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经济代写|发展经济学代写Development Economics代考|Do It Yourself vs. Outsourcing

Dedicated capacity is needed to use new technology tools – capacity that evaluators may not have. Many of the ICT tools require substantial and specific skills and knowledge. This is especially true of emerging technologies such as machine learning, remote sensing and geographic information system (RS\&GIS), and data analytics. Organizations are then faced with a dilemma as to whether to build the capacity in-house or outsource such functions to third-party vendors with specialist capacities. Internal capacity building requires time, organizational orientation and the will to build such capacity in an area with which few senior managers are familiar. In addition, resources are constrained in the environment that development organizations and evaluators work in. An additional resource person for a special function is one person lesser for the core function of evaluation. Outsourcing comes with its own cost implications, lack of institution-specific application of technology, and uncertainty about expected outcomes.

To address such cost and capacity constraints, organizations need to answer some questions about their operations and how a technology tool might help them:
a What is the nature of the organization’s operations at large?
b What are the most commonly measured indicators in the organization’s evaluations?
c Does this new tool help the organization answer its most frequently asked questions?
d How often would the organization need the functions of the tool?
e How easy is it to train staff on the tool?
f Are the costs of outsourcing lower than transaction costs of training staff?
The above questions might give clear answers to some on the need to build certain capabilities in-house or outsource them, while for some others, it may still throw up uncertain results (Box 2.1). As an example, the Independent Evaluation Office of the GEF might be inclined towards building in-house capacity for collecting and analysing remote sensing data, given GEF’s focused environment work and recurrent need for measuring certain environmental indicators (see Case Study 1).

经济代写|发展经济学代写Development Economics代考|Overcoming Data Constraints: In-House or Outsource

The Ministry of Foreign Affairs of Finland planned to evaluate Finland’s interventions in promoting gender equality, an important thematic priority for the ministry. At the outset, the interventions were found to lack primary data. The evaluability study revealed that the only database available was on financing of the projects. To get around such data constraints, the evaluation commissioner decided to explore three options: satellite imagery, Internet-based sourcing of data and data analytics.

Some proxy indicators were laid out for gender equality that could be measured using Internet-sourced data and satellite imagery. When researching some gender-related trends in Africa using the Google Trends database, it was found that Google’s data is still limited in Africa. In many cases, no country-specific data are available, perhaps because country-specific Internet exchange points (IXPs) are still under development in many African countries and Internet penetration is still not high. In addition, it was found to be difficult to attribute outcomes to the project’s activities alone.

Satellite imagery was also considered for indicators such as access to natural resources. Where country- and local-level data was found to be sufficient, the analysis would have required building highly complex models. This would have required much more advanced skills than the ministry possessed. Any external resources would have had budget implications for the evaluation. In addition, finding people with a working knowledge of the requisite technologies, gender issues and evaluation methods was a major stumbling block. In the end, using these tools was found to be too risky and time-consuming with uncertain outcome. Deliberations on training in-house staff were not fruitful due to the costs and perceived non-recurring nature of the indicators laid out.

经济代写|发展经济学代写Development Economics代考|EC982

经济代写|发展经济学代写Development Economics代考|Do It Yourself vs. Outsourcing

使用新技术工具需要专门的能力——评估人员可能没有的能力。许多 ICT 工具需要大量和特定的技能和知识。对于机器学习、遥感和地理信息系统 (RS\&GIS) 以及数据分析等新兴技术来说尤其如此。然后,组织面临两难选择,即是在内部建立能力,还是将这些功能外包给具有专业能力的第三方供应商。内部能力建设需要时间、组织方向和在一个很少有高级管理人员熟悉的领域建设这种能力的意愿。此外,资源在开发组织和评估人员工作的环境中受到限制。一个特殊职能的额外资源人员是评估核心职能的少一个人。外包有其自身的成本影响、缺乏针对特定机构的技术应用以及预期结果的不确定性。

为了解决此类成本和容量限制,组织需要回答一些有关其运营以及技术工具如何帮助他们的问题:
a 整个组织的运营性质是什么?
b 组织评估中最常测量的指标是什么?
c 这个新工具是否帮助组织回答最常见的问题?
d 组织需要多久使用一次工具的功能?
e 对员工进行工具培训是否容易?
f 外包成本是否低于培训人员的交易成本?
上述问题可能会明确回答一些人是否需要在内部构建某些能力或将其外包,而对另一些人来说,它可能仍会产生不确定的结果(框 2.1)。例如,鉴于 GEF 的重点环境工作和经常需要测量某些环境指标(见案例研究 1),GEF 的独立评估办公室可能倾向于建立收集和分析遥感数据的内部能力。

经济代写|发展经济学代写Development Economics代考|Overcoming Data Constraints: In-House or Outsource

芬兰外交部计划评估芬兰在促进性别平等方面的干预措施,这是该部的一个重要主题优先事项。一开始,人们发现干预措施缺乏原始数据。可评估性研究表明,唯一可用的数据库是关于项目融资的。为了绕过这些数据限制,评估专员决定探索三个选项:卫星图像、基于互联网的数据来源和数据分析。

为性别平等制定了一些代理指标,可以使用互联网数据和卫星图像进行衡量。在使用谷歌趋势数据库研究非洲一些与性别相关的趋势时,发现谷歌在非洲的数据仍然有限。在许多情况下,没有特定国家的数据可用,这可能是因为许多非洲国家的特定国家互联网交换点 (IXP) 仍在开发中,互联网普及率仍然不高。此外,发现很难将结果单独归因于项目的活动。

卫星图像也被考虑用于获取自然资源等指标。在发现国家和地方层面的数据足够的情况下,分析将需要建立高度复杂的模型。这需要比事工所拥有的先进得多的技能。任何外部资源都会对评估产生预算影响。此外,寻找对必要技术、性别问题和评估方法有工作知识的人是一个主要的绊脚石。最后,发现使用这些工具风险太大且耗时且结果不确定。由于成本高,而且所列出的指标被认为是非经常性的,有关培训内部员工的讨论没有取得成果。

经济代写|发展经济学代写Development Economics代考

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