Currently there is a great debate about what is understood when we talk about Well-being, and how it is measured, for example we can find people whose income is very high and have many assets and are not happy because Some relative died recently, they are going through a terrible family crisis or they have debts, anyway, but we can also find people who feel very good since they enjoy meeting with their friends and family almost always, however, they may have been fired from work, or do not have enough material assets, well, likewise, it usually happens with nations,Recent studies indicated high suicide rates in European countries of which the majority have high indices of quality of life and well-being at the other extreme are countries such as Paraguay and Peru 'which do not have such a high quality of life, this evidences what has long been known are the limitations of monetary measures of well-being.
In the present work, not only does it strive to deliver a Wellbeing Indicator from an objective measurement, but we also add a few aspects that are relevant to fix the future wellbeing of society, which has a link with the nature of child wellbeing indicators..
Before carrying out the construction of our Peruvian regional social welfare indicator, we chose six social dimensions which are: Education, Employment, Environment, Health, Housing-Basic Services and Information Technology (ICT), within them are the indicators, which are not distinguished, that we collected at the level of each region of all Peru 'and then when constructing our indicator we can contrast them and by doing so find the fragile points where the regions can operate economically, who will be responsible of public policies.
In the first part we will see why a study based on a multidimensional approach to the reality of well-being by regions in our country is interesting, then we mention the methodology under which each of the indicators that were selected in the period 1995-2014 is examined and also how we will compare it, at the end we will propose a defense of our indicator but we will also show its weaknesses and give our conclusions, the sources, also the bibliographic material.
A MULTIDIMENSIONAL APPROACH OF OUR WELL-BEING UNDER OUR REALITY
We have a wide territory on which both cultural and climatic diversity often modify our behavior and our way of living, it is in our country where:
Peru´ ours of every day. by Carlos Amat and León Ch.
Indeed, our way of being, of acting, of perceiving reality is different within each region, for this reason we should not underestimate an examination of the situation of our well-being as a simple task, as Carlos Amat y León masterfully presented it in his book El Peru´ ours of every day, where it indicates:
The study of our reality offers us an outcome from which we can never escape, this would lead us to say that applying a study of well-being here will never be the same in any country, for this reason in this work we will build “Regional Social Well-being Indices” (IBSR) not only to compare them between regions but to see the relationships they maintain among themselves within each region.
First we start with the EDUCATION dimension, in Peru 'the opportunities to access a good education are very restricted and in the interior of the country the possibilities of reaching higher education were almost impossible a few years ago, few people could barely complete their education high school; To illustrate, I will tell you the story of Eric and his two little brothers, when they left early they put some bread in their backpack, they had to cross a hill to get to their school, they arrived soaked and with their hands and feet full of mud on that the night before it had rained and muddied the road, after washing their feet and hands they went to listen to their teacher's classes, the school was precarious in which they only taught a few times a week and for many months it was closed,As Erick many children remain in the same situation until now, they do not have the possibility of accessing a quality education, another very illustrative story is that of the children, from the highlands, who have to wait for hours for an interprovincial bus that can take them to their school, as the living environment of these children there are many more and worrying cases, there are no clearer words to explain reality than in illustrations, and it is for the strong reasons given in the examples that we chose the variables (spending on education), (school attendance) and (approved) for each region.They have to wait for hours for an interprovincial bus that can take them to their school, as the living environment of these children there are many more worrying cases, there are no clearer words to explain reality than in illustrations, and they are due to the strong reasons given in the examples that we chose the variables (education spending), (school attendance) and (approved) for each region.that they have to wait for hours for an interprovincial bus that can take them to their school, as the living environment of these children there are many more worrying cases, there are no clearer words to explain reality than in illustrations, and they are due to the strong reasons given in the examples that we chose the variables (education spending), (school attendance) and (approved) for each region.
Secondly, the EMPLOYMENT dimension is presented, in Peru 'people have jobs, but in the informal sector, without insurance and with low pay; In the present work we expose the other side of employment, we take “unemployment” by region as an indicator.
