Draft. Comments welcome.
Clean Technological Change in Developing-Country Industrial Clusters:
Mexican Leather Tanning
Allen Blackman*
Resources for the Future
Washington, DC and
Arne Kildegaard
Department of Economics
University of Minnesota, Morris
Draft: June 2002
*Corresponding author. Resources for the Future, 1616 P Street, N.W., Washington, DC,
20036, (202) 328-5073 (voice), (202) 939-3460 (fax), www.rff.org, blackman@rff.org
.
This research was funded by the Tinker Foundation and Resources for the Future. We are grateful to CICUR, PPAEG and CIATEC for their assistance, and to our project team at the University of Guanajuato: Federico Arturo, Jorge Barrio, Federico Cantero, Juan
Días, Julio Gasca, Claudia Gomez, Erika del Carmen Gonzalez, Jeremy Heald, Edgar
Isusquiza, Cid Rodriguez, Arcelia Rodríguez, Fabiola Romero, and Eduardo Vargas.

Draft. Comments welcome.
Clean Technological Change in Developing-Country Industrial Clusters:
Mexican Leather Tanning
Abstract
In developing countries, urban clusters of dirty small- and medium-scale enterprises have severe environmental impacts. Since conventional public-sector regulation is generally ineffective in such situations, a promising second-best approach is to promote the voluntary adoption of clean technologies. However, there has been little economic research to guide policymaking. This paper uses original firm-level survey data on a sample of 164 small- and medium-scale leather tanneries in León, Guanajuato—
Mexico’s leather capital—to econometrically identify the determinants of the adoption of five different clean tanning technologies. Contrary to the recent literature, we find that neither conventional regulatory pressure nor “informal regulatory pressure” applied by private-sector agents plays an important role. Rather, key determinants of adoption are the firm’s human capital and stock of technical information, the same factors that drive the adoption of conventional productivity-enhancing technologies. In addition, we find that private-sector trade associations and input suppliers are critical sources of technical information.
JEL codes: Q25, O13, O33, Q28
2

Draft. Comments welcome.
1. Introduction
In developing countries, small and medium enterprises (SMEs) are both plentiful and relatively pollution-intensive. As a result, they generally contribute the lion’s share of industrial pollution in certain economic sectors. In Ecuador, where 80% of the industrial labor force is employed in firms with ten or fewer workers, SMEs are responsible for over 90% of total water pollution associated with vehicle repair and the manufacture of furniture, iron goods, processed foods, pulp and paper, and textiles
(Lanjouw 1997). SMEs create particularly severe environmental problems when they are geographically clustered. For example, emissions of particulate matter from a collection of 350 small-scale brick kilns in Ciudad Juárez, Mexico cause over a dozen cases of premature mortality and hundreds of cases of respiratory illness annually, damages valued at between $20 million to $90 million (Blackman et al. 2001).
Unfortunately, conventional regulatory instruments are generally ineffective in dealing with such problems. Clusters of SMEs large enough to create pressing pollution problems usually have the political power to deflect efforts by local authorities to promulgate or strictly enforce environmental regulations (Blackman 2000). Also, regulators’ political will to strictly enforce environmental standards is often weakened by the perception that, as a leading employer of the poor, SMEs fulfill an important distributional function. Politics aside, enforcing environmental regulations in industrial clusters is difficult because SMEs are exceptionally numerous, and because many are
“informal,” that is, virtually anonymous to the state.
Given these constraints, a promising second-best approach is to promote the adoption of clean technologies which prevent pollution and either reduce production costs, or at least do not raise them significantly. This approach has received considerable attention as a means of surmounting barriers to conventional regulatory approaches in developing countries. Both the seminal Brundtland Commission Report to the United
Nations and the equally influential World Bank Development Report on Development and the Environment advocate this strategy (World Commission on Environment and
Development 1987, World Bank 1992). The hope is that firms will adopt clean technologies voluntarily, or at least with minimal prodding.
Notwithstanding widespread enthusiasm for clean technologies in policy circles, there has been very little rigorous research on why firms—and in particular SMEs—do and do not adopt them. Such research can help to design efficient and effective polices to promote clean technologies. The literature on the diffusion of cost-saving innovations among SMEs in developing countries is broadly relevant, but it does not have much to say about the regulation, externalities, and peculiar political-economy considerations that
3

