Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Argentina in 2006, 2010 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 2006-2017 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

National

Analysis unit

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

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

Sample survey data [ssd]

Sampling procedure

The sample for the 2006-2017 Argentina Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling 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 (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 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.)

Three levels of stratification were used in every country: industry, establishment size, and region.

Industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews. In middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

For the Argentina ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

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.

Cleaning operations

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

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don't know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from "Don't know" responses.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:

1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs.

2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.

3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further three years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS) – and more popularly known as Living in BiH (LiBiH). Birks Sinclair & Associates Ltd. in cooperation with the Independent Bureau for Humanitarian Issues (IBHI) were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for three years following the LSMS, in the autumns of 2002 and 2003 and the winter of 2004. The LSMS constitutes Wave 1 of the panel survey so there are four years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel - Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003 - Wave 4 Fourth interview with sub-sample respondents in Winter 2004

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observations on the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty and movements in an out of poverty are experienced by different types of households and individuals over the four year period. Most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within BiH at a time of social reform and rapid change.

In order to develop base line (2004) data on poverty, incomes and socio-economic conditions, and to begin to monitor and evaluate the implementation of the BiH MTDS, EPPU commissioned this modified fourth round of the LiBiH Panel Survey.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 4 sample comprised of 2882 households interviewed at Wave 3 (1309 in the RS and 1573 in FBiH). As at previous waves, sample households could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions assume the same standard definitions used at Wave 3. While the sample membership, status and eligibility for interview are as follows: i) All members of households interviewed at Wave 3 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview, i.e. younger than 15 years. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules The panel design provides that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in other cases an individual member may move away from their previous wave household and form a new "split-off" household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefits of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are:

i. Movers out of the country altogether i.e. outside BiH This category of mover is clear. Sample members moving to another country outside BiH will be out-of-scope for that year of the survey and ineligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. Personal details of "movers" are passed between the statistical institutes and an interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 4 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko Are followed for interview. When coding, Brcko is treated as the entity from which the household moved.

Feed-forward

Details of the address at which respondents were found in the previous wave, together with a listing of household members found in each household at the last wave were fed-forward as the starting point for Wave 4 fieldwork. This "feed-forward" data also includes key variables required for correctly identifying individual sample members and includes the following: - For each household: Household ID (IDD); Full address details and phone number - For each Original Sample Member: Name; Person number (ID); unique personal identifier (LID); Sex; Date of birth

The sample details are held in an Access database and in order to ensure the confidentiality of respondents, personal details, names and addresses are held separately from the survey data collected during fieldwork. The IDD, LID and ID are the key linking variables between the two databases i.e. the name and address database and the survey database.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Dat entry

As at previous waves, CSPro was the chosen data entry software. The CSPro program consists of two main features intended to reduce the number of keying errors and to reduce the editing required following data entry: - Data entry screens that included all skip patterns. - Range checks for each question (allowing three exceptions for inappropriate, don't know and missing codes).

The Wave 4 data entry program had similar checks to the Wave 3 program - and DE staff were instructed to clear all anomalies with SIG fieldwork members. The program was tested prior to the commencement of data entry. Twelve data entry staff were employed in each Field Office, as all had worked on previous waves training was not undertaken.

Editing

Instructions for editing were provided in the Supervisors Instructions. At Wave 4 supervisors were asked to take more time to edit every questionnaire returned by their interviewers. The SIG Fieldwork Managers examined every Control Form.

Response rate

The level of cases that were unable to be traced is extremely low as are the whole household refusal or non-contact rates. In total, 9128 individuals (including children) were enumerated within the sample households at Wave 4, 5019 individuals in the FBiH and 4109 in the RS. Within in the 2875 eligible households, 7603 individuals aged 15 or over were eligible for interview with 7116 (93.6%) being successfully interviewed. Within co-operating households (where there was at least one interview) the interview rate was higher (98.6%).

A very important measure in longitudinal surveys is the annual individual re-interview rate as a high attrition rate, where large numbers of respondents drop out of the survey over time, can call into question the quality of the data collected. In BiH the individual re-interview rates have been high for the survey. The individual re-interview rate is the proportion of people who gave an interview at time t-1 who also give an interview at t. Of those who gave a full interview at wave 3, 6654 also gave a full

Abstract

The document dataset covers the Enterprise Survey (ES) panel data collected in North Macedonia in 2009, 2013 and 2019.

