Sea Farming Sea Farming Impacts on Household Income

There are only seven individuals who owned safeguard house, four individuals from participant group and the other three individuals from non-participant group. One of the interesting findingsis the respondents’ answer to the question of “What should be improved in sea farming project that would make it more be neficial to local community?”. Most of respondents answered that sea farming project should open a new recruitment because there are relatively lots of people who wants to join the sea farming group. It means that there are interests from non-participants to join sea farming group. New member recruitment will reduce jealousy from non-participants in the society. Other issues which are raised are quality and quantity of the fingerlings, establishment and reinforcement of local hatchery to provide low-cost and high quality fingerling in a timely manner, and information transparency specifically for sea farming member to reduce internal conflict. This conflict caused retransformation of sea farming committee in 2012. Access to credit for local community is another interesting finding. None of the respondents get credit from any banks and other microfinance institutions. The reasons were that they do not know how to apply for credit and no legal recognition for their business. In case of lack of information, socialization about credit program has been conducted every year from local government and some banks, such as Bank DKI, BNI, and BCA. Unfortunately, the program representatives did not actively socialize the program. They were only waiting for someone to come to their desk asking information about credit. In addition to that, sea farming organization certificate which is signed by notary was disappeared. The members suspect that the former chairman of sea farming committee took the certificate away for his own benefit. To sustain sea farming in the future, there are several things to be improved, such as better management, information transparency, and trust between sea farming committee and its members. The new committee is challenged to have better organization, so that they will have good image from participants, non- participants, and local community as a whole. 5 CONCLUSION AND POLICY IMPLICATIONS Conclusion The probit model was used to identify the determinant of sea farming participation in Panggang Island, Kepulauan Seribu, Indonesia. The model shows that factors which significantly influencing the household decision in joining sea farming project are education, occupation, household size, and membership in non-sea farming organization. Contrary to the stated hypothesis, the study shows that all significant factors are reducing the probability of one person to participate in the project. The OLS regression was used to analyze the correlation between participation in sea farming and household income generation. The OLS regression shows that sea farming participation, age, organization member, and mobile phone ownership are variables that have significant relationship to the total income. Participation in sea farming will increase the total income by Rp14.6 million per period, ceteris paribus, significant at 99 percent confidence interval. Compared to other variables, sea farming gives the highest contribution to increase total income. The result is in line with the stated hypothesis that sea farming has positive impact in increasing household income as well as reducing poverty. Sea farming is perceived as beneficial program for the local community particularly in participants’ point of view because it gives alternative source of income, enhances mariculture knowledge and skills, provides capital and input to establish mariculture activity, etc. However, sea farming still faced some constraints which are hampering its potential to contribute in increasing local community’s welfare as expected. Water qualities, security, diseases attack, delay of fingerlings supply, internal and external conflict are some challenges for the success of this project. In summary, sea farming has a good concept to provide economic activity for rural coastal community. Nevertheless, the program requires improvements to give bigger and more significant impacts in increasing household income and alleviating poverty in larger scale. Policy Implications The first model indicates that the project is more attractive for those whom are less educated, have primary occupation not as a fisherman, have less household members, and less involvement in non-sea farming organization.It implies that the project manager and local government should socialize the objectives and positive impacts of the project effectively to attract the fishermen as the main beneficiaries. The second model indicates that the project successfully increase household income. It also shows networking is another important aspect for local community to increase their income. It implies that project manager and local government could optimize organization’s function as the media to gather public attention to join such project and to spread information and knowledge for local community to increase their income. Additionally, mobile phone can be used as information dissemination tools because in both models, sea farming participation and impacts model, show that mobile phone ownership has positive influence to sea farming participation and increasing household income. According to our findings, there are some problems to solve or to get more attention regarding sea farming and mariculture activity in Panggang Island. Thus, the local government should improve in several aspects to optimize sea farming positive impacts such as: 1. Enhancing the role of local hatchery to produce good quality fingerlings. By having local hatcheries that can produce good-quality fingerling, the fish farmers will get cheaper fingerling with higher survival rate in a timely manner. 