Draft. Comments welcome. affect the diffusion of clean technologies. One reason for the lack of research in this area is that hard data on clean technology adoption is exceptionally scarce.
This paper uses original firm-level survey data on a sample of 164 small- and medium-scale leather tanneries in León, Guanajuato (Mexico) to econometrically identify the determinants of the adoption of five clean tanning technologies. León is an archetype of a city where clean technological change represents the best hope for controlling emissions from a highly polluting industrial cluster. The cluster has had severe impacts on human health and the environment, and attempts to mitigate the problem using command-and-control regulation have repeatedly failed. Recently however, a significant percentage of León’s tanneries have voluntarily adopted clean technologies. Ours is the first attempt to explain this phenomenon and distill policy prescriptions.
The remainder of the paper is organized as follows. The second section provides some background on leather tanning in León. The third section briefly reviews the literature. The fourth section develops analytical and econometric models. The fifth section discusses data. The sixth section discusses our results. And the last section summarizes and concludes.
2.1. Efforts to regulate leather tanning in León
The city of León in north-central Mexico produces about two-thirds of the country’s leather. Almost all of it is used in shoemaking, the city’s other hallmark industry. Although exact numbers are not known, local regulators estimate that there are approximately 1,200 tanneries in León. At least three-quarters of the tanneries are smallscale, employing fewer than 20 workers, and about a quarter are unregistered and informal (Villalobos 1999).
Historically, León’s tanneries have dumped untreated effluents directly into municipal sewers which deposit them into the Gomez river, a tributary of the Turbio.
The main pollutants from tanneries are salt (used to preserve raw hides), various chemical compounds of sulfur (used to de-hair hides), chrome (used to render hides biologically inert), dissolved and suspended solids, and solid wastes impregnated with tanning liquors.
The pollution has contaminated surface and ground water, destroyed irrigated agricultural land, and has had serious health impacts. A 1987 study found chromium VI—a highly toxic byproduct of the chromium III used in tanning—in three quarters of the city’s drinking water wells (Hernandez 1987). León’s water pollution problems attracted international attention in 1994 after a die-off of tens of thousands of migratory aquatic birds wintering in a local reservoir contaminated by the city’s wastewater.
Regulations governing tannery pollution have been on the books for decades.
Among other things, current regulations require tanneries to register with environmental authorities, install sedimentation tanks and water gauges, pre-treat wastewater so that daily concentrations of various pollutants do not exceed permissible levels, and handle most solid wastes as hazardous materials. However, these regulations are generally not enforced. By all accounts, the main reason is that, as one of the city’s principal
4

Draft. Comments welcome. employers, tanners have considerable political power. They are represented by the
Cámara de la Industria de Curtiduria del Estado de Guanajuato
(CICUR) a powerful trade organization with a membership of about 400 mostly small- and medium-scale tanners.
Concerted efforts to control pollution from tanneries in León began in July 1987 when tannery representatives signed a convenio (voluntary agreement) in which they agreed to comply with written regulations within four years. But when it became apparent in 1991 that the tanners had not taken any action aside from installing sedimentation tanks desperately needed to prevent sewers from clogging, the convenio was renegotiated. In October 1991 a new convenio essentially granted tanners a second three-year grace period. It also committed the city of the León to build a common effluent treatment plant and a facility for handling solid and hazardous wastes. But at the end of this second grace period, these facilities had not been built and tanneries had still had made no progress in reducing discharges. A third convenio was negotiated in June
1995 and a fourth in March 1997. Neither produced any concrete progress in terms of treating tannery industrial wastes.
In addition to the succession of convenios , there has also been an attempt to control tannery pollution by relocating the industry to a dedicated industrial park called
Parque PIEL . Plans called for the park to house the infrastructure needed to segregate and treat tannery wastewaters. Public funds were to be used to build this infrastructure, and to provide financial incentives for relocation. After some negotiation, 500 tanners committed to the plan and 200 more actually bought land. Unfortunately, progress was brought to a halt by the Mexican financial crisis of 1995 and the discontinuation of government financial assistance. Tanners ultimately refused to foot the costs of relocating themselves and today Parque PIEL sits empty.
To date, the most successful means of controlling tannery emissions in León has been the diffusion of clean tanning technologies. The next section provides some background on such technologies, along with a brief overview of the process of leather tanning.
2.2. Clean tanning technologies
The process of leather tanning is comprised of nine separate stages, each of which produces either water pollution and/or solid waste (Figure 1). Briefly, the nine stages are
(for details, see UNEP 1991):
1. Soaking. To rehydrate salt-cured raw hides (the vast majority in León) and remove salt, dirt and blood.
2. De-hairing and liming. Using a bath of lime and sodium sulfide to dissolve hair.
Together with soaking, this stage is responsible for well over half of the chemical oxygen demand (COD) and biological oxygen demand (BOD) discharged by tanneries.
3. Fleshing. Mechanical removal of fat.
5