In point three we take the Environment dimension, specifically the indicator we take here is "vehicles per thousand inhabitants", the World Health Organization indicates that: "for every person who dies in a car accident another three lose their life because of the contamination of these ”, that is not a problem only of the most industrialized countries, in Latin America the pollution of vehicles kills more people than accidents, the (WHO) indicates that in 2014, Lima has at the Latin American level, the highest indicators of contamination by particulate matter PM 2.5. In particular Lima and Arequipa, are the most polluted cities in Latin America, far surpassing Mexico DF
In fourth place we mention the HEALTH dimension, in which we chose the indicator "maternal mortality", we can say that as a great concern for both individual and social health, it was camouflaged for many years until the conference on safe motherhood in Africa where the paradox of "dying giving life", caused great expectation worldwide. 'When a mother dies there is a great social loss, it is more than an individual tragedy, it causes serious consequences for the family, the community and the local economy, Since women carry out productive tasks within the home and provide the home with basic services, when their breasts die, their young children are also more likely to die, suffer from malnutrition, and are also less likely to attend school.In Peru 'every day 2 women die from complications during pregnancy, childbirth and the puerperium, 856 women suffer pregnancy complications and the probability of deaths from maternal causes is double for women in rural areas compared to women in urban areas. We also consider the rates of morbidity from diarrheal infection and morbidity from respiratory infection, these indicators are closely linked to child malnutrition, and it has been proven that in our country interrupting breastfeeding is a frequent fact, that is why we chose the indicators corresponding to infectious diseases that will help us to measure the adequate feeding and nutrition of infants during the first 5 years of life,In other words, measure children's health in relation to breastfeeding and adequate complementary feeding, since this will be a decisive factor in social well-being, both current and future, by guaranteeing a good intellectual development of the child and a higher work performance in their adult life.
The fifth dimension is HOUSING AND BASIC SERVICES, where we have the indicators' such as (access to drinking water) and (access to improved sanitation service), it is said that greater coverage of these services suggests a reduction in the probability of contracting diseases such as malaria, schistosomiasis, hepatitis, fluorosis, diarrhea and cholera; therefore water and sanitation are the main drivers of public health, people need to have enough water to wash their food, cook and clean themselves and this leads to a good quality of life. Less than 5 percent of the water in our territory goes to the coastal region and where the largest cities are supplied with a small proportion of water which is used by more than 60 percent of the citizens,However, 93 percent of the water is channeled to the eastern region where about 30 percent of the population live, due to this it is evident to find a shortage in cities such as Lima, Arequipa, Ica, many more generated by water stress and deterioration of water quality, we are currently experiencing a dramatic water situation in our basins.
Finally, we have the dimension of Information and Communication Technology, here the chosen variable is the level of mobile service, since in our country the use of mobile telephony is different from that of subscription telephony, and is Way to old. According to Osiptel there are 33,170,000 cell phones compared to 30,000,000 for Peruvians; The use of cell phones allows us to access information and communication and through this increases our opportunities, especially its effects are interesting in neglected and low-income sectors, where farmers have a cell phone, they will make better decisions to buy, produce and sell, as long as informal workers (plumbers, carpenters, painters) have a cell phone,being hired for a job on a certain day is essential for family support; the poorest people are directly and positively affected by their safety, since it allows them to find out about personal emergencies and the location when one leaves home, in short, mobile telephony is much more important for the poorest.
So far we have listed only 6 dimensions, but these are not all that determine an impact on our well-being, its multidimensional nature suggests more possibility, perhaps we could mention that many people who need better opportunities travel to larger cities, where they can get more goods and a better standard of living and thus achieve greater well-being, however, and as in many cases every gain is observed requires a sacrifice, there is a compensatory effect with other variables within our well-being, here the analysis takes an important part of subjective well-being, as in this example, although the well-being of those who migrate for better opportunities on the one hand increases, on the other it decreases because:The place where we are born is our umbilical cord with the earth and that is why nostalgia for the terroir accompanies us throughout our lives.
METHODOLOGY
In this section we are going to explain how we build our Regional Social Welfare Index for the years between 1995 and 2014, we begin by mentioning which are the selected regional social indicators and what was the criterion we considered to choose them, to which dimension they belong and their classification as stated in the article The operationalization of the concept of social welfare: a comparative analysis of different measurements by Eugenio Actis di Pasquale (published in 2008 in the Labor Observatory of the Venezuelan magazine). The respective sources from where we obtained the statistical data will also be attached to this section, and if in which we would have an information deficit in some years, included within our analysis period,We will mention what econometric methodology we use to support the prediction of each time series and a specific case for the AMAZON region will be attached at the end of the work regarding the forecast made (ANNEX 1). Next, we will mention the calculation method and the standardization used to construct the synthetic indicator, as well as comment on the advantages and limitations that we have encountered in the course of preparing this index.We will also discuss the advantages and limitations that we have encountered in the course of preparing this index.We will also discuss the advantages and limitations that we have encountered in the course of preparing this index.
