# A time penalty for the Global South? Inequalities in visa appointment wait times at German embassies and consulates worldwide

Emanuel Deutschmann, Lorenzo Gabrielli, Alexandra Orlova, Niklas Harder, Ettore Recchi

► To cite this version:

Emanuel Deutschmann, Lorenzo Gabrielli, Alexandra Orlova, Niklas Harder, Ettore Recchi. A time penalty for the Global South? Inequalities in visa appointment wait times at German embassies and consulates worldwide. Political Geography, 2025, 10.1016/j.polgeo.2025.103440. hal-05377332

HAL Id: hal-05377332

https://sciencespo.hal.science/hal-05377332v1

Submitted on 21 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Full Length Article

# A time penalty for the Global South? Inequalities in visa appointment wait times at German embassies and consulates worldwide

Emanuel Deutschmann<sup>a,b,*</sup> <sup>●</sup>, Lorenzo Gabrielli<sup>c</sup> <sup>●</sup>, Alexandra Orlova<sup>d</sup> <sup>●</sup>, Niklas Harder<sup>d</sup> <sup>●</sup>, Ettore Recchi<sup>b,e</sup>

ARTICLE INFO

Keywords:

Visa applications
Global inequality
Mobility
Migration
Embassies
Consulates
Chronopolitics

ABSTRACT

Visas are a key tool for states to regulate incoming mobility from abroad, which can have ramifications for the establishment and perpetuation of global inequalities. In this article, we systematically analyze visa appointment wait times in German embassies and consulates worldwide. Using computational methods, we collect—and publish—fine-grained longitudinal data on the closest available appointment dates for various visa types, covering a total of 16,182 visa appointment requests. Our analysis reveals strong and systematic variance: the poorer the country a diplomatic mission is based in, the longer the wait time and the lower the chances of finding an available appointment (which ranges from almost 0 to 100 percent). We also argue that Germany's system is quite opaque compared to other established immigration countries such as the U.S. These core findings raise important questions in light of current debates about global justice, legal pathways to migration, and efforts to attract foreign talent.

1. Introduction

Regulating access to international mobility plays an important role in creating and maintaining global inequalities, and affects people's life chances all over the world (Czaika and Neumayer, 2017; Hobolth, 2014; Laube and Heidler, 2016; Lawson and Roychoudhury, 2016; Mau, 2010). Past research demonstrates that the varying 'power of passports,' visa waiver agreements, migration policies, and visa application costs privilege citizens of rich countries and systematically exclude others (Czaika et al., 2018; Mau, 2023; Mau et al., 2015; Recchi et al., 2021). However, prior work largely overlooks the time spent applying for visas, which can create an important additional burden. Excessively long visa appointment wait times can result in lost educational, political, or professional opportunities and have dire personal consequences for transnational families (e.g., being unable to visit a loved one before they pass away).

Such delays can also hamper international exchange and damage the reputation of receiver countries like Germany (Deutscher Bundestag, 2024). In a note in *Nature* poignantly titled “Exhausted and insulted: my

EU visa ordeal,” a PhD student from Ghana recounted her costly and time-consuming experience requesting a visa to attend an academic conference in Europe, only to be turned down like many others (Owusu-Gyamfi, 2024). In a qualitative study, Saeed (2024) describes how Pakistani applicants are frustrated by the long wait times for visa appointments at German consulates and embassies. Several other personal accounts detail the hassles and humiliations related to visa applications for people in the Global South (Barnard, 2015; Chatterjee, 2022; Daniel, 2022; Reuters, 2023), and qualitative research has produced important insights (e.g., Jacobs, 2022; Ullah et al., 2024; Özdemir and Ayata, 2018). Ours is the first large-N quantitative study that permits systematic statistical comparisons.

This article focuses on visa applications to Germany—the second-most popular destination for international migrants after the United States (IOM, 2021: 25). Germany also consistently ranks in the top ten most visited countries by tourists on the globe (UNWTO, 2023: 6). Data on inequalities in visa appointment wait times at German embassies/-consulates worldwide thus helps build the foundation for a broader global comparative analysis.

<footnote><sup>a</sup> Europa-Universität Flensburg, Germany</footnote>

<footnote><sup>b</sup> Migration Policy Centre, European University Institute, Italy</footnote>

<footnote><sup>c</sup> Istituto di Scienza e Tecnologie dell'Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy</footnote>

<footnote><sup>d</sup> German Centre for Integration and Migration Research (DeZIM), Germany</footnote>

<footnote><sup>e</sup> Sciences Po, Centre for Research on Social Inequalities (CRIS), CNRS, Paris, France</footnote>

<footnote>* Corresponding author. Europa-Universität Flensburg, Germany.
E-mail address: emanuel.deutschmann@uni-flensburg.de (E. Deutschmann).</footnote>
A recent survey establishes that long visa wait times are a major complaint of skilled professionals who were interested in coming (or had moved) to Germany (Liebig & Huerta del Carmen, 2024). Yet until now there has been little information on the length of visa wait times at German diplomatic missions (a term we use interchangeably with ‘embassies and consulates’ to denote representatives of the German state authorized to deliver entry visas for the territory of Germany). The German system is quite opaque compared to other established immigration countries like the United States, despite recent and ongoing efforts by the Foreign Office to unify and digitalize the visa application process under a common online platform called ‘Auslandsportal.’ The U. S. Department of State (2024a) maintains an up-to-date list of estimated wait times to receive an interview appointment at U.S. embassies and consulates worldwide. It also provides an interactive website that allows users to monitor current wait times for different visa types in various cities (U.S. Department of State, 2024b). This transparency gives individual applicants a degree of predictability and enables media outlets to report on backlogs and shifts in wait times (e.g., The Economic Times, 2024). The private service provider VisaGrader.com (2024a, 2024b) even tracks visa appointment wait times for various types of visas at U.S. embassies and consulates over time and offers daily updates. Information on appointment wait times to apply for German visas, by contrast, is limited and comes mainly from parliamentary queries. The German government’s answer to a 2021 query (known as ‘Kleine Anfrage’ in German) by Members of Parliament from the left-wing Die Linke party reported wait times at 32 German embassies/consulates on a single day—April 1, 2021 (Deutscher Bundestag, 2021).

Our study goes beyond this query in five ways. First, the spatial coverage is more comprehensive: we empirically examine data from 130 of Germany’s 187 diplomatic missions. Second, the temporal coverage is richer: we present longitudinal data on 52 time points from November 2, 2023 to September 3, 2024. Third, while the response to the parliamentary query occurred during the height of the COVID-19 crisis (and blames the pandemic for the long wait times), we test whether the situation has improved since then. Fourth, we conduct a statistical analysis of whether the variance in wait times is related to structural factors (GDP per capita, trade, population size, etc.), which is not available in the parliamentary document. Finally, our data collection and analysis are independent and academic.

The article is structured as follows. We first discuss the theoretical underpinnings of our study in the context of ‘chronopolitics’ and present a theoretical model of the structure of visa appointment wait times. Next, we outline the research design and describe the data collection process and modelling strategy. Afterwards, we illustrate the findings, focusing first on the descriptive results and then on the explanatory regression models designed to shed light on the structural factors behind the global variance in visa appointment wait times. We find that the origin country’s economic situation has a significant impact: the poorer the country, the longer the wait time and the lower the chances of getting a visa appointment at all. The conclusion discusses the implications of this and other findings as well as the study’s limitations.

## 2. Time as an instrument of mobility governance: theoretical antecedents

Political geography has long recognized the importance of time as a complement to its spatial foundations. ‘Chronopolitics’ integrates geopolitics and biopolitics to elucidate various facets of power dynamics (Klinke, 2013). Temporal controls, in the form of ‘suspensions,’ ‘stasis,’ ‘postponement,’ ‘deceleration’ and ‘waiting’ (Hage, 2009), are manifestations of power that shape human mobility in space. This contrasts with the dominant trope of ‘acceleration’ that seems to characterize late modernity (Rosa, 2013). Inspired by this theoretical framework, migration research has increasingly evaluated such processes as invisible mechanisms that alter global justice (Khosravi, 2014). The time management of individuals aspiring to international mobility is a key

instrument of ‘b/ordering’ (Van Houtum, 2017) and sorting unwanted from wanted travelers. Governance structures in the Global North have been shown to weaponize time in order to create precariousness, life-altering waits, relational asynchronies, and ultimately to destabilize the futures and agency of potential migrants (Andersson, 2014; Cohen, 2018; Landau, 2019; McNevin and Missbach, 2018).

The idea that ‘the border is not only at the geographical edges of state territories’ (Anderson, 2025: 1) is now a widely accepted principle in migration studies. While the ‘externalization of border controls’ shifts migration gatekeeping outside national territories, temporal disconnections are employed to distance potential migrants from their desired destinations (Tazzioli, 2018; Axelsson, 2022). Critical scholars are keen to emphasize that “immigration governance and biopolitical power operate through time to subjectify, criminalize, punish and exclude in racialized, neo-colonial ways. As a tool of migration governance, time is strategically employed through different rhythms, tempos, conceptualizations and measurements, as well as through the denial or appropriation of people’s time” (Griffiths, 2024: 1232).

This concept has mostly been explored in ethnographic research (for an exception, see Hainmueller et al., 2016) on irregular migrants and asylum seekers, examining the processing of judicial decisions and applications, everyday life in refugee camps and the renewal of residence permits (e.g., Caglar, 2016; Bendixsen and Eriksen, 2018; Jacobsen et al., 2020). However, the same ‘chronocratic’ logic (Kirstoglou and Simpson, 2020) is likely to apply to the wider population of prospective travelers, affecting even larger demographics. In this respect, we surmise that the timing of visa grants is a key mechanism at work, and yet another form of street-level bureaucratic border control that usually escapes policy analysis. We therefore expand the ‘chronopolitics’ framework to examine wait times in migration and tourism management, focusing on a seemingly minor aspect of the ‘border machine’ (Van Houtum, 2010): the time required to obtain a visa appointment at embassies and consulates.