Draft. Comments welcome.
4. Splitting and trimming. Sectioning the hide.
5. Deliming. Removal of lime by copiously rinsing and applying neutralizing chemicals.
6. Bating. Treatment with enzymes to achieve a specified grain, feel and stretch.
7. Pickling. Treatment with common salt and other chemicals to terminate the bating process, adjust the pH, and improve penetrability for later stages of treatment.
8. Degreasing. Removal of grease using solvents or surfactants.
9. Chrome tanning. Soaking hides in a chrome bath to render them biologically inert.
1
The semi-finished hide produced by these nine stages is called a “wet blue” since chrome tanning gives the hide a bluish tint. A number of additional processes are needed to turn wet blues into finished leather, including a second splitting, shaving, re-tanning to achieve special characteristics such as moisture resistance, and dying.
In this paper, we focus on those stages of tanning up to and including chrome tanning, and we disregard subsequent finishing stages. We do this for a number of reasons. First, the wet blue stages are responsible for over 90% of the water pollution from tanning. Second, the wet blue and finishing stages of production are technologically and economically separable. Wet blues are often sold by firms that specialize in manufacturing them, and are purchased by firms that specialize in finishing.
In fact, the finishing stages produce very few effluents. Finally, wet blues are a homogenous product, and the processes used to manufacture them are also fairly homogenous across tanneries. This homogeneity simplifies our analysis considerably.
We consider five clean technologies used in producing wet blues in León (for technical details, see UNEP 1991):
1. High exhaustion. Using special inputs and procedures to ensure that more of the chrome in the tanning bath actually affixes to the hide, and less ends up in waste streams. Although this technique requires a more expensive type of chrome
(“self-basifying”) and a longer soaking period, it offers significant cost savings due to reduced overall chrome use (UNEP 1991).
2. Enzymes in the de-hairing bath. Substituting biodegradable enzymes for lime and sodium sulfide.
3. Precipitation of chrome. Using alkalis to precipitate out the chrome in the tanning liquors, collecting the resultant sludge and processing it to recover the chrome.
Processing the chrome-laden sludge involves some costs, and unless a central facility for the chrome recovery is established to service multiple users, this system is generally less cost-effective than high exhaustion.
1 An alternative to chrome tanning is “vegetable tanning” which involves soaking hides in extracts from tree bark. It is used to produce low grade leather which in Leon is primarily used to make shoe soles. We eliminated tanneries using vegetable tanning from our sample.
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Draft. Comments welcome.
4. Recycling the de-hairing bath. Saving and reusing the contents of the de-hairing bath instead of discharging them into the sewer after a single use. This technology is very simple. It only requires fixed investments in a holding tank, a pump, and a filtering system to remove suspended solids—usually a simple wire mesh screen. Because the chemical inputs into the de-hairing bath are relatively inexpensive, only minor cost saving accompany the environmental benefits.
According to UNEP (1991) a tannery that produces 1,000 wet blues per day could expect to save only US$ 8,000 per year.
5. Recycling the chrome tanning bath. Re-using contents of the tanning bath instead of discharging them into the sewer after a single use. Like recycling the dehairing bath, this clean technology only requires fixed investments in a holding tank, a pump, and a simple filter. It can reduce chrome use by up to 20% (UNEP
1991).
Note that we consider only pollution preventing technologies, not end-of-pipe abatement devices.
2
3. Literature
Among the determinants of technological change discussed in the literature, at least one—regulatory pressure—is unique to clean technologies. The link between formal regulatory pressure and clean technological change is well-established in the theoretical literature (e.g., Millman and Prince 1989) and a number of researchers have found empirical evidence for it (e.g., Kerr and Newell 2000). Even though financial and institutional constraints often preclude effective formal environmental regulation in developing countries, a growing body of recent research shows that “informal regulation”
(also known as “community pressure”) applied by private-sector groups such as neighborhood associations, trade unions and non-governmental organizations, can substitute for formal regulatory pressure (World Bank 1999). For example, Pargal and
Wheeler (1996) analyzed data on releases of water pollution by Indonesian factories during a period when there was effectively no water pollution regulation. They found that lower releases were correlated with a set of proxies for community pressure including per capita income, education, and population density in the vicinity of the plant.
Blackman and Bannister (1998) examined a city-wide effort to persuade small-scale brickmakers in Cd. Juárez (Mexico) to voluntarily substitute clean-burning propane for dirty traditional fuels such as used tires and scrap wood. They found that a key determinant of propane adoption was the extent to which brickmakers were exposed to pressure from local trade unions and neighborhood organizations.
2 The only end-of-pipe abatement devices used in Leon are sedimentation tanks—inexpensive lowtechnology concrete barriers that enable suspended solids to settle out of waste streams. To prevent Leon’s sewer system from clogging—a chronic problem—the city’s municipal water authority (SAPAL) has strictly enforced regulations requiring sedimentation tanks since the late 1980s. In addition, six or eight tanneries in Leon have adopted a pollution prevention technology called direct recycling—a relatively complex system of pumps, pipes, filters and sedimentation tanks that allows the effluent from one stage of the tanning process to be used as the input into other stages.
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Draft. Comments welcome.
The literature identifies a number of non-regulatory determinants of technological change that are potentially relevant (for a review, see Jaffe, Newell and Stavins 2001).
Of these, learning and human capital have probably attracted the most attention. The key idea is that in order to adopt new technologies, firms must first acquire the requisite technical and economic information, a costly enterprise. Information acquisition may be essentially passive, with firms absorbing information via day-to-day contact with business associates (Mansfield 1968), or it can be active with firms engaging in training and technical extension programs. Generally, information acquisition is facilitated and accelerated by the firm’s pre-existing stock of human capital, that is, the education and training of the management and staff. The literature on uncertainty is related to that on information acquisition. As new technologies are diffuse, uncertainty about the economic returns to adoption is reduced. Note that because of passive information acquisition and reductions in uncertainty, adoption generally becomes increasingly attractive over time.
Other drivers of adoption that have received considerable attention in the literature include input prices, firm size, and credit availability. Obviously, firms that face different input prices will have different technological preferences. For example, firms with access to cheap labor may prefer relatively labor-intensive technologies. The majority of the evidence indicates that large firms adopt new technologies faster than small ones. The most obvious explanation is that adoption involves fixed costs that imply economies of scale. Fixed costs may arise from capital indivisibilities or from more subtle informational and transactions costs. Evidence suggests that lack of access to credit is a binding constraint on technological change for small firms and farms even when fixed pecuniary costs of adoption are not large.
4. Model
This section presents a simple model of a tannery’s choice between a clean technology and a dirty one that formalizes the foregoing discussion of the determinants of clean technological change. To simplify the model, we assume tanners decide whether or not to adopt each of the five technologies sequentially and independently. The evidence supports this assumption for some of our five clean technologies. We address complimentarity between the clean technologies in a companion paper.
Each tanner chooses a tanning technology and an output quantity to maximize the discounted present value of profit generated by producing wet blues. The tanners choose between a clean technology and a dirty one indexed by i

(c,d). Time is indexed by t =
(0,1...

