Researchers claim that the Ease of Doing Business (EDBI) is an index that represents only one facet of the conditions of the business environment because the data is collected from companies of a certain size and city. When considering the problem of the representativeness of the EDBI, researchers assume that all the variables in the index vary according to the size of the company or city. In fact, many EDBI variables vary according to the size of the company or city e.g. variables related to public bureaucracy and which are measured by the time and the number of procedures required to do business (World Bank 2018). However, another part of the EDBI variables fits into the classic definition of Transaction Costs. That is, non-operating costs present in all transactions and which resemble transport fees or taxes. Among the EDBI variables, seventeen variables fit this definition because they are precisely taxes and fees regulated by governments that affect companies across the economy (World Bank 2018). This data set is used to create a new index to better represent the conditions of the countries' business environment. The data from twenty countries of Latin America (LA) are retrieved from the World DataBank database (World Bank 2020), which excludes Cuba due to the unavailability of the data.

The selected variables were weighted according to the opinion of ten experts. The evaluation data of these specialists, as well as the calculations used to find the weights are also available.

This layer shows Index Numbers of Yield of Principal Crops in India (2012-24) as per the Economic Survey Report 2024-2025.Data Source: https://www.indiabudget.gov.in/economicsurvey/doc/stat/tab4.2.pdf.Index: Paddy Wheat Pulses Tea Raw- Cotton Raw-Jute Ground-Nut Seed Coal Mineral-Oils Sugar, Molasses & Honey Vegetable & Animal Oil Cotton-Yarn Cotton-Cloth Jute Sacking Cloth Fertilizers and Nitrogen Compounds Cement, Lime & Plaster Iron, Steel & Ferroalloys

1.43 1.03 0.64 0.12 0.66 0.05 0.27 2.14 7.95 1.16 2.64 1.34 0.95 0.32 1.48 1.64 6.55 This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.Note: P: Provisional

Index figures on production prices of dwellings and other buildings reflect the relation between the output value and the output volume and can be used to convert the value of construction output from current prices to fixed prices. The output price index is derived from the series "New dwellings; output indices 2000=100". From the 2nd quarter 2009 on, the figures of the series 2005 = 100 are used and linked to the series 2000 = 100. Statistics Netherlands publishes data on the value of construction output. The volume of construction output, however, cannot be deduced from the value, which is subject to price changes. The price index on the building costs of new dwellings eliminates the effect of price changes. The price index on construction output is calculated by distributing the value of the output (current prices) over the quarters essential to the price setting of the building project. Subsequently, the quarterly output is calculated in fixed prices by using the price index on the building costs of new dwellings. The index figure of the output price is the sum of the current prices divided by the sum of the fixed prices (*100).

Possibilities for selection: - Total construction - Total construction of new dwellings/buildings - New dwellings - New buildings in the private sector - New buildings in the non-commercial sector - Total other buildings - Other dwellings - Other buildings in the private sector - Other buildings in the non-commercial sector

Data available from 1st quarter 2000 till 4th quarter 2016 Frequency: discontinued

Status of the figures: The figures of 2016 are provisional. Since this table has been discontinued, the data will not become definitive.

Changes as of January 29 2018 None, this table is discontinued.

When will new figures become available? This table is succeeded by Production on buildings; price index 2015 = 100. See paragraph 3.

Linking recommendation If you want to compile long-term series with linked price indices on production of buildings, you can link the figures on price level 1995 with the figures on price level 2000. For that, the percentage change from the 2nd quarter 2005 with the 1st quarter 2005 must be calculated, as the price index for the 1st quarter 2005 is the last figure published on price level 1995. This change must then be adjusted to the figures for the 1st quarter 2005 of the series 1995. The 2nd quarter index of the linked series is calculated by calculating the difference between the 1st quarter 2005 and the 2nd quarter 2005 according to the series on price level 2000 and multiplying this by the index for the 1st quarter 2005 according to the series on price level 1995.

In the example: (119/120) x 148=147 (rounded). For the 3rd quarter 2005 the index is calculated analogously, where because of rounding problems the first quarter figures must be used for the link.

