# Data dictionary - Regional Inequality from Outer Space

Companion data for the post **"Regional Inequality from Outer Space: Predicting GDP from Nighttime Lights and Building Inequality Indices in Python"** (<https://carlos-mendez.org/post/python_kuznets_dmsp/>), a Python replication of Lessmann & Seidel (2017). Each dataset ships in two identical forms - **`.csv`** (plain) and **`.dta`** (Stata v118, Stata 14+, with embedded variable labels and value labels). This file documents every dataset and every variable: definition, construction, units, source, coverage, and computed statistics. It is generated by `make_stata.py` and supersedes the post's Appendix A.

> Values are byte-identical across the `.csv` and `.dta` forms; the `.dta` adds metadata only. Long-form Stata `notes` are attached by running `stata_codebook.do` in Stata (see **Using the data**).

## Provenance

- **Replicates:** Lessmann, C. & Seidel, A. (2017). "Regional inequality, convergence, and its determinants - A view from outer space." *European Economic Review* 92: 110-132.
- **Design:** predict regional GDP per capita from DMSP-OLS nighttime lights, build five population-weighted inequality indices, and estimate the spatial (regional) Kuznets curve and its determinants across up to 180 countries, 1992-2012.
- **Panel structure:** *region files* are keyed by region x year over a 1,504-region / 81-country training frame (1992-2010); *country files* are keyed by `Country_ISO` x year over 180 countries (1992-2012). The country-file inequality indices are built from the predicted regional incomes in the region files.

## Data sources

| Source | Provides | Reference / URL |
|---|---|---|
| NOAA/NGDC DMSP-OLS stable lights | Nighttime lights (DMSP-OLS stable lights v4, DN 0-63) | NOAA National Geophysical Data Center. https://www.ngdc.noaa.gov/eog/dmsp.html |
| Gennaioli et al. (2014) | Observed regional GDP per capita (training target) | Gennaioli, La Porta, Lopez-de-Silanes & Shleifer (2014), J. Economic Growth 19(3). |
| World Bank WDI | National accounts, determinants (GDP, trade, FDI, rents, etc.) | World Bank, World Development Indicators. https://databank.worldbank.org/source/world-development-indicators |
| GADM (Global Administrative Areas) | Administrative boundaries, region names, areas, centroids | GADM database of Global Administrative Areas. https://gadm.org |
| GPW v3 (CIESIN) | Gridded population (region and country totals) | CIESIN, Gridded Population of the World v3. https://sedac.ciesin.columbia.edu |
| Polity IV (Center for Systemic Peace) | Democracy-autocracy score (Polity2) | Center for Systemic Peace, Polity IV project. https://www.systemicpeace.org/inscrdata.html |
| GREG (Weidmann et al. 2010) + NOAA/NGDC | Ethnic homelands for the ethnic-inequality light Gini | Weidmann, Rod & Cederman (2010), J. Peace Research 47(4). |
| Lessmann & Seidel (2017) | Original study replicated here; interpersonal Gini (Giniall) | Lessmann & Seidel (2017), 'Regional inequality, convergence, and its determinants - A view from outer space', European Economic Review 92: 110-132. |

## Dataset links (GitHub)

Every dataset is available in both formats - identical data; the `.dta` adds embedded variable labels and value labels. Use a **raw** link to load or download a file in code; use **view** to browse it on GitHub.

**Stata `.dta`**

| File | View on GitHub | Raw (load / download) |
|---|---|---|
| `Prediction_Data.dta` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Prediction_Data.dta) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Prediction_Data.dta) |
| `Table_2_data.dta` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Table_2_data.dta) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Table_2_data.dta) |
| `Table_3_data.dta` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Table_3_data.dta) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Table_3_data.dta) |
| `Table_4_data.dta` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Table_4_data.dta) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Table_4_data.dta) |
| `Table_B4_data.dta` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Table_B4_data.dta) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Table_B4_data.dta) |
| `Figure_5_data.dta` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Figure_5_data.dta) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Figure_5_data.dta) |

**CSV `.csv`**

| File | View on GitHub | Raw (load / download) |
|---|---|---|
| `Prediction_Data.csv` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Prediction_Data.csv) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Prediction_Data.csv) |
| `Table_2_data.csv` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Table_2_data.csv) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Table_2_data.csv) |
| `Table_3_data.csv` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Table_3_data.csv) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Table_3_data.csv) |
| `Table_4_data.csv` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Table_4_data.csv) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Table_4_data.csv) |
| `Table_B4_data.csv` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Table_B4_data.csv) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Table_B4_data.csv) |
| `Figure_5_data.csv` | [view](https://github.com/cmg777/starter-academic-v501/blob/master/content/post/python_kuznets_dmsp/data/Figure_5_data.csv) | [raw](https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/Figure_5_data.csv) |

## Using the data

Each snippet loads straight from GitHub (the **raw** base above) - no manual download needed, except `pyreadstat`, which reads local files only. Swap the file name to load any of the six datasets.

```stata
* Stata 14+ : `use` reads an http(s) URL directly
global BASE "https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/"
use "${BASE}Table_3_data.dta", clear
describe        // variable + value labels
notes           // long-form documentation (after running stata_codebook.do)
* one-time, in a local clone: attach the per-variable notes to every .dta
do stata_codebook.do
```

```python
# Python : pandas reads a .dta URL directly (values + variable labels)
import pandas as pd
BASE = "https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/"
df = pd.read_stata(BASE + "Table_3_data.dta")

# load all six datasets at once
files = ["Prediction_Data", "Table_2_data", "Table_3_data",
         "Table_4_data", "Table_B4_data", "Figure_5_data"]
data = {f: pd.read_stata(BASE + f + '.dta') for f in files}

# pyreadstat exposes the richest metadata but reads LOCAL files -> download first
import pyreadstat, urllib.request
urllib.request.urlretrieve(BASE + "Table_3_data.dta", "Table_3_data.dta")
df, meta = pyreadstat.read_dta("Table_3_data.dta")
# meta.column_labels, meta.variable_value_labels, meta.file_label
```

```r
# R : haven::read_dta auto-downloads an http(s) URL
library(haven)
BASE <- "https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/python_kuznets_dmsp/data/"
df <- read_dta(paste0(BASE, "Table_3_data.dta"))   # labels via attr(df$var, "label")
```