Macedonia ES 2009 was conducted in 2008 and 2009, while Macedonia ES 2013 was conducted between November 2012 and May 2013, and North Macedonia ES 2019 was conducted between December 2018 and October 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

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

National

Geographic coverage notes

Regions covered are selected based on the number of establishments, contribution to employment, and value added. In most cases these regions are metropolitan areas and reflect the largest centers of economic activity in a country.

Analysis unit

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

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 sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Macedonia 2009 ES, Macedonia 2013 ES and of 2019 North Macedonia ES were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Macedonia 2009 ES and for Macedonia 2013 ES, and in the Sampling Note for 2019 North Macedonia ES. Stratified random sampling was preferred over simple random sampling for several reasons:

a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision. b. 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 sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. c. 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. d. 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.) e. 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. f. 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. The original sample design with specific information of the industries and regions chosen is described in Appendix C of the North Macedonia 2019 ES Implementation Report and in Appendix E of the Macedonia 2013 Implementation Report.

Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 3.1 codes 15-37), Retail (ISIC 52), and Other Services (ISIC 45, 50, 51, 55, 60-64, 72).

As it is standard for the ES, the North Macedonia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for North Macedonia ES 2019 was done across three regions: Skopje; Eastern Macedonia comprising Northeastern, Eastern, Southeastern, and Vardar regions; and Western Macedonia comprising Polog, Southwestern and Pelagonia regions. For Macedonia 2013 ES, regional stratification was defined in 4 regions (city and the surrounding business area) throughout Macedonia. And for Macedonia ES 2009, regional stratification was defined in 4 regions which are Eastern, North- West & West, Skopje, and South.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

The Ultra Clear LCD Panel market is experiencing robust growth, driven by increasing demand across diverse applications, including televisions, computers, cell phones, and advertising boards. Technological advancements leading to improved image clarity, higher resolutions, and enhanced color reproduction are key drivers. The market is segmented by panel type (TN, VA, IPS, others) and application, with televisions and computer monitors currently dominating market share. However, the growing penetration of smartphones and the expanding digital signage market are fueling demand for smaller, higher-resolution panels. While the market faces challenges such as the rising cost of raw materials and competition from alternative display technologies like OLED, the overall growth trajectory remains positive. We estimate the 2025 market size to be approximately $15 billion, based on typical industry growth rates and considering the projected CAGR. This figure is expected to grow steadily, reaching an estimated $25 billion by 2033. Major players like Samsung Display, LG Display, and BOE Technology are actively investing in R&D and expanding production capacities to meet the increasing demand. The Asia-Pacific region, particularly China and South Korea, represents a significant market share due to established manufacturing hubs and strong consumer electronics demand. The competitive landscape is characterized by intense competition among established players and emerging companies. Companies are focusing on innovation, strategic partnerships, and mergers and acquisitions to gain a competitive edge. The future of the Ultra Clear LCD Panel market hinges on continued technological advancements, cost optimization, and expansion into new and emerging applications such as augmented reality and virtual reality devices. The market is expected to witness a shift towards more sustainable and energy-efficient panel technologies, influencing the product development strategies of major players. Regional growth will vary based on economic conditions, technological adoption rates, and government policies promoting digital infrastructure development. North America and Europe are expected to show steady growth, while the Asia-Pacific region will continue to be the leading market driver, considering the rapid growth of the consumer electronics industry in this region.

Indonesia Export: Value: Optically Clear Free-Film Adhesives and Optically Clear Curable Liquid Adhesives of A Kind Used Solely or Principally for the Manufacture of Flat Panel Displays or Touch-Sensitive Screen Panels data was reported at 0.054 USD mn in Jan 2025. This records a decrease from the previous number of 0.112 USD mn for Dec 2024. Indonesia Export: Value: Optically Clear Free-Film Adhesives and Optically Clear Curable Liquid Adhesives of A Kind Used Solely or Principally for the Manufacture of Flat Panel Displays or Touch-Sensitive Screen Panels data is updated monthly, averaging 0.053 USD mn from Apr 2022 (Median) to Jan 2025, with 33 observations. The data reached an all-time high of 0.118 USD mn in Feb 2023 and a record low of 0.000 USD mn in Apr 2022. Indonesia Export: Value: Optically Clear Free-Film Adhesives and Optically Clear Curable Liquid Adhesives of A Kind Used Solely or Principally for the Manufacture of Flat Panel Displays or Touch-Sensitive Screen Panels data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAH035: Foreign Trade: by HS 8 Digits: Export: HS35: Albuminoidal Substances, Modified Starches, Glues, Enzymes.

