Decision analysis of pension options for women married into farms reveals gender disparities in pension entitlements in Germany
Alexandra Krause1, Rex Parro2, Julia Schild3, Alina Gombert4
TO DO:
ALINA
- SECTION PROBLEMS/OBSTACLES –> ONLY BULLET POINTS WITH IDEAS UP TO NOW (ALINA)
SOMEONE - PROVIDE SOURCE IN LINE 70 (SECTION INTRO)
NOT YET DONE - DISCUSSION - RESULTS - CONCLUSION - ABSTRACT
1 Abstract
?In our project, we intend to work on gender equality in german farming?.
2 Introduction
The ancient role model of the male, patriarch farmer is under deconstruction (Laoire 2002). When looking at agricultural study courses, practical training classes and social media, several scholars perceive a “feminization” of agriculture in Germany (Inhetveen and Schmitt 2017) - This perception is reflected in agricultural statistics, stating that women make up more than 36 % of the agricultural workers, 23% of agricultural apprentices and 48% of agricultural students (BZL 2021). More and more innovative and successful female farmers overcome traditional role models and claim their territory in this formerly male-dominated field (Padel 2020).
Unfortunately, this “feminization” is not reflected in the share of leadership positions and land ownership of women. Today, only 11 % of farm managers are female and only 1/3 of land is owned by females (Destatis 2020; Tietz, Neumann, and Volkenand 2021). The main reason for this being that women have very little chance of inheriting farm land, as the old-fashioned tradition of male farm succession is still common practice (Pieper and Padel 2021). Oftentimes, the only way to gain access to land is either by starting their own farming business or by marrying into an existing one. Unfortunately, most of the latter then take on the role of the farmers wife, a contributing family member or a seasonal worker (Destatis 2020). The statistically important group of women who married into farms is expected to do the office-, stable- and housework (e.g.cooking,cleaning), and to take care of the children and elderly. This way they contribute to the family business without adequate financial remuneration or security [SOURCE]. When considering old age and pension, it goes without saying that this arrangement puts her at a considerable disadvantage compared to her husband. This is true for housewives in general, however, it is important to note that the agricultural pension system in Germany is unique as it is loosely based on the idea of traditional family farms.
Traditionally, a farmers pension was secured by keeping the family business going by passing it down to his son. However, this system is challenged by structural changes of traditional family farms (e.g.decrease in small farms) (Glauben et al. 2009). Currently, the pension of farmers in Germany is usually a combination of agricultural pension insurance, leasing income from property or monthly allowance from successors(Hagedorn 1991). Here, the pension payment from the insurance is not intended to cover the living costs but rather simply functions as pocket money (BMEL 2021). Therefore, revenues of leasing land during retirement is an important supplement to the agricultural pension but legally goes exclusively to the land owner (i.e. the husband in most cases). The fact that farm property rights are unequally distributed in favor of men is also reflected in the farm decision making process- 40% of farmers’ wives claim that their husband decides alone on the farm (AgriExperts 2019). In addition, eventhough farm wives can also be insured by the agricultural insurance, they often receive a lower pension than men for various reasons (e.g. contributing family members pay and receive less pension, fewer years of contibutuin etc.)(Bundesregierung 2017). In short, despite working full-time on the farm, farmers’ wives often lack decision making power (in regards to the farm) and are financially depend on their husbands and their in-laws.
The imbalance of a high workload on the one hand and a lack of proper financial compensation on the other does not yield an adequate pension. Additionally, divorce or death of the husband constitute major risks for a farm wives’ pension. As a result, hard-working women on family farms bear a high risk of old-age poverty if they do not have a proper pension plan. However, acquiring independent pension entitlements is challenging for women on farms. First, their „patchwork employment biographies“ are disadvantageous in the German pension system and often lead to low pension payouts. Second, patriarchal traditions demand child and elderly care from women and this workload often does not allow for noteworthy off-farm employment in the first place. Third, oftentimes farm wives are not fully aware of their situations and/or of alternatives. Breaking out of old traditions has the potential to create new opportunities and financial security and independence, but it also contains risks and uncertainties.
