AREC 382
Computer-Based Analysis in Agricultural
and Resource Economics
Fall 2012
Assignment 7
Due December 19, 2012 at midnight
(Sorry, absolutely no extensions this time. This is the last day of exams)
Note: This is your last assignment, and the one that carries the most points towards the final grade. There is no final exam for this course.
This final exercise combines many of the skills you have mastered throughout this semester and does not give explicit directions on exactly how and where to do your calculations. These decisions are up to you but please be clear where the answers are and what you are attempting to convey with the answers because partial credit will be harder to come by for this exercise. Please read the entire document at least twice before you attempt to do the exercises. To do the assignment, you will also need to download the accompanying data spreadsheet prescatori.xls.
This document is organized as follows: Section 1 provides the necessary background and motivation. This section introduces the travel cost method, and its use with a group of potential beneficiaries of a natural resource policy (sports anglers). This section also explain that our goal will be to estimate the demand for fishing trips as a function of the price of a trip.
Section 2 explains what we mean by the price of a fishing trip, and how we measure it. In Section 3, I describe the survey of Venice anglers that was used to gather the data for this exercise. Section 4 describes the statistical models, the demand function and the inverse demand function that we estimate from the statistical model, and the welfare measures of interest to natural resource managers. Section 5 describes the accompanying dataset. Section 6 contains the questions you are to answer. There are 12 questions.
Use Excel to do all of your regressions, welfare calculations, and descriptive statistics. If you have not completed the entire assignment by the above date and time, turn in whatever you have been able to do by then.
1. Introduction and Background
Natural resource managers must often make decisions that affect the recreational users of natural resources. Such decisions typically include improving the water quality of streams and lakes and restocking them with fish, allowing access to trails and remote natural areas, providing campsites and campground services in state and national parks, etc.
Economists are interested in computing the monetary value of the benefits associated with such policies. Once the benefits have been monetized (i.e., expressed in euro or dollars), they can be compared with the costs of the policy in a formal benefit-cost analysis. Our first order of business when trying to estimate the benefits of a proposed natural resource management policy is to identify the beneficiaries of the policy, i.e., the persons who gain from the policy itself.
In this assignment, we will focus on water quality improvement and fish restocking in the Venice Lagoon (see map in the Appendix). The Lagoon of Venice is a site of exceptional interest, due to its distinctive hydrological features and ecosystem, and its unique cultural and social significance. As one of the most important wetland sites in the Mediterranean region, the Lagoon of Venice is covered by the European Union’s policy for wetlands preservation, and the harvesting of fish and shellfish in the Lagoon is an important local economic activity.
At this time, however, the Lagoon of Venice is environmentally degraded, due to the industrial pollution from plants and refineries in nearby Porto Marghera, nutrient and pesticides in agricultural runoff, and organic pollutants from urban areas and livestock. In addition, overfishing by commercial outfits has depleted fish stocks, and biodiversity is endangered by an exotic clam species that was artificially introduced for commercial fishing during the 1980s. The introduction of the Tapes philippinarum clam has been blamed for serious changes in the natural lagoon environment, as harvesting this species involves the use of invasive (and illegal) fishing techniques, such as mechanical scrapers, which were eventually prohibited.
Public programs are currently under consideration that would seek to restore environmental balance in the Lagoon by reducing pollution and implementing and managing sustainable commercial fishing practices. The latter would result in increased fish stocks.
The beneficiaries of fish stocks maintenance policies may include commercial fishermen, consumers of the fish and shellfish harvested in the Lagoon, and recreational anglers. In this assignment, we will focus on the latter group—recreational anglers. To assess their benefits from the proposed policy, we will use a technique known as the travel cost method (TCM).
Briefly, the TCM approach reasons that if people travel to a location to fish/hunt/enjoy the scenery/go hiking, and spend money to do so, then the (monetary) value of this activity is equal to, or larger than, the amount of money that they spent to get there and fish/hunt/hike, etc.
To apply the TCM approach to Lagoon recreational anglers, we will estimate their demand for fishing trips to the Lagoon of Venice. This demand function is very similar to any other demand function we have seen in earlier economics classes: It is downward sloping with respect to price. In other words, the higher the price of a trip, the fewer trips one wants to take. The lower the price, the more fishing trips one wants to take.
