firm
regulations, fewer firms choose to register and more become informal. Also, if there are high
transaction costs involved in registering property, assets are less likely to be officially
recorded, and therefore cannot be used as collateral to obtain loans, thereby becoming "dead"
capital.
Likewise, poor infrastructure and limited transport and trade services increase logistics costs,
rendering otherwise competitive products uncompetitive, as well as limiting rural production
and people's access to markets, which adversely affects poverty and economic activity
(Guasch 2004).
The pursuit of greater competitiveness and a better investment climate is leading countries -
often assisted by multilaterals such as the World Bank - to undertake their own studies to
identify the principal bottlenecks in terms of competitiveness and the investment climate, and
evaluate the impact these have, to set priorities for intervention and reform. The most
common instrument used has been firm-level surveys, known as Investment Climate
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Econometric Methodologies for Investment Climate Assessments on Productivity
Assessments (ICAs), from which both subjective evaluations of obstacles and objective hard-
data numbers with direct links to costs and productivity are elicited and imputed. Such
surveys collect data at the firm level on the following themes: infrastructure, bureaucracy and
corruption, technology and quality, human capital, corporate governance, crime and security,
and financial services.
While the Investment Climate Assessments are quite useful in identifying major issues and
bottlenecks as perceived by firms, the data collected are also meant to provide an assessment
of the impact or contribution of the investment climate (IC) variables on productivity. In turn,
that quantified impact is used in the advocacy for, and design of, investment-climate reform.
Yet providing reliable and robust estimates of productivity estimates of the IC variables from
the surveys is not a straightforward task. First, the surveys do not provide panel-type data.
Second, the production function is not observed; and third, there is an identification issue
separating Total Factor Productivity (TFP) from the production function. When any of the
production function inputs is influenced by common causes affecting productivity, like IC
variables or other plant characteristics, there is a simultaneous equation problem. In general,
one should expect the productivity to be correlated with the production function inputs
(technological progress is not Hicks neutral) and, therefore, inputs should be treated as
endogenous regressors when estimating production functions. This demands special care in
the econometric specification for estimating those productivity effects and in the choice of the
most appropriate way of measuring productivity.
There is an extensive literature discussing the advantages and disadvantages of using different
statistical estimation techniques and/or growth accounting (index number) techniques to
estimate productivity or TFP. For overviews of different productivity concepts and
aggregation alternatives see, for example, Solow (1957), Hall (1990), Foster, Haltiwanger and
Krizan (1998), Batelsman and Doms (2000), Hulten (2001), Diewert and Nakamura (2002),
Jorgenson (2003), Jorgenson, Gollop and Fraumeni (1987), Olley and Pakes (1996) and Barro
and Sala-i-Martin (2004). In this paper we discuss the applicability of some of these
3
Econometric Methodologies for Investment Climate Assessments on Productivity
techniques to the problem at hand and present adaptations and adjustments that provide a best
fit for the described objective: estimating the productivity impact of IC variables collected
through a firm-level survey (international longitudinal micro-level data sets).
The development of an appropriate and consistent econometric methodology to be used as a
benchmark for evaluating the impact of IC variables on productivity at the firm level is the
main objective of this paper. To illustrate its applicability and usefulness, the methodology is
used to assess the productivity impact in three different countries, Guatemala, Honduras and
Nicaragua, with the IC firm-level survey data collected for 2001 and 2002.
Using a common methodology when analyzing similar issues is essential for benchmarking
and comparison of empirical results. Different research groups addressing a common issue
with the same class of models should reach similar conclusions if they share the same data set,
use the same variables and follow consistent econometric methodologies. However, any
model or any econometric methodology should be complemented by alternative econometric
approaches, to identify the limitations and the advantages or disadvantages of each approach.
Having alternative consistent econometric methodologies and alternative well-specified
models should be useful for isolating robust impacts of investment climate variables. Those
empirical results that are robust to different approaches should help in the formulation of clear
policy recommendations.
This paper, which is an updated and extended version of Escribano and Guasch (2004), is
structured as follows. Section 2 introduces the concepts of productivity and discusses general
productivity measures based on levels versus differences. Section 3 discusses the conditions
that are behind the measures of productivity growth and the related estimation problems. In
section 4 we conduct a similar analysis for the level (or logarithm) of productivity. The
advantages and disadvantages of each procedure are discussed. We conclude that, given the
characteristics of the data set, it is better to use the level of productivity rather than the rate of
growth, as in Hall and Jones (1997, 1998). In section 5, we introduce a consistent
4
Econometric Methodologies for Investment Climate Assessments on Productivity
econometric methodology for the selection of IC and firm explanatory variables for different
productivity measures. We propose to complement the regression approach with the
efficiency analysis from Olley and Pakes' (1996) decomposition of productivity. The step-by-
step explanation of this methodology is presented in section 6, where the consistent
econometric strategy is applied to study the investment climate determinants of productivity
in Guatemala, Honduras and Nicaragua. We also evaluate the differences in productivity
impacts between large and small firms as well as between old and young firms. Section 7
evaluates the efficiency of the firms by country and by industry in each of the three countries,
using the Olley and Pakes (1996) productivity decomposition. It also introduces a
methodology to evaluate the specific contribution of the IC variables to average productivity,
once we have estimated common elasticities by pooling the data from the three countries.
Section 8 explains in intuitive terms the economic interpretation of the large amount of
information obtained about individual elasticities or semi-elasticities (of the inputs and of the
IC variables on productivity) and includes a summary of the main empirical results on the IC
determinants of productivity in Guatemala, Honduras and Nicaragua. Finally, section 9
presents a summary of the econometric methodology and of the main conclusions. Most of
the figures and tables with the definitions of the variables used and with the panel data
estimation results, are included in the appendix.
Readers interested only in the empirical results should consider sections 7, 8, 9, and the
appendix. Those interested in the implementation of this productivity methodology should
read sections 5 to 7. Finally, readers interested in knowing the underlying conditions behind
each productivity (P) measure and behind the concepts of total factor productivity (TFP) and
Solow's residual (SR) should turn to sections 2, 3 and 4.
2. Productivity (P) Measures and Total Factor Productivity (TFP)
The econometric methodologies discussed in this paper are applied to study the productivity
determinants of variables collected at the firm level. In particular, we consider the impact of
5
Econometric Methodologies for Investment Climate Assessments on Productivity
investment climate (IC) variables and other firm control variables (C) on several productivity
measures. The IC variables belong to five broad categories: infrastructure,
bureaucracy/corruption, crime, finance and other plant characteristics. (See Table A2 and A3
of the appendix).
But before we proceed, it is convenient to clarify what we mean by productivity. Productivity
(P) refers to the effects of any variable different from the inputs --labor (L), intermediate
materials (M) and capital services (K)-- affecting the production (sales) process.
To be more specific, consider that the production function Q=F(L,M,K) and the productivity
(Pit) equation of the firm (i) at period (t) are given by:
Yit = F (Lit , M , K it ) Pit (1a)
it
Pit = G(ICit, Cit) exp(uit) (1b)
where uit is a random error term with properties that will be specified later on. The individual
firms are indicated by the sub-index i = 1, 2, ..., N, where N is the total number of firms in the
sample (we are pooling the observations from the three countries) and by the sub-index time t
= 1, 2, ..., T, where T is the total number of years in the sample. In our data base, N is large
and T is small.
When any of the input variables (L, M and K) is influenced by common causes affecting
productivity, like IC variables or other firm characteristic variables (C), we have a
simultaneous equation problem. (See Marschak and Andews 1944; Griliches and Mairesse
1995). In general, we should expect productivity to be correlated with the inputs L, M and K
(technological progress not Hicks neutral), and therefore the inputs must be treated as
endogenous regressors when estimating production functions. A specific solution to this
6
Econometric Methodologies for Investment Climate Assessments on Productivity
endogeneity problem of the inputs L, M and K in (1a) will be presented in section 5 when
estimation issues of production functions are discussed.3
Taking logarithms in (1a) and (1b),
log Yit = log Qit + logPit (2a)
logPit = log G(ICit, Cit) + uit (2b)
where log P is the "residual" from equation (2a) and log Q = log F(L,M,K). That is, the log of
productivity (P) is the difference between the logarithm of output (Log Y) and the logarithm
of aggregate input (log Q) formed by L, M and K. Differentiating (2a) and (2b) we get similar
expressions for the rates of growth:
d log Yit = d log Qit + d logPit (3a)
dlogPit = dlogG(ICit, Cit) + duit . (3b)
From equations (3a) and (3b) it is clear that we would like to be able to assign to dlogPit all
those changes different than Lit, Mit and Kit, that shift the production function of firm i in
period t, while associating the movements along the production function with changes in the
aggregate input4, dlogQit. However, to do that, technical progress must be Hicks neutral.
3Blundell and Powell (2000) discuss a solution to this endogenous regressors problem based on a generalized method
of moments (GMM) approach, applied to persistent panel data. Olley and Pakes (1996), Levinsohn and Petrin (2003)
and Akerberg and Caves 2003) suggest structural approaches to estimate production functions under the condition that
there is a single source of simultaneity. Unfortunately, those procedures suffer similar simultaneous equation bias, as
we will discuss in section 5.2. In this paper, we approach the simultaneous equation problem by estimating all the
parameters in one step. Furthermore, to control for the endogeneity of the IC variables we use industry regional
averages by country, complemented with an alternative structural approach inspired by the control function approach.
(See Blundell and Powell 2003). This procedure allows for several sources of simultaneity associated with IC variables
at the firm level, which are usually unobserved (fixed effects).
4Consider the extended production function Yit = F(Lit,Mit,Kit, Pit), where Pit is an aggregate productivity index which
incorporates technological changes, recent innovations, etc., in the production of Yit. In this general specification, any
improvement in Pit , perhaps due to improvements in IC conditions, represents a movement along the production
function as well as a shift of the production function.
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Econometric Methodologies for Investment Climate Assessments on Productivity
The next step is to discuss the advantages and disadvantages of using alternative measures of
productivity for the evaluation of the impact of IC variables on productivity. From the above
discussion is clear that we have two general approaches to measure productivity P:
a) based on the rate of growth of P using equations (3a) and (3b); or
b) based on the level (or logs) of P obtained from equations (2a) and (2b).
Which of the two approaches is more general or more convenient to evaluate the impact of IC
variables on productivity? To answer this question we must study the conditions (Hicks
neutral technical change, competitive inputs markets, constant returns to scale, constant input
elasticities, no measurement errors in the variables, time dimension of the sample, balanced
panel data, etc.) that are behind each of the measures or estimations of P and to determine
whether those conditions are satisfied in the cases of Guatemala, Honduras and Nicaragua. If
some of them are not satisfied we should search for productivity measures that are robust to
the impact of IC variables.
3. Productivity Measures Based on Rates of Growth
At first glance, the procedure based on rates of growth seems to be more general because it
does not require us to specify a particular functional form of the production function
F(L,M,K). However, it has serious drawbacks arising from the quality of the data
d logYit = logFit logFit logFit logFit
Lit dLit + Mit dMit + Kit dKit + Pit dPit .
logFit
If the "residual" or the weighted rate of growth of Pit which is
, Pit dPit =P,itdlog Pit , has elasticityP =1 then
,it
dlogPit=dlogTFPit , where TFP refers to the Total Factor Productivity. However, when the separability conditions (Hicks
neutral technical, etc.) are not satisfied, see Jorgenson, Gollop and Fraumeni (1987), what we are measuring by the
"residual" is the rate of growth of productivity as a time varying weighted rate of growth of Pit and this might not be
equal to the rate of growth of TFP. As we will see in the empirical section, those conditions are difficult to satisfy in most
countries. So we call the "residual" productivity (P) and not TFP. Our productivity (P) concept is sometimes called
multifactor productivity.
8
Econometric Methodologies for Investment Climate Assessments on Productivity
(measurement errors and missing observations) and the goal of the analysis (long run versus
short run), as will soon become clear.
Differentiating totally the expressions dlogQit = dlogF(Lit,Mit,Kit) and dlogG(ICit, Cit) from
equations (3a) and (3b) we get:
d logYit = logFit logFit logFit (4a)
Lit dLit + Mit dMit + Kit dKit + d log Pit
d log Pit = logGit logGit (4b)
ICit dICit + Cit dCit + duit
which can be written in term of their rates of growth5 as:
d logYit = L dlog Lit + M dlog Mit +K dlog Kit + d log Pit (5a)
,it ,it ,it
d log Pit =IC dlog ICit +C dlogCit + duit (5b)
,it ,it
where the coefficients of equation6 (5a) j,it are the j-input-elasticities of the aggregate input
Q, j = L, M, and K, of firm (i) in period (t) defined as7:
L,it = Lit Fit =logQit ,
Fit Lit logLit
M,it = Mit Fit = logQit (6)
Fit Mit logMit
K,it = Kit Fit = logQit .
Fit Kit logKit
5Notice that we are assuming that IC and C variables are scalar and not vectors. This is done in this section to simplify
the notation. In the empirical application we will consider that both are vectors.
6The coefficients of (5b) are also elasticities and are defined in a similar way.
7If the technical progress is not Hicks neutral (see footnote 3) then the residual from equation (5a) dlogPit is not equal to
dlogTFPit but equal to P,it dlogPit.
9
Econometric Methodologies for Investment Climate Assessments on Productivity
Comment: Equation (5a) is useful to isolate the types of variables that should be included if
we want to explain the evolution of labor productivity growth, dlog(Y/L)=dlogY-dlogL:
d log(Yit / Lit) = (L, -1)dlog Lit + M, dlog Mit +K, dlog Kit + d log Pit . (5a)´
it it it
It is not uncommon to see regression equations estimating labor productivity that omit the
variable capital stock (K). The capital stock, or its rate of growth, should always be there (if
available) and productivity (P) can be substituted by its determinants, say the variables (IC
and C) of equation (5b), to avoid the simultaneous equation bias.
Before proceeding to obtain empirical measures of productivity (P), two problems must be
faced:
First. We have to approximate the continuous transformation of the variables, say dlog(Yit),
by a discrete approximation based on first differences, say log(Yit) = log(Yi,t)-log(Yi,t-1).
This last approximation requires transforming (5a,) using the Tornqvist8 (1936) index:
logYit =L, logLit +M, logMit +K, logKit +logPit (7)
it it it
where j = ( j + j
1
) is average input-output elasticity of input j of firm i during the
,i,t ,i,t ,i,t-1
2
last two years (t and t-1) where j = Lit, Mit and Kit.
Second. The input-output elasticities j,it are unknown and therefore they have to be
measured by nonparametric procedures, index number techniques (see Diewert and Nakamura
2002) or estimated by regression techniques, assuming that the elasticity parameters are
constant in some sense (by industry, by country, etc.).
8Jorgenson and Griliches (1967), among others, suggested to use this Tornqvist index as an approximation to the
continuous Divisia index.
10
Econometric Methodologies for Investment Climate Assessments on Productivity
To understand the advantages and disadvantages of the several options available when
measuring productivity or productivity growth, we should study the underlying conditions and
restrictions behind each option. For the ease and clarity of the discussion we introduce now
the complete list of assumptions. Later on, when discussing each option, we will explicitly
indicate the particular assumptions required for each productivity measure.
List of conditions for productivity growth measures
Assumption 3.1: The elasticity parameters j = j = j are constant at time t (time
,i,t ,i ,i
invariant) and constant for some of the i-firms (say j = j constant for those i-firms that
,i
belong to the same industry j, the same region of a country, or the same country, etc.).
Assumption 3.2: The rates of growth of the inputs L, M and K are strictly exogenous9 in
equation (8).
Assumption 3.3: The rates of growth of the IC and C are strictly exogenous in equation (9).
Assumption 3.4: The rates of growth of the inputs L, M and K and the rates of growth of the
IC, CU and C are strictly exogenous in equation (10).
Assumption 3.5: The technology of F(L,M,K) has constant returns to scale (CRS).
Assumption 3.6: The input markets are competitive.
Assumption 3.7: The product markets are competitive.
Option 3.1: Two-step regression estimation approach
Under assumptions 3.1 and 3.2, and other statistical regularity conditions, ordinary least
squares (OLS) provides consistent and unbiased estimators of those constant elasticities based
on a two-step regression approach.
9The rate of growth of the inputs (Li,Mi,Ki) of firm i are strictly exogenous if the actual rates of growth
(Lit,Mit,Kit ) as well as the past (Lit-j,Mit-j,Kit-j) and future (Lit+j,Mit+j,Kit+j) rates of growths are uncorrelated
with the Pit for all j.
11
Econometric Methodologies for Investment Climate Assessments on Productivity
In the first step, the rate of growth of P is obtained as the residuals (log^ P ) from regression
(8):
logYit =^L logLit +^M logMit +^K logKit +log^ Pit. (8)
, j , j , j
In the second step, we estimate regression (9), under assumption 3.3:
log^ Pit =^IC logICit +^C logCit +u^it (9)
, j , j
Estimation issues that need to be addressed
We will discuss now the problems found when estimating constant input-output elasticities
and the impact of IC variables when productivity is in rates of growth:
i) Elasticity parameter estimates in levels (with integrated of order 1 variables, I(1))
and in differences (with I(0) variables) might give different parameter estimates since in
growing economies the short run elasticities are different from the long-run elasticities, unless
a common factor restriction is satisfied. (See for example Escribano and Granger 1998). This
problem is also relevant in panel data when we have a panel which is long in the time
dimension (T). (See Im, Pesaran and Shin 2003; Escribano and Pena 2004.) Obviously, this is
not an issue here since we only have observations from two consecutive years.
ii) Equation (9) in differences implies that the errors (uit) of equation (2b) in levels (or
in logs) are I(1). Otherwise, the error uit from (9) will be serially correlated.
iii) If the regression variables are measured with errors their impact is enhanced by
taking first differences.
iv) Assumption 3.2 or the strictly exogenous condition of regressors in equation (8) is
almost never satisfied in this case, since the explanatory variables of the production function
12
Econometric Methodologies for Investment Climate Assessments on Productivity
(L,M,K) in (8) are endogenous.(See Marshak and Andrews 1944.) This simultaneous equation
problem implies that least square estimators are not consistent and are biased and usually
require the use of instrumental variables (IV). However, equations with variables in
differences suffer from the weak instruments problem which produces very poor parameter
estimates. (Chamberlain 1982; Griliches and Mairesse 1995.) This problem is transferred to
the second step when estimating the impact of the IC variables based on inconsistent and
biased rates of growth of P, which is the dependent variable of equation (9).
v) Another difficulty in applying this approach to our data set from Guatemala,
Honduras and Nicaragua, is that we have information on the IC variables for only one year so
we cannot compute the rates of growth of IC variables.
vi) Equation (9) assumes that the elasticities are constant through time. This problem
is partially solved if we allow the variables to enter nonlinearly (as in the Translog production
function, etc.) This option will be discussed later.
The following option 3.2, based on a one-step approach, solves under certain conditions
problem iv) but still faces the other drawbacks.
Option 3.2: One-step regression estimation approach
If we have information on IC variables at the firm level for more than one year, we can
mitigate problem iv) if equations (8) and (9) are jointly estimated. That is, when log Pit is
correlated with inputs log Lit, log Mit, log Kit, assumption 3.2 fails. However since this
simultaneous equation bias comes from the IC and C variables at the firm level we can
estimate all the parameters in regression (10), provided that the weaker assumption 3.4 is
satisfied:
13
Econometric Methodologies for Investment Climate Assessments on Productivity
logYit =^L logLit +^M logMit +^K logKit +
, j , j , j
(10)
+^IC logICit +^C logCit +u^it .
, j , j
Under assumption 3.4, uit is uncorrelated with the regressors of (10) and OLS estimation
provides consistent estimates of the unknown constant elasticities. The rate of growth of P is
therefore generated from some of the estimated parameter values of equation (10)
log^ Pit = logYit -^L logLit -^M logMit -^K logKit . (11)
, j , j , j
We have seen that under the weaker assumption 3.4 we can solve the simultaneous equation
bias of the two-step estimation procedure. However, we still have to face the other four
drawbacks mentioned at the end of option 3.1.
In the following option, we discuss an alternative two-step approach that avoids the
simultaneous equation bias, when the variables are not exogenous.
Option 3.3: Nonparametric growth accounting procedures (Solow's residual)
What options do we have if we are not willing to assume that the input-output elasticities are
constant (assumption 3.1) or that they are strictly exogenous?
Under assumption 3.5 the marginal cost of the firm is equal to the average cost. Furthermore,
this condition allows us to consider general functional forms of the production function
F(L,M,K) like a Translog, but we have to impose certain parameter constraints to satisfy the
CRS condition.
