The Labor Content of Exports (LACEX) database was developed by Calì et al. (2016) on the basis of a panel of global input-output tables and exports from the Global Trade Analysis Project (GTAP) and employment data from the ILO. The database measure the contribution of labor to a given country™s exports “ measured as employees™ compensation or wages (LACEX) or the number of jobs (JOCEX). It also uses gross output in place of exports to construct the labor and jobs content of domestic production. Three datasets are available: LACEX at the 24 sector level between 1995 and 2011, LACEX at the 57 sector level between 1997 and 2011, and JOCEX at the 11 sector level between 1997 and 2011. LACEX covers a maximum of 120 countries and JOCEX 88 countries.

This report is organized as follows. Section 2 describes the current and potential LVC in Argentina, identifying the capacity of development of each sector in the chain. Most of this information has been taken into account to develop the required datasets including the LVC (structures of costs and sales for the 6 sectors in the LVC) and the design of scenarios to be simulated. Section 3 presents the methodological approach. We describe the data sources and treatment to build the MRIO matrix and its associated SAE with the inclusion of the LVC’s sectors. Moreover, in this section we present the characteristics of the MRIO model used for this project. In section 4 we describe the assumptions of the three prospective scenarios concerning the development of the LVC, based on national, regional and global perspectives, particularly on the lithium-ion battery (cells), electromobility sectors and renewable energy storage as demanders of the lithium carbonate. In section 5, we present and analyze the LVC scenario results at national, regional and sectoral level for the three LVC scenarios in the three horizons. Finally, in section 6, the report summarizes and discusses main findings.

Production of goods and services has increasingly globalized since the 1970s but is hard to measure. Standard approaches either overstate the degree of backward integration or underestimate the involvement of some industries—especially services—in Global Value Chain (GVC) activity. This dataset is based on a novel comprehensive method to measure GVC participation using Inter-Country Input-Output (ICIO) linkages in both trade and output. The database and relative data visualizations are also available on the World Bank’s World Integrated Trade Solution website and they are discussed in “Countries and Sectors in Global Value Chains, by Alessandro Borin, Michele Mancini, Daria Taglioni the , World Bank Policy Research Working Paper 9785, September 2021

Investments in infrastructure have been on the development agenda of Latin American and Caribbean (LCR) countries as they move towards economic and social progress. Investing in infrastructure is investing in human welfare by providing access to and quality basic infrastructure services. Improving the performance of the electricity sector is one such major infrastructure initiative and the focus of this benchmarking data. A key initiative for both public and private owned distribution utilities has been to upgrade their efficiency as well as to increase the coverage and quality of service. In order to accomplish this goal, this initiative serves as a clearing house for information regarding the country and utility level performance of electricity distribution sector. This initiative allows countries and utilities to benchmark their performance in relation to other comparator utilities and countries. In doing so, this benchmarking data contributes to the improvement of the electricity sector by filling in knowledge gaps for the identification of the best performers (and practices) of the region. This benchmarking database consists of detailed information of 25 countries and 249 utilities in the region. The data collected for this benchmarking project is representative of 88 percent of the electrification in the region. Through in-house and field data collection, consultants compiled data based on accomplishments in output, coverage, input, labor productivity, operating performance, the quality of service, prices, and ownership. By serving as a mirror of good performance, the report allows for a comparative analysis and the ranking of utilities and countries according to the indicators used to measure performance. Although significant efforts have been made to ensure data comparability and consistency across time and utilities, the World Bank and the ESMAP do not guarantee the accuracy of the data included in this work. Acknowledgement: This benchmarking database was prepared by a core team consisting of Luis Alberto Andres (Co-Task Team Leader), Jose Luis Guasch (Co-Task Team Leader), Julio A. Gonzalez, Georgeta Dragoiu, and Natalie Giannelli. The team was benefited by data contributions from Jordan Z. Schwartz (Senior Infrastructure Specialist, LCSTR), Lucio Monari (Lead Energy Economist, LCSEG), Katharina B. Gassner (Senior Economist, FEU), and Martin Rossi (consultant). Funding was provided by the Energy Sector Management Assistance Program (ESMAP) and the World Bank. Comments and suggestion are welcome by contacting Luis Andres (landres@worldbank.org)

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

This database corresponds to the Russian economy in the year 2001. The database includes social accounting matrices for 88 “Oblasts” (the term “Oblasts” is used not only for Oblasts but also for Republics, Territories, Federal Cities, Autonomous Regions, and Autonomous Districts). For each Oblast, there is a matrix with data for production, consumption and intermediate use of commodities and services, and for bilateral trade with other regions and the rest of the world. The economy in each oblast is represented by 30 industrial sectors producing commodities and services.

