HOUSEHOLDS AND FAMILIES IN ESTONIAN POPULATION

The statistical distribution of households and families by their size and structure can be received from population and household censuses, but it is also important to know this information between the censuses, as changes during the ten-year period can be quite remarkable. It is demonstrated that, since the last census in Estonia in 2011, the share of older age-groups has increased and the rate of children – decreased. The age at first marriage has increased for men and women respectively by 3 and 4 months per year. Consequently, the share of young people living without a partner has increased, but the process is different in men and women. The number of divorces has dropped, but this seems to be caused by the decline of marriages during the last decades. The age of women giving birth has also increased. During the last years, the number of third children has increased, but the number of first children has decreased. The distribution of households by their types was also studied and is presented in Table 1. It is evident that about one-sixth of the population lives alone; single-person households form the most numerous household type in present-day Estonia. From all households containing a couple, somewhat more than two-thirds are households with a married couple, others are households with a cohabiting couple. In average, the first ones are much older than the second ones. The number of households where two or more generations live together is marginal.

population and household censuses. But the problem is that the period of censuses is ten years nowadays, which means that between the censuses the exact data are usually not available, and out-of-date data are used if necessary.
To avoid this problem, a model has been created that gives the estimated values for all household and family types for each year between two censuses. As input for this model, we also use data from different surveys alongside the census data and data from registers.

CHANGE OF POPULATION SIZE IN DIFFERENT AGE GROUPS
Since 2015, the population size in Estonia has been estimated using the residency index that uses more than 20 administrative indexes to establish the persons living permanently in Estonia each year, that is, are residents of the state. It is interesting that the change has not been linear, but, in 2015, a turn took place: the population size that had decreased for more than 25 years started to increase. The reason for this turn is the change in external migration: Estonia, which was an emigration country for long time, has, due to the improved economic situation, gained the status of an immigration country. Also, many people who had emigrated in earlier years, have now returned. The change in population size is well described with help of a quadratic model, see Figure 1. From 2010, the population size has decreased by 0.6% (8500 persons).

CHANGE IN THE SIZE OF ADOLESCENTS' AGE-GROUPS
As the distribution of household and family types depends on the number of children, that is, on the fertility trends during the recent years, we regard the dynamics of sizes in adolescent's age-groups during the last ten years, see Figure 2. The size of the youngest age-group has S-shaped form (parabola of the 3rd order) with the maximum in 2011 and the minimum in 2018 and a general decreasing tendency (total change -5,7%). This form is the reflexion of changes in the number of births during the period.
The size of the two next age-groups -5-9 and 10-14 has been increasing and can be described by parabolae of the second order.
The size of the oldest age group, young people aged 15-19, years has decreased very strongly. The model is again a parabola of the second order.
The total change in the number of adolescents aged <20 years is similar to the change of total population, see Figure 3. The absolute number of adolescents in 2019 was 1% less than in 2000. Hence, the decrease in the number of adolescents was somewhat bigger than that of the total population, but changes in different age-groups are different as shown in Figure 3. The number of seniors, aged 65+ has increased by 12.6%, see Figure 4. The change is not equal in all age-groups; the behaviour of the group 70-74 is especially exceptional, as from 2012-2017, this group included people who were born in the years of the Second World War. Notably big growth -67%occurred in the oldest age-group aged 85+.

CHANGES IN THE SIZE OF SENIOR AGE GROUPS
The number of seniors, aged 65+ has increased by 12.6%, see Figure 4. The change is not equal in all age-groups; the behaviour of the group 70-74 is especially exceptional, as from 2012-2017, this group included people who were born in the years of the Second World War. Notably big growth -67%occurred in the oldest age-group aged 85+.  same time 29 000 marriages were divorced; the number of divorces increased by 7%. The coefficient of the first marriage increased by almost 0.1 and had the value 0.47 for men and 0.53 for women, which is a considerably high value compared with the last decades in Estonia. The change can be described by a linear model showing a 1% increase per year, see Figure 5.

5
During the period 2010-2018 almost 55 000 men and women married; the number of marriages per year increased 1.3 times from 2010 to 2018. At the same time 29 000 marriages were divorced; the number of divorces increased by 7%. The coefficient of the first marriage increased by almost 0.1 and had the value 0.47 for men and 0.53 for women, which is a considerably high value compared with the last decades in Estonia. The change can be described by a linear model showing a 1% increase per year, see Figure 5.

CHANGES IN THE AGE OF MARRIAGE
The age of the first marriage increased by almost two years and reached the value 29.6 years for brides and 32.1 years for grooms. Thus, the age of the first marriage increased by 3 months per year for men and 4 months per year for women, see Figure 6.
C Ch ha an ng ge es s i in n t th he e a ag ge e o of f m ma ar rr ri ia ag ge e The age of the first marriage increased by almost two years and reached the value 29.6 years for brides and 32.1 years for grooms. Thus, the age of the first marriage increased by 3 months per year for men and 4 months per year for women, see Figure 6. The increasing marrying activity and the rising age of marriage have an opposite impact on the number and share of married people in the population.