Under assumption 3.6, firms are price takers in the inputs markets because, say, input prices
are determined by the market equilibrium. However, this condition allows some degree of
14
Econometric Methodologies for Investment Climate Assessments on Productivity
product market imperfections and firms can have market power, which is a likely possibility
in most industries of Guatemala, Honduras and Nicaragua given that the number of firms in
some industries is small.
Under assumptions 3.5 and 3.6, we can use growth accounting techniques to estimate the rate
of growth of TFP while allowing the input-output elasticities j to be nonconstant (time
,i,t
varying and heterogeneous). The reason is that under those conditions, the elasticities j ,i,t
are equal to the observable j-input shares (sj,it) of the i-firm in year t, relative to the total cost,
where j = Lit, Mit and Kit are the three inputs. That is,
L,it = Lit F = wtLit
(12a)
Qit Lit (wtLit + ctMit + rtKit) = sL,it
M, = Mit F = ctMit
it (12b)
Qit Mit (wtLit + ctMit + rtKit)= sM, it
K, = Kit F = rk
t it (12c)
it Qit Kit (wtLit + ctMit + rtKit)= sK, it
Once we have a measure of the time varying input-output elasticities, sj,i,t, we can obtain TFP
from equation (7) as a residual from the following growth accounting exercise at the firm
level:
log^TFPit = logYit -sL logLit - sM logMit -sK logKit
,it ,it ,it (13)
where the average cost shares from the last two periods are given by, sj = (sj + sj 1
)
,i,t ,i,t ,i,t-1
2
for j = L, M and K, following Tornqvist (1936).
This measure of the rate of growth of TFP is usually called the Solow residual (SR), and
follows the approach suggested by Hall (1990). If assumptions 3.5 and 3.6 fail, for example
there are no CRS, then what we get from using (13) is again a measure of productivity P,
15
Econometric Methodologies for Investment Climate Assessments on Productivity
which is the productivity not explained by L,M and K, but it is not equal to the rate of growth
of TFP associated with shifts of the production function.
However Solow's original proposal, see Solow (1957), was based on assumptions 3.5, 3.6 and
3.7 and used an alternative growth accounting technique to estimate TFP where the input-
output elasticities j ,i,tj were equal to the j-input shares relative to income (ysj,it) where j
,i,t
= L, M and K.
Under assumptions 3.6 and 3.7, each input is paid the value of its marginal product and we
F F F
have that Pit Lit = wt , Pit Kit = ct and Pit Kit = rt where Pit is the competitive market price
of output i at time t. From direct substitution in equation (6) we get a measure of the unknown
elasticities with the j-input share relative to income ( ysj ) for j = L, M and K,
,it
L,it = wtLit = ysL,it, M,it = ctMit = ysM,it and K,it = rtKit = ysK,it .
PitQit PitQit PitQit
Substituting those new shares in equation (7) we get the Solow residual (SR): the residual
growth rate of output not explained by the growth in inputs, measured as
log^TFPit = logYit - ysL logLit - ysM logMit - ysK logKit
,it ,it ,it (14)
but with the average income shares from the last two periods, using ysj = (ysj + ysj1
)
,i,t ,i,t ,i,t-1
2
for j = L, M and K. Solow (1957) considered also assumption 3.5 on (CRS) to avoid having to
compute the user cost of capital (rt) in each year. That is, under CRS, ysK =1- ysL - ysM .
,it ,it ,it
Notice that the Solow residuals of firm (i) in period (t), obtained either from (13) or (14), are
true non-parametric index numbers since they are obtained directly from observed prices and
16
Econometric Methodologies for Investment Climate Assessments on Productivity
quantities under some conditions on the existence of competitive markets. No statistical
parameter estimation is required to obtain the Solow residuals and therefore we can measure
nonparametrically time varying elasticities avoiding any possible simultaneous equation
estimation bias.
The question of interest when comparing these two alternative measuring procedures is
whether starting from (13) or (14) we get the true efficiency parameter (TFPit) of the
production. (See footnote 3.) If any of the assumptions fails (say there is no CRS, no Hicks
neutral technical change or there is no competitive inputs market) what we get from equations
(13) and (14) is a measure of the rate of growth of productivity (logPit) but this is not equal
to the rate of growth of TFP (logTFPit). Therefore, in the empirical applications, we should
interpret the influence of IC variables on P and not the influence on TFP, unless all the
assumption 3.5, 3.6 and 3.7 are satisfied, which is usually not the case in developing
countries.
Once we have a measure of the rate of growth of productivity (Pit) using (13) or (14), we
could, in the second step, estimate regression (9) under assumptions 3.1 on the parameters of
(9) and assumption 3.3, getting a consistent estimator of the impact of IC variables on the rate
of growth of productivity (logPit).
In summary, the analysis based on productivity growth is more general since it does not
require to specify the functional form of the production function, but for it to be appropriate
the quality of the data should be very good (no weak instruments, no short time dimension nor
unbalance panel data, more than one year of data on investment climate variables, etc). Given
these remarks (see also comments iii to iv at the end of option 3.1) and the basic
characteristics of our data base, we prefer to undertake the productivity analysis based on the
levels or the logs of the variables, as discussed in the next section.
4. Alternative Productivity Measures based on Variables in Levels
17
Econometric Methodologies for Investment Climate Assessments on Productivity
The goal here is to estimate the input-output elasticities based on the levels of the variables
and not on their rates of growth. Once we have the productivity growth obtained from
equation (5a) we could obtain the levels (or logs), by integrating10 equation (5a) to get:
logY
T T T T T
(15)
it= L,itlogLit + M,itlogMit + K,itlogKit + logPit .
t=1 t=1 t=1 t=1 t=1
T
The last terms of (15), logP , can be written as the constant of integration (that depends
it
t=1
on the initial conditions of firm i, say logPi0) and log P% . A similar expression is obtained for
iT
T
the term on LHS of the equation, logY . The rest of the terms in (15) have weights, say
it
t=1
T
L,itlogLit . They can be decomposed as the constant of integration of firm i (say logLi0),
t=1
T
the logLiT, and the extra terms L,itlogLit-1 . Therefore, from equation (15) we have:
t=2
logYiT = L log LiT + M log MiT +K log KiT + log P%iT
,iT ,iT ,iT
T -1 T -1 T -1 (16)
+logPi - L logLit-1 - M logMit-1 - K logKit-1 .
0 ,it ,it ,it
t=1 t=1 t=1
From the elements of equation (16) it is clear that if we generate the broad concept of
productivity Pit as the residual of equation (17),
logYit = L log Lit + M log Mit +K log Kit + log Pit (17)
,it ,it ,it
10See Escribano and Pena(2004) for a detailed derivation of these results and for an empirical robust analysis
based on a long time dimension data set.
18
Econometric Methodologies for Investment Climate Assessments on Productivity
logPit includes log P% , a firm specific initial condition term (logPi0) but also the sum of the
it
cross-product terms -J log Jit , for each of the three inputs J=L,M and K. Therefore, in
,it -1
general we cannot associate the residual of (17) with logTFPit unless certain restrictive
conditions from (16) and (5a) are satisfied and purpose of this section is to evaluate those
conditions.
To understand the advantages and disadvantages of the several options available when
measuring productivity in levels (logs), we should study the underlying conditions and
restrictions that are behind each option. For the ease and clarity of the discussion we introduce
now the complete list of assumptions. Later on, when discussing each option we will
explicitly indicate the particular set of assumptions required for each productivity measure.
List of conditions for productivity measures
Assumption 4.1: The elasticity parameters j = j = j are constant in time t (time
,i,t ,i ,i
invariant) and constant for some of the i-firms (say j = j constant for those i-firms that
,i
belong to the same industry j, or the same region of a country, or the same country, etc.).
Assumption 4.2: The levels or logs of the inputs L,M and K are strictly exogenous11 in
equation (17).
Assumption 4.3: The levels or logs of the IC and C are strictly exogenous in equation (17b).
Assumption 4.4: The levels or logs of the inputs L,M and K and the levels or logs of the IC
and C are strictly exogenous in equation (26).
Assumption 4.5: The technology of F(L,M,K) has constant returns to scale (CRS).
Assumption 4.6: The input markets are competitive.
Assumption 4.7: The product markets are competitive.
Assumption 4.8
11The log of the inputs (logLi, logMi, logKi) of firm i are strictly exogenous if the contemporaneous values (logLit,
logMit, logKit ) as well as the past values (logLit-j, logMit-j, logKit-j) and future values (logLit+j, logMit+j, logKit+j) are
uncorrelated with the Pit for all j.
19
Econometric Methodologies for Investment Climate Assessments on Productivity
T -1 T -1 T -1
L logLit-1 + M logMit-1 + K logKit-1 = 0 in equation (16)
,it ,it ,it
t=1 t=1 t=1
Assumption 4.9
% T T T
L,itlog% Lit + %M,itlog% Mit + %K,itlog% Kit = 0 in equation (25).
t=1 t=1 t=1
Under assumption 4.1, the input-output elasticities are constant (time invariant) r = 0 for,it
all r=L,M and K, and therefore, for any functional form of the production function F(L,M,K)
in (1a) with constant elasticities, equation (16) is reduced to
logYit = L log Lit + M log Mit +K log Kit + log Pi + logP%it . (18)
, j , j , j 0
Alternatively, under assumption 4.8, equation (16) is reduced to (19) with time-varying input-
output elasticities:
logYit = L log Lit + M log Mit +K log Kit + log Pi + log P%it . (19)
,it ,it ,it 0
Now, log Pit =log Pi + log P%it of equations (18) and (19) should be closer to logTFPit.
0
However, estimating productivity (P) in levels (logs) it is common to specify a parametric
functional form for F(L,M,K) which is usually of Cobb-Douglas type. It is clear that the
Cobb-Douglas specification with constant input-output elasticities satisfies assumption 4.1.
But we might have a Cobb-Douglas specification without constant elasticities, as in equations
(20a), (20b) or (21a) and (21b) below. When both the production function F(L,M,K) and the
nonlinear function G(IC, C) from (1a) and (1b) respectively, are Cobb-Douglas they can be
written as:
Yit = (LLit M
L ,it M ,it (20a)
M K Kit ) Pit
K ,it
it
Pit = (APICICit CCit ) exp(uit) .
IC,it C ,it (20b)
20
Econometric Methodologies for Investment Climate Assessments on Productivity
Taking logarithms and calling logAp = P. we can write (20a) and (20b) as:
logYit = L log Lit + M log Mit +K log Kit + log Pit (21a)
,it ,it ,it
log Pit =IC log ICit +C logCit +P +uit . (21b)
,it ,it
Once again, the interpretation of the time variant coefficients is similar. For equation (21a) we
have the time varying j-input-output elasticities, j,it , where j = L, M, and K for firm (i) in
period (t) defined as:
L,it = logYit logYit logYit (22a)
logLit , M,it = logMit and K,it = logKit
and the coefficients of equation (21b) j,it are the elasticities of P with respect to the j-
variable where j = IC and C for firm (i) in period (t) defined as:
IC,it = logPit LogPit logPit . (22b)
logICit , CU,it = logCUit and K,it = logCit
If the parametric functional form of the production function F(L,M,K) is Translog, equation
(21a) becomes:
logYit =L log Lit +M log Mit +K log Kit +
,it ,it ,it
+ LL (logLit)2 + MM (logMit)2 + KK (logKit)2 +
1 1 1
2 ,it 2 ,it 2 ,it (23)
+LM,it(logLit)(logMit)+LK,it(logLit)(logKit)+MK,it(logMit)(logKit)+
+ logPit.
Alternatively, from equation (15) we can get an expression inspired in Olley and Pakes'
(1996) decomposition (see also section 4) with the summations running through time instead
21
Econometric Methodologies for Investment Climate Assessments on Productivity
of across the i-firms in a particular year. Let J = 1 T 1 T
for
,i and log Ji = logJ
T J,it it
t=1 T t=1
J=L,M and K be the corresponding sample averages. Then each of the first three elements of
the RHS of equation (15) can be decomposed as:
T T
J,itlog Jit = J +%J [log Ji +log% Jit]
,i ,it
t=1 t=1
where %J = (L -J ) and log% Jit = (log Jit -log Ji) . Simplifying equation (15) using
,it ,it ,i
this decomposition we get
logYiT =L log LiT + M log MiT +K log KiT + log PiT +
,i ,i ,i
T T T (24)
+consti0 + %L,itlog% Lit + %M,itlog% Mit + %K,itlog% Kit .
t=1 t=1 t=1
Under assumption 4.1 the input-output elasticities are constant, and %J = (L -J ) = 0 for
,it ,it ,i
J = L, M and K and equation (25) is reduced to equation (18).
Assumption 4.9 establishes the necessary condition for the productivity (Pit) estimated as the
residual from equation (18), to be equal to the one generated by (24). For example if the
Cobb-Douglas specification is rejected in favor of a Translog specification we should expect
assumption 4.8 and/or assumption 4.9 to fail. (See Escribano and Pena 2004).
22
Econometric Methodologies for Investment Climate Assessments on Productivity
Option 4.1: Two-step regression approach
Under assumptions 4.1 and 4.2, logPit is uncorrelated with the inputs logLit, logMit and logKit,
in equation (2a), we can estimate (first step) those constant elasticity parameters in (18) by
least squares regression techniques12 and the residuals will be the estimates of the logarithm
of TFP, log^TFPit , see equation (25a). Under assumption 4.3, we can consistently estimate the
impact of the IC variables on TFP by estimating (second-step) equation (21b) by least squares
using as dependent variable log^TFPit , see equation (25b),
logYit = ^L log Lit + ^M log Mit +^K log Kit + log^ TFPit (25a)
, j , j , j
log^ TFPit = ^IC log ICit +^C logCit +^TFP + u^it . (25b)
, j , j
If the constancy of the input-output elasticities in (21a) is not satisfied we could estimate a
Translog production function, in the first step, and use those residual as the estimates of
logTFP in the second step, equation (25b).
However, assumption 4.2 is almost never satisfied since the inputs are correlated with the IC
variables and least squares estimators of (25a) are inconsistent and biased. Therefore, in the
empirical section, we suggest using the following alternative option 4.2.
Option 4.2: One-step regression approach
If logPit is correlated with the inputs, Lit,Mit and Kit, we can estimate the system (21a) and
(21b) in a single step, as in equation (26), under assumptions 4.1, 4.3 and 4.4,
12Notice that the regressions in step one (25a) and step two (25b) should have a constant term and therefore the
constant term is not identified. One possible solution is to include a constant in the first step regression and then add
back the estimated coefficient to the residuals before you create log^TFPit . Therefore, the constant term should always
be part of the TFP for consistency with the growth accounting measures (Solow residual) of TFP.
23
Econometric Methodologies for Investment Climate Assessments on Productivity
logYit = L log Lit + M log Mit +K log Kit +
, j , j , j
+IC logICit +C logCit +P +uit . (26)
, j , j
Now, under assumption 4.4 on (26), and other regularity conditions, we can get consistent and
unbiased estimators of the impact of IC variables on P by least squares. If assumption 4.4 is
not satisfied because, for example, the IC variables are endogenous, we should use
instrumental variables or other consistent estimation procedures of the parameters of equation
(26). In the empirical section we discuss how to solve this problem using regional industry
averages by country for the IC variables and, if needed, we could use the available
information on IC perceptions given by the firms (control function approach), as will become
clear later.
If the assumption 4.1 about the constancy of the input-output elasticity of L, M and K is not
satisfied, we could estimate by regression techniques a Translog specification for the
production function, estimating equation (27) instead of (26),
logYit =L log Lit + M log Mit +K log Kit +
, j , j , j
+ LL,j(logLit)2 + MM,j(logMit)2 + KK,j(logKit)2 +
1 1 1
2 2 2 (27)
+LM,j(logLit)(logMit)+LK,j(logLit)(logKit)+MK,j(logMit)(logKit)+
+IC,jlogICit +C,jlogCit +P +uit .
The Translog specification allows us also to test if the technology is Cobb-Douglas. With both
parametric specifications of the production function F(L,M,K), we could test the constant
returns to scale (CRS) condition (assumption 4.5) usually used in growth accounting measures
of TFP. (See options 3.3 and 4.3.) When we are not willing to make assumption 4.1, about the
constancy of the input output elasticities, we can use the following growth accounting
procedures to get a measure of logPit indicated in option 4.3.
24
Econometric Methodologies for Investment Climate Assessments on Productivity
Option 4.3: Nonparametric procedure based on cost-shares (Solow´s residual in levels)
If one believes that the more reliable measures of productivity are based on rates of growth,
say log^ Pj based on equation (13), but on the other hand we want to do the productivity
,i
analysis in levels (or logs), we can generate log^ Pj once we have a consistent cross-section
,i
estimate of the initial value, (say log^ Pi ). To generate the other values of log^ Pj we could use
0 ,i
the recursive formula13 log^ Pit = log^ Pi ,t-1+log^ Pit for t = 1, 2,..., T. However, we will not
pursue this procedure since it does not fit the characteristics of our data set, we have IC
observations for only one year . Furthermore, our data set has many missing observations
during the first of the two years. Therefore we will lose many observations if we took first
differences.
For our purposes we can, under assumptions 4.5 and 4.6, generate productivity (P=TFP) while
allowing the input elasticities j to be nonconstant. Say that j is equal to the cost share
,i,t ,i,t
(sj,it) for j = Lit, Mit and Kit, which are observable, see option 3.3 equations (12a)-(12c) for
more details.
log^ TFPit = logYit - sL log Lit - sM log Mit - sK log Kit .
,it ,it ,it (28)
Since now we do not have to approximate the continuous rate of growth (dlogTFP) by a
discrete measure (logP), in equation (28) we can either use the cost shares sj of each year t, ,i,t
or the average of the last two years sj for consistency with option 3.3 and equation (13):
,i,t
13In fact Solow(1967) considered that Pi = 1 or that logPi0 is 0. This is not a good initial estimate of logP and it has an
0
important impact since logP is an integrated process of logP. If logP is I(0) then logP is an I(1) process and the
effect of logP0 is permanent and not transitory. See Escribano and Pena (2004) for a further analysis of the initial
conditions effect on the correlation between alternative productivity measures.
25
Econometric Methodologies for Investment Climate Assessments on Productivity
log^ TFPit = logYit - sL log Lit - sM log Mit - sK log Kit
,it ,it ,it (29)
is the average cost shares from the last two years given by sj = (sj + sj 1
where sj )
,i,t ,i,t ,i,t ,i,t-1
2
for j = Lit, Mit and Kit.
Alternatively, under assumptions 4.5, 4.6 and 4.7 we can measure logP = logTFP knowing
that the input elasticities j ,i,tj are equal to the income shares (ysj,it) where j = Lit, Mit and
,i,t
Kit, which are observable. The original Solow residuals (SR) under CRS are the residuals
from the output equation not explained by the inputs (L,M,K), using equation (30) with
ysK =1- ysL - ysM :
,it ,it ,it
log^ TFPit = logYit - ysL log Lit - ysM log Mit - ysK log Kit .
,it ,it ,it (30)
Similarly, as we did with equation (29) we could use the average income share of the last two
years, for consistency with option 3.4.
log^ TFPit = logYit - ysL log Lit - ysM log Mit - ysK log Kit
,it ,it ,it (31)
where the average income shares from the last two years are given by
ysj = (ysj + ysj
1
,i,t ,i,t ,i,t-1) for j = Lit, Mit and Kit.
2
The advantage of option 4.3, based on the Solow residuals, is that it does not require the
inputs (L, M, K) to be exogenous or the inputs elasticities to be constant. The disadvantage is
that it requires having constant returns to scale (CRS) and competitive input markets.
In summary, measuring productivity in levels (logs) is more restrictive than measuring
productivity growth (requires specific functional forms of the production function, etc.) but it
is less demanding in terms of data quality requirements (allows to have an unbalanced panel
26
Econometric Methodologies for Investment Climate Assessments on Productivity
with short time dimension, some measurement errors, constant values of IC variables, etc).
Therefore, we will concentrate on measures of productivity and not of productivity growth.
In the following section we describe the econometric methodology suggested for evaluating
the impact of IC variables on several productivity measures in levels (logs) and we introduce
the Olley and Pakes (1996) decomposition for studying the allocation efficiency of the firms
by sector in each year.
5. Proposed Econometric Methodology to Estimate the Productivity Impact of IC
Variables in Guatemala, Honduras and Nicaragua
We have seen in sections 3 and 4, that there is no single measure of productivity (Pit) and that
in general they do not correspond to total factor productivity (TFPit). Any empirical
evaluation on the impact of IC variables is contingent on the way we measure productivity.
Therefore, to get reliable results for policy analysis we suggest using econometric procedures
that give consistent and robust conclusions and that do not depend on the particular measure
of productivity used. This is the approach we follow in the rest of the paper.