Temporal borders are established through visa appointments; they act as major gateways to transnational movement. As in refugee camps (Tsagarousianou, 2023), queuing is used to create distance through time management. Our study innovates within this strand of research by using a quantitative and computational approach with a global perspective, rather than the typical small-N and localized case studies. We empirically collect and compare wait times at German embassies and consulates worldwide as a hitherto neglected potential indicator of generalized, population-wide chronopolitics. Our guiding hypothesis based on the chronopolitics literature (and the personal stories of exclusion and life on hold described in the introduction) is that we are unlikely to find a level playing field in which wait times are the same (or vary randomly) worldwide. Rather, we expect a globally hierarchized political geography in which potential travelers from the Global South face systematically longer wait times and lower chances of finding appointments. The following section describes our model in detail.

## 3. An analytical model of visa appointment wait times

The time people spend applying for a visa breaks down into the *visa appointment wait time* and the *visa processing time*. Our proposed model (depicted in Fig. 1) focuses on the former, although the latter can also be a considerable impediment for potential travelers and migrants. Quotas can generate a third type of wait time. In the U.S., for example, quotas for green cards can lead to multiple years, in some cases even decades, of additional wait time over and above the usual bureaucratic obstacle of visa processing (cf. Bier, 2019; Khimm, 2013).

Our outcome variables are the *visa appointment wait time* and the *chances of finding an appointment*. The two are likely correlated: when fewer appointments are available, the wait time will increase. Even though the appointment system is obviously not a free market (because this would entail the absence of state intervention), at a basic level both are a function of supply and demand. In fact, even though diplomatic
<figure><img src="image_2.png" /><figcaption>Fig. 1. Theorizing the structure of visa appointment wait times.</figcaption></figure>

*Source:* Original visualization by the authors.

missions are not profit-maximizing actors and neither higher costs for visas nor larger volumes of applications directly benefit the missions themselves, political decisions to allocate means (staff hours, appointment time slots, digital infrastructure, etc.) strongly influence the *supply* (and thus the ‘price’) of visa appointments. The greater the capacity or willingness of embassies and consulates to deal with visa requests, the higher applicants’ chances of finding available appointments and the shorter the wait time. The higher the *demand* (i.e., the number of potential applicants), the lower the chances of finding an available appointment and the longer the wait time, *ceteris paribus*. Supply and demand are largely a function of three groups of factors. The supply will mainly depend on the situation of the *destination country and its foreign missions*, whereas the demand will primarily be based on the situation in the *origin country*. Relational characteristics of the origin country/destination country dyad will affect both supply and demand.

### 3.1. Destination country factors

Four key factors in the destination country and its foreign missions influence the supply of visa appointment dates. The first is the destination country's *economic situation* and how this affects the budget of the foreign office, embassies, and consulates, and thus the staff hours devoted to visa processing. Since running diplomatic missions and visa application centers is costly, a lack of monetary resources (e.g., due to economic downturns and budget cuts) could lead to fewer available appointments and longer wait times. The second factor is the *political situation*, which may affect how many (and which) potential arrivals are considered desirable, and thus how many visa appointment slots are made available for them. For example, the government's political stance might prioritize arrivals from certain regions and increase the chromopolitical obstacles for travelers from particular other countries, as highlighted in the above-cited literature on time as an instrument of mobility governance. U.S. President Trump's full travel ban on citizens from 12 countries (mainly in Africa and the Middle East) is a recent case of such a distinctive treatment (FitzGerald, 2025). The third factor is

*public health*, which can impact the staffing of embassies/consulates via employee absences.<fnref n="1" /> Fourth, *cultural factors* play a role. For example, holidays and which days of the week are considered business days will affect the availability of appointments at specific diplomatic missions.

### 3.2. Origin country factors

The demand for visa appointments depends on at least four factors in the origin country. The first is its *economic situation*, both in absolute terms and relative to that of the destination country. This factor is especially important for long-term visa applications. Here, different theoretical schools (none of which we subscribe to per se) lead to different predictions: Neoclassical economics (e.g., Beine et al., 2014; Lanati and Thiele, 2024; Lewis and Swannell, 2018; Zelinsky, 1971; Clemens, 2014). Additionally, the situation might be different for tourist visas, since the demand for tourism increases monotonically with rising income levels (Deutschmann and Recchi, 2022). However, this is unlikely to translate directly into a high demand for tourist *visas* in rich origin nations, since these countries usually have visa waiver agreements with other high-income countries (Mau et al., 2015; Recchi et al., 2021). While the economic situation will thus heavily influence the demand for visas, it is difficult to predict which pattern will prevail empirically based on past research and theoretical arguments alone. We therefore include it in our model without making predictions about the directionality.

A second potentially relevant factor is the *political/security situation*: the more conflictual, unstable, violent, and unsafe a country's political situation is, the higher the demand for outmigration and refuge seeking (and thus visa appointments in foreign missions) should be. However, when the situation is *very* bad, there might be few visa applications due to practical impediments such as diplomatic missions closing, collapsing infrastructure, etc. A third factor is the *demographic situation* of the origin country: A larger population could be linked to a greater demand for visa appointments. Finally, the *technological situation* may play a role as well. Since internet access is required to use the German booking system we

<footnote n="1">¹ E.g., due to sick leave or containment rules during outbreaks; the COVID-19 pandemic dramatically altered visa wait times (Deutscher Bundestag, 2021, Bier, 2022).</footnote>
examine empirically, it seems plausible that higher internet penetration in a country could be linked to greater demand for appointments.

The situation in the origin country not only directly affects demand; it can also influence the political situation in the destination country and thus affect the *supply* (denoted by the long arrow in Fig. 1). For example, the U.S. ‘Uniting for Ukraine’ program generated an additional legal pathway for Ukrainians to come to the United States after the Russian invasion (USCIS U.S. Citizenship and Immigration Services, 2024). Germany passed a similar legal regulation based on the EU’s Temporary Protection Directive (Bundesgesetzblatt, 2023).

### 3.3. Relational factors

Further relational factors beyond the GDP per capita differential between the origin and destination countries might affect both demand and supply. For example, political relations may matter: Access to embassies and consulates facilitates visa appointments, yet in some places a diplomatic mission may only be reached in neighboring countries. Economic relations could also play a role. Countries that are important trade partners are treated preferentially: Visa appointments are easily available and wait times short. In addition, cultural proximity (e.g., linguistic, religious) might result in closer relations, better-staffed missions and thus more visa appointment spots. A similar logic could apply to geographic distance: Being far away might reduce demand, since most mobility occurs over short distances (Deutschmann, 2022; Deutschmann et al., 2022). Conversely, better mobility infrastructures between countries (e.g., more commercial flights, and thus a higher volume of air traffic) might improve access to travel, paving the way for a smoother and quicker granting of visa appointments (Deutschmann et al., 2023).

We cannot directly measure supply and demand empirically, nor do we have data on explicit chronopolitical decisions by the German state. We also do not have information on the number of people interested in visa appointments or the appointment capacities of Germany’s foreign missions. We submitted a Freedom of Information request via the NGO FragDenStaat to the Bundesamt für Auswärtige Angelegenheiten on June 8, 2024 regarding the budget and number of employees in German missions abroad. On July 15, 2024, the authority indicated that a response had been issued on July 12, 2024, but it was not received. An additional request submitted on July 24, 2024 remains unanswered. Therefore, we instead test whether the secondary factors that we described as drivers of supply and demand can be directly linked to visa appointment wait times and the likelihood of getting an appointment. There are some restrictions, however: We do not take the public health situation into account, since we lack data on staff absences in embassies/consulates. Nor do we have information on (changes in) the budgets of the foreign office or individual missions. Furthermore, we do not need to assess the GDP differential between Germany and the origin country: Since Germany is the only destination country in our analysis, it is sufficient to test the effect of the origin country’s GDP per capita; the differential would be redundant. The empirical application of the model depicted in Fig. 1 thus requires some simplifications. Still, we are confident that it can elucidate important points.

Finally, we need to consider that, since citizens of multiple countries can apply for visas in a given mission, it does not always make sense to link origin country characteristics to that country’s visa wait times (e.g., the Austrian GDP and visa requests at the German embassy in Vienna). For instance, if citizens do not need a visa to enter a destination country (in our case, citizens from the European Union and other countries who can visit and even work in Germany without a visa), third-country nationals might even constitute a majority of people searching for a visa appointment. We therefore also include information on whether citizens from a certain country need a visa to enter Germany to exclude/control for such cases.

