In 2023, the number of missing person files in the United States equaled 563,389 cases, an increase from 2021 which had the lowest number of missing person files in the U.S. since 1990.

In 2022, there were 313,017 cases filed by the NCIC where the race of the reported missing was White. In the same year, 18,928 people were missing whose race was unknown.

What is the NCIC?

The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide.

Missing people in the United States

A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

While the fear of being kidnapped may persist for one’s entire life, in 2022 the number of missing persons under the age of 21 was much higher than those 21 and over, with 206,371 females under 21 reported missing, and 64,956 females over the age of 21 reported missing.

Why people go missing

There are many reasons why people go missing; some are kidnapped, some purposefully go missing - in order to escape abuse, for example - and some, usually children, are runaways. What persists in the imagination when thinking of missing persons, however, are kidnapping victims, usually due to extensive media coverage of child kidnappings by the media.

Demographics of missing persons

While the number of missing persons in the United States fluctuates, in 2021, this number was at its lowest since 1990. Additionally, while it has been observed that there is more media coverage in the United States of white missing persons, almost half of the missing persons cases in 2022 were of minorities.

NamUs is the only national repository for missing, unidentified, and unclaimed persons cases. The program provides a singular resource hub for law enforcement, medical examiners, coroners, and investigating professionals. It is the only national database for missing, unidentified, and unclaimed persons that allows limited access to the public, empowering family members to take a more proactive role in the search for their missing loved ones.

Number of homicide victims, by Indigenous identity (total, by Indigenous identity; Indigenous identity; First Nations (North American Indian); Métis; Inuk (Inuit); Indigenous person, Indigenous group unknown; non-Indigenous identity; unknown Indigenous identity) and missing person status (total, by missing person status; missing; not missing; missing person status unknown), Canada, 2015 to 2023.

This study reexamined and recoded missing data in the Uniform Crime Reports (UCR) for the years 1977 to 2000 for all police agencies in the United States. The principal investigator conducted a data cleaning of 20,067 Originating Agency Identifiers (ORIs) contained within the Offenses-Known UCR data from 1977 to 2000. Data cleaning involved performing agency name checks and creating new numerical codes for different types of missing data including missing data codes that identify whether a record was aggregated to a particular month, whether no data were reported (true missing), if more than one index crime was missing, if a particular index crime (motor vehicle theft, larceny, burglary, assault, robbery, rape, murder) was missing, researcher assigned missing value codes according to the "rule of 20", outlier values, whether an ORI was covered by another agency, and whether an agency did not exist during a particular time period.

In 2023, around 90.14 thousand missing person reports were filed in Japan, with young adults aged 10 to 19 years old representing the group with the most people missing by 17.73 thousand. The leading reason for people going missing was reported to be illness-related accounting for more than 27.8 percent of missing person reports that year.

The Missing Person Information Clearinghouse was established July 1, 1985, within the Department of Public Safety providing a program for compiling, coordinating and disseminating information in relation to missing persons and unidentified body/persons. Housed within the Division of Criminal Investigation, the clearinghouse assists in helping to locate missing persons through public awareness and cooperation, and in educating law enforcement officers and the general public about missing person issues.

The use of modern missing data techniques has become more prevalent with their increasing accessibility in statistical software. These techniques focus on handling data that are missing at random (MAR). Although all MAR mechanisms are routinely treated as the same, they are not equal. The impact of missing data on the efficiency of parameter estimates can differ for different MAR variations, even when the amount of missing data is held constant; yet, in current practice, only the rate of missing data is reported. The impact of MAR on the loss of efficiency can instead be more directly measured by the fraction of missing information (FMI). In this article, we explore this impact using FMIs in regression models with one and two predictors. With the help of a Shiny application, we demonstrate that efficiency loss due to missing data can be highly complex and is not always intuitive. We recommend substantive researchers who work with missing data report estimates of FMIs in addition to the rate of missingness. We also encourage methodologists to examine FMIs when designing simulation studies with missing data, and to explore the behavior of efficiency loss under MAR using FMIs in more complex models.

Under Section 8 of the Missing Persons Act, 2018, police services are required to report annually on their use of urgent demands for records under the Act and the Ministry of the Solicitor General is required to make the OPP’s annual report data publicly available. The data includes: * year in which the urgent demands were reported * category of records * description of records accessed under each category * total number of times each category of records was demanded * total number of missing persons investigations which had urgent demands for records * total number of urgent demands for records made by OPP in a year.