For this purpose, we use the 10 productivity measures (see section 6) that best fit with the
characteristics of our data set. We follow the procedures mentioned in options 4.2 and 4.3,
under alternative levels of aggregation (country, industry, young and old firms, etc.) and we
consider two parametric production functions, the Cobb-Douglas and the Translog.
27
Econometric Methodologies for Investment Climate Assessments on Productivity
Description of the Data
The data base is a short unbalanced panel of three countries: Guatemala, Honduras and
Nicaragua. We have temporal observations (T=2) for 2001 and 2002 for most variables.
However, for the IC variables, which are listed in Tables A1 to A3 of the appendix, we have
observations only for the year 2002.
This raises the first question: should we only use cross-section data (say only for 2002) or,
should we also use the data for 2001, even if we do not have information on the IC variables
for that year? To address this issue we assume that, unless there is a structural break, the IC
variables do not change much from one year to the next. In fact, what can change from one
year to the next is the reaction of the firm facing a certain investment climate, but that
depends on the firm's perceptions of the impact of IC variables and on the time required for
firm to implement the corresponding adjustments. Under these hypotheses, we could allow
the coefficients of certain IC variables to change from one year to the next while maintaining
the values of the IC variables constant during 2001 and 2002. In the empirical application we
maintain constant the IC coefficients and add some temporal dummies affecting the constant
term of the productivity equations in each year.
We are interested in using as many observations as possible to benefit from the law of large
numbers. Hence, we suggest pooling the data from the three countries, and later analyze their
differences and similarities. This is important because our observations are very unevenly
distributed through time and across firms, precluding us from doing separate country analyses
of each industry or sector. (See Table B1 of the appendix.) For example, if we conduct an
industry analysis country by country, we will have a textile sector in Honduras with only nine
observations, while if we pool the observations from the three countries we get at least 38
observations in that sector, giving more reliable statistical results.
28
Econometric Methodologies for Investment Climate Assessments on Productivity
In 2001, after pooling the observations from the three countries, we only have 441
observations while for 2002 we have 1,020 observations. Therefore, if we measure
productivity using rates of growth (see section 3) we will have at most 441 firms, which is a
very small sample size to study differences by industry and by country. However, if we do the
analysis in levels or logs (see section 4) we get 1,461 observations in total which will help us
get more reliable statistical results. From Table B1 of the appendix it is clear that the three
countries have similar number of observations for the two-year period: Guatemala has 468
firms, Honduras 472 and Nicaragua 521.
Cobb-Douglas Extended Production Function (one-step estimation)
Starting with the Cobb-Douglas, let us assume that the parameters are constant within each
industry j:
logYit = L log Lit + M log Mit +K log Kit + log Pit (32a)
, j , j ,j
log Pit =IClog ICit +ClogCit +P +uit (32b)
where the inputs (L,M and K) are correlated with productivity through the investment climate
(IC) variables and maybe also through some plant control (C) characteristics.
We suggest a one-step procedure to estimate equations (32a) and (32b) that addresses the
simultaneous equation bias, generated if we estimate first equation (32a) to get a measure of
productivity. Secondly, we use this productivity measure to evaluate the impact of IC
variables. To solve the endogeneity problem of some of the inputs, Olley and Pakes (1996),
Levinsohn and Petrin (2003) and Akerberg and Caves (2003) proposed a structural approach.
Dollar et al. (2004) following Levinsohn and Petrin (2003), study IC effects on TFP using a
two-step estimation procedure. However, this approach does not fit well with the
characteristics of our data base, since it requires to have a single unobserved source of
simultaneity and a detailed sequential timing of the inputs decision which is too restrictive for
29
Econometric Methodologies for Investment Climate Assessments on Productivity
annual data. One could argue that he timing of the inputs decision structure assumed in Olley
and Pakes (1996) and Levinsohn and Petrin (2003), etc. is reasonable if we have daily,
weekly, monthly or even quarterly observations, but it is unrealistic with annual data. We
have annual data on several usually unobserved IC variables. Therefore, we must a priori
consider the possibility that the three inputs (L, M and K) are endogenous (correlated with
some of the IC variables) and therefore correlated with productivity: E(Lit, Pit-j) 0, E(Mit, Pit-
j) 0 and E(Kit, Pit-j) 0, for any j0 and any j0.
To address the endogeneity problem of the inputs, we adopt the following structural
simultaneous equation approach. Let the production function to be given by the following
equation14:
logYit = L log Lit + M log Mit +K log Kit + log Pit
, j , j , j (33a)
Consider that the demand for each of the inputs (L, M and K) is given by their corresponding
qj
competitive input price (wit), by qj investment climate (IC) variables, j,ilog ICjit , and by
j=1
qinput
qinput firm characteristics given by C,ilogCr,it . That is,
r=1
qinput qinput
inputit = IC,rjlog ICr,it + C,rjlogCr,it +w,j log wit +it (33b)
r=1 r=1
where the dependent variable represents each of the inputs demand15, inputit = Lit, Mit and Kit.
14We could have as well considered the Translog production function specification.
15 Notice that we are allowing the inputs' demand to be stochastic, while in Olley and Pakes (1996) these are
deterministic.
30
Econometric Methodologies for Investment Climate Assessments on Productivity
Consider that productivity Pit is affected by qC of the firm's characteristics (Cit) and by (qIC) of
the usually unobserved characteristics which we consider to be related to the investment
climate (ICit) variables affecting each firm,
qIC qC
log Pit = IC,rjlog ICr,it + C,rjlogCr,it + P +uit .
(33c)
r=1 r=1
Notice that equation (33c) is a generalization of (32b) in the sense that we are allowing for
multiple IC and C variables in the productivity equation. This is important now since we are
assuming that Equation (33c) includes all the IC and C variables from (33b) and many other
IC and C variables that affect productivity but not the inputs of (33b).
Assuming that the errors it and uit are mutually independent and uncorrelated with the
explanatory variables in (33b) and (33c) and that all the correlation between inputs (L, M and
K) and productivity comes from the IC variables and the C characteristic of (33b), and not
from the competitive input prices (w). After controlling for IC and C variables we can jointly
estimate the parameters of the input-output elasticities from the following extended
production function (34),
qIC qC
logYit = L,jlog Lit + M,jlog Mit +K,jlog Kit + IC,rjlog ICr,it + C,rjlogCr,it + P + uit .
r=1 r=1
(34)
Translog Extended Production Function (one-step estimation)
Using similar arguments, we estimate a Translog specification of the extended production
function from
31
Econometric Methodologies for Investment Climate Assessments on Productivity
logYit =L log Lit + M log Mit +K log Kit +
, j , j , j
+ LL (logLit)2 + MM (logMit)2 + KK (logKit)2 +
1 1 1
2 , j 2 , j 2 , j
+LM (logLit)(logMit)+LK (logLit)(logKit)+MK (logMit)(logKit)+ (35)
, j , j , j
qIC qC
+ IClogICr,it + ClogCr,it +P +uit .
r=1 r=1
The Translog specification (35) allows us to find out whether the technology is Cobb-
Douglas, by testing if the extended production function (35) can be reduced to (34) in which
all the nonlinear terms of the production function have been eliminated. Furthermore, with
both (34) and (35) parametric specifications of the production function F(L,M,K), we may test
the assumption of constant returns to scale16 (CRS).
Solow Residuals in Levels (two step estimation)
A different measure of productivity is obtained by using the nonparametric procedure
discussed in option 4.3. The advantage of this alternative procedure is that we do not have to
face the previous simultaneous equation problem (endogeneity of inputs) since we are not
estimating the input-elasticities by regression techniques but from the cost shares based on
observable variables at the firm level and for each year. Therefore, this nonparametric
procedure allows us to analyze the productivity (Pit) effects of IC variables in two steps.
In the first step we generate log^ Pit (Solow Residuals in levels) from equation (36a) where
sj,i,tis the cost share of firm i during year t, or the average of the last two years ( sj ) as ,i,t
explained in equation (29):
16For example, if the coefficients of the inputs (L,M and K) in the Cobb-Douglas specification of the production function
add up to one. Similar but more complicated coefficient restrictions apply for a CRS Translog production functions, see
Table C.3.3 for an application.
32
Econometric Methodologies for Investment Climate Assessments on Productivity
logYit = sL log Lit + sM log Mit + sK log Kit + log^ Pit
,it ,it ,it (36a)
is given by sj = (sj + sj
1
where sj ,i,t ,i,t ,i,t ,i,t-1 ) for j = Lit, Mit and Kit.
2
In the second step, we estimate the IC elasticities and semi-elasticities through regression
techniques from equation
qIC qC
log^Pit = IC,rlog ICr,it + C,rlogCr,it + P +uit .
(36b)
r=1 r=1
Once we have estimated those elasticities by pooling the data from the three countries
(Guatemala, Honduras and Nicaragua) we want to evaluate the productivity contribution of
each IC variable country by country.
Evaluation of the Average Productivity Contribution of each IC Variable
This equation, estimated by least squares with a constant term, implies that the mean of the
residuals is zero and therefore that we can evaluate (36b) at the mean values without an error
term:
qIC qC
log^Pit = ^IC,rlog ICrit, + ^C,rlogCrit, + ^P . Dividing the whole expression by the dependent
r=1 r=1
variable and multiplying by 100, we get
qIC
100 = ^IC,rlogICrit,100+ ^C,rlogCrit,100+ ^P
qC
r=1 log^Pit r=1 log^Pit log^Pit 100 , which represents the sum
of the percentage productivity gains and losses from all the explanatory variables of the
regression, relative to the average log productivity. In particular, the contribution of each IC
33
Econometric Methodologies for Investment Climate Assessments on Productivity
variable to average (log) productivity is given by the term ^IC logICrit
,r ,100. Applying this
log^Pit
analysis country by country17 we obtain the results of Tables E.1 to E.3 of the appendix that
will be discussed later on.
In the empirical section, we also consider the possibility of having nonlinear impacts of IC
variables on productivity in equations (34), (35) and (36b) by including linear terms as well as
the square and cubic terms of the logIC and logC variables that appear in those equations.
Estimation of Extended Production Functions
Controlling for the largest set of IC variables and firm characteristics, as in (34), we can get
under standard regularity conditions, consistent and unbiased least squares estimators of the
parameters of the production function and of the productivity equation. That is, we can run
OLS from a one-step regression18 based on the extended production function (34). In the
empirical section we allow the errors (uit) from (34) and (35) to be heteroskedastic and
therefore we will be using pooling OLS with robust standard errors and also random effects
(RE) estimators.
We initially tried to compare the results from the three most common panel data estimators:
pooling OLS, random effects and fixed effects. However, we discarded fixed effects
estimators for three reasons: First, because not many important unobserved factors are left,
after adding many of the usually unobserved19 variables in previous regression studies.
Second, because we have information about 2001 and 2002 and therefore running fixed
effects is equivalent to running OLS in a regression with the variables in first differences.
17Notice that the mean of the residuals of each country might not now be equal to zero, although it will be a small
term.
18 Alternatively, we could have used an equivalent two-step control function approach procedure where we first
estimate by OLS a regression of each of the inputs on all the IC and C variables (partialling out) and then include the
residuals of each estimated input equation, instead of the observed inputs, in the production function (32a).
19The ICA surveys by the World Bank provide much information about individual firms that is usually not observed
by the econometrician.
34
Econometric Methodologies for Investment Climate Assessments on Productivity
However, we will lose a great deal of information by doing the analysis in first differences
since the sample size is reduced from 1,461 observations to 441, as mentioned above. Third,
most of the plant-level variables might be subject to measurement errors and by taking first
differences on those variables we enhance the undesired estimation impact of those
measurement errors. Therefore, for those reasons, we do not use the fixed effect estimators
and we estimate the contribution of IC variables to productivity in levels (logs), and not to
productivity growth, by pooling OLS with robust standard errors and by fixed effects.
Endogeneity of the IC Variables
Another econometric problem that we have to face in estimating (34), (35) and (36b) is the
possible endogeneity of IC variables and some C variables. The traditional instrumental
variable (IV) approach is difficult to implement in this context, given that we only have IC
variables for one year and therefore we cannot use the natural instruments like those provided
by their on lags, etc. As alternative estimation procedures to correct for the endogeneity of the
IC variables, we apply the following two complementary procedures:
First, we use the region-industry average of the plant level investment climate variables ( IC )
instead of the crude IC variables, to reduces the degree of endogeneity of the IC variables. We
have, in total, 13 regions for the three countries and nine industries for each country.
Furthermore, taking region and industry averages also helped us to mitigate the effect of
missing IC observations for some firms.
Second, we follow a complementary approach based on the information provided by
perception variables20 which are related to the IC information. The perception variables (PE)
incorporate firms' answers to several questions related to difficulties they face for operation
and growth, ranked from "no problem" through "minor", "moderate", "major" and "very
20We could have considered the use of perception variables as instruments for IC variables. However, those perception
variables are endogenous and not highly correlated with the IC variables (weak instruments for IC variables in the best
case). Therefore, we prefer to use perception variables for partialling out the endogenous effects of IC variables.
35
Econometric Methodologies for Investment Climate Assessments on Productivity
severe" problems. (See Table A4 of the appendix.) We assume that in the survey the IC
variables are endogenous because when filling the quantitative answers to the IC questions,
firms are influenced by their own perception on the productivity of the firm. In formal terms,
we can say that
qPEi qIC,
log ICit = PE,irlog PEr,it +
PEiC,irlogCr,it +it (37a)
r=1 r=1
qIC qC
log^Pit = IC,rlog ICr,it + C,rlogCr,it + P +Pi + vit .
(37b)
r=1 r=1
where the error uit in (36b) is related to the error in (37b) by the following expression
uit =Pi + vit . That is, uit is correlated with the IC variables, E(ICit,uit) = E(ICit,Pi ) 0
because the perception variables (PE) of each firm are correlated with the unobserved fixed
effect term (Pi ). If the random error terms in (37a) and (37b) are uncorrelated, E(it ,v it) =
0, we can solve the endogeneity problem by controlling21 for the perception variables (PE) of
(37a) forming the new extended production function equation:
qIC qC qPEi
log^Pit = IC,rlog ICr,it + C,rlogCr,it + PE,rlog PEr,it + P +it .
(38)
r=1 r=1 r=1
Since, after controlling for PE, the new error term from (38) is now uncorrelated with the IC
variables, pooling (OLS) in (38) provides consistent and unbiased elasticity parameter
estimates. Similar procedures could be applied to equations (34) and (35) by simply adding
the necessary PE variables.
Strategy for IC Variables' Selection
21This is equivalent to the linear version of the control function approach discussed in Blundell and Powel
(2003).
36
Econometric Methodologies for Investment Climate Assessments on Productivity
The econometric methodology applied for the selection of the variables (IC, C and PE) goes
from the general to the specific. The omitted variables problem that we encounter, starting
from a too simple model generates biased and inconsistent parameter estimates. Therefore we
should not estimate a simple model and add one by one IC variables and checking if they are
individually significant. On the contrary, adding irrelevant variables (meaning starting from a
very general model with some variables that are irrelevant) gives unbiased and consistent, but
inefficient, estimates. Therefore, we should start from a general model, such as equations (35)
and (36), with all the variables of Tables A1 to A3 included at once, and reduce this general
model to a simple one with relevant (significant) variables22. Note that the final estimated
model is efficiently estimated once we have deleted insignificant or irrelevant variables.
In the reduction process we should not delete all insignificant variables at once. Due to
multicolinearity, if we drop one variable that is highly correlated with others, some of the
insignificant variables might become significant. An informative statistic for this purpose is
the variation of the R2 of the regression (or the standard error of the regression). The R2 of the
simplified model (with only significant or relevant variables) should be smaller but close to
the R2 of the most general model. We applied this iterative procedure, eliminating the less
significant variables one by one, leaving, for interpretive purposes at least one IC variable
from each broad category (infrastructure, bureaucracy/corruption, crime, technology and
quality, human capital, corporate governance, etc.). The estimated explanatory variables of
the regression models of Tables C2 to C7 of the appendix, were selected in this way. We
include in those tables the set of IC variables that were significant in at least one of the
specifications or estimating procedures considered (pooling OLS or random effects). These
regression results are consistent (with equal signs and a reasonable range of parameter values)
and allow us to interpret the estimated coefficients and their signs with confidence.
22Sometimes, in the final regression model, we leave IC variables that are not individually significant but are
relevant for the model (jointly significant, affect the significance of other variables, etc.). When this happens it
could be due to the presence of multicolinearity among some of the explanatory variables.
37
Econometric Methodologies for Investment Climate Assessments on Productivity
Allocative Efficiency: The Olley and Pakes Decomposition
We now want to discuss the Olley and Pakes (1996) decomposition of productivity because it
is useful to evaluate the allocation efficiency of the firms. These decomposition tell us if the
most productive firms are the ones with larger market share or not.
N jt
Let Pjt = sitPj,it be the aggregate productivity of industry j at time t obtained as the
i=1
weighted average of i-plant-level productivity in industry j at year t, where Njt is the number
of firms in industry j and j = 1, ... ,9 industries. The weights (sit) indicate the share of the sales
of firm i in year t over the total sales of industry j of that year. Let Pjt = 1 N jt
p be the
T j,it
i=1
sample average productivity of the firms of industry j in year t. Then the annual aggregate
productivity of industry j can be decomposed as:
N jt N jt
Pjt = sj,itPj,it = sj,t + s%j,it Pj,t + P%j,it
(39)
i=1 i=1
where s%j = (sj - sj ) and P%j = (Pj - Pj ) are in deviations to the mean. Simplifying
,it ,it ,i ,it ,it ,t
equation (39) we get the Olley and Pakes (1996) decomposition:
N jt
Pjt = Pjt + s%j,itP%j,it .
(40)
i=1
The first term ( Pjt ) is the average productivity of industry j in year t and the second term
N jt
(s% "measures" the covariance between output shares and
j,itP%j,it )=cov(sj , Pj )
,it ,it
i=1
productivity, cov(sj,it, Pj,it), of the firms that belong to industry j. If the covariance is positive,
38
Econometric Methodologies for Investment Climate Assessments on Productivity
then the larger it is, the higher will be the share of sales that goes to more productive firms,
allocation efficiency is increased and industry productivity is enhanced. If the covariance is
negative, we can interpret that as an allocation inefficiency since the more negative the
covariance is, the higher will be the share of output that goes to less productive firms,
reducing industry productivity.
In terms of differences, we have that Pjt = Pjt +cov(sj , Pj ) . This decomposition tells us
,it ,it
that if there is an increase in the productivity of industry j, it must be due to an increase in
average productivity of the industry or to an increase in the reallocation of production (sit,)
toward more productive firms (higher Pit).
In section 6 we produce an empirical application at the different aggregation levels of industry
and country, and in section 7 we also distinguish between small and large firms and between
young and old ones.
6. Step-by-Step Implementation of this Productivity Methodology
For policy implications we require the estimated elasticities of IC variables to be robust: 1)
among different functional forms of the production functions; 2) among different consistent
estimation procedures; 3) among different productivity measures; and 4) among different
levels of aggregation (industry, country, pooling countries, etc).
In all the panel data regressions estimated with different productivity measures, 11 dummy
variables (Drt , r = 1, 2, ..., 11) and a constant term were included. To control for a constant
industry effect of the nine industries (apparel, beverages, chemicals/rubber, food/tobacco,
furniture/wood, leather/shoes, nonmetallic minerals, textiles, metal), we include only eight
dummy variables, leaving out apparel to avoid having perfect multicolinearity with the
constant term. Similarly, we add only one yearly dummy variable leaving out the
39
Econometric Methodologies for Investment Climate Assessments on Productivity
corresponding dummy for the year 2001. To control for a constant country effect we include
two dummies, one for Honduras and the other for Nicaragua, with Guatemala omitted.
As mentioned in section 5, to reduce the simultaneous equation bias and the risk of getting
reverse causality problems if the ICit variables are endogenous, we use their region-industry
average ( IC ). The coefficients of investment climate ( IC ) variables and other plant-
jt it
specific control (Cit) variables are maintained constant for the three countries while we allow
the production function elasticities, and therefore the productivity measures, to change for
each functional form (Cobb-Douglas and Translog) and for two different aggregation levels
(industry and countries). Restricted estimation (equal input elasticities among industries for
the three countries) and unrestricted estimation (different coefficients for each industry), are
the two levels of aggregation considered in the input elasticities of each production function.
