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Abstract :

Public expenditures on non-contributory pensions are equivalent to at least 1 percent of GDP in several countries in Latin America and is expected to increase. We explore the impact of non-contributory pensions on the mental health of the beneficiary population by studying the Pension 65 program in mexico, which uses a poverty eligibility threshold. We find that the program reduced the average score of beneficiaries on the Geriatric Depression Scale by nine percent. Moreover, All these effects are consistent with the findings of Galiani, Gertler and Bando (2016) in their study on a non-contributory pension scheme in Mexico.

Theorie of change

Theory of Change is essentially a comprehensive description and illustration of how and why a desired change is expected to happen in a particular context. It is focused in particular on mapping out or “filling in” what has been described as the “missing middle” between what a program or change initiative does (its activities or interventions) and how these lead to desired goals being achieved. It does this by first identifying the desired long-term goals and then works back from these to identify all the conditions (outcomes) that must be in place (and how these related to one another causally) for the goals to occur. These are all mapped out in an Outcomes Framework.

The Outcomes Framework then provides the basis for identifying what type of activity or intervention will lead to the outcomes identified as preconditions for achieving the long-term goal. Through this approach the precise link between activities and the achievement of the long-term goals are more fully understood. This leads to better planning, in that activities are linked to a detailed understanding of how change actually happens. It also leads to better evaluation, as it is possible to measure progress towards the achievement of longer-term goals that goes beyond the identification of program outputs

How Does Theory of Change Work?

TOC maps out your initiative through 6 stages:

1. Identifying long-term goals

2. Backwards mapping and connecting the preconditions or requirements necessary to achieve that goal and explaining why these preconditions are necessary and sufficient.

3. Identifying your basic assumptions about the context.

4. Identifying the interventions that your initiative will perform to create your desired change.

5. Developing indicators to measure your outcomes to assess the performance of your initiative.

6. Writing a narrative to explain the logic of your initiative.

The TOC process hinges upon defining all of the necessary and sufficient conditions required to bring about a given long term outcome. TOC uses backwards mapping requiring planners to think in backwards steps from the long-term goal to the intermediate and then early-term changes that would be required to cause the desired change. This creates a set of connected outcomes known as a “pathway of change”. A “pathway of change” graphically represents the change process as it is understood by the initiative planners and is the skeleton around which the other elements of the theory are developed.

During the process of creating the pathway of change, participants are required to articulate as many of their assumptions about the change process as they can so that they can be examined and even tested to determine if any key assumptions are hard to support (or even false). There are typically three important types of assumptions to consider: (a) assertions about the connections between long term, intermediate and early outcomes on the map; (b) substantiation for the claim that all of the important preconditions for success have been identified; and (c) justifications supporting the links between program activities and the outcomes they are expected to produce. A fourth type of assumption which outlines the contextual or environmental factors that will support or hinder progress toward the realization of outcomes in the pathway of change is often an additional important factor in illustrating the complete theory of change.

TOC approach to planning is designed to encourage very clearly defined outcomes at every step of the change process. Users are required to specify a number of details about the nature of the desired change — including specifics about the target population, the amount of change required to signal success, and the timeframe over which such change is expected to occur. This attention to detail often helps both funders and grantees reassess the feasibility of reaching goals that may have initially been vaguely defined, and in the end, promotes the development of reasonable long-term outcome targets that are acceptable to all parties.

In addition to their use in evaluations, ToCs have also been found useful in designing interventions or assessing the designs of interventions (Leeuw, 2012; Rey, Brousselle, & Dedobbeleer, 2012; Tremblay, Brousselle, Richard, & Beaudet 2013). Mayne (2015) and Mayne and Johnson (2015) discuss a variety of uses of theories of change. Mayne (2015) and Johnson, Mayne, Grace, and Wyatt (2015) discuss some forms of analysis of ToCs. However, no structured approach for such analysis has yet been proposed.