These social accounting matrices are compiled from a number of data sources. The most important includes the input-output table for Russia for the year 2001, data from the publication the “Regions of Russia” with data on 88 regions of Russia, and exports and imports by sector and region. Although all source data are from Rosstat, the compilation into social accounting matrices is due to the authors and is not available from Rosstat. It is necessary to modify the original source data in order to assure accounting consistency of a social accounting matrix. Given the generally less reliable quality of the original export and import data, we allowed these data to adjust the most in the balancing process. Thus, our regional export and import data should not be considered reflective of the original Rosstat data.

The database is used in “Regional Impacts of Russia’s Accession to the World Trade Organization”, by Thomas Rutherford and David Tarr (World Bank Policy Research Working Paper 4015, September 2006). This paper also describes the construction of the database.

Governments of developing countries typically spend between 20 and 30 percent of gross domestic product. Hence, small changes in the efficiency of public spending could have a major impact on aggregate productivity growth and gross domestic product levels. Therefore, measuring efficiency and comparing input-output combinations of different decision-making units becomes a central challenge. This paper gauges efficiency as the distance between observed input-output combinations and an efficiency frontier estimated by means of the Free Disposal Hull and Data Envelopment Analysis techniques. Input-inefficiency (excess input consumption to achieve a level of output) and output-inefficiency (output shortfall for a given level of inputs) are scored in a sample of 175 countries using data from 2006–16 on education, health, and infrastructure. The paper verifies empirical regularities of the cross-country variation in efficiency, showing a negative association between efficiency and spending levels and the ratio of public-to-private financing of the service provision. Other variables, such as inequality, urbanization, and aid dependency, show mixed results. The efficiency of capital spending is correlated with the quality of governance indicators, especially regulatory quality (positively) and perception of corruption (negatively). Although no causality may be inferred from this exercise, it points at different factors to understand why some countries might need more resources than others to achieve similar education, health, and infrastructure outcomes.

Abstract

The Hunger Safety Net Programme (HSNP) is a social protection project being conducted in the Arid and Semi-Arid Lands (ASALs) of northern Kenya. The ASALs are extremely food-insecure areas highly prone to drought, which have experienced recurrent food crises and food aid responses for decades. The HSNP is intended to reduce dependency on emergency food aid by sustainably strengthening livelihoods through cash transfers. The pilot phase ran from 2009 to 2013. The second phase has been launched in July 2013 and contracted to run until March 2018. Oxford Policy Management (OPM) was responsible for the monitoring and evaluation (M&E) of the programme under the pilot phase, as well as the second phase of implementation.

Within the impact evaluation component for Phase 2, OPM used a range of analytical methods within an overarching mixed-method approach. The quantitative impact evaluation of HSNP Phase 2 compares the situation of HSNP2 beneficiaries and control households, relying on the Regression Discountinuity approach, integrated by a targeted Propensity Score Matching approach. In addition to the analysis at the household level, a Local Economy-Wide Impact Evaluation (LEWIE) was conducted to investigate the impact of the HSNP2 on the local economy, including on the production activities of both beneficiary and non-beneficiary households.

A single round of data collection based on a household and business survey underpins the household quantitative impact evaluation and the LEWIE study. The objective of the survey is to collect household and business data to provide an assessment of the programme's impact on the local economy, as well as beneficiary households.

The household survey is a survey of 5,979 people, carried out between 13 February and 29 June 2016 in 187 sub-locations across the four counties of Mandera, Marsabit, Turkana and Wajir. The survey covered modules on household demographic characteristics, livestock, assets, land, transfers, food and non-food consumption, food security, saving and borrowing, jobs, business, livestock trading and subjective poverty.