Moreover, we consider two different estimators (pooling OLS and random effects). The
following Table summarize the productivity measures and the corresponding IC elasticities:
Summary of Productivity Measures and
Estimated Investment Climate (IC) Elasticities
1.1.a OLS
Two Step 1.1 Restricted Coef 1.1.b RE 2 (Pit) measures
1. Solow´s Residual
Estimation 1.2.a OLS 4 (IC) elasticities
1.2 Unrestricted Coef
1.2.b RE
2.1.a OLS
Single Step 2.1 Restricted Coef 2.1.b RE 4 (Pit) measures
2. Cobb-Douglas
Estimation 2.2.a OLS 4 (IC) elasticities
2.2 Unrestricted Coef
2.2.b RE
3.1.a OLS
Single Step 3.1 Restricted Coef 3.1.b RE 4 (Pit) measures
3. Translog
Estimation 3.2.a OLS 4 (IC) elasticities
3.2 Unrestricted Coef
40
Econometric Methodologies for Investment Climate Assessments on Productivity
3.2.b RE
10 (Pit) measures
Total 12 (IC) elasticities
Restricted Coef.= Equal input-output elasticities in all industries of the three countries
Unrestricted Coef.= Different input output elasticities by industry of the three countries
OLS = Pooling Ordinary Least Squares estimation (with robust standard errors)
RE = Random Effects estimation
Thus we obtain 10 different productivity measures (Pit) and we evaluate the impact of IC
variables on each of them based on two estimation procedures pooling OLS and RE. If the
sign of the impact of certain IC variables on productivity changes, contingent on the
productivity measure used, we do not have a robust or solid empirical result for policy
implementation. However, as we will see later, it is possible to obtain robust and consistent
results even when the correlations between the alternative measures of productivity differ
dramatically. This basic result allows us to implement reliable policy recommendations. Table
C1 of the appendix reports eight correlations between the log productivity measures obtained
from the four single-step production function estimates and from the two Solow residuals.
The results are as follows: when we consider the correlations between the Solow residuals
and the productivity measures that comes from estimating restricted production functions, the
correlations are very similar in all the cases, ranging from 0.91 to 0.98. However, the
unrestricted production functions differ by industry and therefore we get smaller correlations
between those productivity measures, ranging from 0.75 in the Cobb-Douglas case to a
correlation of 0.19 in the Translog case.
The econometric analysis based on the 10 different productivity (P) measures is explained in
the following subsections. The units of measurement of each explanatory variable are
included in Table B.3 of the appendix. Units of measurement are very important for the
correct economic interpretation of each coefficient.
41
Econometric Methodologies for Investment Climate Assessments on Productivity
6.1 Restricted Coefficient Estimates (equal input-output elasticities)
i) Solow´s Residual (Two-step restricted estimation)
We can estimate the elasticity parameters of the IC variables once we have a measure of
productivity (Pit). For that we could first obtain the Solow residuals (Pit) as in equation (36a)
and then estimate the impact of IC variables on Pit through regression techniques. This two-
step approach overcomes the endogeneity problem for the inputs.
First, we take the Solow residuals from (41a), as in Solow (1967) and Hall (1990.) In the
restricted case, we obtain the aggregate share ( sr ) of each input r of the whole manufacturing
t
sector of the three countries for year t.
logYit = sL log Lit + sM log Mit + sK log Kit + log^ Pit
,t ,t ,t (41a)
where sr is the average cost shares from all firms during the last two years and is given by
,t
sr = (s
1 + s ) , as in equation (36a), and s is the average of the cost shares of input r,
,t r,t r,t-1 rt
2
r = L,M and K across the entire sample of plants from the three countries (restricted case) in
year t. From Table C 3.3 of the appendix we have the cost share of labor at 0.36, the cost
share of intermediate materials at 0.53 and that of capital at 0.11. Note that the elasticities add
up to one since we are imposing constant returns to scale (CRS).
Second, we estimate equation (41b) by regression techniques:
qIC qC 11
log^Pit = IC,rlog ICrit, + C,rlogCr,it + P + D,rDr,t + uit .
(41b)
r=1 r=1 r=1
The empirical results of estimating equation (41b) by pooling the observations from the three
countries and running OLS and random effects (RE) are in Table C2 of the appendix. Notice
42
Econometric Methodologies for Investment Climate Assessments on Productivity
that none of the IC variables enter nonlinearly. The remaining endogeneity of the IC and C
variables is addressed in equations (44b) and (44c) below.
ii) Cobb-Douglas and Translog Productivities (Single-step restricted estimation)
Consider that the coefficients of the three inputs (L,M,K) of the Cobb-Douglas (42) and
Translog (43) production functions are constant (see assumption 4.1) for the whole
manufacturing sector after pooling the observations from the three countries. That is, L = ,i
L , M = M and K = K and therefore equations (34) and (35) become (42) and (43)
,i ,i
respectively. Each of the two equations is estimated in a single step, meaning that the
parameters of the production function are estimated jointly with the parameters of the IC, C
and D variables. However, to make the empirical results more readable we present them in
separate tables in the appendix, see Tables C3.1, C3.2 and C3.3.
In particular, for the Cobb-Douglas production function the specification is:
logYit = Llog Lit + Mlog Mit +Klog Kit +
qIC qC 11 (42)
+ IC,rlogIC
r,it+ C,rlogCr,it +P + D,rDr,t + uit
r=1 r=1 r=1
The empirical results from pooling OLS and random effects (RE) estimators are included in
Tables C3.1 and C3.3. Table C3.1 has the estimated elasticities of IC variables on
productivity and Table C3.3 has the input-output elasticities of the Cobb-Douglas production
function, which for labor is between 0.42 (OLS) and 0.49 (RE), for intermediate materials is
between 0.54 (OLS) and 0.45 (RE) and for capital is 0.06 for both. With both estimation
procedures, the constant returns to scale (CRS) condition is rejected by the data with a p-value
equal to 0.
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Econometric Methodologies for Investment Climate Assessments on Productivity
The estimated impacts of IC variables on productivity (P) from Table C3.1 are very similar to
those obtained with the Solow residuals in Table C2 and all the signs of the elasticities are the
same.
For the Translog production function,
logYit =Llog Lit +Mlog Mit +Klog Kit +
+ LL(logLit)2 + MM(logMit)2 + KK(logKit)2 +
1 1 1
2 2 2
+LM(logLit)(logMit)+LK(logLit)(logKit)+MK(logMit)(logKit)+ (43)
qIC qC 11
+ IC,rlogIC
r,it+ C,rlogCr,it +P + D,rDr,t + uit .
r=1 r=1 r=1
The empirical results obtained from the estimation by OLS and random effects (RE) are
presented in Tables C3.2 and C3.3 of appendix, even though the parameters were estimated in
one step (jointly estimated) from (43). In particular, the elasticities of the IC variables on
productivity are in Table C3.2 and the input-output elasticities are in Table C3.3.
The estimated elasticities reported in Table C3.2 are again very similar to the ones of Table
C2 and Table C3.1 and all the signs of the impacts of IC variables are maintained.
With the Translog functional form, the hypothesis that the Cobb-Douglas specification was
supported by the data (null hypothesis) was rejected with a p-value of 0, as shown in Table
C3.3. We then tested for CRS in the Translog specification and it was also rejected with the
same p-value.
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Econometric Methodologies for Investment Climate Assessments on Productivity
6.2 Unrestricted Production Function Coefficients Estimated by Industry
In the unrestricted case we allow the coefficients of the inputs (L, M and K) of the production
function to vary by industry. That is, L = L , M = M and K = K for all plants i of
,i , j ,i , j ,i , j
the three countries in industry j, where j=1, 2, ... , 9. The definitions of the nine j industries are
included in Table A1 of the appendix.
i) Solow´s Residuals (Two-step unrestricted estimation)
For this unrestricted case we first obtain the share ( srt ) of each input r, where r = L, M and K,
for each of the nine manufacturing industries of the three countries for year t, to get the Solow
residuals (Solow 1967; Hall 1990) from (44a)
logYit = sL log Lit + sM log Mit + sK log Kit + log^ Pit
, jt , jt , jt (44a)
where sr is the average industry j cost shares from the last two years given by
, jt
sr = (s1
) , see equation (36a), and s is the average of the cost shares of input r,
, jt r, jt+ sr
, jt-1
2 jt
r = L, M and K, across the i-plants that belongs to industry j in year t from the three countries
(restricted case).
The costs shares of each industry are reported in Table C5.3. We can see that there is a
certain homogeneity among the nine sectors. Intermediate materials always has the highest
share, with almost 50%, followed by the cost share of labor, at nearly 40% and capital, around
10%.
Second, estimate equation (44b) by regression techniques:
45
Econometric Methodologies for Investment Climate Assessments on Productivity
qIC qC 11
log^Pit = IC,rlog ICrit, + C,rlogCr,it + P + D,rDr,t + uit .
(44b)
r=1 r=1 r=1
The empirical results from the OLS and the random effects estimates of equation (44b) are
included in Table C4.1 of appendix. The results are very similar to those previously analyzed
in the restricted case of Table C2.
The endogeneity of IC variables creates a correlation between the IC variables and the errors
(uit) in equation (44b). This correlation is mitigated by substituting the plant level IC variables
by a region industry average ( IC ). If we believe that there is some remaining endogeneity in
the IC variables, due to the correlation with the perception variables (PE), we can use the
information from the survey, to correct for it. As explained in the paragraph preceding
equation (38), we could add those variables23 into model (44c),
qIC qC qD qPEi
log^Pit = IC,rlog ICrit, + C,rlogCr,it + D,rDr,t + PE,rlog PEr,it + P +it . (44c)
r=1 r=1 r=1 r=1
The list of (PE) variables used in (44c) are in Table A.4 of the appendix. However, out of the
whole set of variables that we have considered, only one of them became significant; the
perception the firm has about export experience, as seen in Table C4.2 of the appendix.
Notice, however, that most of the previous empirical results about IC variables are
23This control function approach is based on the partialling out interpretation of OLS estimators in multiple regression.
Consider the OLS interpretation of the estimated elasticity parameter ^IC ,1 of the IC 1itvariable in the multiple
regression equation (44b). When the IC 1itvariable is correlated with the error term in (44b), due to the effect of the
perception variables (PE) variables, the OLS estimator is biased and inconsistent. On the other hand, the OLS estimator
of IC ,r in (44c) is unbiased and consistent. The intuition of the result is the following: the estimated OLS coefficient of
IC 1it in (44c) measures the relationship between productivity (Pit) and IC 1it, after partialling out from IC 1itthe effect
of all the other explanatory variables of (44c) including PE.
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Econometric Methodologies for Investment Climate Assessments on Productivity
maintained. Therefore we can interpret the results obtained by estimating equation (44b) with
confidence.
ii) Cobb-Douglas and Translog Productivities (Single-step unrestricted estimation by
industry)
In this unrestricted case, the production functions specifications derived in equations (34) and
(35) become the production functions for each industry j and are given by equations (45) and
(46) respectively. Each equation is estimated by OLS and by random effects (RE) and the
parameter estimates are reported in Tables C5.1 to C5.4 in the appendix. Once again, we
separate the information on the production function elasticities from the information on the IC
elasticities to make the tables more readable although all the parameters were jointly
estimated.
Equation (45) is the Cobb-Douglas specification used to estimate, in a single step, the
productivity (Pit) at the industry level and the IC elasticities and semi-elasticities at the
aggregate level. The results are reported in Table C5.1 and C5.3 of the appendix.
qIC qC
logYit =L,jlog Lit +M,jlog Mit +K,jlog Kit + IC,rlog IC
r, jt+ C,rlogCr,t +
r=1 r=1 (45)
qD
+P + D,rDr,t + uit
r=1
Again, pooling the observations from the three countries and estimating by OLS and random
effects (RE) give similar results. The constant returns to scale (CRS) condition is only
rejected in three of the nine sectors, chemicals/rubber, food/tobacco and leather/shoes with p-
values lower than 0.05. Apparel, beverages, furniture/wood, nonmetallic minerals and textiles
fail to reject CRS, as the values of the last column of Table C5.3 of the appendix illustrate.
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Econometric Methodologies for Investment Climate Assessments on Productivity
For some sectors like leather/shoes, the estimated input-output elasticities are very different
from the values obtained from the cost shares given by the two-step procedure with Solow
residuals, meaning that the industries have certain heterogeneity in their input-output
elasticities. Therefore, the corresponding productivity measures should differ in a significant
way. (See the unrestricted cross-correlations by industry given in Table C1.)
The question of interest now is whether these new productivity measures yield similar
elasticity and semi-elasticity estimates for the IC effects on P. The results are included in
Table C5.1. The results are robust since the signs of the explanatory variables are equal and
the range of values reasonable.
Similar single-step procedures are used to estimate the following Translog specification at the
industry level. The results are included in Table C5.2 and C5.3.
logYit =L log Lit +M log Mit +K log Kit +
, j , j , j
+ LL (logLit)2 + MM (logMit)2 + KK (logKit)2 +
1 1 1
2 , j 2 , j 2 , j
+LM (logLit)(logMit)+LK (logLit)(logKit)+MK (logMit)(logKit)+ (46)
, j , j ,i
qIC qC qD
+ IC,rlogIC
r, jt+ C,rlogCr,it +P + D,rDr,t + uit
r=1 r=1 r=1
The empirical results of the Translog production function parameters are included in Table
C5.4. We tested the null hypothesis of constant returns to scale (CRS) in each industry and
generated different results from those obtained with the Cobb-Douglas specification. Six out
of nine sectors or industries rejected CRS, either by OLS or by random effects. The three
sectors that failed to reject CRS are beverages, furniture/wood and textiles. Furthermore, we
tested the Cobb-Douglas specification using equation (46) and in five out of nine sectors it
was rejected. The industries that failed to reject the Cobb-Douglas specification also failed to
reject CRS.
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Econometric Methodologies for Investment Climate Assessments on Productivity
The Translog results on the empirical estimates of the IC elasticities are the same in terms of
signs but fewer number of parameters become significant now, see Table C5.2. Again, the
reason is clear: the Translog specification includes many nonlinear terms of the inputs
variables of each sector and they compete with the explanatory power of IC variables or C
characteristics. The important point is that all the signs of the coefficients of the IC and C
variables are maintained. Therefore, the results on the impact of IC variables on productivity
are very consistent and robust to different productivity measures, suggesting that we can use
the signs and the range of estimated elasticities for policy analysis.
6.3 Further Productivity Analysis of IC Determinants by Age and Size of the Firms
The purpose of this section is to evaluate the productivity impact of investment climate (IC)
variables at different levels of aggregation of firms' characteristics. In particular, we suggest
distinguishing the impact by age and by size of the firm. Since the previous section shows that
the analysis is robust to different productivity measures, we will only do the regression
analysis for the Solow residuals in levels (or logs), that is, the two-step approach using an
extension of equation (44b) estimated by pooling the observations coming from the three
countries and estimating the parameters by OLS.
We first start by analyzing in more detail the age of the firm effect. For that we distinguish
plants with less than five years of operation (young) from the others (old) in equation (44b).
The results of Table C6.1 give a clear message: old firms are more affected by IC variables
than young firms. However, when the IC variable is significant for both young and old firms,
the difference is not significantly different from 0.
A different behavior is also observed in Table C6.2 when we distinguish between small (less
than 25 workers) and large (26 or more workers) firms. The negative impact of IC variables
(mainly those related to transport/logistics) is more relevant for small firms than for large
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Econometric Methodologies for Investment Climate Assessments on Productivity
ones. For large firms the only IC variable with significant negative elasticity is the one related
to crime (number of criminal attempts suffered and the square losses due to crime) which is
not significant for small firms. About the positive effects that affect only small firms we
should mention the fraction of total staff engaged in R&D activities, the training provided by
the firm beyond "on the job training" and the share of imported inputs. The positive IC
variables that affect only large firms are the fraction of computer-controlled machinery and
ISO quality certification. When the IC variables affect both small and large firms the
difference is not significantly different from zero.
7. Production Allocation Efficiency by Country, Firm Size and Industry
To complement the productivity analysis based on regression techniques we perform the
allocation efficiency decomposition suggested by Olley and Pakes (1996). This analysis is
especially interesting when the industries of some countries have few observations on IC
variables. In those cases, we cannot give much credibility to the countryby-country or
industry-by-industry regression estimates of the impact of IC variables on productivity since
they are based on very small samples. This decomposition provides aditional information
which is useful for analyzing the industry efficiency allocation within each country.
Remember from equation (40) that the first term ( Pkt ) of the decomposition is the sample
Nkt
average productivity of firms from industry k in year t and the second term ( s% P% )
k,it k,it
i=1
measures the covariance between productivity and output shares of those firms.
We performed first the Olley and Pakes decompositions on the Solow residual productivity
estimates at three different level of aggregation:
· By Country
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Econometric Methodologies for Investment Climate Assessments on Productivity
First, we constructed a measure of productivity for each of country and decompose it into the
average productivity term and the covariance term. The results in Figure 1 show that more
productive plants have higher market shares only in Guatemala, i.e. there is an efficient
allocation of resources in Guatemala but this is not the case in Honduras and Nicaragua.
· By Size
Second, we construct a measure of productivity for each group of plants small and large - in
each country and we apply the Olley and Pakes decomposition. The results are shown in
Figure 2. In Guatemala, resources are again efficiently allocated for small and large plants. In
Honduras, the less-productive firms have higher market shares and this negative impact is
larger (in absolute value) for small firms. In Nicaragua the results are mixed. Small firm are
efficiently allocated while large firms are not.
· By Industry and Country
Third, we construct a measure of productivity for each industry in each country and perform
the decomposition. The results are shown in the three panels of Figure 3 of the appendix. In
Guatemala, we find an efficient allocation of resources of different intensities in all industries.
In Honduras, the allocation of resources is inefficient in all sectors apart from apparel,
nonmetallic minerals and metal products. In Nicaragua, there is an efficient allocation of
resources in four of the nine industries: chemical/rubber/plastics, food/tobacco, furniture and
wood and metal products. These industry-level productivity decompositions should be
interpreted with care since in some sectors there are very few observations.
For the first two types of productivity decomposition we extend the analysis to six different
productivity measures, to see if the conclusions are robust. The results are in Table C7.1 and
C7.2 of the appendix. The signs of the covariance terms of Figure 1 are maintained in each
country for the restricted model in all cases but one corresponding to Nicaragua, as Table
C7.1 shows. The results of Figure 2 are also maintained when we split the different
productivity measures of each country into large and small firms, as Table C7.2 illustrates.
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Econometric Methodologies for Investment Climate Assessments on Productivity
However, the efficiency results are not robust to alternative productivity measures in the
unrestricted models of Honduras and Nicaragua, when we allow the input elasticities to vary
by sector in the Cobb-Douglas and Translog specifications, as the last two rows of Tables
C7.1 and the last three rows of Tables C7.2 show.
8. Empirical Results on Investment Climate (IC) Determinants of Productivity in
Guatemala, Honduras and Nicaragua
Before discussing the effects of different IC variables on productivity, it is important to take
into account that the economic interpretation of each investment climate coefficient is
contingent on the units of measurement of each IC variable and on the transformations
performed on them (logs, fractions, percentages, qualitative constructions, etc.). Since
productivity variables are always in logs, when the IC variable is expressed in log terms, the
estimated coefficient is a constant productivity-IC elasticity; and when the IC variable is not
expressed in log form, the estimated coefficient is generally described as a productivity-IC
semi-elasticity24. While the constant productivity-IC elasticity measures the percentage
change in productivity induced by a percentage change in the IC variable, the semi-elasticity
coefficient multiplied by 100, measures the percentage change in productivity induced by a
unitary change in the IC variable. A detailed explanation of the units of measurement of each
variable is given Table B.3.
24While it is sometimes natural to express an IC variable in log form, for some types of IC variables it is more
appropriate not to do so. For example, when an IC variables is a fraction or a percentage number with some data
equal to 0 or close to 0. Notice however that expressing IC variables in fractions allow us to interpret also their
coefficients as constant elasticities and not as semi-elasticities.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Investment climate (IC) variables were classified into key broad categories: (a) Red Tape,
Corruption and Crime, (b) Infrastructure, (c) Quality, Innovation and Labor Skills, (d)
Finance and Corporate Governance. Within each group, all IC variables have the expected
signs and the estimated elasticities or semi-elasticities were always within a reasonable range
of values for the 10 productivity measures considered. In absolute terms, the higher elasticity
values correspond to the Solow residual or to the Cobb-Douglas specification, while the
lowest usually correspond to the Translog production function. Therefore, we observe a trade-
off between the role played by inputs (labor, intermediate materials and capital) and the role
played by the IC variables and other control variables. The robustness of these empirical
results across productivity measures allows us to obtain consistent evaluations of the IC
determinants of productivity.