Although a large literature seeks to explain the "missing middle" of mid-sized firms in developing countries, there is surprisingly little empirical backing for existence of the missing middle. Using microdata on the full distribution of both formal and informal sector manufacturing firms in India, Indonesia, and Mexico, we document three facts. First, while there are a very large number of small firms, there is no "missing middle" in the sense of a bimodal distribution: mid-sized firms are missing, but large firms are missing too, and the fraction of firms of a given size is smoothly declining in firm size. Second, we show that the distribution of average products of capital and labor is unimodal, and that large firms, not small firms, have higher average products. This is inconsistent with many models explaining "the missing middle" in which small firms with high returns are constrained from expanding. Third, we examine regulatory and tax notches in India, Indonesia, and Mexico of the sort often thought to discourage firm growth and find no economically meaningful bunching of firms near the notch points. We show that existing beliefs about the missing middle are largely due to arbitrary transformations that were made to the data in previous studies.

Example data sets for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the data sets used in both example analyses (Examples 1 and 2) in two file formats (binary ".rda" for use in R; plain-text ".dat").

The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:

ID = group identifier (1-2000)
x = numeric (Level 1)
y = numeric (Level 1)
w = binary (Level 2)

In all data sets, missing values are coded as "NA".

Since 1952, more than 300,000 persons have been reported as missing in Mexico. About 65 percent of them have been found, either alive or dead. In 2023 alone, there were almost 30,000 reports of people missing in the North American country.

Pharmaceutical obesity RCTs used to evaluate the scope of the missing data problem.pdf (0.27 MB DOC)

Series Name: Number of deaths and missing persons attributed to disasters (number)Series Code: VC_DSR_MMHNRelease Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationTarget 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersGoal 1: End poverty in all its forms everywhereFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Puget Sound Missing Data includes Land Cover for EverettKirklandRedmondBellevueBonney LakeCovingtonEverettSteilecoomDes MoinesUrban Growth Areas UnincorporatedUrban Growth Areas

The National Incidence Studies of Missing, Abducted, Runaway, and Thrownaway Children (NISMART) were undertaken in response to the mandate of the 1984 Missing Children's Assistance Act (Pub.L. 98-473) that requires the Office of Juvenile Justice and Delinquency Prevention (OJJDP) to conduct periodic national incidence studies to determine the actual number of children reported missing and the number of missing children who are recovered for a given year. The first such study, NISMART-1 (NATIONAL INCIDENCE STUDIES OF MISSING, ABDUCTED, RUNAWAY, AND THROWNAWAY CHILDREN (NISMART), 1988 [ICPSR 9682]), was conducted from 1988 to 1989 and addressed this mandate by defining major types of missing child episodes and estimating the number of children who experienced missing child episodes of each type in 1988. At that time, the lack of a standardized definition of a "missing child" made it impossible to provide a single estimate of missing children. As a result, one of the primary goals of NISMART-2 was to develop a standardized definition and provide unified estimates of the number of missing children in the United States. Both NISMART-1 and NISMART-2 comprise several component datasets designed to provide a comprehensive picture of the population of children who experienced qualifying episodes, with each component focusing on a different aspect of the missing child population. The Household Survey -- Youth Data and the Household Survey -- Adult Data (Parts 1-2) are similar but separate surveys, one administered to the adult primary caretaker of the children in the sampled household and the other to a randomly selected household youth aged 10 through 18 at the time of interview. The Juvenile Facilities Data on Runaways (Part 3) sought to estimate the number of runaways from juvenile residential facilities in order to supplement the household survey estimate of the number of runaways from households. And the Law Enforcement Study Data, by case perpetrator, and victim, (Parts 4-6) intended to estimate the number of children who were victims of stereotypical kidnappings and to obtain a sample of these cases for in-depth study.

Large majorities in nearly every country support democracy, according to studies of cross-national surveys. But many of these reports have treated as missing data persons who did not provide a substantive response when asked to offer an opinion about the suitability of democracy as a regime type for their country, which has led to substantial overestimates of expressed support for democracy in some countries. This article discusses the consequences of excluding such nonsubstantive responses and offers suggestions to improve the study of popular support for democracy.

Missing data is a common problem in many research fields and is a challenge that always needs careful considerations. One approach is to impute the missing values, i.e., replace missing values with estimates. When imputation is applied, it is typically applied to all records with missing values indiscriminately. We note that the effects of imputation can be strongly dependent on what is missing. To help make decisions about which records should be imputed, we propose to use a machine learning approach to estimate the imputation error for each case with missing data. The method is thought to be a practical approach to help users using imputation after the informed choice to impute the missing data has been made. To do this all patterns of missing values are simulated in all complete cases, enabling calculation of the “true error” in each of these new cases. The error is then estimated for each case with missing values by weighing the “true errors” by similarity. The method can also be used to test the performance of different imputation methods. A universal numerical threshold of acceptable error cannot be set since this will differ according to the data, research question, and analysis method. The effect of threshold can be estimated using the complete cases. The user can set an a priori relevant threshold for what is acceptable or use cross validation with the final analysis to choose the threshold. The choice can be presented along with argumentation for the choice rather than holding to conventions that might not be warranted in the specific dataset.

Missing presumed ...? : the police response to missing persons is a book. It was written by Geoff Newiss and published by Home Office, Policing and Reducing Crime Unit in 1999.