Indonesia Construction Index: Business Problem Index: Bangka Belitung data was reported at 21.250 2010=100 in Dec 2018. This records an increase from the previous number of 21.230 2010=100 for Sep 2018. Indonesia Construction Index: Business Problem Index: Bangka Belitung data is updated quarterly, averaging 21.675 2010=100 from Mar 2011 (Median) to Dec 2018, with 32 observations. The data reached an all-time high of 50.000 2010=100 in Dec 2017 and a record low of 8.330 2010=100 in Sep 2012. Indonesia Construction Index: Business Problem Index: Bangka Belitung data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Construction and Properties Sector – Table ID.EE007: Construction Index: Business Problem Index: by Province.

Abstract

The study was conducted in Serbia between October 2008 and February 2009 as part of the first round of The Management, Organization and Innovation Survey. Data from 135 manufacturing companies with 50 to 5,000 full-time employees was analyzed.

The survey topics include detailed information about a company and its management practices - production performance indicators, production target, ways employees are promoted/dealt with when underperforming. The study also focuses on organizational matters, innovation, spending on research and development, production outsourcing to other countries, competition, and workforce composition.

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 is defined as a separate production unit, regardless of whether or not it has its own financial statements separate from those of the firm, and whether it has it own management and control over payroll. So the bottling plant of a brewery would be counted as an establishment.

Universe

The survey universe was defined as manufacturing establishments with at least fifty, but less than 5,000, full-time employees.

Kind of data

Sample survey data [ssd]

Sampling procedure

Random sampling was used in the study. For all MOI countries, except Russia, there was a requirement that all regions must be covered and that the percentage of the sample in each region was required to be equal to at least one half of the percentage of the sample frame population in each region.

In most countries the sample frame used was an extract from the Orbis database of Bureau van Dijk, which was provided to the Consultant by the EBRD. The sample frame contained details of company names, location, company size (number of employees), company performance measures and contact details. The sample frame downloaded from Orbis was cleaned by the EBRD through the addition of regional variables, updating addresses and phone numbers of companies.

Examination of the Orbis sample frames showed their geographic distributions to be wide with many locations, a large number of which had only a small number of records. Each establishment was selected with two substitutes that can be used if it proves impossible to conduct an interview at the first establishment. In practice selection was confined to locations with the most records in the sample frame, so the sample frame was filtered to just the cities with the most establishments.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys. For Serbia, the percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 26.7% (82 out of 307 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

Two different versions of the questionnaire were used. Questionnaire A was used when interviewing establishments that are part of multiestablishment firms, while Questionnaire B was used when interviewing single-establishment firms. Questionnaire A incorporates all questions from Questionnaire B, the only difference is in the reference point, which is the so-called national firm in the first part of Questionnaire A and firm in Questionnaire B. Second part of the questionnaire refers to the interviewed establishment only in both Questionnaire A and Questionnaire B. Each variation of the questionnaire is identified by the index variable, a0.

Response rate

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

Survey non-response was addressed by maximising efforts to contact establishments that were initially selected for interviews. Up to 15 attempts (but at least 4 attempts) were made to contact an establishment for interview at different times/days of the week before a replacement establishment (with similar characteristics) was suggested for interview. Survey non-response did occur, but substitutions were made in order to potentially achieve the goals.

Additional information about sampling, response rates and survey implementation can be found in "MOI Survey Report on Methodology and Observations 2009" in "Technical Documents" folder.

The dataset contains 150 Math Word Problems(MWP). Each problem consists of textual math problems that involve the application of first and second-degree mathematical equations for their resolution. To create this set, academic and educational sources containing first and second-degree math problems were selected, and some original problems were also included.

Each problem in the dataset is structured as follows:

"question": A textual description of the math problem in Spanish "question_english": A textual description of the math problem in English "mathml_equations": The corresponding equation for the problem, expressed in MathML format to facilitate processing and manipulation by machine learning models. "Difficulty": The number of variables in the equation. "Grade": The grade of the equation, with 1 indicating a linear equation and 2 indicating a quadratic equation. "Index: A unique identifier for each problem in the dataset. "Author": The creator or source of the problem. "Ref": The source or citation for the problem, if applicable.