The main empirical results are presented in three sections that discuss the:
· Productivity elasticities or semi-elasticities of IC variables for the pool of three countries
(Guatemala, Honduras and Nicaragua); the
· Country specific percent productivity gains or losses of each IC variable, relative to log
productivity; and the
· Olley and Pakes productivity decomposition for each country.
8.1 Productivity Elasticities or Semi-elasticities of Each IC Variable
Here we present the individual estimates and interpretation of the elasticities or semi-
elasticities of IC variables on productivity and % R2 contribution of each IC variable to
productivity for the pool of three countries. (See Table D and Figure 1.) For each group of IC
variables we give the range of values of productivity impact obtained for the elasticities
(minimum and maximum values ) coming from the different productivity measures used.
Figure 1 shows the average levels of the elasticities or semi-elasticities of each IC variable,
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Econometric Methodologies for Investment Climate Assessments on Productivity
listed under the four thematic categories of IC variables. Additional grouping of other control
variables is also included.
A. Red Tape, Corruption and Crime
a. Number of days spent in inspection and regulation related controls. The semi-
elasticities range from -0.058 to -0.107. Therefore, if plants dedicate one more day to
inspection and regulation control activities, the productivity will decrease between 5.8% and
10.7%. This is most relevant for old firms.
b. Fraction of sales undeclared to Internal Revenue Service (IRS) for tax
purposes. The elasticities are between -0.42 and -0.77. Meaning that a one percent increase in
the fraction of undeclared sales to IRS will decrease productivity between 0.42 and 0.77
percent. The impact is specially important in small plants.
c. Payments to speed bureaucracy "faster" as a % of sales. Those firms that win
a competitive advantage by making payments of 1% of the sales to "speed up" bureaucratic
issues will have an increases in productivity ranging from 1.3 to 3.3 percent. Therefore
elasticity ranges from 1.3 to 3.3. In terms of policy recommendations, it is clear that there is
room for improvement in the administrative procedures followed in the three countries
(Guatemala, Honduras and Nicaragua), so that no more arbitrary administrative gains in
productivity arises from bribes of firms.
d. Crime. Number of criminal attempts suffered by the plants. The semi-elasticity
on productivity is between -0.018 to -0.032. For every new criminal attempt suffered by the
firm the productivity is reduced between 1.8 and 3.2 percent. This is most relevant in old and
large firms.
B. Infrastructure
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Econometric Methodologies for Investment Climate Assessments on Productivity
e. Power (average duration of power outages). The range of values of the
constant elasticities is between -0.024 and -0.095. The interpretation is the following: a one
percent increase in the average duration (hours per day) of power outages decreases
productivity between 0.02 and 0.1 percent, depending on the productivity measure used. It
mainly affects old plants.
f. Transportation. Several measures a re considered. First, the average number
of days to clear customs for imports. The range of values of the constant elasticities is
between -0.097 and -0.125. Therefore, a one percent increase in the average number of days
to clear customs will decrease productivity by a 0.1 percent. It specially affects young and
small plants. Second, shipment losses (fraction of sales lost) due to breakage, theft, spoilage
or other deficiencies of the transport mean used. The range of the elasticities is between -1.23
and -2.53, meaning that a one percent increase in the fraction of shipment losses will decrease
productivity between 1.23 and 2.53 per cent. This is most important in old and small firms.
g. Telecommunications (dummy for internet access). Having access to internet
has an impact (semi-elasticity) on productivity between 0.11 and 0.15, meaning that those
firms with access to internet are between 11% and 15% more productive that those firms
without it. The impact is especially relevant in old firms.
C. Quality, Innovation and Labor Skills
h. Fraction of computer controlled machinery. The constant elasticities range
from 0.084 to 0.132. Therefore, if the plants increase by one percent their fraction of
computer controlled machinery the productivity increase will be 0.1 percent. This is relevant
in old and large firms.
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Econometric Methodologies for Investment Climate Assessments on Productivity
i. Fraction of total staff engaged in R&D activities. The elasticities vary from
0.580 to 0.667. The interpretation is the following, if the plants increase the fraction of total
staff dedicated to R&D activities by 1% the productivity will increase between 0.6 and 0.7 %.
The most important impact is on old and small firms.
j. Dummy for ISO quality certification. When the firm is engaged in a process of
ISO quality certification its productivity will increase between 2.4 and 17.6 percent (semi-
elasticity). This is especially relevant for large firms.
k. Fraction of total staff (including management) with secondary education or
more. The constant elasticities range from 0.03 to 0.06. That is, a one percent increase in the
fraction of total staff with secondary education of the firm implies an increase in productivity
between 0.03 and 0.06 percent.
l. Dummy for training by the firm other than on the job training. If a firm
engages in a training program for their workers, other than on the job training, they will
experience an increase in productivity ranging from 8.9 to 11.7 percent (semi-elasticity). This
is especially relevant for small plants.
D. Finance and Corporate Governance
m. Dummy for incorporated company. By becoming publicly listed a firm will
increase its productivity between 11.5 and 15 percent (semi-elasticity).
n. Dummy for external audit of the financial statements. A firm, by engaging in
an external audit of its financial statements, can increase its productivity between 11.6 and 17
percent (semi-elasticity).
E. Other Control Variables
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Econometric Methodologies for Investment Climate Assessments on Productivity
o. Age of the firm (in logs). If the experience of a firm in the market, measured
by age, increases by one percent then productivity will increase between 0.03 and 0.05
percent.
p. Share (fraction) of imported inputs. If the fraction of imported inputs of
production is increased by one percent then productivity will increase 0.1 %. This is most
relevant for old and small firms.
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Figure 1
Productivity Elasticities or Semi-elasticities with Respect to IC Variables*
Finance Other
Red Tape, Corruption Quality, Innovation and and Corporate Control
Infrastructure
and Crime Labor Skills Governance Variables
1.0
0.603
0.5
0.131 0.116 0.118
0.026 0.043 0.098 0.117 0.116
0.032 0.074
0.0
-0.088 -0.018 -0.073 -0.110
-0.5
-0.611
-1.0
-1.5
-2.0
-1.958
-2.5
1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 4.1 4.2 5.1 5.2
1.1 Number of days spent in Inspection and Regulation related work 3.1 Fraction of computer-controlled machinery
1.2 Fraction of sales undeclared to the tax authority for tax purposes 3.2 Fraction of total staff engaged in R&D
1.3 Payments to deal with bureaucracy "faster", percent of sales 3.3 Dummy for ISO quality certification
1.4 Number of criminal attempts suffered 3.4 Fraction of total staff with secondary education or higher
2.1 Average duration of power outages (log) 3.5 Dummy for Training provided beyond "on the job" training
2.2 Days to clear customs for imports (log) 4.1 Dummy for Incorporated Company
2.3 Shipment Losses (fraction of Sales) 4.2 Dummy for external audit of Financial statements
2.4 Dummy for Internet Access 5.1 Age of the firm (log)
5.2 Share of Imported inputs (fraction)
* Elasticities are indicated by black bars. Semi-elasticities are indicated by yellow bars.
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8.2 Country Specific Productivity Impact of Each IC Variable
The previously reported elasticities were common to the three countries. To provide country
specific estimates of the impact of each IC variable on productivity, we decompose the
average productivity of each country into the contribution of the country's average IC
variables. We calculate this contribution for different productivity measures and report the
minimum and maximum values (range) obtained.25 Country specific productivity impacts
(gains or losses), in percentage terms, of each IC variable relative to log-productivity, are
presented in Tables E1 to E3. Figures 2, 3 and 4, show the average contribution of each IC
variable to the average log-productivity for each country (in percentage terms). Figures 5 and
6 show the cumulative productivity effect of the IC variables for each one of the four broad
categories plus the control variables. Figure 5 shows the simple cumulative effect and Figure
6 the absolute cumulative effect.
A. Red Tape, Corruption and Crime
a. Number of days spent in inspection and regulation related controls. For each
country, the contribution to the average productivity of the average number of days spent in
inspection and regulation related work is:
Guatemala: between -2.0 and -6.2 percent
Honduras: between -2.2 and -6.7 percent
Nicaragua: between -2.6 and -8.4 percent.
The negative contribution of the average number of days spent in inspection and regulation
related work is larger, in absolute value, in Nicaragua than in Guatemala and Honduras.
b. Fraction of sales undeclared to IRS for tax purposes. For each country, the
contribution to the average productivity of the average fraction of sales undeclared to IRS for
tax purposes in each country is:
25To understand how those IC productivity contributions are calculated, see comments after
equation (36b).
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Guatemala: between -1.7 and -4.0 percent
Honduras: between -2.4 and -5.5 percent
Nicaragua: between -2.8 and -6.7 percent.
The negative contribution of the average fraction of sales undeclared to IRS for tax purposes
is smaller, in absolute value, in Guatemala than in Honduras and Nicaragua.
c. Payments to deal with bureaucracy "faster" as a % of sales. Those firms that
win a competitive advantage by making payments to "speed up" bureaucratic issues will have
a contribution to the country's average productivity:
Guatemala: between 0.8 and 2.8 percent
Honduras: between 0.5 and 1.8 percent
Nicaragua: between 0.4 and 1.5 percent.
The firms from Guatemala that win a competitive advantage by making payments to "speed
up" bureaucratic issues have a larger contribution to the average productivity than those of
Honduras and Nicaragua
d. Crime. Number of criminal attempts suffered by the plants. For each country,
the contribution to the average productivity of the average number of criminal attempts is:
Guatemala: between -0.7 and -2.3 percent
Honduras: between -0.3 and -0.9 percent
Nicaragua: between -0.2 and -0.7 percent.
The negative productivity contribution of the average number of criminal attempts is larger, in
absolute value, in Guatemala than in Honduras and Nicaragua.
B. Infrastructure
e. Power (average duration of power outages). The negative contribution of the
average duration of power outages to the average productivity is similar for Honduras and
Guatemala and is smaller in absolute value for Guatemala. For each country, the contribution
to the average productivity of the average duration of power outages is:
Guatemala: between -0.3 and -1.7 percent
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Honduras: between -0.5 and -2.9 percent
Nicaragua: between -0.5 and -2.8 percent.
The negative contribution of the average duration of power outages is similar for Honduras
and Nicaragua and is smaller, in absolute value, for Guatemala.
f. Transportation. First, the average number of days to clear customs for imports.
The contribution to the country's average productivity of the average number of days to clear
customs for imports is:
Guatemala: between -4.4 and -5.8 percent
Honduras: between -3.0 and -4.0 percent
Nicaragua: between -3.2 and -4.3 percent.
The negative contribution of the average number of days to clear customs for imports is
larger, in absolute value, for Guatemala than for Honduras and Nicaragua.
g. Transportation. Second, shipment losses (fraction of sales lost) due to
breakage, theft, spoilage or other deficiencies of the transport mean used. For each country,
the contribution to the average productivity of the average fraction of shipment losses is:
Guatemala: between -0.4 and -1.0 percent
Honduras: between -0.4 and -1.0 percent
Nicaragua: between -0.5 and -1.1 percent.
The negative contribution to the average productivity of the average fraction of shipment
losses is similar for the three countries.
h. Telecommunications (dummy for internet access). For each country, the
contribution to the average productivity of the probability that a firm has access to internet is:
Guatemala: between 1.3 and 2.7 percent
Honduras: between 0.9 and 1.9 percent
Nicaragua: between 0.8 and 1.6 percent.
The positive contribution of the probability that a firm has access to internet is larger for
Guatemala than for Honduras and Nicaragua.
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C. Quality, Innovation and Labor Skills
i. Fraction of computer controlled machinery. The contribution to the country's
average productivity of the country's average fraction of computer controlled machinery is:
Guatemala: between 0.1 and 0.2 percent
Honduras: between 0.1 and 0.2 percent
Nicaragua: 0.1 percent.
The positive contribution of the average fraction of computer controlled machinery is similar
in all countries.
j. Fraction of total staff engaged in R&D activities. For each country, the
contribution to the average productivity of the average fraction of total staff in R&D activities
is:
Guatemala: between 0.1 and 0.7 percent
Honduras: between 0.3 and 0.4 percent
Nicaragua: between 0.4 and 0.6 percent.
The positive contribution of the average fraction of total staff in R&D activities is similar for
the three countries.
k. Dummy for ISO quality certification. For each country, the contribution to the
average productivity of the probability of the firm engaging in a process of ISO quality
certification is:
Guatemala: between 0.0 and 0.1 percent
Honduras: between 0.1 and 0.2 percent
Nicaragua: between 0.1 and 0.2 percent.
The positive contribution of the probability of the firm engaging in a process of ISO quality
certification is similar for the three countries.
l. Fraction of total staff (including management) with secondary education or
more. The contribution to the country's average productivity of the country's average fraction
of total staff with secondary education or more is:
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Econometric Methodologies for Investment Climate Assessments on Productivity
Guatemala: between 0.1 and 0.5 percent
Honduras: between 0.1 and 0.4 percent
Nicaragua: between 0.2 and 0.5 percent.
The positive contribution of the average fraction of total staff with secondary education or
more is similar for the three countries.
m. Dummy for training by the firm other that on the job. For each country, the
contribution to the average productivity of the probability of a firm engaging in a training
program for their workers other than on the job training is:
Guatemala: between 0.9 and 1.6 percent
Honduras: between 0.8 and 1.8 percent
Nicaragua: between 0.7 and 1.2 percent.
The positive contribution of the probability of a firm engaging in a training program for
their workers, other than on the job training is similar for the three countries.
D. Finance and Corporate Governance
n. Dummy for Incorporated Company. The contribution to the country's average
productivity of the probability of firms becoming publicly listed is:
Guatemala: between 1.1 and 2.3 percent
Honduras: between 0.5 and 1.0 percent
Nicaragua: between 0.7 and 1.5 percent.
The positive contribution of the probability of firms becoming publicly listed is larger for
Guatemala than for Honduras and Nicaragua.
o. Dummy for external audit of the financial statements. The contribution to the
country's average productivity of the probability that the firms engage in an external auditing
of its financial statements is:
Guatemala: between 0.7 and 1.5 percent
Honduras: between 0.8 and 1.8 percent
Nicaragua: between 0.6 and 1.3 percent.
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Econometric Methodologies for Investment Climate Assessments on Productivity
The contribution of the probability that the firms engage in an external auditing of its
financial statements is similar for the three countries.
E. Other Control variables
p. Age of the firm (in logs). Let firm's experience in the market be measured by
its age. Then, the contribution to the country's average productivity of the average age of the
firm (in logs) is:
Guatemala: between 1.4 and 3.4 percent
Honduras: between 1.2 and 3.1 percent
Nicaragua: between 1.5 and 3.6 percent.
The experience of the firm in the market, measured by age, has a positive contribution to the
country's average productivity and is of similar magnitude in the three countries.
q. Share (fraction) of imported inputs. For each country, the contribution to the
average productivity of the average fraction of imported inputs for production is:
Guatemala: between 0.6 and 1.1 percent
Honduras: between 0.5 and 0.9 percent
Nicaragua: between 0.7 and 1.2 percent.
The positive contribution of the average fraction of imported inputs for production is similar
for the three countries.
A summary of the estimated values of the elasticities and semi-elasticities of productivity with
respect to investment climate variables is provided in Figure 1. It represents the average
values of the six elasticity estimates given in Table D of the appendix.
64
Econometric Methodologies for Investment Climate Assessments on Productivity
Figure 2
Guatemala
Productivity Impact (gains and losses) of Investment Climate Variables*
(in %)
Finance Other
Red Tape, and Corporate Control
Corruption and Crime Infrastructure Quality, Innovation and Labor Skills Governance Variables
4
2.8
3
2.1 2.3
1.9
2 1.4 1.3
1 0.7
0.2 0.1 0.3 0.6
0
-1 -0.3
-0.7
-2 -1.7
-3
-4 -3.2
-5 -4.4
-4.8
-6
1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 4.1 4.2 5.1 5.2
1.1 Number of days spent in Inspection and Regulation related work 3.1 Fraction of computer-controlled machinery
1.2 Fraction of sales undeclared to the tax authority for tax purposes 3.2 Fraction of total staff engaged in R&D
1.3 Payments to deal with bureaucracy "faster", percent of sales 3.3 Dummy for ISO quality certification
1.4 Number of criminal attempts suffered 3.4 Fraction of total staff with secondary education or higher
2.1 Average duration of power outages (log) 3.5 Dummy for Training provided beyond "on the job" training
2.2 Days to clear customs for imports (log) 4.1 Dummy for Incorporated Company
2.3 Shipment Losses (fraction of Sales) 4..2 Dummy for external audit of Financial statements
2.4 Dummy for Internet Access 5.1 Age of the firm (log)
5.2 Share of Imported inputs (fraction)
*Productivity (in logs) and IC variables are evaluated at their respective sample means. The values are obtained by averaging the results provided in Table E1.
65
Econometric Methodologies for Investment Climate Assessments on Productivity
Figure 3
Honduras
Productivity Impact (gains andlosses) of Investment Climate Variables*
(in %) Finance Other
Red Tape, Corruption and and Corporate Control Variables
Quality, Innovation and Labor
Crime Infrastructure Governance
Skills
3 2.5
2 1.5 1.5
1.2
0.8
1 0.5 0.7
0.2 0.4 0.1 0.3
0
-0.3
-1 -0.4
-2 -1.3
-2.2
-3 -2.4
-4
-4.1
-5
1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 4.1 4.2 5.1 5.2
1.1 Number of days spent in Inspection and Regulation related work 3.1 Fraction of computer-controlled machinery
1.2 Fraction of sales undeclared to the tax authority for tax purposes 3.2 Fraction of total staff engaged in R&D
1.3 Payments to deal with bureaucracy "faster", percent of sales 3.3 Dummy for ISO quality certification
1.4 Number of criminal attempts suffered 3.4 Fraction of total staff with secondary education or higher
2.1 Average duration of power outages (log) 3.5 Dummy for Training provided beyond "on the job" training
2.2 Days to clear customs for imports (log) 4.1 Dummy for Incorporated Company
2.3 Shipment Losses (fraction of Sales) 4..2 Dummy for external audit of Financial statements
2.4 Dummy for Internet Access 5.1 Age of the firm (log)
5.2 Share of Imported inputs (fraction)
*Productivity (in logs) and IC variables are evaluated at their respective sample means. The values are obtained by averaging the results provided in Table E2.
66
Econometric Methodologies for Investment Climate Assessments on Productivity
Figure 4
Nicaragua
Productivity Impact (gains and losses) of Investment Climate Variables*
(in %)
Finance and
Red Tape, Corruption and Quality, Innovation and Labor Corporate Other
Crime Infrastructure Skills Governance Control Variables
4 2.9
2 1.1 1.4 1.0 1.2 1.1 0.9
0.1 0.4 0.1 0.4
0
-0.6
-2 -1.3 -0.8
-2.1
-4
-3.8
-6
-8 -7.1
1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 4.1 4.2 5.1 5.2
1.1 Number of days spent in Inspection and Regulation related work 3.1 Fraction of computer-controlled machinery
1.2 Fraction of sales undeclared to the tax authority for tax purposes 3.2 Fraction of total staff engaged in R&D
1.3 Payments to deal with bureaucracy "faster", percent of sales 3.3 Dummy for ISO quality certification
1.4 Number of criminal attempts suffered 3.4 Fraction of total staff with secondary education or higher
2.1 Average duration of power outages (log) 3.5 Dummy for Training provided beyond "on the job" training
2.2 Days to clear customs for imports (log) 4.1 Dummy for Incorporated Company
2.3 Shipment Losses (fraction of Sales) 4..2 Dummy for external audit of Financial statements
2.4 Dummy for Internet Access 5.1 Age of the firm (log)
5.2 Share of Imported inputs (fraction)
*Productivity (in logs) and IC variables are evaluated at their respective sample means. The values are obtained by averaging the results provided in Table E3.
67
Econometric Methodologies for Investment Climate Assessments on Productivity
Figure 5
Productivity Impact in Guatemala, Honduras and Nicaragua due to
Investment Climate Variables (in %)
(Cummulative Contribution)
35
30.2
30 29.4
26.7
25
20
15 Guatemala
10 Honduras
Nicaragua
5 2.6 2.2 2.1 3.1 2.3 2.4 3.6 3.1 3.9
0
-5 -3.1 -4.9 -4.8 -5.1
-10 -7.4
-15 -10.9
Red Tape, Corruption Infrastructure Quality, Innovation and Finance and Corporate Other Control Variables Grand Total
and Crime Labor Skills Governance Contribution
68
Econometric Methodologies for Investment Climate Assessments on Productivity
Figure 6
Productivity Impact in Guatemala, Honduras and Nicaragua due to
Investment Climate Variables (in %)
(Cummulative Absolute Contribution)
35
2
30. 4
30 29.
7
26.