COHABITATION
The only source of information about non-registered cohabitation is available from census data, see Figures 7-9. From Figure 7, we can see that the partnership patterns were quite similar at the time of both censuses, but there is a small shift towards later start. If we look the age when 50% of people live together with a partner, we can see that in 2000, this age was 26.8 years for men and 24.4 years for women. In 2011, this situation appeared when men were 28.4 and women 25.2 years old, which means that, in twelve years, the starting point of family life had shifted to more than one year later.   From Figure 7, we can see that the partnership patterns were quite similar at the time of both censuses, but there is a small shift towards later start. If we look the age when 50% of people live together with a partner, we can see that in 2000, this age was 26.8 years for men and 24.4 years for women. In 2011, this situation appeared when men were 28.4 and women 25.2 years old, which means that, in twelve years, the starting point of family life had shifted to more than one year later. From Figure 7, we can see that the partnership patterns were quite similar at the time of both censuses, but there is a small shift towards later start. If we look the age when 50% of people live together with a partner, we can see that in 2000, this age was 26.8 years for men and 24.4 years for women. In 2011, this situation appeared when men were 28.4 and women 25.2 years old, which means that, in twelve years, the starting point of family life had shifted to more than one year later.   The share of people living together with a partner among the population aged 15 years and more has also changed. In 2000, 56.5% of men and 46.2% of women had partner; by 2011 this number had dropped and was 55.1% for men and 46.2% for women.
As there are many widows among aged women, we also compared the populations of age groups 15-49 in both years. In 2000, -49.0% of men and 50.7% of women had a partner; in 2011, the share of men having a partner had dropped to 46.4%, but the share of women having a partner had somewhat increased to 51.1%.
To explain the different ratios of women and men, it is important to consider that the partners do not always belong to the same age group. Another reason explaining the change is that the ratio of women and men in fertile age has dramatically changed. In 2000, women were dominating among the population aged 15-49 years -per 100 men there were almost 104 women, but in 2011 the situation was the opposite: per 100 men there were 98 women. Here, the two main reasons were the more active emigration of young women and the dropped death rate (increased life expectation) of men.
The ratio of two partnership types -married couples and unmarried cohabiting couples -has also somewhat changed -in 2000, 19.5% of all couples were not married, in 2011, this share was 31.0%. Among the people aged 15-49 years, the share of unmarried partners was 28.5% in 2000 and 44.3% in 2011. The share of people living together with a partner among the population aged 15 years and more has also changed. In 2000, 56.5% of men and 46.2% of women had partner; by 2011 this number had dropped and was 55.1% for men and 46.2% for women. As there are many widows among aged women, we also compared the populations of age groups 15-49 in both years. In 2000, -49.0% of men and 50.7% of women had a partner; in 2011, the share of men having a partner had dropped to 46.4%, but the share of women having a partner had somewhat increased to 51.1%.
To explain the different ratios of women and men, it is important to consider that the partners do not always belong to the same age group. Another reason explaining the change is that the ratio of women and men in fertile age has dramatically changed. In 2000, women were dominating among the population aged 15-49 years -per 100 men there were almost 104 women, but in 2011 the situation was the opposite: per 100 men there were 98 women. Here, the two main reasons were the more active emigration of young women and the dropped death rate (increased life expectation) of men.
The ratio of two partnership types -married couples and unmarried cohabiting couples -has also somewhat changed -in 2000, 19.5% of all couples were not married, in 2011, this share was 31.0%. Among the people aged 15-49 years, the share of unmarried partners was 28.5% in 2000 and 44.3% in 2011. Probably the tendency of increase of unmarried couples continues but in a slowing tempo. Most married couples (60-78%) have experienced cohabitation before marriage, see Figure 11. Probably the tendency of increase of unmarried couples continues but in a slowing tempo. Most married couples (60-78%) have experienced cohabitation before marriage, see Figure 11. Probably the tendency of increase of unmarried couples continues but in a slowing tempo. Most married couples (60-78%) have experienced cohabitation before marriage, see Figure 11. It is also quite common to marry after the couple already has a child or several children, see Figure 12. The number of divorces was high in Estonia from the middle of the 20 th century already and increased extremely in the 1990s when the number of marriages dropped. Now, the ratio of divorces and marriages has gone down and has stabilized at the level of 50 divorces per 100 marriages, see Figure 13. Still, this fact does not demonstrate that half of marriages continue until the death of partners, as among all marriages, the number of second or third marriages for partners changes between 0.25 and 0.33, having a modest tendency of falling.
Unfortunately, there is very few information on the stability of cohabitation; probably it is not higher than the stability of legal marriages.

DIVORCES
The number of divorces was high in Estonia from the middle of the 20 th century already and increased extremely in the 1990s when the number of marriages dropped. Now, the ratio of divorces and marriages has gone down and has stabilized at the level of 50 divorces per 100 marriages, see Figure 13. Still, this fact does not demonstrate that half of marriages continue until the death of partners, as among all marriages, the number of second or third marriages for partners changes between 0.25 and 0.33, having a modest tendency of falling. Unfortunately, there is very few information on the stability of cohabitation; probably it is not higher than the stability of legal marriages. The number of divorces was high in Estonia from the middle of the 20 th century already and increased extremely in the 1990s when the number of marriages dropped. Now, the ratio of divorces and marriages has gone down and has stabilized at the level of 50 divorces per 100 marriages, see Figure 13. Still, this fact does not demonstrate that half of marriages continue until the death of partners, as among all marriages, the number of second or third marriages for partners changes between 0.25 and 0.33, having a modest tendency of falling.
Unfortunately, there is very few information on the stability of cohabitation; probably it is not higher than the stability of legal marriages. More than half of divorcing couples have common children. The average number of common children in divorcing couples is about 0.6, and, consequently, almost 3000 children lose one of their parents each year; more often this is the father and a single-mother family arises.
More than half of divorcing couples have common children. The average number of common children in divorcing couples is about 0.6, and, consequently, almost 3000 children lose one of their parents each year; more often this is the father and a single-mother family arises. A great change has happened in the share of cohabiting mothers. In 2000 their share was 28.8% and in 2014 almost twice more -53.9%. But this rate has not changed in the same direction all the time: from 2014, the share of cohabiting mothers has decreased and was 19.7% in 2018, still remaining the most common family status of child-bearing mothers.
The most dramatic change has happened in the number of women who have decided to give birth without having stabile partner, that is, as single mother. In 2000 their percentage was 26.3%, and, during the period 2002-2003, it increased to 36% but, after that, started to decrease quite rapidly, having its minimum value (4.2%) in 2015. In 2018, the percentage of parturients who did A great change has happened in the share of cohabiting mothers. In 2000 their share was 28.8% and in 2014 almost twice more -53.9%. But this rate has not changed in the same direction all the time: from 2014, the share of cohabiting mothers has decreased and was 19.7% in 2018, still remaining the most common family status of child-bearing mothers.
The most dramatic change has happened in the number of women who have decided to give birth without having stabile partner, that is, as single mother. In 2000 their percentage was 26.3%, and, during the period 2002-2003, it increased to 36% but, after that, started to decrease quite rapidly, having its minimum value (4.2%) in 2015. In 2018, the percentage of parturients who did not have a husband or a cohabiting partner was 7%. Maybe big jumps in this time-series have some technical reasons, but it is still clear that the number of women who have chosen the way to have a child without a partner is less than 10 %.

AGE OF NEWBORNS' PARENTS
The fact that mothers tend to postpone childbearing and that mothers' average age has increased for several years is generally known. Naturally, fathers' age has also increased, but, surprisingly, much less, see Figure 17. If the average age of women has increased by four years from 2000, the increase in men's age forms only 66% from this number. Especially remarkable is the stabilization of fathers' age during the period 2012-2018.

BIRTH ORDER OF CHILDREN
Birth order of children born in a year gives some information about the popular children's pattern in a certain time-period: if second children dominate among the newborns, then it is evident that single-children households are not very popular. In a similar way, the big share of third and following children might forecast the growing popularity of bigger families. During the last decade, the share of second children has almost reached the level of the share of first children, and the share of third children has also increased. Obviously, the biggest problem in Estonian family policy is postponing of the first birth, see Figure 18.

TYPES OF HOUSEHOLDS AND NUCLEAR FAMILIES
The traditional definition of the household is a group of people, usually living in a common dwelling, who share the available household facilities (common budget and food); a person living alone is also a household.
In register-based terminology, the household is a group of people (relatives or not) who live in a common dwelling. The household consists of nuclear families and additional household members. In the most typical case, the household has no nuclear families (this is single person's household) or one nuclear family, but households with two and also three nuclear families also exist -these are, usually, multigenerational households where grandparents, parents and children live together. In nuclear families, all members are related to each other. In the following text, we will mainly use the traditional definition of the household.
There exist the following types of nuclear families: 1. Married or cohabiting couple without children. 2. Married or cohabiting couple with a child or children.
a. At least one child is adolescent (aged less than 18 years). b. All children are adults. 3. Single parent's family with an adolescent or adult child or children.
c. Single father's family. d. Single mother's family.
To characterize the society, it is important to know the distribution of households and also the distribution of types of nuclear families.

