Report No. 47193GT
Guatemala
Investment Climate Assessment
(In Two Volumes) Volume II: Background Notes on Productivity
June 26, 2008
Finance and Private Sector Unit
Poverty Reduction and Economic Management Unit
Latin America and the Caribbean Region
Document of the World Bank
TABLE CONTENTS OF
SECTION 1. METHODOLOGICAL NOTES ................................................................... 1
1. REPLACEMENT STRATEGY FOR MISSINGPRODUCTION F U N C T I O N VARIABLES ...................... 1
2. ENDOGENEITY PROBLEMS ............................................................................................................. 6
SECTION 2. PRODUCTIVITY RESULTS ........................................................................ 8
SECTION 3. INTERPRETATIONS OF THE PRODUCTIVITY RESULTS ...............40
1. SECURITY COSTS AND THEIREFFECT ON PRODUCTIVITY ........................................................ 40
2. EFFECTSICBLOCKVARIABLES PRODUCTrVITY...........................................................
OF ON 42
3. GENDER IMPACT ............................................................................................................................ 43
4. DIFFERENCES INSAMPLING(2003VS. 2007DATI) .................................................................. 44
Table 1.1: Observations available for regression analysis in Guatemala by Industry and size and percentage o f
observations lost .............................................................................................
Table 1.2: Observations available for regression analysis in Guatemala by Industry and region and percentage
of observations lost ..
Table 1.3: Extended
Table 2.1: General Info
Table 2.2: Investment climate (IC) and control (C) variables ....
Table 2.3: Investment climate (IC) and control (C) variables (c
Table 2.4: Investment c
Table 2.5: Investment climate (IC) and control (C) variables
Table 2.6: Total number o f observations before and after cleaning missingvalues and outliers in production
function CpF)variables .......................................................................................................................................................... 14
Table 2.7: Representativeness of production function variables before and after cleaning for missing values
and outliers ............... ........................... ............. ......
Table 2.8: Total number o f observations and response rate o f I C and C variables inthe original sample ..........15
16
Table 2.9: Correlation matrix among productivity measures....
Table 2.10 Correlation
Table 2.11: I C A elastic
Table 2.12: I C A elas
industry,region and size ..................
Table 2.13: Percentag
Aggregate Productivity
Table 2.14: I C impact on the decomposition by inputs of the efficiency term of the Olley and Pakes
decomposition inlogs ........................................................................................................................................................... 24
Table 2.15: Twostage least squares (2SLS) estimation o f employment equation.
Table 2.16: Two stage least squares (2SLS) estimation o f real wages equation ......
Table 2.17: T w o stage least squares (2SLS) estimation of probability o f exporting equation ................................ 27
Table 3.2: Percentage of female workers instaff, average by industry
Table 3.3: Percentage o f female workers instaff, average by size ...........................................
Table 3.6: Differences inSampling (2003 vs. 2007) ...................................................................................................... 44
i
Figures
Figure 2.1: Olley and Pakes Decomposition in Levels by Industry and Region of Aggregate Productivity
................................................................................... 29
..................................................... .......................
.........................................................................................
Figure 2.5: Relative I C effects by groups of variab
efficiency (mixed O&P decomposition and simulations of a 20% improvement inI C and C variables) .............30
Figure 2.6: Relative I C effects on aggregate productivity @fixed O&P decomposition)..
Figure 2.7: Relative I C effects on average productivity (Ahxed O&P decomposition) ........................
Figure 2.8: Relative I C effects on efficiency @fixed O&P decomposition)...........................................
Figure 2.9: Relative I C effects by groups of variables on average productivity (Decomposition in Logs); b y
sue ........................................... ............................
Figure 2.10: I C A Percentage Absolute Contrib
Figure2.11: Relative IC effects on average loge
Figure 2.12: Relative I C effects on average logr ................................................................... 35
Figure2.13: Relative IC effects on the probab
Figure 2.14 Relative I C A effects on the probab
Figure 2.15: Relative I C effects by groups of va
Figure2.16: RelativeI C A effects by groups ofvariables on average logrealwages; by size ................................ 37
Figure 2.17: Relative ICA effects by groups of variables on the probability of exporting; b y size
Figure2.18: Relative ICA effects by groups ofvariables on the probability of rece
Figure 2.19: Managers' perceptions; percentage of firms that considers each one of the
a severe obstacle to firms' economic performance .................................................
This report was preparedby Stefka Slavova andJorge Peiia.
ii
Section 1. METHODOLOGICAL NOTES
1. REPLACEMENTSTRATEGY FORMISSINGPRODUCTIONFUNCTIONVARIABLES
The sample size o f the Guatemala Enterprise Survey used inproductivity regressions (see
Appendix 2) i s augmented by approximately 20% through replacing missing information
on sales/workers/capital/materials with the corresponding locationsectorsize averages
(Tables 1.6 and 1.7) in Appendix 2. Such replacement i s problematic in crosssectional
data as it could be biasing the estimates.
Incomplete data i s a common problem that standard econometric and statistical methods
have nothing to say about or how to solve it, and constitutes a continuous source o f
problems for researchers. The problem of having too many missing values in a dataset
may bias the representativeness of the data, causes losses o f efficiency in regression
analysis and, like inthe case o f Enterprise Surveys, implies losing a large number of very
expensive interviews, both inpecuniary and time terms.
The Guatemala ICA is not an exception. Table 1.1 shows the distribution of the missing
values by industry and size. From this table it i s clear that for instance in the food sector
we would have lost 26.3% o f observations for small firms, while after replacing missing
values, we only lose 5%. This problem i s common to all sectors and sizes, and it i s clear
that the pattern o f missing values does not follow a clear correlation by size of firms or
industries; all the industries and sizes suffer from this problem.
Table 1.1: Observations available for regression analysis inGuatemala by Industry and
size and percentageof observations lost
Total Without
replacing 116 17.1 93 21.8 53 23.2 262 20.1
With replacing 136 2.9 117 1.7 65 5.8 318 3.0
1
The same can be said about the pattern of missing values by industries and regions o f
Table 1.2. Again, the pattern of missing values i s uncorrelated with the distribution o f
industries and regions. Both firms located in Guatemala City and firms located inthe rest
o f the country refusedto answer the productivity questions of the I C survey. For instance
we lost 21% o f `food' firms located in Guatemala City and 21.4% of firms of the same
sector located in the rest o f the country, while after replacing missing observations we
only lost 4.8% o f the food firms in Guatemala City and 3.6% inthe rest o f the country.
Table 1.2: Observations available for regression analysis inGuatemala by Industry
There are a number o f approaches to deal with the problem of missing data. These
approaches may be grouped into two different families o f methods: maximum likelihood
and multiple imputation, see Allison (2001) and Little and Rubin (1987) for a review.
The objective of these methods i s not to augment the sample size, but to maintain the
sample representativeness and to gain efficiency inthe estimation.
Our method of imputing missing data, which we call ZCA method, shares the expectation
step of the ExpectationMaximization (EM) algorithm proposed in the seminal paper o f
Dempster, Laird and Rubin (1977), method that, within the maximum likelihood
approaches, has been widely applied in several scientific fields, see McLachlan and
Krishnan (1997). In particular, the replacement strategy used in the ICA of Guatemala
departs from the expectation o f the production function variables conditional on the
industry, region and size the corresponding observation belongs to, in other words we
replace the missing value with the expectation o f the distribution of the variable,
conditional on the information on industry, region and size according to the equation:
E(JiIDT,i 7OI,i7DS,i=PO +P T ,J DT,i+PT,JOI,i +PT ,.I
DS,i = L,
` 9 7
(1)
Y, L,M and K represent output, labor, materials and capital and DT,DI and Ds are time,
industry and size dummies respectively. Estimated values to replace incomplete data are
given by
2
The ICA method has the advantage o f imputingmissing data without a population model,
which i s the main advantage over EM algorithm.' Imputation of missing data without a
population model i s the main characteristic of the second family o f approaches: the
multiple imputation methods. Our strategy is, in fact, a general multiple imputation
method in which we assume that each imputed variable can be represented as a linear
function of other variables (dummies of industry, region and size) and therefore the fitted
values can be used to replace missing data. The second condition that needs to hold for
the multiple imputation method to work well i s that all the variables, including those
replaced and those used to replace them, have normal distributions. Although these are
strong assumptions the multiple imputation method seems to work well even when the
variables have distributions that are manifestly not normal, see Schafer (1997).
Therefore, under these assumptions our method leads to a consistent estimation o f the
ICAparameters, but at the same time it can be argued that a more efficient method can be
used. Notice that by imputing missing values we are modifying the population
distribution of replaced variables. In particular, if the two conditions mentioned in the
previous paragraph hold, the sample average of the modified distribution of the variable
converges to the population expectation. Unfortunately, this does not hold for the case of
the standard deviation. With the replacement strategy we are reducing the variability of
the distribution and therefore any statistical inference will be based in downwardbiased
standard errors.
