ABSTRACT

Value for Money is an internationally recognized investment return evaluation approach; nevertheless, in the context of Pakistan, it is a new phrase, notably among policymakers, aid agencies, and implementing organizations in the development sector. Although there is no universally accepted definition, Vfm in the Department for International Development (DFID) programme implies “we maximize the impact of every pound spent to improve the lives of poor people” (DFID, 2011). According to my understanding, it is the best use of resources, i.e., effectiveness, quality, and sustainability, as well as striking the right balance between the four “E’s” of economy, efficiency, effectiveness, and equity. The Vfm technique has long been used by the private sector commercial and manufacturing industries, and in recent years, there has been a significantly growing tendency among aid agencies and development sector organizations to gradually use the Vfm technique in order to analyze whether aid agencies are getting enough results to justify their spending or simply to reassure us that money is not being thrown away. Vfm helps assistance agencies and development organizations to examine the extent to which their funding supports their strategic aims.

This research study is expected to guide the policy makers, development sector aid agencies and implementing partners in putting in-place a system for developing an ongoing and integrated mechanism for assessing the key Vfm metrics. This is an attempt to understand how data is being maintained under a particular livelihood intervention programme by an implementation agency. The study reveals that currently the aid and implementation agencies focus is on the outputs rather than the outcomes. The Vfm approach will provide a more outcome-oriented perspective to these agencies. What needs to be highlighted at the outset though is that the most important aspect of taking a Vfm approach is not in the specific metrics that it will reveal, but in helping put in place a system which asks more relevant questions, makes some of the underlying assumptions of theory of change, helps to generate the evidence base for it and adds to the overall efficiency, cost-effectiveness, and impact of the organization.

The data gathered through the household survey questionnaire was initially analyzed using the summary statistics of the model used for measuring the impact of given assets on the livelihood of beneficiary households, which included the percentage, frequencies, past and current asset values, and increase in income, among other things. Descriptive statistics for explanatory variables such as beneficiary CIs membership, Women Empowerment, and Social Inclusion are also included.

This research study was conducted with the National Poverty Graduation Programme (NPGP) tangible asset transfer beneficiaries. Apart from the Vfm analysis of the target audience, the study will further provide a brief write-up that may lead Project Management Unit (PMU) in the introduction of an on-going and streamlined framework for evaluating the core importance of money indicators for its ongoing work. This is an effort to explain how data should be managed in the context of numerous initiatives under the program. However, the most important part of taking a Vfm strategy is not in the concrete metrics that it can show, but in trying to bring in motion a framework that raises more meaningful questions, making some of the fundamental principles of philosophy of transformation clearer, it helps to build a foundation for facts which contributes to the total performance, cost-effectiveness, and influence of the organization.

According to the survey results of 345 HHs, non-livestock assets have a slightly higher monthly income than livestock assets, assuming the same baseline income. Seventy-eight percent of HHs reported an increase in income, resulting in an average monthly incremental income of PKR. 4,136. Non-livestock beneficiaries saw a 39% monthly increase in income, while livestock saw a 31% increase, with an average income increase of about 16%.