Distribution of household types
The following table gives the distribution of household and family types measured in the census of 2019. Explanations: Hh -household, ms -members, c -couple, os -opposite sex, ss -same sex; Hh without nucleus -household containing more than one member but no nuclear family, multinucleus household -household containing more than one nuclear family.
The share of different household types and their members is represented in Figure 19.
Cohabiting ss without children Explanations: Hh -household, ms -members, c -couple, os -opposite sex, ss -same sex; Hh without nucleus -household containing more than one member but no nuclear family, multinucleus household -household containing more than one nuclear family.
The share of different household types and their members is represented in Figure 19. The most common household type in 2011 was the single person's household (39.9%). Married couples' household type was on the second place (31.1%). On the third and fourth place, there were households of cohabiting opposite sex couples (13.6%) and single mothers' households (11.8%). All other household types were very rare, covering in total less than 5% of all households. If family members are considered, the order is different. The biggest number of people The most common household type in 2011 was the single person's household (39.9%). Married couples' household type was on the second place (31.1%).
On the third and fourth place, there were households of cohabiting opposite sex couples (13.6%) and single mothers' households (11.8%). All other household types were very rare, covering in total less than 5% of all households. If family members are considered, the order is different. The biggest number of people (41.5%) lived in married couples' households, 19.2% in households of cohabiting opposite-sex couples and 18.7% of all people lived alone -94 thousand men and 145 thousand women, more than half of them widows. 6.3% of all persons lived in single mothers' households, and 3.9% of all household members lived in the biggest households which had two or more nuclei. Very few households had a cohabiting same-sex couple as the nucleus. This household type was measured in Estonia for the first time, and it is possible that it is under-reported and also under-covered. The households with several members but without a nucleus were also rather rare.

ADDITIONAL MEMBERS OF HOUSEHOLDS
More than 110 000 persons are members of households but do not belong to the nuclear family constituting the household; that is 8.7% of the population. Often, they are relatives, e.g. parents of members of belonging to the nucleus. Sometimes they are also helpers or subtenants. Of the households with (married or unmarried) couples, about 7% have additional members. Most often the additional members belong to single-parent households; there are additional members in 15% of all cases. Obviously, single parents need more help from relatives and other people than the households of couples.  Figure 20. Households with nuclear families containing adolescent children

CHILDREN IN HOUSEHOLDS
In Table 2 and Figure 20, some households with children are missing -these are households without the family nucleus, e.g. the households of grandparents with children. The number of such households is small.
The decision to have the following child depends on the household and family type, see Figure 21. In married couples, the frequency of having the second child is the highest and in single parent households -the lowest. The frequency of having the third child (and following children) is also higher in married couples than in cohabiting couples.

Figure 20. Households with nuclear families containing adolescent children
In Table 2 and Figure 20, some households with children are missing -these are households without the family nucleus, e.g. the households of grandparents with children. The number of such households is small. The decision to have the following child depends on the household and family type, see Figure 21.  Table 2 and Figure 20, some households with children are missing -these are households without the family nucleus, e.g. the households of grandparents with children. The number of such households is small.
The decision to have the following child depends on the household and family type, see Figure 21. In married couples, the frequency of having the second child is the highest and in single parent households -the lowest. The frequency of having the third child (and following children) is also higher in married couples than in cohabiting couples.
C Ch ha an ng ge e i in n t th he e d de ef fi in ni it ti io on n o of f t th he e h ho ou us se eh ho ol ld d  In married couples, the frequency of having the second child is the highest and in single parent households -the lowest. The frequency of having the third child (and following children) is also higher in married couples than in cohabiting couples.
Households and families in Estonian population | 103

CHANGE IN THE DEFINITION OF THE HOUSEHOLD
As already mentioned, there are two possible definitions of the household, and, until now, we have used the traditional one. But the number of inhabited dwellings is about 50 000 smaller than the number of traditional households, which means that the distribution of households by the number of their members will change when using the new definition of the household, see Figure 22.
dwellings is about 50 000 smaller than the number of traditional households, which means that the distribution of households by the number of their members will change when using the new definition of the household, see Figure 22. It is evident that when the number of households decreases, the share of small households will decrease, and the share of large households will increase. The biggest decrease is in the number of one-person households: about 24% of them disappear. That means, 57 thousand single persons who live in the same dwelling with another household will be additional members of a household in the new statistical context.
The same happens with two-person households -their number decreases by 4%, and more than 7000 two-person households will be joined with another household. As a result, the number of households with two or more nuclei increases.
The number of bigger households will increase -the number of households with six and seven persons will increase by 43% and 76% respectively; the number of households with more than seven persons will duplicate.
C Co on nc cl lu us si io on n It is evident that when the number of households decreases, the share of small households will decrease, and the share of large households will increase. The biggest decrease is in the number of one-person households: about 24% of them disappear. That means, 57 thousand single persons who live in the same dwelling with another household will be additional members of a household in the new statistical context. The same happens with two-person households -their number decreases by 4%, and more than 7000 two-person households will be joined with another household. As a result, the number of households with two or more nuclei increases.
The number of bigger households will increase -the number of households with six and seven persons will increase by 43% and 76% respectively; the number of households with more than seven persons will duplicate.


Women are invisible?! A literature survey on gender gap and financial training

Most of the existing definitions and measurements of financial literacy have been tailored for developed countries. Although financial literacy and financial behaviour are usually assessed at the household level, the gender dimension of household agents is less explored which may overshadow the women’s low level of financial literacy. This paper is a selective systematic review aiming to survey the existing literature on financial training where the focus is on two main issues, namely, financial training and gender disparity in financial literacy. Based on a thorough literature review, we identify research gaps in each of these dimensions. The identified gaps are then used to formulate two promising research ideas. Specifically, the promising research ideas seek to investigate the gender disparity and propose a novel method for financial training.

Introduction
Inequalities are unfortunately the common characters in all the societies and fighting against them is the unique symbol of the new era. In this sense, the gender difference in access and usage of financial services has become debatable among researchers. Although many efforts have been made to eliminate this inequality, it has not been reduced significantly. Unfortunately, this gap is striking in financially underdeveloped countries, and women are denied education because of socio-economic factors. Some associated this disparity to the lower level of financial literacy among women as compared to men; however, the level of financial literacy is not satisfactory even among men.
The relevance of gender to financial behaviours has been widely discussed in the literature (Fisher, 2010). There is now clear evidence that the level of financial literacy is lower among women than men (Lusardi and Mitchell, 2007a). It is believed that women do not pay enough attention to financial matters, and since they live longer, and their work life is shorter, the possibility of encountering financial crises is higher. In general, women are less likely to have a plan for the old ages as opposed to men (Lusardi and Mitchell, 2007b). Hence, probing the roots of this difference, in order to eliminate it, is the primary way to secure the financial future of women, especially in underdeveloped countries.
In this context, the first part of this discussion paper is going to review the existing literature on gender inequality in financial literacy and financial behaviours, and its causes. If there is any gap, what is the pragmatic way to close it? How can we improve the level of financial literacy among women? And more importantly, what is financial literacy?
As Sholevar and Harris (2019) defined financial literacy as the awareness and knowledge of exploiting the available financial services from essential financial needs to potential financial inclusion, an effective programme should consider the needs and availability of financial options in every context. They argued that this concept is characterised by the demand and supply sides; awareness and need are related to the demand side, while supply-side encompasses the availability of financial services.
Financial literacy is the main player in making a good financial decision, which not only improves the level of welfare but also has a great impact on economic development. Yet, how can we identify financially literate and illiterate individuals? What kind of programmes are required to improve the process of financial decision making?
After the 2008 financial crisis, concern about the level of financial literacy among consumers has risen, and as a consequence, the number of educational programmes in various forms have been massively increased. Many of these programmes are informational and providing financial information about financial services and products. However, the key question is whether just giving information can change financial behaviours? What is the connection between 'what to know' and 'what to do'? How can financial literacy programmes be more effective? More importantly, could financial literacy be taught? It is evident that a proper education or training with regard to financial matters would be necessary to reduce financial illiteracy.
It seems that financial training could assist with solving this problem; hence, the next section will be focused on financial interventions. Does improving the level of financial literacy have any impact on people's economic well-being? The following section will discuss the effect of financial training on financial behaviour.
Finally, a conclusive outlook of all concepts is provided as a possible roadmap.