To correct for this, a plausible and elegant solution is to do a resampling a given number
of times and to replace the missingdata ineach sample so we can obtain a distribution of
the estimators of interest under different replacements o f the missing data, and thus use
the bootstrap standard errors to do statistical inference. As Escribano et al. (2008) show,
the replacing strategy proposed introduces enough variability in the distribution which
allows making correct statistical inferences. The variability comes from the number o f
industriesused multiplied by the number of regions and sizes.
When the two assumptions mentioned above do not hold, our replacement strategy i s no
longer consistent. Very little can be said about the asymptotic distributions o f the
estimators obtained under such circumstances. In general, in these cases we can treat our
replaced variables as variables measured with error. Thus, the parameters obtained from
the regression analysis would be consequently downward biased, and the magnitude of
the bias will depend on the standard deviation of the error term relative to the standard
deviation of the variable and the proportion of replaced values.
Another question that needs to be taken into account i s the nature of the mechanism that
generates the missing data. Our replacement strategy leads to unbiased results when the
pattern of missing values i s completely missing at random (CMAR), missing at random
(MAR) (no relation with the population model), and inthe exogenous sampling selection,
in which the pattern of missing values depends on the explanatory variables of the
1The EM algorithm imputes missingdata conditional o n a given population model, and therefore chooses the
candidate values to replace the missing cells that maximize the likelihood fmction conditional o n a vector of
parameters of that model.
3
population model. To see it, let us suppose the next extended production function as in
Escribano and Guasch (2005 and 2008):
yi, =a,+aLlj, +aMmj,+aKki,+P,ic,,, +P2i~2,it +...+P,ic,,, +qc,,, +62~2,it+...+6jcj,it+ui,
(3)
where y, 1, m and k represents output, labor, materials and capital all in logs, ic are the
investment climate variables and c are other control variables. Let the pattern o f missing
values for each observation i at moment t be given by sit, where sir=O if missing value and
1otherwise. So what we observe is:
Ifthe pattern of missingvalues isM.A.R,then the necessary conditions for equation (4) to
be identified are
E(s,u,,)=0
E[(si,Jit)(si,uil)] E[(sitJituil)] 0
= = J =l,m,k,ic,,...,ic,,c, ,...,ck
Andinthe case of exogenoussample selection we need that
That is, for the identification condition in this case to hold we need to control for any
exogenous variable affecting the pattern o f missing values, and this i s the way we shall
proceed in the estimation o f the productivity equations. Note that in these cases the
complete case (deletion of observations with any missing value) also leads to unbiased
I C A parameters, although at the cost o f losing efficiency and in some cases the
representativeness of the original sampling frame.
If the pattern of missing values is endogenously determined (it is correlated with output
('y)in equation (4)) and there is selfselection in our model, the replacing strategy may
lead to inconsistent estimates. Inthese cases one has to implement the Heckman modelto
correct for selfselection, since OLS applied either on the complete case or on the sample
with replacement estimates biasedparameters.
Table 1.3 offers a review o f the different methods that can be used to estimate the
parameters of equation (4). The first column uses our method or the ICA method, the
second column i s the same method with bootstrap standard errors with 1500 replications.
The third column i s the complete case and the last column i s the Heckman model. There
are no great differences in the standard errors between column 1 and 2, which supports
the idea that the ICA method can be used to make inferences .when there is enough
variability by industry, region and size. The complete case of column 3 leads to slightly
4
different production function and ICA parameters, what poses some doubts about the
representativeness o f the complete case, notice that in this column the significance of IC
variables i s generally reduced, although there are no significant changes in the magnitude
o f the parameters. Finally, Heckman's Lambda in the last column i s not significant,
indicating that the selection model does not make sense. Anyway, even in this case the
Heckman model leadto similar results as the ICA method.
Table 1.3: Extendedproductionfunction of Guatemala
ard Complete Heckman
case selection model
[0.014] [0.002] i0.002
0.097 0.084 0.06!
[0.050] rO.0651 [0.054] r0.054
ter from public sources (b) 0.002** 0.002 0.002*** 0.003**:
[0.001] [0.002] [0.001] [0.001
O.OlO*** 0.010 0.011*** 0.011*:
[0.006] [0.007] [0.006] [0.006
0.073 0.073 0.179 0.16~
[0.116] rO.1161 [0.128] [0.125
0.208 0.208*** 0.262" 0.25)
[O. 1511 [0.049] [0.155] [0.176
0.524*** 0.524*** 0.386*** 0.405 ** :
[O. 1241 [0.053] [0.109] [0.098
0.097 0.097 0.046 0.03~
[0.0791 [0.151] [0.084] [0.086
nager's time in bureaucratic issues (a) 0.026** 0.026*** 0.017' 0.019*'
rking capital financed by informal sources
ngcapital financed by nonbank 0.004 0.004 0.005** 0.001
cia1institutions [0.003] [0.021] [0.003] [0.006
mmy for checking or saving account 0.166 0.166 0.142 0.16:
[0.102] [0.115] [0.119] [0.118
mmy for credit line 0.161* 0.161*** 0.126 O . l l r
[0.057] 10.0821 [0.085
5
I C A method.
Bootstrap standard Complete Heckman
ICAmethod errors, 1500 case selection model
repetitions.
Dummyfor I S 0 Certification (b) 0.297' 0.297* 0.499*** 0.422***
[0.176] [0.160] [0.182] [0.154]
Percentage of female workers instaff (b) 0.002 0.002* 0.002 0.002
[0.001] [0.0011 [0.0021 [0.002]
Training to nonproduction workers (b) 0.002 0.002** 0.002 0.002*
[0.001] [0.001] [O.OOl] [0.001]
Share of importedinputs (b) 0.003** 0.003 0.002* 0.003**
[O.OOl] [0.0021 [0.0011 [0.001]
Percentage of unionized workforce 0.021** 0.021*** 0.019* 0.015
[0.009] [0.007] [0.011] [0.015]
Dummyfor FDI 0.25 0.250*** 0.111 0.084
[0.190] [0.024] [0.196] [0.175]
Share of exports 0.006** 0.006*** 0.006** 0.006***
[0.002] [0.002] [0.002] [0.002]
Dummy for large firm 0.446*** 0.446*** 0.441** 0.447***
[0.169] [0.035] [0.175] [0.161]
Constant 1.845** 1.845 1.346 1.441***
[0.928] [0.986] [0.753]
Observations 318 318 263 277
Rsquared 0.891 0.891 0.91
Heckman's Lambda (inverse of Mill'sratio) 0.46C
[0.408]
2. ENDOGENEITY PROBLEMS
Our regressions of TFP in IC variables might suffer from endogneity problems, i.e. the
use o f explanatory variables which might be correlated with the error term and thus result
in biased and inconsistent estimates. Some of the literature emphasizes the potential
endogeneity of access to finance variables to firm productivity and other measures o f
firmperformance.
Endogeneity i s yet an unresolved issue in econometrics. In fact it i s difficult to test
whether a variable i s endogenous or not, and too often one has to rely in economic
intuition and to make aprioristic assumptions on the plausible exogeneity o f the variables.
Moreover, in the context of IC data it i s difficult to use Hausmantype tests to check for
the presence of endogeneity given the low power o f these tests and the highrejection rate
of the nullhypotheses.
A possible solution to the endogeneity of IC variables i s to use the industryregionsize
averages. While this endogeneity correction has been proven to work well when there are
no industryregionsize (IRS) processes correlated with the error term, sometimes it i s
difficult to get good instruments o f crude plantlevel IC variables. Some o f the
explanatory variables in table 2.11 in Appendix 2 are in this form. For other variables
their IRS average i s not a good instrument and we have to rely on the crude variable.
Note that excluding these variables from the regression, although they were endogenous,
6
i s not a good solution provided we are modeling the expectation o f TFP on the whole
investment climate firms are facing and the exclusion o f any relevant variable may result
in a omitted variables problem which is another serious problem that causes biases inthe
rest of the parameters o f the model.
Our models should be interpreted in terms of conditional expectations. What we are
doing i s modeling the conditional expectation o f productivity given all the firmlevel
information we have on the investment climate which has an effect on firms'
performance and efficiency. This means that the coefficients o f IC variables cannot be
interpreted in causal terms; the term reverse causality does not make sense in our model,
the data lack the optimal properties to do Grangercausality inference. Rather, the
coefficients should be interpreted as marginal effects on the conditional expectation o f
productivity. Whether this effect i s driven by a simultaneous effect i s something we a
priori do not know and a more thorough analysis should be made to provide more
insights into this. A possible solution would be to model TFP and finance in a
simultaneous model and estimate it by 2SLS or 3SLS techniques. Nevertheless this
solution should imply an enormous system o f simultaneous equations. We prefer our
model for its parsimony and simplicity.
Therefore, even though it could be true that the effect o f finance variables may be driven
by simultaneous forces, the relative contribution on productivity of this group of
variables holds interms of conditional expectations although it does not do so in terms of
causal relations.
