Graph and download economic data for Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States (IRLTLT01USM156N) from Apr 1953 to May 2025 about long-term, 10-year, bonds, yield, government, interest rate, interest, rate, and USA.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

Indian Electoral Bonds Data

This dataset presents a detailed overview of the expenditures made by various companies through Indian Electoral Bonds since 2019. Electoral bonds, introduced by the Government of India in 2018, were aimed to be a mechanism for political funding. The Government of India had introduced them for transparency in the electoral process, however, the Supreme Court of India declared them unconstitutional on February 15, 2024, on the grounds of bypassing the right to information of the citizens of India.

Context: The dataset emerges amidst significant legal and regulatory developments surrounding electoral bonds in India. The Election Commission of India (ECI), in compliance with orders from the Supreme Court (SC), mandated the State Bank of India (SBI) to furnish details regarding electoral bonds. Following the SC's ruling declaring the 2018 scheme unconstitutional, the SBI was directed to disclose electoral bond data to the ECI by March 15, 2024. This dataset encapsulates the information disclosed by the ECI on its website.

Contents: 1. Purchaser Details: This section includes information on the purchasers of electoral bonds, encompassing names, dates of purchase, and denominations. 2. Political Party Beneficiaries: It delineates the political parties receiving donations through electoral bonds, featuring details such as dates, denominations, and parties encashing the bonds. 3. Financial Metrics: The dataset encompasses financial aspects of electoral bond transactions, offering insights into the amounts donated through bonds of varying denominations (₹1 lakh, ₹10 lakh, and ₹1 crore).

Significance: This dataset holds paramount importance in shedding light on the intersection of corporate interests and political funding in India. By revealing the identities of donors, their associated companies, and the recipient political parties, it facilitates a deeper understanding of political financing dynamics. It is to be noted that the data of the money spent by a party is not mapped to the data of the money received by the corresponding political party, the reason for this, as stated by the SBI, was that these data were maintained in two physical silos and the SBI needed time till June 30, 2024, to map them (one month after the National elections 2024 in India).

Implications: Stakeholders ranging from policymakers and researchers to journalists and civil society can leverage this dataset to scrutinize the flow of funds within the political landscape. It provides a foundation for assessing the influence of corporate entities on electoral processes and policy formulation.

Key Insights: The dataset showcases contributions from diverse corporate entities, including prominent names like Megha Engineering and Infrastructure, Future Gaming and Hotel Services (Lottery Martin), Sun Pharma, Lakshmi Mittal, Sula Wine, and DLF Commercial Developers. Furthermore, it highlights major political parties such as the BJP, Congress, AITMC, BRS, AIDMK, TDP, YSR Congress, AAP, SP, and JD(U) as recipients of electoral bond donations.

Usage: Researchers can utilize this dataset to conduct in-depth analyses on patterns of political funding, donor preferences, and the impact of electoral bonds on democratic processes. Additionally, journalists can employ this data to produce investigative reports, enhancing public awareness and accountability.

Ethical Considerations: While this dataset provides valuable insights, it raises ethical questions regarding the influence of corporate interests on democratic institutions. It underscores the importance of robust regulatory frameworks and transparency measures to safeguard the integrity of electoral processes.

Conclusion: This Kaggle dataset serves as a valuable resource for elucidating the intricacies of political financing in India. By fostering transparency and accountability, it empowers stakeholders to engage in informed discussions and advocate for reforms aimed at strengthening democratic governance.

Abstract copyright UK Data Service and data collection copyright owner.

The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material.
The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files.

Main Topics:

The files in this dataset relate to the datafiles held in the Leicester database in the directory /rjb/malet/*.*, excluding the derived datafiles, which are held in SN 3096. These data on seventeenth century French revenues and expenditure supplied by the Project Director, Professor Richard Bonney, draw upon J.R. Malet, Comptes rendus de l'administration des finances du royaume de France (London, 1789). For a discussion of this source in English, consult Bonney, R.J., 'Jean Roland Malet: historian of the finances of the French monarchy', French History, 5 (1991), 180-233.
File Information:
g068md01.* Malet's figures for royal expenditure in France, 1600-10
g068md02.* Malet's figures for royal expenditure in France, 1611-42
g068md03.* Malet's figures for royal expenditure in France, 1643-56
g068md04.* Malet's figures for royal expenditure in France, 1661-88
g068md05.* Malet's figures for royal expenditure in France, 1689-95
g068md06.* Malet's figures for receipts from the pays d'elections, 1600-10
g068md07.* Malet's figures for receipts from the pays d'elections, 1611-42
g068md08.* Malet's figures for receipts from the pays d'elections, 1643-56
g068md09.* Malet's figures for receipts from the pays d'elections, 1661-88
g068md10.* Malet's figures for receipts from the pays d'elections, 1661-88 (charges)
g068md11.* Malet's figures for receipts from the pays d'elections, 1661-88 (net to Treasury)
g068md12.* Malet's figures for receipts from the pays d'elections, 1689-95
g068md13.* Malet's figures for receipts from the pays d'elections, 1689-95 (charges)
g068md14.* Malet's figures for receipts from the pays d'elections, 1689-95 (net to Treasury)
g068md15.* Malet's figures for receipts from the pays d'etats, 1600-10
g068md16.* Malet's figures for receipts from the pays d'etats, 1611-42
g068md17.* Malet's figures for receipts from the pays d'etats, 1643-56
g068md18.* Malet's figures for receipts from the pays d'etats, 1661-88
g068md19.* Malet's figures for receipts from the pays d'etats, 1661-88 (charges)
g068md20.* Malet's figures for receipts from the pays d'etats, 1661-88 (net to Treasury)
g068md21.* Malet's figures for receipts from the pays d'etats, 1689-95
g068md22.* Malet's figures for receipts from the pays d'etats, 1689-95 (charges)
g068md23.* Malet's figures for receipts from the pays d'etats, 1689-95 (net to Treasury)
g068md24.* Malet's figures for dons gratuits from the pays d'etats, 1661-88
g068md25.* Malet's figures for dons gratuits from the pays d'etats, 1661-88 (charges)
g068md26.* Malet's figures for dons gratuits from the pays d'etats, 1661-88 (net to Treasury)
g068md27.* Malet's figures for dons gratuits from the pays d'etats, 1689-95
g068md28.* Malet's figures for dons gratuits from the pays d'etats, 1689-95 (charges)
g068md29.* Malet's figures for dons gratuits from the pays d'etats, 1689-95 (net to Treasury)
g068md30.* Malet's figures for receipts from the revenue farms, 1600-10
g068md31.* Malet's figures for receipts from the revenue farms, 1611-42
g068md32.* Malet's figures for receipts from the revenue farms, 1643-56
g068md33.* Malet's figures for receipts from the revenue...