Japan Index: NSE: Stock Price Index: 1st Section Nagoya Issues data was reported at 3,368.710 04Jan1968=100 in Oct 2018. This records a decrease from the previous number of 3,749.150 04Jan1968=100 for Sep 2018. Japan Index: NSE: Stock Price Index: 1st Section Nagoya Issues data is updated monthly, averaging 2,038.020 04Jan1968=100 from Feb 1999 (Median) to Oct 2018, with 237 observations. The data reached an all-time high of 3,994.950 04Jan1968=100 in May 2015 and a record low of 1,381.220 04Jan1968=100 in Mar 2003. Japan Index: NSE: Stock Price Index: 1st Section Nagoya Issues data remains active status in CEIC and is reported by Nagoya Stock Exchange. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

This dataset contains Austin Watershed Reach Index and Problem Scores by the City of Austin’s Environmental Resource Management Division.

Abstract

The survey was conducted in Ethiopia between July 2011 and July 2012 as part of the Africa Enterprise Survey 2011 rollout, an initiative of the World Bank. Data from 644 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

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

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 private 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. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Ethiopia was selected using stratified random sampling. Three levels of stratification were used in this country: firm sector, firm size, and geographic region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry and one service as defined in the sampling manual. The manufacturing industry had a target of 340 interviews and service industry had a target of 240 interviews.

Size stratification was defined following the standardized definition for the Enterprise Surveys: 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.

Regional stratification was defined in five regions (city and the surrounding business area): Addis Ababa, Oromya, SNNPR, Amhara, and Tigray.

For the Ethiopia ES, three sample frames were used. The first sample frame was produced by Ethiopia Ministry of Trade and Industry. A copy of that frame was sent to the TNS statistical team in London to select the establishments for interview. However, the quality of the sample frames was not optimal and additional sample frames were acquired during the implementation of the survey in order to reach the target number of interviews. The second sample frame used was the Dun & Bradstreet (D&B) database and the third sample frame was the Ethiopia Yellow Pages 2011.

The enumerated establishments with five or more employees were then used as the sample frame for the Ethiopia Enterprise Survey with the aim of obtaining interviews at 600 establishments.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of noneligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone or fax numbers so the local contractor had to screen the contacts by visiting them. Due to response rate and ineligibility issues, additional sample had to be extracted by the World Bank in order to obtain enough eligible contacts and meet the sample targets.

Given the impact that non-eligible units included in the sample universe may have on results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 21% (392 out of 1,873 establishments) and 12% (37 out of 310 establishments) for the ES firms for the Ministry of Trade and D&B sample frames respectively. The non-eligibility rate for the Yellow Pages sample frame was 16% (98 out of 607 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire [ISIC Rev.3.1: 15-37] - Services Module Questionnaire [ISIC Rev.3.1: 45, 50, 51, 52, 60, 61, 62, 63, 64 & 72] - Screener Questionnaire.

The survey is fielded via manufacturing or services questionnaires in order not to 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.

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 "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.

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.

The number of contacted establishments per realized interview was 0.16, 0.38, and 0.36 for formal ES firms using the sample frames from the Ministry of Industry and Trade, D&B, and Yellow Pages respectively. 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 0.06, 0.05 and 0.007 using the sample frames from the Ministry of Industry and Trade, D&B, and Yellow Pages respectively.

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Ethiopia ES 2011 Implementation" in Technical Documents.

The Environmental Integrity Index (EII) is a tool developed by the City of Austin’s Environmental Resource Management Division to monitor and assess the ecological integrity and the degree of impairment in Austin’s watersheds. This feature class provides the most recent results from the EII for the City of Austin Watershed Protection Department’s Masterplanning process. Similarly, the Austin Lakes Index was designed to provide a yearly assessment of the ecological integrity of Lake Austin, Lady Bird Lake, and Lake Long. Index scores (from both the EII and ALI) are an integer between 0 and 100. Excellent 88-100 | Very Good 76-87 | Good 63-75 | Fair 51-62 | Marginal38-50 | Poor 26-37 | Bad 13-25 | Very Bad 0-12. Problem Scores are an integer between 1 and 100 with 1 being "No Problem" and 100 being a highest priority. EII Methodology: http://www.austintexas.gov/watershed_protection/publications/document.cfm?id=186267 Master Plan Problem Score Methodology: http://www.austintexas.gov/watershed_protection/publications/document.cfm?id=186352 Lake Index Methodology: http://www.austintexas.gov/watershed_protection/publications/document.cfm?id=196479

Median Consumer Price Index is a part of the Median CPI indicator of the Federal Reserve Bank of Cleveland.