). In each period, revenues are equal to the product of price and quantity, N it
= p t q it
. Costs are comprised of four separate elements: production costs, C i
(•), environmental regulatory costs, R it
(•), one-time pecuniary fixed costs associated with adoption of the clean technology, F c0
(•), and one-time non-pecuniary fixed transactions and learning costs associated with adoption, T c0
(•). Several of these costs depends on the tanner’s technology choice: regulatory costs are lower for adopters than for non-adopters and non-adopters obviously do not pay fixed adoption costs. In addition, production costs may be lower for adopters than non-adopters. Revenues and the two recurrent costs
8

Draft. Comments welcome. are discounted using a subjective discount rate,

.
Each of the four components of costs are functions of underlying tannery characteristics. Production costs are increasing in input prices, “ v it
”, (where v it
is a vector) and are decreasing in both human capital, “u t
”, and the scale of the plant “y t
”.
Regulatory costs are an increasing function of formal government regulatory pressure,
“g t
”, and informal community pressure, “o t
”. Pecuniary fixed costs are increasing in the price the tannery pays for physical capital, “r t
”. In addition, for some of our technologies
(precipitation of chrome and recycling of the de-hairing and chrome baths) pecuniary fixed costs are increasing in firm scale, “y t
”, since firms with more tanning drums need to purchase more equipment. Non-pecuniary fixed costs are decreasing in human capital,
“u t
”, since more educated and experienced tanners learn the new technology more quickly. We assume that the cost function is a twice-differentiable, increasing convex function of output holding constant input prices, human capital, and firm scale.
Thus, the tanner’s optimization problem may be written, max
( q it
, i )


0
[ N it

C it
(q it
; v it
, u t
, y t
)

R it
( g t
, o t
)] e
  t dt

F i 0
( y
0
, r
0
)

T i 0
( u
0
) (t = 0, 1, ...

) (1) where for non-adopters,
F d0
(y
0
,r
0
) = T d0
(u
0
) = 0.
The tanner chooses whether of not to adopt the clean technology by comparing the maximum profit that can be obtained from the clean technology and the dirty technology.
More specifically, for each technology, the tanner chooses a stream of output quantities, q it
* for t = (0, 1, ...

) so as to maximize the present discounted value of profit. Then the tanner compares maximized profit for the two technologies. Maximized profit may be expressed as a function of the underlying tannery characteristics,
 i
(g t
,o t
,r t
, u t
, v it
,y t
) i = (c,d); t = (0, 1, ...

). (2)
In order to be able to express
 i
as a function of period zero tannery characteristics, we assume that the tanner knows the intertemporal paths of the recurrent costs C it
and R ct
and the price of output.
3
Specifically, we assume that the time path of these parameters may be described by equations of the form,
W t
= W
0 f(t),
3 We require a model in which
 i
is a function of period zero prices and costs because we do not have a panel of data that would enable us to estimate a true intertemporal model. The assumption that tanners know the intertemporal path of prices costs is less restrictive than the alternative assumptions yielding the same result: (a) tanners make their technology choices by simply comparing the profits that accrue in period zero (a common assumption in the literature), or (b) costs and prices are stationary, in which case tanners’ output decisions are identical in each period and the intertemporal model collapses to a static one.
9

Draft. Comments welcome. where f(t) is a bounded, non-negative function of time. Then maximized profits may be written,
 i
(g
0
,o
0
,r
0
, u
0
, v i0
,y
0
) = S iN
N
0
- S iC
C i0
( v i0
,y
0
) - S
R
R i0
(g
0
,o
0
)
- F i0
(r
0
,y
0
) + T i0
(u
0
) i = (c,d) (3) where,
S iN
 

0 f
Ni
( t ) e
  t dt
S iC
 

0 f
Ci
( t ) e
  t dt
S iR
 

0 f
Ri
( t ) e
  t dt i

( c , d )
The tanner chooses between the two technologies by calculating,
I* =
 c
(g
0
,o
0
,r
0
, u
0
, v i0
,y
0
) -
 d
(g
0
,o
0
, v i0
,y
0
). (4)
The tanner will adopt as long as I* > 0. Using this framework, it is straightforward to show that, all other things equal, a tannery is more likely to adopt the clean technology the lower its cost of capital, the more human capital it has, the more intense its exposure to formal and informal regulatory pressure, and the higher are the prices it pays for inputs used more intensively in the dirty technology than the clean one. Firm scale has an ambiguous impact on the probability of adoption. On one hand, it enables tanners to spread fixed costs over a greater number of units of output. But on the other hand, it necessitates higher fixed pecuniary adoption costs since set up costs are higher in relatively large tanneries.
Our econometric model is a reduced form of equation (4). For each technology we estimate,
I* j
= C j