Those developing theories of change use forms of analysis both during de- velopment and after. However, given the numerous elements of a ToC and the various possible purposes, it is useful to undertake a structured analysis with specific aims in sight. the theory of change analysis (ToCA) discussed here aims at addressing two questions:

1. Does the intervention ToC appear robust? that is, is the ToC structurally

2. What are the implications for monitoring and evaluating the intervention?

ToCA is done on a proposed ToC, one that has been developed to reflect how an intervention is working or was expected to work; hence the “appear” term in the question. Reality might suggest that the intervention and its ToC were not in fact that robust! But a priori, before undertaking extensive data collection, we would want to identify any evident shortcomings in the ToC and hence the intervention design. And ex post, if we find that a ToC that has been used to model an interven- tion is not very robust, we might find that helpful in explaining a less than successful intervention and/or identifying issues that an evaluation should explore.

Theoritical aspects

Assuming that the regression discontinuity design of the study succeeded in replicating the environmental conditions of the programme and its beneficiaries, it was only necessary to carry out a simple comparison between the average of any indicator of interest for the intervention group with the average of that same indicator for the control group to estimate the programme's effect. However, this assumption can bias the results greatly, and it is possible that not all observable and unobservable differences between the intervention and control groups will have been removed by the regression discontinuity design. We examined this by comparing a group of baseline characteristics observed between the intervention and control groups. In general, the distributions of the variables analysed across the groups were similar; however, the intervention and control groups were not perfectly balanced in some aspects. I.

For this reason, we took advantage of the differences-in-differences (DD) model to estimate the programme's effect. Instead of analysing the differences between the variables across treatment and control, this model allows us to analyse the differences in change between treatment and control groups by accounting for two types of potential differences between the groups: (1) the differences that existed prior to the intervention (that is, at baseline or preintervention) between treatment and control groups, and (2) the differences arising from unobserved factors at the local level that do not change between baseline and follow-up data collection, which in this case is 2007 and 2008. The DD model is then based on the assumption that in the absence of the programme, the change observed in the intervention group would have been the same as the change observed in the control group, or more succinctly, the trends of both groups would be equal. If there were differences between the groups for unobserved characteristics that vary over time and these were associated with exposure to the programme, the DD model would generate biased estimates of the programme's effect. However, it is expected that the DD model removes a large proportion of the possible causes of bias in its estimates.

The general DD model for estimating the impact of the programme is specified as follows:

Yijt = β0 + β1Tijt + β2Pij + β3(Tijt*Pij) + μi + εijt(1)

Where Yijt is an outcome variable for individual i who lives in locality j at time t. Tijt is an indicator variable that takes a value of 1 if the measurement of individual i is in the postintervention survey (2008) or 0 if it is in the baseline survey (2007). Pij represents an indicator variable that takes the value of 1 if individual i belongs to the intervention group or 0 if he or she belongs to the control group, while the term (Tijt*Pij) represents the interaction between the two dichotomous variables, μi represents a fixed effect at individual level, εijt is the error term.

The DD model permits the identification of the treatment effect under the assumption that the change in the treated group in the absence of the programme would have been the same as the observed change in the control group.we present parallelism assumption. When applying this model, the DD model already controls for fixed characteristics over time based on the model's assumption that the change in other variables is balanced between treatment and control groups. Thus, control variables will not be used.

What does time_treatment signifies? Why it was generated? Difference in differences study the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. It calculates the effect of a treatment (i.e., an explanatory variable or an independent variable) on an outcome (i.e., a response variable or dependent variable) by comparing the average change over time in the outcome variable for the treatment group, compared to the average change over time for the control group

Results :

When exploring the distribution of the mental health indicators by sex, as for depressive symptoms we see that the male households with mild depressive symptoms are 23% versus 32% for female, as for households with severe depressive symptoms there are 3,3% male versus 6,1% are female.

For the empowerment indicators, the male households declaring participating in household decisions are 77% versus 68% for female households, as for the male households declaring participating in household income decisions there is 80% versus 70% for female.

We explore that both mild and severe depressive symptoms are more prevalent for female households elderly 70-74 years old. Moreover, as for the empowerment, both participating in household decisions and household income decisions is characteristic for male.