To regenerate every `.dta`, this README, and `stata_codebook.do` from the CSVs (in a local clone):

```bash
python make_stata.py
```

## The six datasets

| File | Grain | Rows x Cols | Years | Coverage | Purpose |
|---|---|---|---|---|---|
| `Prediction_Data` | region-year | 5,258 x 30 | 1992-2010 | 1,504 regions in 81 countries | Train the light->income prediction model (Table 1). |
| `Table_2_data` | region-year | 5,258 x 8 | 1992-2010 | same 1,504-region training frame | Validate the inequality indices (Table 2). |
| `Table_3_data` | country-year | 3,675 x 9 | 1992-2012 | 180 countries | Kuznets curve: GDP + five indices (Table 3). |
| `Table_4_data` | country-year | 3,675 x 17 | 1992-2012 | 180 countries (determinants sparser) | Determinants of regional inequality (Table 4). |
| `Table_B4_data` | region-year | 5,258 x 14 | 1992-2010 | 1,504 regions in 81 countries | Conley spatial-HAC standard errors (+ lat/lon). |
| `Figure_5_data` | country-year | 3,675 x 5 | 1992-2012 | 180 countries (Giniall: 153 ctry) | Regional vs interpersonal inequality (Figure 5). |

## Cross-file variable index

Which file each variable appears in (● = present).

| Variable | Prediction_Data | Table_2_data | Table_3_data | Table_4_data | Table_B4_data | Figure_5_data |
|---|---|---|---|---|---|---|
| `Aid` |  |  |  | ● |  |  |
| `Arable_land` |  |  |  | ● |  |  |
| `COVW_pred_GDP_pc` |  |  | ● |  |  |  |
| `Country_ISO` | ● | ● | ● | ● | ● | ● |
| `Country_NAME` | ● |  | ● | ● |  | ● |
| `FDI_share_of_GDP` |  |  |  | ● |  |  |
| `GDP_pc_Country` |  |  | ● | ● |  |  |
| `GDP_pc_Region` | ● | ● |  |  |  |  |
| `GE_0W_pred_GDP_pc` |  |  | ● |  |  |  |
| `GE_1W_pred_GDP_pc` |  |  | ● |  |  |  |
| `GE_m1W_pred_GDP_pc` |  |  | ● |  |  |  |
| `GINIW_Eth_light` |  |  |  | ● |  |  |
| `GINIW_pred_GDP_pc` |  |  | ● | ● |  | ● |
| `Giniall` |  |  |  |  |  | ● |
| `Latitude` |  |  |  |  | ● |  |
| `Light_Country` |  | ● |  |  |  |  |
| `Light_Region` |  | ● |  |  |  |  |
| `Longitude` |  |  |  |  | ● |  |
| `Polity2` |  |  |  | ● |  |  |
| `Pop_Country` | ● | ● |  | ● |  |  |
| `Pop_Region` | ● | ● |  |  |  |  |
| `Region_NAME` | ● |  |  |  |  |  |
| `Resources_rents_share_of_GDP` |  |  |  | ● |  |  |
| `School_enrollment_secondary` |  |  |  | ● |  |  |
| `Trade_GDP_share` |  |  |  | ● |  |  |
| `area` |  |  |  | ● |  |  |
| `code_Coutry_Region` | ● |  |  |  | ● |  |
| `eap` | ● |  |  |  |  |  |
| `eca` | ● |  |  |  |  |  |
| `fedelupd2` |  |  |  | ● |  |  |
| `id_t_j` | ● |  |  |  |  |  |
| `lac` | ● |  |  |  |  |  |
| `log_GDP_pc_Country` | ● |  |  |  |  |  |
| `log_GDP_pc_Region` | ● |  |  |  | ● |  |
| `log_Light_ppix_Region` | ● |  |  |  | ● |  |
| `log_N_pix_low_cod_1_ppix` | ● |  |  |  |  |  |
| `log_N_pix_top_cod_1_ppix` | ● |  |  |  |  |  |
| `log_area` | ● |  |  |  |  |  |
| `log_region` | ● |  |  |  |  |  |
| `log_region_X_log_area` | ● |  |  |  |  |  |
| `mena` | ● |  |  |  |  |  |
| `pred_GDP_pc_Region` |  | ● |  |  |  |  |
| `price_gasoline` |  |  |  | ● |  |  |
| `sa` | ● |  |  |  |  |  |
| `satyear_1` | ● |  |  |  | ● |  |
| `satyear_2` | ● |  |  |  | ● |  |
| `satyear_3` | ● |  |  |  | ● |  |
| `satyear_4` | ● |  |  |  | ● |  |
| `satyear_5` | ● |  |  |  | ● |  |
| `satyear_6` | ● |  |  |  | ● |  |
| `satyear_7` | ● |  |  |  | ● |  |
| `ssa` | ● |  |  |  |  |  |
| `year` | ● | ● | ● | ● | ● | ● |

## Construction & formulas

All five inequality indices are computed **within each country-year, across that country's
regions**, on predicted regional income `y = pred_GDP_pc_Region`, weighted by the regional
population share `p_i = Pop_Region_i / Pop_Country`. Let `ybar = sum_i p_i * y_i` be the
population-weighted mean.

- **Population-weighted Gini** (`GINIW_pred_GDP_pc`, 0-1):
  `GINIW = ( sum_i sum_j p_i p_j |y_i - y_j| ) / (2 * ybar)`.
- **Coefficient of variation** (`COVW_pred_GDP_pc`, >=0):
  `COVW = sqrt( sum_i p_i (y_i - ybar)^2 ) / ybar`.
- **Generalized entropy** `GE(alpha)` (`GE_1W`, `GE_0W`, `GE_m1W`):
  for `alpha not in {0,1}`: `GE(alpha) = 1/(alpha(alpha-1)) * sum_i p_i [ (y_i/ybar)^alpha - 1 ]`;
  `GE(1)` = Theil = `sum_i p_i (y_i/ybar) ln(y_i/ybar)`;
  `GE(0)` = mean log deviation = `sum_i p_i ln(ybar/y_i)`;
  here `GE_m1W` uses `alpha = -1`.

