Keith E. Maskus, University of Colorado at Boulder
Tsunehiro Otsuki, Osaka University
John S. Wilson, World Bank
World Bank Policy Research Working Paper 3590, May 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.
Keith E. Maskus, Department of Economics, UCB 256, University of Colorado, Boulder CO 80309,
maskus@colorado.edu.
Tsunehiro Otsuki, Osaka School of International Public Policy, 1-31 Machikaneyama, Toyonaka, Osaka 560-0043,
Japan, otsuki@osipp.osaka-u.ac.jp.
John S. Wilson, World Bank, 1818 H Street N.W., Washington DC 20433, jswilson@worldbank.org
Non-technical Summary
Standards and technical regulations exist to protect consumer safety or to achieve other goals,
such as ensuring the interoperability of telecommunications systems, for example. Standards and
technical regulations can, however, raise substantially both start-up and production costs for
firms. We develop econometric models to provide the first estimates of the incremental
production costs for firms in developing nations in conforming to standards imposed by major
importing countries. We use firm-level data generated from 16 developing countries in the
World Bank Technical Barriers to Trade (TBT) Survey Database. Our findings indicate that
standards do increase short-run production costs by requiring additional inputs of labor and
capital. A 1 percent increase in investment to meet compliance costs in importing countries
raises variable production costs by between 0.06 and 0.13 percent, a statistically significant
increase. We also find that the fixed costs of compliance are non-trivial; approximately $425,000
per firm, or about 4.7 percent of value added on average.
Our results may be interpreted as one indication of the extent to which standards and technical
regulations might constitute barriers to trade. While the relative impact on costs of compliance
are relatively small, these costs can be decisive factors driving export success for companies. In
this context, there is scope for considering that the costs associated with more limited exports to
countries with import regulations may not conform to World Trade Organization rules
encouraging harmonization of regulations to international standards, for example. Policy
solutions then might be sought by identifying the extent to which subsidies or public support
programs are needed to offset the cost disadvantage that arises from non-harmonized technical
regulations.
2
1. Introduction
Technical regulations, such as product certification requirements, performance mandates,
testing procedures, conformity assessments, and labeling standards, exist to ensure consumer
safety, network reliability, or other goals. However, such regulations can significantly raise
setup and production costs. As a consequence, they may act as impediments to competition by
blocking firm entry and expansion within a country or, as is frequently alleged by exporting
firms, as barriers to trade.1 Indeed, there has been a rising use of technical regulations as
instruments of commercial policy in the unilateral, regional, and global trade contexts (Maskus
and Wilson, 2001). As traditional barriers to trade have fallen, these non-tariff barriers have
become of particular concern to firms in developing countries, which may bear relatively larger
costs in meeting their requirements than their counterparts in developed nations.
Developing countries are typically "standards takers" rather than "standards makers"
since, at the national level, developing their own standards tends to be more costly than adopting
those of the major markets (Stephenson, 1997). At the firm level, complying with differing
standards in such major export markets as the European Union (EU), the United States, and
Japan can add costs and limit export competitiveness.
These costs associated with foreign standards and technical regulations may be borne
publicly and privately. But developing countries typically have neither the public resources
required to provide national laboratories for testing and certification nor the capability for
collective action to raise their standards. As a result, a significant portion of meeting the costs of
standards may be borne by individual firms.
1See the case studies in Wilson and Abiola (2003).
3
Despite the evident importance of this question, to date the impacts of technical standards
imposed by importing nations on the production costs of firms in developing countries have not
been studied systematically in an econometric framework. Quantification of these effects is
important for several reasons. First, it is useful to shed light on competing claims about the
efficiency and cost impacts of foreign standards and regulations, including how these rules affect
labor and capital usage. To the extent that costs are increased or input use is distorted the
prospects for efficient industrial development could be impeded. Second, the estimates should
be informative for governments in setting domestic standards by illustrating their potential costs.
In this context, harmonization with international standards may not be optimal. Third, a finding
that costs are raised would support the view that technical regulations may be used to limit
market access. In cases where the importing country's regulations may not conform to WTO
obligations, the empirical results could help assess the damages to the exporting country's trade
benefits. Thus, information on the cost impacts could facilitate the resolution of trade disputes
(Maskus and Wilson, 2001).
In this paper we develop econometric models to estimate the incremental production
costs of enterprises in several developing nations associated with conforming to standards and
technical regulations imposed by major importing countries. We use firm-level data generated
through the World Bank Technical Barriers to Trade Survey Database. Our sample includes 159
firms in 12 industries located in 16 developing countries in Eastern Europe, Latin America, the
Middle East, South Asia, and Sub-Saharan Africa. We employ transcendental logarithmic cost
functions to separate impacts of initial compliance cost from variable cost elements in production.
Our results suggest that the need to comply with foreign technical standards has a significantly
4
positive impact. Specifically, the elasticity of (variable) production costs with respect to
standards and technical regulations is estimated to range between 0.06 and 0.13. Evaluated at
sample means, this result implies an increase in variable costs of a dollar magnitude that is
similar to the rise in initial compliance costs.
In Section 2 we provide background information regarding central issues of technical
standards, costs, and trade. In Section 3 we specify the econometric model for assessing the cost
effects of meeting foreign standards and technical regulations. In Sections 4 and 5 we discuss
the survey data and econometric results, respectively. In Section 6 we make concluding
observations.
2. Background
In principle, product standards2 play a variety of useful roles in overcoming market
failures (Stephenson, 1997). For example, emission standards oblige firms to internalize the
costs of maintaining an acceptably low degree of environmental damage. Food safety standards
ensure that consumers are protected from health risks and deceptive practices, information about
which would not ordinarily be available in private markets. For consumers, efficient and non-
discriminatory standards allow comparison of products on a common basis in terms of regulatory
characteristics, permitting enhanced competition. From the producers' point of view,
production of goods subject to recognized standards could achieve economies of scale and
reduce overall costs. Since standards themselves embody information about technical
2The terms "standards" and "standards and technical regulations" are used interchangeably throughout this paper.