In our study, we display different pension options that farm wives have. However, given the high system complexity, the long-term benefits of pension schemes are difficult to anticipate. This study aimed to integrate uncertainty into long-term performance projections for pension scheme interventions in Germany by applying decision analysis and probabilistic modeling approaches to produce economic ex-ante assessments for pension schemes. With our research we would like to show that risks and uncertainties do not need to be a reason to stick to a status quo that discriminates against females in farming. We would like to encourage female farmers to be courageous and look into their options and not be satisfied with practice as usual.
3 Decision
When looking at gender equality, the freedom to act of the group that is discriminated against is usually limited and entails a number of risks. That being the case we were particularly interested to investigate the decision making process and options from the woman’s point of view. In order to do that we set up a realistic and specific case study.
Our decision maker can either stick to the status quo or decide for an intervention to improve her pension.
Default option: In the The default option our decision maker follows the traditional structures (i.e. contributing full time to the family business without renumeration),and relies on her husband and his farming business to secure her pension. In this case she is officially registered as a farm wife and the mandatory agricultural insurance is paid for her by her husband.
Interventions to improve the pension: In our model, we evaluate 14 different interventions where she pursues a pension plan to ensure financial security once she retires. There is a considerable amount of different pension schemes to chose from along with a number of possible ways to finance her pension independently of her husband (e.g. off-farm employment).
4 Methods
After extensive literature research on female farmers in Germany, we identified the statistically important group of women married into farms and narrowed down the focus on the pension issue for these women in Germany. In order to illustrate possible options and interrelations that help to facilitate a decision making process, we created a first draft of a conceptual model (see Figure 4.1). In the course of this project, we solely focused our attention on the monetary values, excluding any emotional conditions and personal preferences, due to the limited scope and timeframe of this module. Next, we took the participatory approach and reached out to stakeholders and experts to update our model. We then used the updated version (see Figure 4.3) as a guideline for our model code. The model was then created according to Luedeling et al. (2021). We also made use of the tools by Wickham (2021). In a final step, this report was compiled using tools from the Xie (2021) team.
4.1 Decision maker
We characterized our decision maker as a 25 year old woman. The farm she married into is still owned by her in-laws resulting in her to be registered as a contributing family member for the next five years, until the farm is being transferred to her husband. During that time, the amount of money paid into her agricultural pension and the payout she will receive in old age are only half of that of her husband’s. At the age of 30, her husband will become farm owner and her and her husband’s status will be the same in regards to the agricultural pension. There are a number of additional individual characteristics that affect her options to take precautions for her financial future. We determined her to be a gardener by trade, she did not bring any noteworthy wealth or property into the marriage and the couple already has one young child.
4.2 Draft of conceptual model
Our inital model draft was based on literature and personal experiences (Pieper and Padel 2021) (Figure 4.1). It displays both decision options that lead to her pension i.e. the decision option 1 aka default option and decision option 2 of choosing a pension plan. We chose the three most commonly mentioned ways in the literature to secure a safe pension, namely owning property, investing in private insurance and paying into state insurance. As ways to finance these pension schemes we decided on four different financing options. Self-emplyoment on the farm (e.g. as a gardener), setting up a co-ownership of the farm with her husband, convincing her in-laws/husband to pay her fully or working in her chosen profession as a gardner off farm. All of these options could be used for investment into property or private insurance. In order to make use of state insurance she would have to be officially employed on the farm, or take on an occupation off farm. In a brainstorming session, we collected different risks for each option. If she sticks to status quo (i.e. default option) she is financially dependend on her family-in-law. Events that could highly threaten her pension in this case are a possible divorce, a fatal accident at work of her husband, or bancruptcy of the farm. Obstacles that we thought likely to make it difficult for her to choose a pension plan is the objection of the husband himself and the need to cover child and eldery care, which is mainly seen as her responsibility.
Figure 4.1: Model draft
4.3 Stakeholders and Participatory approach
Our goal was to gain a better understanding of the actual and current pension situation of farmers wives’ in Germany in order to define a realistic case of a decision maker and to update and adjust our initial conceptual model. For this, we first identified consultants in private as well as agricultural insurance companies (i.e social insurance for agriculture,forestry and horticulture SVLFG), the agricultural chamber (i.e. Landwirtschaftskammer Nordrhein-Westfalen), lawyers for agricultural family law and the German woman farmers association (i.e. deutscher Landfrauenverband) as our experts. We then conducted several interviews via phone and the video communications platform zoom.us. Additionally, we used the social networking sites facebook.com and instagram.com to approach potential decision makers (i.e.farm wives). We then organized an online workshop over zoom with the previously identified experts, stakeholders and potential decision makers (Figure 4.2).