Other constructed variables:

- **Log nighttime light** (`log_Light_ppix_Region`): `ln(meanDN)`, where the region mean DN is
  set to `0.01` when it equals 0 so the log is defined (DMSP DN range 0-63).
- **Prediction model** (eq. 1): a random-effects regression of `log_GDP_pc_Region` on
  `log_Light_ppix_Region`, the top-/low-coded pixel counts, `log_area`, `log_region`,
  `log_region_X_log_area`, the World Bank region dummies, the satellite-era dummies, and
  `log_GDP_pc_Country`. `pred_GDP_pc_Region` is the back-transformed fitted value.
- **Transport cost** (paper's proxy, not a stored column): `area * price_gasoline`.


## Per-dataset documentation

### `Prediction_Data`

- **Grain:** region-year &nbsp;|&nbsp; **Rows x Cols:** 5,258 x 30 &nbsp;|&nbsp; **Years:** 1992-2010 &nbsp;|&nbsp; **Coverage:** 1,504 regions in 81 countries
- **Panel key:** code_Coutry_Region x year
- **Purpose:** Train the light->income prediction model (Table 1).
- **Columns (CSV order):** `Region_NAME`, `Country_NAME`, `Country_ISO`, `id_t_j`, `code_Coutry_Region`, `year`, `Pop_Region`, `Pop_Country`, `GDP_pc_Region`, `log_GDP_pc_Region`, `log_Light_ppix_Region`, `log_GDP_pc_Country`, `log_N_pix_top_cod_1_ppix`, `log_N_pix_low_cod_1_ppix`, `log_area`, `log_region`, `log_region_X_log_area`, `eap`, `eca`, `lac`, `mena`, `sa`, `ssa`, `satyear_1`, `satyear_2`, `satyear_3`, `satyear_4`, `satyear_5`, `satyear_6`, `satyear_7`

**Variable dictionary**

| Variable | Stata label | Definition | Construction | Units | Source | Coverage |
|---|---|---|---|---|---|---|
| `Region_NAME` | First-level administrative region name | Name of the first-level admin unit (state/province/canton) | GADM admin-1 name | string | GADM (Global Administrative Areas) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `Country_NAME` | Country name | Country name (English) | From GADM country attributes | string | GADM (Global Administrative Areas) | country/region files |
| `Country_ISO` | Country code (ISO 3166-1 alpha-3) | Three-letter country identifier | Assigned per country | string | GADM (Global Administrative Areas) | all files |
| `id_t_j` | Country-year key (year+ISO, e.g. 2010CHE) | Concatenated year and ISO code | year concatenated with Country_ISO | string | Authors' replication archive | region frame (Prediction) |
| `code_Coutry_Region` | Numeric region key (orig. spelling 'Coutry' kept) | Numeric identifier for a region (unique within country) | Region identifier carried verbatim from the authors' archive | integer | Authors' replication archive | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `year` | Calendar year | Year of observation | - | year | - | per file (see summary) |
| `Pop_Region` | Regional total population (persons) | Total population of the region | Population density x region area, rounded up (min 1); 5-yr waves interpolated to annual | persons | GPW v3 (CIESIN) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `Pop_Country` | Country total population (persons) | Total population of the country | Sum of regional populations | persons | GPW v3 (CIESIN) | region & country frames |
| `GDP_pc_Region` | Observed regional GDP per capita (2005 PPP US$) | Observed regional GDP per capita (training target) | Regional accounts, constant 2005 PPP US$ | US$ (2005 PPP) | Gennaioli et al. (2014) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_GDP_pc_Region` | Log observed regional GDP per capita | Natural log of GDP_pc_Region | ln(GDP_pc_Region) | log US$ | Gennaioli et al. (2014) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_Light_ppix_Region` | Log avg nighttime light per pixel (region) | Natural log of the region mean DMSP-OLS stable-lights digital number | ln(mean DN); mean set to 0.01 when 0 so the log is defined; DN ranges 0-63 | log DN | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_GDP_pc_Country` | Log national GDP per capita | Natural log of national GDP per capita | ln(national GDP per capita) | log US$ | World Bank WDI | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_N_pix_top_cod_1_ppix` | Log count of top-coded pixels (DN=63) | Log number of saturated (top-coded) pixels in the region | ln(count of DN=63 pixels) per region; controls for sensor saturation | log count | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_N_pix_low_cod_1_ppix` | Log count of low-coded pixels (DN=0) | Log number of dark (low-coded) pixels in the region | ln(count of DN=0 pixels) per region; controls for sparse/rural area | log count | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_area` | Log region area (km^2) | Natural log of the region polygon area | ln(region area in km^2) | log km^2 | GADM (Global Administrative Areas) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_region` | Log number of regions in the country | Log count of first-level regions per country | ln(number of regions in the country) | log count | GADM (Global Administrative Areas) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_region_X_log_area` | Interaction: log_region x log_area | Product of log_region and log_area | log_region * log_area | - | This study (derived) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `eap` | World Bank region dummy: East Asia & Pacific | 1 if the country is in East Asia & Pacific (North America = reference) | World Bank regional grouping indicator | 0/1 | World Bank WDI | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `eca` | World Bank region dummy: Europe & Central Asia | 1 if the country is in Europe & Central Asia (North America = reference) | World Bank regional grouping indicator | 0/1 | World Bank WDI | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `lac` | World Bank region dummy: Latin America & Caribbean | 1 if the country is in Latin America & Caribbean (North America = reference) | World Bank regional grouping indicator | 0/1 | World Bank WDI | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `mena` | World Bank region dummy: Middle East & North Africa | 1 if the country is in Middle East & North Africa (North America = reference) | World Bank regional grouping indicator | 0/1 | World Bank WDI | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `sa` | World Bank region dummy: South Asia | 1 if the country is in South Asia (North America = reference) | World Bank regional grouping indicator | 0/1 | World Bank WDI | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `ssa` | World Bank region dummy: Sub-Saharan Africa | 1 if the country is in Sub-Saharan Africa (North America = reference) | World Bank regional grouping indicator | 0/1 | World Bank WDI | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_1` | Satellite/sensor-era dummy 1 (of 7) | 1 for DMSP satellite/sensor configuration era 1 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_2` | Satellite/sensor-era dummy 2 (of 7) | 1 for DMSP satellite/sensor configuration era 2 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_3` | Satellite/sensor-era dummy 3 (of 7) | 1 for DMSP satellite/sensor configuration era 3 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_4` | Satellite/sensor-era dummy 4 (of 7) | 1 for DMSP satellite/sensor configuration era 4 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_5` | Satellite/sensor-era dummy 5 (of 7) | 1 for DMSP satellite/sensor configuration era 5 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_6` | Satellite/sensor-era dummy 6 (of 7) | 1 for DMSP satellite/sensor configuration era 6 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_7` | Satellite/sensor-era dummy 7 (of 7) | 1 for DMSP satellite/sensor configuration era 7 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |

**Computed statistics** (from the CSV; dummies: Mean = share coded 1; identifiers/strings: numeric stats omitted)

| Variable | N | Miss% | Distinct | Min | Mean | Median | Max | SD |
|---|---|---|---|---|---|---|---|---|
| `Region_NAME` | 5,258 | 0.0 | 1,483 | — | — | — | — | — |
| `Country_NAME` | 5,258 | 0.0 | 82 | — | — | — | — | — |
| `Country_ISO` | 5,258 | 0.0 | 81 | — | — | — | — | — |
| `id_t_j` | 5,258 | 0.0 | 277 | — | — | — | — | — |
| `code_Coutry_Region` | 5,258 | 0.0 | 1,504 | — | — | — | — | — |
| `year` | 5,258 | 0.0 | 19 | 1992 | 2002.2 | 2000 | 2010 | 5.51 |
| `Pop_Region` | 5,258 | 0.0 | 5,258 | 928.4 | 3,705,986 | 964,556 | 199,528,672 | 11,695,457 |
| `Pop_Country` | 5,258 | 0.0 | 277 | 1,193,269 | 103,683,791 | 38,461,096 | 1,328,343,680 | 228,114,600 |
| `GDP_pc_Region` | 5,258 | 0.0 | 5,207 | 226.3 | 14,371 | 8,770.2 | 150,768 | 13,450 |
| `log_GDP_pc_Region` | 5,258 | 0.0 | 5,161 | 5.42 | 9.10 | 9.08 | 11.92 | 1.06 |
| `log_Light_ppix_Region` | 5,258 | 0.0 | 5,184 | -4.61 | 0.957 | 1.25 | 4.14 | 1.77 |
| `log_GDP_pc_Country` | 5,258 | 0.0 | 277 | 6.07 | 9.28 | 9.26 | 11.45 | 0.939 |
| `log_N_pix_top_cod_1_ppix` | 5,258 | 0.0 | 3,820 | -20.75 | -10.53 | -12.37 | 4.20e-05 | 4.31 |
| `log_N_pix_low_cod_1_ppix` | 5,258 | 0.0 | 5,135 | -15.16 | -1.55 | -0.523 | -8.34e-05 | 2.83 |
| `log_area` | 5,258 | 0.0 | 81 | 9.91 | 13.17 | 13.01 | 16.61 | 1.74 |
| `log_region` | 5,258 | 0.0 | 34 | 1.39 | 3.19 | 3.18 | 4.34 | 0.698 |
| `log_region_X_log_area` | 5,258 | 0.0 | 83 | 17.15 | 42.57 | 42.05 | 72.16 | 12.98 |
| `eap` | 5,258 | 0.0 | 2 | 0 | 0.201 | 0 | 1.00 | 0.401 |
| `eca` | 5,258 | 0.0 | 2 | 0 | 0.468 | 0 | 1.00 | 0.499 |
| `lac` | 5,258 | 0.0 | 2 | 0 | 0.165 | 0 | 1.00 | 0.372 |
| `mena` | 5,258 | 0.0 | 2 | 0 | 0.041 | 0 | 1.00 | 0.198 |
| `sa` | 5,258 | 0.0 | 2 | 0 | 0.044 | 0 | 1.00 | 0.204 |
| `ssa` | 5,258 | 0.0 | 2 | 0 | 0.034 | 0 | 1.00 | 0.181 |
| `satyear_1` | 5,258 | 0.0 | 2 | 0 | 0.016 | 0 | 1.00 | 0.125 |
| `satyear_2` | 5,258 | 0.0 | 2 | 0 | 0.004 | 0 | 1.00 | 0.062 |
| `satyear_3` | 5,258 | 0.0 | 2 | 0 | 0.230 | 0 | 1.00 | 0.421 |
| `satyear_4` | 5,258 | 0.0 | 2 | 0 | 0.022 | 0 | 1.00 | 0.146 |
| `satyear_5` | 5,258 | 0.0 | 2 | 0 | 0.251 | 0 | 1.00 | 0.434 |
| `satyear_6` | 5,258 | 0.0 | 2 | 0 | 0.246 | 0 | 1.00 | 0.431 |
| `satyear_7` | 5,258 | 0.0 | 2 | 0 | 0.032 | 0 | 1.00 | 0.176 |

### `Table_2_data`

- **Grain:** region-year &nbsp;|&nbsp; **Rows x Cols:** 5,258 x 8 &nbsp;|&nbsp; **Years:** 1992-2010 &nbsp;|&nbsp; **Coverage:** same 1,504-region training frame
- **Panel key:** Country_ISO x year (region-year frame)
- **Purpose:** Validate the inequality indices (Table 2).
- **Columns (CSV order):** `Country_ISO`, `year`, `pred_GDP_pc_Region`, `GDP_pc_Region`, `Light_Region`, `Light_Country`, `Pop_Region`, `Pop_Country`