The only difference between the demand for fishing trips in this final project and demand functions that you have studied in other economics courses is that in this project we will trace out one demand function for trips for each angler, whereas in other courses you probably studied the market demand for a certain good (e.g., ice cream) by all consumers in that market.
In this assignment, we will work with the simplest possible shape for the demand function for fishing trips. Specifically, we will use a linear demand function. The formula for a linear demand function is:
(1) ,
Where y is the number of fishing trips to the Lagoon of Venice taken by an angler in a year, p is the price of a fishing trip, and and are the intercept and the slope, respectively. Figure 1 shows an example of a linear demand function. Please note that the true and are both unknown to us, and must be estimated using regression analysis and a sample of data. Also note that the slope coefficient, , must be negative.
Figure 1.
It is, of course, possible to work with other functional forms for the demand function. For example, the demand function could be the curve shown in Figure 2.
Figure 2.
2. How Do We Measure the Price of a Trip?
The price of a fishing trip is comprised of several components. Fishing in the Venice Lagoon requires you to use a private or a public transportation boat, which means that first you will have to drive to a launching ramp or a to a public transportation boat (“battello”) stop. The price of a trip is thus comprised of
1. the cost of gas for your car and parking fees, if any;
2. the cost of fuel for the boat, or the fare for the battello;
3. food and beverages consumed during the trip, and the cost of lodging if you spend the night;
4. the cost of bait;
5. any access fees (or fishing license fees, if they are paid every time you go fishing); and
6. the opportunity cost of the time spent traveling to and at the fishing site.
To recreational demand economists, item 6 is as important as items 1-5 in computing the price of a trip. They reason that if an individual does not go fishing, he could gainfully use this time to earn money. For example, if instead of going fishing for six hours you spent the same amount of time doing economics and statistics consulting, you would earn an amount of money equal to your hourly rate times 6 hours.
It is clear that the price per trip is different across different anglers for three reasons. First, anglers live at different distances from the Lagoon of Venice and own different type of cars, which means that the cost incurred under item 1. will vary across anglers. Second, not everyone owns the same boat, and some people will rely on public transportation boats, which means that item 2. will also differ from one angler to the next. The same applies to items 3., 4. and 5. Third, different people have different opportunity cost of time (different wage rates).
We therefore conclude that the cost of a fishing trip tends to be different across anglers. This is important, and is essential for our goal of estimating the coefficients in equation (1) using regression analysis.
As mentioned above, we will estimate the demand function for trips by anglers that live in the Province of Venice using data collected from a sample of them. In the next section of this document, we will describe the questionnaire used to gather these data and the survey administration.
Please note that it is relatively easy to measure items 1-5 when we survey anglers (or hunters), but that it is very challenging to measure item 6, the opportunity cost of time. In past research efforts, researchers have used a person’s hourly wage rate, or about 30% of a person’s hourly wage rate, to measure the opportunity cost of time. But this approach does not work very well when the angler is a retired person, a homemaker, a person that is paid on a salary—rather than on an hourly—basis, or a person that works a fixed number of hours (e.g., eight hours a day).
For this reason, in this final project the price of a trip has been computed by adding together items 1-5. The opportunity cost of time, which is not measured reliably in this study, was ignored.
3. Study Design and Administration
A. Study Design
In April-July 2002, we conducted a mail survey of anglers with valid fishing licenses for the Lagoon of Venice. The questionnaire is reported in the Appendix. Question 4 explicitly asks the respondents to tell us how many times they typically go fishing in the Lagoon in a year. The price per trip is reported by the respondent when he or she answers question 12. The answers to question 4 and question 12, therefore, are the basic variables needed to estimate equation (1).
But we wanted more information. Specifically, we wanted to know how many times the respondent would go fishing if the price per trip changed, or the catch rate was improved (which is the goal of the policy currently under consideration), or both. Accordingly, we asked the respondent (question 22) how many trips he would take in a year if the price per trip increased by a specified percentage. Different respondents were given different percentages, which were selected at random from five possible values (20%, 40%, 60%, 80% and 100%).