We identified four major stakeholders in this decision. The farmer is the husband of our decision maker. He may depend on the support of his wife when running his farm. He may expect his wife to support him, as he most probably grew up in a farm, where his mother contributed without formal employment contract. Our second stakeholder category are children, that grow up on a farm and may encounter the expectation to take the farm over at some point. In case of a farm take over, The successor child will take responsibility for the financial security of its parents during their pension. To minimize his/ her financial obligations, the successor should be in favor of a good pension plan. Our third stakeholder category are the in-laws of the decision maker. When the husband owns the farm and his wife supported him full time, the wife self -sufficient in her pension but rather dependent on her husband. The In-laws may be skeptical about investing in the pension of the decision make, because they believe in the eternity of marriage. Another stakeholder are people that would like to start a farming business without inheriting one. Here, the
Figure 4.2: Invitation to the Workshop
Table 4.1 shows an overview of the participants, their status in our research, and whether they partook in an interview and/or in the workshop. In total, 15 participants attended our workshop. After a brief introduction and a general initial discussion, the participants were given three questions, namely “What pension options does a farm wife have?” “What are potential risks and obstacles?” and “How could she finance the pension options?” After answering these questions in groups of three, we collected all thoughts and ideas in plenum. After a lively discussion we were able to update our model.
| Participant | Status | Interview | Workshop.participation |
|---|---|---|---|
| Farmer’s wife | decision-maker | no | yes |
| Farmer | stakeholder and co-decision-maker | no | no |
| Farm Children | stakeholder | no | yes |
| Rural woman association | expert | yes | yes |
| Agricultural Insurance agency | expert | yes | yes |
| State insurance agency | expert | no | no |
| Private insurance consultant | expert | yes | no |
| Agriculral chamber consultant | expert | yes | yes |
| Lawyer for agricultural family law | expert | no | yes |
| Young woman farmers | stakeholder | no | yes |
Table 4.1 shows an overview of the participants, their status in our research, and whether they partook in an interview and/or in the workshop. In total, 15 participants attended our workshop. After a brief introduction and a general initial discussion, the participants were given three questions, namely “What pension options does a farm wife have?” “What are potential risks and obstacles?” and “How could she finance the pension options?” After answering these questions in groups of three, we collected all thoughts and ideas in plenum. After a lively discussion we were able to update our model.
4.4 Adjustment of conceptual model
Based on the outcomes of our research we adjusted and specified our decision option 2 (i.e. financing her own retirement plan) in our conceptual model (Figure 4.3).
Figure 4.3: Adjusted decision option 2 pathways of financing her own retirement
As a first step we eliminated the options of farm co-ownership and self- emplyoment we previously considered as a possible financing option. We learned in the workshop that co-ownerships are hardly done in reality as it is not feasible for most farming businesses and self-employment of both parents is hardly manageable when simultaneuosly raising a child. In the end, we were left with four financing options. Three of which require intial investment costs in form of covering child and elderly care. Establishing an own branch on the farm itself, particuarly in form of setting up a farm shop, delivery service or holiday housing seemed to be popular among workshop participants. Identified risks for this option were the circumstance of divorce and the bancruptcy of the farm. Getting an off farm job or negotiating with the in-laws to be officially employed at the farm was also mentioned frequently.Here, the risks are related to the farm and therefore identical with the ones for ‘own branch on farm’ (i.e. risk of divorce, bancruptcy). Another option we had not thought of before the workshop is to receive money from her husband which she can then invest into a pension plan. Howere, the risks here are equal to the default option as she would also rely on her husband for her pension (i.e. risk of divorce,bancruptcy, death of husband) When it comes to pension schemes, we excluded own property and only focused on the investment into different insurances. Here, we explicitly follow the available information on german pension schemes as stated by the pension consultants. If choosing the options of own branch or receiving family money she would pay into the agricultural insurance by herself, and on top of that has the options to invest into private insurance or ETFS or a mix of both. The other two options of on and off farm job imply the mandatory state insurance, which she then could also boost with additional private insurance, ETFs or a mix. We would like to highlight that the greatest obstacle to option 2 (i.e. pension plan) we were able identify from the workshop was in fact the husband/in-laws themselves. Every option in the retirement plan itself contains the risk, that they oppose her decision to plan for her retirement. Mainly because they do not see the necessity for it as farmers generally prefer to invest into their farming business rather than into their wives pension schemes. Additionally, the investment costs and having to find suitable child and eldery care is a further deterrent.This risk is illustrated by a black circle in the model and affects every option within the pension plan decision. The default option and associated risks remained the same in the updated version. The only change was that of design to match the design of the new decision options 2 pathway (Figure 4.4).