**Variable dictionary**

| Variable | Stata label | Definition | Construction | Units | Source | Coverage |
|---|---|---|---|---|---|---|
| `Country_ISO` | Country code (ISO 3166-1 alpha-3) | Three-letter country identifier | Assigned per country | string | GADM (Global Administrative Areas) | all files |
| `year` | Calendar year | Year of observation | - | year | - | per file (see summary) |
| `pred_GDP_pc_Region` | Predicted regional GDP per capita (2005 PPP US$) | Model-predicted regional GDP per capita | Back-transformed fitted values of the eq.-1 random-effects model | US$ (2005 PPP) | This study (derived) | region frame (Table_2) |
| `GDP_pc_Region` | Observed regional GDP per capita (2005 PPP US$) | Observed regional GDP per capita (training target) | Regional accounts, constant 2005 PPP US$ | US$ (2005 PPP) | Gennaioli et al. (2014) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `Light_Region` | Regional total nighttime lights (summed DN) | Sum of pixel digital numbers over the region | Sum of DMSP-OLS stable-lights DN over the region's pixels | summed DN | NOAA/NGDC DMSP-OLS stable lights | region frame (Table_2) |
| `Light_Country` | Country total nighttime lights (summed DN) | Sum of pixel digital numbers over the whole country | Sum of DMSP-OLS stable-lights DN over all country pixels | summed DN | NOAA/NGDC DMSP-OLS stable lights | region frame (Table_2) |
| `Pop_Region` | Regional total population (persons) | Total population of the region | Population density x region area, rounded up (min 1); 5-yr waves interpolated to annual | persons | GPW v3 (CIESIN) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `Pop_Country` | Country total population (persons) | Total population of the country | Sum of regional populations | persons | GPW v3 (CIESIN) | region & country frames |

**Computed statistics** (from the CSV; dummies: Mean = share coded 1; identifiers/strings: numeric stats omitted)

| Variable | N | Miss% | Distinct | Min | Mean | Median | Max | SD |
|---|---|---|---|---|---|---|---|---|
| `Country_ISO` | 5,258 | 0.0 | 81 | — | — | — | — | — |
| `year` | 5,258 | 0.0 | 19 | 1992 | 2002.2 | 2000 | 2010 | 5.51 |
| `pred_GDP_pc_Region` | 5,258 | 0.0 | 5,254 | 360.1 | 13,422 | 8,324.9 | 70,638 | 11,689 |
| `GDP_pc_Region` | 5,258 | 0.0 | 5,207 | 226.3 | 14,371 | 8,770.2 | 150,768 | 13,450 |
| `Light_Region` | 5,258 | 0.0 | 5,199 | 44.00 | 213,456 | 58,401 | 7,904,552 | 465,243 |
| `Light_Country` | 5,258 | 0.0 | 277 | 12,106 | 7,477,449 | 1,733,508 | 83,312,528 | 14,801,072 |
| `Pop_Region` | 5,258 | 0.0 | 5,258 | 928.4 | 3,705,986 | 964,556 | 199,528,672 | 11,695,457 |
| `Pop_Country` | 5,258 | 0.0 | 277 | 1,193,269 | 103,683,791 | 38,461,096 | 1,328,343,680 | 228,114,600 |

### `Table_3_data`

- **Grain:** country-year &nbsp;|&nbsp; **Rows x Cols:** 3,675 x 9 &nbsp;|&nbsp; **Years:** 1992-2012 &nbsp;|&nbsp; **Coverage:** 180 countries
- **Panel key:** Country_ISO x year
- **Purpose:** Kuznets curve: GDP + five indices (Table 3).
- **Columns (CSV order):** `Country_NAME`, `Country_ISO`, `year`, `GDP_pc_Country`, `GINIW_pred_GDP_pc`, `COVW_pred_GDP_pc`, `GE_1W_pred_GDP_pc`, `GE_0W_pred_GDP_pc`, `GE_m1W_pred_GDP_pc`

**Variable dictionary**

| Variable | Stata label | Definition | Construction | Units | Source | Coverage |
|---|---|---|---|---|---|---|
| `Country_NAME` | Country name | Country name (English) | From GADM country attributes | string | GADM (Global Administrative Areas) | country/region files |
| `Country_ISO` | Country code (ISO 3166-1 alpha-3) | Three-letter country identifier | Assigned per country | string | GADM (Global Administrative Areas) | all files |
| `year` | Calendar year | Year of observation | - | year | - | per file (see summary) |
| `GDP_pc_Country` | National GDP per capita (2005 PPP US$) | National GDP per capita | World Bank WDI, constant 2005 PPP US$ | US$ (2005 PPP) | World Bank WDI | 1992-2012 · 180 ctry · country frame |
| `GINIW_pred_GDP_pc` | Pop-weighted regional Gini (predicted income) | Population-weighted Gini of predicted regional income within a country-year | Gini of pred_GDP_pc_Region across regions, weighted by Pop_Region, per country-year | 0-1 | This study (derived) | 1992-2012 · 180 ctry · country frame |
| `COVW_pred_GDP_pc` | Pop-weighted coefficient of variation (pred income) | Population-weighted coefficient of variation of predicted regional income | pop-weighted SD / pop-weighted mean of pred_GDP_pc_Region, per country-year | >=0 | This study (derived) | 1992-2012 · 180 ctry · country frame |
| `GE_1W_pred_GDP_pc` | Pop-weighted Theil index GE(alpha=1) | Population-weighted Theil index of predicted regional income | Generalized entropy GE(alpha=1) of pred_GDP_pc_Region, pop-weighted, per country-year | >=0 | This study (derived) | 1992-2012 · 180 ctry · country frame |
| `GE_0W_pred_GDP_pc` | Pop-weighted mean log deviation GE(alpha=0) | Population-weighted mean log deviation of predicted regional income | Generalized entropy GE(alpha=0) of pred_GDP_pc_Region, pop-weighted, per country-year | >=0 | This study (derived) | 1992-2012 · 180 ctry · country frame |
| `GE_m1W_pred_GDP_pc` | Pop-weighted generalized entropy GE(alpha=-1) | Population-weighted GE(-1) of predicted regional income | Generalized entropy GE(alpha=-1) of pred_GDP_pc_Region, pop-weighted, per country-year | >=0 | This study (derived) | 1992-2012 · 180 ctry · country frame |