+ R j

+ F j

+ T j

+ Z j
 e j
(5) where: j
I* j
C j
R j
F j
T j indexes individual tanneries is an unobserved latent variable is a vector of firm-specific variables that influence production costs is a vector of firm-specific variables that influence regulatory costs is a vector of firm-specific variables that influence pecuniary fixed adoption costs is a vector of firm-specific variables that influence non-pecuniary fixed
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Draft. Comments welcome. adoption costs
Z j is a vector of miscellaneous firm-specific variables that influence one or several types of costs

are vectors of parameters, and e j is an error term.
4
Although I* j
is latent and unobserved, we do observe an indicator variable, I j
, which takes the value of one if the clean technology is adopted and zero otherwise, that is, we observe,
I j
= 1 if I* j
> 0
I j
= 0 if I* j
≤ 0.
Using I j
as our dependent variable, we estimate equation (5) as a simple logit.
5. Data and variables
Our data comes from an original survey of 164 tanneries in León. The survey was administered in person in January 2000. Missing data reduces the number of usable records somewhat. By far, the variables with the most missing observations are the prices of inputs since some respondents did not have relevant records on hand at the time of the interview. For the econometric models that include the prices of sulfur and labor, we have 106 complete records (sample A) and for the models that only include the price of labor, we have 137 complete records (sample B).
To estimate equation (5) for each of our technologies, we use data on 13 variables associated with costs in the manner hypothesized in the analytical model. Table 1 lists the variables in our models and presents summary statistics for samples A and B.
[Insert Table 1 here]
Regarding the determinants of the production cost function, we include the prices of one or two inputs, depending on the technology in question. We include the natural logarithm of the price of labor ( lnp_lab2 ) in the models of all five technologies.
5 The technologies associated with the de-hairing bath—enzymes and the recycling—reduce the quantity of sulfur used in wet blue production. Therefore, we include the natural
4 Note that in order to be able to estimate the model with our data, we are forced to make a number of assumptions and abstractions. First, we implicitly assume that the discount factors, S iPj
, S iCj
, and S iRj
, and therefore discount rates,
 j
, are constant across tanners. We note, however, that the literature suggests that discount rates may vary considerably across producers (Pender 1997). Second, we abstract entirely from uncertainty which is often a significant influence on investment decisions (Pindyk 1991). Finally, we abstract from variations in producers risk attitudes which may also have been significant (Antle 1987).
5 Following the convention in Mexico, we distinguished between four different classes of labor—“obreros”,
“técnicos”, “supervisores” and others. We use the price of obreros in our adoption models because the lion’s share of workers in leather tanneries are obreros, and because our data is most complete for this variable.
11

Draft. Comments welcome. logarithm of the price of sulfur ( lnp_sulf ) in these adoption models.
6
Our measure of the scale of the firm is the natural logarithm of the number of wet blues produced per week
( ln_no_wba ).
As for the determinants of regulatory costs, we use two variables to proxy for the intensity of formal regulatory pressure: a dummy variable indicating whether the respondent believed that the technology in question would be required by law within five years ( he_req, ed_req, pc_req, rp_req, and rc_req ) and the number of visits per year by
SAPAL, the local water authority charged with enforcing certain regulatory requirements
( r_nv_sa ). Generally, the number of annual inspections by an environmental regulatory authority is endogenous since dirtier plants are inspected more often. However, SAPAL adheres to a fixed schedule of monthly visits. As the summary statistic for r_nv_sa indicate, although the vast majority of tanners in our sample were visited once a month, some were visited less frequently, a phenomenon that suggests they are subject to relatively lax formal regulatory pressure. Our proxy for informal regulatory pressure is a dummy variable indicating whether or not the tannery belongs to CICUR, the tannery trade association that has promoted clean technologies ( cicur ).
As for the determinants of fixed pecuniary adoption costs, our proxy for the cost of capital is a dummy variable that identifies tanneries which were not able to get any loans from formal banks between 1990 and 2000 because their applications were rejected
( cc_b ). We also include a dummy variable identifying tanneries that own (versus rent) their plants ( own_sp ). Renters pay a higher effective price for new equipment since it is costly for them to recover investments in equipment when they switch locations.
As for the determinants of non-pecuniary fixed adoption costs, we use a number of variables to measure human capital including: a categorical variable measuring the owner’s education ( edow_df where 1 = less than high school [“preparatoria”]; 2 = high school; and 3 = more than high school); the number of employees in the tannery with a bachelor’s degree in engineering or chemistry [“licenciatura en ingeniería or química”], or a technical degree in tanning [“carrera técnica”] ( no_li_ct ); and the number of years since the owner first learned of the existence of the technology ( he_fshd3, ed_fshd3, pc_fshd3, rp_fshd3, and rc_fshd3 ). Regarding the last variable, we follow the literature in assuming that the longer the owner has known about the technology, the greater her stock of knowledge about it. These variables are categorical. (“0” = this year; “1” = less than five years; “2” = five to nine years; “3” = ten to 14 years; “4” = 15 to 19 years; and
“5” = more than 20 years.) The variable cicur may also proxy for human capital since trade union members may have easier access to technical information and assistance.
For two of the three technologies that have been actively promoted by chemical supply companies—high exhaustion, enzymes in the de-hairing bath, and the
6 High exhaustion, the precipitation of chrome and recycling the tanning bath all reduce chrome use.
However, we are unable to include chrome prices in these models because the type of chrome tanneries purchase—and therefore, the price they pay for it—depends on whether or not they adopt. For example, tanneries that adopt high exhaustion use self-basifying chrome which is more expensive than conventional chrome.
12