The WTO provides a clear distinction between standards and technical regulations; the former are voluntary and the
latter are mandatory technical requirements. In many cases "standards" cover mandatory technical requirements.
5
knowledge, conformity to efficient standards encourages firms to improve the quality and
reliability of their products.
Standards also may reduce transaction costs in business by increasing the transparencyof
product information and compatibility of products and components (David and Greenstein, 1990).
This is possible as technical regulations can increase the flow of information between producers
and consumers regarding the inherent characteristics and quality of products. In short,
consumers can reduce uncertainty in determining product quality due to standardization of
products (Jones and Hudson, 1996).
International standards, in the absence of multilateral consensus on the appropriate level
or setup of standards, also provide common reference points for countries to follow so that
transaction costs can be reduced. For example, in 1961 Codex Alimentarius was developed as a
single international reference point in order to draw attention to the field of food safety and
quality. Similarly, international standards developed by the International Standards Organization
(ISO) provide a basis especially for the developing countries to choose norms that are recognized
in foreign markets. In this regard, conformity to global standards can increase export
opportunities.
Despite their potential to expand competition and trade, standards may be set to achieve
the opposite outcomes. In general, standards selection could act to raise the compliance costs of
some firms (e.g., new entrants) relative to other firms (e.g., incumbents) thereby restricting
competition (Fischer and Serra, 2000). This outcome may be most likely in the context of
international trade, where governments might choose technical regulations to favor domestic
firms over foreign rivals, thereby restricting trade. This issue could be particularly problematic
6
for small exporting firms from developing countries, for they would need to absorb the fixed
costs of meeting multiple international regulations without enjoying domestic scale advantages.
Because economic theory suggests that technical regulations can either enhance or
impede trade, it is unsurprising that empirical evidence is mixed. Some studies support the claim
of an efficiency-increasing effect. Swann et al (1996) studied the impacts of standards on British
exports and imports over the period 1985-1991. Standards data were constructed as a simple
count of the number of standards by industry. Their findings concluded that adherence to British
national standards tended to raise both imports and exports. Moenius (1999) found that
standards shared by two countries had a positive and significant effect on trade volumes in a
gravity model. Gasiorek et al (1992) employed a CGE approach to find that harmonization of
standards in the EU would reduce trade costs by 2.5 percent.
In contrast, the fact that regulations can act as barriers to trade is evident in three recent
studies. Otsuki, Wilson, and Sewadeh (2001) estimated the impact of changes in the EU
standard on maximum aflatoxin levels in food using trade and regulatory survey data for 15
European countries and nine African countries between 1989 and 1998. The results suggested
that implementation of proposed new aflatoxin standards in the EU would reduce African
exports of cereals, dried fruits, and nuts to Europe by 64% or US$ 670 million. Wilson and
Otsuki (2002) studied the impact of pesticide standards on banana trade. The authors examined
regulatory data from 11 OECD importing countries and trade data from 19 exporting countries.
The results indicated that a ten-percent increase in regulatorystringency--tighter restrictions on
the pesticide chlorpyrifos--would lead to a decrease in banana imports of 14.8 percent. In
another paper Wilson, Otsuki and Majumdar (2002) addressed the question of whether cross-
7
country standards for maximum tetracycline (a widely used antibiotic) affected beef trade. They
examined the effects of the tetracycline standard on beef trade between six importing and 16
exporting countries. The results suggested that a ten-percent more stringent regulation on
tetracycline use would cause a decrease in beef imports by 6.2 percent.
Survey evidence also points to cost-raising characteristics of technical regulations. A
survey by the OECD (2000) as well as the interviews conducted by the United States
International Trade Commission (1998) shed some light on the size of standards-related costs.3
According to the OECD study, which was based on 55 firms in three sectors in the United States,
Japan and the United Kingdom, the additional costs of complying with foreign standards can be
as high as 10 percent. The United States International Trade Commission informally interviewed
representatives of the U.S. information technology industry. Interview responses revealed that
standards-related costs are considered the most significant trade barrier in that sector.
Overall, therefore, theoretical models and empirical evidence are mixed on the trade
impacts of foreign standards. However, the empirical studies undertaken to date adopt indirect,
and potentially misleading, approaches to understanding the cost impacts of regulatory
requirements. Specifically, the econometric investigations estimate reduced-form or gravity
models of bilateral trade in which standards are entered as a determinant of trade flows. The
survey evidence is informative but fails to incorporate the responses directly into a well-specified
cost function. Thus, a significant omission in this literature is that none of these studies has
taken a systematic and parametric approach to estimating the actual cost impacts of complying
with international standards. It is of considerable interest to study the extent to which variable
3See the discussion in Maskus, Wilson, and Otsuki (2001).
8
production costs are raised by these compliance needs and whether such compliance efforts have
impacts on factor demand within firms. This is the task to which we turn next.
3. Modeling the Cost Effects of Standards
A full accounting of the implications of a firm's decision to comply with standards
requires close study of both the costs and benefits of doing so. Our focus here is strictly on the
supply side and we leave aside the demand for compliance.4 Thus, our aim is to provide an
initial quantification of the costs incurred by firms in developing countries as they meet technical
regulations required in major export markets. It is of considerable interest to determine whether
such cost increases are significant.
