washinvestments

Dataset on multilateral development bank (MDB) investment in water supply and sanitation associated with the paper "Multilateral development banks investment behaviour in water and sanitation: Findings and lessons from 60 years of investment projects in Africa and Asia" (Heidler et al. 2023).

https://github.com/openwashdata/washinvestments

Keywords

Repository

Dataset on multilateral development bank (MDB) investment in water supply and sanitation associated with the paper "Multilateral development banks investment behaviour in water and sanitation: Findings and lessons from 60 years of investment projects in Africa and Asia" (Heidler et al. 2023).

README.Rmd

---
output: github_document
always_allow_html: true
editor_options: 
  markdown: 
    wrap: 72
  chunk_output_type: console
bibliography: references.bib
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  message = FALSE,
  warning = FALSE,
  fig.retina = 2,
  fig.align = 'center'
)

library(tidyverse)
library(epoxy)
library(washinvestments)
```

# washinvestments


[![License: CC BY
4.0](https://img.shields.io/badge/License-CC_BY_4.0-orange.svg)](https://creativecommons.org/licenses/by/4.0/)
[![R-CMD-check](https://github.com/openwashdata/washinvestments/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/openwashdata/washinvestments/actions/workflows/R-CMD-check.yaml)
[![DOI](https://zenodo.org/badge/770378167.svg)](https://zenodo.org/doi/10.5281/zenodo.11203434)


The goal of washinvestments is to provide users documentation on the data set published alongside the article "Multilateral
development banks investment behaviour in water and sanitation: Findings
and lessons from 60 years of investment projects in Africa and Asia"
[@heidler2023multilateral].

## Installation

You can install the development version of washinvestments from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("openwashdata/washinvestments")
```

Alternatively, you can download the dataset as a CSV or XLSX file from
the table below.

```{r, echo=FALSE, message=FALSE, warning=FALSE}

extdata_path <- "https://github.com/openwashdata/washinvestments/raw/main/inst/extdata/"

read_csv("data-raw/dictionary.csv") |> 
  distinct(file_name) |> 
  dplyr::mutate(file_name = str_remove(file_name, ".rda")) |> 
  dplyr::rename(dataset = file_name) |> 
  mutate(
    CSV = paste0("[Download CSV](", extdata_path, dataset, ".csv)"),
    XLSX = paste0("[Download XLSX](", extdata_path, dataset, "_utf8.xlsx)")
  ) |> 
  knitr::kable()

```

## Project goal

Multilateral development banks (MDBs) significantly influence water and
sanitation infrastructure development. However, data on their investments is
dispersed and hard to compare. This project presents a new data set
compiled by drawing on 3,639 water and sanitation projects that aims at
assessing "territorial trends, technology choices, distribution of
financial burdens, and reforms to institutional arrangements to analyze
the investment behaviour of the World Bank, ADB, and AfDB in water
supply and sanitation from their first operations in the 1960s until
2020 and distil how they responded to trends in urbanization and the
policy debates about governing access to basic services."
[@heidler2023multilateral]

## Data

The data set includes information about water and sanitation projects
conducted in Africa and Asia between 1960 and 2020. The package provides
access to one data set.

```{r, echo = TRUE}
library(washinvestments)
```

The `washinvestments` data set has `r ncol(washinvestments)` variables
and `r nrow(washinvestments)` observations. For an overview of the
variable names, see the following table.

```{r, eval=FALSE}
washinvestments
```

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "washinvestments.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable()
```

## Example: Investment trends in Africa and Asia over 60 years

Here is a basic example illustrating the analysis of financing trends in the WASH sector over 60 years, with a focus on Africa and Asia as outlined in the paper. The first plot displays the aggregated number of investment projects in the WASH sector from 1960 to 2020, grouped in 10-year intervals. It is notable that a higher number of projects in Asia have secured financing, with a significant increase observed since around the 2000s. Moreover, there is a discernible upward trend in the number of investment projects over the years. Subsequently, we explore investment trends within Asia and Africa separately. The second plot illustrates investment patterns in Asia, examining various regions within the continent. Likewise, the third plot focuses on investment trends in Africa, with a breakdown by different regions.

![](man/figures/africa_asia_10.png)
![](man/figures/asia.png)
![](man/figures/africa.png)

```{r, eval=FALSE, message=FALSE, warning=FALSE, include=TRUE, paged.print=FALSE}
library(washinvestments)
library(ggplot2)
library(countrycode)
library(dplyr)
----------------------------------------------------------------------------------------------------------------------------
# Add a new column for continent information as well as a new column for 10-year intervals
washinvestments <- washinvestments |> 
  mutate(continent = countrycode(iso_country_code, "iso3c", "continent"),
         year_interval = cut(year, breaks = seq(1960, 2020, by = 10),
          labels = seq(1960, 2010, by = 10)))