TYPES OF INDICATORS WITHIN THE SYNTHETIC INDEX
According to the concept of well-being, it can be classified as: Positive or Negative in this way: if the highest value corresponds to a situation of well-being, we can say that it is a Positive indicator, on the contrary, if it corresponds to a situation of discomfort, we say that It is a Negative indicator. Expressing graphically it can be observed:
Differentiating the indicators:
Positive Indicators:
-Expenditure on Education: it is the expenditure for initial, primary, secondary, productive technical, alternative basic, special basic, non-university superior and university superior education, measured in current soles per student, and by department.
-Approved: is the total of approved as a percentage of enrollment, by department.
-School Attendance: is the school attendance as a percentage of the average of the total population enrolled in the educational system, by department.
-Access to Drinking Water: is the proportion of households with access to improved sources of water supply, by department.
-Improved Sanitation Service: is the proportion of households with access to improved sanitation service, by department.
-Mobile Service: mobile phone service, by department.
Negative indicators:
-Unemployment: total unemployment as a percentage of the EAP, by department.
-Vehicles per thousand Inhabitants: number of vehicles per thousand inhabitants, by department.
-Maternal Mortality: direct and indirect maternal deaths are considered, by department.
-Morbidity due to diarrheal infection: rate of morbidity due to acute diarrheal infection of the total population of children under five years of age per one hundred thousand inhabitants, by department.
-Morbidity from Respiratory Infection: morbidity rate from acute respiratory infection of the total population of children under five years of age per one hundred thousand inhabitants, by department.
Deployment of Dimensions
We can see that our indicator comprises a total of 11 component indices, below we show from where we collect the corresponding information that favored us when making our indicator.
STAGE I
METHODOLOGY AND
The time range of our data is highly variable if we compare it between departments, so we decided to standardize them according to the forecast with the Box Jenkins methodology.
This econometric methodology is based on estimating the process of a variable, through the identification and estimation phase of an ARIMA model (p, d, q) where it can depend on its own lag or on the lags of the disturbances, in other In other words, the dynamic structure of a given time series is explained by its past information.
Having already explained the concept of this methodology, the stages that we must pursue to forecast, for example, the morbidity rate from respiratory infection for Amazonas will be explained.
- Using the Dickey Fuller test (the unit root test) to determine if this variable is stationary or not, if it is not, we can transform it into its difference or its logarithm, so that it can achieve stationarity. We observe the correlogram and We estimate the ARIMA process of the morbidity rate, and suppose that this estimated model meets all the requirements in the validity analysis, here it is satisfied that the coefficients are significant and that the inverted roots are within the unit circle, if it is the AR part it would meet the stationarity condition and if it is the MA part it would meet the pourable condition, and that the model residuals are white noise, an independent and identically distributed variable. Finally, the morbidity rate forecasts are obtained,This is the expectation of the morbidity rate h periods forward, given the information available up to the last observation period, this procedure is used for the point estimation, in our specific case of the investigation, most of the indicators They have the data from 2000 to 2014, so we can use this procedure for a projectionbackwards which is feasible, in this way to complete the five missing data from 95 to 99.
STAGE II
THE TREATMENT FOR CALCULATION AND NORMALIZATION
This section focuses on specifying the exact digits as a representative of the value of regional social welfare, which are the key to the objective of this document; Initially we collected the data already predicted, with this we began to examine within each one of the departments, throughout the 20 years (1995-2014). We must obtain the maximum and minimum values (also called extreme values) of the data observed in each department, here we clarify in the case of the maximum values, if our component index is a percentage, then the maximum value will be 100 percent, if our component index is measured in a non-percentage fixed quantity, then the maximum value is the one observed in the component index throughout the 20 years,It may also be the case that a minimum value is agreed as a reference.
Once the operation is done, we proceed to calculate the component index, in this way we obtain:
As we well know, our indicator refers to a value of satisfaction and prosperity, also called well-being, for which it is proposed to store within it clearly positive values; Taking this into account, we will alternate with our component indices of discomfort, such as: unemployment, vehicles per inhabitant, maternal mortality, morbidity rate from diarrheal infection, morbidity rate from respiratory infection. (see figure 1)
(1 - V normalized) = V standardized
Now, we already have the 11 component indices that reflect the collective well-being for each of the 24 departments, and in order to calculate our INDICATOR, in this case we equitably distribute the weights of each of the dimensions, then we average between the number total component indices; This result obtained, normally different between departments, fluctuates around between 0 and 1; we will interpret a result that tends towards one as the ideal, it will be understood that the region is reaching maximum well-being. Then we carry out the indicated process.