3.1 Cost Function
Consider a firm exporting a product to a foreign market that mandates conformity with
standard s. We assume that the firm's compliance with any domestic standard is a sunk cost and
does not affect its decision to meet the foreign requirement. In principle the foreign standard
could affect both the firm's fixed costs (e.g., by requiring product redesign) and its variable costs
(e.g., by devoting more labor to product certification). To capture this possibility, we model
initial investment in compliance with the standard as a quasi-fixed factor and estimate a short-run
variable cost function.5 In this view, fixed costs are incurred in investing in compliance while
4Our data are insufficient for the analysis of demand for compliance. Such an analysis will require data on unit
prices of products that comply with standards and those that do not in each export market. This data is not currently
available.
5See Berndt and Hesse (1986), Morrison (1988), and Badulescu (2003) for further discussion. Badulescu sets out a
similar specification in which R&D is a quasi-fixed input across countries.
9
firms alter their capital and labor usage to meet recurring costs. Thus, our cost estimates reflect
short-run equilibrium and cannot be considered estimates of full adjustment to the long run.
In general, then, the cost function for the firm is specified as
C = C(w, y;s, z) (1)
Here, w refers to a vector of factor prices, y is output, s indicates the stringency of the foreign
standard, and z is a vector of other variables affecting firm-level costs. The firm minimizes
variable costs wx, where x is the vector of variable inputs. The cost function is assumed to have
standard properties: non-decreasing in w and y, concave in w, and homogeneous of degree one
with respect to w.
This general cost function has the stringency of standards and technical regulations, s, as
an argument because differential standards and technical regulations should affect the choice of
inputs for producing a given output level. That is, firms are informed about the technical
regulations required to sell their products in foreign markets. They make input allocation
decisions between production activities in the traditional sense and efforts that are devoted to
comply with the standards and technical regulations.
3.2 Estimation Models
We estimate firm-level parametric cost functions. This approach requires three central
assumptions. The first is that all firms, across industries and countries, share the same
technology. Application of the transcendental logarithmic (translog) function to industry-level
production data across OECD countries shows that this assumption is unlikely to hold (Harrigan,
1997). In the most general case we should estimate firm-level fixed effects and fully flexible
quadratic terms between these effects and all cost-related variables in order to permit factor
10
biases in technical differences. Unfortunately, such a specification would more than exhaust the
available degrees of freedom and is infeasible. Thus, we include in vector z industry and country
fixed effects in every specification to control for differences in technology relative to the
benchmark function. Nonetheless, this approach requires making the residual assumptions that
firms within an industry within each country share the same cost functions and that efficiency
differences by industry and country are Hicks-neutral.
A second problem is that estimation of a cost function incorporating intermediate inputs
requires firm-level data on prices of materials and intermediates, which our survey data do not
provide. Accordingly, we specify equation (1) as the cost of producing net output, or value
added, introducing only labor and capital as variable inputs. Thus, we assume that the value-
added cost function is weakly separable from the aggregator for raw materials and intermediate
inputs. The weak separability of the cost function implies that the choice of relative labor and
capital inputs will be independent of material and intermediate input prices.6
The cost function that reflects this technology is rewritten as
C(w, y;s, z) = (C1(y,w1;s,z),C2(y,w2; s, z)) , (2)
where w1 ={wL ,wK ) and w2 is the vector of prices for variable inputs other than labor and capital.
These subcomponents of the overall cost function should be homogeneous of degree one in w1
and w2, respectively, in order to be consistent with the linear homogeneity of C in w. Thus, this
cost function allows for each subcomponent to be estimated separately. Our goal is to estimate
6In our particular case, the separability condition is written as
C(w, y;s, z)/wL = 0, j L, K or
wj C(w, y;s, z)/wK wj K(w, y;s, z)
L(w, y;s,z) = 0, j L, K .
11
the elasticity of value-added cost (which corresponds to C1) with respect to standards. This
elasticity may be written as
C1 s = ln C1 /ln s (3)
s s C1
The third assumption is that factor prices are exogenous to firms, permitting their input
choices to be made endogenously. However, inspection of our survey data shows that direct
application of this assumption to a cross-section of firms is untenable because firms inevitably
report different average wage rates (or annual salaries) and returns to capital. Put differently,
direct construction of labor and capital prices from the survey data makes use of variables that
are endogenous, both in principle and in fact.
Consider, for example, the calculation of average salary per firm, which we define as
total payroll divided by firm employment. This computation generates figures for annual wage
rates that vary across firms within each country, as suggested by the summary data in Table 1.
Thus, the notion that firms inside a country, or even within an industry, face a common wage in a
competitive labor market is questionable. Similarly, we calculate an average capital price per
firm as operating surplus (value added less payroll), divided by the value of fixed assets. As may
be seen in Table 1, these constructed prices vary across firms as well.
One approach to resolving this difficulty would be to apply a national-average (or
industry-average) salary and price of capital to all firms. Such aggregate prices could be justified
as exogenous to each enterprise. However, to do so would sacrifice the cross-sectional variation
in factor prices needed to identify the cost function. To cope with this problem we employ an
instrumental variables technique in which we recognize that variations in factor prices across
12
firms depend on other characteristics of firms (Roberts and Tybout, 1997; Bernard and Jensen,
2000). Specifically, we estimate first-stage regressions of constructed labor and capital prices on
national-average factor prices, country and industry dummies, firm age (years since founding),
and dummy variables indicating the structure of firm ownership.
wL = a0 + a1wL + a2wK + Sa3jDj + Sa4kDk + a5AGEijk + Sa6mDm
ijk k k (4)
wK = b0 + b1wL + b2wK + Sb3jDj + Sb4kDk + b5AGEijk + Sb6mDm
ijk k k (5)
Here, superscripts i, j, and k refer, respectively, to firm, industry, and country, while superscript
m refers to type of ownership. In the data there are four types of ownership: privately held
domestic firms, publicly traded domestic firms (including domestic subsidiaries and joint
ventures with domestic firms), subsidiaries of multinational firms (including joint ventures with
multinational firms), and state-owned or collective enterprises. In principle, age and ownership
are past decisions that should be exogenous to current employment levels. Thus, the
instrumentation procedure should generate predicted wages that are exogenous to the second-
stage cost function estimation.