7
Section 2. PRODUCTIVITYRESULTS
Table 2.1: GeneralInformation at Plant Leveland Production FunctionVariables'
General Industrial classification (a) Food; (b) Apparel and Textiles; (c) Other manufacturing.
Informationat
PlantLevel Regional classification (a) Guatemala City; (b) Rest of the country ~ ~~
Production Sales Used as the measure of output for the production function
Function estimation. Sales are defined as total annual sales in 2005. The
Variables series are deflated by using the Producer Price Index (PPI), base
2002.
Employment Total number of permanent and temporary workers.
Total hours worked per Total number of employees multiplied by the average hours
year worked per year.
Materials Total costs of intermediate and raw materials used inproduction
(excluding fuel). The series are deflated using the Producer
Price Index (PPI), base 2002.
Capital stock Net book value of machinery and equipment. The series are
deflated using the Producer Price Index (PPI), base 2002.
User cost of capital The user cost of capital is defined interms of the opportunity
I costmachinery
of using capital; it is defined as 15% of the net book value
of and equipment.
Labor cost I Total expenditures on personnel. The series are deflated using
the Producer Price Index (PPI), base 2002.
Dependent Exports Dummy variable that takes the value of 1if exports are greater
Variables in than 10%.
Regression Foreign Direct Dummy variable that takes the value of 1if any part of the
Equationsand Investment capital of the firm i s foreign.
Linear Wages Real wage is defined as the total expenditure on personnel
Probability (deflated by the Producer Price Index, base 2002) divided by the
Models total number of permanent and temporary workers.
Employment Total number of permanent and temporary workers.
8
Table 2.2: Investment climate (IC) and control (C)variables
Descriptionof the variable
9
Table 2.3: Investment climate (IC) and control (C) variables (continued)
Blocksof IC Variable name Descriptionof the variable
Variables
Redtape, Sales reported for tax Percentageof total annual sales that a typical firmoperating inplant's sector reports
corruptionand purposes for tax purposes.
crime Workforce reported for tax Percentageof total workforce that atypical firm operating in plant's sector reports
purposes for tax purposes.
Dummy for conflicts with Dummy taking the value of 1if the plant has conflicts with clients with a third party
clients involved.
Dummy for conflicts in Dummy taking the value of 1ifthe plant has conflicts with clients with a court
courts involved (conditional on having conflicts with clients with a thirdparty involved).
Weeks to judgment Number of weeks that took the court to deliverjudgment inthe latest conflict with
clients (conditionalon having conflicts with clients with a third party involved).
Dummy for security Dummy taking the value of 1if the plant has security expenses.
Security cost Security expenses as apercentage of total annual sales.
Dummy for crime
Dummy for gifts to obtain a Giftsexpected or requestedto obtain aconstruction permit,conditional on applying
construction permit for a construction permit.
Wait for an operating license Days to obtain a main operating license (conditional on applying for an operating
license).
Dummy for gifts for Giftsexpected or requestedto obtain anoperating license, conditional on applying
operating license for an operating license.
10
Table 2.4: Investment climate (IC) and control (C) variables
Blocks of IC
variables Variable name Descriptionof the variable
Finance and Largest shareholder Percentage of firm's capital owned by the largest shareholder.
corporate Initialinvestment: private Percentage of the investment needed to start operations received from
governance banks private commercial banks.
Initial investment: public banks Percentage of the investment needed to start operations received from
stateowned banks and/or government agencies.
Purchases paidbefore delivery IIPercentage of annual purchases paid for before delivery.
IPurchases paid on delivery I Percentage of annual purchases paid for on delivery. I
Purchases paid after delivery Percentage of annual purchases paid for after delivery.
Sales paidbefore delivery Percentage of annual sales paid for before delivery.
Sales paid on delivery Percentage of annual sales paid for on delivery.
Sales paid after delivery Percentage of annual sales paid for after delivery.
Working capital financed by Percentage of firm's working capital financed with internal funds.
internal funds
Working capital financed by Percentage of firm's working capital financed by private commercial
private banks banks.
Working capital financed by Percentage of firm's working capital financed by stateowned banks.
stateowned banks
Working capital financed by Percentage o f firm's working capital financed by family/friends.
family/friends
Working capital financed by Percentage of firm's working capital financed by nonbanking financial
nonbank financial institutions institutions.
Working capital financed by Percentage of firm's working capital financed with credit from suppliers.
credit from suppliers
Working capital financed by Percentage of firm's working capital financed by informal sources.
informal sources
New fixed assets financed by Percentage of investments in new fixed assets financed with internal
internal funds funds.
New fixed assets financed by Percentage of investments in new fixed assets financed by private
private banks commercial banks.
New fixed assets financed by Percentage of investments in new fixed assets financed by stateowned
stateowned banks banks.
New fixed assets financed by Percentage of investments in new fixed assets financed by family/friends.

familylfriends
New fixed assets financed by Percentage of investments innew fixed assets financed by nonbanking
nonbank financial institutions financial institutions.
New fixed assets financed by Percentage of investments in new fixed assets financed with credit from
credit from suppliers L
suppliers.
New fixed assets financed by Percentage of investments in new fixed assets financed by informal
informal sources sources.
Checking or savings account Dummy taking the value of 1if the plant has a checking or savings
account.
Owner of the land Percentage of the land on which the plant operates owned by the firm.
Dummy for credit line Dummy that takes the value of 1if the firm has access to a credit line or
overdraft facility.
Dummy for loan Dummy that takes the value of 1 if the firm has access to a loan.
Dummy for loan with collateral Dummy that takes the value of 1 if the firm has access to a loan which
requires collateral (conditional on having a loan).
Value of collateral Value of collateral as a Dercentaee of the loan value (conditional on

having a loan with collateral).
Dummy for debt Dummy taking the value of 1if the number of rejected loan applications
i s larger than the number of applications for a loan.
Dummyno loanbecause Dummy that takes the value of 1 if the firm did not apply for a loan
complexity because of complexity.
11
Blocks of IC
variables Variable name Descriptionof the variable
Dummy no loanbecause cost Dummy that takes the value of 1if the firm did not apply for a loan
because of its cost.
Dummy no loanbecause Dummy that takes the value of 1if the firm did not apply for a loan
collateral because of the collateral required.
Rejected credit applications Percentage of rejected credit applications.
Accepted credit applications Percentage of accepted credit applications.
External audit Dummy that takes the value of 1if the firm has its annual financial
statements externally audited.
12
Table 2.5: Investment climate (IC) and control (C) variables
Blocks of IC
variables Variable name Descriptionof the variable
Quality, Dummy for quality Dummy taking the value of 1ifthe firm has any kind of quality certification.
innovationand certification
labor skills Dummy for foreign Dummy taking the value of 1if the plant uses technology licensed from a
technology foreignowned company.
Dummy for product Dummy taking the value of 1if the plant has introduced any product innovation
innovation inthe last 3 years.
Dummy for process Dummytaking the value of 1if the planthas introduced any productionprocess
innovation improvement in the last 3 years.
Outsourcing Percentage of total annual sales subcontracted.
Dummy for R&D Dummy that takes the value of 1if the firm performed R&D activities during the
last year.
R&Dexpenditures R&Dexpenditures as a percentage of total annual sales.
Staff  production workers Percentage of production workers out of all staff.
Staff  female workers Percentage of female workers out of all staff.
Staff  skilled workers Percentage of skilled production workers out of all staff.
Staff university education Dummy taking the value of 1if the typical production worker has at least one
year of university education.
Dummy for training Dummy taking the value of 1if the firm provides formal (beyond onthejob)
training to its employees.
Trainingto production Percentage of production workers receiving formal (beyond onthejob) training
workers
Training to nonproduction Percentage of nonproductionworkers receiving formal (beyond onthejob)
IManager experience I Manager experience in years.
Other control Age Age of the firm in 2005.
variables Capacity utilization Percentage of total firm productive capacity utilized.
Trade union Percentage of workforce unionized
Dummy for incorporated Dummy that takes the value of 1ifthe firm i s an incorporated company.
company
Dummy for limited liability I Dummy that takes the value of 1if the firm i s a limited liability company.
company
Dummy for FDI Dummy that takes the value of 1if any part of the firm's capital i s foreign.
Dummy for public capital Dummy that takes the value of 1if any part of the firm's capital i s public.
Exporting experience Number of years of exporting experience.
Dummy for local monopoly IIDummy takingthe value one of 1if the firm i s a local monopoly.
Dummy 5 or more Dummy taking the value of 1if the plant has 5 or more competitors in the local
competitors market.
Dummy less than 5 Dummy taking the value of 1if the plant has less than 5 competitors in the local
competitors market.
Dummy increased sales Dummy taking the value of 1if the plant has increased its sales.
Dummy decreased sales Dummy taking the value of 1if the plant has decreased its sales.
Dummyfor importer Dummy takingthe value of 1if the firm imports more than 10%of the total
purchases of intermediate materials.
