Documento dall'Università degli Studi di Milano-Bicocca su Network e Social Capital. Il Pdf esplora il capitale sociale e le reti sociali, analizzando le prospettive di misurazione micro e macro, con contributi di Coleman, Putnam e Granovetter, utile per Economia a livello universitario.
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Scienze dell'organizzazione (Università degli Studi di Milano-Bicocca) Scansiona per aprire su Studocu Studocu non è sponsorizzato o supportato da nessuna università o ateneo. Scaricato da Arm (photoarmmela@gmail.com)1
When we talk about Social Capital we're referring to a relational concept that deals with sociability, social networks, trust, norms of reciprocity and cooperative actions. It's also a multidimensional concept that refers to individuals (micro, Coleman), to organisations (meso), to societies (macro, Putman), depending on the issue researchers are investigating. Lastly, it's a spatially based concept that refers to countries, regions, cities, virtual/digital communities. For example, we can say that our city has a higher social capital than other cities. Important difference: Social networks and social capital are two different things. With Social Networks, we refer to all relations one actor has, meaning that we also include conflictual relations, avoidance, blaming, non cooperative ones (every relationship in one's life, either positive or negative) while Social capital refers to those always cooperative relations 'embedded in the social structure useful to reach an aim otherwise difficult or impossible to reach (Coleman, 1990). Social capital is always positive in the short term of the micro level, while it isn't always positive in the macro level = INCORPORATO/ INCASTONATO
Social Capital can be divided into three strands:
| Approach | Micro | Macro | Meso |
| Discipline | Sociology | Economics/Political Science | Sociology |
| Unity of analysis | Individual actor/relations | Territorial Unit | Relations, Organizations, Territories |
| Contents | Recognition/Belonging | Norms, values, trust, civicness | Recognition/Belonging |
| Nature of the good | Individual | Collective | Individual/club/Collective |
| Method | SNA | Survey | Case study |
| Benefits | Individual actor | Society | Individual actor/club |
| Definition | Cooperative relations/Networks useful to reach an aim | Norms, values, all elements of the social structure fostering cooperative actions | Cooperative relations/Networks in a given territory |
| Downsize of SC | Social inequality Social exclusion | Corruption, collusion, clientelism (opportunism) | |
| Authors | Coleman, Lin, Granovetter, Burt, Pizzorno | Coleman, Putnam, Cartocci, Paxton, Fukuyama | Coleman, Trigilia, Bagnasco, Ramella, Woolcock |
The effects of SC can be put into two different definitions: benefits and downsize. We could notice that you can have different kinds connected to different levels:
If we want to measure SC on a micro level we can use three different techniques: This document is available free of charge on studocu Scaricato da Arm (photoarmmela@gmail.com)2
1) Name Generator: It measures social relationships at individual levels, meaning that you have to interview single individuals with questions such as:
With questions like the ones above, we can use the answers to obtain names and construct a data table. After that,to create a structure of the individual's network, you need to ask: "do these people know each other?" An output for a name generator would end up looking like this:
Table nº2: Friends
| Name | Sex | Age | Birthplace | Family situation | Degree | Profession | Place of residence | Frequency of contacts | With children present? | How long have you known him/her? | Where did you meet? |
| 1 | |||||||||||
| 2 | |||||||||||
| 3 | |||||||||||
| 4 |
We can now build a proximity/adjacency matrix (A proximity is a measurement of the similarity or dissimilarity, broadly defined, of a pair of objects. If measured for all pairs of objects in a set, the proximities are represented by an object-by-object proximity matrix) that would end up looking like this:
| Alberta | Guido | Veronica | Noe mi | Luca | |
| Alberta | - | 1 | 1 | 0 | 1 |
| Guido | 1 | - | 1 | 0 | 0 |
| Veronica | 1 | 1 | . | 1 | 1 |
| Noemi | 0 | 0 | 1 | - | 1 |
| Luca | 1 | 0 | 1 | 1 | - |
Out of this we can get a graph, which will be our outcome (the visual representation of the matrix): ->nodes=dots We can see that Veronica covers a very important position in this structure, she's crucial. If we delete her the two parts are not connected anymore. We can now see how the structure of the network is crucial to understand the spread of information, economic behaviours, careers ... etc ...