With these assumptions, we can develop an estimable translog cost function. Again, we
treat the standard with which a firm must comply to be a quasi-fixed factor and estimate a short-
run variable cost function. The notion is that for a firm to export it must meet the required
compliance cost and therefore it sets aside that component of cost before allocating labor and
capital to production activities. We specify the translog form to permit a flexible second-order
approximation to a cost structure depending on output, input prices, and standards. Thus, our
central specification of costs for firm i is as follows.
13
ln C~i = 0 + y ln yi + L ln wLi + K ln wKi + LL (ln wLi )2 + KK (ln wKi )2
1 1
2 2
+ yy (ln yi)2 + LK ln wLi ln wKi + Ly ln wLi ln yi + Ky ln wKi ln yi + s ln si
1
2 (6)
+ Ls ln wLi ln si + Ks ln wKi ln si + ln yi ln si + ss(ln si )2
1
ys 2
N C
+ z +
zn n zc c
z + DDdom +i
n=1 c=1
~
where C denotes value-added (cost of labor and capital, referred to as production cost hereafter),
wL denotes the instrumented wage rate, wK denotes the instrumented unit price of capital, y
denotes sales as a measure of output, and s denotes the firm-specific measure of standards.
Summary data on these variables are provided in Table 1 for the estimation sample. The
variables zn and zc denote industry-specific and country-specific factors, respectively, affecting
firm costs. We capture these additional factors by means of industry and country fixed effects.
For this purpose we use the four-industry aggregation listed in Table 2 and the 16 countries in
Table 3.
Our setup cost for compliance is designed specifically in the survey to measure cost
associated with foreign technical regulations and standards. Some of the surveyed firms
indicated that it is also necessary to comply with domestic technical regulations and standards in
order to sell their products in the domestic market. Because information is not available on the
cost of complying with domestic technical regulations and standards, a dummy variable ( Ddom)
is used to control for the possible cost difference associated with the domestic requirement. It
takes the value one if a firm reports that it is required to comply with domestic technical
14
regulations and standards, and the value zero otherwise. The variable i is the error term, which
is assumed normally distributed with zero mean.
Equation (6) is the translog cost function, which we estimate simultaneously with the
following equation for the share of labor in variable costs:
SLi = L + LL ln wLi + LK ln wKi + Ly ln yi + Ls ln si + µi (7)
The error term is also assumed normally distributed with zero mean and it reflects stochastic
disturbances in cost minimization. We eliminate the capital-share equation from the estimation
because it is fully determined by equations (6) and (7) and the constraints below.
Note that in writing these equations we have imposed the required symmetry in cross-
variable coefficients. Further, the linear homogeneity condition imposes the following
constraints:
L + K = 1
KK + LK = 0 (8)
LL + LK = 0
L + Ky = 0
y
Ls + Ks = 0
Equations (6) and (7) are estimated jointly in an iterative three-stage least squares
procedure (I3SLS), subject to the constraints in equations (8). When one of the share equations
is dropped, the I3SLS produce is the preferred approach since the estimators are consistent and
asymptotically efficient (Berndt and Wood 1975). The I3SLS procedure guarantees identical
translog cost parameters irrespective of which share equation is dropped. The parameters for the
15
dropped equation can be recovered by using the symmetry condition and the conditions in
equations (8).
From equation (6) we can determine the direct elasticity of production costs with respect
to foreign standards as = s + ss ln si , which varies with the level of standards. We are
d
s
interested as well in the impacts of the standards on factor demands. The coefficient Ls in the
share equation (7) measures the bias in labor use (impact on labor share) from an increase in the
foreign standard (Ls SL ln s = Ls ), and likewise for the bias in capital use
(Ks SK ln s = Ks ). In effect, the need to meet this standard could generate an overall
increase in costs, along with a bias in factor use toward labor or capital.
While the direct cost elasticity is of some interest, we can calculate the total elasticity of
cost with respect to a change in the stringency of standards, accounting for impacts on factor use,
as
S ln C~ ln s = s + ss ln si + Ls ln wLi + Ks ln wKi + ys ln yi . (9)
This elasticity will vary with different observations on factor prices and output. Likewise, we
can calculate the total elasticity of scale as
ln C ln y = y + ln yi + Ly ln wLi + Ky ln wKi + ln si .
~
(10)
y yy ys
Finally, the Allen partial elasticities of substitution between inputs i and j ( ij ) are:
ii = ii + Si - Si
2
, i = L or K
Si
ij = ij + SiS j
, i = L, j = K . (11)
SiS j
16
4. Data and Variable Construction
The data used for cost estimation are taken from a new survey undertaken by the World
Bank explicitly for the purpose of assessing compliance costs of firms in developing countries
facing technical standards in their potential export markets. Because the data are constructed
from firm-level surveys we provide an overview of their development.
4.1 The World Bank Technical Barriers to Trade Survey Data
The World Bank Technical Barriers to Trade Survey is the first comprehensive
questionnaire designed to elicit information from individual firms in developing countries about
how their operations are affected by foreign technical requirements.7 The survey was
administered in the year 2002 to 689 firms in 17 developing countries. The objective of the
survey is to obtain informationon the relevant standards, government regulations, and technical
barriers to trade confronting exporters from developing countries seeking to enter major
developed-country markets.