2. Optimizing the role of bank and other microfinance institutions, thus the local community would be able to expand their business. However, careful implementation of opening access to the credit is also necessary. Otherwise, over-harvesting of fish production will occur and cause lower price for fish production. 3. Creating legal rules and strengthening the law enforcement. One of the sea farming effects is the emerging grouper culture by both project’s participants and non-participants. This phenomenon could be good for local economic activity but it could cause negative effects to the environment since marine environment is common and open access property. Thus, there should be a basic rule of how mariculture activity can be conducted in the area. The rules should consider fairness for all local community members and the carrying capacity of the area. The distance between cages is also another important thing to consider because if the distance between cages is too narrow and the fish density is too high, then there is a possibility of disease outbreaks as experienced by Chilean salmon business which collapsed and caused severe losses in 2007. 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Mayfield. 1978. “Non-adoption of Innovations: Evidence from Discriminant Analysis”. Economic Geography 54: 145-156. Available online: http:www.jstor.orgstable10.2307142849. Zbinden, S. and D.R. Lee. 2005. “Paying for Environmental Services: An Analysis of Participation in Costa Rica’s PSA Program”. World Development 332: 255-272. Available online: http:intranet.catie.ac.cr- intranetposgradopolitica_gober2011GOBERNANZA20INTRANET 20MODULO20IIIseminario203Lecturas20Seminario2031220P aying20for20environmental20services.pdf. APPENDICES Appendix 1 Indicators to Measure Poverty No Indicators Remarks 1 Poverty Rate or Headcount Index HCI Definition : The proportion of the population for whom consumption or other measures of living standard is less than the poverty line. Formula : HCI = Headcount index H = Number of people with incomes below poverty line N = Total population number Advantages : Easy to compute and interpret. Disadvantages : a. The headcount index ignores differences in well-being between different poor households. b. The headcount index does not take the intensity of poverty into account. c. Over time, the index does not change if individuals below the poverty line become poorer or richer. 2 Poverty Gap Definition : The average dif ference between poor households’ expenditure and the poverty line. Formula : TPG = Total poverty gap AVG = Average poverty gap NPG = Normalized poverty gap = Poverty line = Household income Advantages : No discontinuity at the poverty line, takes into account intensity of poverty. Disadvantages : a. Do not capture differences in the severity of poverty and ignore inequality among the poor. b. Violates transfer principle. 3 Square Poverty Gap Index SPGI or Definition : The average of the square relative poverty gap of the poor. Formula : SPGI = Square poverty gap index N = Total population number = Poverty line = Household income Advantage : SPGI takes inequality among the poor into account. Disadvantages : Difficult to read and interpret. 4 Foster-Greer- Thorbecke Index FGT or Definition : a generalized measure of poverty within an economy. It measures the outfall from the povertyline and is weighted by . Formula : ;   0 FGT = Foster-Greer-Thorbecke Index N = Total population number = Poverty line = Income of the poor individual i  = Weighting factor a measure of the sensitivity of the index to poverty If α=0, we have HCI; α =1, we have PG; and α =2, we have the SPGI. 5 Amartya Sen’s Poverty Index ASP Formula : ASP = Amartya Sen’s Poverty Index NPG = Normalized poverty gap Gini poor = Gini coefficient 22 6 Multi Dimensional Poverty Index MPI MPI is measurement to capture the severe deprivations that each person faces at the same time. The MPI reflects both the incidence of multidimensional deprivation and its intensity. It can be used to create a comprehensive picture of people living in poverty and permits comparisons both across countries, regions, and the world as well as within countries. 22 Gini coefficient or Gini ratio is a summary statistic of the Lorenz curve and a measure of inequality in a population. If G=0 implies that all households in a country have exactly the same amount of wealth, while if G=1 means a single household has all the country’s income Lexicon 2012. Appendix 2 System and Location for Mariculture Activity and Protected Shalow Open Sea in Semak Daun Island