## 4. Data and methods

This study leverages the fact that most German consulates and embassies worldwide use a unified online system. The German Foreign Office maintains a list of links to the websites of all German embassies and consulates to which one can apply for an appointment to obtain a visa (https://service2.diplo.de/rktermin/extern/choose_locationList.do, accessed February 24, 2024). For most embassies and consulates, the link directs users to a site where applicants first have to select a visa type (e.g., Schengen visa, long-term work visa, etc.). In the next step, they are led to one of several appointment booking systems. The most common is a list-based system: After solving a captcha (i.e., inserting a combination of six distorted letters and numbers to prove to be a human), applicants can select from a list of available dates. The second-most common type of application procedure is a form-based system that requires filling in a form with personal information (name, profession, phone number, email address, address, purpose of visit, etc.), solving a captcha and submitting an appointment request. An email with an appointment is then sent to the applicant. In addition to these two main systems, which are run via the Foreign Office’s own ‘service2.diplo.de’ provider, several visa application systems that are run by private service providers exist as well (see the Supplementary Material for further details). For ethical reasons, we only collected data from the default list-based system, which presents the available dates without needing to reserve one of them or to provide any personal information. We thus did not enter false information and book actual dates as required by the form-based system, which could have increased the wait times for actual applicants. This approach allowed us to cover 68.3 percent of all cases in which a working link was provided and 86.5 percent of all countries (109 out of 126) with a German diplomatic mission and a working visa booking link. The countries covered are home to 65.4 percent of the world’s population (cf. Fig. 2).

### 4.1. Data collection

To collect data from the list-based service2.diplo.de system, we developed a Python script that first solves the captcha and then collects the name of the embassy/consulate, the visa type and subtype, the date of the request, and the earliest available visa appointment date. It also computes the difference between the two dates, which is the wait time in days, our main outcome variable. The Python script also records when no date was listed (when the website returns the following message: "Unfortunately, there are no appointments available at this time. New appointments will be made available for booking at regular intervals").

Since a potentially relevant factor on the supply side is which day of the week the request is made, we collected data on available visa appointment dates every 6 days. This rolling weekday window covers all 7 days of the week after seven rounds. By collecting data 52 times over a period of 10 months (November 2, 2023 to September 3, 2024), we are able to state with relative certainty what share of requested appointments were (not) available, allowing us to compute the chances of finding an appointment—our second outcome variable of interest. In short, the longitudinal and automatized collection based on computational methods (see the Supplementary Material for further details) enabled us to generate a rich and fine-grained dataset containing information from 16,182 visa appointment requests.

### 4.2. Analytical strategy

The data has a complex structure: Observations for specific request dates are nested within visa subtypes, which are nested within visa types, which are nested within foreign missions, which are nested within origin countries. Origin countries constitute the bottleneck with regard to statistical degrees of freedom. There are 109 countries in our sample, and most of the structural factors that are likely to influence visa wait times and the chances of seeing an available appointment date are based at the country level. Due to this bottleneck, we cannot simply include all
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** List-vs. form-based systems used for visa requests at German diplomatic missions as of October 2023
*Note:* When multiple visa systems are used for different types of visas, ‘list’ is given preference, followed by ‘form’ in this visualization.</figcaption></figure>

of these factors simultaneously in a regression model as explanatory variables. We employ a two-stage analytical strategy to address this issue. In a first step, we look at bivariate correlations. In a second step, we run multivariate regression models but only include country-level factors that have significant bivariate correlations with the wait time and the chances of seeing an available appointment date. In addition, we check for multicollinearity and drop variables if necessary. The following subsections describe the variables used in this process.

### 4.2.1. Dependent variable

The main dependent variable is the *wait time* (in days), i.e., the number of days between the day of the request and the closest available visa appointment date. However, this variable can only partially capture the visa appointment situation since in many cases, no appointments are available. To account for this limitation, we also explore a second dependent variable, the *chances of being offered an appointment date*. This variable is binary at the date-of-request level (date available or not) but, resulting from a count of 52 requests, it is continuous at the aggregated mission and country levels (chance from 0 to 1). Since the regression models use the date-of-request level as individual units, we run logistic regression models if the chances of seeing an available appointment is the dependent variable, and ordinary least squares (OLS) regression models when the continuous wait time is the dependent variable.

### 4.2.2. Independent and control variables

For supply-side factors linked to the destination country and its foreign missions, we test for the influence of the *world region* where the embassy/consulate is located, the number of *Schengen visas* issued to citizens of that country in 2022 (Auswärtiges Amt, 2023) and the number of *first residence permits* issued to citizens of that country in 2022. Eurostat (2024), the data source, defines residence permits as “authorisation[s] issued by the competent national authority allowing a citizen of a non-member (non-EU) country (also known as a third country national) to stay for at least three months on its territory.” We also control for the *weekday of the request* and the *visa category*. When creating the latter variable, we needed to consider that different missions refer to apparently identical visa types and subtypes differently in the booking system. Given this terminological variability, we reclassified them into consistent categories (cf. Table 1). However, this revealed that the visa types do not all operate at the same level of generality: In 6.7 percent of the cases there is just a general category, ‘visa,’ for all

<table><caption>**Table 1**
Classification of visa types in German diplomatic missions.</caption><thead><tr><th>Type</th><th>Freq.</th><th>Percent</th></tr></thead><tbody><tr><td>Au pair/voluntary service (Freiwilligendienst)</td><td>274</td><td>1.7</td></tr><tr><td>Accelerated specialist procedure with prior approval<br>(Beschleunigtes Fachkräfteverfahren mit Vorabzustimmung)</td><td>725</td><td>4.5</td></tr><tr><td>Family reunion</td><td>1361</td><td>8.4</td></tr><tr><td>National visa</td><td>5285</td><td>32.7</td></tr><tr><td>Schengen visa</td><td>2544</td><td>15.7</td></tr><tr><td>Ethnic German repatriates (Spätaussiedler)</td><td>102</td><td>0.6</td></tr><tr><td>Student visa</td><td>935</td><td>5.8</td></tr><tr><td>Vander Elst visa</td><td>260</td><td>1.6</td></tr><tr><td>Visa</td><td>1079</td><td>6.7</td></tr><tr><td>Re-entry (Wiedereinreise)</td><td>156</td><td>1.0</td></tr><tr><td>Work visa</td><td>2062</td><td>12.7</td></tr><tr><td>Working holiday/youth mobility program</td><td>1399</td><td>8.7</td></tr><tr><td>Total</td><td>16,182</td><td>100.0</td></tr></tbody></table>

types of visas. Many cases distinguish between national visas (32.7 percent) and Schengen visas (15.7 percent), while others have more specific categories, including family reunion (8.4 percent), Vander Elst visas (1.6 percent), and working holiday/youth mobility programs (8.7 percent). Since this inconsistency makes it difficult to use these categorizations in a meaningful way in our regression models, we classify visas into three simpler categories: long term (77.6 percent), short term (15.7 percent), and mixed (6.7 percent).

To measure the origin-country factors that are closely linked to demand (but which may also indirectly affect supply by influencing political decisions in Germany), we examine the role of *GDP per capita* (World Bank, 2024a). We also control for *visa freedom*, i.e. whether a person can enter Germany without a visa. We differentiate between two forms of visa freedom. The first gauges freedom in a strict sense as whether the citizens of the country where the mission is based can enter Germany without a visa for *all* purposes, including to work. This applies to members of the European Economic Area (27 EU countries + 4 European Free Trade Association countries) and selected other states (Australia, Canada, Israel, Japan, New Zealand, South Korea, the United Kingdom, and the United States, cf. The Federal Government, 2024). Second, we experiment with an alternative variable that captures a looser sense of visa freedom—countries whose citizens can enter Germany without a visa for at least *some* purposes (e.g., a short-term stay)
and under at least some conditions (e.g., if they have a biometric passport), based on *Auswärtiges Amt* (2024a).

To assess the political/security situation, we include an indicator of political stability and absence of violence/terrorism from the Worldwide Governance Indicators. This meta measure combines a range of indicators from various sources including armed conflict, violent demonstrations, social unrest, security risk, interstate and civil war, ethnic tensions, and terrorism (Kaufmann et al., 2010 via World Bank, 2024b). To assess the demographic situation, we include the population size of the country of origin based on World Bank (2024c) data. Finally, we include internet penetration (i.e., the percentage of the population that uses the internet) to proxy for the origin country's technological situation (World

Bank, 2024d).

The final group of factors is relational. Below the country level, we control for the *type of mission* as an indicator of political relations (which may also be related to the budget and thus the capacity for managing appointments). Overall, 78.7 percent of the cases come from embassies (Botschaften), 20.7 percent from consulates (Generalkonsulate), and 0.6 percent from the German Institute (Deutsches Institut) in Taipei (this special case does not appear in the regression models below due to missing values for Taiwan on other variables). We include the amount of trade with Germany, measured via the turnover (i.e., exports plus imports) in euros (Destatis, 2024), to indicate economic relations. We also test the influence of *geographic distance*, *linguistic proximity*, and *religious*

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Variance in visa appointment wait times at German diplomatic missions across time and space.
*Note*: Panels B, C, and D are based on the 55.9 percent of cases with a wait time reported in Panel A.</figcaption></figure>
proximity, all based on CEPI data (Melitz and Toubal, 2014). Linguistic proximity is evaluated using the indicator ‘common spoken language,’ which measures the share of the two populations that should be able to understand each other. Finally, we assess the role of the number of *air passengers* who travelled to Germany from each origin country in 2023 (based on data from Sabre, an online reservation system) as an indicator of the mobility infrastructure.

## 5. Results

We first describe the variance in visa appointment wait times and then move to the explanatory analysis, including the results from the regression models.

### 5.1. Descriptive results

Fig. 3 charts a first set of descriptive findings. For 44.1 percent of all visa appointment requests at German embassies and consulates globally, no visa appointment dates were available during the study period (covering 10 months between 2023 and 2024). This highlights the importance of our second dependent variable, the chances of finding an appointment. For the 55.9 percent of requests for which an appointment was available, Panel B of Fig. 3 depicts the distribution of wait times. It resembles a power-law pattern: Most wait times are relatively short (1 day is the most common) and there is a long tail of relatively few very long wait times. The longest wait time was 98 days, recorded three times: For a long-term visa request in Luanda, Angola on November 2, 2023, a Schengen visa request in Kabul, Afghanistan (handled via the embassy in Islamabad but listed as ‘Kabul Visa’ in the Foreign Office system) on August 20, 2024, and a family reunification visa request in Muscat, Oman on June 23, 2024.