The countries cover a range of economic development and export experience yet have
sufficiently deep agricultural and industrial structures to permit sectoral comparisons. Countries
were selected for study in five regions. These include Poland, the Czech Republic, and Bulgaria
(East Europe); Argentina, Chile, Panama, and Honduras (Latin America); Jordan and Iran
(Middle East); India and Pakistan (South Asia); and South Africa, Nigeria, Uganda,
Mozambique, Kenya, and Senegal (Sub-Saharan Africa). Information on the number of firms
interviewed in each country and included in the estimation sample is listed in Table 3.
7Wilson and Otsuki (2003) describe this survey in detail.
17
The survey also embodies a diverse sectoral composition. The majority of firms are
categorized as manufacturing. The largest single industry is textiles and apparel (46 firms)
followed by raw agricultural products (18 firms) and processed food and tobacco (24 firms; see
Table 2). For analytical purposes we group the industries into four broad categories, namely raw
food; processed food, tobacco, drug and liquor; equipment; and textiles and materials.8
Firms were asked to provide information about numerous characteristics, including
product composition, age, form of ownership, employment, payroll, value of fixed assets,
intermediate inputs, raw materials, and others. Of particular interest is the export orientation of
firms. The majority of the respondent companies in the sample export at least some of their
products. The procedure for selecting firms meant that the sample consists of firms that are
either currently exporting or are willing to export but have chosen not to do so for some reason.
The number of firms that are currently exporting is 646 or 93.6 percent of the total. The number
of firms that are clearly not exporting is 43 or 6.4 percent of the total. Seventy percent of the
firms in the total sample face the need to comply with technical regulations (as defined in the
survey) in their export markets.
Across all five regions, 55 percent of the firms may be categorized as the headquarters
location of a privately held, non-listed company. About 20 percent are the headquarters location
of a publicly traded or listed company and 18 percent are subsidiaries or joint ventures of a
domestic enterprise. About 6.5 percent are subsidiaries of foreign firms or joint ventures with
foreign partners. Only a small portion of firms are state-owned or collective enterprises.
8Standards in the services sector are no less important than product standards as is evident in trans-border
operations of education, postal and telecommunication services. Collection of data on services standards, however,
would require expansion of the definition of standards as attributes of services outputs which are different from and
more complex than those of goods.
18
4.2 A Measure of Standard
A direct measure of the stringency of foreign standards and technical regulations
confronting a variety of industries and importing partner countries is difficult to define.
However, the relative increase in setup cost incurred for complying with these standards is a
good proxy for their stringency. One advantage of using reported investment to represent
stringency is that this measure is expressed in dollar terms and therefore is comparable across
industries and countries. In practical terms such an aggregation is necessary because the precise
specifications of technical standards facing firms vary across industries and cannot be
meaningfully aggregated at that stage. Another advantage is that expenditure for compliance can
be interpreted as a quasi-fixed factor, permitting us to specify a short-run variable cost function.
Our measure of foreign standards and technical regulations is constructed from
respondents' answers to the question summarized in Table 4. Respondents were asked the
following question: "What are the approximate costs of the items below as a percentage of your
total investment costs over the last year?" As may be seen, three categories were listed and
respondents indicated such costs within broad ranges.9 To focus on incremental investment as a
measure of quasi-fixed costs, we construct a standards-cost aggregate from the first three
categories. Weighted-average setup costs with regard to each category were computed by
multiplying the midpoint percentage within each range by reported investment cost of each firm,
yielding a dollar figure per category per firm. To develop the overall measure per firm we
simply added these various cost categories. Thus, to quantify the perceived impact of meeting
foreign standards and technical regulations we develop a measure of incremental contributions to
9The survey also asked two questions about measures of recurrent labor costs, which we do not employ in this
paper.
19
setup costs arising from additional plant and equipment and product redesigns (in total and for
multiple markets).
Unfortunately, not all firms responded to all three categories. Thus, to include only those
cases with responses in all of these categories greatly would reduce the number of observations
available for the regression analysis. We therefore aggregated these standards variables by
summing across the three categories, assigning a category value of zero to firms with missing
responses, for those firms where at least one category response was positive. Presumably, this
procedure understates the severity of such costs and should result in conservative cost
estimates.10
Therefore, we use the increase in previous year's reported investment cost for compliance
as a measure of the short-run fixed cost of standards and technical regulations. As shown in
Table 1, the total standard cost varies from a minimum of $357 to a maximum of $12.3 million.
Reported setup costs for compliance obviously are greater for larger firms.
5. Estimation Results
The first-stage regressions to develop instrumented labor and capital prices were run
based on equations (4) and (5). The instruments used include per capita GDP, real interest rates,
firm age, country and industry dummies, and dummy variables indicating the structure of firm
ownership. Per capita GDP and real interest rates were used to represent national average wage
rates and national average price of capital, respectively. We used the lending interest rate
10This selection procedure rais es a significant concern about selectivity bias. To control for this we included in
supplemental regressions a dummy variable taking on the value of 1 for firms that answered all three categories and
a value of zero otherwise. This made virtually no difference in the results.
20
available from the World Development Indicators. The interest rates were adjusted for inflation
as measured by the GDP deflator. These two equations were estimated jointly using seemingly
unrelated regression (SUR). The instrumented wage rates and capital prices were then used in
the cost function and share equation regressions.
In the second stage a cost function was run under alternative specifications. The
maximum number of observations included in these regressions was 159. As mentioned earlier,
this loss in observations is largely due to the low response to the questions regarding compliance
withthe foreign standards and technical regulations. The translog cost function was estimated
with the labor share equation jointly by using maximum likelihood estimation with iterated
three-stage least squares. The I3SLS method was used to obtain consistent estimators by
guaranteeing invariance of the estimated coefficients of the share equations irrespective of which
of the share equations is dropped (Berndt and Wood, 1975).