Figure 4.4: Decision option 1 aka default option after the workshop
4.5 Model Inputs
The previous determination of our decision maker (see section 3.1 Decision maker) allowed us to fill our input table with values (Table 4.1). After receiving a calibration training in the DA-Course, we considered ourselves calibrated experts and were able to estimate reasonable ranges based on a mixed approach of literature reasearch and expert opinion. The time frame of our intervention amounts to 40 years (i.e. retirement at the age of 65). Based on this and according to the Methodology of Do, Luedeling, and Whitney (2020), we tried to assign reasonable uncertainties given this long time horizon. Based on her appointed profession as gardener, we estimated her possible yearly income and amount of payment into the different insurance options. We repeated this process for own farm branch, on farm job and receiving family money. Hereby, we agreed that she would be able to invest at least 10 % of her income into private insurance schemes (i.e. private insurance, ETFs, or mix of both). After retiring, it is likely that she lives and receives her pension for another 17 years until the age of 82, which is the average female life expectancy in Germany (Statista 2021). Pension estimation calculators such as the brutto-netto-rechner, Allianz-Rechner-Privatrente, SVLFG-Rentenschätzung and etf-sparplanrechner were used to narrow down reasonable monetary amounts of each option within the pension plan option. The monthly payment into and payout of the agricultural insurance depends on her status (i.e. wife of farm owner or contributing family member). In our case she is a contributing family member for five years, until her husband becomes farm owner. As soon as that happens the contributions into the insurance and her pension increase by 50%. It becomes obvious that a late farm transfer could have a negative impact on her pension.
| Description | label | variable | distribution | lower | median | upper |
|---|---|---|---|---|---|---|
| Agricultural insurance payout for 17 years | Agricultural insurance payout (Euro/Year) | Agri_insurance | posnorm | 7092 | NA | 7103.28 |
| Agricultural insurance investment for 40 years (5 years as MIFA + 35 years as full member) | Agricultural insurance investment (Euro/Year) | Agri_insurance_inv | posnorm | 2893 | NA | 2902.56 |
| Private insurance payout for 17 years off farm | Private insurance payout with off farm job (Euro/Year) | Private_insurance_off_farm | posnorm | 6360 | NA | 7560.00 |
| Private insurance payout for 17 years on farm | Private insurance payout with on farm job (Euro/Year) | Private_insurance_on_farm | posnorm | 4680 | NA | 5880.00 |
| Private insurance payout for 17 years own branch | Private insurance payout with own business branch (Euro/Year) | Private_insurance_own_branch | posnorm | 3480 | NA | 5160.00 |
| Private insurance payout for 17 years family money | Private insurance payout with family money (Euro/Year) | Private_insurance_family_money | posnorm | 2160 | NA | 8640.00 |
| Private insurance investment for 40 years (10% of net income) off farm | Private insurance investment with off farm job (Euro/Year) | Private_insurance_inv_off_farm | posnorm | 1800 | NA | 2040.00 |
| Private insurance investment for 40 years (10% of net income) on farm | Private insurance investment with on farm Job (Euro/Year) | Private_insurance_inv_on_farm | posnorm | 1320 | NA | 1560.00 |
| Private insurance investment for 40 years (10% of net income) own branch | Private insurance investment with own branch (Euro/Year) | Private_insurance_inv_own_branch | posnorm | 960 | NA | 1440.00 |
| Private insurance investment for 40 years (10% of net income) family money | Private insurance investment with family money (Euro/Year) | Private_insurance_inv_family_money | posnorm | 600 | NA | 2400.00 |
| State insurance payout for 17 years off farm | State insurance payout with off farm job (Euro/Year) | State_insurance_off_farm | posnorm | 9840 | NA | 10920.00 |
| State insurance payout for 17 years on farm | State insurance payout with on farm job (Euro/Year) | State_insurance_on_farm | posnorm | 7440 | NA | 7680.00 |
