ICT Infrastructure Subsidies Analysis

In Argentina, as in many other countries, there is a fund made up of contributions from companies in the ICT sector with the goal of bringing services to populations that have no access to them for various reasons. In this post, I participated in a panel where I talked about the history of this fund in Argentina, called the Universal Service Trust Fund (FFSU), its regulatory framework and the different programs it has today.

In this post, I will develop a small exploratory analysis of some of the data regarding the two most important programs of the FFSU: the Connectivity Program and the Vulnerable Neighborhoods Program. The analysis covers the years between 2020 and 2023, for which data is available. The data was extracted from the minutes of the meetings of the Board of Directors of the National Communications Entity (ENACOM), which are published in PDF on the agency’s website.

Connectivity Program

# Me conecto a la base de datos y leo la tabla del Programa Conectividad
# Connect to the database and read the Conectividad Program table

from dotenv import load_dotenv
from sqlalchemy import create_engine
import os
import pandas as pd

load_dotenv()

host = os.getenv("HOST")
port = os.getenv("PORT")
database = os.getenv("DBNAME")
user = os.getenv("USER")
password = os.getenv("PASSWD")

engine = create_engine(f"postgresql://{user}:{password}@{host}:{port}/{database}")

anr_prog_con = pd.read_sql_table(table_name="conectividad_aprob_georef", con=engine)

The Figure 1 shows the number of localities that were beneficiaries from ANRs from the Connectivity Program approved by province, between 2020 and 2023.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax = anr_prog_con.groupby("provincia_indec").size().sort_values(ascending=False).plot(kind='bar', figsize=(20,10), legend=False)

ax.set_xlabel("Provincia", fontsize=15)

Figure 1: Localidades beneficiarias de ANRs del Prog. Conectividad por provincia

The Figure 2 shows the number of localities that were beneficiaries of ANRs from the Connectivity Program by year.

anr_prog_con['anio'] = anr_prog_con['fecha'].apply(lambda x: x.strip().split(' ')[1] if len(x.split(' ')) > 1 else None)

fig, ax = plt.subplots()

anr_prog_con.groupby("anio").size().plot(ax=ax, kind='bar', figsize=(12,6), legend=False)

ax.set_xlabel("Año", fontsize=15)

Figure 2: Localidades beneficiarias de ANRs del Prog. Conectividad por año

The Figure 3 shows the number of localities that were beneficiairies from ANRs from the Connectivity Program by year and province.

import textwrap

anr_prog_con_prov_anio = anr_prog_con.groupby(['anio', 'provincia_indec']).size().unstack().fillna(0)

fig, ax = plt.subplots(4,1)

for i, anio in enumerate(anr_prog_con_prov_anio.index):
    anr_prog_con_prov_anio.loc[anio].sort_values(ascending=False).plot(ax=ax[i], kind='bar', figsize=(12,26), legend=False)
    ax[i].set_title(f"{anio}", fontsize=20)
    ax[i].set_xlabel("")
    ax[i].set_xticklabels([textwrap.fill(label.get_text(), 10) for label in ax[i].get_xticklabels()], rotation=45, fontsize=8, ha='right')  # Wrap labels

Figure 3: Localidades beneficiarias de ANRs del Prog. Conectividad por año y provincia

Vulnerable Neighborhoods Program

# Me conecto a la base de datos y leo la tabla del Programa Barrios Populares
# Connect to the database and read the Barrios Populares Program table

import geopandas as gpd

anr_prog_renabap = gpd.read_postgis("SELECT * FROM renabap_aprob", con=engine, geom_col="geometry")

The Figure 4 shows the number of neighborhoods that were beneficiaries of ANRs from the Vulnerable Neighborhoods Program by province, between 2021, the year in which the program began, and 2023.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax = anr_prog_renabap.groupby("provincia").size().sort_values(ascending=False).plot(kind='bar', figsize=(20,10), legend=False)

ax.set_xlabel("Provincia", fontsize=15)

Figure 4: Barrios beneficiados con ANRs del Prog. Barrios Populares por provincia

The Figure 5 shows the number of neighborhoods that were beneficiaries of ANRs from the Vulnerable Neighborhoods Program by year.

anr_prog_renabap['anio'] = anr_prog_renabap['fecha'].apply(lambda x: x.strip().split(' ')[1] if len(x.split(' ')) > 1 else None)

fig, ax = plt.subplots()

anr_prog_renabap.groupby("anio").size().plot(ax=ax, kind='bar', figsize=(12,6), legend=False)

ax.set_xlabel("Año", fontsize=15)

Figure 5: Barrios beneficiados con ANRs del Prog. Barrios Populares aprobados por año

The Figure 6 shows the number of neighborhoods that were beneficiaries of ANRs from the Vulnerable Neighborhoods Program by year and province.

import textwrap

anr_prog_renabap_prov_anio = anr_prog_renabap.groupby(['anio', 'provincia']).size().unstack().fillna(0)

fig, ax = plt.subplots(3,1)

for i, anio in enumerate(anr_prog_renabap_prov_anio.index):
    anr_prog_renabap_prov_anio.loc[anio].sort_values(ascending=False).plot(ax=ax[i], kind='bar', figsize=(12,26), legend=False)
    ax[i].set_title(f"{anio}", fontsize=20)
    ax[i].set_xlabel("")
    ax[i].set_xticklabels([textwrap.fill(label.get_text(), 10) for label in ax[i].get_xticklabels()], rotation=45, fontsize=8, ha='right')  # Wrap labels

Figure 6: Barrios beneficiados por ANRs del Prog. Barrios Populares aprobados por año y provincia

Beyond these graphs about the number of localities and vulnerable neighborhoods that were beneficiaries, it would be interesting to analyze the impact of these programs on the connectivity of the beneficiary populations. We will try to approach this issue in a future post.