The EU Timber Regulation (EUTR) is a key element in the efforts of the European Union to curb the trade in illegal timber products. This study help remedy the lack of systematic, statistical analysis of potential impacts of the EUTR on international trade in timber products. Using cointegration intervention — or shock — models we quantify potential shifts in import prices and quantities of tropical hardwood lumber and oak lumber after the entry into effect of the EUTR. We further estimate import demand models to assess the relation between temperate and tropical hardwood products and whether there was a structural change in demand elasticities after the entry into force of the EUTR. The shock model analysis indicates, for most of the bilateral trade flows where we observe cointegration and a significant shock variable, increasing import prices and decreasing import quantities of tropical hardwood lumber following the EUTR start date, consistent with a contraction of the supply of tropical timber. The results of the import demand models do not give a clear indication as to whether oak lumber is a complementary or substitute product for tropical hardwood lumber, and there are no clear signs of structural changes in demand elasticities. Besides the analysis per se, an important contribution of the paper is the procedure for building as long and homogeneous time series of tropical hardwood lumber as possible.

The Optical Clear Adhesive (OCA) for Touch Panels market has emerged as a critical segment within the electronics and display industries, primarily catering to the rising demand for high-performance touchscreens in smartphones, tablets, and various electronic devices. Optical Clear Adhesive serves a pivotal role by

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Uzbekistan in 2008, 2013 and 2019.

The Uzbekistan ES 2008 was conducted between 2008 and 2009. The Uzbekistan ES 2013 was conducted between January 2013 and October 2013. Finally, the Uzbekistan ES 2019 was conducted between February 2019 and August 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

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

National

Analysis unit

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 take 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

For the Uzbekistan ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Uzbekistan ES 2008, 2013, 2019 were were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Uzbekistan ES 2008 and Uzbekistan ES 2013, and in the Sampling Note for 2019 Uzbekistan ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are described in the "Republic of Uzbekistan Enterprise Surveys Data Set" report for Uzbekistan ES 2008 and "The Uzbekistan 2013 Enterprise Surveys Data Set" report for Uzbekistan ES 2013, Appendix E. For Uzbekistan 2019 ES, specific information of the industries and regions chosen is described in the "The Uzbekistan 2019 Enterprise Surveys Data Set" report, Appendix C.

For Uzbekistan ES 2008, industry stratification was designed in the way that follows: the universe was stratified into 23 manufacturing industries, 2 services industries -retail and IT-, and one residual sector as defined in the sampling manual. Each sector had a target of 120 interviews. For Uzbekistan ES 2013, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services). Finally, for Uzbekistan ES 2019, industry stratification was designed in the way that follows: the universe was stratified into six manufacturing industries and two services industries: Food and Beverages (ISIC Rev. 3.1 code 15), Textiles (ISIC 17), Garments (ISIC code 18), Rubber and Plastics Products (ISIC code 25), Non-Metallic Mineral Products (ISIC code 26), Other Manufacturing (ISIC codes 16, 19-24, 27-37), Retail (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees).

For Uzbekistan ES 2008, regional stratification was defined in 3 regions. These regions are Tashkent, Samarkandskaya, and Tashkentskaya.

For Uzbekistan ES 2013, regional stratification was defined in 3 regions (city and the surrounding business area) throughout Uzbekistan.

For Uzbekistan ES 2019, Regional stratification was done across nine regions: Andijan Region, Fergana Region, Qashqadaryo Region, Samarqand Region, Tashkent Region, Tashkent, Karakalpakstan, Navoiy and Jizzakh Region, and Surxondaryo Region.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues. Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know (-7). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For Uzbekistan ES 2008 and 2013, survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2008, the number of contacted establishments per realized interview was 1.61. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (1.61) suggests that the main source of error in estimates in the Uzbekistan may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 33%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 11%.

Finally, for 2019, the number of interviews per contacted establishments was 37.9%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 36.3%.

Has the Supreme Court become just another political branch? Public perceptions of Court approval and legitimacy in a post-*Dobbs* world

https://doi.org/10.5061/dryad.m63xsj48k

Description of the data and file structure

Figure 1: Data for Figure 1 comes from publicly available Gallup data:

Supreme Court: https://news.gallup.com/poll/4732/supreme-court.aspx

Congress: https://news.gallup.com/poll/1600/congress-public.aspx

Executive Branch: https://news.gallup.com/poll/4729/presidency.aspx

This figure can be replicated using the Figure1_Gallup_Trust.R script and the Figure1_Gallup_Trust.csv data.