STAGE III
THE
CASE: AMAZONAS
- Spending on education
Expenditure on education has increased about 15 times in the last 20 years, which is one of the largest growth rates in the entire country, as the index is measured in current soles, we will take the extreme values of the table.
- The Approved
Because it is a percentage character, here we will take 100 percent as the maximum value, since it reports greater well-being to the region the higher the number of approved, because it has a correlation (not absolute) with learning.
- School Attendance
It is also measured in percentages, so we consider the maximum value as 100 percent, ´ this reports greater well-being, as it corresponds to the collective or social concern of the parents of the region regarding education.
- Unemployment
The procedure is similar to the previous one, because it is measured in percentages, however "this is a component index of unrest, for which we will proceed to standardize it. It should be noted that the Amazon region has the highest unemployment in all of Peru."
(1 - 0, 1042) = 0, 89
- The number of Vehicles per thousand inhabitants
We collect the corresponding extreme values from the table, as it is a component index of discomfort, it is standardized.
(1 - 0,909) = 0, 09
- Maternal Mortality
A procedure similar to the previous one is followed.
(1 - 0, 125) = 0, 88
- Diarrheal Infection Morbidity
The procedure is similar to the previous one.
(1 - 0, 048) = 0, 95
- Morbidity from Respiratory Infection
The process is similar to the previous one
(1 - 0, 541) = 0, 46
In the last 10 years, cases of respiratory infection in children under five years of age have more than doubled.
- Access to Drinking Water
This index is measured as a percentage of families with access to drinking water, so the maximum value taken is 100 percent. The Amazon region has a very good position in this index, compared to the majority.
- The Improved Sanitation Service
Here we follow the same procedure as the previous one, we discover that the Amazon region leads in this component index at the national level.
- The Mobile Service
The maximum values are acquired from the observation.
After Huancavelica and Madre de Dios, Amazonas had one of the lowest records in regards to mobile telephony, which shows how difficult it was to communicate with marginal sectors of this region, however, in the last 15 years it has had a very significant improvement, far surpassing several regions such as Moquegua, Pasco and Tumbes. All this is reflected in a greater well-being of the population.
REGIONAL SOCIAL WELFARE INDICATOR (AMAZONAS)
The next step will be to find the indicator (IBSR) for Amazonas. As we already mentioned, we must add the component indices of the mentioned region and divide it by the number of component indices.
Calculated:
The result shows a relative good phase of the well-being of this region, as the indicator is passing above 0.5, especially it can be said that Amazonas has achieved a Regional Social Welfare among the most outstanding, strengthening Spending on Education, Employment, reducing Maternal Mortality, the Morbidity Rate from Diarrheal Infection, providing greater access to the Drinking Water service, Improved Sanitation and improving their Mobile Telephony communications.
The results of the well-being indicators for the other regions (25) are briefly presented below, following the same procedure.
REGION
The result that we observe may be that it becomes difficult to accept, we see that Apurímac and Ayacucho have the highest Regional Well-being Indicators (0.7), and Ucayali has the lowest levels of Well-being (0.4). Leaving aside prejudices, we know that Ayacucho was a region that was hit hard by terrorism in the past, and Apurímac a region of our sierra that was often forgotten. Such statements are consistent with the high levels of poverty that still reign in these regions.
In the regions of Apurímac and Ayacucho, the improved sanitation service has increased, as has the access of families to drinking water compared to the other regions in recent years, a result that also defends but with greater force the high degree of The level of well-being acquired in these regions is the minimum amount of maternal deaths that has been registered in the last decade, especially Apurímac; which is related to the progress of medical care in the region.
We are going through a process in which the peripheral regions push to develop more than the main cities and much more than the capital, we are embarking on a new economic and social phenomenon, where inequality is also reducing, and where the most remote towns The capital is in progress, an improvement of their care systems, these regions get the best marks in terms of well-being; The highlights of the carefully made observations are shown below:
Most of the regions allocate higher spending on education each year, but especially the poorest regions. Currently Moquegua not only registers the highest spending on education at the national level but has also increased spending on education dramatically.
As of 2014, the region with the highest number of approved (nationwide) is Arequipa, despite this Apurímac reports greater well-being, since the region has made a lot of effort to overcome one of the lowest levels of 'this' index.
Moquegua is the region with the most responsible students, since as of 2014, it has the highest attendance at classes, however, Ancash has the highest well-being.
The region that achieves the lowest unemployment is Callao, and the one with the greatest drop in unemployment in recent years is Puno and Piura.
Metropolitan Lima has the largest automobile fleet, followed by Tacna and Arequipa, and the one with the highest levels of well-being due to the reduced number of cars are Loreto and Tumbes.