Next, we asked respondents how many trips they would take (i) if there was a 50% increase in the catch rate (question 26), and (ii) for a simultaneous increase in the price per trip and in the catch rate (question 27).
In summary, we have a total of four price-trip pairs for each respondent. The first of these four pairs tells us the actual price and number of trips at the current catch rate, while the remaining three are hypothetical. The four pairs are summarized in table 1.
Table 1. Trips reported by the respondent in the survey.
Scenario Type Price per trip Catch rate
1 Actual Actual price per trip pi0 Current conditions (C=0)
2 Hypothetical Higher price: pi1 = (1+X)?pi0, where X is selected at random out of {0.20, 0.40, 0.60, 0.80, 1.00} Current conditions (C=0)
3 Hypothetical Actual price per trip pi0 Increase of 50% over current conditions (C=1)
4 Hypothetical Higher price: pi1 = (1+X)?pi0, where X is the same as in scenario 2 Increase of 50% over current conditions (C=1)
Please note that if you wish to estimate the demand function based on the actual price per trip, the current catch rate, and actual trips taken by the respondent, you will use equation (1) and use one observation per respondent. If, instead, you wish to use all four observations for each respondent, it is necessary to modify the demand function as follows:
(2) ,
where C is a dummy variable that takes on a value of 0 if the catch rate is the current one (scenarios 1 and 2), and 1 of the catch rate is the improved catch rate (scenarios 3 and 4) (see table 1).
Equation (2) implies that when the catch rate is improved, the demand function shifts out: In other words, when the catch rate is increased, at any given price people take more trips than they do at the current catch rate. Note that the slope of the demand function remains the same, and that the demand function is moved up by exactly . This is shown in Figure 3. This assumption—that people will go fishing more, all else the same, when the quality of the site gets better—is a key feature of the TCM.
You can read the rest of the questionnaire for yourself. You will notice that in questions 6-10 and 14-15 the respondent is queried about the type of boat. This is important for the Lagoon of Venice, where the type of boat and boat traffic determines wave motion, which damages the buildings in Venice and has potentially serious consequences on the Lagoon environment.
Figure 3.
In questions 16-21 we try to find out about the respondent fishing habits and behaviors, including locations, type of fish caught, time of the day he or she goes fishing, etc.
You will notice that questions 23-25 probe the respondent about his perception of the Lagoon. Specifically, question 23 asks about the respondents perception of and knowledge of exotic species (the Tapes philippinarum clam) in the Lagoon, while question 24 investigates the respondent’s perceptions of pollution in the Lagoon. In question 26, we inquire whether the respondent had previously changed his fishing behavior as a result of such problems.
B. Survey Administration
The survey was administered by mail in April-July 2002. In a first wave of mailings, we sent questionnaires to 3000 anglers. This sample was drawn from the universe of all holders of fishing licenses in the Province of Venice, stratified by distance of the angler’s place of residence from the Lagoon and age. The survey packet included a participation notice card, which was filled out and returned by about 500 anglers.
In our second wave of mailings, we sent a reminder card to the remaining 2500 anglers. We received about 500 more participation cards after this reminder. In sum, the participation notice card was filled and returned to us by a total 1048 anglers. Of these, 605 stated that they did not intend to participate in the survey, while the remaining 443 filled out the questionnaire. Out of the latter 443, 268 had gone fishing in the Venice Lagoon in the previous 12 months. In this assignment, we will work with the survey data collected from these 268 individuals.
4. The Statistical Model
A. Basic Statistical Models
As explained in sections 1 and 3, the goal of this final project is to estimate the trip demand function from our sample of anglers. In practice, we will estimate several variants of the trip demand function. The simplest trip demand function is the one presented in equation (1). A more complex variant is the one presented in equation (2).
In an even more complex variant, we allow individual characteristics of the respondents to affect the number of trips taken by the respondent. For example, we can write the equation
(3)
Where INCOME is a variable measuring the respondent’s income, RETIRED is a dummy variable that takes on a value of 1 if the respondent is a retired person and zero otherwise, and BOAT is a dummy variable that takes on a value of 1 if the respondent goes fishing by his or her own boat, and zero otherwise (for example, if he uses a friend’s boat or the public transportation system).