Figure 2: Data for Figure 2 comes from a content analysis of The New York Times coverage of the U.S. Supreme Court. To produce those results, we used the New York Times API, we first downloaded the corpus of news articles from 1 January 2008 – 20 June 2023 (the last date available at the time we performed our content analysis) that included the words “Suprem...

Indonesia Import: Volume: Optically Clear Free-Film Adhesives and Optically Clear Curable Liquid Adhesives not of A Kind Used Solely or Principally for the Manufacture of Flat Panel Displays or Touch-Sensitive Screen Panels data was reported at 4.586 kg mn in Jan 2025. This records an increase from the previous number of 3.858 kg mn for Dec 2024. Indonesia Import: Volume: Optically Clear Free-Film Adhesives and Optically Clear Curable Liquid Adhesives not of A Kind Used Solely or Principally for the Manufacture of Flat Panel Displays or Touch-Sensitive Screen Panels data is updated monthly, averaging 2.940 kg mn from Apr 2022 (Median) to Jan 2025, with 34 observations. The data reached an all-time high of 4.943 kg mn in Jul 2024 and a record low of 1.931 kg mn in Apr 2023. Indonesia Import: Volume: Optically Clear Free-Film Adhesives and Optically Clear Curable Liquid Adhesives not of A Kind Used Solely or Principally for the Manufacture of Flat Panel Displays or Touch-Sensitive Screen Panels data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAH133: Foreign Trade: by HS 8 Digits: Import: HS35: Albuminoidal Substances, Modified Starches, Glues, Enzymes.

The global PE Coated Aluminum Composite Panel market is experiencing steady growth, projected to reach a market size of $699.7 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 3.4% from 2025 to 2033. This growth is driven by several key factors. The increasing demand for aesthetically pleasing and durable building materials in the construction sector is a significant driver. Furthermore, the rising adoption of PE coated aluminum composite panels in advertising displays and transportation applications fuels market expansion. The panels' lightweight yet robust nature, ease of fabrication, and ability to be customized with various colors and finishes contribute to their widespread appeal. While specific constraints are not detailed, potential challenges could include fluctuating raw material prices, competition from alternative cladding materials, and environmental concerns related to manufacturing and disposal. Market segmentation reveals significant opportunities in fire-resistant, anti-bacterial, and anti-static panels across diverse sectors like construction, advertising, and transportation. The market's geographical distribution shows a robust presence across North America, Europe, and Asia-Pacific, with China and the United States as leading regional markets. Continued innovation in panel technology, such as improved fire resistance and enhanced aesthetics, is expected to drive further market penetration. The competitive landscape is characterized by a mix of established global players like Arconic and Mitsubishi Chemical, alongside several regional manufacturers in China and Taiwan. These companies are constantly striving to innovate their offerings and expand their distribution networks to capture a greater market share. Future growth will likely be influenced by technological advancements, government regulations related to building safety and sustainability, and the overall health of construction and advertising industries globally. The market is projected to witness continued, albeit moderate, expansion throughout the forecast period. Further research into specific application areas and regional market dynamics would provide deeper insights into the growth potential. This in-depth report provides a comprehensive overview of the global PE Coated Aluminum Composite Panel (ACP) market, projecting a valuation exceeding $15 billion by 2028. It delves into market dynamics, key players, and future growth prospects, offering invaluable insights for industry stakeholders. The report utilizes rigorous data analysis and expert forecasts to provide a clear picture of this rapidly evolving sector. Keywords: Aluminum Composite Panel, PE Coated ACP, ACP Market, Building Materials, Advertising Materials, Fire-resistant ACP, Anti-bacterial ACP, Composite Panel Manufacturers.

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modelling the complexities of human behaviour, the notion of universal panel data - in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated - can further enhance exploitation of the richness of panel information. The NPS Universal Panel Questionnaire (UPQ) consists of both survey instruments and datasets, meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the NPS. The NPS-UPQ provides a consistent and straightforward means of conducting not only user-driven analyses using convenient, standardized tools, but also for monitoring MKUKUTA, FYDP II, and other national level development indicators reported by the NPS.