Our estimates should be considered conservative because when the wait times are long at a consulate/embassy, there is also a high chance that no appointments will be available. This is evident from the negative correlation between wait times and the chances of finding an appointment ($r = -0.54, p < 0.001$, see Fig. 5) and example trends for specific cases (Supplementary Material Fig. S3 describes the case of Accra, Ghana). Thus, some people also have to wait *before* they can reserve an appointment (i.e., they must repeatedly check the website until an appointment becomes available). This period is unquantifiable to us. In the form-based system, which we did not analyze and which is often used when there is a lot of strain in the system (see the Supplementary Material), wait times can be much longer than those recorded here. The German embassy in Yerevan, Armenia, one of the few diplomatic missions to provide such information, stated that the wait time for Schengen visas was 7 months (i.e., approximately 210 days) at the time of writing (Auswärtiges Amt, 2024b).

Panel C of Fig. 3 illustrates how the average wait time evolved during the study period (November 2, 2023–September 3, 2024). There are wave-like fluctuations over time. For instance there was a clear peak in December 2023, which could be explained by a higher demand for visas in the pre-holiday season, a lower supply of appointment dates due to staff leave over the holiday period, or the large COVID-19 wave that occurred in many countries that month (in Germany, for example, this wave climaxed in mid-December, cf. RKI, 2024, coinciding with the peak in visa wait times). The figure also depicts a continuous rise after a period of shorter wait times at the beginning of 2024, which could be capturing a seasonal effect in which more people are interested in obtaining visas to Germany during the summer months in Europe/the northern hemisphere. However, it is hard to test this hypothesis without long-term data.

There are also significant differences in wait times between world regions of origin (Panel D). They are particularly high in Africa and North America, and lowest in Latin America and the Caribbean as well as Oceania. Detailed embassy-level analyses reveal that the high wait times in North America are driven by especially long wait times in Chicago

(see Supplementary Material Fig. S2). Since neither Canadian nor U.S. citizens need a visa to enter Germany, it seems plausible that the (perhaps surprisingly) high wait times in North America are driven by third-country nationals. Within Africa, wait times also differ enormously between embassies/consulates: Ouagadougou, Antananarivo, Kampala, and Kinshasa stand out with high average wait times of over 40 days (Fig. S2).

Fig. 4 explores the variance in average visa wait times and the chances of finding an appointment across countries and displays corresponding 95 percent confidence intervals. The left-hand side reveals very strong variance in the share of requests that produce an available visa appointment date, ranging from 0.7 percent in Uganda and 1.3 percent in Pakistan at the bottom end to 100 percent in Venezuela, Tunisia, Trinidad and Tobago, and quite a few other countries. Similarly, Panel B illustrates that the average wait time for requests that lead to available appointments also varies tremendously between countries. Whereas the average wait time was particularly low in Uzbekistan (1.0 day), Singapore (2.0 days), and Cuba (2.7 days), it exceeded a month in 17 countries and was especially high in Cyprus (60.7 days), Madagascar (71.3), and Burkina Faso (75.7). Note, however, that these times are conditional on an appointment being available. For example, Panel A establishes that hardly any requests in Uzbekistan yielded an available date. Thus, the short wait time in Panel B is based on a single request with an available date. Singapore, by contrast, has very short wait times and nearly always has an available appointment. This divergence highlights the need to look at the wait time *and* the chances of finding an appointment *together*. Furthermore, Fig. 4 reveals that several African countries are among the nations that host the embassies/consulates with the longest wait times and the lowest chances of offering appointments. In addition, some countries whose citizens enjoy visa freedom with Germany (e.g., Austria, Sweden) also appear to have rather unfavorable conditions for prospective visa applicants—a pattern that we explore more closely in the next section.

### 5.2. Explanatory results

This section examines whether (and how) visa wait times and the chances of getting visa appointments are related to structural factors. As described above, we proceed in two steps, beginning with bivariate correlations. Fig. 5 displays a correlation heatmap at the country level (i.e., based on the mean wait time and chances of obtaining a visa appointment across all observations within a country). It reveals that only 3 of the 11 country-level explanatory factors under scrutiny are significantly related to visa wait times: GDP per capita, trade, and internet penetration. The correlation with GDP per capita is negative: The poorer the country, the longer its applicants have to wait for a visa appointment ($r = -0.28, p < 0.01$). The correlation with trade is also negative ($r = -0.17, p < 0.05$), in line with our theoretical considerations. The negative correlation between wait times and internet penetration ($r = -0.35, p < 0.001$), by contrast, runs counter to our expectation that when more people have access to the internet, demand in the online booking system should be higher. The heatmap also reveals a very strong positive correlation between internet penetration and GDP per capita ($r = 0.90, p < 0.001$), suggesting that internet penetration, when examined in isolation, operates more like a proxy for economic wellbeing/development, which would explain the negative correlation with wait times.

Note that while none of the other factors is significantly correlated with wait times, this does not necessarily mean they have no influence on the demand for (or supply of) visas. It is possible that the Foreign Office and individual embassies/consulates *do* take them into account and allocate resources accordingly—for instance, by assigning more officers where there have been more travelers to Germany in the past. Therefore, there would be no visible correlation even though these factors do, in the background, affect the structure of visa appointments. Unfortunately, as mentioned above, we lack access to data on each diplomatic mission's human and financial resources, which would be
<figure><img src="image_5.png" /><figcaption>Fig. 4. Share of requests with an available date and mean number of days per request for visa appointment, by country with a German diplomatic mission.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 5. Correlation heatmap of the wait time for a visa appointment at German diplomatic missions worldwide.</figcaption></figure>

Note: \*p < 0.05, \*\*p < 0.01, \*\*\*p < 0.001. Darker red (blue) indicates a stronger positive (negative) correlation. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

needed to assess this possibility.

The second column in Fig. 5 establishes that GDP per capita is also significantly related to the likelihood of seeing an available visa appointment on the mission's website: The richer the country, the higher the chances of finding an appointment ($r = 0.19, p < 0.05$). Three other factors are also significant: (1) population size (the larger the population, the lower the chances of finding an appointment, in line with our demand-based theoretical considerations, $r = -0.22, p < 0.05$), (2) geographic distance (locations further from Germany are more likely to find appointments, $r = 0.20, p < 0.05$, an unexpected pattern that will, however, disappear once we take the visa-freedom of predominantly close-by countries into account), and (3) internet penetration (higher internet usage is connected to higher chances of finding appointments, $r = 0.26, p < 0.01$), which again runs counter to our theoretical intuition and highlights the need for more thorough, multivariate analyses.

Fig. 6 plots the relationship between GDP per capita (potentially the most central predictor variable) and visa wait times on the one hand and the chances of finding an appointment on the other hand. Panel A presents the correlation with wait times and reveals a pattern that, as anticipated in the theory section, needs to be taken into consideration: Several Global North countries whose citizens do not need a visa to visit or work in Germany (strictly defined, highlighted via dark blue dots) have surprisingly high wait times, which is at odds with the general negative correlation between wait times and GDP per capita. These countries include Cyprus, Austria, Sweden, Belgium, Canada, the U.S., and Japan. Some of them lie on popular migration routes (e.g., Cyprus in the Mediterranean, Bulgaria and Austria on the Balkan route). Thus, it seems plausible that this pattern is driven mainly by third-country nationals applying for visas at German embassies/consulates (known as ‘secondary movements’ within the EU free movement area). Therefore, correlating these countries’ macro-level characteristics with wait times

would be erroneous since most applicants originate from other countries. Excluding these states strengthens the correlation (Panel C), which supports the argument that economic prosperity is significantly associated with shorter visa wait times ($r = -0.42, p < 0.001$). When we apply the looser definition of visa freedom (i.e., when we also exclude countries whose citizens can sometimes/under some conditions enter Germany without a visa), the correlation further strengthens ($r = -0.49, p < 0.001$), but fewer countries remain in the sample (Panel E).

Panels B, D, and F demonstrate the same phenomenon for the chances of getting a visa appointment. Panel B visualizes how the chances of finding an available appointment tend to be lower for German embassies/consulates in poorer countries than in richer countries ($r = 0.19, p < 0.05$), although they vary considerably. Several visa-free countries with high per capita GDPs host German consulates/embassies with surprisingly low chances of listing available visa appointment dates. They include Austria, Belgium, Cyprus, Czech Republic, Greece, Hungary, Italy, Japan, Poland, Spain, South Korea, Sweden, and the Netherlands. Again, the dominance of countries in the Mediterranean and along Eastern European migration routes appears to support the argument that the presence of third-country nationals in these countries might be driving the high demand and thus (perhaps combined with a chronopolitical reluctance to increase the supply) the low chances of seeing available visa appointments. When strictly visa-free countries are excluded, the positive correlation between GDP per capita and the chances of obtaining a visa appointment becomes even stronger and highly significant ($r = 0.47, p < 0.001$, Panel D). Applying the looser definition of visa freedom further strengthens the correlation ($r = 0.55, p < 0.001$, Panel F).