At this point, you should notice that equations (1)-(3) are really mathematical models. In other words, they assume that fishing trips can be perfectly predicted from price and catch rate conditions.
In real life, however, people are not so easily predictable. For this reason, instead of a perfect mathematical relationship, we write a statistical model, which contains an “error term” e. This error term captures factors that influence the demand for trips, but are not easily predictable or known to the researcher. If we add error terms to equations (1)-(3), we obtain:
(4) ,
(5) , and
(6) ,
respectively. The error terms are e, ? and ?, respectively.
The beta coefficients are estimated from the sample data reported by our anglers using the method of ordinary least squares. In other words, the software places the regression line (i.e., finds the values of the ?s) to make the sum of the square distances between the price-trip points and the regression line itself as small as possible. You illustrated these concepts in Ex 6.
B. Demand Functions
The estimated coefficients are used to do three things. First, we use them to trace out the demand function for trips. For example, if we use the estimated coefficient from equation (4), which we denote as b0 and b1 (we reserve the symbols and for the true coefficients, while b0 and b1 denote the estimated coefficient), we can ignore the error term e and trace out the trip demand function. To do this, we simply draw the straight line with intercept equal to b0 and slope equal to b1. Price is on the x-axis and trips are on the y-axis.
The second thing that we can do after we estimate the coefficients is to draw the inverse demand function . This time, we put trips on the horizontal axis and prices on the vertical axis. To draw this line, suppress the error term in equation (4) and write:
(7) .
C. Welfare Statistics
The third thing we can do once (7) has been drawn is to calculate several measures of welfare. The first is the willingness to pay for the total trips y1 taken by the respondent, which is the area under the inverse demand function up to y1 in the lower of the two inverse demand functions in Figure 4 (the blue one). Although the respondent is willing to pay all of this money to go fishing, in reality he only pays the area of the rectangle between the origin O, E, C, and y1, because the current price is p0.
The difference between the total willingness to pay and total expenditure is the consumer welfare. In figure 4, the consumer surplus for the current price and fishing conditions, is equal to the area of the triangle BEC. The consumer surplus is frequently used as a measure of the welfare in applied policy work. Here, it captures the value of access to the fishing sites of the Lagoon.
Figure 4. Inverse Demand Functions and Consumer Surplus
The consumer surplus under the current catch rate conditions is therefore:
(8) ,
where p0 represent the current price per trip faced by the respondent.
Suppose now we wish to find out how the consumer surplus associated with the improved catch rate. To do this, it is necessary to estimate regression equation (5). On suppressing the error term, we obtain the regular demand function, which is equal to . As before, we can derive the inverse demand function, which is now:
(9) .
We can draw this demand function for C=1, as shown by the higher line in Figure 4 (the pink line), and for C=0 (the blue line in Figure 4). For C=1 (improved catch), the consumer surplus is equal to:
(10) ,
While for C=0 (current condition), the consumer surplus is .
What is the change in consumer surplus experienced by the angler when catch rates are improved? This change in consumer surplus is CS1-CS0, and it is equal to
(11) .
In sum, Figure 4 depicts the consumer surplus with and without the improvement in the catch rate. The surplus with the improvement in the catch rate is equal to the area of the triangle AED while the surplus without the improvement is equal to the area BEC. So the improvement in the consumer surplus is equal to the area ABCD.
5. The Data
The companion spreadsheet contains data collected through the survey of Venice anglers. Completed questionnaires were supplied by 268 anglers, but the dataset contains a total of 1072 rows because each respondent contributed 4 observations on the number of annual trips.
The individual respondent is denoted by his ID number. The variable SCENARIO keeps a track of which observation on trips the respondent is providing. When SCENARIO=1, the respondent is telling us how many fishing trips he truly takes in the average year. When SCENARIO=2, he is telling us how many he would take if the price increased, when SCENARIO=3 he is telling us how many trips he would take if the catch rate were improved, and when SCENARIO=4 he is telling how many he would take if the price were raised and the catch rate was also increased.
The variable TRIPS is the number of trips per year. PRICEE denote the price per trip. The variable CATCH is a dummy that takes on a value of 1 when the respondent was asked to consider a scenario with improved catch rates, and 0 when he was asked to consider the current conditions.