| State insurance investment for 40 years off farm | State insurance investment with off farm job (Euro/Year) | State_insurance_inv_off_farm | posnorm | 2400 | NA | 2760.00 |
| State insurance investment for 40 years on farm | State insurance investment with on farm job (Euro/Year) | State_insurance_inv_on_farm | posnorm | 1680 | NA | 1920.00 |
| ETF payout for 17 years off farm | ETF payout with off farm job (Euro/Year) | ETF_off_farm | posnorm | 15600 | NA | 18840.00 |
| ETF payout for 17 years on farm | ETF payout with on farm job (Euro/Year) | ETF_on_farm | posnorm | 12000 | NA | 14160.00 |
| ETF payout for 17 years own branch | ETF payout with own business branch (Euro/Year) | ETF_own_branch | posnorm | 8640 | NA | 12000.00 |
| ETF payout for 17 years family money | ETF payout with family money (Euro/Year) | ETF_family_money | posnorm | 5400 | NA | 21840.00 |
| ETF investment for 40 years off farm | ETF investment with off farm job (Euro/Year) | ETF_inv_off_farm | posnorm | 1800 | NA | 2040.00 |
| ETF investment for 40 years on farm | ETF investment with on farm Job (Euro/Year) | ETF_inv_on_farm | posnorm | 1320 | NA | 1560.00 |
| ETF investment for 40 years own branch | ETF investment with own branch (Euro/Year) | ETF_inv_own_branch | posnorm | 960 | NA | 1440.00 |
| ETF investment for 40 years family money | ETF investment with family money (Euro/Year) | ETF_inv_family_money | posnorm | 600 | NA | 2400.00 |
| Mixing ETF and private insurance payout for 17 years off farm (10% of net income divided into 5% ETF anf 5% private insurance) | Mixed payout with off farm job (Euro/Year) | Mix_off_farm | posnorm | 10920 | NA | 13020.00 |
| Mixing ETF and private insurance payout for 17 years on farm (10% of net income divided into 5% ETF anf 5% private insurance) | Mixed payout with on farm job (Euro/Year) | Mix_on_farm | posnorm | 8424 | NA | 9192.00 |
| Mixing ETF and private insurance payout for 17 years own branch (10% of net income divided into 5% ETF anf 5% private insurance) | Mixed payout with own business branch (Euro/Year) | Mix_own_branch | posnorm | 6120 | NA | 9180.00 |
| Mixing ETF and private insurance payout for 17 years family money (10% of net income divided into 5% ETF anf 5% private insurance) | Mixed payout with family money (Euro/Year) | Mix_family_money | posnorm | 3840 | NA | 15360.00 |
| Mixing ETF and private insurance investment for 40 years off farm (10% of net income divided into 5% ETF anf 5% private insurance) | Mixed investment with off farm job (Euro/Year) | Mix_inv_off_farm | posnorm | 1800 | NA | 2040.00 |
| Mixing ETF and private insurance investment for 40 years on farm (10% of net income divided into 5% ETF anf 5% private insurance) | Mixed investment with on farm Job (Euro/Year) | Mix_inv_on_farm | posnorm | 1320 | NA | 1560.00 |
| Mixing ETF and private insurance investment for 40 years own branch (10% of net income divided into 5% ETF anf 5% private insurance) | Mixed investment with own branch (Euro/Year) | Mix_inv_own_branch | posnorm | 960 | NA | 1440.00 |
| Mixing ETF and private insurance investment for 40 years family money (10% of net income divided into 5% ETF anf 5% private insurance) | Mixed investment with family money (Euro/Year) | Mix_inv_family_money | posnorm | 600 | NA | 2400.00 |
| Coefficient of variation | Coefficient of variation | var_slight | const | 1 | NA | 1.00 |
| Discout rate | Discount rate | discount_rate | const | 1 | NA | 1.00 |
| Years of receiving pension | Pension years | pension_years | const | 17 | NA | 17.00 |
| Years of paying into pension | Working years | working_years | const | 40 | NA | 40.00 |
4.6 Coding the model
The model was created in accordance with Luedeling et al. (2021). Since the pension yield and input demand depend on the age of the decision maker, all benefits and costs were modeled in relation to individual circumstances of the decision maker (e.g. profession,age etc). The heart of our model are the yearly payments into agricultural or state pension for 40 years (€/year) and the yearly pension payout for 17 years (€/year).