According to the Global Hunger Index 2024, which was adopted by the International Food Policy Research Institute, Somalia was the most affected by hunger and malnutrition, with an index of 44.1. Yemen and Chad followed behind. The World Hunger Index combines three indicators: undernourishment, child underweight, and child mortality. Sub-Saharan Africa most affected The index is dominated by countries in Sub-Saharan Africa. In the region, more than one fifth of the population is undernourished . In terms of individuals, however, South Asia has the highest number of undernourished people. Globally, there are 735 million people that are considered undernourished or starving. A lack of food is increasing in over 20 countries worldwide. Undernourishment worldwide The term malnutrition includes both undernutrition and overnutrition. Undernutrition occurs when an individual cannot maintain normal bodily functions such as growth, recovering from disease, and both learning and physical work. Some conditions such as diarrhea, malaria, and HIV/AIDS can all have a negative impact on undernutrition. Rural and agricultural communities can be especially susceptible to hunger during certain seasons. The annual hunger gap occurs when a family’s food supply may run out before the next season’s harvest is available and can result in malnutrition. Nevertheless, the prevalence of people worldwide that are undernourished has decreased over the last decades, from 18.7 percent in 1990-92 to 9.2 percent in 2022, but it has slightly increased since the outbreak of COVID-19. According to the Global Hunger Index, the reduction of global hunger has stagnated over the past decade.

A new basis for an international poverty measurement is proposed based on linear programming for specifying the least cost diet and explicit budgeting for nonfood spending. This approach is superior to the World Bank's $1-a-day line because it is (i) clearly related to survival and well being; (ii) comparable across time and space since the same nutritional requirements are used everywhere while nonfood spending is tailored to climate; (iii) adjusts consumption patterns to local prices; (iv) presents no index number problems since solutions are always in local prices; and (v) requires only readily available information. The new approach implies much more poverty than the World Bank's, especially in Asia.

Indonesia Construction Index: Business Problem Index: North Maluku data was reported at 19.460 2010=100 in Dec 2018. This records an increase from the previous number of 19.420 2010=100 for Sep 2018. Indonesia Construction Index: Business Problem Index: North Maluku data is updated quarterly, averaging 30.635 2010=100 from Mar 2011 (Median) to Dec 2018, with 32 observations. The data reached an all-time high of 42.590 2010=100 in Dec 2014 and a record low of 15.230 2010=100 in Jun 2018. Indonesia Construction Index: Business Problem Index: North Maluku data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Construction and Properties Sector – Table ID.EE007: Construction Index: Business Problem Index: by Province.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

In the 2023 edition of the globalization index, Switzerland had the highest index score at 90.75. Belgium followed behind, with the Netherlands in third. Overall, globalization declined in 2020 due to the COVID-19 outbreak, but increased somewhat in 2021, even though it was still below pre-pandemic levels.

About the index

The KOF Index of Globalization aims to measure the rate of globalization in countries around the world. Data used to construct the 2023 edition of the index was from 2021. The index is based on three dimensions, or core sets of indicators: economic, social, and political. Via these three dimensions, the overall index of globalization tries to assess current economic flows, economic restrictions, data on information flows, data on personal contact, and data on cultural proximity within surveyed countries.

Defining globalization

Globalization is defined for this index as the process of creating networks of connections among actors at multi-continental distances, mediated through a variety of flows including people, information and ideas, capital and goods. It is a process that erodes national boundaries, integrates national economies, cultures, technologies and governance and produces complex relations of mutual interdependence.

The Division of Criminal Justice Services (DCJS) collects crime reports from more than 500 New York State police and sheriffs’ departments. DCJS compiles these reports as New York’s official crime statistics and submits them to the FBI under the National Uniform Crime Reporting (UCR) Program. UCR uses standard offense definitions to count crime in localities across America regardless of variations in crime laws from state to state. In New York State, law enforcement agencies use the UCR system to report their monthly crime totals to DCJS. The UCR reporting system collects information on seven crimes classified as Index offenses which are most commonly used to gauge overall crime volume. These include the violent crimes of murder/non-negligent manslaughter, forcible rape, robbery, and aggravated assault; and the property crimes of burglary, larceny, and motor vehicle theft. Firearm counts are derived from taking the number of violent crimes which involve a firearm. Population data are provided every year by the FBI, based on US Census information. Police agencies may experience reporting problems that preclude accurate or complete reporting. The counts represent only crimes reported to the police but not total crimes that occurred. DCJS posts preliminary data in the spring and final data in the fall.