The variable ZONA denotes the area where the respondent lives. For example, if ZONA=1, the respondent lives in the city of Venice. If ZONA=2, he lives in Zone B, if ZONA=3 he lives in Zone C, etc (see footnote 8). The variable DQAP tells us how many years the respondent has been fishing, and thus captures experience, which is likely to be a good proxy for fishing skills.
VOGO is a variable equal to 1 if the respondent believes that the exotic species of claim has affected fish stocks and catch rates. In other words, this is the answer to question 23. INQUI is an indicator taking on a value of 1 if the respondent answered yes to question 24, and 0 otherwise. CAMBIA means that the respondent told us that he did change his fishing behavior in response to pollution problems and to the introduction of the exotic species of clam (question 25).
The other variables in the dataset are:
• yearborn = the year of birth of the respondent;
• householdsize = the number of members of the respondent’s household;
• retired = dummy equal to one if the respondent is a retired person, and zero otherwise;
• student = dummy equal to one if the respondent is a student, and zero otherwise;
• pcapinc = annual income of the family divided by the number of household members.
• Fasciaa, fasciab, fasciac, fasciad = dummies equal to one if the respondent lives in Zone A, B, C or D, respectively.
• Boat = dummy equal to one if the respondent uses his own boat to go fishing;
• Education = schooling in years;
• Age = age of the respondent in years;
• Male = dummy equal to one if the respondent is zero.
6. Homework questions.
Question 1. Section 1 of this document. Consider equation 1, and assume that the intercept is equal to 20 and the slope to -0.5. What is the number of trips when the price of a trip is equal to 30 euro? By how much do I need to raise the price for trips to decline by one?
Question 2. Use the information provided in Section 3 to calculate the survey response rate, which is defined as the number of questionnaires received by the researchers, divided by the number of questionnaires they mailed out. Do you judge this response rate to be low, high, or just about right? What is the response rate recommended for mail surveys to be used for government work? Please cite your source for this.
Question 3. Consider equation (6) in section 4 of this document. Which sign do you expect for the coefficients on price, catch rate, income, retired and boat? Why?
Question 4. Before we begin to estimate the demand for fishing trips, it is necessary to get a good sense of the survey data. Provide descriptive statistics of the sample by answering the following questions:
(a) what is the percentage of males in the sample? Studies by the Department of the Interior in the US have found that 95% of sports fishermen are men. Do the data from the Venice study find similar results?
(b) What is the minimum, maximum, and average number of actual fishing trips in a year? To answer this question, make sure that you select only the data for SCENARIO=1. To do this, copy the data to sheet 2, sort them by SCENARIO and then by ID, then delete the rows corresponding to scenarios 2, 3 and 4 from sheet 2. Rename this sheet {SCENARIO 1}. (You will use this sheet to answer items (c)-(g) too). Then select Descriptive Statistics from the Data Analysis menu, and apply it to the trips variable. Is the max. realistic? Could it be a data entry problem?
(c) What is the minimum, maximum, and average price of a trip? Are they realistic? In answering this question, again please select only the observations with SCENARIO=1 (i.e., use the sheet SCENARIO 1). Do the data confirm our reasoning in section 2 of this document that the price of a trip varies across respondents?
(d) In the SCENARIO 1 sheet, based on the examining the variable DQAP (=years of fishing experience), do you believe this sample to be comprised primarily of experienced anglers?
(e) What are the percentages of ZONE A, B, C, D and E respondents in our sample?
(f) What is the average age of our respondents? The average number of years of schooling? The percentage of retired persons in our sample?
(g) What is the percentage of the sample that changed their fishing behavior as a result of pollution and exotic species?
Question 5. We would expect fewer trips when we raise the price of a trip (in scenario 2) relative to now. Is that the case? To get insights about this, compute the average number of trips for scenario=2 and compare with the average number of trips for scenario=1. What do you notice?
Are there any respondents who will give up fishing altogether when the price of a trip is raised?
Question 6. We have previously argued that when the quality of the site is improved (by raising the catch rate), the number of trips should increase. To check if this expectation is borne out in the survey data, compute the average number of trips for scenario=1 and compare it with the average number of trips for scenario=3.