**Computed statistics** (from the CSV; dummies: Mean = share coded 1; identifiers/strings: numeric stats omitted)

| Variable | N | Miss% | Distinct | Min | Mean | Median | Max | SD |
|---|---|---|---|---|---|---|---|---|
| `Country_NAME` | 3,675 | 0.0 | 180 | — | — | — | — | — |
| `Country_ISO` | 3,675 | 0.0 | 180 | — | — | — | — | — |
| `year` | 3,675 | 0.0 | 21 | 1992 | 2002.1 | 2002 | 2012 | 6.03 |
| `GDP_pc_Country` | 3,675 | 0.0 | 3,675 | 126.4 | 12,572 | 6,864.4 | 119,068 | 15,364 |
| `GINIW_pred_GDP_pc` | 3,675 | 0.0 | 3,674 | 0.001 | 0.064 | 0.061 | 0.163 | 0.033 |
| `COVW_pred_GDP_pc` | 3,675 | 0.0 | 3,674 | 0.003 | 0.127 | 0.116 | 0.365 | 0.069 |
| `GE_1W_pred_GDP_pc` | 3,675 | 0.0 | 3,675 | 4.13e-06 | 0.010 | 0.007 | 0.058 | 0.010 |
| `GE_0W_pred_GDP_pc` | 3,675 | 0.0 | 3,675 | 4.12e-06 | 0.010 | 0.007 | 0.051 | 0.009 |
| `GE_m1W_pred_GDP_pc` | 3,675 | 0.0 | 3,675 | 4.12e-06 | 0.010 | 0.007 | 0.047 | 0.009 |

### `Table_4_data`

- **Grain:** country-year &nbsp;|&nbsp; **Rows x Cols:** 3,675 x 17 &nbsp;|&nbsp; **Years:** 1992-2012 &nbsp;|&nbsp; **Coverage:** 180 countries (determinants sparser)
- **Panel key:** Country_ISO x year
- **Purpose:** Determinants of regional inequality (Table 4).
- **Columns (CSV order):** `Country_NAME`, `Country_ISO`, `year`, `GINIW_pred_GDP_pc`, `GDP_pc_Country`, `Pop_Country`, `Resources_rents_share_of_GDP`, `Arable_land`, `Trade_GDP_share`, `FDI_share_of_GDP`, `area`, `price_gasoline`, `Aid`, `School_enrollment_secondary`, `GINIW_Eth_light`, `Polity2`, `fedelupd2`

**Variable dictionary**

| Variable | Stata label | Definition | Construction | Units | Source | Coverage |
|---|---|---|---|---|---|---|
| `Country_NAME` | Country name | Country name (English) | From GADM country attributes | string | GADM (Global Administrative Areas) | country/region files |
| `Country_ISO` | Country code (ISO 3166-1 alpha-3) | Three-letter country identifier | Assigned per country | string | GADM (Global Administrative Areas) | all files |
| `year` | Calendar year | Year of observation | - | year | - | per file (see summary) |
| `GINIW_pred_GDP_pc` | Pop-weighted regional Gini (predicted income) | Population-weighted Gini of predicted regional income within a country-year | Gini of pred_GDP_pc_Region across regions, weighted by Pop_Region, per country-year | 0-1 | This study (derived) | 1992-2012 · 180 ctry · country frame |
| `GDP_pc_Country` | National GDP per capita (2005 PPP US$) | National GDP per capita | World Bank WDI, constant 2005 PPP US$ | US$ (2005 PPP) | World Bank WDI | 1992-2012 · 180 ctry · country frame |
| `Pop_Country` | Country total population (persons) | Total population of the country | Sum of regional populations | persons | GPW v3 (CIESIN) | region & country frames |
| `Resources_rents_share_of_GDP` | Natural-resource rents (% of GDP) | Total natural-resource rents as a share of GDP | Oil + gas + coal + mineral + forest rents, % of GDP | % GDP | World Bank WDI | 177 ctry · N=3,620 |
| `Arable_land` | Arable land (share of land area) | Arable land as a share of land area (FAO definition) | Arable land / total land area | share | World Bank WDI | 178 ctry · N=3,603 |
| `Trade_GDP_share` | Trade openness (exports+imports)/GDP | Trade as a share of GDP | (Exports + imports) / GDP | ratio | World Bank WDI | 176 ctry · N=3,509 |
| `FDI_share_of_GDP` | FDI openness: net FDI inflows / GDP | Net foreign direct investment inflows as a share of GDP | Net FDI inflows / GDP | ratio | World Bank WDI | 174 ctry · N=3,477 |
| `area` | Country land area (km^2) | Total land area excluding inland water | World Bank WDI land area | km^2 | World Bank WDI | 1992-2012 · 180 ctry · country frame |
| `price_gasoline` | Gasoline pump price (2005 PPP US$/litre) | Pump price for gasoline | Pump price, PPP constant 2005 US$/litre; paper's transport cost = area x price_gasoline | US$/litre | World Bank WDI | 162 ctry · N=1,366 |
| `Aid` | Net official development assistance (2011 US$) | Net official development assistance received | Net ODA received, constant 2011 US$ | US$ (2011) | World Bank WDI | 155 ctry · N=2,964 |
| `School_enrollment_secondary` | Gross secondary-school enrolment (% gross) | Gross secondary-school enrolment ratio (>100% with over-age pupils) | Secondary enrolment / age-eligible population | % gross | World Bank WDI | 172 ctry · N=2,566 |
| `GINIW_Eth_light` | Ethnic inequality: pop-weighted light Gini | Population-weighted light-Gini computed across ethnic homelands | Light Gini across ethnic homelands (method of Alesina et al. 2016) | 0-1 | GREG (Weidmann et al. 2010) + NOAA/NGDC | 173 ctry · N=3,528 |
| `Polity2` | Polity IV democracy-autocracy score (-1..+1) | Rescaled Polity IV combined democracy-autocracy score | Polity IV combined score rescaled -1 (autocracy) to +1 (democracy) | -1..+1 | Polity IV (Center for Systemic Peace) | 157 ctry · N=3,158 |
| `fedelupd2` | Federal-state dummy (1=federal) | 1 if the country is federally organised | Federalism indicator from the authors' archive | 0/1 | Authors' replication archive | 1992-2009 · 154 ctry · N=2,724 |