Gender gap and its drivers
Financial literacy and financial behaviour have a close relationship; however, this link depending on the time and circumstances would change. As a result, the disparity in the level of financial literacy among different groups of the community could be observed, especially in those with regard to gender.

Problem statement
In financially underdeveloped countries, the gender difference is bolder than developed ones, and rules and norms do favour men more as compared to women. The higher life expectancy, shorter working-life and rapid changes in the financial markets have made women more vulnerable as compared with men. Furthermore, the role of making financial decisions are normally played by men rather than women, which widened the gender gap in financial literacy. Notwithstanding, inequality in provided resources for financial literacy is the main cause of the gender gap. Finding a solution to facilitate women's access to financial services and enhance their financial knowledge could improve financial behaviour. In general, managing money has become a common problem for everyone, considering the complexity and diversity of financial services, especially for women from under developing countries. Although there are numerous works on financial literacy and its importance in the literature, the function of gender in this field has been neglected or at least has not received the attention this imperative matter requires.
As for financial literacy, many different organisations, through surveying, measured financial literacy around the world. It has been revealed that women are less financially literate as compared to men and they are aware of their scarcity in financial literacy (Bucher-Koenen et al., 2017). To the best of our knowledge, there is no work focusing on the assessment of their willingness to learn more. Lusardi and Mitchell (2011) suggested that age and gender influenced the level of financial knowledge, as women and older generations had lower financial literacy compared to other groups.
There are a few research teams who tried to root out the gender disparity in financial literacy. For example, an American team (Fonseca et al., 2012) used the collected data from the RAND American life panel and tried to explain the gender gap in financial literacy through the Blinder-Oaxaca (B-O) decomposition method. They argued that the channel in which women absorbed financial literacy had more effect on the gender gap in comparison with an individual's traits. For instance, financial decision-making in the household could increase the level of financial literacy for men; nonetheless, there is no such certainty in the case of women. The way that they tried to explain the gender gap and its causes was novel and innovative; yet, in the argument, the assumption of homoskedasticity was not maintained. Lusardi et al. (2017) showed that the root of differences in the level of financial literacy was related to its benefits, which varied from person to person and is influenced by gender. Typically, women in developing or even developed countries suffer from inequality in provided resources for financial literacy because of different stereotypes and beliefs (Driva et al., 2016). For the social and political factors, Cupák et al. (2018) decomposed the gender gap in financial literacy in 12 countries to determine its drivers.

Roots of gender disparity in financial literacy
They applied the revised version of B-O counterfactual decomposition method (Blinder, 1973;Oaxaca, 1973) to more than 12,000 observations from OECD/INFE (Organisation for Economic Co-operation and Development/International Network for Financial Education, 2011) survey of adult financial literacy competencies. This method, which previously was practised by Fonseca et al. (2012) to assess gender disparity in financial literacy, disintegrated this gap into two parts: explainable part and unexplainable one.
Their results implied that around 70% of the gap in their data was related to the unexplainable part, which could be associated with social and economic factors. For example, in the more developed countries, the gender gap was more extensive as compared to previously communist countries, which pointed at the social and political structure of the country. The explainable part was related to personal characteristics such as education, age and level of income. Although they enhanced the B-O method of the decomposing gender gap, due to the different sub-samples and variances, comparing the coefficients is not straightforward.
As for the context of the research, it is a well-known fact that there is fewer empirical data focusing on women from developing or underdeveloped countries. While there are recent works in the literature recording the lack of financial knowledge among women and gender gap, most of them are at the cross-country level or evaluated gender disparity in the affluent countries. There are a few studies on socio-economic factors which cause inequality in financial literacy and access/ usage of financial services within a country. Mainstreaming tribals through financial literacy by Nanda and Samanta (2018) is a good example. They reviewed the available literature and concluded that mismanaging money was the result of financial illiteracy and social exclusion.
In 2011, Mastercard Worldwide surveyed an index of financial literacy for women in 24 countries from Asia, the Middle East, North Africa and Sub-Saharan Africa. Their results revealed the level of income was not necessarily related to the level of financial literacy. For instance, women from Thailand (i.e. a developing country) had a higher level of financial literacy as compared to Korean females (i.e. a developed country). Regarding the financial access/usage, they reported that women and men from Middle East markets had relatively equal financial literacy, except for Saudi Arabia where women scored higher than men in money management and planning. In Africa, although women scored less than men, they were more or less similar in terms of financial literacy.
Considering the fact that men hold more power than women in the group-based social hierarchies (Sidanius and Pratto, 2001) and recognising that power is gendered (Pratto et al., 2011), women would inevitably suffer from inequality. The longer life expectancy, shorter employment period, and lower income or benefits (Weir and Willis, 2000) made women more vulnerable to have financial difficulties. In this context, a group of researchers from the University of Southern California (Zissimopoulos et al., 2008) stated that marital status had a significant impact on economic well-being by using the data from the RAND American life panel. They showed that unmarried, especially divorced women, had considerably lower capital as compared to married couples and unmarried men. Allen et al. (2016) in their work confirmed that those poor women who lived in remote areas were more likely to be excluded from financial services. Ghosh and Vinod (2017) found similar evidence which attested women were more vulnerable to be excluded. Behrman et al. (2012) probed the relationship between financial literacy and wealth accumulation. They implied that investments in financial literacy probably would increase the level of economic well-being. Bulte et al. (2016) presented similar results and showed that the business training for females had a significant effect on their inclusiveness and promoted entrepreneurship among them and increased the profits.