What information do we get from the name generator technique? You can have information about the network by obtaining the following measures:
> You can have information about the individuals and their relations:
2) Position Generator this technique is used to understand the position/prestige in the society. All of the positions in the labour market have a prestige score, the prestige of a lawyer is different from that of a taxi driver. The idea of the position generator is that society can be understood as a pyramid and in order to build that pyramid, we have to ask the following question: "Of your friends, relatives, acquaintances .. etc, is there anyone who is a lawyer, builder etc ...? " This document is available free of charge on studocu Scaricato da Arm (photoarmmela@gmail.com)4
| Relation | Sex | Age | Since when do you know hanter | When did you -Profe-ssam | Place of residence |
| 1. Cohabitant | |||||
| 2. Non-cohabitant family | |||||
| 3. Friend | |||||
| 4. Acquaintance | |||||
| 5. Work colleague | |||||
| 1. Lawyer | |||||
| 2. Police officer | |||||
| 3. Businessman | |||||
| 4. Builder | |||||
| 5. University Professor | |||||
| 6. Engineer | |||||
| 7. High School teacher | |||||
| 8. Technician | |||||
| 9. High-rank civil servant | |||||
| 10. Politician | |||||
| 11. Truck driver | |||||
| 12 Bishop (or similar for other religions) | |||||
| 13. Priest (or similar for other religions) | |||||
| 14. Mechanic | |||||
| 15. Postman | |||||
| 16. Craftsman |
By using this technique, you won't get a graph because you don't ask if they know each other, you don't have names and, therefore, you won't have the affiliation matrix.
3) Resource Generator: If you use this method, you are interested in understanding the support network of the individual. In this case, the question to ask is: "Is there someone among your acquaintances, friends or relatives whom you could ask for assistance in the following situations? If you know more than one, refer to the one you have known for longer." With this question you can't get a graph because you are not asking for names, the only case you can have a graph is the name generator because you have a list of names.
| 1: Solve a peoblom ul wont | |
| 2 Heb wmh noung | |
| 3 Heb wrh small mpans | |
| 4. Shopping when | |
| Discuss poltes | |
| ( Ostan medical advice | |
| 7. Gef atvice for a family problem | |
| Borrow money in 0: 10,000€) | |
| 9: Help to buy sports ticket | |
| SU: Help to buy tickets for an art or cultural evert | |
| tt. Take cam ọt chájun for several days | |
| 12. Get adoce to send cháfram atroad to study English | |
| 13 Gel a recommendation for a | |
| 14: Help to solve problems with the local adminstration | |
| 15. Get financial advice | |
| 56 Borrow a hobday home |
With the resource generator you obtain a table with percentages:
| Milan | Lyon | Deris | Madrid | ||
| % of cases | Type of relation ** | Type of relation | Type of relation | Type of relation | |
| MATERIAL | |||||
| Can help when moving house | 79% *** | Family (48%) **** | 12% Friend (42%) | (67%) | 65% Friend (50%) |
| Can help with small jobs around the house | 66% Family (48) | 35% Family (48) | 63% Friend (64) | 72% Friend (62) | |
| EMOTIONAL | |||||
| Can give advice concerning a conflict with family members | 70% Friend (55) | 25% Friend (54) | 66% Friand (52) | 59% Family (53) | |
| CARE | |||||
| Can do your shopping when you are il | 89% Family (74) | 13% Friend (40) | 57% Family (57) | 61% Family (53) | |
| Can look after your children when you cannot | 60% Family (74) | 21% Family | 54% Family (56) | 58% Friand (53) | |
| LEISURE | |||||
| Can provide sports events tickets | 50% Friend (54) | NA | NA | 60% Friend (38) | 48% Friend (45) |
| Can give advice re sending children abroad | 44% Friend (54) | 3% Friend (68) | 46% Family (67) | 47% Friend (29 | |
| Can land you a holiday home | 55% Friend (60) | 16% Family (00) | 40% Friend (52) | 32% Friend (79) | |
| Can provide tickets for cultural events | 60% Friend (60) | NA | N.A | 78% Friend (100 |
Place of residence Since when do you know him/her They are not listed according to the prestige Scaricato da Arm (photoarmmela@gmail.com)