Enterprise Survey 2009-2017, Panel Data - Liberia

Abstract
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The documented dataset covers Enterprise Survey (ES) panel data collected in Liberia in 2009 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2009-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage
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National

Analysis unit
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The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe
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The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data
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Sample survey data [ssd]

Sampling procedure
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The sample for the 2009-2017 Liberia Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random was preferred over simple random sampling for several reasons:
- To obtain unbiased estimates for different subdivisions of the population with some known level of precision.
- To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except subsector 72, IT, which was added to the population under study), and all public or utilities sectors.

- To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions.
- To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)
- Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous.
- The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Three levels of stratification were used in this country: industry, establishment size, and region. Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries. Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72).
For the Liberia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
Regional stratification for the Liberia ES was done across three regions: Montserrado, Margibi, and Nimba.

Mode of data collection
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Face-to-face [f2f]

Research instrument
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The current survey instruments are available:
- Services and Manufacturing Questionnaire
- Screener Questionnaire.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Cleaning operations
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Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate
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There was a high response rate especially as a result of positive attitude towards the international community in collaboration with the government in their reconstruction efforts after a period of civil strife.There was also very positive attitude towards World Bank initiatives.

摘要
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本记录的数据库涵盖了2009年和2017年在利比里亚收集的企业调查（ES）面板数据，作为世界银行企业调查倡议的一部分。指标调查与企业调查类似；它在较小的经济体中实施，因为企业调查固有的抽样策略通常不适用于企业有限的全域，故而无法实施。
2009-2017年企业调查的目标是从客户国的企业获取对私营部门状况的反馈，并建立一个企业数据面板，以便随着时间的推移追踪商业环境的变动，并允许进行例如改革影响的评估。通过访谈制造业和服务业的企业，指标调查数据提供了关于私营部门增长限制的信息，并用于创建跨国的、具有统计学意义的商业环境指标。
作为其旨在创造投资环境、就业和可持续增长的战略目标的一部分，世界银行推广改善商业环境作为发展的关键策略，这导致了对各国企业数据的系统收集。企业调查（ES）是世界银行持续的项目，旨在收集基于企业经验的客观数据以及企业对其运营环境的感知。
地理覆盖范围
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全国
分析单元
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研究的主要抽样单元为机构。机构是指开展业务和进行工业操作或提供服务的地方。一个公司可能由一个或多个机构组成。例如，啤酒厂可能有几个灌装厂和几个分销机构。为了本调查的目的，一个机构必须做出自己的财务决策，并拥有与其公司财务报表分开的财务报表。机构还必须拥有自己的管理和对其工资单的控制。
全域
企业调查的全域，即研究的全域，是非农业经济。它包括：根据ISIC修订版3.1分组分类的所有制造业部门（组D）、建筑部门（组F）、服务业部门（组G和H）以及运输、仓储和通信部门（组I）。请注意，此人口定义排除了以下部门：金融中介（组J）、房地产和租赁活动（组K，除子部门72，IT外，该子部门被纳入研究人口），以及所有公共或公用事业部门。
数据类型
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样本调查数据 [ssd]
抽样程序
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2009-2017年利比里亚企业调查（ES）的样本是根据抽样说明中解释的方法使用分层随机抽样选择的。由于以下几个原因，分层随机抽样被优先选择，而不是简单随机抽样：
- 为了获得对不同人口子集的无偏估计，并具有一定的精确度。
- 为了获得对整个人口的无偏估计。整个人口，或研究的全域，是非农业经济。它包括：根据ISIC修订版3.1的分组分类的所有制造业部门：（组D）、建筑部门（组F）、服务业部门（组G和H）以及运输、仓储和通信部门（组I）。请注意，此定义排除了以下部门：金融中介（组J）、房地产和租赁活动（组K，除子部门72，IT外，该子部门被纳入研究人口），以及所有公共或公用事业部门。
- 确保最终的总样本包括来自所有不同部门的机构，并且样本不是集中在某一两个行业/规模/地区。
- 利用分层抽样的优势，在大多数情况下，人口估计将比使用简单随机抽样方法（即，标准误差更低，其他条件相同）更精确。
- 如果层内的测量是同质的，分层可能产生比相同大小的简单随机样本更小的估计误差界限。
- 通过将人口元素分层到方便的分组中，可以降低调查中每个观察值的成本。
在本国使用了三个层级的分层：行业、机构规模和地区。行业分层设计如下：全域被分层为制造业和服务业。制造业（ISIC Rev. 3.1代码15-37），服务业（ISIC代码45，50-52，55，60-64，和72）。
对于利比里亚ES，规模分层定义如下：小型（5至19名员工）、中型（20至99名员工）和大型（100名或更多员工）。
利比里亚ES的区域分层跨越三个地区：蒙塞拉多、马吉比和尼姆巴。
数据收集方式
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面对面 [f2f]
研究工具
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当前的调查工具包括：
- 服务业和制造业问卷
- 筛选问卷。
标准企业调查主题包括公司特征、性别参与、融资获取、年销售额、投入/劳动力成本、劳动力构成、贿赂、许可、基础设施、贸易、犯罪、竞争、产能利用率、土地和许可、税收、非正式性、商业-政府关系、创新和技术以及绩效指标。超过90%的问题客观地确定了国家商业环境的特征。剩余的问题评估调查受访者对公司增长和绩效障碍的看法。
数据清理操作
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数据输入和质量控制由承包商实施，数据以批量（通常是10%、50%和100%）的形式提交给世界银行。这些数据交付将检查逻辑一致性、超出范围的值、跳过模式以及重复条目。问题由世界银行标记，并由实施承包商通过数据检查、回访和重新访问机构进行纠正。
响应率
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响应率很高，特别是在与政府在经历内战后的重建工作中与国际社会积极合作后。对世界银行的倡议也持非常积极的态度。