Draft. Comments welcome. precipitation of chrome—we also include dummies that identify the tanneries’ principal suppliers ( ds_grija ). Our assumption is that in cases where chemical supply companies provided information and technical assistance, these dummies proxy for human capital.
There are a dozen chemical supply companies which service ten or more of tanneries.
Because we have too few observations to include this subset of suppliers, we tested each dummy (in a model with the remaining independent variables) and only included those that were significant.
To control for miscellaneous firm characteristics that may affect the costs of adoption, we include a dummy variable that identifies tanneries which primarily produce wet blues made of cow hide, as opposed to those made of goat, horse, lamb and pig hide
( dth_c ).
Finally, note that in preliminary specifications, we included dummies identifying each tannery’s location in León. We developed a list of 12 locations by aggregating
“colonias”—that is, neighborhoods included in all León addresses—in a way that accounts for natural barriers such as large roads, rivers and railroad lines. We believed the location dummies might proxy for human capital and regulatory pressure. If demonstration effects are important, than one would expect firms in neighborhoods where tanneries are “thick on the ground” to pay lower learning costs than tanneries which are relatively isolated. In addition, one might expect spatial patterns to monitoring and enforcement activities. Surprisingly however, none of the location dummies were correlated with adoption in any of our five models. We omit them in the models presented in the paper to preserve degrees of freedom.
6. Results
Table 2 presents our regression results. With regard to the production cost variables, the most striking result is that firm scale as proxied by weekly output
( lno_wba ) is not significant in any of the five models. Evidently, economies of scale associated with these technologies are not important and/or are counterbalanced by disincentives to adoption due to the magnitude of fixed adoption costs. Several of the price variables are significant, however. The price of sulfur ( lnp_sulf ) is significant at the
5% level in the models of the adoption of enzymes and recycling the de-hairing bath. In the recycling model, the sign is positive, indicating that, as our model predicts, tanneries paying higher sulfur prices are more likely to adopt this sulfur-saving technology.
However, in the enzymes model, the sign of lnp_sulf is negative indicating that tanneries paying higher sulfur prices are less likely to adopt. The most likely explanation is that unobserved differences in the type of sulfur are correlated with price, that is, the type of sulfur used in combination with enzymes tends to be relatively inexpensive.
[Insert Table 2 here]
Surprisingly, few of the regulatory variables are significant. The indicator variable for membership in CICUR, the tannery trade association ( cicur ), is significant the 10% level in the high-exhaustion model. The number of annual visits by SAPAL, the
13

Draft. Comments welcome. city water authority ( r_nv_sa ), is positive and significant at the 5% level in the recyclingof-the-tanning-bath model. The dummy variable indicating the tanner expects the environmental regulatory authority will eventually require adoption is not significant in any of the five models.
Of the proxies for the fixed pecuniary costs of adoption, the dummy identifying firms that have been rationed out of formal credit markets ( cc_b ), is negative and significant at the 5% level in the high exhaustion model. This suggests that credit constraints may inhibit adoption. In addition, the dummy identifying firms that own their plant ( own_sp ) is positive and significant at the 5% level in the recycling-the-de-hairingbath model.
The most striking results concern the human capital variables that proxy for the non-pecuniary fixed set-up costs. The number of years the tannery owner has been aware of the technology ( he_fshd3, ed_fshd3, pc_fshd3, rp_fshd3, rc_fshd3 ), a proxy for the owner’s accumulated stock of knowledge about the technology, is positive and significant at the 1% level in every one of the models except that for recycling the tanning bath. The number of trained environmental specialists ( no_li_ct ) is positive and significant in the precipitation of chrome model (at the 10% level) and in the recycling the tanning bath model (at the 5% level). The tannery’s chemical supplier ( ds_griga ) a highly significant predictor or adoption in the chrome precipitation model.
The level of the owners’ education ( edow_df ) is significant in the models of high exhaustion, chrome precipitation, recycling the dehairing bath and recycling the tanning bath. However, in each of these cases, it is negatively correlated with adoption, indicating that more educated owners are less likely to adopt, all other things equal. This surprising finding is most likely due to an omitted variables problem. One possibility is that education proxies for the owner’s susceptibility to expert advice: less educated owners may have been more liable to blindly accept the recommendations of clean technology promoters. A second possibility is that education proxies for absentee ownership: less educated owners may have been on-site more often, and therefore, may have been quicker to take advantage of opportunities to cut regulatory and production costs.
7
Finally, the variables that control for firm-specific characteristic—the dummy that identifies firms using cow hides to produce wet blues ( dth_c )—is positive and significant at the 5% level in the enzymes and chrome precipitation models.
To summarize briefly, the econometric results suggest that the factors that best explain which firms in León have adopted clean tanning technologies are the same
7 Another possibilities is that education proxies for the owner’s age or experience. However, including age and experience variables in the regressions does not affect the results. A final possibility is that lesseducated owners were more likely to have falsely reported adopting. However, a variety of tests— including checking for internal inconsistencies in survey responses that would suggest a false report and discarding all records classified by the enumerator as anything less than completely reliable—discounts this explanation.
14