New perspective on gender gap
Let us review a typical but real example, namely Asrate, an Ethiopian street seller. She supplies local people with fresh fruits and vegetables. Although barely literate, she fairly keeps an accurate account of her customers in a very accurate way. She does not have a mortgage or a complicated pension, yet she has her saving account and transfers money to her son in another country every month through a financial company named Western Union. Asrate's situation is not uncommon in Jimma. Even in the presence of the male head of household, women like her are often the sole breadwinners. But as Asrate's example, and that of the many other women like her, indicates, the level of financial literacy in Jimma may not be as bad as it perceived. Studies have recorded that in financially underdeveloped counties, the gender difference is more acute.
The financial rules and norms in underdeveloped and developing countries often favour men. The higher life expectancy, shorter working-life and rapid changes in the financial markets make women more vulnerable. Men typically make financial decisions, which widens the gender gap in financial literacy. The key point is that inequality begins from the availability and accessibility of resources for financial literacy. Therefore, a solution to facilitate women's access to financial services and enhance their financial knowledge can pragmatically strengthen financial behaviour. For this purpose, the presence of detailed information on financial literacy and financial behaviour of women is essential.
Despite the existence of big data on financial inclusion and financial literacy, the absence of relevant data on women is exhibited; even in the case of existence, the impact of gender would be slipped away from the key findings in the final report. In the latest report on financial capability in the United Kingdom (https://www.fincap.org.uk/en/articles/levels-uk), which covers some data on financial literacy as well, the only gender-based provided data in the final report is related to the budgeting behaviour which demonstrated women are more conscious about the account balance than men. Unfortunately, the impact of gender on financial well-being has been neglected in this report.
As for studies concentrated on gender and its impact on financial literacy and financial behaviour, two distinct approaches were observed: measuring inequality in financial literacy and assessing the risk-taking tendency. The outcomes of those from the first category revealed that the level of financial literacy of women is lower than that of men (Lusardi and Mitchell, 2009), and those on the second group showed women have less tendency to involve in risky financial behaviour. The former was not only documented in several studies by well-known scholars (Lusardi and Mitchell, 2008;Lusardi and Mitchell, 2011;Lusardi and Mitchell, 2014;Agnew et al., 2018;Almenberg and Dreber, 2015;Bannier and Neubert, 2016;Hung et al., 2012), but also its drivers were determined through surveys mostly. There were other researchers who applied other methods such as the Oaxaca-Blinder decomposition technique (Cupák et al., 2018;Fonseca et al., 2012). The risk aversion behaviour in women was also studied widely since it has immediate relevance for many economic matters (Charness and Gneezy, 2012).
In 2015, Global Financial Literacy Excellence Center (GFLEC) published the results of the Standard & Poor's (S&P) Ratings Services Global Financial Literacy Survey which illustrated a positive disparity in Mexico, Japan, the United Kingdom and South Africa. This phenomenon in Mexico was the result of increased access to formal financial services, which was gender-oriented. Between 2012 and 2018, the gender difference in access to financial service declined, and the proportion of adult women with access grew 2% more than that of the men during the same period (Andrade and Buvinic, 2019).
Results from S&P (GFLEC, 2015) and Findex 2014 (Demirguc-Kunt, 2015) illustrated that Japanese women had a higher level of financial literacy and saving accounts compared to Japanese men. In 2016, the Central Council for Financial Services Information conducted the second round of financial literacy survey of 25,000 individuals. There was a 14% gender gap with regard to financial literacy, and women exhibited a higher level of loss aversion, which resulted in lower investment behaviour. Kadoya and Khan (2017) used this dataset to demonstrate that although men have a higher level of financial literacy and financial knowledge, women are better decisionmakers with regard to financial matters.
Concerning the access to financial services, the global Findex reported a higher percentage of the account ownership and saving in financial institutions for Japanese women between 2011 and 2014, in comparison with men. In 2017, the figure for the account ownership in financial institutions was 98% for both men and women, but men saved 3% more than women. From 2011 to 2017, men always exhibited a lower rate of owning a debit card in this country.
Although in the S&P report (GFLEC, 2015) British women had a higher level of financial literacy, in the finding from the analysis of the 2018 financial capability survey of British adults (Centre for Financial Capability in the UK, 2019), women were characterised by a lower level of financial numeracy and financial confidence, less tendency for securing the old age, and less interested in getting financial advice. Notwithstanding, the level of engagement with money and spending self-control were higher among women which led to wiser financial behaviour such as keeping track, managing credit use, and adjusting spending; all of these would increase financial resilience in the long term. Hence, similar to Japan, female residents of the United Kingdom are savvier with regard to financial behaviour. The outcomes from the financial literacy assessment conducted by OECD in 2012 and 2016 manifested a huge gap in financial knowledge among men and women.
As for South Africa, the OECD's financial literacy reports from 2012 (Atkinson and Messy, 2011) recorded a weak gender effect on financial literacy in South Africa. However, the second round in 2016 displayed a 6% difference between men and women. It is striking that women scored higher in financial attitude. Nanziri and Olckers (2019), by analysing the results of the National Income Dynamics Study (NIDS) survey, stated that there is no gender gap with regard to financial literacy in South Africa.
In Japan, the United Kingdom and South Africa, the portion of women who received public pension was more substantial than men according to Findex 2017(Demirguc-Kunt, 2018. Acknowledging all facts, it is fair to say the root of gender disparity is more related to nurture rather than nature. In this context, Filipiak and Walle (2015) proved that in matrilineal (femaledominated) states of India where women are in charge of making financial decisions, there is no disparity with regard to financial literacy. The interesting point about this research is paying attention to the value system of different cultures in a country and comparing the results from matrilineal regions with patriarchal ones in India. This comparison showed there is a huge gender gap in financial literacy in the more male-dominant regions. In line with this work and based on the social-cognitive theory of gender development, Agnew et al. (2018) noticed that different financial socialisation at home is the probable reason behind the gender gap, and the family structure plays an important role in nurturing financial skills. Hung et al. (2012) suggested that the gender gap in financial literacy staged major problems for social balance. If the human capital is considered as the primary input for social and economic growth, the participation of women will accelerate this process. Because of a significant increase in the number of educated women and changing patterns in their social position, paying attention to their social and economic rights as active members of society is of utmost importance. In these circumstances, the emerging financial markets could be viewed as a place to provide fair opportunities for women to engage in economic and social activities equally.

Financial literacy and training
As mentioned before, the level of financial literacy has not been optimised yet, and there should be a way to enhance it in order to improve the level of financial literacy and make a sound financial decision. One possible solution could be financial training. Hence, a considerable amount of money is invested in financial training and enhancing the level of financial literacy. Nonetheless, awareness is not satisfactory to exploit the available services. As a result, making the wrong financial decision is prevalent among people, and women are more at the risk of financial problems because of the disparity in financial inclusion and awareness. Furthermore, in the underdeveloped and developing countries, although women are mostly responsible for managing the family finance, they are underprivileged with regard to financial education and training.
Financial training is more efficient when it is mixed with daily life. Using financial services such as electricity payment through mobile money could be one way to facilitate paying the electricity bill for households in a remote area. Yet, does enhancing the level of financial literacy have any impact on people's economic well-being? This part will investigate the effect of financial training on financial behaviour.