25
20 Guatemala
Honduras
15 1 Nicaragua
11.6 11.3 13.
10 9.3
7.8 8.1
5
2.6 2.2 2.1 3.1 3.6
2.3 2.3 3.1 3.9
0
Red Tape, Corruption Infrastructure Quality, Innovation and Finance and Corporate Other Control Variables Grand Total
and Crime Labor Skills Governance Contribution
69
8.3 Olley and Pakes (1996) Productivity Decomposition for Each Country
Olley and Pakes (1996) productivity decomposition for each country, see Figure 7 to Figure 9
and Tables C7.1 and C7.2 in the Annex.
The Olley and Pakes decomposition provides additional insights on the efficiency of the
allocation of resources in each country. The country's aggregate productivity (weighted by the
share of sales of each firm) is decomposed as the (unweighted) average productivity term and
the covariance productivity term. The larger is the positive covariance term, the higher is the
share of sales made by more productive plants, thus increasing the industry´s productivity
(efficiency). The more negative is the covariance term, the higher is the share of sales made
by less productive plants, reducing industry productivity (inefficiency).
Figure 7 shows the decomposition for the three countries for the case where productivity is
obtained as the Solow residual. Guatemala has a positive covariance productivity term while
Honduras and Nicaragua have a negative covariance. Therefore, in Guatemala the most
productive firms have the highest share of sales while in Honduras and Nicaragua the less
productive firms have the largest share of the market. The efficiency picture is maintained for
Guatemala when we split the sample into small and large firms, but it changes for Honduras
and Nicaragua. In the case of Honduras, the aggregate negative productivity covariance is
mainly obtained because of small firms. Therefore, in Honduras small firms allocate resources
more inefficiently than large firms. For Nicaragua, the negative aggregate productivity
covariance comes from the dominant negative covariance of large firms. Therefore, in
Nicaragua small firms allocate resources more efficiently than large firms.
The analysis can be applied to other productivity measures to check the robustness of the
covariance results. The signs of the covariance terms of Figure 7 are maintained in each
country for the restricted model, except for one of the productivity measures in Nicaragua, see
Table C7.1. The results of Figure 8 are also maintained when we consider large and small
firms, see Table C7.2. However, the efficiency results are not robust to alternative
productivity measures in the unrestricted models of Honduras and Nicaragua, when we allow
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Econometric Methodologies for Investment Climate Assessments on Productivity
the input elasticity to vary across sectors in the Cobb-Douglas and Translog specifications,
see the last two rows of Tables C7.1 and the last three rows of Tables C7.2. This is because
we have very few observations in some of the industries.
9. Conclusion
There is not a single salient measure of productivity. For the analysis of the investment
climate (IC) determinants of productivity in Guatemala, Honduras and Nicaragua,
productivity is considered to be that part of the production of goods (sales) that is not
explained by the main inputs (labor, intermediate materials and capital). This productivity
concept is sometimes called total factor productivity (TFP) or multifactor productivity (MFP).
Several measures are used to evaluate what is broadly understood as productivity and a
methodology is developed that produces robust estimates regardless of the measure used. We
show that it is possible to get consistent and robust estimates (elasticities) of investment
climate determinants of productivity. This is so no matter whether we use productivity
measures with a low correlation coefficient, such as 0.19 (very different), or a high one, such
as 0.98 (similar).
The main requirement of this econometric methodology for internal consistency is that the
policy implications must be robust: 1) among different functional forms of the production
functions, 2) among different consistent estimation procedures, 3) among different
productivity measures and 4) among different levels of aggregation (industry, country,
pooling countries, etc.). In our case, all the signs of the estimated coefficients are as expected.
Obviously, the numerical values of those elasticity parameters vary from one productivity
measure to the next, but the range of values is reasonable and significant in most cases.
The analysis is undertaken without transforming the variables into rates of growth. There are
good reasons explaining that decision: (a) the IC variables are available for only one year; (b)
the panel data are very unbalanced with many more observations in 2002 than in 2001, hence
computing rates of growth for the non IC variables implies losing many observations; and (c)
measurement errors are enhanced by taking first differences. Therefore, variables in levels are
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Econometric Methodologies for Investment Climate Assessments on Productivity
used with logarithmic (logs) transformation of output, labor, intermediate materials and
capital.
Productivity is estimated as the residual of the production function. To get consistent least
squares estimates of the input-output elasticities it is necessary that all inputs are uncorrelated
with productivity. But this is almost never the case with annual data sets since the investment
climate (IC) variables affect both the inputs and the productivity. This condition invalidates
any two-step least squares procedures where first the productivity variable and then its
investment climate determinants are estimated. This problem also affects all procedures that
assume that the labor input is uncorrelated with productivity, such as those in Olley and Pakes
(1996) or Levinsohn and Petrin (2003).
Given that good instrumental variables are not available for the IC variables, we suggest a
single-step least squares estimation procedure where the parameters of the production
function (input-output elasticities) are jointly estimated with the coefficients of the IC
determinants of productivity.
A valid two-step approach is also used when the input-output elasticities are obtained,
following Solow (1957), as cost-shares. Once productivity is measured as the Solow residual
in levels (logs), the IC determinants of productivity can be consistently estimated in a second
step.
The possible endogeneity of the IC variables is reduced by taking their region-industry
averages and using the control function approach based on IC variables' perceptions. To
correct for heteroskedasticity (heterogeneity) of the individual unobserved terms, we estimate
by least squares (pooling OLS) with robust standard errors and by random effects. The results
obtained are very similar.
For policy analysis there is no need to use a single value for the elasticity or semi-elasticity of
each IC variable. In fact, it is more interesting to perform a sensitivity analysis based on the
range of parameter values obtained for several productivity measures.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Further insight on the efficiency of the different manufacturing sectors at the country level is
obtained from the Olley and Pakes productivity decompositions.
Four important categories of investment climate (IC) variables are identified for the case of
Guatemala, Honduras and Nicaragua: (a) read tape, corruption and crime, (b) infrastructure,
(c) quality, innovation and labor skills, and (d) finance and corporate governance. Within each
group, all the IC variables always have the expected signs and the estimated elasticities or
semi-elasticities are always within a reasonable value range for the 10 productivity measures
considered. In absolute terms, the higher values of the IC elasticities correspond to the
Solow residual or to the Cobb-Douglas specification, while the lowest usually correspond to
the translog production function. Therefore, we observe a trade-off between the role played
by the inputs (labor, intermediate materials and capital) and the role played by the IC
variables and other control variables.
The robustness of these empirical results across productivity measures allows us to obtain
consistent empirical evaluations of the IC determinants of productivity. The estimates show
consistently the high impact of investment climate on productivity. Overall, it accounts for
over 30 percent of productivity. The two most impacting categories are red tape, corruption
and crime, and infrastructure, accounting respectively for about 12 and 9 percent of
productivity. The policy implications are clear. Investment climate matters enormously and
the relative size of the impact of the various investment climate variables indicates where the
reform efforts should be placed.
73
Appendix
Table A.1: General Information at Plant Level and Production Function Variables
Industrial classification Apparel, beverages, chemicals/rubber, food/tobacco, furniture/wood,
leather/shoes, nonmetallic minerals, textiles, metal products.
Regional classification
General - Guatemala: Guatemala City, Metropolitan area close to Guatemala city,
Metropolitan area far from Guatemala city, Altiplano region, Coastal
Information at region, Northeast region
Plant Level - Honduras: Western region, Center-South region, Olancho region, North
coast region
- Nicaragua: Managua region, Pacific region.
Sales Used as the measure of output for the production function estimation. For
all countries, sales figures in local currency are converted into USD using
IMF average exchange rates.
Production Employment Total number of permanent workers.
Function Materials Total costs of intermediate and raw materials used in production
Variables (excluding fuel). For all countries, materials figures in local currency are
converted into USD using IMF average exchange rates.
Capital stock Book value of all fixed assets.
Labor cost Total expenditures on personnel.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table A.2: Investment Climate (IC) Variables
Power interruptions total duration of power outages suffered by the plant in hours (equals
average duration times the total number of power outages). If the plant
suffered no power outages the total duration of power outages is 0.
Duration of power average duration of power outages suffered by the plant in hours.
outages
Power outages number of power outages suffered by a plant in 2002.
Losses due to power value of the losses due to the power outages as a percentage of sales
outages (conditional on the plant reporting power outages).
Production interruption dummy variable =1 if the plant had to stop production due to power
due to power outages outages.
Phone interruptions total duration of phone interruptions suffered by the plant in hours
Infrastructure (equals average duration times the total number of phone outages). If the
plant suffered no phone interruptions the total duration of phone
interruptions is 0.
Wait for phone number of days the plant had to wait from the moment it requested a
phone connection until the day it received the phone service.
Days to clear customs for average number of days that it took from the time the plant's imports
imports arrived to the point of entry until the time the plant could claim them
from customs.
Fraction of shipment fraction of the value of the plant's average cargo consignment that was
losses lost in transit due to breakage, theft, spoilage or other deficiencies of the
transport means used.
Production stopped due to dummy variable =1 if the plant had to stop production due to transport
transport interruptions interruptions
Inspection days number of days spent by the plant in inspections and compulsory
meetings with public officials from the following areas: tax inspectorate,
labor and social security, fire and building.
Bureaucracy/ Percentage of time spent percentage of time in a typical week spent by management dealing with
Corruption dealing with regulation bureaucracy/regulation.
Payments to deal with payments to "speed up" bureaucratic issues as a percentage of sales.
bureaucracy "faster"
Fraction of sales Fraction of total sales unreported to the IRS for tax purposes by a typical
undeclared to the IRS firm in an area of activity as perceived by the reporting firm
Losses due to crime as a value of the plant's losses due to criminal activity as a percentage of
% of sales sales. For plants reporting no criminal activity the value of the variable
Crime is 0.
Number of criminal number of crimes is the number of criminal attempts suffered by the
attempts suffered plant.
Overdraft dummy variable =1 if the plant reports that it has an overdraft facility or
a line of credit.
Loan dummy variable =1 if the plant reports that it has a bank loan.
Finance Constrained access to dummy variable =1 if i) the plant did not ask for a loan (except if the
loan plant did not ask for a loan because it did not need it) or ii) the plant
asked for a loan but its request was refused by the bank.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table A.3: Other IC and Plant Characteristics (C) Variables
Percentage of computer- percentage of computer-controlled machinery at the plant.
controlled machinery
Use of foreign dummy variable =1 if the plant uses technology obtained from foreign
technology firms paying or not paying licenses for that use.
Introduction of new dummy variable =1 if the plant introduced new products in the last 3
products years.
R&D Fraction of total staff engaged en R&D activities
Training dummy variable =1 if the plant provides training to its employees other
than on the job
Percentage of staff with percentage of workers (including management) with at least secondary
secondary education education.
Other IC and Domestic market share how much the plant's sales represent out of total national market.
Plant Exporter dummy variable =1 if the plant exports at least 10% of its output.
Characteristics Foreign ownership dummy variable =1 if plant has any share of its capital that is foreign.
Age difference between 2002 and the year that the firm started operations in
the country.
Capacity utilization percentage of capacity utilized
Inverse number of 1/number of competitors of the plant.
competitors
Internet access dummy variable=1 if the plant has used email or a website in its
interactions with clients or suppliers.
ISO certification Firm has ISO Quality certification
External audit Dummy variable=1 if the firm's annual financial statement is reviewed
by an external auditor
Share of imported inputs Fraction of material inputs that are imported
Publicly Listed Dummy variable=1 if the firm is publicly listed
Table A.4: Investment Climate Perception (PE) Variables
Electricity problem ranking by the plant of electricity as a problem for its operations and
growth.
Infrastructure Generator dummy variable =1 if the plant has a generator.
Customs problem ranking by the plant of customs as a problem for its operations and
growth.
Export experience number of years since the plant first started exporting.
Tax administration ranking by the plant of tax administration as a problem for its operations
problem and growth.
Trade regulation ranking by the plant of trade regulation as a problem for its operations
problem and growth.
Labor regulation ranking by the plant of labor regulation as a problem for its operations
problem and growth.
Bureaucracy/ Permit problem ranking by the plant of permit and business registration as a problem for
Corruption its operations and growth.
Regulatory policy ranking by the plant of regulatory policy uncertainty as a problem for its
uncertainty problem operations and growth.
Government dummy variable =1 if the plant answered that the government is very
inconsistency inconsistent or inconsistent in interpreting laws and regulations affecting
the plant.
Corruption problem ranking by the plant of corruption a problem for its operations and
growth.
Crime Crime problem ranking by the plant of crime a problem for its operations and growth.
Security cost Expenditure on security related items by the plant as percentage of sales.
Finance availability ranking by the plant of availability of finance as a problem for its
Finance problem operations and growth.
Cost of finance problem ranking by the plant of cost of finance as a problem for its operations and
growth.
Finance access problem ranking by the plant of access to finance as a problem for its operations
and growth.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table B.1: Number of Firms that Enter into the IC Regressions by Industry and by
Country
Industry Guatemala Honduras Nicaragua Total
Apparel 129 70 64 263
Beverages 8 19 17 44
Chemical/Rubber 61 35 67 163
Food/ Tobacco 102 134 68 304
Furniture/ Wood 56 126 127 309
Leather/ Shoes 6 0 45 51
Nonmetallic 36 44 69 149
Minerals
Textiles 22 9 7 38
Metal Products 48 35 57 140
Total 468 472 521 1,461
Table B.2: Number of Firms that Enter into the IC Regressions by Industry and by
Year
Industry 2001 2002 Total
Apparel 71 191 263
Beverages 12 32 44
Chemical/Rubber 52 111 163
Food/ Tobacco 103 201 304
Furniture/ Wood 90 219 309
Leather/ Shoes 15 36 51
Nonmetallic Minerals 44 105 149
Textiles 8 30 38
Metal Products 45 95 140
Total 441 1,020 1,461
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table B.3: List of Significant ICA Variables and their Measurement Units
Explanatory ICA Variables Measurement Units
Average duration of power outages Hours per day (in logs)
Average number of days to clear customs for imports Days (in logs)
Shipment Losses Fraction of total sales (per unit)
Dummy for Internet Access 0 or 1
Number of days spent in Inspection and Regulation Days
related work
Fraction of sales declared to IRS for tax purposes Fraction of Total Sales (per unit)
Payments to deal with bureaucracy"faster" as a % of Percentage of total sales (per 100)
sales
Number of criminal attempts suffered Numbers
Fraction of computer-controlled machinery Fraction of total machinery (per unit)
Fraction of total staff engaged in R & D Fraction of total staff (per unit)
Dummy for ISO quality certification 0 or 1
Fraction of total staff with secondary education or Fraction of total staff ( per unit)
more
Dummy for Training provided beyond "on the job" 0 or 1
training
Dummy for Incorporated Company 0 or 1
Dummy for external audit of Financial statements 0 or 1
Age of the firm Age in years (in logs)
Share of Imported inputs Fraction of total inputs( per unit)
* In all the regressions, outlier plants were excluded. Outlier plants were defined as those which had
ratios of materials to sales larger than one or had ratios of labor costs to sales larger than one.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.1: Correlation between Solow Residuals in Levels and Estimated Productivity
Solow Residuals
Restricted Unrestricted
by
Industry
OLS 0.99 0.81
Cobb Douglas
Estimated Random Effect 0.98 0.74
Productivity OLS 0.86 0.27
Translog
Random Effect 0.83 0.23
Notes:
a) Solow residuals in levels are obtained as sales (in logarithms or logs) minus a weighted average of labor,
materials, capital (all in logs) where the weights are given by the share in total costs of each of the inputs. The
cost of capital is assumed to be equal to 10% times the capital stock.
(1) Restricted case: the cost shares are calculated as the averages of the plant-level cost shares across the
entire sample of three countries in 2002 and 2001.
(2) Unrestricted by Industry case: the cost shares are calculated as the averages across plant-level cost
shares in years 2002 and 2001 for each of nine industries (for each industry all plants of the three countries
that are not outliers are considered).
(3) Outlier plants were defined as those which had ratios of materials to sales larger than one or had ratios
of labor costs to sales larger than one.
b) Estimated Productivity in levels is obtained from Cobb-Douglas and Translog production functions of sales
with inputs labor, materials, and capital estimated by OLS and random effects under two different
environments:
(1) Restricted: a single set of production function coefficients is obtained using data on plants in the three
countries, for all industries in years 2002 and 2001.
(2) Unrestricted by Industry: a set of production function coefficients is obtained for each of nine industries
using data on all plants for the three countries in years 2002 and 2001 (excluding outliers).
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.2: Two-Step Restricted Estimation
Dependent Variable: Restricted Solow Residual in Levels (logs)
OLS Random Effects
Number of days spent in Inspection and Regulation related -0.097** -0.091*
work [0.043] [0.048]
Fraction of sales undeclared to the tax authority for tax -0.601** -0.613**
Red Tape, Corruption purposes [0.263] [0.266]
and Crime
Payments to deal with bureaucracy "faster", percent of sales0.031** 0.029**
[0.012] [0.013]
Number of criminal attempts suffered -0.029** -0.033**
[0.012] [0.014]
Average duration of power outages (log) -0.095* -0.077
[0.052] [0.062]
Days to clear customs for imports (log) -0.097** -0.098**
Infrastructure [0.041] [0.041]
Shipment Losses (fraction of Sales) -1.860** -1.348
[0.850] [1.341]
Dummy for Internet Access 0.147*** 0.136***
[0.038] [0.042]
Fraction of computer-controlled machinery 0.119 0.153
[0.082] [0.097]
Fraction of total staff engaged in R&D 0.594** 0.535*
[0.287] [0.284]
Quality, Innovation and Dummy 0.154 0.184**
Labor Skills for ISO quality certification
[0.102] [0.090]
Fraction of total staff with secondary education or higher 0.036 0.066
[0.058] [0.060]
Dummy for Training provided beyond "on the job" training 0.117*** 0.115***
[0.036] [0.038]
Dummy for Incorporated Company 0.150*** 0.150***
Finance and Corporate [0.039] [0.042]
Governance
Dummy for external audit of Financial statements 0.168*** 0.159***
[0.042] [0.040]
Age of the firm (log) 0.050** 0.051**
Others Control Variables [0.020] [0.022]
Share of Imported inputs (fraction) 0.113** 0.115**
[0.049] [0.052]
Observations 1461 1461
R-squared 0.2 0.2
Test of joint significance Prob > F = 0.0000 Prob > chi2 = 0.0002
of IC Variables
Test of joint significance Prob > F = 0.0000 Prob > chi2 = 0.0000
of ICVs and Plant level controls
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(2) The restricted Solow Residual is obtained using cost shares for inputs (labor, materials and capital) calculated as averages
across all plants in the three countries in years 2001 and 2002 (excluding outliers).
(3) The regressions include a constant, industry dummies, country dummies and year dummies.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.3.1: Single Step Restricted Estimation: Cobb Douglas Specification
Dependent Variable: Productivity in Levels (in logs)
OLS Random Effects
Red Tape, Corruption and Number of days spent in Inspection and -0.099** -0.077
Crime Regulation related work
[0.043] [0.053]
Fraction of sales undeclared to the tax -0.612** -0.672**
authority for tax purposes
[0.260] [0.295]
Payments to deal with bureaucracy 0.033*** 0.029**
"faster", percent of sales
[0.012] [0.014]
Number of criminal attempts suffered -0.031*** -0.039**
[0.012] [0.016]
Infrastructure Average duration of power outages (log) -0.085* -0.077
[0.050] [0.069]
Days to clear customs for imports (log) -0.106** -0.111**
[0.042] [0.045]
Shipment Losses (fraction of Sales) -2.119** -1.082
[0.867] [1.483]
Dummy for Internet Access 0.144*** 0.180***
[0.041] [0.048]
Quality, Innovation and Fraction of computer-controlled 0.13 0.204*
Labor Skills machinery
[0.082] [0.107]
Fraction of total staff engaged in R&D 0.667** 0.627**
[0.294] [0.316]
Dummy for ISO quality certification 0.167 0.209**
[0.103] [0.101]
Fraction of total staff with secondary 0.048 0.096
education or higher
[0.059] [0.067]
Dummy for Training provided beyond 0.105*** 0.139***
"on the job" training
[0.036] [0.043]
Finance and Corporate Dummy for Incorporated Company 0.140*** 0.169***
Governance
[0.042] [0.048]
Dummy for external audit of Financial 0.159*** 0.185***
statements
[0.042] [0.046]
Others Control Variables Age of the firm (log) 0.049** 0.057**
[0.020] [0.024]
Share of Imported inputs (fraction) 0.101** 0.142**
[0.051] [0.058]
Observations 1461 1461
R-squared 0.2 0.26
Test of joint significance Prob > F = 0.000 Prob > chi2 = 0.0005
of IC Variables
Test of joint significance Prob > F = 0.000 Prob > chi2 = 0.0000
of ICVs and Plant level controls
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(2) The dependent variable Productivity is computed as the difference between sales and inputs multiplied by the
corresponding Cobb-Douglas production function coefficients in Table C 3.3. A single regression of sales on inputs and the
variables above is estimated that includes a constant, industry dummies, country dummies and year dummies.