Draft. Comments welcome. factors long known to drive the adoption of conventional cost-saving technologies: the owner’s stock of knowledge about the technology, and the human capital embodied in the firm’s staff. Although there is some evidence that formal and informal regulatory pressure have played an important role in the adoption of some technologies, it does not appear to have been consistently determinative. Also, the five clean tanning technologies are evidently scale-neutral: small tanneries were just as likely to adopt as large ones.
7. Additional survey data
Survey data not used in the econometric analysis both support and embellish the conclusion that the owner’s stock of knowledge is the key driver of clean technology adoption. For each technology, Table 3 presents non-adopters’ most important reasons for not adopting. In each case, the plurality of non-adopters said the key reason was a lack of relevant technical information.
[Insert Table 3 here]
Table 4 shows how long the survey respondents had been aware of each technology. In each case, less than a third had been aware more than four years. Thus, although they have been in use in other countries for decades, these five clean technologies are new to tanners in León, and as a result, information is evidently relatively scarce.
[Insert Table 4 here]
Table 5 presents data on how the adopters in our sample first became aware of each technology. For enzymes in the de-hairing bath, almost half of adopters leaned about the technology from chemical supply companies. For every other case, the plurality of adopters first heard of the technology from CICUR, the tannery trade union.
[Insert Table 5 here]
8. Conclusion
Our results run counter to the economic literature’s emphasis on the role of regulatory pressure in driving clean technological change. That formal regulatory pressure was not important in León is not all that surprising given such regulation is relatively weak. That informal regulatory pressure was not important is more surprising.
As discussed in Section 3, there is now a significant body of literature demonstrating that in developing countries, informal regulatory pressure is a key driver of environmental performance.
Two factors explain our somewhat unconventional results. One has to do with the nature of the pollution in question. While emissions from industrial sources of air pollution are relatively easy to detect and also impact specific neighborhoods disproportionately, damages from individual tanneries (odors aside) are not easy to detect
15

Draft. Comments welcome. and are not concentrated around the tannery. Tannery effluents are discharged into common sewers. Thus, it is difficult for private sector organizations such as neighborhood organizations to identify particularly dirty tanneries, and moreover, there is little incentive for them to do so.
A second explanation for the lack of informal regulation has to do with the political economy of León. As discussed above, tanneries are a leading employer and powerful political force in the city, and are able to derail efforts to enforce environmental regulations. Unfortunately, neither one of these two factors is likely to be unique to our case study: one would expect them to be present in most large clusters of water polluters.
The implication is that one can not expect informal regulation to be a generalizable solution to SME pollution problems.
A number of policy prescriptions follow from our positive results. First, given the importance of learning effects, the diffusion of clean technologies can be hastened by facilitating the dissemination of technical information. Our survey data indicate that in
León, the principal purveyors of such information have been private sector institutions, namely chemical supply companies and CICUR, the tannery trade association. Given chronic constraints on public sector finances and expertise for promoting clean technologies in developing countries, this is welcome news. Also encouraging is the fact that learning and demonstration effects are typically cumulative and self-perpetuating.
As more firms adopt, the rate of adoption should accelerate, at least until some threshold is reached. One would expect this dynamic to occur even in the absence of subsidies to technical assistance.
Finally, our results suggest that efforts to promote clean tanning technologies should be as successful among small firms as they are among medium and large ones.
This message is heartening given that such a large proportion of the firms in industrial clusters are typically small-scale.
16

Draft. Comments welcome.
References
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Draft. Comments welcome.
Pindyck, R. 1991. Irreversibility, Uncertainty, and Investment. Journal of Economic
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Oxford U. Press: New York.
18

Draft. Comments welcome.
Table 1. Variables in econometric model
Proxy for: Name
Variable
Description
Sample A
(n = 137) mean s.d.
Sample B
(n = 106) mean s.d.
I*
I*
I*
I*
I*
C(v) he_ado2 ed_ado2 pc_ado2 rp_ado2 rc_ado2 lnp_sulf
C(v) lnp_lab2
C(y), F(y) lno_wba
R(g) r_nv_sa
R(o), T(u) cicur
R(g)
R(g)
R(g)
R(g)
R(g) he_req ed_req pc_req rd_req rc_req
F(r)
F(r)
T(u) cc_b own_sp edow_df
T(u)
T(u)
T(u)
T(u)
T(u) no_li_ct he_fshd3 ed_fshd3 pc_fshd3 rd_fshd3 rc_fshd3 ds_grija
Endogenous
Adopted high exhaustion?
†
Adopted enzymes in de-hairing bath?
†
Adopted precipitation of chrome?
†
Adopted recycling of de-hairing bath?
†
Adopted recycling of chrome bath?
†
Exogenous
0.59
--
0.20
--
0.49
--
0.40
--
--
0.38
--
0.22
--
0.49
--
0.41
Ln price sulfur
Ln price labor
Ln no. wet blues produced per week
0.19 0.40
-- --
6.31 0.35
5.37 1.17
Credit applications rejected 1990 - 2000?
†
0.09 0.28
Own (vs. rent) plant?
†
0.57 0.50
Owner’s education < = > high school §
2.04 0.90
--
1.90
6.31
5.29
Visits per year by SAPAL
Member CICUR?
†
10.96 6.16
0.89 0.31
High exhaustion required w/in 5 yrs?
†
0.82 0.39
Enzymes “ “ “ “ ?
†
0.49 0.50
10.69
0.89
0.83
0.50
5.01
0.32
0.38
0.50
Precipitation
“ “ “ “ ?
†
Recycle de-hairing. “ “ “ “ ?
†
Recycle tanning
“ “ “ “ ?
†
0.64
0.72
0.78
0.48
0.45
0.42
0.64
0.73
0.78
0.48
0.45
0.41
0.10
0.56
1.96
--
0.32
0.37
1.13
0.31
0.50
0.93
Number professionals on staff
Yrs. since first heard of high exhaustion
§
“ “ “ “ “ enzymes §
“ “ “ “ “ precipitation §
“ “ “ “ “ recycle de-hair §
“ “ “ “ “ recycle tanning §
Chemical supplier dummy
†
T(u)
T(u)
Z dth_c Wet blues made of cow hide?
†
†
zero/one dummy variable
§
categorical dummy variable
1.64 5.10
1.69 1.00
1.04 0.90
1.31 1.15
1.23 0.92
1.44 0.94
0.10 0.30
0.88 0.32
1.11
1.63
1.00
1.25
1.21
1.41
0.11
0.86
1.69
0.94
0.88
1.15
0.93
0.92
0.31
0.34
19