Since pension payment and payouts are usually paid per month, we first used a monthly time unit in our code. However, doing that created a wide variety of outputs for 684 months (40 years of working and paying into her pension plus 17 years of receiving pension) making it highly complicated to create a straightforward output in the Luedeling et al. (2021). Therefore we decided to use yearly time untis instead as intended by Luedeling et al. (2021).
Our model entails 14 „ways“, divided according to different income sources in 4 branches. In each branch, she pays a mandatory insurance (either state or agricultural insurance). In addition there are differnt options to top this mandatory insurance with personal investments like ETF, private insurance or a mix of both. Each of the 14 ways is compared to the default option (i.e. decision option 1). Here, our decision maker choses not to invest financially into her retirement and only relies on her husband/family-in-law since the mandatory agricultural insurance is paid by them.
- Branch 1 = Default vs. Own farm branch (Way 1, 2, 3)
- Branch 2 = Default vs. Off-Farm Job (Way 4, 5, 6, 7)
- Branch 3 = Default vs. Payed on farm job (Way 8, 9, 10, 11)
- Branch 4 = Default vs. Family money (Way 12, 13, 14)
Our case study expands over 57 years and is split into a period of 40 years of payment and a period of 17 years of payout. For all insurances, investments and pensions we produced time series with variation.
#Agricultural insurance
Agri_insurance <- c(rep (0,working_years),vv(var_mean = Agri_insurance,
var_CV = var_slight,
n = pension_years))
Agri_insurance_inv <- c(vv(var_mean =Agri_insurance_inv,
var_CV = var_slight,
n = working_years), rep(0,pension_years))In way 1, our decision maker sets up her own business branch. Analogue to the default option, she continues to be part of the agricultural insurance. Here however, she pays into it herself. On top, she invests about 10 % of her income into private insurance. In part A, her benefits from the private insurance and agricultural insurance were summed up, whereas part B sums up the cost for both insurances.
By subtracting the cost from the benefit, we obtained the NPV. The NPV was then discounted with a discount rate of 1.
PartA <- Private_insurance_own_branch + Agri_insurance
PartB <- Agri_insurance_inv + Private_insurance_inv_own_branch
profit_with_Own_business_branch_1 <- (PartA - PartB)
NPV_profit_with_Own_business_branch_1 <- discount(profit_with_Own_business_branch_1,
discount_rate = 1, calculate_NPV = TRUE)In the default option, the agricultural insurance is paid for by the family. Therefore, the decision maker benefits from receiving pension without any own investment.
PartA <- Agri_insurance + Default_option
PartB <- Agri_insurance_inv
# The variables Default_option and Agri_insurance_inv are equal and cancel each other out.
profit_Default <- (PartA - PartB)
NPV_no_branch <- discount(profit_Default,
discount_rate = 1, calculate_NPV = TRUE) To calculate the NPV of the decision, we subtracted the default option from way 1. We perform this calculation on all 14 ways.
NPV_decision_profit_with_Own_business_branch_1 <- NPV_profit_with_Own_business_branch_1 - NPV_no_branchIn our return list, we included the NPV of the default option, the NPVs of the different interventions, the NPV of the decisions and the cashflow.
return(list( NPV_no_branch = NPV_no_branch,
#way 1
NPV_profit_with_Own_business_branch_1 = NPV_profit_with_Own_business_branch_1,
NPV_decision_profit_with_Own_business_branch_1 = NPV_decision_profit_with_Own_business_branch_1,
Cashflow_decision_gender_way_A = profit_with_Own_business_branch_1,
#way 2
[...]
#way 14
[...]
)5 Results and Discussion
Depending on the possibilities on farm and in the region, our decision maker can chose among different sources of income. The decision maker may either start her own branch on the family farm, pursue an off-farm job, ask her husband for a formal working contract or ask her husband to pay for her retirement.