To account for the possibility that the relationship between economic prosperity and the demand for visas could be non-linear (cf. theory section), we also display locally weighted regression (lowess) curves
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** GDP per capita and visa wait times/chances of getting a visa appointment at German diplomatic missions worldwide.</figcaption></figure>

Note: Panels A and B denote countries that have strictly defined visa freedom with Germany in dark blue. The short-dashed grey lines are based on a lowess smoother with the standard smoothing parameter of 0.8. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
(the short-dashed grey lines in Fig. 6). However, this does not generate an inverted u-shaped pattern as mobility transition theory would predict. If anything, the raw patterns (Panels A and B) are slightly u-shaped (i.e., the exact opposite) and as soon as the implausible cases are excluded (Panels C to F), the smoothed lowess lines closely follow the straight correlation lines. This suggests that the wait time indeed decreases (and chances increase) overall rather monotonically with ln GDP per capita.

These observations inform our modelling strategy for the second step, which entails multivariate regressions. Since GDP per capita appears to be the main structural factor of influence, we place it center stage in our models. We also include a binary variable that denotes strictly visa-free countries. And rather than including the country-level factors that are significant in Fig. 5, we include (to the extent that multicollinearity issues and the limited degrees of freedom at the country level allow) those that are significant in Appendix Figure A1, which displays the correlation heatmap without strictly visa-free countries and which can thus be deemed more reliable. We use the *strict* definition of visa freedom in these analytical steps, since in countries where only the loose definition applies, citizens may be applying for visas at the missions (since under *some* conditions they do need visas), making an exclusion less plausible.

Table 2 presents the results of a series of OLS regression models that predict the visa appointment wait time in days. The first model only contains a logarithmic version of GDP per capita, which again shows a significant effect. The second model controls for whether the country of origin has visa freedom with Germany (strictly defined) for the reasons described above. In line with the previous analyses, the regression coefficient of GDP per capita (ln) becomes stronger. Model 3 adds several mission-, visa-, and date-level variables: The type of mission (consulates feature shorter wait times than embassies) and the type of visa. We find that short-term and mixed visa types have longer wait times than long-term visas, perhaps due to a higher demand for short-term visas since tourism is much more prevalent than migration (Deutschmann et al.,

2022: 171). Wait times are highest when initiating a search for appointments on Tuesdays to Fridays (for a more detailed analysis of the role of the weekday, see the Supplementary Material).

Model 4 adds the world region of origin; compared to requests from embassies/consulates located in Africa, those in Asia, Europe, Latin America and the Caribbean, and Oceania have shorter visa appointment wait times. Only applicants based in North America have to wait slightly longer (even controlling for the fact that both Canada and the U.S., the only two countries in this region according to the classification used, have visa freedom with Germany). Controlling for the region only slightly reduces the effect of GDP per capita, and it remains significant. Finally, the fifth model adds the three country-level variables besides GDP per capita (ln) that had a significant bivariate relation with appointment wait times after excluding strictly visa-free countries (see Figure A1): Air passengers, trade and internet penetration. Of these, only trade has a significant effect: The more trade with Germany, the shorter the wait times, in line with our theoretical assumptions. GDP per capita retains a highly significant effect across all five models (and remains about double the size of the effect of trade in the full model), leading us to conclude that poorer countries indeed have significantly longer visa appointment wait times than richer countries. No multicollinearity issues occur in these models.

Table 3 replicates the modelling process employing the chances of seeing an available appointment date as the dependent variable, using a logistic regression since this outcome is binary. Again, all models indicate a significant effect of GDP per capita: The poorer the country a mission is based in, the less likely an applicant is to find available visa appointment dates. We also find that consulates feature shorter wait times than embassies (as shown above) and a lower chance of available visa appointment dates—pointing to a trade-off between searching at embassies (higher chance) and consulates (shorter wait times). We observe a similar pattern for the type of visa. Table 2 reveals that visas in the broader mixed category have longer wait times than those in the specialized long-term category, but Table 3 establishes that visas in the

<caption>Table 2</caption>

OLS regressions predicting visa appointment wait times (in days) at German diplomatic missions worldwide.

<table><thead><tr><th></th><th>(1)<br>GDP</th><th>(2)<br>+ Visa free</th><th>(3)<br>+ Supply</th><th>(4)<br>+ Regions</th><th>(5)<br>Full</th></tr></thead><tbody><tr><td>GDP per capita (ln)</td><td>-0.132***</td><td>-0.225***</td><td>-0.188***</td><td>-0.123***</td><td>-0.098***</td></tr><tr><td>Strictly visa free (1 = true)</td><td></td><td>0.171***</td><td>0.153***</td><td>0.164***</td><td>0.181***</td></tr><tr><td>Type of mission</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Embassy (Botschaft)</td><td></td><td></td><td>ref.</td><td>ref.</td><td>ref.</td></tr><tr><td>Consulate (Generalkonsulat)</td><td></td><td></td><td>-0.044***</td><td>-0.115***</td><td>-0.083***</td></tr><tr><td>Type of visa</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Long term</td><td></td><td></td><td>ref.</td><td>ref.</td><td>ref.</td></tr><tr><td>Short term</td><td></td><td></td><td>0.087***</td><td>0.066***</td><td>0.057***</td></tr><tr><td>Mixed</td><td></td><td></td><td>0.036***</td><td>0.062***</td><td>0.064***</td></tr><tr><td>Day of the week</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Monday</td><td></td><td></td><td>ref.</td><td>ref.</td><td>ref.</td></tr><tr><td>Tuesday</td><td></td><td></td><td>0.043**</td><td>0.041**</td><td>0.041**</td></tr><tr><td>Wednesday</td><td></td><td></td><td>0.041**</td><td>0.039**</td><td>0.038**</td></tr><tr><td>Thursday</td><td></td><td></td><td>0.052***</td><td>0.051***</td><td>0.051***</td></tr><tr><td>Friday</td><td></td><td></td><td>0.054***</td><td>0.054***</td><td>0.054***</td></tr><tr><td>Saturday</td><td></td><td></td><td>-0.017</td><td>-0.021</td><td>-0.021</td></tr><tr><td>Sunday</td><td></td><td></td><td>-0.016</td><td>-0.018</td><td>-0.018</td></tr><tr><td>Region of origin</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Africa</td><td></td><td></td><td></td><td>ref.</td><td>ref.</td></tr><tr><td>Asia</td><td></td><td></td><td></td><td>-0.068***</td><td>-0.069***</td></tr><tr><td>Europe</td><td></td><td></td><td></td><td>-0.218***</td><td>-0.222***</td></tr><tr><td>Latin America & the Caribbean</td><td></td><td></td><td></td><td>-0.123***</td><td>-0.134***</td></tr><tr><td>Northern America</td><td></td><td></td><td></td><td>0.048***</td><td>0.043**</td></tr><tr><td>Oceania</td><td></td><td></td><td></td><td>-0.068***</td><td>-0.078***</td></tr><tr><td>Air passengers (ln)</td><td></td><td></td><td></td><td></td><td>-0.013</td></tr><tr><td>Trade (ln)</td><td></td><td></td><td></td><td></td><td>-0.052*</td></tr><tr><td>Internet penetration</td><td></td><td></td><td></td><td></td><td>-0.003</td></tr><tr><td>N</td><td>8657</td><td>8657</td><td>8657</td><td>8281</td><td>8281</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.02</td><td>0.04</td><td>0.05</td><td>0.08</td><td>0.09</td></tr></tbody></table>

Standardized beta coefficients * p < 0.05, **p < 0.01, ***p < 0.001.
<table><caption>**Table 3**
Logistic regression predicting the chances of finding a visa appointment at German diplomatic missions worldwide.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr><tr><th></th><th>GDP</th><th>+ Visa free</th><th>+ Supply</th><th>+ Regions</th><th>Full</th></tr></thead><tbody><tr><td>GDP per capita (ln)</td><td>0.332***</td><td>1.192***</td><td>1.343***</td><td>0.941***</td><td>0.887***</td></tr><tr><td>Strictly visa free (1 = true)</td><td></td><td>-1.383***</td><td>-1.573***</td><td>-2.416***</td><td>-2.645***</td></tr><tr><td>Type of mission</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Embassy (Botschaft)</td><td></td><td></td><td>ref.</td><td>ref.</td><td>ref.</td></tr><tr><td>Consulate (Generalkonsulat)</td><td></td><td></td><td>-0.511***</td><td>-0.386***</td><td>-0.336***</td></tr><tr><td>Type of visa</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Long term</td><td></td><td></td><td>ref.</td><td>ref.</td><td>ref.</td></tr><tr><td>Short term</td><td></td><td></td><td>-0.119***</td><td>-0.027</td><td>-0.015</td></tr><tr><td>Mixed</td><td></td><td></td><td>0.539***</td><td>0.502***</td><td>0.421***</td></tr><tr><td>Weekday</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Monday</td><td></td><td></td><td>ref.</td><td>ref.</td><td>ref.</td></tr><tr><td>Tuesday</td><td></td><td></td><td>0.156**</td><td>0.156**</td><td>0.150**</td></tr><tr><td>Wednesday</td><td></td><td></td><td>0.124**</td><td>0.128**</td><td>0.123*</td></tr><tr><td>Thursday</td><td></td><td></td><td>0.082</td><td>0.081</td><td>0.068</td></tr><tr><td>Friday</td><td></td><td></td><td>0.006</td><td>-0.002</td><td>-0.008</td></tr><tr><td>Saturday</td><td></td><td></td><td>-0.012</td><td>-0.020</td><td>-0.019</td></tr><tr><td>Sunday</td><td></td><td></td><td>-0.007</td><td>-0.016</td><td>-0.022</td></tr><tr><td>Region of origin</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Africa</td><td></td><td></td><td></td><td>ref.</td><td>ref.</td></tr><tr><td>Asia</td><td></td><td></td><td></td><td>0.229***</td><td>0.276***</td></tr><tr><td>Europe</td><td></td><td></td><td></td><td>1.508***</td><td>1.498***</td></tr><tr><td>Latin America & the Caribbean</td><td></td><td></td><td></td><td>0.333***</td><td>0.396***</td></tr><tr><td>Northern America</td><td></td><td></td><td></td><td>0.740***</td><td>0.698***</td></tr><tr><td>Oceania</td><td></td><td></td><td></td><td>0.379***</td><td>0.391***</td></tr><tr><td>Political stability and absence of violence</td><td></td><td></td><td></td><td></td><td>0.012</td></tr><tr><td>Common spoken language</td><td></td><td></td><td></td><td></td><td>0.436***</td></tr><tr><td>N</td><td>15,773</td><td>15,773</td><td>15,773</td><td>15,357</td><td>14,530</td></tr><tr><td>AIC</td><td>21,612.96</td><td>20,501.89</td><td>20,083.26</td><td>19,169.09</td><td>18,173.91</td></tr><tr><td>BIC</td><td>21,628.30</td><td>20,524.89</td><td>20,175.25</td><td>19,298.96</td><td>18,318.00</td></tr></tbody></table>