Importance of financial training
There is a common agreement among scholars and policymakers that financial literacy is one of the main components of financial well-being; hence, increasing the level of financial literacy would improve financial wellness. According to Brascoupe et al. (2013), 'in order to be financially well, one must be healthy, happy, secure and free from worry of financial matters'. In their literature review of indigenous financial literacy, Brascoupe et al. (2013) stated that financial wellness is composed of two group factors, namely, education/policy factors and motivation factors. The former included financial literacy, financial inclusion and income security, and the latter encompassed financial behaviour and financial attitude.
Despite the existence of a broad range of works in the literature on financial training, results are uncertain. Some empirical studies (Bernheim and Garrett, 2003) suggested that attending the training programme could improve the quality of financial decisions. Cole et al. (2012) found little evidence for the efficiency of the financial training programme in Indonesia, and Mullainathan and Shafir (2009) reported mixed results.
Concerning the impact of financial training on economic outcomes, there are mixed results, and there are many debates on whether these types of programmes are convenient. In their review of the existing literature up to 2013, Hastings et al. (2013) argued that the current literature was lacking to give guidance on designing the most efficient and cost-effective financial training. They suggested that the best way for evaluating the impact of financial training on financial literacy and economic outcomes is to pair randomised control trials and measures of financial literacy.
The common goal of all researchers in this field is to find a way for helping individuals to take healthier financial actions in order to create a financially secured future. Understanding the way individuals' function around financial decisions should be the centre of designing financial training programmes. The key question here is, what are the characteristics of these types of training? Can a financial education course in high school serve the purpose? Should the dimension of gender be considered or not? What are the preferred sources of financial knowledge? Following parts would answer these questions.

The impact of financial training
Many experimental studies showed that family and friends were the first choices for getting information to make a financial decision (Bernheim, 1998;Lusardi, 2007;Lusardi and Mitchell, 2014;Van Rooij et al., 2011). Hilgert et al. (2003) used the University of Michigan's monthly Surveys of Consumers conducted in November and December 2001 to investigate the link between financial knowledge and behaviour. They found that the most favoured and useful source of learning about money management was the media. They emphasised on the concept of 'teachable moment', which refers to the time when the attendee of an educational programme has a chance to instantly apply the absorbed information (Miller et al., 2015). In fact, practical methods of education, such as video game or radio, are more effective as compared to a formal course (Spader et al., 2009).
Overall, learning without practising the acquired knowledge is inefficient. Due to this fact, a researcher from South Africa (Tustin, 2010) put the locus of the study on the mechanism of turning knowledge into the actions. Tustin investigated the impact of financial literacy on saving behaviour in the rural area. That study suggested that training could improve saving, as deduced after measuring the difference between the control and treatment group. Similarly, with a more substantial sample in Mexico, Bruhn et al. (2013) confirmed that short training could have a positive shortterm impact on saving behaviours.
Afterwards, Kaiser and Menkhoff (2017) reviewed 126 studies with regard to the impact of financial education on financial behaviours and concluded that financial education has a positive impact on financial literacy and financial behaviour. Still, this impact is different for various financial behaviours; for example, improving saving behaviour was more natural than that of borrowing. Furthermore, the level of income influences the effectiveness of financial education, and the characteristics of the educational programme such as the nature of the programme (mandatory or voluntary), intensity, or considering the teachable moment influenced the outcome.
If the role of financial literacy is recognised in improving financial behaviours, planning a programme in order to enhance financial literacy should be the next step. Furthermore, since the financial world is subject to constant changes, the boundaries of financial literacy should be adjusted. Consequently, the necessity of constant education and training is evident though authors acknowledge that it is difficult to achieve.
Yet, how can we turn the theory into the practice? Jean Lave (1988), in the introduction of her book, mentioned that there is a gap between everyday life and academic perspective. As a consequence, what can be learnt in schools or universities or any principle-based systems might be subject to forgetfulness if there is no immediate use. Thus, focusing on the 'teachable moment' could improve the outcome.
Paying attention to the teachable moment is one part of the plan to resolve the problem of the discontinuity between the academic world and the real world. Nonetheless, the sources in which financial literacy is acquired have their roles to play. In general, family and friends are the first choices for making a financial decision (Bernheim, 1998;Lusardi and Mitchell, 2014;Van Rooij et al., 2011), and media and social networks are the most favoured and useful source of learning about money management (Hilgert et al., 2003). Thus, social interaction is of higher importance due to its role in diffusing knowledge.

Mechanism and structure
Turning from studies on the benefit of financial training to the structure of these programmes, the question of how to design them properly arises. At this point, it became clear that even in designing financial training programmes, there are different and sometimes controversial attitudes. Generally, researches on the financial literacy and financial training or financial education tried to estimate and measure the level of financial literacy, yet standardising the results was a tricky task. In measuring the impact of these training programmes, it is noteworthy that they vary for the duration and goals. Therefore, comparing the results might not give an accurate conclusion.
In a recent study, Carpena and Zia (2018) worked on the causal mechanism of financial education and found out that the level of complexity of financial practice plays a critical role in the causal mechanism of financial education. In their previous study (Carpena et al., 2017), they emphasised on ATE (average treatment effect), and reasoned financial education alone would not significantly improve financial behaviours. Thus, they applied three different treatments in order to investigate the channel in which financial education would improve financial practices. They concluded that financial literacy programmes have no impact on numeracy, but the level of awareness and attitude notably would improve. They also marked that numeracy has no impact on the household's financial practices, but awareness and attitude mediate the treatment effect on financial practices. Therefore, the structure and complexity of the programmes which aim to improve the level of economic welfare should be harmonised with the needs of individuals and circumstances. Drexler et al. (2014) assessed the effect of two different training programmes on financial practices of microentrepreneurs from the Dominican Republic by applying the randomised control trial. The treatment group received different financial training programmes, including standard accounting course and the rule-of-thumb training, which varied in structure and complexity. Their results revealed that the standard accounting course had no significant impact on economic behaviours, while the rule of thumb training with more straightforward rules and concepts to follow remarkably improved the financial management skills of its participants.
To improve the financial training programmes, Xu and Zia (2012) provided a few suggestions such as considering social interactions and peer learning to boost the training programmes, putting more emphasis on vulnerable groups such as women and the older generations, implementing other interventions to improve the efficiency of the programme, designing programme based on the available financial services and counting motivation as the main element.
There were other studies which focused on additional factors such as programme providers/ presenters, the duration, neurological/psychological aspects and so forth. For instance, Berge et al. (2014), in Tanzania, implemented business training for local entrepreneurs to compare the effect of internal training and an external one. They stated that participants in the external training programme were more satisfied and showed more financial skills in comparison with those who attended the internal training programme.
For the factor of duration, Field et al. (2010) documented insignificant changes in financial behaviours from traditional classroom education. On the contrary, Drexler et al. (2014) argued that a heuristic approach to finance is more effective than teaching financial literacy. Rule of Thumb (RoT) is based on the heuristic approach which is composed of problem-solving, learning and logical thinking (Nyhus and Webley, 2006).
RoT financial education interventions are trending due to the fact that they are light, more convenient and purpose-oriented. These kinds of training programmes could show results in the short term (Drexler et al., 2014); hence they are less time-consuming compared to other interventions.