City Planning Staff built a GIS model to help prioritize locations in the city for safety improvements based on severity of crashes and need of the surrounding community. Essentially, the model aggregated crash data points into street segments, weights the crashes by severity, and factors other context, like proximity to schools, senior housing, buses, into a final score.Both the final index score and the various aggregate crash numbers are useful for understanding safety conditions on Cleveland's streets and identifying the worst problem intersections.MethodologyBreak up Cleveland's street network in comparable segments including intersections.Aggregate crash data within those segments (sum crashes by severity type)Score the crash history for the segment (Crash_Score_Total)Identify context points of interest in vicinity (schools, senior centers, bus stops so far) of the street.Score the context for how many things are nearby that require extra proactive attention.Identify the social health and vulnerability of the street segment using CDC's Social Vulnerability Index and how it ranks within ClevelandCombine the crash score and context score.Boost scores based on social vulnerability, e.g. elevate streets in neighborhoods experiencing more poverty, racial discrimination, housing and transportation challenges.Data GlossaryFor all crashes: Click here, then click on "Fields" to view documentation.For bike and pedestrian classes: Click here, then click on "Fields" to view documentation.Update FrequencyNeverContactsCleveland City Planning Commission

Post-earthquake emergency logistics faces significant challenges such as limited resources, uncertain casualty numbers, and time constraints. Developing a scientific and efficient rescue plan is crucial. One of the key issues is integrating facility location and casualty allocation in emergency medical services, an area rarely explored in existing research. This study proposes a robust optimization model to optimize the location of medical facilities and the transfer of casualties within a three-level rescue chain consisting of disaster areas, temporary hospitals, and general hospitals. The model accounts for limited medical resources, casualty classification, and uncertainty in casualty numbers. The Trauma Index Score (TIS) method is used to classify casualties into two groups, and the dynamic changes in their injuries after treatment at temporary hospitals are considered. The objective is to minimize the total TIS of all casualties. A robust optimization approach is applied to derive the corresponding robust model, and its validity is verified through case studies based on the Lushan earthquake. The findings show that data variability and the uncertainty budget play a critical role in determining hospital locations and casualty transportation plans. Temporary hospital capacity significantly influences the objective function more than general hospitals. As the problem size increases, the robust optimization model performs better than the deterministic model. Furthermore, uncertainty in casualty numbers has a more significant impact on serious casualties than moderate casualties. To enhance the model’s applicability, it is extended into a two-stage dynamic location-allocation model to better address the complexity of post-disaster scenarios.

This dataset contains replication files for "The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely" by Susan Athey, Raj Chetty, Guido Imbens, and Hyunseung Kang. For more information, see https://opportunityinsights.org/paper/the-surrogate-index/. A summary of the related publication follows. The impacts of many policies, such as efforts to increase upward income mobility or improve health outcomes, are only observed with long delays. For example, it can take decades to see the effects of early childhood interventions on lifetime earnings. This problem has greatly limited researchers’ and policymakers’ ability to test and improve policies and arises frequently in our own work at Opportunity Insights on the determinants of economic opportunity. In this study, we develop a new method of estimating the long-term impacts of policies more rapidly and precisely using short-term proxies. We predict long-term outcomes (e.g., lifetime earnings) using short-term outcomes (e.g., earnings in early adulthood or test scores). We then show that the causal effects of policies on this predictive index (which we term a “surrogate index”, following terminology in the statistics literature) can help us learn about their long-term impacts more quickly under certain assumptions that are described in the full paper. We apply our method to analyze the long-term impacts of a job training experiment in California. Using short-term employment rates as surrogates, we show that one could have estimated the program’s impact on mean employment rates over a 9 year horizon within 1.5 years, with a 35% reduction in standard errors. The success of the surrogate index in this job training application suggests that our method could be applied to predict the long-term impacts of other programs as well. Going forward, we hope to build a public library of early indicators (surrogate indices) for social science by harnessing historical experiments along with the large-scale datasets we have built. If you would like to contribute to this effort by reporting a surrogate index that predicts long-term impacts estimated in an experiment, as in the GAIN program, please contact us.