The design of the NPS-UPQ combines the four completed rounds of the NPS - NPS 2008/09 (R1), NPS 2010/11 (R2), NPS 2012/13 (R3), and NPS 2014/15 (R4) - into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

The universe includes all households and individuals in Tanzania with the exception of those residing in military barracks or other institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING PROCEDURE While the same sample of respondents was maintained over the first three rounds of the NPS, longitudinal surveys tend to suffer from bias introduced by households leaving the survey over time, i.e. attrition. Although the NPS maintains a highly successful recapture rate (roughly 96% retention at the household level), minimizing the escalation of this selection bias, a refresh of longitudinal cohorts was done for the NPS 2014/15 to ensure proper representativeness of estimates while maintaining a sufficient primary sample to maintain cohesion within panel analysis. A newly completed Population and Housing Census (PHC) in 2012, providing updated population figures along with changes in administrative boundaries, emboldened the opportunity to realign the NPS sample and abate collective bias potentially introduced through attrition.

To maintain the panel concept of the NPS, the sample design for NPS 2014/2015 consisted of a combination of the original NPS sample and a new NPS sample. A nationally representative sub-sample was selected to continue as part of the “Extended Panel” while an entirely new sample, “Refresh Panel”, was selected to represent national and sub-national domains. Similar to the sample in NPS 2008/2009, the sample design for the “Refresh Panel” allows analysis at four primary domains of inference, namely: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. This new cohort in NPS 2014/2015 will be maintained and tracked in all future rounds between national censuses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The format of the NPS-UPQ survey instrument is similar to previously disseminated NPS survey instruments. Each module has a questionnaire and clearly identifies if the module collects information at the individual or household level. Within each module-specific questionnaire of the NPS-UPQ survey instrument, there are five distinct sections, arranged vertically: (1) the UPQ - “U” on the survey instrument, (2) R4, (3), R3, (4) R2, and (5) R1 – the latter 4 sections presenting each questionnaire in its original form at time of its respective dissemination.

The uppermost section of each module’s questionnaire (“U”) represents the model universal panel questionnaire, with questions generated from the comprehensive listing of questions across all four rounds of the NPS and codes generated from the comprehensive collection of codes. The following sections are arranged vertically by round, considering R4 as most recent. While not all rounds will have data reported for each question in the UPQ and not each question will have reports for each of the UPQ codes listed, the NPS-UPQ survey instrument represents the visual, all-inclusive set of information collected by the NPS over time.

The four round-specific sections (R4, R3, R2, R1) are aligned with their UPQ-equivalent question, visually presenting their contribution to compatibility with the UPQ. Each round-specific section includes the original round-specific variable names, response codes and skip patterns (corresponding to their respective round-specific NPS data sets, and despite their variance from other rounds or from the comprehensive UPQ code listing)4.

• Household identification;
• Survey staff details;
• Household member roster;
• Education,
• Health,
• Labour;
• Food outside the household;
• Subject welfare;
• Food security;
• Housing, water and sanitation;
• Consumption of food over the past one week;
• Non-food expenditures (past one week & one month);
• Non-food expenditures (past twelve months);
• Household assets;
• Family/household non-farm enterprises;
• Assistance and groups;
• Credit;
• Finance;
• Recent shocks to household welfare;
• Deaths in the household;
• Household recontact information;
• Filter questions;
• Anthropometry.

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modeling the complexities of human behavior, the notion of universal panel data – in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated – can further enhance exploitation of the richness of panel information.

This Basic Information Document (BID) provides a brief overview of the Tanzania National Panel Survey (NPS), but focuses primarily on the theoretical development and application of panel data, as well as key elements of the universal panel survey instrument and datasets generated by the four rounds of the NPS. As this Basic Information Document (BID) for the UPD does not describe in detail the background, development, or use of the NPS itself, the round-specific NPS BIDs should supplement the information provided here.

The NPS Uniform Panel Dataset (UPD) consists of both survey instruments and datasets, meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the NPS. The NPS-UPD provides a consistent and straightforward means of conducting not only user-driven analyses using convenient, standardized tools, but also for monitoring MKUKUTA, FYDP II, and other national level development indicators reported by the NPS.

The design of the NPS-UPD combines the four completed rounds of the NPS – NPS 2008/09 (R1), NPS 2010/11 (R2), NPS 2012/13 (R3), and NPS 2014/15 (R4) – into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, other urban, rural, Zanzibar.