Many of our decision options have a negative NPV. The reason is that we compared each of our ways to the default option, in which the woman receives very little retirement, but as her family covers the cost for the mandatory insurance, she has no cost at all.
Figure 5.1: NPV decision of own business branch
In the first branch our decision-maker sets up her own business branch on her husbands farm. Here, she continues to be part of the agricultural insurance, but also invests about 10 % of her income in private insurance, etf or a mixed investment. In Figure 5.1 the NPVs from the decision clearly show, that these options are not attractive to our decision maker. Even tough the pension payout is considerably higher, the default option with the small pension comes for free.
Figure 5.2: NPV decision of off farm job
In the second branch , our decision maker gets a gets an off-farm job. Here, she stops to be part of the agricultural insurance.Instead, she is part of the mandatory state insurance. Within this branch, the decision maker can either invests no income in pension or invest about 10% of her income in private insurance, ETF or mixed investment. In Figure 5.2 the NPVs from the decision show, that only the investments in ETF and Mixed investment are more attractive than the default. Even tough the pension payout with any pension plan is considerably higher then the default, the default option with the small pension comes for free.
Figure 5.3: NPV decision of on farm job
In the third branch, our decision maker gets an official working contract and gets officially paid on farm by her husband. Here, she stops to be part of the agricultural insurance. Instead, she is part of the mandatory state insurance. With the income from this on farm job, the decision maker can either invests no income in pension or invest about 10 % of her income in private insurance, ETF or mixed investment. In Figure 5.3 the NPVs from the decision show, that most options are not attractive to our decision maker. Even tough the pension payout is considerably higher, the default option with the small pension comes for free.
Figure 5.4: NPV decision of family money
In the fourth branch, the decision maker convinces her husband to invest in her pension. Here, she continues to be part of the agricultural insurance, She uses the money from her husband to invest in a private insurance or ETF or mixed investment. In Figure 5.4 the NPVs from the decision show, that most options are barely more attractive than the default to our decision maker. Even tough the pension payout is considerably higher, the default option with the small pension comes for free.
5.1 Cashflow
Figure 5.5: Cashflow of way 1
The cashflow displays the biography of the decision maker. During the 40 years of work, the decision maker invests in her retirement. The payout is spread over the 17 years of pension. The cashflow shows how the structure of pension investments - decision makers have to deal with long years of investment and negative cashflow and the return comes only in old age.
(figure caprion) Cashflow: Here we plot the distribution of annual cashflow over the entire simulated period for the intervention (n_years) for way xx. For this we use the plot_cashflow() function which uses the specified cashflow outputs from the mcSimulation() function. ## PLS
Figure 5.6: PLS way 1
When checking the PLS for our differnt ways, it becomes apparent that the respective pension scheme has the highest impact . For example for way 1 our decision maker combines the mandatory agricultural insurance with private insurance. In the PLS, the investments in insurance appear negative whereas the payouts have a positive impact on the NPV of the decision. As our model is quite simple, this corresponds with our expectations. (See Fig. 5.6). When looking at the Pls of other
5.2 Discount rate
Long-term payments directed to woman retirement incur high establishment and maintenance costs and will generate net losses in the first few years but return substantial benefits to the wife and family in the long term. Analogue to Do, Luedeling, and Whitney (2020), best option for a decision maker may largely depend on their discount rate. In our Model, we have used the dicount rate f 1. It would be desirable to provide a range of different discount rates, to incoperete the different dicout rates of different decision makers.
5.3 Risks
In our model building process, we identified several risks which have the potential to impair the model outcome. We estimated the likelihood of these risks to occur and incooperated them in our initial version of the model. Also, we identified their potential impact on the NPV. However, implementing the risks lead to misleading results, as our code applied the risks over and over, and was not sensitive to the time in the employment biography of the woman.