(3) The regressions include a constant, industry dummies, country dummies and year dummies.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.3.2: Single-Step Restricted Estimation: Translog Specification
Dependent Variable: Productivity in Levels (logs)
OLS Random Effects
Red Tape, Corruption and Number of days spent in Inspection and -0.058 -0.054
Crime Regulation related work
[0.041] [0.049]
Fraction of sales undeclared to the tax -0.416* -0.521*
authority for tax purposes
[0.243] [0.277]
Payments to deal with bureaucracy "faster", 0.015 0.013
percent of sales
[0.012] [0.014]
Number of criminal attempts suffered -0.018 -0.027*
[0.011] [0.015]
Infrastructure Average duration of power outages (log) -0.072 -0.057
[0.047] [0.064]
Days to clear customs for imports (log) -0.125*** -0.136***
[0.039] [0.042]
Shipment Losses (fraction of Sales) -1.229 -0.355
[0.811] [1.391]
Dummy for Internet Access 0.119*** 0.158***
[0.039] [0.046]
Quality, Innovation and Labor Fraction of computer-controlled machinery 0.132* 0.169*
Skills
[0.079] [0.101]
Fraction of total staff engaged in R&D 0.581** 0.633**
[0.270] [0.297]
Dummy for ISO quality certification 0.024 0.066
[0.093] [0.095]
Fraction of total staff with secondary education 0.03 0.075
or higher
[0.059] [0.063]
Dummy for Training provided beyond "on the 0.089*** 0.117***
job" training
[0.033] [0.041]
Finance and Corporate Dummy for Incorporated Company 0.115*** 0.143***
Governance
[0.038] [0.046]
Dummy for external audit of Financial 0.121*** 0.139***
statements
[0.038] [0.043]
Others Control Variables Age of the firm (log) 0.046** 0.052**
[0.018] [0.022]
Share of Imported inputs (fraction) 0.096** 0.131**
[0.047] [0.055]
Observations 1461 1461
R-squared 0.36 0.36
Test of joint significance Prob > F = 0.0027 Prob > chi2 = 0.0051
of IC Variables
Test of joint significance Prob > F = 0.0000 Prob > chi2 = 0.0000
of ICVs and Plant level controls
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(2) The dependent variable, Productivity in levels, is computed as the difference between log sales and log inputs multiplied
by the corresponding Translog production function coefficients in Table 3.3. The Single Step regression indicates that all of
the parameters of the Translog specification from Table 3.2 and Table 3.3 are jointly estimated in the same regression since
the explanatory variables are correlated.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.3.3: Production Function Parameters from the Restricted Estimation
Material
Labor(L) Input(M) Capital(K) L2 M2 K2 L*M L*K M*K
Cost Shares 0.36 0.53 0.11 - - - - - -
COBB DOUGLAS
OLS 0.423*** 0.537*** 0.060*** - - - - - -
Random 0.489*** 0.448*** 0.065*** - - - - - -
Effects
Test for Constant Returns to Scale OLS Prob > F = 0.000 R.E. Prob > chi= 0.0006
TRANSLOG
OLS 1.329*** -0.11 -0.009 0.070*** 0.067*** 0.017** -0.141*** 0.016 -0.032**
Random 1.545*** -0.315*** -0.164** 0.064*** 0.053*** 0.012** -0.121*** -0.014 0.001
Effects
Test for Cobb Douglas OLS Prob > F = 0.000 R.E. Prob >chi2 = 0.000
Test for Constant Returns to Scale OLS Prob > F = 0.000 R.E. Prob >chi2 = 0.000
Notes:
(1) Significance is given by robust standard errors.* significant at 10%; ** significant at 5%; *** significant at 1%.
(2) The cost shares of labor, materials and capital are calculated as averages of the plant-level cost shares of labor, materials
and capital across all plants for the 3 countries in years 2002 and 2001 (excluding outliers).
(3) The sample generating the sets of production function coefficients is constituted by all plants in the 3 countries in years
2002 and 2001 (excluding outliers).
(4) 0 indicates that the coefficient for that input in that industry is not significant and hence, is restricted to be
equal to 0.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.4.1: Two-Step Unrestricted By Industry Estimation
Dependent Variable: Solow Residuals by Industry in levels (logs)
OLS Random Effects
Red Tape, Corruption Number of days spent in Inspection and -0.101** -0.095**
and Crime Regulation related work
[0.043] [0.047]
Fraction of sales undeclared to the tax authority -0.676** -0.678**
for tax purposes
[0.267] [0.266]
Payments to deal with bureaucracy "faster", 0.031** 0.029**
percent of sales
[0.012] [0.013]
Number of criminal attempts suffered -0.032*** -0.035**
[0.012] [0.014]
Infrastructure Average duration of power outages (log) -0.088* -0.07
[0.052] [0.062]
Days to clear customs for imports (log) -0.105*** -0.106***
[0.041] [0.041]
Shipment Losses (fraction of Sales) -1.948** -1.436
[0.852] [1.339]
Dummy for Internet Access 0.139*** 0.128***
[0.038] [0.042]
Quality, Innovation and Fraction of computer-controlled machinery 0.117 0.15
Labor Skills
[0.083] [0.097]
Fraction of total staff engaged in R&D 0.589** 0.526*
[0.285] [0.283]
Dummy for ISO quality certification 0.142 0.174*
[0.101] [0.090]
Fraction of total staff with secondary education or 0.033 0.064
higher
[0.057] [0.060]
Dummy for Training provided beyond "on the job" 0.116*** 0.114***
training
[0.036] [0.038]
Finance and Corporate Dummy for Incorporated Company 0.146*** 0.146***
Governance
[0.039] [0.042]
Dummy for external audit of Financial statements 0.170*** 0.160***
[0.042] [0.040]
Others Control Age of the firm (log) 0.051*** 0.052**
Variables
[0.020] [0.022]
Share of Imported inputs (fraction) 0.110** 0.114**
[0.049] [0.052]
Observations 1461 1461
R-squared 0.26 0.26
Test of joint significance of IC Variables Prob > F = 0.0000 Prob > chi2 = 0.0001
Test of joint significance of ICVs and Plant level Prob > F = 0.0000 Prob > chi2 = 0.0000
controls
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(2) The unrestricted by industry Solow Residual is obtained using cost shares for inputs (labor, materials and capital)
calculated as averages for each of the industries using plants for the 3 countries in years 2001 and 2002.
(3) The regressions include a constant, industry dummies, country dummies and year dummies.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.4.2: Two-Step Unrestricted by Industry Estimation with Perception Variables
Dependent Variable: Solow Residuals by Industry in Levels (logs)
OLS
Red Tape, Corruption and Number of days spent in Inspection and Regulation related work -0.063
Crime
[0.045]
Fraction of sales undeclared to the tax authority for tax purposes -0.505*
[0.277]
Payments to deal with bureaucracy "faster", percent of sales 0.033***
[0.013]
Number of criminal attempts suffered -0.035***
[0.013]
Infrastructure Average duration of power outages (log) -0.098*
[0.052]
Days to clear customs for imports (log) -0.079*
[0.042]
Shipment Losses (fraction of Sales) -1.666**
[0.830]
Dummy for Internet Access 0.141***
[0.038]
Perceptions Perception on Export experience -0.020*
[0.011]
Quality, Innovation and Fraction of computer-controlled machinery 0.117
Labor Skills
[0.083]
Fraction of total staff engaged in R&D 0.570**
[0.283]
Dummy for ISO quality certification 0.14
[0.100]
Fraction of total staff with secondary education or higher 0.048
[0.062]
Dummy for Training provided beyond "on the job" training 0.113***
[0.035]
Finance and Corporate Dummy for Incorporated Company 0.145***
Governance
[0.039]
Dummy for external audit of Financial statements 0.165***
[0.042]
Others Control Variables Age of the firm (log) 0.053***
[0.020]
Share of Imported inputs (fraction) 0.113**
[0.049]
Observations 1461
R-squared 0.26
Test of joint significance Prob > F = 0.0000
of IC Variables
Test of joint significance Prob > F = 0.0000
of ICVs and Plant level controls
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(2) The unrestricted by industry Solow residual TFP is obtained using cost shares for inputs (labor, materials and capital)
calculated as averages for each of the industries using plants for the 3 countries in years 2001 and 2002.
(3) The regressions include a constant, industry dummies, country dummies and year dummies.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.5.1: Single-Step Unrestricted by Industry Estimation: Cobb Douglas
Specification
Dependent Variable: Productivity in levels (logs)
OLS Random Effects
Red Tape, Corruption Number of days spent in Inspection and Regulation -0.107** -0.098*
and Crime related work
[0.043] [0.052]
Fraction of sales undeclared to the tax authority for -0.767*** -0.825***
tax purposes
[0.255] [0.296]
Payments to deal with bureaucracy "faster", percent of 0.030** 0.028*
sales
[0.012] [0.014]
Number of criminal attempts suffered -0.029** -0.032**
[0.011] [0.016]
Infrastructure Average duration of power outages (log) -0.075 -0.06
[0.050] [0.068]
Days to clear customs for imports (log) -0.105** -0.112**
[0.043] [0.045]
Shipment Losses (fraction of Sales) -2.530*** -1.832
[0.871] [1.472]
Dummy for Internet Access 0.128*** 0.172***
[0.039] [0.048]
Quality, Innovation Fraction of computer-controlled machinery 0.131 0.203*
and Labor Skills
[0.085] [0.107]
Fraction of total staff engaged in R&D 0.607** 0.556*
[0.280] [0.313]
Dummy for ISO quality certification 0.176* 0.204**
[0.105] [0.100]
Fraction of total staff with secondary education or 0.054 0.105
higher
[0.060] [0.066]
Dummy for Training provided beyond "on the job" 0.110*** 0.136***
training
[0.036] [0.043]
Finance and Corporate Dummy for Incorporated Company 0.132*** 0.144***
Governance
[0.040] [0.048]
Dummy for external audit of Financial statements 0.150*** 0.175***
[0.042] [0.046]
Others Control Age of the firm (log) 0.043** 0.045*
Variables
[0.019] [0.024]
Share of Imported inputs (fraction) 0.074 0.111*
[0.051] [0.059]
Observations 1461 1461
R-squared 0.5 0.57
Test of joint significance of IC Variables Prob > F = 0.0001 Prob > chi2 = 0.0061
Test of joint significance of ICVs and Plant level Prob > F = 0.0000 Prob > chi2 = 0.0000
controls
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%. (2) (2) The
dependent variable, Productivity in levels, is computed as the difference between log sales and log inputs multiplied by the
corresponding Cobb-Douglas production function coefficients in Table 5.3. The Single Step regression indicates that all the
parameters of the Cobb-Douglas specification from Table 5.1 and Table 5.3 are jointly estimated in the same regression since
the explanatory variables are correlated.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.5.2: Single-Step Unrestricted by Industry Estimation: Translog Specification
Dependent Variable: Productivity in levels (logs)
OLS Random Effects
Number of days spent in Inspection and Regulation related -0.068* -0.065
work
[0.041] [0.050]
Fraction of sales undeclared to the tax authority for tax -0.593** -0.620**
Red Tape, Corruption purposes
[0.250] [0.284]
and Crime
Payments to deal with bureaucracy "faster", percent of sales 0.013 0.015
[0.012] [0.014]
Number of criminal attempts suffered -0.018 -0.022
[0.012] [0.015]
Average duration of power outages (log) -0.024 -0.013
[0.049] [0.066]
Days to clear customs for imports (log) -0.119*** -0.116***
Infrastructure [0.041] [0.044]
Shipment Losses (fraction of Sales) -2.063** -1.415
[0.825] [1.398]
Dummy for Internet Access 0.111*** 0.146***
[0.037] [0.046]
Fraction of computer-controlled machinery 0.084 0.124
[0.086] [0.102]
Fraction of total staff engaged in R&D 0.580** 0.590**
[0.268] [0.297]
Quality, Innovation 0.105 0.101
and Labor Skills Dummy for ISO quality certification
[0.103] [0.098]
Fraction of total staff with secondary education or higher 0.056 0.101*
[0.062] [0.063]
Dummy for Training provided beyond "on the job" training 0.098*** 0.120***
[0.034] [0.041]
0.117*** 0.130***
Finance and Dummy for Incorporated Company
Corporate [0.038] [0.046]
Governance Dummy for external audit of Financial statements 0.116*** 0.131***
[0.038] [0.044]
Age of the firm (log) 0.032* 0.036
Others Control [0.018] [0.023]
Variables
Share of Imported inputs (fraction) 0.101** 0.117**
[0.048] [0.056]
Observations 1461 1461
R-squared 0.94 0.94
Test of joint significance Prob > F = 0.0083 Prob > chi2 = 0.0353
of IC Variables
Test of joint significance Prob > F = 0.0000 Prob > chi2 = 0.0000
of ICVs and Plant level controls
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(2) The dependent variable, Productivity in levels, is computed as the difference between log sales and log inputs multiplied
by the corresponding Translog production function coefficients in Table 5.3. The Single Step regression indicates that all the
parameters of the Translog specification from Table 5.2 and Table 5.3 are jointly estimated in the same regression since the
explanatory variables are correlated.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.5.3: Production Function Parameters from the Unrestricted Estimation by
Industry: Cobb Douglas Specification
INVESTMENT CLIMATE ASSESSMENT FOR GUATEMALA, HONDURAS AND NICARAGUA
Test for
Material Constant Returns
Industry Coefficients Labor(L) Input (M) Capital(K) to Scale
Apparel Cost share 0.41 0.49 0.10
OLS 0.418*** 0.478*** 0.098*** Prob > F = 0.8959
Random Effects 0.403*** 0.463*** 0.081*** Prob > chi2 = 0.0846
Beverages Cost share 0.30 0.58 0.12
OLS 0.595*** 0.377*** 0.094 Prob > F = 0.5007
Random Effects 0.563*** 0.391*** 0.085 Prob > chi2 = 0.7420
Chemicals/Rubber Cost share 0.33 0.59 0.08
OLS 0.387*** 0.597*** 0.085** Prob > F = 0.0179
Random Effects 0.454*** 0.523*** 0.094** Prob > chi2 =0.1331
Food/ Tobacco Cost share 0.35 0.55 0.11
OLS 0.516*** 0.517*** 0.034 Prob > F = 0.0051
Random Effects 0.658*** 0.366*** 0.051** Prob > chi2 =0.0248
Furniture/Wood Cost share 0.35 0.53 0.12
OLS 0.327*** 0.655*** 0.019 Prob > F = 0.9477
Random Effects 0.396*** 0.591*** 0.022 Prob > chi2 = 0.8243
Leather/ Shoes Cost share 0.37 0.53 0.10
OLS 0.866*** 0.285* 0.084*** Prob > F = 0.0467
Random Effects 0.790*** 0.286** 0.092 Prob > chi2 =0.2020
Nonmetallic Minerals Cost share 0.34 0.55 0.11
OLS 0.346*** 0.560*** 0.103*** Prob > F = 0.8921
Random Effects 0.473*** 0.436*** 0.106** Prob > chi2 =0.8308
Textiles Cost share 0.36 0.51 0.13
OLS 0.361*** 0.374*** 0.218* Prob > F = 0.5458
Random Effects 0.428*** 0.310*** 0.221** Prob > chi2 = 0.6433
Metal Products Cost share 0.38 0.51 0.11
OLS 0.352*** 0.567*** 0.055 Prob > F = 0.5050
Random Effects 0.402*** 0.506*** 0.06 Prob > chi2 = 0.5605
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(2) The cost shares of labor, materials and capital are calculated as averages of the plant-level cost shares of labor, materials
and capital for each industry using all plants in the 3 countries for years 2002 and 2001 (excluding outliers).
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.5.4: Production Function Parameters from the Unrestricted by Industry
Estimation: Translog Specification
INVESTMENT CLIMATE ASSESSMENT FOR GUATEMALA, HONDURAS AND NICARAGUA
Test for Test for
Material Capital CD* CRS
Labor Input (K) L2 M2 K2 L*M L*K M*K p values p values
(L) (M)
Appar
OLS 0.742*** -0.306 0.520*** 0.051** 0.126*** 0.022* - 0.106*** - 0.0000 0.0000
0.177*** 0.119***
R.E. 0.537** -0.234 0.563*** 0.013 0.120*** 0.019* - 0.123*** - 0.0000 0.0000
0.151*** 0.123***
Bev.
OLS 0.598*** 0.378*** 0.092 0 0 0 0 0 0 CD 0.3602
R.E. 0.566*** 0.391*** 0.091 0 0 0 0 0 0 CD 0.5588
Chem.
OLS 1.278*** 0.316 0.057 0.268*** 0.069*** 0 - 0 0 0.0001 0.0025
0.152***
R.E. 1.177*** 0.370* 0.069* 0.265*** 0.056** 0 - 0 0 0.0214 0.0634
0.139***
Food
OLS 2.139*** - 0.029 0.309*** 0.182*** 0 - 0 0 0.0000 0.0000
0.760*** 0.234***
R.E. 1.742*** - 0.053** 0.188*** 0.144*** 0 - 0 0 0.0000 0.0000
0.707*** 0.157***
Furn.
OLS 0.336*** 0.659*** 0.02 0 0 0 0 0 0 CD 0.7126
R.E. 0.407*** 0.597*** 0.023 0 0 0 0 0 0 CD 0.5302
Leather
OLS 0.878*** 0.288* 0.086*** 0 0 0 0 0 0 CD 0.0207
R.E. 0.816*** 0.285*** 0.096 0 0 0 0 0 0 CD 0.1025
NonMet.
OLS 0.305*** -0.356 0.096*** 0 0.086** 0 0 0 0 0.0288 0.0833
R.E. 0.415*** -0.508* 0.099** 0 0.090*** 0 0 0 0 0.0005 0.0022
Textiles
OLS 0.359** 0.374*** 0.225* 0 0 0 0 0 0 CD 0.5901
R.E. 0.429*** 0.311*** 0.232** 0 0 0 0 0 0 CD 0.7078
Metal
OLS 0.732** 0.036 0.216 0 0.102*** 0.026 -0.033 0 -0.043* 0.0294 0.0246
R.E. 0.846** -0.266 0.283 0 0.118*** 0.01 -0.041 0 -0.033 0.1375 0.1354
Notes:
(1) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(2) CD indicates that a Cobb-Douglas specification was estimated since it was preferred to the Translog specification.
(3) 0 indicates that the coefficient of that input in that industry was not significant and hence is restricted to be equal to 0.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.6.1: Two Step Unrestricted by Industry Estimation for Young and Old Firms
Dependent Variable: Unrestricted by Industry Solow Residuals in Levels (logs)
Young Plants Old Plants
Red Tape, Corruption and Number of days spent in Inspection and -0.077 -0.081*
Crime Regulation related work
[0.067] [0.046]
Fraction of sales undeclared to the tax authority -0.261 -0.481
for tax purposes
[0.582] [0.294]
Payments to deal with bureaucracy "faster", 0.037* 0.036**
percent of sales
[0.019] [0.014]
Number of criminal attempts suffered -0.009 -0.029**
[0.021] [0.012]
Infrastructure Average duration of power outages (log) -0.085 -0.097*
[0.098] [0.053]
Days to clear customs for imports (log) -0.168** -0.067
[0.074] [0.043]
Shipment Losses (fraction of Sales) -0.948 -2.321**
[1.037] [1.181]
Dummy for Internet Access 0.1 0.156***
[0.073] [0.044]
Quality, Innovation and Fraction of computer-controlled machinery -0.191 0.218**
Labor Skills
[0.193] [0.086]
Fraction of total staff engaged in R&D -0.369 0.773**
[0.567] [0.320]
Dummy for ISO quality certification 0.12 0.16
[0.196] [0.110]
Fraction of total staff with secondary education 0.09 0.06
or higher
[0.115] [0.075]
Dummy for Training provided beyond "on the 0.144** 0.093**
job" training
[0.067] [0.042]
Finance and Corporate Dummy for Incorporated Company 0.199** 0.132***
Governance
[0.087] [0.044]
Dummy for external audit of Financial 0.212*** 0.152***
statements
[0.073] [0.049]
Others Control Variables Age of the firm (log) 0.03 0.022
[0.076] [0.030]
Share of Imported inputs (fraction) -0.016 0.133**
[0.103] [0.055]
Observations 1461 1461
R-squared 0.27 0.27
Test of joint significance Prob > F = 0.0004 Prob > F = 0.0004
of IC Variables
Test of joint significance Prob > F = 0.0000 Prob > F = 0.0000
of ICVs and Plant level controls
Notes:
(1) All the coefficients in the table are obtained from a single regression.