Standardized beta coefficients * p < 0.05, **p < 0.01, ***p < 0.001.

broader mixed category also have a higher chance of obtaining an appointment. In other words, when missions offer all kinds of visas under the same umbrella category, there are typically more available slots but longer wait times. The day of the week the request was made matters here as well (cf. the Supplementary Material for a more detailed analysis of which days are ‘optimal’ for applicants): Compared to Mondays, the chances of finding an appointment are elevated on Tuesdays and Wednesdays. One possible explanation is that embassies/ consulates enter new dates into the system at the beginning of the week that become available on Tuesdays. Applicants in Africa have a lower chance than those from all other regions of seeing free visa appointments to choose from. Applicants in Europe have the highest chances by far.

In line with our analytical strategy, Model 5 adds the additional country-level variables that had a significant bivariate correlation with the chances of getting a visa appointment when strictly visa-free countries were excluded (see Figure A1). Since including all six of them creates serious multicollinearity issues and unstable effects, we exclude, most factors that are highly correlated with GDP per capita. We keep the variables *common spoken language* and *political stability*. In this full model, which features no multicollinearity issues, political stability does not have a significant effect. The cultural effect of a common spoken language, by contrast, is highly significant and positive: The larger the share of the two populations that can understand each other because they speak the same language, the higher the chances of finding available appointments. This cultural relational factor is in line with our theoretical assumptions.

## 6. Discussion

This article theorizes visa appointment wait times as a specific form of chronopolitics and introduces a novel way to collect pertinent data from German embassies and consulates around the world. Our analysis of this fine-grained data—gathered in short intervals from November 2023 to September 2024 and published open access together with this

article—reveals that:

1. There are huge inequalities between German diplomatic missions worldwide: The chances of getting a bookable appointment date range from almost 0 to 100 percent of all requests, and wait times range from 0 to 98 days.

2. There are significant differences between world regions and a structural gradient that reflects global economic inequalities: The poorer the country, the longer the wait time and the lower the chances of obtaining a visa appointment.

3. Controlling for visa waivers, applicants in Europe have the shortest wait times and the highest chances of getting an appointment; those in Africa have the longest wait times (similar to those in North America) and the lowest chances.

From a theoretical standpoint, these findings contribute to, expand and partially challenge an emerging body of research on chronopolitics and the temporal dimension of mobility governance. While previous studies document how irregular migrants and asylum seekers experience bureaucratic time through indefinite waiting, postponement or deceleration (e.g., Bendixsen & Eriksen, 2018; Caglar, 2016; Jacobsen et al., 2020), our data establishes that similar dynamics affect a broader population of prospective travelers. The unequal distribution of appointment availability could be seen as a form of temporal bordering (Tazzioli, 2018; Griffiths, 2024), selectively delaying access to mobility depending on applicants’ geographic and economic origin. Our large-N study is thus able to disclose something that small-scale qualitative studies, which by definition have to focus on geographically and otherwise restricted (e.g., group-specific) cases, are blind to. For very good reasons, such research often tends to focus on the clearest, most dramatic cases of discrimination and misery, for example exposing the ‘modes of governing by choking lifetime’ that asylum seekers detained in Greece are subjected to (Tazzioli, 2018). But our statistical, global comparative analysis reveals that entire countries and world regions
face ‘time penalties’ that can affect anyone from tourists to students to business travelers. They take effect before any move is made, and the length of these penalties constitutes a quantifiable continuum that largely follows a global economic hierarchy (rather than a binary distinction such as migrant/non-migrant or undesirable/desirable). German missions in poorer countries tend to have longer wait times and lower appointment availability, even when using the same digital booking infrastructure. This economic gradient and the additional significant effects of world regions (which suggest further discriminatory mechanisms beyond a purely economic logic) create a political geography of visa wait times that puts a time penalty on citizens of Global South countries—and even, it seems, on third-country nationals applying from well-off countries in the Global North. This latter finding also points to a relevant and understudied role of diplomatic missions in processes of externalization or the management of migratory routes. For Global North citizens, the results indicate a ‘mobility premium’ that facilitates access regardless of personal traits. Whereas research on ‘citizenship by investment’ and golden passports indicates that *individual wealth* can facilitate access (Surak, 2023), ours shows that *aggregated national wealth* can override individual economic/personal characteristics and predetermine applicants’ fates.

From the perspective of potential applicants from around the world, our findings raise important questions about inequality, injustice, and exclusion. Would-be travelers from the Global South often describe the visa application process as an expensive, nerve-racking, and humiliating experience (e.g., Barnard, 2015; Chatterjee, 2022; Owusu-Gyamfi, 2024). Based on comprehensive, systematic data, our analysis confirms for the first time that it is also a statistically more time-consuming and difficult process for applicants from less affluent countries. Our findings thus align with theories that describe time as being weaponized by state authorities to create synchronies, precarities, and deterrents (Andersson, 2014; Cohen, 2018; Landau, 2019; McNevin and Missbach, 2018)—even if we were not able to analyze the degree to which this pattern is intentional. A political rationale in which long wait times are created deliberately as a ‘cooling out’ (Goffman, 1952) mechanism to discourage applicants from less prosperous countries certainly seems plausible, even though it is hard to prove empirically. But regardless of whether the unequal wait times are an accidental byproduct of over-strain and an inability to keep up with high demand or an intentional strategy to keep unwanted travelers out, from a cosmopolitan global justice perspective it is an unsatisfactory situation. In principle, everyone should at least have an equal right to be heard and to be assessed (if not to be freely mobile). From utilitarian or materialist perspectives, overly long visa appointment wait times can lead to lost opportunities, reduced productivity and other undesirable outcomes. For destination countries like Germany, these patterns may be economically irrational as they hinder the arrival of *all* applicants from a specific country/region, including highly qualified and sought-after ones. It would thus be economically reasonable to adjust administrative resources based on demand, creating balanced wait times globally. Yet the discrepancies we identify suggest Germany may be either unable or unwilling to do so.

Our findings speak to two current debates in Europe. The first is about legal pathways to migration. To lower the death toll in the Mediterranean and other dangerous channels, Germany (and other EU member states) should ensure equal access to visa appointments, regardless of the country of origin. Second, given the necessity to attract high-skilled labor from abroad, since it needs 1.5 million immigrants per year to keep its labor force stable (Deconinck, 2023), Germany has a clear interest in making the visa process faster and more transparent. Providing up-to-date information about wait times would help create such transparency and increase predictability and convenience for applicants. The world's most popular country for immigrants, the U.S.—while certainly not a role model in every aspect of its treatment of immigrants (e.g., Massey, 2020; Vega, 2017)—demonstrates what such a transparent system can look like. Our computational research design

indicates that such information can also be created for Germany.

Our study suffers from at least three limitations. First, since we lacked information on staff numbers at missions and the underlying decision-making processes, we cannot determine whether the inequalities and ‘time penalty for the poor’ that we detect are primarily demand driven or reflect supply-side gatekeeping. A second limitation is the lack of information about applicants’ nationality, which may affect the workload and appointment policies, especially in Global North missions dealing with secondary movements within the Schengen area. Finally, for ethical reasons, we excluded the form-based system (21.6 percent of cases) and private service providers (7.3 percent); thus we did not cover all types of visas and embassies/consulates/countries (cf. the Supplementary Material). These omissions are not random, since missions often switch to the form-based system if they experience high levels of demand (Deutscher Bundestag, 2024). The embassies/consulates could take a more transparent approach to fix this issue, since they have direct knowledge of average wait times and could easily address it without ethical or major technical obstacles. A more proactive information-sharing approach would increase transparency. In turn, shorter and fairer wait times would make Germany a more attractive destination for all mobile populations—from holiday goers to sought-after professionals—and reduce global inequality and injustice.