# Filter the data for Africa and Asia
washinvestments_asia_africa <- washinvestments |> 
  filter(continent %in% c("Africa", "Asia"))

# Count the number of projects per continent and arrange it in descending order
continent_counts <- washinvestments_asia_africa |> 
  group_by(continent, year_interval) |> 
  summarise(count = n()) |> 
  arrange(desc(year_interval))

# Encode the continent column as factor with chosen levels for plotting
washinvestments_asia_africa$continent <- factor(washinvestments_asia_africa$continent, levels = continent_counts$continent)
----------------------------------------------------------------------------------------------------------------------------
# Filter the data set for Asia
washinvestments_asia <- washinvestments |>
  filter(region %in% c("Central Asia", "Eastern Asia", "Polynesia", "South-eastern Asia", "Southern Asia", "Western Asia"))

# Count the number of projects per region and arrange it in descending order
asia_counts <- washinvestments_asia |> 
  group_by(region) |> 
  summarise(count = n()) |> 
  arrange(desc(count))

# Encode the region column as factor with chosen levels for plotting
washinvestments_asia$region <- factor(washinvestments_asia$region, levels = asia_counts$region)
----------------------------------------------------------------------------------------------------------------------------
# Filter the data set for Africa
washinvestments_africa <- washinvestments |>
  filter(region %in% c("Northern Africa", "Eastern Africa", "Middle Africa", "Western Africa", "Southern Africa"))

# Count the number of projects per region and arrange it in descending order
africa_counts <- washinvestments_africa |> 
  group_by(region) |> 
  summarise(count = n()) |> 
  arrange(desc(count))

# Encode the region column as factor with chosen levels for plotting
washinvestments_africa$region <- factor(washinvestments_africa$region, levels = africa_counts$region)
----------------------------------------------------------------------------------------------------------------------------
# Create bar plots of investment trends
ggplot(continent_counts, aes(x = year_interval, y = count, fill = continent)) +
  geom_bar(stat = "identity", position = "dodge") +  # Position dodge for side-by-side bars
  labs(title = "Investment trends in Africa and Asia (aggregated every 10 years)",
       x = "Year interval",
       y = "Number of investment projects",
       fill = "Continent") +
  scale_fill_brewer(palette = "Set2") +
  theme(plot.title = element_text(hjust = 0.5, face = "bold", color = "#333333"))

ggplot(washinvestments_asia, aes(x = year, fill = region)) +
  geom_bar() +
  scale_x_discrete(breaks = seq(1960, 2020, 10)) +
  labs(title = "Investment trends in Asia",
       x = "Year",
       y = "Number of investment projects",
       fill = "Region") +
  scale_fill_brewer(palette = "Set2") +
  theme(plot.title = element_text(hjust = 0.5, face = "bold", color = "#333333"))

ggplot(washinvestments_africa, aes(x = year, fill = region)) +
  geom_bar() +
  scale_x_discrete(breaks = seq(1960, 2020, 10)) +
  labs(title = "Investment trends in Africa",
       x = "Year",
       y = "Number of investment projects",
       fill = "Region") +
  scale_fill_brewer(palette = "Set2") +
  theme(plot.title = element_text(hjust = 0.5, face = "bold", color = "#333333"))
```

## License

Data are available as
[CC-BY](https://github.com/openwashdata/washinvestments/LICENSE.md).

## Citation

To cite this package, please use:

```{r}
citation("washinvestments")
```

Owner

Citation (CITATION.cff)

# -----------------------------------------------------------
# CITATION file created with {cffr} R package, v1.0.0
# See also: https://docs.ropensci.org/cffr/
# -----------------------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "washinvestments" in publications use:'
type: software
license: CC-BY-4.0
title: 'washinvestments: Multilateral development banks investment behaviour in water
  and sanitation'
version: 0.0.1
doi: 10.5281/zenodo.11203434
abstract: Dataset on multilateral development bank (MDB) investment in water supply
  and sanitation between 1960 and 2020 compiled by drawing on on 3,639 water and sanitation
  projects to assess territorial trends, technology choices, distribution of financial
  burdens, and reforms to institutional arrangements.
authors:
- family-names: Götschmann
  given-names: Margaux
  email: margauxg@ethz.ch
  orcid: https://orcid.org/0009-0002-2567-3343
- family-names: Heidler
  given-names: Andri
  orcid: https://orcid.org/0000-0003-2138-4675
- family-names: Nesi
  given-names: Muhil
- family-names: Nikiema
  given-names: Josiane
  orcid: https://orcid.org/0000-0001-8802-0564
- family-names: Lüthi
  given-names: Christoph
date-released: '2024-05-16'
contact:
- family-names: Götschmann
  given-names: Margaux
  email: margauxg@ethz.ch
  orcid: https://orcid.org/0009-0002-2567-3343

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