| Risk | Description | Lower…. | Upper…. |
|---|---|---|---|
| Risk in-laws decide | Percentage risk that in-laws/husband overrule daughter in-law/wife | 45.00 | 50.00 |
| Risk of divorce | Percentage risk that the marriage will be divorced at some point | 20.00 | 40.00 |
| Risk of fatal accident of husband | Percentage risk that farmer husband has a fatal accident at work | 0.01 | 0.02 |
| Risk of farm bancruptcy | Percentage risk that farm goes bancrupt | 1.00 | 1.70 |
this is my favorite table (Table5.1)
Risks: Problem with binominal distribution (0,1) and possible unlimitedness repetitions over our time period E) Risks that cannot occur each year, but have to end like death of husband. The husband does not have a death risk if he already is dead. The ongoing 0/1 distribution would have to end F) Risks that do not mean the receiving of no money at all like in other cases (insect pests that destroy whole harvest). Risks would lower payment depending on when(!) they occure: if a farm wive pays into her insurances for many years, she should still get money out of the insurance if she stops paying in. Bancruptcy f.e. would not mean to get no pension at all. But depending on how long she pays money into the insurance, she would get more or less money. Only a function “knowing” exactly how the insurance gives out money after how many years paying in would work fine. If her husband would die even after 20 years paying for insurances, she would receive no money at all with the binominal distribution. The death of the husband is a risk that affects her working conditions and the money she could pay into the insurance from the year of the death onwards, but does not mean a total loss of pension. G) husband_risk means the power of the husband to prevent his wife from getting payed/ doing a job. This only occurs once. If she has a job, she does not discuss with her husband each year if she can still do it. This risk also is hard to value as a number -> instead of using the risks of the decision function, new risk functions would have to be written! Complicated, because no knowledge about the exact way the insurances pay what amounts of money under which conditions.
5.4 Problems/Obstacles/Uncertainties/what we did not implemement
There are severalrisks, that would even decrease the value of the default option.
Risk of no /late farm transfer: In our Model the Farm is transfered to the husband in time. It is important to keep in mind that this is not always the case. As we learned in the workshop (see section Participatory research), a large number of farms chose to cancel the woman’s agricultural pension because they prefer to invest the money into the farm directly.Also, the transfer of a family farm to the successor is oftentimes not a smooth process and might be realized later then expected. when the farm actually does not belong to the partner, but still to the parents in law, the situation of the woman is very insecure. She will not be registered as a wife. Without marriage, she can not be registered as a contributing family member.
Case of no marriage: When the woman is not married but “only” a partner, she will mot be eligible to the agricultural pension. If she does not pursue an outside job, she is not eligible for any pension.
Within the scope of this model, it was not possible to include the emotinal hurdles to pursue the rationally mot advisabme way. in our workshop woman emphazized, that asking for money is such a big step thyat many woman are to shy to even ask. Whith our model, we may identify the most attractive option in monetary terms, but probanly the monetary terms are not the most important one for woman.
child care & elderly care was difficult to implemement into the our model. Initially, we included child and elderly care as a cost. however, this cost in combination with the cost for pension investment mader the desion NPV incredibly negative. For the sake of a simple model we left it out, however, it would be desirable to include it in future.
Uncertainties related to the communication culture in farming families, farmers’ values, farm profitability, and pension returns appear to have the great influence on whether a retirement plan emerges as the preferable option. Better access to information and self confidence to ask for a fair share of farming income are prerequisites to implement pension plans for in-married woman in german farms. Narrowing these key knowledge gaps may offer additional clarity on farmers’ wives optimal course of action and provide guidance for agencies promoting insurance interventions in Germany. Our model produced a set of plausible ranges for net present values and highlighted critical variables, more clarity on which would support decision-making under uncertainty. Our research approach proved effective in providing forecasts of uncertain outcomes and can be useful for informing family farms pursuing a pension plan.–>IS THAT TRUE? I THOUGHT OUR MODEL WAS RATHER CONFUSING
Do, Luedeling, and Whitney (2020) Fabian (2018) Müller (2010) Hadler et al. (2020) Oedl-Wieser, Schmitt, and Seiser (2020) Bundesregierung (2017)
5.5 Recommendation
Right mixture of options is important –> for each individual case–> depends on age, wealth before wedding, profession, in-laws (whether they see the need for a retirement plan) etc.
Annual profits from farming mostly benefit the farm owner, which is rarely is the women. Farmers likely prefer reinvesting the available money in farming technology due to the relatively early incomes and short time-lag on returns. However, structural changes in agriculture and the high give-up rates impose high insecurity on the future of the farm and may raise the awareness for proper pension plans.