Share of imports Share of imported inputs over total purchases of intermediate materials and
Dummy for exporter Dummy taking the value of 1ifthe firm exports more than 10% of its total
annual sales.
Share of exports Share o f exports intotal annual sales.
Small Dummy taking the value of 1if the firm has less than 20 employees.
Medium Dummy taking the value of 1if the firm has 20 or more, but less than 100
13
Table 2.6: Total number of observations before and after cleaning missingvalues
and outliers inproduction function (PF) variables
Observationsbefore Observationsafter
c1eaning c1eaning
Missingobservations 61 4
Of which:
firms with one PF variable missing 33 0
firms with two PFvariables missing 21 0
firms with three PFvariables missing 3 0
firms with four PFvariables missing 4 4
Outliers 5 6
of which:
outliers only in materials (ratio of materials to
sales > 1) 5 6
outliers only in labor cost (ratio of labor cost to
sales > 1) 0 0
outliers in both materials and labor cost 0 0
Usefulobservations (outliersandmissing
excluded) 262 318
The cleaningprocess i s performedinthree steps.
I. Those firms with missingvalues in all the PFvariables (sales, materials, labor cost andcapital) are dropped
from the sample.For the rest of the missingvalues we applythe proceduredescribedinI1and111.
II. We replacethose observationswith ratios of materials to sales or of laborcost to sales greater than one
(outliers)following step 111.
Ill. We replacethe missingvalues of the PF variablesby their correspondingindustryregionsizemedians. Ifwe
do not haveenoughobservationsinsome cells, we replacethemby the correspondingindustrysizemedians.Ifwe still
do not haveenoughobservationsinthose cells, inthe next step we replacethe missingvaluesby the regionsize
medians.Ifstill necessary,inthe last step we computethe mediansonly by size andor by industryto replacethose
missingvalues.
The last row of the table summarizesthe number of useful observationsfor regressionanalysis beforeand after the
cleaningprocess.
14
Table 2.7: Representativeness o f production function variables before and after
cleaning for missingvalues and outliers
By industry and region
Industry Guatemala City Rest of the country Total
#Obs Perc. #Obs Perc. #Obs Perc.
Food 62 24.3 28 38.4 90 27.4
59 24.0 27 37.5 86 27.0
By industry and size I
Industry I Medium I Large I Total I
#Obs Perc. #Obs Perc. #Obs Perc.
pefore cleaning 38 27.1 27 22.7 25 36.2 90 27.4
I hfter cleaning II36 II26.5 I 261 I 22.2 I 24 I 36.9 I 86 I 27.0 I
34 I 28.6 16 23.2 83 I 25.3
33 I 28.2 16 24.6 82 I 25.8
Other Before cleaning 69 49.3 58 48.7 28 40.6 155 47.3
manufacturing After cleaning 67 49.3 58 49.6 25 38.5 150 47.2
Total Before cleaning 140 100.0 119 100.0 69 100.0 328 100.0
After cleaning 136 100.0 117 100.0 65 100.0 318 100.0
15
Table 2.8: Total number of observations and response rate of I C and C variables in
the original sample
Blocks of I C 1 I I
variables Name of the variable #Observations Responserate
Infrastructure
Red tape,
corruption and
crime
16
Blocks of IC
variables Name of the variable #Observations Response rate
Finance and Largest shareholder "
376 99 A
,,. .
corporate Initial investment: privatebanks
governance 324 98.8
Initial investment: public banks 325 99.1
Purchasespaid before delivery 328 100.0
Purchasespaid on delivery 338 innn
Quality, Dummy for quality certification 317 96.6
innovationand Dummy for foreign technology
labor skills 312 95.1
Dummy for product innovation 312 95.1
Dummy for process innovation 312 95.1
Outsourcing 2737 aa I
Dummy for R&D 312 95.1
R&D expenditures in5 37 n
Staff productionworkers I 312 I 95.1
Staff female workers 311 94.8
Staff skilled workers 312 95.1
Staff  university education ?in 94 5
17
Blocks of I C
variables Name of the variable #Observations
Other control
variables
18
Table 2.9: Correlationmatrix among productivity measures
1 TWO steps solow
 1 Single step  Single step 
residual Restricted
Restricted Unrestricted Dou `Obb las I Unrestricted
Translog `Obb 1
Doug1as Translog
Single step  CobbDouglas 0.89 0.92 1
Restricted Translog 0.91 0.93 0.97 1
Single step  CobbDouglas 0.08 0.04 0.22 0.17 1
Unrestricted ~~~~~l~~ 0.11 0.09 0.15 0.11 0.80 1
b) EstimatedProductivityinlogs is obtainedfrom CobbDouglas andTranslogproductionfunctions of sales with inputs
labor, materials,andcapitalestimatedby OLS under two differentenvironments:
(1) Restricted: a singleset of productionfunction coefficientsis obtainedusingdataonplants, for all industries
(excludingoutliers).
(2) Unrestrictedby industry:a set of productionfunction coefficientsi s obtained for each one of eight industriesusing
data on all plants (excludingoutliers).
19
Table 2.11: ICA elasticities and semielasticities with respect to productivity, robust
White standard errors
Blocksof ICA ExplanatoryICA variables
governance
* significantat 10%; ** significantat 5%; *** significantat 1%; Eachregressionincludesa set of industrydummies and aconstant term.
(a) Variables instrumented with the industryregionsizeaverage. (b) Variables approximated with a proxy (only missingvalues replaced
by the industryregionsizeaverage).
Estimation of IC elasticities and semielasticities:
A) Two steps: In the first step compute the Solow residual with restricted (or unrestricted)inputoutput elasticities. In the second step
compute the IC coefficientsby regressingthe set of IC variables on the Solowresidualby OLS.
B) Single step:
i)CobbDouglas: ComputetheICcoefficientsjointly with therestricted(orunrestricted)inputoutputelasticities byOLSinanextended
CobbDouglas productionfunctionof the form:
log Y, = a,log L, t a,w M , a x l o g K ,
log + + a',cIci+ a;c,+(Y;Jl, + ap+ u,
where L, M and K denote the inputs employment, materials and capital respectively,IC and C denote the investmentclimate and other
control variables andDj, denotes the industrydummies.
ii)Translog: Compute the IC coefficientsjointly with the restricted(or unrestricted) inputoutputelasticitiesby OLS in an extended
Translogproductionfunction of the form:
lQ3Y=qlQ34+4%Y+%1%4 %k4)2t,%kY,'t,%oopgi +~~(logq)oC9Y)+%(4~Q34)+~k~~Q3~)+~~~
1 1 1
+2 +&++$D, +4+Y
Restricted estimation: equal inputoutputelasticities for all firms inthe country.
Unrestricted by industry estimation: equalinputoutputelasticities for all firms in the same industry.
21
Dummyfor largefirm 0.117 0.106 0.446** 0.462** 0.419* 0.308**
Observations 318 318 318 318 318 318
Rsquared 0.273 0.257 0.891 0.897 0.912 0.925
Note: We employ a general correction for heteroskedasticity: the robust White standard errors (Table 1.10). This
method corrects for any general pattern of heteroskedasticity in the residuals of the productivity regressions. In fact,
clustering standard errors i s a particular case of the White standard errors which allow for common variation within
clusters. Table 2.11 shows the estimation results with `White' robust standard errors and Table 2.12 shows the results
with standard errors clustered by industry, region and size. It i s clear that the significance of the ICA variable
coefficients does not change significantly if we cluster the standard errors.
22
Table 2.13: Percentage Contribution of IC and C Variables to the Olley and Pakes
Decompositionof the Aggregate Productivity inLogs; Restricted Solow Residual
Infrastructure
Red tape,
corruption
and crime
Dummy for security (b) 0.36 0.23 0.13
Dummy for crime (b) 0.06 0.06 0.01
Manager's time in bureaucratic issues (a) 0.21 0.21 0.00
Payments to speed up bureaucracy (b) 0.00 0.01 0.01
corporate Working capital financed by informal sources 0.00 0.01 0.01
governance ~ T  . . L : ^  ,,..:*,1 C,"..,,A l.., L,.lr c^^^^:"1
I^ I I I

Dummyfor credit line 0.12 0.07 0.05
Quality, Dummyfor I S 0 Certification (b) 0.16 0.05 0.11
_ I
innovation Percentage o f female workers in staff (b) 0.04 0.05 0.01
and labor Training to nonproduction workers (b)
I skills 0.08 0.08 0.00
Other control Share o f imported inputs (b) 0.09 0.06 0.03
variables Percentage o f unionized workforce 0.01 0.00 0.01
Dummyfor FDI 0.08 0.02 0.06
Share of exports 0.30 0.13 0.17
Dummyfor large firm 0.10 0.02 0.08
Industry Apparels and textiles 0.01 0.02 0.01
dummies Other manufacturing 0.08 0.07 0.01
Constant 0.72 0.72 0.00
Residual 0.14 0.00 0.14
Total 2.33 1.65 0.67
Notes: ~~ ~~
* Resultspresentedare
** The relative to aggregate productivity (mixed: share of sales inlevels andproductivity in logs).
productivity measure used to construct the tables i s the restricted Solow residualandhence the elasticities and semielasticities
usedare obtainedfrom the first column of Table 2.11.