Question 7. The Venice authorities and researchers have recently wondered whether residents of the city of Venice (Zone A anglers) generally go fishing in the Lagoon more often than people that live in Lagoon waterfront areas. Is this true? To answer this question, consider only the observations for scenario=1 (sheet SCENARIO 1), and divide up this sample into ZONA=1 and all others. Do a 2 tailed t test with unequal variances of the null hypothesis that the mean number of trips for ZONA=1 respondents is the same as the mean number of trips for all others.
Question 8. Do retired persons go fishing more than the others? To answer this question, consider only observations for scenario=1. Break this sample into retired persons (RETIRED=1) and all others (RETIRED=0), and do a t test on the means of these two groups, following the procedure you used to answer question 7.
Question 9. Now it is time to estimate a very simple demand function, namely that represented by equation (4). To do this, first make sure that you use only the observations with scenario=1. Then, use the TOOLS | Data Analysis | Regression to run a linear regression. Your dependent variable will be TRIPS, and the independent variable is PRICEE (do include the intercept in this regression). Based on the regression results,
(a) is the sign of the coefficient on price the right one?
(b) by how much do we need to raise the price of a trip to see trips decline by one?
(c) At what price will the angler forgo fishing altogether?
Question 10. Using the regression results from question 9, compute the consumer surplus (CS0) for every person in the sample following equation (8), where b0 and b1 should be set equal to the estimated intercept and slope from the regression. Make sure you only use the observations with scenario=1.
(a) What is the average consumer surplus for this sample?
(b) What is the average consumer surplus for Zone A residents?
Question 11. Now we will use the entire sample of 1072 observations, and will run regression (5). Here, the dependent variable is TRIPS, and the independent variables are PRICEE and CATCH.
(a) is the sign of the coefficient on CATCH the right one?
(b) What is the interpretation of this coefficient?
(c) Does the coefficient on price change very much relative to what it was in question 9? How do you interpret this finding?
Question 12. Now we want to see whether the number of trips is affected by other variables, such as a person’s income, boat ownership, and whether he or she is retired from the workforce. Now run a regression where the dependent variable is TRIPS and the independent variables are PRICEE, CATCH, PCAPINC, BOAT and RETIRED.
(a) Report the regression results and comment on the sign and magnitude of the coefficients.
(b) Do the coefficients on PRICEE and CATCH change very much relative to the results from question 11?
Appendix. Translated questionnaire.
The Universities of Padua and Venice are conducting a study about the most important aspects of recreational use of the Lagoon of Venice. We would like to ask you to help us by answering a few questions about sport fishing in the Lagoon of Venice. Your opinion is very important for our research. Your answers to our questions are strictly confidential and your name will not be associated with your answers.
1) How many times do you go fishing in a year, on average?______
2) How many years have you been fishing? _________ years.
3) Do you ever go fishing in the Lagoon of Venice?
? NO (terminate)
? SI (continue)
4) How many times a year, on average, do you go fishing in the Lagoon of Venice?_________
5) Who do you go fishing with?
? By myself
? With my family
? With friends
6) Do you use a boat to get to your fishing site(s)?
? YES
? NO
7) If yes, which of the following best describes most of your fishing trips?
? Own boat
? Friends’ boat
? Boat owned by an association
? Rental boat
? Public transportation boat (vaporetto)
8) If you use your own boat, please describe it below for us.
Characteristics Please circle the appropriate type
Type of boat Oars Sail Motor
Type of skull Wood Fiberglass Dinghy
Length of the boat
Vintage of the boat
9) If your boat has a motor, what are its characteristics?
Characteristics Please explain
Strokes Two Four
Horsepower
Vintage of the motor
10) If you use a boat, where do you keep it?
? At home
? In a canal. ? Which canal and where? __________________________________________________________
? In a marina. ? Which marina and where? ____________________________________________________________?
11) In the last twelve months, how much did you spend to go fishing in the Lagoon of Venice?
Category Expenditure (lire)
Gear (nets, rods, etc.)
Boat and motor maintenance
Boat mooring (marina, insurance, etc.)