## 7. Conclusion and policy recommendations

Our analysis provides the first large-scale empirical evidence of systemic inequalities in visa appointment wait times across German embassies and consulates worldwide. The disparities we identify underscore how visa systems function as instruments of mobility control that disproportionately disadvantage applicants from poorer countries, reflecting global economic inequalities. These patterns align with critical theories of chronopolitics, in which time is used as a tool of control, deterrence and exclusion, constituting temporal borders. Our study advances this literature by extending this framework to examine the impact on a wider population of travelers and applying a quantitative and global approach. Our results have broad implications. From a global justice perspective, unequal access to visa appointments undermines procedural fairness and moral equality. From an economic standpoint, inefficient and opaque systems hinder talent mobility, affect productivity, and compromise the strategic interest in attracting skilled migrants (a point that applies not just to Germany but to many other nations with an aging population and lack of skilled workers). Based on our findings, we propose the following policy recommendations for the German Foreign Office that may also be useful for other destination countries with high and unequal visa wait times:

1. *A transparency initiative.* A digital platform should be established that allows transparent public access to up-to-date data on current and past appointment and processing wait times for all diplomatic missions and visa types, similar to the U.S. Department of State's publicly available system. This would be a fitting addition to Germany's new online visa portal. Improved transparency would allow applicants to plan their actions accordingly and could improve satisfaction rates among (potential) arrivals, resulting in reputational gains.

2. *A fairness & efficiency initiative.* Consular resources should be allocated based on demand and regional needs. A unified automated identification system could be introduced to identify missions with a growing demand and the need for additional resources. Overall, shorter and more equitable wait times would improve access to legal migration pathways, help reduce reliance on dangerous routes, and support labor market needs.

Future research could extend our framework to other countries or regions. Comparative studies could help identify the uniqueness or indicative nature of the chronopolitical patterns we detect. To explore the possible ‘cooling out’ mechanism, future research would require
incorporating staffing data for diplomatic missions. These new avenues
would deepen our understanding of how mobility regimes and visa
systems (re)produce global inequalities and how they might be
reformed, which is essential not only for procedural fairness but also for
advancing a more just and inclusive system of international mobility.

CRediT authorship contribution statement

**Emanuel Deutschmann:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Investigation, Formal analysis, Data curation, Conceptualization. **Lorenzo Gabrielli:** Validation, Software, Methodology, Data curation. **Alexandra Orlova:** Writing – review & editing, Validation, Project administration, Investigation, Data curation. **Niklas Harder:** Writing – review & editing, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization. **Ettore Recchi:** Writing – review & editing, Validation, Conceptualization.

Declaration of competing interest

We have nothing to declare.

Acknowledgements

We are grateful for helpful comments made by participants of the
Tuesday meeting at the Institute for Analytical Sociology at Linköping
University, the MZES Colloquium at the University of Mannheim, the
FB2 Colloquium at Germany's Federal Institute for Population Research,
and the Lunchtime Seminar of the Research Group Welfare, Inequalities
and Mobilities at the University of Copenhagen, where earlier versions
of this work were presented. Selcan Mutgan, Anastasia Menshikova,
Heiko Rüger, Mary A. Shiraef were among the people who provided
helpful suggestions and support. Finally, we thank Luca Bernasconi for
preparing some of the data used in the regression models, Kelley Friel for
first-class proof-reading and language-polishing, and three anonymous
reviewers at *Political Geography* for excellent advice that helped improve
the paper a lot. Emanuel Deutschmann, Lorenzo Gabrielli, and Ettore
Recchi acknowledge support from the Global Mobilities Project (GMP)
at the Migration Policy Centre (MPC) of the European University Insti-
tute (EUI). Alexandra Orlova and Niklas Harder acknowledge funding
from the German Federal Ministry for Education, Family Affairs, Senior
Citizens, Women and Youth.

Appendix

<figure><img src="image_8.png" /><figcaption>Fig. A1. Correlation heatmap, excluding countries with comprehensive visa freedom.</figcaption></figure>

Supplementary material

Supplementary material to this article can be found online at https://doi.org/10.1016/j.polgeo.2025.103440.
## Data availability

The German Visa Appointment Dataset and replication files are available open access here: https://zenodo.org/records/17484492.

## References

Anderson, B. (2025). Introduction: Rethinking migration-challenging borders, citizenship and race. In B. Anderson (Ed.), *Rethinking migration* (pp. 1–20). Bristol: Bristol University Press.

Anderson, R. (2014). Time and the migrant other: European border controls and the temporal economics of illegality. *American Anthropologist*, *116*(4), 795–809.

Auswärtiges Amt. (2023). Statistik zu erteilten Visa. At: https://www.auswaertiges-amt.de/de/service/visa-und-aufenthalt/-/2231558. (Accessed 20 March 2024).

Auswärtiges Amt. (2024a). Übersicht zur Visumpflicht bzw. -freitrei bei Einreise in die Bundesrepublik Deutschland. 1.1.2024. At: https://www.auswaertiges-amt.de/dc/service/visa-und-aufenthalt/staatenliste-zur-visumpflicht/207820. (Accessed 30 October 2024).

Auswärtiges Amt. (2024b). Terminvergabesystem des Auswärtigen Amts — Eriwan. http://service2.diplo WP/2013/01/31/how-long-is-the-immigration-line-as-long-as-24-years/. (Accessed 29 March 2024).

Axelsson, L. (2022). Border timespaces: Understanding the regulation of international mobility and migration. *Geografiska Annaler: Series B, Human Geography*, *104*(1), 59–74.

Barnard, H. (2015). The elephant that didn't get a Visa to be in the room. *AIB Insights*, *15*(1), 12–14.

Beine, M., Bertoli, S., & Fernandes-Huertas Moraga, J. (2014). *A practitioners' guide to gravity models of international migration*. FERDI Policy Brief. No. B102.

Bendixsen, S., & Eriksen, T. H. (2018). Time and the other: Waiting and hope among irregular migrants. In K. Janeja, & A. Bandak (Eds.), *Ethnographies of waiting: Doubt, hope and uncertainty* (pp. 87–112). Abingdon: Routledge, 2018.

Bier, D. J. (2019). Immigration wait times from quotas have doubled: Green card backlogs are long, growing, and inequitable. In *Policy analysis, 873*Washington, DC: Cato Institute. At: https://www.cato.org/publications/policy-analysis/immigration-wait-times-quotas-havedoubled-green-card-backlogs-are-long. (Accessed 28 March 2024).

Bier, D. J. (2022). Record nonimmigrant visa waits Even as ¼ of consulates remain partially closed. CATO Institute. https://www.cato.org/blog/record-nonimmigrant-visa-waits-even-1/4-consulates-remain-partially-closed. (Accessed 27 February 2024).

Bundesgesetzblatt. D. (2022). Ukraine-Aufenthaltserlaubnis-Fortgeltungsverordnung. https://www.recht.bund.de/bgbl/1/2023/334/VO.html. (Accessed 20 September 2024).

Caglar, A. (2016). Still 'Migrants' after all those years: Foundational mobilities, temporal frames and emplacement. *Journal of Ethnic and Migration Studies*, *42*(6), 952–969.

Chatterjee, D. (2022). How international conferences fail scholars from the global South. *The Medium*. At: https://medium.com/international-affairs-blog/how-international-al-conferences-fail-scholars-from-the-global-south-fbde14e5d1f1. (Accessed 29 March 2024).

Clemens, M. A. (2014). Does development reduce migration? In R. E. B. Lucas (Ed.), *International handbook on migration and economic development* (pp. 152–185). Cheltenham: Edward Elgar.

Cohen, E. (2018). *The political value of time*. Cambridge: Cambridge University Press.

Czaika, M., De Haas, H., & Villares-Varela, M. (2018). The global evolution of travel visa regimes. *Population and Development Review*, *44*(3), 589–622.

Czaika, M., & Neumayer, E. (2017). Visa restrictions and economic globalisation. *Applied Geography*, *84*, 75–82.

Daniel, A. (2022). *Why visas are a hot topic right now at the international AIDS conference*. NPR. At: https://www.npr.org/sections/goatsandsoda/2022/07/13/11111 35757/the-visa-hurdle-why-conference-applicants-from-the-global-south-cant-alwa ys-clea. (Accessed 29 March 2024).

Deconinck, C. (2023). Economic expert says Germany needs 1.5 million immigrants a year. Brussels Signal. At: https://brusselssignal.eu/2023/07/economic-expert-says-germany-needs-1-5-million-immigrants-a-year/. (Accessed 1 November 2024).

Destatis. (2024). Außenhandel. Ranking of Germany's trading partners in foreign trade (preliminary results). 2023. At: https://www destatis.de/EN/Themes/Economy/ Foreign-Trade/Tables/order-rank-germany-trading-partners.html. (Accessed 3 May 2024).

Deutscher Bundestag. (2021). Antwort der Bundesregierung auf die Kleine Anfrage der Abgeordneten Ulla Jelpke, Dr. André Hahn, Gökay Akbulut, weiterer Abgeordneter und der Fraktion DIE LINKE. *Drucksache*, *19/29430*.

Deutscher Bundestag. (2024). Experten: Visa-Probleme behindern Austausch in der Wissenschaft. At: https://www.bundestag.de/dokumente/textarchiv/2024/kw08- pa-bildung-internationalisierung-989318. (Accessed 30 October 2024).

Deutschmann, E. (2022). *Mapping the Transnational World: How We Move and Communicate Across Borders, and Why It Matters*. Princeton: Princeton University Press.

Deutschmann, E., Gabrielli, L., & Recchi, E. (2023). Roads, rails, and checkpoints: Assessing the permeability of nation-state borders worldwide. *World Development*, *164*, 106175.

Deutschmann, E., & Recchi, E. (2022). Europeanization via Transnational Mobility and Migration. In S. Büttner, M. Eigmüller, & S. Worschech (Eds.), *Sociology of Europeanization* (pp. 283–306). Berlin: De: Gruyter Oldenburg.

Deutschmann, E., Recchi, E., & Vespe, M. (2022). Assessing Transnational Human Mobility on a Global Scale. In S. Pöttschke, & S. Rinken (Eds.), *Migration Research in a Digitized World: Using Innovative Technology to Tackle Methodological Challenges* (pp. 169–192). Cham: Springer.