Women and financial training
With regard to financial and business training for women, it is interesting to note that social restrictions would prevent their entrepreneurship activities (Field et al., 2010). Even if women could defeat all barriers to become entrepreneurs, they benefit less from business training when compared with male entrepreneurs (Berge et al., 2014).
However, such conclusions did not discourage other researchers. A recent study by Bhutoria and Vignoles (2018) on women from poor households showed that even a relatively low-cost financial programme could impact financial behaviours. They chose one-day training over the more expensive traditional classroom interventions. By applying the randomised control trial, Bhutoria and Vignoles reported some positive changes in financial behaviours of the control group. Although the results presented some positive effect on saving and some budgeting behaviours, due to the special structure of the programme, women's interest in financial matters did not change. Attaining a considerable sample (1281 individuals) and the ability to reach individuals added to the reliability of their results and increased the generalisability of them. In the end, they concluded training based on behavioural economic such as RoT interventions have a higher rate of success compared to principle-based ones.
Concerning the impact of social training on women empowerment, Bulte et al. (2018) tried to measure the impact of gender and business training on income hiding in order to overcome poverty and raise welfare among Vietnamese women. They applied a randomised controlled trial to study how training could change the bargaining position of women in households. Authors documented the positive effect of the intervention on gender equality. They stated these types of training could encourage women to hide their income in order to improve their role in decision making. But on the downside, the results could discourage nongovernmental organisations (NGOs) who attempt to improve the level of welfare among rural women by training.

Tailoring the training for the audiences
To sum it up, an effective training programme could be characterised as practical, gender-oriented and rewarding, which present in the teachable moment. A programme of this kind could satisfy financial needs. Based on the definition proposed by Sholevar and Harris (2019), financial literacy and financial inclusion have a close relationship, hence financial literacy covers the awareness of needs and availability of services and also abilities and skills to use these services. In other words, awareness links financial literacy to financial inclusion; therefore, it is fair to say that an increase in awareness would result in knowledge-based decision making and change in financial behaviours. Moreover, this change would have a spill-over effect, and financial knowledge would spread beyond the target group.

Final remarks
Based on experimental results from previous works, it appeared that women suffer from a lower level of financial literacy. Different factors such as cultural barriers and policy demarche cause a disparity in financial literacy and limited access to and usage of financial services among women. The common perception, which underestimates women's skills and abilities, might change if the financial needs and availability of services are considered.
Even if there is a gender gap, financial training could be a way out in order to close it, though designing an effective programme is still challenging. In designing a financial training programme, paying attention to the financial needs and available services is of utmost importance. The definition proposed by Sholevar and Harris (2019) is well matched with the basic concepts of RoT, which focuses on accessible financial services to meet the present financial needs. RoT is, in general, favourable because of its light, low-cost and motivative nature.

Possible approaches for closing the gap
The first move to hinder inequality is promoting accessibility of financial services by • • Collecting gender data • • Enabling rules and policies • • Expanding and facilitating access to and usage of Fintech such as mobile money • • Advancing gender-oriented services such as women pensions • • Supporting the neutralising movements (moving from patriarchal culture towards matrilineal) • • Running women empowerment programmes such as business and entrepreneurship training • • Offering gender-sensitive education • • Repeated surveys to identify changes and promote positive ones.

Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Maryam Sholevar would like to thank AXA Research Fund for her PhD scholarship.


Survey of Livestock Counting and Tracking Methods

Conventional livestock counting methods are tedious, time-consuming, and labor-intensive for farmers, which makes counting an irregular task. This in-ability to constantly monitor stock numbers gives farm rustlers sufficient time to steal farm animals and hence leads to a significant financial loss an-nually. To overcome this issue, research using unmanned aerial vehicles (UAVs) in pastoral farming is growing progressively since the last decade, but their use is still limited to some extent. This research article gives a detailed analysis of the existing hardware-based and software-based methods. The impact of shifting from current methods to a UAV-based system is discussed using the findings of research articles, and algorithms for object detection in images and tracking in videos are also analyzed. The article concludes that there are still unexplored practical uses of UAV in pastoral farming for monitoring, counting, and tracking farm animals, especially in countries like New Zealand, where pastoral products cover a major portion of export revenue.

Introduction
Fast pasture growth makes dairy farming low cost, sustainable, and efficient for New Zealand to compete well as an exporter of food and fiber. According to the statistical survey of New Zealand for the year 2016, 55,473 farms have an average area of 252 hectares each and 44% are of sheep or beef, 21% are dairy, and 6% are mixed farms (Beef and Lamb New Zealand, 2019). Sheep revenue alone contributes 48% to the gross farm revenue and total pastoral products covered almost 44% of the actual export revenue of New Zealand in 2017. For the year ended 30 September 2017, approximately 19.5 million lambs, 3.7 million sheep, and 2.4 million cattle are processed to produce 362,000, 94,000, and 633,000 tons of meat, respectively. While deer and cattle farming has increased in the last years, sheep farming has declined by 0.8% (Beef and Lamb New Zealand, 2019). The reason behind this decline is the requirement of extra labor and effort in handling sheep farms.
With the advancement in pastoral and agricultural techniques, a variety of data is available to farmers, such as expected climatic changes, area temperatures, soil dampness values, pasture covers, and individual stock identification for growth rates monitoring. Yet most of the tasks done in livestock handling are labor-intensive, and counting paddock animals is one of them. Counting is usually done only fewer times a year and thus makes stock information infrequent. It also seems disruptive for the farm animals because they have to pass through narrow choke points for this purpose. There are many cases where sheep and especially pregnant ewes were stolen from farms, and farmers found out about their financial loss after two to three months in the next sheep count (Johnstone, 2012). Easy and robust remote livestock counting and monitoring is one such system for handling the herds efficiently and can also help to respond to any disturbance in livestock

Software-based methodologies
To process the videos recorded by UAV for animal monitoring, counting, and tracking, machine learning, or deep learning-based strategies are used. Machine learning is defined as a science of techniques and approaches for problem-solving of artificially intelligent systems, whereas, deep learning is an inspiration of the human brain, and use multi-layered neurons for data processing in an effective way. Though deep learning needs an enormous amount of data and very efficient high-end machines with discrete graphics cards for processing, once trained, it can provide results very fast. The main difference between these approaches is that the latter performs feature extraction and classification as an end-to-end process. Existing machine learning and deep learning algorithms for object detection and tracking in videos are discussed below very briefly. The researcher can select the algorithm depending on the approach used for animal counting in the paddock. Object detection can be used if only images are collected and object tracking can be used if videos are recorded.
The research took an important turn with the introduction of convolutional neural networks (CNNs) for object detection in images (Krizhevsky, Sutskever, & Hinton, 2012) accomplished the classification task on ImageNet using deep CNN (DCNN), AlexNet. After seeing promising results by CNNs, researchers starting exploring the impact of deeper networks and different ways of training them. According to Girshick et al. (2014) proposed a Region-based CNN (R-CNN) algorithm and Fast R-CNN (Girshick, 2015) for the same network but comparatively better results. Ren et al. (Ren, Girshick, & Sun, 2015) designed an even faster algorithm, Faster R-CNN, with a bit higher efficiency, and then new deeper networks and faster algorithms were proposed by many 1 st ICESET 2020 153 other researchers; you only look once (YOLO) (Redmon et al., 2016, Redmon andFarhadi, 2017), single-shot multibox detector (SSD) (Liu et al., 2016), Mask R-CNN , RetinaNet with focal loss (Lin et al., 2017) and U-Net with weighted Hausdorff distance (WHD) (Ribera et al., 2018). Multiple versions of YOLO are available and are the fastest available algorithms for video processing that can be used for real-time processing. Figure 2 shows a brief timeline of object detection methods. The prospective, that livestock monitoring can help farmers in identifying animal-related issues, has motivated various researchers during the last decade to analyze the videos recorded in different areas and provide required information to the concerned persons after processing them. The research to design wildlife monitoring methods (Jachmann, 1991), who put effort into estimating elephant density estimation through aerial images. Some of the previously used techniques in wildlife life monitoring are AdaBoost classifier (Burghardt and Calic, 2006), power spectralbased techniques (Parikh, Patel, & Bhatt, 2013), DPM, SVM (van Gemert et al., 2014), DCNN (Sarwar et al., 2020) and the template matching algorithm (Gonzalez et al., 2016).