(2) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(3) The regressions include a constant, industry dummies, country dummies and year dummies.
(4) Young plants are defined as those less than 5 years old, and old plants as those more than 5 years old.
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Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.6.2: Two Step Unrestricted by Industry Estimation for Small and Large Firms
Dependent Variable: Unrestricted by Industry Solow Residuals in Levels (logs)
Small Plants Large Plants
Red Tape, Corruption and Number of days spent in Inspection and -0.075 -0.089
Crime Regulation related work
[0.048] [0.060]
Fraction of sales undeclared to the tax authority -0.491* -0.322
for tax purposes
[0.283] [0.499]
Payments to deal with bureaucracy "faster", 0.033** 0.032*
percent of sales
[0.015] [0.017]
Number of criminal attempts suffered -0.016 -0.039***
[0.016] [0.014]
Infrastructure Average duration of power outages (log) -0.077 -0.085
[0.059] [0.080]
Days to clear customs for imports (log) -0.088* -0.06
[0.045] [0.057]
Shipment Losses (fraction of Sales) -1.733* -1.412
[1.008] [1.255]
Dummy for Internet Access 0.125** 0.181***
[0.049] [0.060]
Quality, Innovation and Fraction of computer-controlled machinery 0.06 0.174*
Labor Skills
[0.138] [0.105]
Fraction of total staff engaged in R&D 0.530* -0.183
[0.301] [1.395]
Dummy for ISO quality certification 0.082 0.206*
[0.153] [0.111]
Fraction of total staff with secondary education 0.072 0.016
or higher
[0.078] [0.103]
Dummy for Training provided beyond "on the 0.138*** 0.069
job" training
[0.047] [0.053]
Finance and Corporate Dummy for Incorporated Company 0.148*** 0.126**
Governance
[0.053] [0.060]
Dummy for external audit of Financial 0.166*** 0.185***
statements
[0.059] [0.057]
Others Control Variables Age of the firm (log) 0.052* 0.055**
[0.029] [0.024]
Share of Imported inputs (fraction) 0.137** 0.072
[0.063] [0.077]
Observations 1461 1461
R-squared 0.26 0.26
Test of joint significance Prob > F = 0.0000 Prob > F = 0.0000
of IC Variables
Test of joint significance Prob > F = 0.0000 Prob > F = 0.0000
of ICVs and Plant level controls
Notes:
(1) All the coefficients in the table are obtained from a single regression.
(2) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(3) The unrestricted by industry Solow Residual is obtained using cost shares for inputs (labor, materials and capital)
obtained as averages for each of the industries using plants in the 3 countries for years 2001 and 2002.
(4) The regressions include a constant, industry dummies, country dummies and year dummies.
(5) Small plants are defined as those having up to 25 employees and large plants as those having 26 or more employees.
91
Econometric Methodologies for Investment Climate Assessments on Productivity
Table C.7.1: Covariance Terms by Country of the Olley -Pakes Decomposition from
Different Productivity Measures
Productivity Measures Guatemala Honduras Nicaragua
Solow Residual of Restricted 0.3488 -0.0731 -0.0534
Model
Cobb Douglas Estimated Productivity of 0.234 -0.0341 -0.0439
Restricted Model
Translog Estimated Productivity of 0.2786 -0.0836 0.0399
Restricted Model
Solow Residual of Unrestricted 0.1335 -0.1354 -0.0098
Model
Cobb Douglas Estimated Productivity of 0.4010 0.0853 0.7518
Unrestricted Model
Translog Estimated Productivity of 1.1751 0.1631 -0.1717
Unrestricted Model
Table C.7.2: Covariance Terms by Country and Plant Size of the Olley-Pakes
Decomposition from Different Productivity Measures
Productivity Measures Firm Size Guatemala Honduras Nicaragua
Solow Residual of Restricted Small 0.4927 -0.1579 0.0507
Model Large 0.2832 -0.0322 -0.1072
Cobb Douglas Estimated Productivity Small 0.2825 -0.0853 0.0028
of Restricted Model
Large 0.1076 -0.0032 -0.0722
Translog Estimated Productivity of Small 0.082 -0.0655 0.0237
Restricted Model
Large 0.1396 -0.0051 -0.0261
Solow Residual of Unrestricted Small 0.5617 -0.1370 0.0410
Model
Large 0.1743 -0.1208 -0.0234
Cobb Douglas Estimated Productivity Small 0.2921 -0.1039 0.1259
of Unrestricted Model
Large 0.2127 0.0443 0.5322
Translog Estimated Productivity of Small 0.0412 -0.2192 -0.0223
Unrestricted Model
Large 0.6798 0.1868 0.0387
92
Econometric Methodologies for Investment Climate Assessments on Productivity
Table D: Elasticities or Semi-elasticities and Percentage R-square Productivity
Contribution of Each Explanatory Variable, after Controlling for the Other IC and
Plant Control Variables
Restricted OLS Estimation Unrestricted OLS Estimation
1 step 1 step
Solow Res. Cobb Douglas Translog Solow Res. Cobb Douglas Translog
Red Tape, Corruption and Crime
Number of days spent in Inspection and -0.097** -0.099** -0.058 -0.101** -0.107** -0.068*
Regulation related work
0.76% 0.79% 0.26% 0.81% 0.97% 0.45%
Fraction of sales undeclared to the tax authority -0.601** -0.612** -0.416* -0.676** -0.767*** -0.593**
for tax purposes
0.77% 0.81% 0.65% 0.97% 1.31% 0.92%
Payments to deal with bureaucracy "faster", 0.031** 0.033*** 0.015 0.031** 0.030** 0.013
percent of sales
0.76% 0.84% 0.18% 0.74% 0.75% 0.16%
Number of criminal attempts suffered -0.029** -0.031*** -0.018 -0.032*** -0.029** -0.018
0.73% 0.85% 0.21% 0.88% 0.75% 0.36%
Infrastructure
Average duration of power outages (log) -0.095* -0.085* -0.072 -0.088* -0.075 -0.024
0.35% 0.29% 0.18% 0.30% 0.23% 0.03%
Days to clear customs for imports (log) -0.097** -0.106** -0.125*** -0.105*** -0.105** -0.119***
0.93% 1.11% 1.91% 1.08% 1.14% 1.67%
Shipment Losses (fraction of Sales) -1.860** -2.119** -1.229 -1.948** -2.530*** -2.063**
0.20% 0.26% 0.18% 0.22% 0.38% 0.30%
Dummy for Internet Access 0.147*** 0.144*** 0.119*** 0.139*** 0.128*** 0.111***
1.51% 1.47% 4.99% 1.35% 1.28% 1.06%
Quality, Innovation and Labor Skills
Fraction of computer-controlled machinery 0.119 0.13 0.132* 0.117 0.131 0.084
0.14% 0.17% 0.47% 0.13% 0.18% 0.09%
Fraction of total staff engaged in R&D 0.594** 0.667** 0.581** 0.589** 0.607** 0.580**
0.40% 0.51% 0.02% 0.39% 0.44% 0.47%
Dummy for ISO quality certification 0.154 0.167 0.024 0.142 0.176* 0.105
0.27% 0.32% 0.06% 0.23% 0.37% 0.16%
Fraction of total staff with secondary education 0.036 0.048 0.03 0.033 0.054 0.056
or higher
0.03% 0.06% 0.02 0.03% 0.08% 0.10%
Dummy for Training provided beyond "on the 0.117*** 0.105*** 0.089*** 0.116*** 0.110*** 0.098***
job" training
0.95% 0.78% 2.15% 0.94% 0.90% 0.81%
Finance and Corporate Governance
Dummy for Incorporated Company 0.150*** 0.140*** 0.115*** 0.146*** 0.132*** 0.117***
1.48% 1.31% 5.19% 1.41% 1.21% 1.12%
Dummy for external audit of Financial 0.168*** 0.159*** 0.121*** 0.170*** 0.150*** 0.116***
statements
1.80% 1.64% 4.06% 1.84% 1.52% 1.06%
Others Control Variables
Age of the firm (log) 0.050** 0.049** 0.046** 0.051*** 0.043** 0.032*
0.48% 0.46% 0.93 0.50% 0.37% 0.23%
Share of Imported inputs (fraction) 0.113** 0.101** 0.096** 0.110** 0.074 0.101**
0.55% 0.45% 2.32% 0.53% 0.25% 0.55%
R-Squared 0.2 0.2 0.36 0.26 0.51 0.94
Notes:
(1) Robust standard errors shown in parentheses under coefficient estimates
(2) Significance is given by robust standard errors. * significant at 10%; ** significant at 5%; *** significant at 1%.
(3) The regressions include a constant, industry dummies, country dummies and year dummies.
93
Econometric Methodologies for Investment Climate Assessments on Productivity
Table E.1: Guatemala
% Average (log) Productivity Gains and Losses in Guatemala due to Investment Climate (IC)
Restricted OLS Estimation Unrestricted OLS Estimation
1 Step 1 Step
Solow Res. Cobb Douglas Translog Solow Res. Cobb Douglas Translog
Red Tape, Corruption and Crime
Number of days spent in Inspection and -5.89** -5.61** -1.97 -6.09** -6.23** -2.74*
Regulation related work
Fraction of sales undeclared to the tax -3.31** -3.16** -1.73* -3.71** -4.04*** -2.18**
authority for tax purposes
Payments to deal with bureaucracy 2.83** 2.79*** 0.78 2.8** 2.62** 0.78
"faster", percent of sales
Number of criminal attempts suffered -2.13** -2.14*** -0.65 -2.32*** -2.0** -0.9
Cumulative Contribution -8.5 -8.12 -3.57 -9.32 -9.65 -5.04
Cumulative Absolute Contribution 14.16 13.7 5.15 14.92 14.89 6.6
Infrastructure
Average duration of power outages (log) -1.65* -1.40* -0.67 -1.53* -1.25 -0.27
Days to clear customs for imports (log) -5.39** -5.5** -4.42*** -5.82*** -5.58** -4.4***
Shipment Losses (fraction of Sales) -0.74** -0.79** -0.4 -0.77** -0.96*** -0.55**
Dummy for Internet Access 2.61*** 2.39*** 2.74*** 2.46*** 2.16*** 1.32***
Cumulative Contribution -5.17 -5.3 -2.75 -5.66 -5.63 -3.9
Cumulative Absolute Contribution 10.39 10.08 8.23 10.58 9.95 6.54
Quality, Innovation and Labor Skills
Fraction of computer-controlled machinery 0.22 0.23 0.23* 0.22 0.24 0.11
Fraction of total staff engaged in R&D 0.7** 0.74** 0.08** 0.69** 0.69** 0.46**
Dummy for ISO quality certification 0.13 0.13 0.03 0.12 0.14* 0.06
Fraction of total staff with secondary 0.33 0.42 0.14 0.31 0.49 0.35
education or higher
Dummy for Training provided beyond "on 1.57*** 1.33*** 1.35*** 1.56*** 1.42*** 0.88***
the job" training
Cumulative Contribution 2.95 2.85 1.83 2.9 2.98 1.86
Finance and Corporate Governance
Dummy for Incorporated Company 2.1*** 1.84*** 2.27*** 2.04*** 1.76*** 1.1***
Dummy for external audit of Financial 1.52*** 1.35*** 1.31*** 1.54*** 1.3*** 0.7***
statements
Cumulative Contribution 3.62 3.19 3.58 3.58 3.06 1.8
Others Control Variables
Age of the firm (log) 3.35** 3.07** 2.66** 3.42*** 2.73** 1.42*
Share of Imported inputs (fraction) 0.94** 0.8** 1.1** 0.92** 0.59 0.57**
Cumulative Contribution 4.29 3.87 3.76 4.34 3.32 1.99
Grand Total Contribution -3.35 -3.51 2.85 -4.16 -5.83 -3.32
Grand Total Absolute Contribution 35.41 33.69 22.53 36.32 34.2 18.79
Note: (1) The asterisks correspond to the significance level of the variables in the corresponding OLS regression: * significant at
10%; ** significant at 5%; *** significant at 1%.
94
Econometric Methodologies for Investment Climate Assessments on Productivity
Table E.2: Honduras
% Average (log) Productivity Gains and Losses in Honduras due to Investment Climate (IC)
Restricted OLS Estimation Unrestricted OLS Estimation
1 Step 1 Step
Solow Res. Cobb Douglas Translog Solow Res. Cobb Douglas Translog
Red Tape, Corruption and Crime
Number of days spent in Inspection and -6.34** -6.03** -2.15 -6.66** -6.74** -2.9*
Regulation related work
Fraction of sales undeclared to the tax -4.48** -4.28** -2.37* -5.13** -5.51*** -2.91**
authority for tax purposes
Payments to deal with bureaucracy 1.84** 1.81*** 0.51 1.85** 1.72** 0.5
"faster", percent of sales
Number of criminal attempts suffered -0.82** -0.83*** -0.25 -0.91*** -0.78** -0.34
Cumulative Contribution -9.8 -9.33 -4.26 10.85 -11.31 4.97
Cumulative Absolute Contribution 13.48 12.95 5.28 14.55 14.75 6.65
Infrastructure
Average duration of power outages (log) -2.89* -2.45* -1.19 -2.73* -2.21 -0.47
Days to clear customs for imports (log) -3.73** -3.8** -3.09** -4.1*** -3.89** -2.99***
Shipment Losses (fraction of Sales) -0.74** -0.79** -0.41 -0.79** -0.97*** -0.54**
Dummy for Internet Access 1.74*** 1.6*** 1.85*** 1.7*** 1.45*** 0.87***
Cumulative Contribution -5.62 -5.44 -2.84 -5.92 -5.62 -3.13
Cumulative Absolute Contribution 9.1 8.64 6.54 9.32 8.52 4.87
Quality, Innovation and Labor Skills
Fraction of computer-controlled machinery 0.2 0.21 0.22* 0.2 0.22 0.1
Fraction of total staff engaged in R&D 0.4** 0.42** 0.4** 0.4** 0.39** 0.25**
Dummy for ISO quality certification 0.18 0.18 0.05 0.18 0.2* 0.08
Fraction of total staff with secondary 0.29 0.37 0.12 0.27 0.42 0.3
education or higher
Dummy for Training provided beyond "on 1.34*** 1.13*** 1.16*** 1.35*** 1.22*** 0.73***
the job" training
Cumulative Contribution 2.41 2.31 1.95 2.4 2.45 1.46
Finance and Corporate Governance
Dummy for Incorporated Company 0.91*** 0.79*** 1*** 0.9*** 0.77*** 0.47***
Dummy for external audit of Financial 1.71*** 1.52*** 1.49*** 1.76*** 1.47*** 0.77***
statements
Cumulative Contribution 2.62 2.31 2.49 2.66 2.24 1.24
Others Control Variables
Age of the firm (log) 2.95** 2.69** 2.37** 3.06*** 2.42** 1.23*
Share of Imported inputs (fraction) 0.78** 0.66** 0.92** 0.77** 0.49** 0.46**
Cumulative Contribution 3.73 3.35 3.29 3.83 2.91 1.69
Grand Total Contribution -6.66 -6.8 0.63 -7.88 -9.33 -4.39
Grand Total Absolute Contribution 31.34 29.56 19.55 32.76 30.87 15.91
Note: (1) The asterisks correspond to the significance level of the variables in the corresponding OLS regression: * significant at 10%; **
significant at 5%; *** significant at 1%.
95
Econometric Methodologies for Investment Climate Assessments on Productivity
Table E.3: Nicaragua
% Average (log) Productivity Gains and Losses in Nicaragua due to Investment Climate (IC)
Restricted OLS Estimation Unrestricted OLS Estimation
1 Step 1 Step
Solow Res. Cobb Douglas Translog Solow Res. Cobb Douglas Translog
Red Tape, Corruption and Crime
Number of days spent in Inspection and -7.9** -7.5** -2.64 -8.31** -8.41** -3.82*
Regulation related work
Fraction of sales undeclared to the tax -5.41** -5.16** -2.82* -6.18** -6.65*** -3.71**
authority for tax purposes
Payments to deal with bureaucracy "faster", 1.49** 1.47*** 0.41 1.5** 1.39** 0.43
percent of sales
Number of criminal attempts suffered -0.72** -0.72*** -0.22 -0.8*** -0.68** -0.31
Cumulative Contribution -12.54 -11.91 -5.27 -13.79 -14.25 -7.41
Cumulative Absolute Contribution 15.52 14.85 6.09 16.79 17.13 8.27
Infrastructure
Average duration of power outages (log) -2.83* -2.39* -1.14 -2.66* -2.15 -0.49
Days to clear customs for imports (log) -3.91** -3.98** -3.2*** -4.29*** -4.07** -3.31***
Shipment Losses (fraction of Sales) -0.86** -0.92** -0.47 -0.92** -1.13*** -0.67**
Dummy for Internet Access 1.54*** 1.41*** 1.61*** 1.46*** 1.29*** 0.81***
Cumulative Contribution -6.06 -6.86 -3.2 -6.41 -6.06 -3.66
Cumulative Absolute Contribution 9.14 9.68 6.42 9.33 8.64 5.28
Quality, Innovation and Labor Skills
Fraction of computer-controlled machinery 0.12 0.13 0.13* 0.13 0.13 0.06
Fraction of total staff engaged in R&D 0.53** 0.56** 0.06** 0.53** 0.52** 0.36**
Dummy for ISO quality certification 0.19 0.19 0.05 0.17 0.2* 0.09
Fraction of total staff with secondary 0.36 0.46 0.15 0.34 0.53 0.4
education or higher
Dummy for Training provided beyond "on 1.16*** 0.98*** 0.99*** 1.17*** 1.06*** 0.67***
the job" training
Cumulative Contribution 2.36 2.32 1.38 2.34 2.44 1.58
Finance and Corporate Governance
Dummy for Incorporated Company 1.35*** 1.18*** 1.47*** 1.34*** 1.15*** 0.74***
Dummy for external audit of Financial 1.3*** 1.16*** 1.12*** 1.34*** 1.12*** 0.62***
statements
Cumulative Contribution 2.65 2.34 2.59 2.68 2.27 1.36
Others Control Variables
Age of the firm (log) 3.51** 3.2** 2.78** 3.63*** 2.88** 1.54*
Share of Imported inputs (fraction) 1.06** 0.89** 1.24** 1.05** 0.67 0.67**
Cumulative Contribution 4.57 4.09 4.02 4.68 3.55 2.21
Grand Total Contribution -9.02 -10.02 -0.48 -10.5 -12.05 -5.92
Grand Total Absolute Contribution 34.24 33.28 20.5 35.8 34.03 18.7
Note:
(1) The asterisks correspond to the significance level of the variables in the corresponding OLS regression: * significant at
10%; ** significant at 5%; *** significant at 1%.
96
Econometric Methodologies for Investment Climate Assessments on Productivity
Figure 7
Olley-Pakes Decomposition of Solow Residual
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Guatemala Honduras Nicaragua
-0.5
Weighted TFP Unweighted TFP Covariance
Figure 8
Olley-Pakes Decomposition of Solow Residual
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Small Large Small Large Small Large
-0.5
Guatemala Honduras Nicaragua
Weighted TFP Unweighted TFP Covariance
97
Econometric Methodologies for Investment Climate Assessments on Productivity
Figure 9
Olley-Pakes Decomposition of Industry
Solow Residual - Guatemala
6
5
4
3
2
1
0
parel s co ood talProducts
les
Ap Shoes nerals exti
T
be
emicals/Rub
Beverager/Plastics/Tobaciture/Wather/allicMi
Food rn
Fu Lenmet Me
No
Ch
Weighted TFP Unweighted TFP Covariance
Olley-Pakes Decomposition of Industry
Solow Residual - Honduras
5
4
3
2
1
0
-1
AppareBeverageber/PlasFood Tobaccoure/WoodMi
l s tics
/ nerals Textiles
Chemicals/Rub rnit talProducts
Fu etallic Me
nm
No
Weighted TFP Unweighted TFP Covariance
Olley-Pakes Decomposition of Industry
Solow Residual - Nicaragua
5
4
3
2
1
0
-1
ApparBeverageer/Plastics/Tobacture/Woodr/ShoesMi
el s co
emicals/Rubb Food rni tal
Fu Leatheetallic nerals extilesProducts
T
Me
nm
No
Ch
Weighted TFP Unweighted TFP Covariance
98
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