In addition to the household survey, a business questionnaire was conducted in the three main commercial hubs of each county. Overall, 282 business questionnaires were administered in the four counties. The purpose of the survey was to learn more about local economic activities and livelihoods in the HSNP counties, and the data was used for the LEWIE analysis. The aim was to capture information on three main sectors of the local economy: - Retailing - shops that sell retail goods on which a price mark-up is applied; - Services; - Producers - businesses that transform inputs into outputs.

Lastly, since livestock trading is a very important activity in the HSNP counties, livestock traders have been interviewed to understand better how the market works. In each county, three main livestock markets were targeted for interviews.

Geographic coverage

• Household survey: four counties where the HSNP was implemented, Mandera, Marsabit, Turkana and Wajir.

• The business survey data is no representative and was collected from three main commercial hubs of each of the four HSNP counties. Overall, 282 business questionnaires were administered in the four counties.

• The livestock trader survey data is no representative and was collected from three main livestock markets of each of the four HSNP counties. Overall, 48 livestock traders have been interviewed.

Analysis unit

• Individuals
• Households
• Businesses

Universe

• At the household level, the study population consists of all the households in the four HSNP counties (i.e. Mandera, Marsabit, Turkana and Wajir). Within a household, the survey covered all de jure household members (usual residents).

• At the market level, the survey covered a random sample of businesses in the three main commercial hubs of each county. The aim was to capture information on three main sectors of the local economy:

1. Retailing - shops that sell retail goods on which a price mark-up is applied;
2. Services;
3. Producers - businesses that transform inputs into outputs.

The following categories of businesses were excluded from the listing:

1. Temporary stalls or mobile sellers located outside permanent kiosks;
2. Banks;
3. Education institutions (schools, universities etc.);
4. Health facilities.

• The livestock trader survey was conducted in the three main livestock markets of each county. To the extent possible, livestock traders have been sampled in order to achieve a balance between those trading large animals, those trading small or medium value animals, those trading only within the HSNP counties and those who also trade outside the HSNP counties.

Kind of data

Sample survey data [ssd]

Sampling procedure

HOUSEHOLD SURVEY

The household survey used a two-stage sampling approach, for which the sample frame was defined by sub-locations and households in the HSNP Management Information System (MIS) data. The MIS data are data from a census of nearly all households in the four HSNP counties. The census contains the information that was gathered in respect of these households during the registration for the HSNP programme, their Proxy Means Test (PMT) score and their assignment to the HSNP cash transfers, as well as information about all payments received by all households since the start of Phase 2. The HSNP acknowledges that a small number of the population was recognised to be missed and was registered at a later date. The sampling procedure was intended to cover the different sample requirements of the impact evaluation approaches, including the Local Economy-Wide Impact Evaluation (LEWIE), the quantitative impact evaluation based on the Regression Discontinuity (RD) approach, and the Propensity Score Matching (PSM) back-up.

Drawing the sample consisted of two stages: 1. First stage: sampling of sub-locations 2. Second stage: sampling of households within a sub-location.

First stage of sampling

In the first stage, a first stratification was performed, based on sub-counties within each of the four counties. Sub-locations were then selected within each sub-county. The sampling frame, i.e. list of all sub-locations in each county, was obtained using the HSNP MIS data. An explicit stratification of sub-locations was carried out with the aim of identifying sub-locations that can be defined as towns, nearby villages and remote areas. This was guided by the settlement type classification that has already been made by the programme as part of its sub-location mapping exercise.

Sub-locations were selected using the probability proportional to size (PPS) method. This method implies selecting larger EAs, as defined by the household population, with a higher probability. Sub-locations served as primary sampling units (PSUs) in each county. Before drawing the sample of sub-locations, sub-locations that did not have sufficient households in them to make up the minimum sample size required for the analysis, were dropped. A total of 45 sub-locations from the sample frame that had fewer than 14 households with PMT scores above or below the eligibility cut-off were dropped. In addition, six sub-locations per county (that is, 24 in total) were sampled with certainty, which were county capitals and main trading gateways, as these commercial hubs were important to include in the sample for the LEWIE. After also removing from the sample frame 24 sub-locations which were sampled with certainty, this led to a sample frame size of 433 sub-locations from which to select the remainder of the sub-location sample.