The parameter estimates with respect to translog models are presented in Table 5, with
standard errors reported in parentheses. In the first specification we exclude the quadratic term
on standards and the cross-terms on standards, input prices, and output. Thus, this model tests
for the notion that technical regulations affect costs only directly, without secondary impacts
through scale and variable inputs. The second equation contains the full translog specification
and is consistent with theory. Both of these regressions employ the instrumented factor prices
from the first stage. The third equation also follows the full specification but for comparison
purposes uses the raw (uninstrumented) wage rates and unit prices of capital. Finally, the fourth
model is estimated under the full translog but employs a different definition ofthe standards
variable, one that only contains the categories for one-time product redesign costs (excluding
21
plant and equipment investment). In this case the sample size falls to 96. Our interest here is in
seeing if the redesign costs alone have different impacts on costs.
All equations include industry and country fixed effects. The fit of each model is good
with adjusted R-squared coefficients of around 0.9. According to the procedures described in
Berndt and Wood (1975), we examined local concavity in input prices and positivity of input
shares for the translog model. Our fully specified translog cost functions were found to satisfy
these conditions.
The results of the translog model estimation suggest that the signs for the coefficients for
the linear and quadratic terms of the wage rate and capital price are all positive and statistically
significant. However, the signs and significance of the coefficients for the linear and quadratic
terms of the log of standards are mixed. In the restricted model I, the direct coefficient S is
positive, suggesting that costs rise with the relative severity of foreign standards. However, in
the general models II, III, and IV both the linear and quadratic coefficients on standards are
negative, suggesting that the direct effect of standards is negative or cost saving.
However, such direct impacts fail to account for the impacts of foreign technical
regulations through factor use and scale. We compute the total elasticity of costs with respect to
standards as in equation (9), reporting the results in Table 6. We evaluate this elasticity at the
mean and first and third quartiles of standards, sales, and input prices. It may be seen that the
total elasticity of domestic costs in producing value added with respect to variations in foreign
standards ranges from0.055 to 0.325, depending on the estimation approach and sample quartile.
This estimate is significantly positive at the mean in Model II and consistently positive and
significant in Models III and IV.
22
These differences require some explanation. The highest elasticities are registered in
Model III, in which the variable factor prices are not instrumented. Taken literally, the result
would suggest a quantitatively large impact of the severity of foreign standards on variable input
costs in exporting firms. That is, having satisfied the fixed setup costs required by foreign
technical regulations, variable costs would increase via a large induced increase in labor and
capital demand. Indeed, the computed elasticities of labor and capital demand in Table 7 are
highest in this specification, suggesting that a one-percent rise in foreign standards would induce
an 0.3-percent increase in labor and an 0.24-percent increase in capital employment.
However, these estimates fail to account for the endogeneity between production costs
and factor prices in our firm-level data. The instrumental variables approach in Models II and IV
should offer more reliable estimates. It may be seen that, using the fuller specification of
standards costs in Model II, including both plant and equipment charges and redesign costs, the
estimated cost elasticity in Table 6 is approximately 0.06, which is significantly positive only at
the mean of the sample. Thus, our estimate with the preferred econometric approach and the
larger sample suggests that increases in foreign standards compliance costs modestly affect
variable cost.
Interestingly, however, the estimated total cost elasticity is considerably higher in Model
IV, which incorporates only the product-redesign costs as a fixed factor. In that specification the
estimated elasticity is around 0.13 and is highly significant at the sample mean. This finding
indicates that the need to reorient product characteristics to meet foreign standards adds
significantly to short-run variable costs. While the results in Models II and IV are not strictly
comparable because of the different samples, this provides some indication that it is the need to
23
meet foreign requirements on product characteristics that matters rather more for sustaining
export positions. As may be seen in Table 7, the need for redesign implies induced increases in
demand for labor and capital of perhaps 0.12 - 0.15 percent.
While the estimated elasticities of variable cost with respect to the severity of foreign
standards seem modest, the implied cost impacts should be kept in perspective. As noted in
Table 8, at the sample mean a one-percent increase in compliance costs amounts to $4,250 for
the larger sample ($1,620 for the smaller sample). In turn, the table lists the dollar increment in
variable costs implied by the elasticities in each model at the sample mean. As may be seen, this
increase is $5,270 in Model II and $12,904 in Model IV. Thus, the implied expansion of
variable costs is, in fact, of a similar magnitude to the rise in required investment to meet
compliance costs. Viewed this way the impact on overall costs for the average firm, including
both compliance expenditures and variable charges, is economically significant.
Estimates of the scale elasticity (equation (10) are also presented in Table 6. This
parameter measures the percentage change in variable cost with respect to a one-percentage
change in output and may be interpreted as the ratio of marginal cost to average cost. These
scale elasticities range between 0.91 and 1.11. It is therefore not clear whether the average firm
in our sample exhibits economies of scale or diseconomies of scale.
We have assumed so far that the elasticity of costs with respect to standards is constant
across industries. Unfortunately, we do not have sufficient numbers of observations to run a
separate cost function regression per industry even using the aggregated industries. We instead
examine the constancy of the elasticity by letting the elasticity vary across industries in a pooled
regression. That is, we estimate equations (6) and (7), incorporating interaction terms between
24
the standards variables and four aggregate industry dummies. Let j denote jth industry.