Analysis unit

• Households
• Individuals

Universe

The universe includes all households and individuals in Tanzania with the exception of those residing in military barracks or other institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

While the same sample of respondents was maintained over the first three rounds of the NPS, longitudinal surveys tend to suffer from bias introduced by households leaving the survey over time; i.e. attrition. Although the NPS maintains a highly successful recapture rate (roughly 96% retention at the household level), minimizing the escalation of this selection bias, a refresh of longitudinal cohorts was done for the NPS 2014/15 to ensure proper representativeness of estimates while maintaining a sufficient primary sample to maintain cohesion within panel analysis. A newly completed Population and Housing Census (PHC) in 2012, providing updated population figures along with changes in administrative boundaries, emboldened the opportunity to realign the NPS sample and abate collective bias potentially introduced through attrition.

To maintain the panel concept of the NPS, the sample design for NPS 2014/2015 consisted of a combination of the original NPS sample and a new NPS sample. A nationally representative sub-sample was selected to continue as part of the “Extended Panel” while an entirely new sample, “Refresh Panel”, was selected to represent national and sub-national domains. Similar to the sample in NPS 2008/2009, the sample design for the “Refresh Panel” allows analysis at four primary domains of inference, namely: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. This new cohort in NPS 2014/2015 will be maintained and tracked in all future rounds between national censuses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The format of the NPS-UPD survey instrument is similar to previously disseminated NPS survey instruments. Each module has a questionnaire and clearly identifies if the module collects information at the individual or household level. Within each module-specific questionnaire of the NPS-UPD survey instrument, there are five distinct sections, arranged vertically: (1) the UPD - “U” on the survey instrument, (2) R4, (3), R3, (4) R2, and (5) R1 – the latter 4 sections presenting each questionnaire in its original form at time of its respective dissemination.

The uppermost section of each module’s questionnaire (“U”) represents the model universal panel questionnaire, with questions generated from the comprehensive listing of questions across all four rounds of the NPS and codes generated from the comprehensive collection of codes. The following sections are arranged vertically by round, considering R4 as most recent. While not all rounds will have data reported for each question in the UPD and not each question will have reports for each of the UPD codes listed, the NPS-UPD survey instrument represents the visual, all-inclusive set of information collected by the NPS over time.

The four round-specific sections (R4, R3, R2, R1) are aligned with their UPD-equivalent question, visually presenting their contribution to compatibility with the UPD. Each round-specific section includes the original round-specific variable names, response codes and skip patterns (corresponding to their respective round-specific NPS data sets, and despite their variance from other rounds or from the comprehensive UPD code listing)4.

This dataset is based on a full-scale research performed on two adjacent apartment buildings in Amsterdam.

One roof has a bitumen surface with a solar PV system, the other is a blue-green capillary irrigated solar roof with

grey (shower-) water suppletion, with a constructed wetroof for grey water purification.

In the research, the solar output of both roofs was measured and compared, including temperature, irradiation and humidity measurements.

The dataset covers 5 months, June to October 2022, and includes only day data (hours with solar irradiation).

The dataset is being made public to act as supplementary data for publication(s) and the PhD thesis of Els van der Roest.

Also, it might be used by other researchers.

This dataset is a result of the TKI project Urban Photosynthesis and is co-financed with PPS funding

from the Topconsortia for Knowledge & Innovation (TKI’s) of the Ministry of Economic Affairs and Climate.

Abstract of the paper:

With an increasing demand for climate resiliency, water sensitivity, nature inclusiveness and energy efficiency in dense urban environments, the call for layered and multifunctional use of rooftops is rising. Vegetated roofs combined with Photo-Voltaic (PV) installations are an example of multifunctional and more effective use of available space, and well-irrigated systems could have an enhanced cooling effect. This research investigated a blue-green capillary irrigated solar roof with grey (shower-) water suppletion, with a constructed wetroof for grey water purification. Two full-scale commercial PV systems on twin rental apartment blocks in Amsterdam were analyzed, on a blue-green roof (BGR) versus a bitumen roof (BiR). The energy output, PV panel temperature, relative humidity and air temperature under the panels were monitored during 5 warmer months (June-October 2022). On average, a solar panel on the BGR is expected to produce 4.4% more energy than a solar panel on the BiR at similar irradiation. A clear difference in panel temperature on the roofs is only seen when the surface temperature of the roofs differs by at least 4.64°C. Otherwise, other factors such as wind or albedo have probably more influence on the PV panel temperature and thus on PV power output.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

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

National

Analysis unit

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 take 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

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

Snapshot img

Solar Panel Coatings Market Size 2024-2028

The solar panel coatings market size is forecast to increase by USD 3.84 billion at a CAGR of 13.8% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for solar energy as a renewable power source. Superhydrophilic coatings are gaining popularity for enhancing the efficiency of solar panels by reducing water contamination and improving self-cleaning properties. Photoactive coatings, which possess electrical conductivity and light transparency, are another trend in the market, enabling solar cells to convert a broader spectrum of light into electricity.