**Computed statistics** (from the CSV; dummies: Mean = share coded 1; identifiers/strings: numeric stats omitted)

| Variable | N | Miss% | Distinct | Min | Mean | Median | Max | SD |
|---|---|---|---|---|---|---|---|---|
| `Country_NAME` | 3,675 | 0.0 | 180 | — | — | — | — | — |
| `Country_ISO` | 3,675 | 0.0 | 180 | — | — | — | — | — |
| `year` | 3,675 | 0.0 | 21 | 1992 | 2002.1 | 2002 | 2012 | 6.03 |
| `GINIW_pred_GDP_pc` | 3,675 | 0.0 | 3,674 | 0.001 | 0.064 | 0.061 | 0.163 | 0.033 |
| `GDP_pc_Country` | 3,675 | 0.0 | 3,675 | 126.4 | 12,572 | 6,864.4 | 119,068 | 15,364 |
| `Pop_Country` | 3,675 | 0.0 | 3,675 | 2,690.0 | 34,542,604 | 7,563,883 | 1,353,431,168 | 126,525,234 |
| `Resources_rents_share_of_GDP` | 3,620 | 1.5 | 3,454 | 0 | 9.93 | 3.48 | 100.4 | 15.00 |
| `Arable_land` | 3,603 | 2.0 | 2,492 | 4.31e-04 | 0.148 | 0.105 | 0.661 | 0.138 |
| `Trade_GDP_share` | 3,509 | 4.5 | 3,509 | 0.003 | 0.841 | 0.761 | 5.32 | 0.469 |
| `FDI_share_of_GDP` | 3,477 | 5.4 | 3,476 | -0.829 | 0.044 | 0.025 | 4.31 | 0.105 |
| `area` | 3,675 | 0.0 | 180 | 50.00 | 728,582 | 155,360 | 16,380,084 | 1,922,363 |
| `price_gasoline` | 1,366 | 62.8 | 834 | 0.019 | 0.882 | 0.844 | 2.35 | 0.417 |
| `Aid` | 2,964 | 19.3 | 2,914 | -1,218,120,000 | 557,102,709 | 265,405,000 | 25,985,650,000 | 977,685,466 |
| `School_enrollment_secondary` | 2,566 | 30.2 | 2,566 | 5.16 | 74.19 | 82.79 | 160.6 | 31.42 |
| `GINIW_Eth_light` | 3,528 | 4.0 | 3,178 | 0 | 0.273 | 0.200 | 0.830 | 0.256 |
| `Polity2` | 3,158 | 14.1 | 21 | -1.00 | 0.341 | 0.600 | 1.00 | 0.656 |
| `fedelupd2` | 2,724 | 25.9 | 2 | 0 | 0.138 | 0 | 1.00 | 0.345 |

### `Table_B4_data`

- **Grain:** region-year &nbsp;|&nbsp; **Rows x Cols:** 5,258 x 14 &nbsp;|&nbsp; **Years:** 1992-2010 &nbsp;|&nbsp; **Coverage:** 1,504 regions in 81 countries
- **Panel key:** code_Coutry_Region x year
- **Purpose:** Conley spatial-HAC standard errors (+ lat/lon).
- **Columns (CSV order):** `code_Coutry_Region`, `Country_ISO`, `year`, `Latitude`, `Longitude`, `log_GDP_pc_Region`, `log_Light_ppix_Region`, `satyear_1`, `satyear_2`, `satyear_3`, `satyear_4`, `satyear_5`, `satyear_6`, `satyear_7`

**Variable dictionary**

| Variable | Stata label | Definition | Construction | Units | Source | Coverage |
|---|---|---|---|---|---|---|
| `code_Coutry_Region` | Numeric region key (orig. spelling 'Coutry' kept) | Numeric identifier for a region (unique within country) | Region identifier carried verbatim from the authors' archive | integer | Authors' replication archive | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `Country_ISO` | Country code (ISO 3166-1 alpha-3) | Three-letter country identifier | Assigned per country | string | GADM (Global Administrative Areas) | all files |
| `year` | Calendar year | Year of observation | - | year | - | per file (see summary) |
| `Latitude` | Region centroid latitude (degrees) | Latitude of the region polygon centroid | GADM polygon centroid | degrees | GADM (Global Administrative Areas) | region frame (Table_B4) |
| `Longitude` | Region centroid longitude (degrees) | Longitude of the region polygon centroid | GADM polygon centroid | degrees | GADM (Global Administrative Areas) | region frame (Table_B4) |
| `log_GDP_pc_Region` | Log observed regional GDP per capita | Natural log of GDP_pc_Region | ln(GDP_pc_Region) | log US$ | Gennaioli et al. (2014) | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `log_Light_ppix_Region` | Log avg nighttime light per pixel (region) | Natural log of the region mean DMSP-OLS stable-lights digital number | ln(mean DN); mean set to 0.01 when 0 so the log is defined; DN ranges 0-63 | log DN | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_1` | Satellite/sensor-era dummy 1 (of 7) | 1 for DMSP satellite/sensor configuration era 1 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_2` | Satellite/sensor-era dummy 2 (of 7) | 1 for DMSP satellite/sensor configuration era 2 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_3` | Satellite/sensor-era dummy 3 (of 7) | 1 for DMSP satellite/sensor configuration era 3 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_4` | Satellite/sensor-era dummy 4 (of 7) | 1 for DMSP satellite/sensor configuration era 4 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_5` | Satellite/sensor-era dummy 5 (of 7) | 1 for DMSP satellite/sensor configuration era 5 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_6` | Satellite/sensor-era dummy 6 (of 7) | 1 for DMSP satellite/sensor configuration era 6 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |
| `satyear_7` | Satellite/sensor-era dummy 7 (of 7) | 1 for DMSP satellite/sensor configuration era 7 | Sensor-era indicator; DMSP sensors change and age over 1992-2010 | 0/1 | NOAA/NGDC DMSP-OLS stable lights | 1992-2010 · 1,504 reg (81 ctry) · region frame |