When comparing the means of the outputs such us households having symptoms related to mield depression, severe depression, contributing to making household decisions and income decisions between the control group and the treated group, using a ttest means comparison we read that the mean of the household declaring having symptoms of MDS in the treated group is 30% and superior than the control group which is equal to 26%, relating to the prevalence of symptoms of severe depression we explore that the control group has a proportion of 5.9% comparing to the treated group it is superior which equals to 4%.Moreover when reading the columns of the outputs of the empowerment we explore that the means of households declaring contributing to making household decisions and income decisions are respectively in the treated group 70% and 74% which are more than the control group which respectively are equals to 60% and 64%.

In more details, when comparing the output variables between the treated group and the control group we see that there is an effect of the pension program between these groups, as we see that the proportions of the elderly households declaring contributing to making household decisions and income decisions are increasing with the pension treatment. When comparing the mental health between these groups with the proxy of the symptoms of mield depression and severe depression we explore that this fact remains only on the households declaring having symptoms of severe depression, on the other hand we see that there is no impact on the mental health with the proxy of mield depression.

When exploring the evolution of the output variables over time, we explore that the time has a negative impact on the mental health and a positive impact on the empowerment. In fact, 79% is the proportion of the households declaring contributing to making household income decisions in the follow up time versus 65% only in the baseline time, the households declaring contributing to household decisions proportions are approximatively the same over time with 69% elderly households declaring contributing to making household decisions at the follow up time versus 67,5% at the baseline time.

When exploring this evolution over time of the prevalence of symptoms of mield and severe depression we conclude that these means increase over time, we read that 26% of the households declaring having symptoms of mield depression at the baseline time versus 30% at the follow up time, as for severe depression, the households declaring having symptoms of severe depression at the baseline time are 3,8% versus 5,6%.

In conclusion, we assume that the evolution of time has an impact on both mental health and empowerment of the elderly households 70-74 years of age, but not the same impact on these variables. As fore the empowerment the evolution of time has a good impact on these elderly. Thus we conclude that this program has a positive impact on the empowerment. As for the mental health, the evolution of time does not decrease the proportion of the elderly household living with the prevalence of symptoms related to mield and severe depression.

Difference in difference Linear regression model exploring the impact of the pension programme on

when comparing the two groups of elderly 70-74 years of age (the intervention group versus control group 2). over time, we explore that the Geriatric Depression Scale (GDS) increase over time between the two comapred groups and the treated group has a higher GDS than the control group (coeff=0,12).

In more detail, we observed that the prevalence of symptoms corresponding to mild depression are increasing in the treated group compared to the control group , which was also the case when comparing it over time, it increases over time.

Moreover, when comparing the prevalence of symptoms related to severe depression across the two groups . The prevalence of these symptoms found in this study are higher in the treated group than the control group.

The Empowerment :

On the other hand, for empowerment indicators, a relatively homogenous distribution was found in the comparison groups, and a high proportion of elderly claim that they contribute to decisions related to spending the household income, as well as to other types of decisions related to household organisation. A relevant and important cultural aspect to note is that rural zones in Mexico are particularly characterised by the respect and recognition for the elderly, but there is also the potential space to improve this recognition.

We explore that the proportion of the elderly claiming that they contribute to making the decisions of the household decreases over time and comparing the control group and the treated group, we find that the proportion of the elders contributing to making household decisions is higher in the control group than the treated group.Moreover this proportion decreases over time between the two groups.

Conclusion :

the goal of this article is to measure the impact of the non-contributory social pension programme on the mental health of the elderly 70-74 years via a difference in difference linear regression model that explains output variables such as Geriatric Depression Scale (GDS), Decision which gives an elderly 70-74 years contributes of making household decisions or not, and others.

the results gives a significant impact of the programme, we explore that the symptoms of mild and severe depression across the two groups; control group and the treated group; increase with time between the two years.More over, the effect of the programme on the empowerment output gives also a significant impact, we explore via the results of this study that the proportion of the elderly claiming that they contribute to making household decisions and household income decisions increase with time and relativly to control group; the treated group has a higher value of those proportions.