For the remaining sub-locations in our sample frame, the PPS process was implemented. This starts by first generating a list of all sub-locations in the sample frame. This list was sorted into groups for each of the four counties, which amounts to implicit stratification by county. A sampling step was then calculated, based on the cumulative sum of population sizes and the number of sub-locations to be drawn. The sampling step is used to select sub-locations from the list, beginning from a random start.

Due to variation in the population sizes of some sub-locations, the PPS procedure in this instance leads to some sub-locations being selected more than once. If a sub-location was selected more than once then the number of households selected from that sub-location would be increased (doubled, if the sub-location was picked twice, and tripled if selected three times).

Second stage of sampling

In the second stage, a fixed number of households were randomly selected within each sub-location. 24 households were selected for the purpose of the household quantitative impact evaluation. Eight households were added to the sample for the analysis of the HSNP impact on the local economy (i.e. LEWIE sample). The selection of a fixed number of households in the second stage in theory delivers a sample that is self-weighted (compensating for the oversampling of larger sub-locations in the first PPS stage). In practice, analysis weights are still required also to account for non-response, as outlined further below.

The RD approach required a sample of treatment and control households with a PMT score within a small neighbourhood of the HSNP PMT eligibility

Malaysia GDP: Growth: Gross Value Added: Industry: Manufacturing data was reported at 5.983 % in 2017. This records an increase from the previous number of 4.437 % for 2016. Malaysia GDP: Growth: Gross Value Added: Industry: Manufacturing data is updated yearly, averaging 9.164 % from Dec 1971 (Median) to 2017, with 47 observations. The data reached an all-time high of 22.518 % in 1973 and a record low of -13.418 % in 1998. Malaysia GDP: Growth: Gross Value Added: Industry: Manufacturing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank: Gross Domestic Product: Annual Growth Rate. Annual growth rate for manufacturing value added based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

Uganda UG: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data was reported at 8.189 % in 2017. This records a decrease from the previous number of 8.731 % for 2016. Uganda UG: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data is updated yearly, averaging 7.112 % from Jun 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 10.542 % in 2012 and a record low of 1.869 % in 1981. Uganda UG: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uganda – Table UG.World Bank.WDI: Gross Domestic Product: Share of GDP. Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average; Note: Data for OECD countries are based on ISIC, revision 4.

Costa Rica CR: GDP: 2015 Price: USD: Gross Value Added Per Worker: Agriculture, Forestry, and Fishing data was reported at 0.011 USD mn in 2023. This records an increase from the previous number of 0.010 USD mn for 2022. Costa Rica CR: GDP: 2015 Price: USD: Gross Value Added Per Worker: Agriculture, Forestry, and Fishing data is updated yearly, averaging 0.009 USD mn from Dec 1991 (Median) to 2023, with 33 observations. The data reached an all-time high of 0.012 USD mn in 2009 and a record low of 0.007 USD mn in 1991. Costa Rica CR: GDP: 2015 Price: USD: Gross Value Added Per Worker: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Gross Domestic Product: Real. Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2015 U.S. dollars. Agriculture corresponds to the International Standard Industrial Classification (ISIC) tabulation categories A and B (revision 3) or tabulation category A (revision 4), and includes forestry, hunting, and fishing as well as cultivation of crops and livestock production.;Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database.;Weighted average;

Peru PE: GDP: 2010 Price: USD: Gross Value Added Per Worker: Agriculture data was reported at 0.002 USD mn in 2017. This records an increase from the previous number of 0.002 USD mn for 2016. Peru PE: GDP: 2010 Price: USD: Gross Value Added Per Worker: Agriculture data is updated yearly, averaging 0.002 USD mn from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 0.003 USD mn in 2013 and a record low of 0.001 USD mn in 1992. Peru PE: GDP: 2010 Price: USD: Gross Value Added Per Worker: Agriculture data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Peru – Table PE.World Bank: Gross Domestic Product: Real. Value added per worker is a measure of labor productivity—value added per unit of input. Value added denotes the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data are in constant 2010 U.S. dollars. Agriculture corresponds to the International Standard Industrial Classification (ISIC) tabulation categories A and B (revision 3) or tabulation category A (revision 4), and includes forestry, hunting, and fishing as well as cultivation of crops and livestock production.; ; Derived using World Bank national accounts data and OECD National Accounts data files, and employment data from International Labour Organization, ILOSTAT database.; Weighted Average;