**Computed statistics** (from the CSV; dummies: Mean = share coded 1; identifiers/strings: numeric stats omitted)

| Variable | N | Miss% | Distinct | Min | Mean | Median | Max | SD |
|---|---|---|---|---|---|---|---|---|
| `code_Coutry_Region` | 5,258 | 0.0 | 1,504 | — | — | — | — | — |
| `Country_ISO` | 5,258 | 0.0 | 81 | — | — | — | — | — |
| `year` | 5,258 | 0.0 | 19 | 1992 | 2002.2 | 2000 | 2010 | 5.51 |
| `Latitude` | 5,258 | 0.0 | 1,504 | -54.33 | 29.81 | 38.45 | 69.95 | 24.33 |
| `Longitude` | 5,258 | 0.0 | 1,504 | -156.4 | 22.45 | 23.12 | 163.0 | 67.31 |
| `log_GDP_pc_Region` | 5,258 | 0.0 | 5,161 | 5.42 | 9.10 | 9.08 | 11.92 | 1.06 |
| `log_Light_ppix_Region` | 5,258 | 0.0 | 5,184 | -4.61 | 0.957 | 1.25 | 4.14 | 1.77 |
| `satyear_1` | 5,258 | 0.0 | 2 | 0 | 0.016 | 0 | 1.00 | 0.125 |
| `satyear_2` | 5,258 | 0.0 | 2 | 0 | 0.004 | 0 | 1.00 | 0.062 |
| `satyear_3` | 5,258 | 0.0 | 2 | 0 | 0.230 | 0 | 1.00 | 0.421 |
| `satyear_4` | 5,258 | 0.0 | 2 | 0 | 0.022 | 0 | 1.00 | 0.146 |
| `satyear_5` | 5,258 | 0.0 | 2 | 0 | 0.251 | 0 | 1.00 | 0.434 |
| `satyear_6` | 5,258 | 0.0 | 2 | 0 | 0.246 | 0 | 1.00 | 0.431 |
| `satyear_7` | 5,258 | 0.0 | 2 | 0 | 0.032 | 0 | 1.00 | 0.176 |

### `Figure_5_data`

- **Grain:** country-year &nbsp;|&nbsp; **Rows x Cols:** 3,675 x 5 &nbsp;|&nbsp; **Years:** 1992-2012 &nbsp;|&nbsp; **Coverage:** 180 countries (Giniall: 153 ctry)
- **Panel key:** Country_ISO x year
- **Purpose:** Regional vs interpersonal inequality (Figure 5).
- **Columns (CSV order):** `Country_ISO`, `Country_NAME`, `year`, `GINIW_pred_GDP_pc`, `Giniall`

**Variable dictionary**

| Variable | Stata label | Definition | Construction | Units | Source | Coverage |
|---|---|---|---|---|---|---|
| `Country_ISO` | Country code (ISO 3166-1 alpha-3) | Three-letter country identifier | Assigned per country | string | GADM (Global Administrative Areas) | all files |
| `Country_NAME` | Country name | Country name (English) | From GADM country attributes | string | GADM (Global Administrative Areas) | country/region files |
| `year` | Calendar year | Year of observation | - | year | - | per file (see summary) |
| `GINIW_pred_GDP_pc` | Pop-weighted regional Gini (predicted income) | Population-weighted Gini of predicted regional income within a country-year | Gini of pred_GDP_pc_Region across regions, weighted by Pop_Region, per country-year | 0-1 | This study (derived) | 1992-2012 · 180 ctry · country frame |
| `Giniall` | National interpersonal income Gini (0-100) | Household-survey interpersonal income Gini | Reported household income Gini on a 0-100 scale (note: regional indices are 0-1) | 0-100 | Lessmann & Seidel (2017) | 1992-2012 · 153 ctry · N=1,330 (Figure_5) |

**Computed statistics** (from the CSV; dummies: Mean = share coded 1; identifiers/strings: numeric stats omitted)

| Variable | N | Miss% | Distinct | Min | Mean | Median | Max | SD |
|---|---|---|---|---|---|---|---|---|
| `Country_ISO` | 3,675 | 0.0 | 180 | — | — | — | — | — |
| `Country_NAME` | 3,675 | 0.0 | 180 | — | — | — | — | — |
| `year` | 3,675 | 0.0 | 21 | 1992 | 2002.1 | 2002 | 2012 | 6.03 |
| `GINIW_pred_GDP_pc` | 3,675 | 0.0 | 3,674 | 0.001 | 0.064 | 0.061 | 0.163 | 0.033 |
| `Giniall` | 1,330 | 63.8 | 384 | 17.50 | 39.55 | 37.80 | 74.30 | 10.11 |

## Known limitations & caveats

- **DMSP top-coding / saturation.** Sensor digital numbers are capped at 63, so the brightest
  city cores are censored; `log_N_pix_top_cod_1_ppix` controls for this.
- **Sensor drift across satellite eras.** Six+ DMSP sensors span 1992-2010 with differing
  calibration and aging; the `satyear_1`..`satyear_7` dummies absorb these era effects.
- **Sparse determinants.** Several Table 4 determinants are observed for far fewer country-years
  than the core panel (e.g. `price_gasoline` N=1,366; `School_enrollment_secondary` N=2,566), so
  determinant regressions run on shifting subsamples - compare columns descriptively, not as
  nested models. The authors' ICRG "bureaucratic quality" index is licensed and **not** included.
- **Scale of `Giniall`.** The interpersonal income Gini is on a **0-100** scale, whereas the
  regional indices (`GINIW_*`, etc.) are on a **0-1** scale - do not mix them without rescaling.
- **`code_Coutry_Region` spelling.** The misspelling "Coutry" is preserved deliberately so the
  key matches the authors' original replication archive.


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*Generated by `make_stata.py`. Stata format: .dta v118 (Stata 14+). Definitions follow Lessmann & Seidel (2017) and the post's Appendix A.*