Draft. Comments welcome.
Table 2. Logit adoption function estimates
Proxy for:
C(v)
C(v)
C(y),F(y)
Name Description
R(g)
R(o),T(u)
R(g)
F(r)
F(r)
T(u)
T(u)
T(u)
T(u) r_nv_sa cicur
**_req cc_b own_sp edow_df no_li_ct
**_fshd3 ds_grija
Visits per year by SAPAL
Member CICUR?
-0.025
(0.035)
1.326***
(0.704)
Required by law w/in 5 yrs? -0.053
(0.539)
Credit apps. rejected
Own (vs. rent) plant?
-1.518**
(0.722)
-0.081
(0.415)
Owner’s educ. < = > H. S.
No. professionals on staff
Yrs. since first heard of **
Chemical supplier dummy
-0.808***
(0.254)
0.183
(0.140)
0.720***
(0.246)
--
Z lnp_sulf lnp_lab2 lno_wba dth_c
Variable
Ln price sulfur
Ln price labor
Ln no. wet blues per week
Wet blues of cow hide?
Constant
Log likelihood
Model 1 Model 2 Model 3 Model 4 Model 5
HE ED PC RD RC
(n = 137) (n = 106) (n = 137) (n = 106) (n = 137)
0.093
(0.572)
0.004
(0.217)
-1.504*
(0.799)
0.078
(0.656)
0.009
(0.271)
-0.677
(0.713)
-0.094
(0.298)
1.662*
(0.973)
0.267
(0.826)
0.035
(0.295)
-1.224
(0.706)
-0.282
(0.249)
0.691
(0.675)
-1.224
(3.883)
-75.7
-0.044
(0.050)
0.384
(0.881)
0.295
(0.576)
1.016
(0.851)
-0.524
(0.522)
0.365
(0.325)
0.062
(0.176)
1.608***
(0.444)
--
2.027*
(1.110)
-2.475
(4.809)
-48.7
0.007
(0.043)
1.217
(1.252)
0.785
(0.700)
0.136
(0.990)
-0.183
(0.574)
-0.958***
(0.328)
0.130*
(0.075)
0.833***
(0.251)
2.263***
(0.775)
1.985
(1.267)
-0.178
(4.937)
-48.1
0.033
(0.059)
0.479
(1.194)
0.551
(0.911)
-0.270
(1.018)
1.4097**
(0.659)
-0.651*
(0.356)
0.281
(0.188)
1.041***
(0.381)
--
0.540
(0.959)
-9.550
(6.040)
-40.9
0.108**
(0.046)
0.935
(1.166)
1.082
(0.858)
-0.612
(0.908)
0.171
(0.534)
-0.829***
(0.321)
0.188**
(0.082)
0.434
(0.279)
--
1.108
(1.150)
4.182
(4.887)
-52.2
* significant at 10% level
** significant at 5% level
*** significant at 1% level
20

Draft. Comments welcome.
Table 3. Most important reason for not adopting technology:
% non-adopters’ responses in each category
Reason HE
Lack tech. info. 49
ED
(n=76) (n=50) (n=76) (n=85) (n=97)
44
PC
46
RD
41
RC
37
Uncertainty
Fixed costs
Variable costs
Ruins quality
Other
6
23
4
0
17
14
12
2
4
24
5
21
5
8
14
12
25
1
2
19
10
24
4
9
15
Table 4. Years since respondent first became aware of technology:
% responses in each category
(n = 137)
0
Years
Less than 5
5 - 9
10 - 14
15 - 20
HE ED PC RD RC
5 28 23 19 9
47 46 45 51 56
29 20 16 20 21
14 3 9
4 2 4
More than 20 1 0 1
8
1
1
12
1
1
Table 5. How respondent first became aware of technology:
% responses in each category
Source
Tanner
Input supplier
CICUR
CIATEC
Other
Do not recall
HE ED PC RD RC
(n=130) (n=98) (n=104) (n=111) (n=124)
15
21
30
9
46
19
16
14
27
19
8
32
23
9
35
9
23
2
12
13
0
16
24
2
13
27
2
12
22
0
21