*** Each term of the Olley and Pakes decomposition of aggregate productivity (PJ can be expressed in terms of the investment
climate variables according to the following expression:
P. = & ' , I C j + f ~ ' , C ~ + & ' ~ +&, 6+~i Ji + N j d ' , ~ ~ o v ( s j , i , I C j , i ) + N J f L ' c ~ o v ( s j j , C , , ~ ) i N j & ' D ~ ~ o v ( s j , i , D j ) + N j ~ o v ( s j , i , , ~ J , i )

I ~ 5
IC is the vector of investment climate variables, C the vector of other control variables, Djthe vector of industry dummies, ui and up
are the residual and the constant of the productivity regression of the restricted Solow residuals on the IC and C variables (see
Escribano et al. (2007) for details). Table 2.13 presents the percentage contributions to aggregate productivity (in logs).
23
Table 2.14: IC impact on the decomposition by inputs of the efficiency term of the
Olley and Pakes decomposition inlogs
Share of exports 5.66 6.53 1.02 7.16 20.37
Dummy for large firm 2.72 3.99 0.64 1.18 8.52
Industry Apparels and textiles 0.02 0.58 0.15 0.18 0.57
dummies Other manufacturing 0.55 0.54 0.09 0.13 0.06
Residual 3.68 36.12 1.04 71.07 30.23
Total 12.71 13.18 2.70 97.77 100.00
Where I C i s the vector of investment climate variables, C the vector of other control variables, D,the vector of industry dummies, utand
are respectively the residual and the constant of the productivity regression on the I C and C variables and using the restricted Solow residual
as dependent variable (see Escribano et. al. (2007) for details). Table 2.14 presents the percentage contributions to efficiency which are
obtained from the next equation:
24
Table 2.15: Twostage least squares (2SLS) estimation of employment equation
Blocksof ICA % Contribution
%
variables Explanatory ICA variables Coefficient Contribution Coefficient
Productivity 0.377* 27.31 0.369' 25.50
Real wages
Infrastructure
Red tape,
corruption and
crime
Financeand
corporate
governance
Quality,
innovationand
labor skills
Other control
variables Trade union 10.049** 10.34 I0.049*** 10.46 I
Dummy for FDI 0.692** 1.27 pi
0.681** 1.47
Share of exports 0.005 ** 1.90 0.005* 2.01
Instruments I First stage Rsauared 10.29 I
evaluation III Hansen
Partial Rsouared
Uvalue of uartial Rsauared 10.00 0.00
test (Uvalue) 10.61 1II 10.6 I
I I Observations I316 I I316 I I
Notes:
* significantat 10%; ** significantat 5%; *** significantat 1% (robust standard errors).
Each regression includes a set of industry and year dummies and a constant term.
(a) Variables instrumented with the industryregionsize average.
(b) Variables approximated with a proxy (only missingvalues replacedby the industryregionsize average).
Productivity i s endogenous and the list of variables used as excluded instruments is: days to clear customs to export, dummy for gifts in
inspections, electricity from generator, shipment losses in exports (IndustryRegionSize average), dummy for conflicts with clients, dummy for
crime, working capital financed by informal sources, working capital financed by nonbanking financial institutions, dummy for checking or
current account, dummy for credit line, staff  female workers, training to nonproduction workers.
The percentage contributions of productivity, IC and C variables to average log employment are computed according to the next expression:
IC is the vector of investment climate variables, C the vector of other control variables, Dj the vector of industry dummies, vL and pL are
respectively the residual and the constant of the real wages 2SLS regression.
First stage Rsquared from the regression of productivity on both the included and the excluded instruments.
The partial Rsquared measuresthe squared partial correlation between the excluded instruments and productivity.
Ftest ofjoint significance of the excluded instruments that corresponds to the partial Rsquared.
The Hansen test i s a test of overidentifying restrictions. The nullhypothesis i s that the instruments are valid instruments, that is, uncorrelated
with the error term, and therefore the excluded instruments are correctly excluded from the estimated equation.
25
Table 2.16: Two stage least squares (2SLS) estimation of realwages equation
Blocks of I C A % %
variables Explanatory I C A variables Coefficient Contribution Contribution
Productivity 0.608** 13.75 0.530* 11.92
Infrastructure Losses due to power outages (b) 0.023* 0.72 0.023* 0.72
Red tape, corruption Sales reportedto taxes (a) 0.002* 2.49 0.002* 2.05
~
and crime Paymentsto obtain a contract with the 0.036* 0.89 0.035 0.87
evaluation
I Hansentest (Uvalue) 10.93 I 10.93 I
I I Observations I316 I I316 I I
Notes:
* significantat 10%; ** significant at5%; *** significantat 1% (robust standarderrors).
Eachregressionincludesa set of industryandyear dummies andaconstant term.
(a)Variables instrumentedwith the industryregionsizeaverage.
(b) Variablesapproximatedwith aproxy (only missingvaluesreplacedby the industryregionsizeaverage).
Productivity is endogenous andthe list of variables usedas excluded instruments is: days to clear customsto export, electricity from
generator, water from public sources, dummy for security, payments to speed up bureaucracy, working capital financed by non
bankingfinancial institutions.
* The percentage contributions of productivity, IC and C variables to average log real wages are computed according to the next
expression:
IC is the vector of investmentclimate variables, C the vector of other control variables, D, the vector of industrydummies, vw and
are respectivelythe residualandthe constantof the real wages 2SLS regression.
First stageRsquaredfrom the regressionof productivity onboththe includedandthe excludedinstruments.
The partial Rsquaredmeasures the squaredpartial correlationbetweenthe excludedinstruments andthe productivity.
Ftest ofjoint significanceof the excludedinstrumentsthat correspondsto the partial Rsquared.
The Hansen test i s a test of overidentifying restrictions. The null hypothesis i s that the instruments are valid instruments, that is,
uncorrelated with the error term, andtherefore the excludedinstruments are correctly excluded from the estimatedequation.
26
Table 2.17: Two stage least squares (2SLS) estimation of probability of exporting
equation
Hansen test (pvalue)
Notes:
* significant at 10%; ** significantat 5%; *** significant at 1%(robust standard errors).
Each regression includes a set of industry dummies and a constant term.
(a) Variables instrumented with the industryregionsize average.
(b) Variables approximated with aproxy (only missing values replaced by the industryregionsize average).
Productivity i s endogenous and the list of variables used as excluded instruments is: Security expenses, crime losses, initial
investment: private banks, dummy for process innovation, staff skilled workers, trade union.
The percentage contributions of productivity, IC and C variables to the probability of exporting are computed according to the
next expression:
IC is the vector of investment climate variables, C the vector of other control variables, Djthe vector of industry dummies, vExpand
6Expare respectively the residual and the constant of the probability of exporting 2SLS regression.
First stageRsquared from the regression of productivityon boththe included and the excluded instruments.
The partial Rsquared measures the squared partial correlation between the excluded instruments andthe productivity.
Ftest ofjoint significance of the excluded instruments that corresponds to the partial Rsquared.
The Hansen test i s a test of overidentifying restrictions. The nullhypothesis i s that the instruments are valid instruments, that is,
uncorrelated with the error term, and therefore the excluded instruments are correctly excluded from the estimated equation.
21
Table 2.18: Two stage least squares (2SLS) estimation of probability of exporting
I equation
Of IC* % %
variables ExplanatoryICA variables Coefficient Contribution Coefficient Contribution
Productivity 0.106* 357.26 0.11* 367.63
Infrastructure Dummyfor own generator (b) 0.086" 21.43 0.087* 21.60
Wait for a water sumh (b) 0.013* 10.44 0.013" 10.15
Redtape,
corruption
andcrime
corporate
innovation
andlabor
skills
Other control
variables
Instruments First stage Rsquared 0.19 10.18 I
Partial Rsquared 0.07
I Hansen
Dvalue of partial Rsauared 0.00 HI
test (pvalue) 10.95 I 10.94
I I Observations I318 I I318 I I
Notes:
* significantat 10%; ** significantat 5%; *** significantat 1%(robuststandarderrors).
Eachregressionincludes a set of industrydummies and a constantterm.
(a)Variables instrumentedwith the industryregionsizeaverage.
(b) Variables approximatedwith a proxy (only missingvalues replacedby the industryregionsizeaverage).
Productivityis endogenousandthe list of variables usedas excludedinstruments is: dummy for gifts ininspections, electricity from
generator, water from public sources, sales reportedto taxes (IRS avg.), working capitalfinanced by informal sources, dummy for
I S 0 certification,staff female workers, training to nonproductionworkers, share of importedinputs.