Equations (6) and (7) will be rewritten as:
ln C~i = 0 + y ln yi + L ln wLi + K ln wKi + LL (ln wLi )2 + KK (ln wKi )2
1 1
2 2
+ yy (ln yi)2 + LK ln wLi ln wKi + Ly ln wLi ln yi + Ky ln wKi ln yi +
1 jD ln s
j j
2 s i
j
(12)
+ j j j j j j j j
LsD ln wLi ln s ji + KsD ln wKi ln s +i ysD ln yi ln s i
j j j
N C
+ 1 j D (ln s ji)2 +
j
2 ss znzn + zc c
z + DDdom + i
j n=1 c=1
SLi = L + LL ln wLi + LK ln wKi + Ly ln yi + j j (13)
LsD ln si + µi
j
where Dr =1 if j = r and Dr = 0 if j r . The fifth constraint in (8) should also be rewritten
accordingly:
Ls + Ks = 0 where j =1,..,J
j j (14)
This revision of the equations and a constraint permits us to compute elasticities for four
aggregated industries, including equipment, textiles and materials, raw food, and processed food.
The jth industry's total elasticity of cost with respect to standards is:
= s + ss ln si + Ls ln wLi + Ks ln wKi + ys ln yi .
j j j j j j (15)
s
The results for each model are presented in Table 9. There appear to be no significant
impacts on variable costs in processed foods, drugs, and liquors. Estimated cost elasticities are
25
consistently positive in the other sectors and standards seem to affect variable costs especially in
equipment (Model II) and textiles and material (Model IV).
Finally, Table 9 displays the elasticities of labor and capitaldemand with respect to
standards. These may be defined as
(16)
Ls ln L/ln s = ln C /ln s - ln SL /ln s
Ks ln K / ln s = ln C /ln s - ln SK /ln s
Using the elasticity of cost with respect to standards, evaluated at the mean, the full translog
model with instrumented input prices (Model II) implies that =0.060 and =0.056. This
Ls Ks
indicates that a rise in compliance setup costs increases both labor and capital usage, with a
slightly greater increase in labor demand. As noted above, these effects are larger in Model IV.
The Allen partial elasticities of substitution in Table 10 indicate a moderate substitutability
between labor and capital ( ) in the sample. The own-elasticity estimates indicate that labor
KL
is highly elastic with respect to its own price and that capital is much less elastic.
6. Conclusions
This paper estimates the impact on short-run costs of complying with standards and
technical regulations required by importing countries using firm-level data on technical barriers
to trade for 16 developing countries based on the World Bank Technical Barriers to Trade
Survey Database. The translog model results indicate that incremental production costs are
greater for a firm confronting more stringent standards and technical regulations. Using the
broader measure of standards in Model II, variable production costs are 0.058 percent higher
26
when the initial setup cost for compliance with foreign standards is increased by 1 percent. In
this case 0.060 percent additional labor and 0.056 percent additional capital are employed.
Using the narrower cost definition, focusing on product redesign costs, the impacts on variable
costs are considerably higher, at 0.13 - 0.14 percent, with correspondingly higher impacts on
variable factors. We focus on only labor and capital cost, but other types of input costs may arise
as additional plants and production units will require additional raw material, energy and
intermediate inputs.
Our analysis demonstrates the possible supply response in developing country enterprises
when changes in foreign standards and technical regulations take place. It can also be inferred
how much more (less) cost is incurred when a firm switches between export markets that vary in
the severity of standards and technical regulations. It is conceivable that firms might avoid
higher-cost markets in light of the impacts on production expenditures.
The results may be cautiously interpreted as indications ofthe extent to which standards
and technical regulations constitute non-tariff barriers to trade. While the relative impact on
costs is small in terms of the underlying elasticity, it could be decisive for particular firms and
countries. In this context, there is scope for assessing the damages to the exporting country's
trade benefits where the importing country's regulations may not conform to WTO obligations.
Policy solutions then might be sought by identifying the extent to which subsidies or public
support programs are needed to offset the cost disadvantage that stems from international
technical regulations. Furthermore, disaggregation of the cost disadvantage into those associated
with initial setup and variable production costs would help identify policy solutions regarding
standards and technical regulations. The existence of both setup and variable costs would imply
27
that a discrete action, such as upgrading infrastructure and training through government
programs and assistance from international organizations, for example, would be necessary to
overcome the cost disadvantage in addition to ad valorem subsidies.
28
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30
Table 1. Data Summary
Variable Mean Std. Dev. Min Max
Value Added (US$1,000) 9,087 22,744 13 189,463
Sales (US$1,000) 21,382 49,297 48 336,216
Wage rate (US$1,000) 3.14 3.14 0.11 15.38
Wage rate instrumented (US$1,000)* 2.47 1.78 0.34 8.15
Unit price of capital (US$1,000) 1.92 4.10 0.00 29.91
Unit price of capital instrumented (US$1,000)* 0.82 0.63 0.06 4.01
Per capita GDP (US$1,000) 2.22 1.89 0.26 7.47
Real interest rate (lending) (%) 9.00 4.78 1.68 29.09
Number of years since foundation 27.58 23.71 2 142
Standards (compliance costs of previous year) (US$1,000) 425 1,441 0.357 12,310
*Please see Section 5 for the instruments used for the wage rate and the unit price of capital.