Antireflective properties are crucial for enhancing the absorption of sunlight, making coatings with hybrid functionalities increasingly desirable. Furthermore, the development of underwater coatings for solar cells is a promising trend, expanding the application areas of solar energy. However, durability issues with solar panel coatings remain a challenge, necessitating the development of self-healing and antimicrobial coatings to ensure long-term performance and prevent degradation.

What will be the Size of the Solar Panel Coatings Market During the Forecast Period?

Request Free Sample

The use of fossil fuels for energy generation continues to have detrimental effects on the earth's atmosphere, leading to increase in greenhouse gases and environmental concerns. In response, the renewable energy sector, particularly solar energy, has gained significant traction as a viable alternative. Solar panel technology has seen remarkable advancements in recent years, with a primary focus on enhancing solar panel efficiency. Solar panel efficiency is crucial for maximizing energy generation from solar panels.



However, several factors, such as reflection, dust accumulation, and surface morphology, can negatively impact the performance of solar panels. To address these challenges, the market for solar panel coatings has emerged as a promising solution. Coatings are thin films applied to the surface of solar panels to improve their functionality and durability. Self-cleaning coatings, for instance, help prevent dust accumulation, ensuring optimal energy transmittance and reflection. Anti-reflection coatings reduce reflection, allowing more sunlight to reach the solar cells and thereby increasing energy generation. Moreover, the development of photoactive coatings has been a significant breakthrough in the solar panel coating market. These coatings possess antireflective properties, self-healing capabilities, and hybrid functionalities that include antimicrobial activity. Furthermore, superhydrophobic and superhydrophilic coatings are gaining popularity due to their ability to repel water and enhance the cleaning process, respectively. The air/glass interface is another critical aspect of solar panel performance. Coatings with anti-fogging properties help maintain clear visibility on the solar panels, ensuring optimal energy generation.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Power utilities
  Commercial
  Residential


Type

  Anti-reflective
  Hydrophobic
  Self-cleaning
  Anti-soiling
  Others


Geography

  APAC

    China
    India
    Japan


  Europe

    Germany
    UK
    France
    Italy


  North America

    US


  Middle East and Africa



  South America

    Brazil

By End-user Insights

The power utilities segment is estimated to witness significant growth during the forecast period.

The Market encompasses the production and application of coatings on renewable energy systems, specifically solar panels, to enhance their efficiency and durability. In the United States, the adoption of solar energy is on the rise due to the increasing demand for renewable sources of electricity. Power utilities are a significant end-user segment in this market, given their role in supplying electricity to meet the expanding demand. The US electricity consumption is surging, fueled by the expansion of data centers, manufacturing industries, and the electrification trend.

However, challenges such as delays in project implementation and escalating electricity costs may hinder the sustained growth of the power utilities segment. Solar panel coatings play a crucial role in optimizing the performance of solar panels by reducing the adverse effects of environmental factors like dust, dirt, snow, and ice. These coatings minimize light scattering, ensuring maximum energy absorption. Moreover, they offer protection against extreme weather events, ensuring the longevity of solar panels. The companies specializing in solar pa

The reference system consists of six PV modules arranged at fixed angles on the roof of the Shaw Theatre at NAIT's Main Campus- near the corner of Princess Elizabeth Avenue and 106 Street. The angles correspond to those typically used for solar installations in Edmonton. Each of the six modules is paired with a duplicate module placed at exactly the same angle. During the winter months- snow will be cleared from the left side of the modules. By pairing modules at exactly the same angles- researchers will be able to compare the effect of snow clearing versus snow cover on energy production. The degree angle of rows are 90- 53- 45- 27- 18- 14 degrees.

You can find the actual picture at the "About" tab. In the dataset column name- 90_LEFT means the left panel and 90 degree angle and so on.

The City is not the owner of this data, it is provided to us and owned by NAIT