a. System and Location for Mariculture Activity in Semak Daun Island CCMRS-IPB 2006

Sea Farming Demarcated Fishing Rights Area

b. Protected Shallow Open Sea in Semak Daun Island CCMRS-IPB 2007

Appendix 3 Total Income and Monthly Income per Capita of Sample Households No Number of HH Head SF Participation Household Size Total Income Monthly Income per Capita hhn sfp hhsz totinc capinc 1 16 non_participant 6 18,900,000 350,000 2 17 non_participant 7 9,900,000 157,143 3 26 non_participant 4 13,500,000 375,000 4 27 non_participant 6 13,500,000 250,000 5 33 non_participant 3 43,700,000 1,616,667 6 35 non_participant 5 9,000,000 200,000 7 40 non_participant 6 22,500,000 416,667 8 42 non_participant 5 27,900,000 620,000 9 43 non_participant 5 31,500,000 700,000 10 44 non_participant 3 27,000,000 1,000,000 11 45 non_participant 2 27,000,000 1,500,000 12 47 non_participant 4 15,300,000 425,000 13 50 non_participant 4 18,000,000 500,000 14 51 non_participant 4 13,500,000 375,000 15 52 non_participant 4 8,100,000 225,000 16 55 non_participant 5 13,500,000 300,000 17 56 non_participant 4 27,000,000 750,000 18 57 non_participant 5 35,100,000 780,000 19 58 non_participant 6 13,500,000 250,000 20 60 non_participant 5 13,500,000 300,000 21 8 non_participant 3 72,000,000 2,666,667 22 9 non_participant 7 28,400,000 450,794 23 11 non_participant 4 37,500,000 1,041,667 24 19 non_participant 7 30,500,000 484,127 25 20 non_participant 3 28,500,000 1,055,556 26 23 non_participant 4 28,500,000 791,667 27 24 non_participant 4 41,000,000 1,138,889 28 25 non_participant 6 26,300,000 486,111 29 29 non_participant 3 31,500,000 1,166,667 30 32 non_participant 6 41,500,000 768,519 31 34 non_participant 4 26,300,000 729,167 32 36 non_participant 6 35,000,000 648,148 33 37 non_participant 4 33,000,000 916,667 34 38 non_participant 8 30,300,000 420,139 35 39 non_participant 3 25,800,000 955,556 36 41 non_participant 4 29,000,000 805,556 37 46 non_participant 6 35,000,000 648,148 38 48 non_participant 6 33,000,000 611,111 39 49 non_participant 4 29,900,000 830,556 40 53 non_participant 9 32,500,000 401,235 No Number of HH Head SF Participation Household Size Total Income Monthly Income per Capita hhn sfp hhsz totinc capinc 41 54 non_participant 5 37,500,000 833,333 42 59 non_participant 4 90,000,000 2,500,000 43 61 non_participant 3 30,500,000 1,129,630 44 2 Participant 2 17,000,000 944,444 45 3 Participant 3 38,300,000 1,416,667 46 5 Participant 4 50,000,000 1,388,889 47 6 Participant 6 31,300,000 578,704 48 7 Participant 4 32,000,000 888,889 49 10 Participant 5 44,000,000 977,778 50 12 Participant 3 36,500,000 1,351,852 51 13 Participant 6 26,300,000 486,111 52 14 Participant 6 78,500,000 1,453,704 53 15 Participant 3 18,400,000 679,630 54 18 Participant 5 59,000,000 1,311,111 55 21 Participant 4 66,900,000 1,858,333 56 22 Participant 6 41,900,000 775,463 57 28 Participant 5 54,000,000 1,200,000 58 30 Participant 4 87,000,000 2,416,667 59 31 Participant 5 28,500,000 633,333 60 62 Participant 5 28,500,000 633,333 61 63 Participant 5 16,800,000 373,333 62 68 Participant 4 24,900,000 690,972 63 69 Participant 4 23,100,000 641,667 64 70 Participant 3 26,000,000 962,963 65 71 Participant 6 23,100,000 427,778 66 72 Participant 7 28,000,000 444,444 67 73 Participant 3 43,300,000 1,601,852 68 74 Participant 4 34,000,000 944,444 69 75 Participant 3 26,800,000 991,482 70 76 Participant 4 26,000,000 722,222 71 77 Participant 5 28,500,000 633,333 72 78 Participant 4 21,800,000 604,167 73 79 Participant 4 34,000,000 944,444 74 80 Participant 5 21,100,000 468,889 75 81 Participant 5 38,900,000 863,889 76 82 Participant 4 23,100,000 641,667 77 83 Participant 5 18,800,000 416,667 Appendix 3 Total Income and Monthly Income per Capita of Sample Households cont’d Appendix 4 Picture of Cage Culture and Pen Culture in Panggang Island a Cage Culture b Cage from Local Government c Pen Culture Appendix 5 Stata Output for the Determinants of Sea Farming Participation a. Probit Model b. Marginal Effects _cons 3.200172 1.519209 2.11 0.035 .2225775 6.177767 bt05 .6786631 .4108673 1.65 0.099 -.126622 1.483948 mp05 .4629199 .394178 1.17 0.240 -.3096548 1.235495 tv05 .3628455 .3182177 1.14 0.254 -.2608498 .9865407 org_member -2.620722 .7022593 -3.73 0.000 -3.997125 -1.244319 hhsz -.4255302 .1971478 -2.16 0.031 -.8119327 -.0391277 fishermen -2.385916 .9595941 -2.49 0.013 -4.266686 -.5051461 educt -2.290199 .8454526 -2.71 0.007 -3.947256 -.6331427 age .022184 .0294612 0.75 0.451 -.0355589 .079927 sfp Coef. Std. Err. z P|z| [95 Conf. Interval] Log likelihood = -25.640236 Pseudo R2 = 0.5148 Prob chi2 = 0.0000 LR chi28 = 54.41 Probit regression Number of obs = 77 Iteration 5: log likelihood = -25.640236 Iteration 4: log likelihood = -25.640236 Iteration 3: log likelihood = -25.640268 Iteration 2: log likelihood = -25.654653 Iteration 1: log likelihood = -26.653023 Iteration 0: log likelihood = -52.845155 . probit sfp age educt fishermen hhsz org_member tv05 mp05 bt05 dydx is for discrete change of dummy variable from 0 to 1 bt05 .2576824 .15851 1.63 0.104 -.052982 .568347 .428571 mp05 .1757667 .14918 1.18 0.239 -.116626 .46816 .350649 tv05 .1377693 .11993 1.15 0.251 -.097282 .37282 .714286 org_me~r -.6645339 .09587 -6.93 0.000 -.852426 -.476641 .298701 hhsz -.1615701 .07536 -2.14 0.032 -.309271 -.013869 4.63636 fisher~n -.7158144 .1354 -5.29 0.000 -.981198 -.450431 .831169 educt -.4936739 .10086 -4.89 0.000 -.691351 -.295997 .142857 age .0084231 .01121 0.75 0.453 -.013553 .030399 37.8182 variable dydx Std. Err. z P|z| [ 95 C.I. ] X = .37656683 y = Prsfp predict Marginal effects after probit . mfx