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Can modern technologies facilitate spatial and temporal price analysis?

Marko Rissanen's picture

The International Comparison Program (ICP) team in the World Bank Development Data Group commissioned a pilot data collection study utilizing modern information and communication technologies in 15 countries―Argentina, Bangladesh, Brazil, Cambodia, Colombia, Ghana, Indonesia, Kenya, Malawi, Nigeria, Peru, Philippines, South Africa, Venezuela and Vietnam―from December 2015 to August 2016.

The main aim of the pilot was to study the feasibility of a crowdsourced price data collection approach for a variety of spatial and temporal price studies and other applications. The anticipated benefits of the approach were the openness, accessibility, level of granularity, and timeliness of the collected data and related metadata; traits rarely true for datasets typically available to policymakers and researchers.

The data was collected through a privately-operated network of paid on-the-ground contributors that had access to a smartphone and a data collection application designed for the pilot. Price collection tasks and related guidance were pushed through the application to specific geographical locations. The contributors carried out the requested collection tasks and submitted price data and related metadata using the application. The contributors were subsequently compensated based on the task location and degree of difficulty.

The collected price data covers 162 tightly specified items for a variety of household goods and services, including food and non-alcoholic beverages; alcoholic beverages and tobacco; clothing and footwear; housing, water, electricity, gas and other fuels; furnishings, household equipment and routine household maintenance; health; transport; communication; recreation and culture; education; restaurants and hotels; and miscellaneous goods and services. The use of common item specifications aimed at ensuring the quality, as well as intra- and inter-country comparability, of the collected data.

In total, as many as 1,262,458 price observations―ranging from 196,188 observations for Brazil to 14,102 observations for Cambodia―were collected during the pilot. The figure below shows the cumulative number of collected price observations and outlets covered per each pilot country and month (mouse over the dashboard for additional details).

Figure 1: Cumulative number of price observations collected during the pilot

Ghanaian firms experience improved access to finance and electricity but challenges remain

Silvia Muzi's picture

The private sector continues to be a critical driver of job creation and economic growth. However, several factors can undermine the private sector and, if left unaddressed, may impede development.  Through rigorous face-to-face interviews with managers and owners of firms, the World Bank Group’s Enterprise Surveys benchmark the business environment based on actual experiences of firms.
This blog focuses on Ghana, where 720 firms were surveyed covering six business sectors—(i) Food, (ii) Chemicals, Plastics, & Rubber (iii) Basic Metals, Fabricated Metals, Machinery & Equipment (iv) Other Manufacturing (v) Retail (vi) Other Services.

Use of financial services for investments and working capital on the rise
According to the 2012 Ghana Enterprise Surveys (ES), 21% of firms used banks to finance investments (vs. 16% in 2007) and 25% used banks to finance working capital (vs. 21% in 2007). However, while access to financial services has improved, it is still lower compared to the average for around 135 countries with ES data. The corresponding global averages for bank finance for investments and working capital are 25% and 30%, respectively. Moreover, in Ghana, 23% of the firms surveyed had a bank loan or line of credit, compared to the global average of 34%.


How do we manage revisions to GDP?

Soong Sup Lee's picture

Gross Domestic Product (GDP) estimates are some of the most heavily requested and used data published on  And as many users notice, the estimates are sometimes revised, occasionally  resulting in large changes from previously published values. Why do revisions happen, what information do we publish about those revisions, and where do you find it?