In Apurímac, Ica, Madre de Dios, Moquegua, Tacna and Tumbes, are the departments where the number of maternal deaths is reduced, even zero in some cases, however, Huánuco, Moquegua, Puno and Tacna reflect the greatest well-being to the date, for having surprisingly reduced their figures, and on the other hand the places that list the most maternal deaths are Callao, Pasco and Ucayali.
With regard to access to drinking water, Apurímac is the department with the greatest well-being, and access to improved sanitation services has Amazonas as the highest representative in terms of well-being.
Huancavelica, certainly, far exceeds its communication needs compared to the rest of the country, which is reflected in the astonishing figures that OSIPTEL offers us; and those who most took advantage of the appearance of mobile telephony were Apurímac and Huancavelica itself, since they have a greater value in their well-being.
ADVANTAGES AND DISADVANTAGES
* the biggest Advantages of our Indicator are:
- The dimensions chosen are also those that occupy the greatest importance in the economic life of citizens. The amount of component indices is acceptable, and shows a greater approximation to the calculation of well-being. The Health variables are linked to the study of Child Well-being. We have a reliable source.
* the disadvantages of our Indicator are:
- the scarce regional statistical information in our country. Due to the first point, we had to opt for an econometric methodology to forecast the data, which partly complicated the development of the main reason for this document, to measure well-being.
CONCLUSIONS
The highest Regional Social Welfare Indicator (IBSR), according to our work, is Apurímac with 0.698, and Ayacucho is very close with 0.697, which in recent years have had as support the improvement of the HOUSING AND BASIC SERVICES dimensionHowever, the regions that lag behind are Ucayali (0.43) and Pasco (slightly) (0.51), but in general terms, almost all the departments are 0.5 to 1 as a measure of well-being, to give accuracy of well-being The average is 0.61 among all the departments, the most vulnerable regions, as is the case of Ucayali, have a high level of maternal mortality in common, and as it was guessed, the data show that here there is the lowest level of school attendance, but also the most worrying level, after Loreto, of disapproved children.
And as if that were not enough, it also has the lowest level of access to drinking water, and is the most unhealthy region among all, these results reinforce the idea of which one agrees when it is said that when a mother dies, the child has the least possibilities of to be in school, to have a decent quality of life, greater probability of suffering from malnutrition and dying; So, not only must we create the means for people to develop and have the same opportunities, in this case the children of this region, but we must also focus on the health and well-being of those who determine the future of those who determine the well-being future, we must focus on the long term and give a chance now to those who will also decide for the well-being of tomorrow.
On the other hand, it can also be observed that access to drinking water is closely related to the morbidity rate due to diarrheal infection, the critical case is presented by Cajamarca, in this region infants under 5 years of age tend to get sick because there is not enough water that people can use to wash their food, clean themselves and cook.
It is also discovered that there is a strong interrelation of our IBSR in the economic sphere of production, since, coincidentally, according to the BCR reports between 2007 and 2013, Ayacucho (the second region with the highest value in the Regional Well-being Index) it has shown an average growth of 9 percent, exceeding the national average by 3 points, mainly due to mining; Fishing has grown at a rate that far exceeds the national average and construction grew some 7 points above the national average, driven by spending on works such as schools and highways. The situation with respect to 2007 has improved, since as a percentage of the national average the value added per inhabitant in Ayacucho registers an advance of 8 points (from 46 to 54 percent).
BIBLIOGRAPHY
* Amat and León Chávez, Carlos (2006). Nine essays to discuss and decide: El Perú´ ours of every day. Pacific university.
* Gonzales Well, Gabriel. A proposal for a system of indicators on child welfare in Spain. Unicef-Spain.
* Molederos Abeyes, María. Approach methods to the measurement of Well-being: an overview. Department of Applied Economics-University of Valladolid.
* De la Torre, Rodolfo. Measuring welfare and social progress: a human development perspective. International Journal of Statistics and Geography.
* Giarrizo, Victoria. Subjective economic well-being: beyond 'growth. University of the Andes-Venezuela.
* Consulting support. The impact of telecommunications on development: the case of mobile telephony in rural areas.
* International Agency for Research on Cancer (IARC) -WHO: Diesel engine exhaust carcinogenic. Lyon, France, June 12, 2012
* Di Pasquale, Eugenio (2008) / VOL1. The operationalization of the concept of Social Welfare: a comparative analysis of different measurements. Venezuelan magazine labor observatory.
* Economic and social report Ayacucho region. Central Reserve Bank of Peru´, 2014.
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