* The percentagecontributions of productivity, IC andC variablesto the probability of exportingare computed accordingto the next
expression:
3xprespectivelythe
IC is the vector of investmentclimatevariables, C the vector of other control variables, DJthe vector of industrydummies, vExpand
are residualandthe constant of the probability of exporting2SLS regression.
First stageRsquaredfrom the regressionof productivity on boththe includedand the excludedinstruments.
The partialRsquaredmeasuresthe squaredpartial correlationbetweenthe excluded instruments andthe productivity.
Ftestofjoint significance of the excludedinstrumentsthat correspondsto the partialRsquared.
The Hansentest i s a test of overidentifyingrestrictions. The nullhypothesis i s that the instruments are valid instruments, that is,
uncorrelatedwith the error term, andtherefore the excludedinstruments are correctlyexcluded from the estimatedequation.
28
Figure 2.1: Olley and Pakes DecompositioninLevels by Industry and Region of
Aggregate Productivity (Restricted Solow Residual)
18.0
160
140
120
100
8 0
6 0
4.0
2.0
0 0
Figure 2.2: Olley and Pakes Decomposition inLevels by Size and Age ofAggregate
Productivity (Restricted Solow Residual)
size nse
14.0
120
iao
a0
60
40
20
ao
Figure 2.3: Mixed Olley and Pakes Decompositionby Industry and Region of
Aggregate Productivity (Rt itricted Solow Residual)
l n d u a y
3.0 Bn
25
2 0
1 5
1.o
0 5
0 0
29
Figure 2.4: Mixed Olley and Pakes Decompositionby Size andAge ofAggregate
Productivity (Restricted Solow Residual)
size
25 1 N I ase
20
1.5
1.0
05
00
small Medurn LerBe I furrs a d
Figure 2.5: Relative I C effects by groups of variables on aggregate productivity,
average productivity and efficiency (mixed O&P decomposition and simulations of a
20% improvement inI C and C variables)
A E A E
ari tothe nixed Ulevand Blest%ccnoCsitimof the
Assfsate Raluctivii (WedWclec: RoductivRy measuredin w,shareafsaleswed I
inlevds).
RSnulatiorrsof aChargeinIC4Vars and%changemAggfsate Rocclciityandmthecbrrponsnts d
the aley and we^ Rim.
Irrhastructures IWtape,comptionandcrime
rnFimeandcaporategoLernance 0 Cbality,innmationandlaborskils
Iahererddables
30
I I Aggregate productivity Average productivity
I IEfficiency term 1
Infrastructures 15.3 25.7 16.7 23.9 22.6 17.4
R e d tape, corruption and crime 45.2 37.3 51.4 45.4 25.1 38.7
Finance and corporate governance 9.1 6.3 9.8 7.8 8.5 5.3
Quality, innovation and labor skills 9.9 6.2 8.7 7.4 12.2 5.9
Other control variables 16.4 24.3 10.1 12.8 26.5 29.7
Total 100 100 100 100 100 100
Notes:
A: The percentage contributions of I C and C variables of table C.2a were transformed such that the relative impact of the
group `s' to aggregate and average productivity and efficiency may be expressed according to the next expressions
(1.A)Wsngg = [il l%LTil+l% cov(s, ,icf,p )I1[jl l+l% cov(si ,icf,pI + ji=ll.). I+1d;r
vrts
x x 'ricJr I
a ' cov( s! ,ic;,jr I ]
1
(2.A)wsmg= [5p=l ~d&I][ p=1
5 I%`pl$+v!'#$ ? lqrE;,1]
C J r l
cov(sir .i~f,~
ll[LIIN%
C cov(s, ,icf,p )I + ?lNqr
Vr#s j,=l
C COV(S~,.icS.jr )I ]
For all s,r=infrastructures; red tape, corruption and crime; finance and corp. gov.; quality, innov. and labor skills; other
control variables, and p=l,..,n are the number o f variables belonging to the group s, andjr=l,..,mr are the variables in the
group r, such that r#s.
B:The relative impact of each group of IC and C variables is expressed interms of the % change of each term of the O&P
decomposition from t=O (before simulation) to t=l (after simulation) (see Escribano et. al. (2007) for details) according to
the following expressions
(1.B)yngg = [ilI] [il 1
AIPp' AIPp"l+ Vr#s j,=1? Alp;
X I]
(2.B)wsmg= [5 1
?=I AlFil][ ?=I Vrtrj=I
2 AlFil + 1 3AlFil]
(3.B)yeff= [~lA~~~~(~:,.Pp",ii~j,Alc~v(~;,i.Pp',i)I
] [ ~ , A ~ C O " ( ~ ~ , . i ~ ~ ~ , t ) ~
I
Note that the industry dummies and the constant are excluded from the computations of both cases A and B.
31
Figure 2.6: Relative I C effects on aggregate productivity (Mixed O&P
decomposition)
, Infrastructures F&i tape,corrytim&crim ~ i  &corp. p. Clcalbinmatim aher contrd
70 &I& sldlls variables
II II
x )
=i I
452
0.3
3.4
n 00 00
T.l 11 12 13 14 15 16 T2 2.1 22 2.3 2.4 2.5 2.6 2.7 T.3 3.1 32 3.3 3.4 3.5 T.4 4.1 42 4.3 T.5 5.1 52 5.3 5.4
T.l Total infrastructures 2.3Dummyforconflictsincourts T.4 Total quality, innov. and labor skills
I l w t ociedrcustomSt0exp3rtinteraction 2.4DummyforseurityRTmss 4.1DummyforiSOqualityce!tification
wthfinstktcb eqmtt
12OEctricityfmmaQmaator 2.5Dummyforcrime
2.6Mana@stimespentintu.issues 42Stafffemelemrkers
4.3Trainingto mnpocbctionmrkefs
13Dummyfor'Ejfts'to o h i neleztncitysupply 2.7P a p e mto speeduptureaucmcy
14vIclteroutages T.5 Total other control variables
15vIclterfromplblicsources T.3 Total finance andcorporate governance 5,191areof imp3rtedirputs
16Wpnentlosses,imptts 3.1Initialinvestment:privatetenks 52Percentageofunionizslmrkforce
32Vlbrkingcapitalfimdbyinfodsources 5,3DummyforFDi
T.2 Total redtape corruption and crime 3.3Vlbrkingcapitalfimdbymrrbankingfimial 5,4of exp3tts
PlSelesrep3rldtOtaES institutions
22Dummyforconflictswthclients 3.4Dmmyforcheckingorsa\n'rgaccount
3.4Dummyforcrejt line
Figure 2.7: Relative I C effects on average productivity (Mixed O&P decomposition)
Infrastructures Fedtape,corrlptim&crim &cap. p. W iimovatim aher contrd
&laborsldlls variables
514
242
8 7
Ql
a7
62
n 0.1 09
T.l 11 12 13 14 15 16 T2 21 22 2.3 24 25 26 27 T.3 3.1 3.2 3.3 2.4 36 T.4 4.1 42 4.3 T.5 5.1 52 53 5.4
T.l Total infrastructures 2.3Dummyforconflictsincourts
11Daysto clearcustomsto eqmtt  interaction 2.4Dummyforseurityees T.4 Total quality, innov. and labor sklils
4.1DurnmyforiSOqualityce!tification
nithfimsthadcbeqmtt 2.5Dummyforcrime 4.2Staff femalemhers

12Eetricityfromagenemor 4.3Trainingto mrquductionwrkers
13Dummyfor'gifts'to ottainde3ticitysupFiy 2.7Payrentsto speedupburmuracy
2.6M~stimespentinbur.issues
14V&erOuta+ T.5 Total other control variables
15V&erfromplblicsources T.3 Total finance andcorporate governance 5,,Shareof impttdiws
16shipmentlosses,imptts 3.1initialiwestment:privatebanks
32Vlbrkingcapitalf i m dbyinfodsources 5.2Percentawofunionizslmrkforce
5,3DummyforFDI
T.2 Total redtape corruption and crime 3,SVlbhingcapitalfimdbymrrbankingfimial 5,4Shareofwtts
2.lSelesrepottdto taxs institutions
22Dummyforconflictsdthclients 3.4DummyforcheMrgorsavingaccount
3.4Dummyforcre3itline
32
Figure 2.8: Relative I C effects on efficiency (Mixed O&P decomposition)
6 Infrastructures F k itap, corrlption&crirre f i m e &cap. q,w. W iinnovation a k r cctitrd variables
&labor skills
T.l 11 12 13 14 15 16 T2 21 22 2.3 2.4 2.5 2.6 27 T.3 3.1 3.2 3.3 3.4 3.5 T.4 4.1 42 4.3 T.5 5.1 5.2 5.3 5.4
.