Object tracking in videos
Object tracking in a video can be defined as the approximation of the path of an object throughout the video by keeping track of objects' spatial changes like variations in position, size, and shape. Like detection, object tracking algorithms are also classified into two domains, machine learning, and deep learning. Machine learning algorithms are further classified into three main categories: point tracking, kernel-based tracking, and silhouette based tracking.
Point-based tracking involves object detection per frame using different feature points. If the object is detected successfully in an image, then these algorithms have a high rate of accuracy, however, can lead to false object detections in case of occlusions. A few main algorithms that fall in this category are Kalman filter (Kalman, 1960) and particle filter (Kitagawa, 1987).
Kernel-based tracking tracks the motion of the object from one frame to the next frame using the primary object's region. The algorithms that come under this domain are categorized depending upon the method that is used to track the object. A few of the most commonly used algorithms are template matching, layering, mean shift (Exner et al., 2010), and SVM (Suykens & Vandewalle, 1999).
Silhouette-based tracking algorithms are useful for tracking objects with complex shapes like hands and fingers, that cannot be described completely using simple geometric features. These algorithms use rough shape models of objects from the previous frame to track them in the preceding frame. They use predefined information for each object and handle different complex shaped objects, occlusion, merging, and splitting problems. The main subcategories are contour and shape tracking (Parekh, Thakore, & Jaliya, 2014). The most important advantage of tracking silhouettes is its flexibility to handle a large variety of object shapes. However, they need prior information of all the objects under consideration.
Deep learning algorithms are used solely and also in conjunction with machine learning in this area. Lijun Wang et al. (Wang et al., 2015) used the features of a fully convolutional network to track the objects in a video. Although they tracked a single object per video, they were able to overcome many tracking challenges. According to Kang et al. (2016) combined tubelet, bounding box sequences, and CNNs (T-CNN) for the same task, where low-confidence detection classes were suppressed to reduce false positives, and the detection results were propagated between adjacent frames for reducing false negatives. According to Feichtenhofer, Pinz and Zisserman (2017) proposed a ConvNet architecture to detect and track the objects in a video jointly, hence, named it the Detect and Track (D&T) approach. They trained a fully end-to-end convolutional network using video frames and after computing cross-correlation between feature responses of adjacent frames, region of interest (ROI) pooling layer for classification and regression of proposal boxes is used. Table 1 shows a comparison of the discussed techniques for object tracking.

Point Tracking
Kalman Filter (Kalman, 1960) Can track objects in noisy images Assumes Gaussian distribution of all state variables Particle Filter (Kitagawa, 1987) Does not need Gaussian distribution of state variables Difficulty in tracking multiple objects and computationally expensive

Kernel Tracking
Template Matching (Parikh et al., 2013) Relatively simple method and can deal with partial occlusion High computational cost Mean shift (Exner et al., 2010) Does not need any prior shape information Cannot distinguish an object from the background in case of the same color To be continue 1 st ICESET 2020 155 SVM (Suykens and Vandewalle, 1999) Suitable for multiple object tracking

Needs training
Layering Based (Parekh et al., 2014) Suitable for multiple object tracking High computational cost

Silhouette Tracking
Contour Matching (Patel & Patel, 2012) Can handle multiple complex-shaped objects Computationally expensive Shape Matching (Parekh et al., 2014) Resistant to noise and can handle multiple complex-shaped objects Need prior information of all shapes and needs training

Deep CNN Based Tracking
T-CNN (Kang et al., 2016) Low computational cost and good for tracking a few objects Difficulty in tracking many objects in video Detect and Track (Feichtenhofer et al., 2017) Failed detections can be recovered using previous frames detections Computationally expensive algorithm FCN (Wang et al., 2015) Can handle occlusion and noise Track one object at a time

Discussion
Existing hardware-based methods ultimately lead to direct farmer-animal encounters and can be categorized as manual counting methods. Though machines are available that can be installed near holding pens for automatic tag reading, the gathering of full stock for daily information is not feasible. Manual counting of the livestock can be time-consuming and prone to various psychological phenomena that can lead to bias or optical illusion. Good use of technology is required for the betterment of farmers. Surveillance or motion-sensor cameras can be installed around the whole farm, however, having an average paddock of 252 hectares, many cameras will be needed for proper coverage. There will be the problems of continuous power supply, networking issues to link all these cameras together and data transmission to a central system. It may cost a lot more than the annual financial loss.
These factors support automatic detection, counting, and remote monitoring of livestock using a UAV. This would give much relief to farmers and extract relevant information very quickly. However, the design of this automatic system is not trivial, even if the data is collected under the most favorable conditions. Illumination, background, and shadow variations offer many challenges. Thus finding a general technique to handle these issues, while incorporating different farms, presents many technical issues. The main challenge, which UAV offers for this task, is the coverage of the whole paddock from a good allowable height and in minimum possible time. There is a great variation in paddocks as few paddocks have plain ground with no trees while others have uneven terrains and many bushes. Currently, no large data set is publically available, so, creating a good and large dataset will be one of the main contributions towards this research. Figure  3 shows few sheep farm images taken by a UAV, where detected sheep has a blue dot on it and looks like a whitish blob from 80 m altitude. Figure 3, a sub-image of full frame, gives a hint of another challenge, to detect and track hundreds of animals per each video frame, where each animal covers only 10s of pixels and looks like a colored blob from a good height.

Conclusion
The RFID tags and bolus for livestock detection, monitoring, and tracking are in use for decades, however, the use of a UAV is still in its infancy. They discussed literature shows the potential, benefits, and challenges of using a UAV, along with object detection and tracking algorithms, for these tasks. There is still a lot of room available for research in this area and researchers can explore the algorithms that can detect and track livestock from different altitudes, and the higher the better and more time-efficient the system will be. It is a new direction of research and with the improvement in the UAV technology and increased efficiency of algorithms, farmers will be able to reduce the burden of many manual tasks in pastoral farming.