Eurostat. (2024). *Residence permits - Statistics on first permits issued during the year*. At: http://ec.europa.eu/eurostat/statistics-explained/index.php?title=Residence-permits_statistics_on_first-permits_issued_during_the_year&oldid=611391 (Accessed 2 March 2024)

FitzGerald, J. (2025). Why are these 12 countries on Trump's travel-ban list? BBC. https://www.bbc.com/news/articles/cz6329yyvdwo. (Accessed 25 August 2025).

Goffman, E. (1952). On cooling the mark out: Some aspects of adaptation to failure. *Psychiatry*, *15*(4), 451–463.

Griffiths, M. (2024). Epilogue: 'claiming time'. *Journal of International Migration and Integration*, *25*(3), 1231–1247.

Hage, G. (2009). *Waiting*. Melbourne: Melbourne Univ. Publishing.

Hainmueller, J., Hangartner, D., & Lawrence, D. (2016). When lives are put on hold: Lengthy asylum processes decrease employment among refugees. *Science Advances*, *2*(8), Article e1600432.

Hobolt, M. (2014). Researching mobility barriers: The European visa database. *Journal of Ethnic and Migration Studies*, *40*(3), 424–435.

IOM. (2021). *World Migration Report 2022*. Geneva: International Organization for Migration.

Jacobs, E. (2022). Work visas and return migration: How migration policy shapes global talent. *Journal of Ethnic and Migration Studies*, *48*(7), 1647–1668.

Jacobsen, C., Karlsen, M.-A., & Khosravi, S. (Eds.) (2020). In *Waiting and the temporalities of irregular migration*. Abingdon: Routledge.

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The worldwide governance indicators: Methodology and analytical issues. *World Bank Policy Research Working Paper No.* 5430.

Khimm, S. (2013). How long is the immigration 'line'? As long as 24 years. *Washington Post*. At: https://www.washingtonpost.com/news/wonk/wp/2013/01/31/how-long-is-the-immigration-line-as-long-as-24-years/. (Accessed 29 March 2024).

Khosravi, S. (2014). Waiting. In B. Anderson, & M. Keith (Eds.), *Migration: A COMPAS anthropology*. Oxford: COMPAS.

Kirstoglou, E., & Simpson, B. (2020). Introduction: The time of anthropology: Studies of contemporary chronopolitics and chronocracy. In E. Kirstoglou, & B. Simpson (Eds.), *The time of anthropology: Studies of contemporary chronopolitics* (pp. 1–30). London: Taylor & Francis.

Klinke, I. (2013). Chronopolitics: A conceptual matrix. *Progress in Human Geography*, *37*(5), 673–690.

Lanati, M., & Thiele, R. (2024). The link between economic growth and emigration from developing countries: Does migrants' skill composition matter? *Kyklos*, *77*(1), 213–248.

Laube, L. (2019). A chronotope of containment development: Europe's migrant crisis and Africa's reterritorialisation. *Antipode*, *51*, 169–186.

Laube, L., & Heidler, R. (2016). Democratic centre, autocratic isolates: The changing field of international visa policies. *Journal of Ethnic and Migration Studies*, *42*(15), 2548–2572.

Lawson, R. A., & Roychoudhury, S. (2016). Do travel visa requirements impede tourist travel? *Journal of Economics and Finance*, *40*(4), 817–828.

Lewis, J., & Swannell, M. (2018). The macroeconomic determinants of migration. *Bank of England Working Paper No. 729*.

Liebig, T., & Huerta del Carmen, M. (2024). *Der Weg nach Deutschland: Ergebnisse der Schlussbefragung einer Längsschnittstudie von an Deutschland interessierten Fachkräften aus dem Ausland (Oktober-Dezember 2023)*. Paris/Berlin: OECD.

Massey, D. S. (2020). The real crisis at the Mexico-US border: A humanitarian and not an immigration emergency. *Sociological Forum*, *35*(3), 787–805.

Mau, S. (2010). Mobility citizenship, inequality, and the liberal state: The case of visa policies. *International Political Sociology*, *4*(4), 339–361.

Mau, S. (2023). *Sorting machines: The reinvention of the border in the 21st century*. Cambridge: Polity Press.

Mau, S., Gülzau, F., Laube, L., & Zaun, N. (2015). The global mobility divide: How visa policies have evolved over time. *Journal of Ethnic and Migration Studies*, *41*(8), 1192–1213.

McNevin, A., & Missbach, A. (2018). Luxury limbo: Temporal techniques of border control and the humanitarianisation of waiting. *International Journal of Migration and Border Studies*, *4*(1/2), 12–34.

Melitz, J., & Toubal, F. (2014). Native language, spoken language, translation and trade. *Journal of International Economics*, *93*(2), 351–363.

Owusu-Gyamfi, S. (2024). 'Exhausted and insulted': How harsh visa-application policies are hobbling global research. *Nature*, *627*, 705–705.

Özdemir, Z., & Ayata, A. G. (2018). Dynamics of exclusion and everyday bordering through Schengen visas. *Political Geography*, *66*, 180–188.

Recchi, E., Deutschmann, E., Gabrielli, L., & Kholmatova, N. (2021). The Global Visa Cost Divide: How and Why the Price for Travel Permits Varies Worldwide. *Political Geography*, *86*, 102350.

Reuters. (2023). *Visa justice: Border rules block poorer nations from world summits*. At: http://www.reuters.com/article/idUSL8N38C3RT/ (Accessed 29 March 2024).

RKI. (2024). Abwassersurveillance zu SARS-CoV-2. AMELAG-Wochenbericht vom 27.03.2024. At: https://www.rki.de/DE/Content/Institut/OrgEinheiten/Abt3/ FG32/Abwassersurveillance/Bericht_Abwassersurveillance.html?_blob=publicati onFile (Accessed 29 March 2024).

Rosa, H. (2013). *Social acceleration: A new theory of modernity*. New York: Columbia University Press.

Saeed, A. (2024). *Issues in visa application process for Germany - A case study from Pakistan*. Europa-Universität Flensburg. Unpublished M.A. thesis.

Surak, K. (2023). *The golden passport: Global mobility for millionaires*. Cambridge, MA: Harvard University Press.
Tazzioli, M. (2018). The temporal borders of asylum. Temporality of control in the EU border regime. *Political Geography*, 64, 13–22.

The Economic Times. (2024). Tourist visa appointment wait times down by 75%, US processes record number of visas to Indians. At: https://economictimes.indiatimes.com/nri/visit/tourist-visa-appointment-wait-times-down-by-75-us-processes-record-breaking-visas-to-indians/articleshow/107232727.cms?from=mdr (Accessed 27 February 2024).

The Federal Government. (2024). Do I need a visa? At: https://www.make-it-in-germany.com/en/visa-residence/procedure/do-i-need-visa. (Accessed 30 October 2024).

Tsagarousianou, R. (2023). Time and mobility/immobility: The chronopolitics of mobility and the temporalities of suffering and hope in situations of encampment. *Mobilities*, 18(2), 267–281.

Ullah, A. A., Chattoraj, D., & Kathy, A. A. (2024). Mobility in a globalised world: How countries regulate mobility with passports and visas. *Journal of Public Affairs*, 24(3), Article e2932.

UNWTO. (2023). International Tourism – 2023 starts on a strong note with the Middle East recovering 2019 levels in the first quarter. *World Tourism Barometer*, 21(2), 1–9.

U.S. Department of State. (2024a). Global Visa wait times. At: https://travel.state.gov/content/travel/en/us-visas/visa-information-resources/global-visa-wait-times.html. (Accessed 27 February 2024).

U.S. Department of State. (2024b). Visa appointment wait times. At: https://travel.state.gov/content/travel/en/us-visas/visa-information-resources/wait-times.html. (Accessed 27 February 2024).

USCIS (U.S. Citizenship and Immigration Services). (2024). Uniting for Ukraine. At: https://www.uscis.gov/ukraine (Accessed 22 April 2024).

van Houtum, H. (2010). Human blacklisting: The global apartheid of the EU's external border regime. *Environment and Planning D*, 28(6), 957–976.

van Houtum, H. (2017). *B/ordering space*. Routledge.

Vega, I. I. (2017). Empathy, morality, and criminality: The legitimation narratives of U.S. Border Patrol agents. *Journal of Ethnic and Migration Studies*, 44(15), 2544–2561.

VisaGrader.com. (2024a). Current US Visa appointment wait times in Germany. At: https://visagrader.com/us-visa-appointment-wait-times/germany-deu. (Accessed 27 February 2024).

VisaGrader.com. (2024b). US Visa appointment wait times Worldwide by Country at their US Embassy, US Consulates. At: https://visagrader.com/us-visa-appointment-wait-times/worldwide-current. (Accessed 27 February 2024).

World Bank. (2024a). GDP per capita, PPP (current international $). Last updated 21.2.2024. At: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD. (Accessed 21 February 2024).

World Bank. (2024b). Political stability and absence of Violence/Terrorism: Estimate. last updated 21.2.2024. At: https://data.worldbank.org/indicator/PV.EST. (Accessed 21 February 2024).

World Bank. (2024c). Population, total. At: https://data.worldbank.org/indicator/SP.POP.TOTL. (Accessed 20 March 2024).

World Bank. (2024d). *Individuals using the internet (% of population)*. At: https://data.worldbank.org/indicator/IT.NET.USER.ZS. (Accessed 30 October 2024).

Zelinsky, W. (1971). The hypothesis of the mobility transition. *Geographical Review*, 61 (2), 219–249.