T.lTotal infrastructures 2.3Dummyforconflictsinmutts
11Daystoclearcustomstoewrtinteraction 2.4Dummyforsecurityexperses T.4 Total quality, innov. and labor skills
Mithfirrnsthatdowtt 25Dummyforcrime 4.lDummyforISOqditycertification
12Eectricityfromagor 2.6Mampfstimespent inbur.issues 4.2Stafffemalenorkern
',fzA"d,
13Dummyfor'gifts'toobtalndectricitysupply 2JPa)nnentstospeedupbureawracy 4.3Tminingto mmprochtionuorkers
14VIBteroutages
15Waberfromplblicsources T.3Total finance and corporate governance variables
~~~~~~
16Ship~lent losses,imprts 3.1Initialiwestrnent:privateb k s
3pMbrkingcapital f i  e d ~ i ~ osources
~ 5.2Percentapofunionizednorkforce
T.2 Total redtape corruption and crime 3.3Mbrkingcapitalfinancedbyrambankingfinancial 53DummyforFDI
5,4Shareofe ~ t t s
2.1Salesreprtedtotax% institdions
22Dumrnyforco~lictsMiithclients 3.4Dumnyforcheckkgorsavingaccount
3.4Dummyforcredit line
Figure 2.9: Relative I C effects by groups ofvariables on average productivity
(Decomposition inLogs); by size
lnfrastuctures Fi?dtape, corruption Financeandcorporate Wity, innovationand Other controlvariable3
andcrime governance laborskills
33
Figure 2.10: ICA PercentageAbsolute Contribution on Economic Performance
Variables
._.. 
Roductivity Errployrnent Realwages &ports ~ FDI
Realwages
Redtape, corruption and crime
Financeand corporate gowrnance 0 Quality, innovationand laborskils
Other control variables
I Total I 100 I 100 I 100 I 100 I 100 I
Notes:
Let the percentage contribution of the IC variable i,to the average value of the dependent variable of equation q be given by
(1)
where q = logP, lo&, ZogW, EXP, FDI, (see notes on tables D.lD.4).From (l),relative percentage contribution of the
the
group 's' of IC variables to the average value of the dependent variable of equation q (e: i s therefore given by
)
(2)
For all s,r=infrastructures; red tape, corruption and crime; finance and corp. gov.; quality, innov. and labor skills; other control
variables, and i=l ...,nare the number of variables belonging to the group s, andjr=l...,mrare the variables inthe group r, such
that r#s.
* n.s. means that no variables of this group of variables were significant in the corresponding regression.
34
Figure 2.11: Relative I C effects on average logemployment
~ ___
,o Prod. W. Infrastructures Redtape, corr. & Fin & Quality inn. & labor skills Other ctrlvars
crime corp gov.
I
''5 O
1'22
I I
ii 78
3.5
T.l T.2 1'.3 3.1 3.2 3.3 3.4 3.5 3.6 T.4 4.1 4.2 4.3 T.5 5.1 5.2 T.6 6.1 6.2 6.3 6.4 6.5 T.7 7.1 7.2 7.3 7.4
T.l Productivity T.4 Total red tape corr. and crime 6.3DummyforR&D
4.lWorkforcereportedto taxes 6.4 Staff  skilled workers
T.2 Real wages 4.2 Securityexpenses 6.5 Dummyfo rt raining
4.2 Crime losses
T.3 Total infrastructures T.7 Total other control variables
3.1Days to clearcustomsto import interaction
 T.5 Total finance and corporate 7.1Shareof imported inputs
with firms that do import governance 7.2 Trade union
3.2 Dummyforomgenerator 5.3 initiai investment: private banks 7.3DummyforFDI
3.3 Water outages 5.2 Dummyforioan wth collateral 7.4Shareof eworts
3.4 Water from public sources
3.5 Dummyfor internet T.6 Total quality, innovation and
3.6 Dummyforemaii labor skills
6.lDummyfor IS0 qualitycertification
6.2 Dummyfor process innovation
Figure 2.12: Relative IC effects on average logreal wages
1 % Prod Infrast. Redtape, Finance& Quality innovation & Other control variables
rate gov labor slulls
70 ptton & crtm cc
60
50
40
30
20
M
5 7 4 2
0
1 T I Ti! 21 T.3 3.1 3.2 T.4 4.1 4.2 T.5 5.1 5.2 T.6 6.1 6.2
T.l Productivity T.4 Total finance and corporate T.6 Total other control variables
governance 6.1Exportingexperience
T.2 Total infrastructures 4.1Purchases paid beforedelivery 6.2 Dummyfor large firm
2 liossesdueto poweroutages 4.2Working capital financed byinformalsources
T.3 Total red tape corruption and T.5 Total quality, innovation and labor
crime skills
3.1Sales reportedto taxes 5.1Staff femaieworkers
3.2Paymentstoobtainacontractwththe 5.2 Staff productionworkers
government
35
Figure 2.13: Relative IC effects on the probability of exporting
______
6 Rod Infrastructures Redtape, corr, &crime Finance& Quality inn. & labor skills Other ctrl variables
corn. aov
5 {,
I
0 I
15 13 7
K)
5
0
T.l T 2 2.1 2.2 2.3 2.4 2.5 T.3 3.1 3.2 3.3 3.4 T.4 4.1 4.2 4.3 T.4 4.1 4.2 4.3 4.4 T.5 5.1 5 2 5.:
T.l Productivity T.3 Total red tape corr. and crime T.5 Total quality, innov. and labor skills
3.1Workforce reported to taxes 5.1Dummyfor R&D
T.2 Total infrastructures 3.2 Dummyfor absenteeism due to crime 5.2 Staff productionworkers
2.lDays to clear customsto import  3.3 Number of inspections 5.3 Dummyfor training
interaction with firms that do import 3.4 Dummyfor gifts in inspections 5.4 Manager's experience
2.2 Average duration of powroutages
2.3Water from public sources T.4 Total finance and corporate gov. T.6 Total other control variables
2.4 Dummyfor internet 4.1Largest shareholder 6.1Shareof imported inputs
2.5 Shipment losses in exports 4.2Working capitaifinanced byfamilyifriends 6.2Dummymore5competitors
4.3 Dummyfor loan with collateral 6.3 Dummyformediumfirms
Figure 2.14: Relative ICA effects on the probability of receiving FDI
Rod. Infrastructures RedtaDe. corruDtions &crime Finance& Qualitv innovation Other control variables
3 4 2 5
T 6 6 1 6 2 6 3
T.l Productivity T.4 Total finance and corporate gov. T.6 Total other control variables
4.1Initialinvestment: private banks 6.1Share of exports
T.2 Total infrastructures 4.2 Working capital financed bynon banking 6.2 Dummyfor local monopoiy
2.lDummyforowr generator financial institutions 6.3Dummyfor largefirrns
2.2Waitforawatersupply
T.5 Total quality, innov. and labor skills
T.3 Total red tape corr. and crime 5.1 Durnmyforforeigntechnology
3.lDummyforconflicts with clients 5.2 Staff  skilledworkers
3.2 Dummyforconflicts in courts
3.3Dummyforgifts in inspections
3.4Cost of entry
36
Figure 2.15: Relative I C effects by groups ofvariables on average logemployment;
by size
0 I 27.0
17.3

5.1
4.2
Productivity Real Wages Infrastructures Red tape, Finance and Quality, Other control
corruption and corporate innovation and variables
crime governance labor skills
I &# Smll firm 0 Mediumfirms 0 Largefirms I
I I
Figure 2.16: Relative ICA effects by groups of variables on average logrealwages;
by size
/c
'5
70 67.3
35
30
55
50
L5
LO
35
30 27 3
15
10
15
IO
5 1.0 0.7 2 8 3 1 2.9 2.9 4'3
0
Productivity Infrastructures Red tape, Finance and Quality, Other control
corruption and corporate innovationand variables
crime governance labor skills
I
~ Smallfirm Mediumfirm 0 Largefirms
Figure 2.17: Relative ICA effects by groups of variables on the probability of
exporting; by size
zo
~
25 N
P
.I
20
115
10
5
i o
I Productivity Infrastructures Redtape, Finance and Quality, Other control 1
corruption and corporate innovation and variables
crime governance labor skills
I 0 Largefirms
I
Figure 2.18: Relative ICA effects by groups of variables on the probability of
receiving FDI;by size
k
io
P
1.5 x
1.0
15
io
!5
!O
5
0
5
0 
Productivity Infrastructures Red tape, Finance and Quality, Other control
corruption and corporate innovation and variables
crime governance labor skills
0 Smallfirms 0 Mediumfirms _ 0 Large firms
38
Figure 2.19: Managers' perceptions; percentage of firms that considers each one of
the following problems as a severe obstacle to firms' economic performance
Infrastructures Redtape, co rruption and crime finance Labor skills Total"
11 1.2 1.3 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2 8 2.9 3.1 3.2 3 3 4.1 4.2 5 6
1 Infrastructures. 2 Red Tape, Corruption and Crime. 3. Finance.
1.1Electricity 2.1 Corruption 3.1Access to Land
1.2 Transportation 2.2 Crime.theft and disorder 3.2 Access to Finance
1.3 Customand traderegulatiom 2.3 Anticonpeitiveor InformalPracticeS 3.3 Macroeconomc uncertainty
2.4 Legal SystenVConflict ReSolUtion
2.5 Regulatory Policy Uncertainty 4. Labour Skills.
2.6TaxRats 4.1 Labor Regulations
2.7Tax Administration 4.2 Skillsand Educationof Avalable Workforce
2.8 Business Licensingand Operating Permits
2.9 Environmentalregulations 5. Total relative weights.
&. Average group relative weights.