Table 2. Industries in the Sample
Aggregate Industry Sub-industry Count
Raw food Raw agricultural and meat products 18
Subtotal 18
Processed food, tobacco, Processed food, tobacco, drug and liquor
drug and liquor 24
Subtotal 24
Equipment Electronics 11
Industrial equipment 4
Transportation equipment, and auto parts 10
Other equipment 6
Subtotal 31
Textiles and Materials Metal and mineral 15
Chemical 11
Leather 3
Plastics material 9
Textiles and apparel 46
Wood product 2
Subtotal 86
Total 159
31
Table 3. Number of Surveys Used for the Analysis by Country
Region Country Count
East Europe Bulgaria 23
Czech Republic 6
Poland 9
East Europe Total 38
Latin America & Caribbean Argentina 5
Chile 7
Honduras 3
Panama 6
Latin.America & Caribbean Total 21
Middle East Iran 14
Jordan 6
Middle East Total 20
South Asia India 33
Pakistan 30
South Asia Total 41
Sub-Saharan Africa Kenya 8
Nigeria 1
Senegal 2
South Africa 25
Uganda 5
Sub-Saharn Africa Total 39
16 Country Total 159
Table 4. Question on Cost Impact of Complying with Foreign Standards as a Share in Total
Investment (number of firms)
Share of investment costs 1-10% 11- 26- 51- 76- >100% Total
25% 50% 75% 100%
Additional plant or 62 32 14 6 3 3 120
equipment
One-time product redesign 70 17 5 3 1 0 96
Product redesign for each 57 15 4 4 0 0 80
market
32
Table 5. Cost Function Estimation (Fixed Effects: Industry, Country)
Model I Model II Model III Model IV
Parameters (I3SLS) (I3SLS) (I3SLS) (I3SLS)
0 -0.810 -1.585** 0.031 -1.751
(0.660) (0.804) 0.977 (1.146)
y 0.761*** 1.068*** 1.153*** 1.181***
(0.145) (0.219) 0.309 (0.296)
yy 0.019 -0.040 -0.116** -0.067
(0.018) (0.034) 0.016 (0.041)
L 0.351*** 0.376*** 0.286*** 0.416***
(0.083) (0.087) 0.067 (0.104)
K 0.649*** 0.624*** 0.714*** 0.584***
(0.083) (0.087) 0.067 (0.104)
LL 0.079*** 0.077*** 0.078*** 0.065***
(0.013) (0.013) 0.005 (0.012)
KK 0.079*** 0.077*** 0.078*** 0.065***
(0.013) (0.013) 0.005 (0.012)
LK -0.079*** -0.077*** -0.078*** -0.065***
(0.013) (0.013) 0.005 (0.012)
Ly -0.011 -0.016 0.006 -0.016
(0.011) (0.012) 0.51 (0.014)
Ky 0.011 0.016 -0.006 0.016
(0.011) (0.012) 0.51 (0.014)
s 0.055* -0.254* -0.528** -0.391
(0.031) (0.153) 0.015 (0.257)
ss -0.050** -0.084** -0.079**
(0.025) 0.018 (0.037)
Ls -0.002 -0.024*** -0.016
(0.010) 0.004 (0.014)
Ks 0.002 0.024*** 0.016
(0.010) 0.004 (0.014)
ys 0.058** 0.133*** 0.090**
(0.026) 0.037 (0.036)
D 0.008 0.013 -0.355*** 0.002
(0.113) (0.111) 0.025 (0.172)
Fixed Effects Industry, Country Industry, Country Industry, Country Industry, Country
wL and wkInstrumented yes yes no yes
Standards Redesign and Equipment Redesign and Equipment Redesign and Equipment One-time Redesign
Statistics
N 159 159 159 96
Adjusted R-squared 0.923 0.923 0.873 0.924
Log likelihood -95.435 -92.754 -108.765 -47.915
Note: The adjusted R-squared is computed as one minus the ratio of the residual sum of squares to the total sum of
squares, adjusted by the degrees of freedom. Figures in parentheses are standard errors and coefficients are
significantly different from zero as indicated by *** (1%), ** (5%) and *(10%).
Table 6: Elasticity of Variable Cost with respect to Standards and Scale
Elasticity with Elasticity
respect to evaluated at Model I Model II Model III Model IV
Standards 25 percentile na 0.055 0.207*** 0.142*
(1.473) (4.320) (1.894)
mean 0.055* 0.058* 0.270*** 0.132***
(1.760) (1.765) (6.188) (2.619)
75 percentile na 0.056 0.325*** 0.146***
(1.436) (6.177) (2.882)
Scale 25 percentile
0.893*** 0.998*** 0.851*** 0.876***
(21.031) (12.927) (7.785) (13.705)
mean 0.914*** 1.112*** 1.068*** 1.086***
(23.734) (11.217) (7.404) (17.460)
75 percentile 0.939*** 1.242*** 1.296*** 1.255***
(19.446) (9.609) (6.945) (14.515)
Note: Numbers in parentheses denote asymptotic t-values.
Table 7: Effect of Standards and Technical Regulations on Input Demand
Model I Model II Model III Model IV
Labor Demand
( Ls) na 0.060 0.299 0.148
Capital Demand
( Ks) na 0.056 0.240 0.116
Table 8. Estimated Impact on Mean Dollar Variable Costs of One-Percent Increase in
Mean Setup Costs
Model I Model II Model III Model IV
One-percent Increase in $4,250 $4,250 $4,250 $1,620
Mean Setup Costs
Mean Impact $4,998 $5,270 $24,535 $12,904
34
Table 9: Elasticity of Variable Cost with respect to Standards by Industry
Model Model I Model II Model III Model IV
Machinery and Equipment 0.114** 0.322*** 0.475*** 0.225
(2.000) (3.862) (3.888) (1.409)
Processed Food, Tobacco, Drug, and Liquor -0.004 -0.053 0.077 -0.026
(-0.060) (-0.633) (0.667) (-0.148)
Raw Food 0.018 0.079 0.419*** 0.190
(0.310) (1.175) (4.795) (1.177)
Textiles and Materials 0.058* 0.033 0.236*** 0.124**
(1.740) (0.866) (4.738) (2.214)
Note: Numbers in parentheses denote asymptotic t-values.
Table 10: Substitution Elasticity Estimates
Model I Model II Model III Model IV
Allen Elasticity of
substitution between L and 0.639 0.636 0.627 0.694
K ( KL )
Own elasticity of L ( LL) -1.456 -1.450 -1.404 -1.600
Own elasticity of K ( KK) -0.280 -0.279 -0.280 -0.301
35