Clothing
Other (please explain)____________________________________
12) What is the average cost per trip to the Lagoon for each of the following categories?
Category Cost (lire)
Bait (only if you buy bait)
Boat fuel
Launching from a boat ramp
Other (please explain)__________________________
13) If you do not buy bait, how much time do you spend gathering or preparing bait for each fishing trip?
Number of hours_____________
14) If you own a boat, do you believe that your fixed costs are high, relative to your income?
? yes
? no
* By fixed costs we mean expenses that are incurred every year regardless of the number of trips (insurance, marina, maintenance, etc.)
15) If you own a boat, what is, in your opinion, the minimum number of trips that should be undertaken to justify the fixed costs of owning a boat?
? The minimum number of trips is___________________
? There is no minimum number of trips, because I use my boat for other purposes as well
16) Please mark on the following map the sites from which you usually access the Lagoon MAP 1
17) Please mark on the following map the sites where you normally go fishing
MAP 2
18) Which species do you usually fish? (Please check all applicable species.)
? Branzino (seabass)
? Orata (gilthead)
? Sogliola (Sole)
? Mormora
? Passera (flounder)
? Seppie (squid)
? Vongole (clams)
? Go
? Cefalo (mullet)
? Other, please explain________________
19) What time of the day do you usually go fishing?
? Morning
? Afternoon
? Evening
? Night
20) How many hours do you spend at the fishing site on a typical fishing trip?
? Up to 3 hours
? 3 to 7 hours
? 7 to 12 hours
? more than 12 hours
21) Which season do you usually go fishing? (Please check one or more boxes)
? Spring
? Summer
? Fall
? Winter
22) If the cost per Lagoon fishing trip increased by ____X____%, would you keep your number of fishing trips the same?
? Yes
? No, I would decrease them to _______________ per year
? No, i would stop going fishing in the Lagoon of Venice altogether
23) In your experience as a fisherman, would you say that the commercial fishing of vongola filippina (caparozzolo) (Philippines clam), as is currently done, has had an adverse effect on catch rates in the Lagoon?
? Yes
? No
24) In your experience as a fisherman, would you say that polluted sediments have had an adverse effect on catch rates in the Lagoon?
? Yes
? No
25) Have you changed the number of your fishing trips and your fishing sites in the Lagoon as a result of the pollution of the Lagoon and illegal clam fishing?
? Yes
? No
26) Suppose that illegal fishing of the Philippines clam were subject to regulation, and that polluted sediments were removed, resulting in a 50 % increase in catch rates in the Lagoon. If the cost per trip were the same, would you increase your number of Lagoon fishing trips?
? Yes. ? Could you tell us how many more times you would go fishing in the Lagoon per year? _______
? No. ? Why?_________________________________
27) Suppose that illegal fishing of the Philippines clam were subject to regulation, and that polluted sediments were removed, resulting in a 50 % increase in catch rates in the Lagoon. If the cost per trip increased by ____X___%, would you keep the number of your Lagoon fishing trips the same?
? Yes, I would keep it the same
? No, I would increase it by ____ times a year
? No, I would decrease it by ______ times a year
? No, i would stop going fishing in the Lagoon altogether
Individual characteristics of the respondent
28) Gender M F
29) Year of birth __________________
30) Place of Residence
__________________________________________________________________
31) Education
? Elementary school
? Junior high school diploma
? High school diploma
? College degree
? Other (please explain)______________
32) Occupation
? Teacher
? Business owner
? Professional
? Self-employed
? Manager
? Clerk
? Factory worker
? Homemaker
? Retiree
? Student
? Other (please explain)_______________
33) How many people is your household comprised of? ______
34) Which of the following categories best describes your pre-tax household income? (Your answers are confidential.)
? Less than 15 million lire
? 15 to 20 million lire
? 20 to 30 million lire
? 30 to 50 million lire
? 50 to 80 million lire
? 80 to 150 million lire
? More than 150 million lire
35) Which percentage of total household income do you contribute?
? Less than 50%
? 50% to 75%
? More than 75%
This is the end of the questionnaire. The Universities of Padua and Venice thank you for your attention and for taking the time to complete the questionnaire.