1E Infrastructure E3 Red tape, corruption and crime Financeand corporate governance Labor skills 1
'Totalsarecomputed astherelative weighof eachgroup of perceptlomoverthesumof all perceptiom'weights
39
Section 3. INTERPRETATIONS OFTHE PRODUCTIVITY RESULTS
1. SECURITY COSTSAND THEIR EFFECT ONPRODUCTIVITY
The regression results show a significant positive effect of security costs on productivity
compared to declines in productivity associated with speed payments and time dealing
with officials. One interpretation i s this as declines in crime improving productivity.
However, it seems that a plausible interpretation i s that security costs rise with crime and
this positive association with productivity is then puzzling. There is also a concern that
the measure might be endogenous.
The interpretation of the dummy for security costs i s as follows: those firms having any
security costs are on average 37% more productive that the remaining firms. This positive
effect on productivity i s independent o f the amount o f money spent on security, it only
matters whether the firms spend some money on security or not. The interpretation that
'security costs rise with crime' would be true if the significant variable would have been
the total amount spent on security as a percentage of total sales. This variable i s available
in the survey and has been incorporated in the selection of significant variables, but
finally it was not statistically significant. In fact, in other productivity analyses done for
other countries (Africa, Asia, Latin America) where the percentage o f security costs i s
significant on TFP, the effect i s negative. That i s in line with the interpretation above,
where more crime implies more security expenses and less productivity.
We can put more empirical underpinning to this issue by going one step further and
computing the interactive effect o f having security expenses (dummy) and losses due to
crime in the productivity regressions. This effect i s significant, with the coefficient
ranging from 0.015 to 0.019, depending on the productivity specification we are using,
see Table 2.19. The interpretation in this case i s straightforward. Let productivity
equation be given by:
P=bO+bl *durn_secur+b2*(dumsecur*crimeloss)+b3 *IC3+...+bn *ICn+e
where P i s productivity, dumsecur i s a dummy for security expenses, crimeloss i s
losses due to crime, IC3,...,ICn represent other IC and C variables and e i s a error term.
Here b2 measures the interaction effect and b l the direct effect o f the dummy for security
on productivity. The effect o f having security expenses i s given by:
AP=(bl +b2*crimeJoss) *Adum~secur=(0.5180.015*crime~loss)*Adum~secur
That is: if a firm incurs any security expenses, holding everything else constant,
productivity increases on average by 0.5180.015*crimeloss. This indicates that the
positive effect o f security expenses decreases as the intensity o f criminal activity
increases (more losses due to crime). For some firms with a large proportion o f sales lost
due to crime the effect o f having security expenses may be even negative.
40
So far the effect o f security expenses on crime losses i s still unresolved. The linear
correlation between 'dummy for security expenses' and 'losses due to crime' i s negative (
0.27). Although no causal relationships can be derived from the correlation, it indicates
that security may prevent crime, since those firms with security expenses are less likely
to suffer criminal attempts. Another plausible interpretation i s that the more losses due to
crime (as percentage of sales) the less probability o f having security expenses. The linear
regression of crimeloss on dumsecur and a constant i s
crimeloss CoefSicient Standard error Tstat
dumsegur 1I 6.124 1.333 4.6
constant 1 12.828 1.173 10.9
This linear regression shows an intuitive negative linear relation between the percentage
of sales lost due to crime and dummy for security expenses, so having security expenses
reduces losses due to crime. Or, in other words, those firms spending some money
preventing crime reduce the losses due to crime. Obviously, this i s a naive linear
regression that does not take into account possible endogenous problems and many other
econometric issues. Insteadit i s pretty intuitive on the nature o f the relation we are trying
to clarify.
Table 3.1: IC elasticities and semielasticities with respect to productivity
TWO steps estimation 1 Single step estimation
Water from public sources (b) 0.003*** 0.003*** 0.002"" 0.002"" 0.002"" 0.002**
Shipment losses, import (b) 0.009"" 0.009"" 0.009*** 0.008** 0.009** 0.012***
Red tape, Sales reportedto taxes (a) 0.008 0.008 0.013** 0.013"" 0.008 0.005
corruption and Dummy for conflicts with 0.18 0.185 0.085 0.096 0.112 0.118
crime clients
Dummyfor conflicts incourts 0.126 0.129 0.198 0.127 0.185 0.214
Dummyfor security(b) 0.518*** 0.519*** 0.703*** 0.619*** 0.695*** 0.643***
Interaction (Dummy for 0.015*** 0.015*"* 0.019*** 0.017**" 0.018*** 0.017***
security*Lossesdue to crime)
Dummy for crime (b) 0.151 0.15 0.093 0.034 0.094 0
Manager's time inbureaucratic 0.01 0.006 0.021** 0.016" 0.022" 0.013
issues (a)
Payments to speed up 0.006 0.006 0.007 0.006 0.006 0.004
41
governance
instaff(6)
Trainingto nonproduction I 0.001 Io.001 I0.002 I0.002 10.002 Io.001
Finally, the dummy for security expenses may be an endogenous variable. Unfortunately
the industryregionsize average o f this variable i s not a good instrument so we had to use
the crude plantlevel variable inthe regressions.
2. EFFECTSICBLOCKVARIABLESONPRODUCTIVITY
OF
The effects are accumulated in absolute terms across variables in a block (rather than
allowing for some variables to have offsetting effects). Thus the positive effect o f having
security costs i s added to the absolute effect o f management time, speed payments and
sales reported to authorities to make "red tape, corruption and crime" the block o f I C
measures with the biggest impact.
The idea i s to add up all the effects in each group in absolute terms and to compute the
absolute percentage relative contribution o f each group with respect to the absolute
contribution of the investment climate as a whole. This way, we do not offset the positive
effects o f some variables with the negative effects of other variables. By using the
absolute contributions we measure what the productivity gain i s the investment climate
factors improve, and this implies reducing the IC constraints with negative effects and
increasingthe IC factors with positive effects.
42
3. GENDER IMPACT
The survey questionnaire includes information on the gender o f the principal owner. It
would be interesting to note the sector and size distributions of where women are more
economically active, whether women report differences in constraints or whether there
are differences inthe impact o f objective conditions on performance.
The following two tables show the average of the percentage of female workers in staff
by industry and size. The sectors where women are more active are garments, textiles,
food and chemicals.
Table 3.2: Percentageoffemale workersinstaff, averageby industry
IIIFoods 34.38
Garments 53.09
Textiles III 34.29III
Machinery and equipment 20.00
Chemicals 31.55
Nonmetallic min 10.55
Other manufacturing 18.98
By size the distribution of the percentage of women among firm staff is very uniform. Inall
categories o f size the percentage o f female workers in staff i s close to 30%.
Table 3.3: Percentage of female workers instaff, average by size
Small 28.89
Medium 31.33
30.85
We can compute the absolute percentage contribution o f the variable percentage of
female workers in staff by industry and size. Table 3.4 shows that the sectors where
women are more economically active the relative absolute contribution o f this variable i s
larger.
Table 3.4: Percentage of female workers instaff, percentage absolute contributions
to average logproductivity by industry
Foods 12.93
Garments 13.91
Textiles 2.59
Machinery and equipment 1.41
Chemicals 2.19
Nonmetallic minerals 1.27
Other manufacturing 1.65
43
Table 3.5 shows the relative contributions by sizes. Inthis case the contributions are uniformly
distributed among categories of sizes.
Table 3.5: Percentage of female workers instaff, percentage absolute contributions
to average logproductivity by size
Small
I Large
Medium
12.02
4. DIFFERENCESSAMPLING (2003VS. 2007DATA)
IN
A legitimate question is whether differences in perceived constraints between 2003 and
2007 are driven by differences in the sampling. The latest survey includes services as
well as manufacturing firms. There may also be differences in the geographic / size /
sectoral distribution of respondents.
We include the same percentages computed with the sample that we have used to
construct the 20032007 panel and test changes in the IC coefficients. The panel uses the
same sectors, industries and regions in both 2003 and 2007. So the results shouldn't be
driven by sampling differences. The percentages are very similar to those of Figure 1.1
(Chapter 1) in almost all the cases, except by some minor changes in the second decimal.
So we would conclude that the sampling differences are not affecting the results.
Table 3.6: Differences in Sampling (2003 vs. 2007)
2003 2007
Corruption 84.07 62.3 1
Electricity 26.64 51.68
I Macroeconomic instability 62.83 45.99
Crime I 84.07 I 33.64 1
44