# Conversation Transcript

**User**

[fh_ratings.csv](fh_ratings.csv) [wb_gdp_data.csv](wb_gdp_data.csv) can you describe these 2 datasets and give a high level overview of what they contain?

**Assistant**

I’m inspecting both CSVs for schema, size, and a few representative rows first. Then I’ll summarize what each dataset appears to measure and the main dimensions they cover.

<details>
<summary>Background terminal finished with wc -l fh_ratings.csv wb_gdp_data.csv</summary>

Command

```bash
wc -l fh_ratings.csv wb_gdp_data.csv
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc 'wc -l fh_ratings.csv wb_gdp_data.csv'
Chunk ID: 05708a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 16
Output:
    4306 fh_ratings.csv
     271 wb_gdp_data.csv
    4577 total
```

</details>

<details>
<summary>Background terminal finished with sed -n '1,8p' fh_ratings.csv</summary>

Command

```bash
sed -n '1,8p' fh_ratings.csv
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "sed -n '1,8p' fh_ratings.csv"
Chunk ID: 0fbc1f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 71
Output:
country,year,political_rights_score,civil_liberties_score,status
Afghanistan,2000,7,7,Not Free
Afghanistan,2001,7,7,Not Free
Afghanistan,2002,6,6,Not Free
Afghanistan,2003,6,6,Not Free
Afghanistan,2004,5,6,Not Free
Afghanistan,2005,5,5,Partly Free
Afghanistan,2006,5,5,Partly Free
```

</details>

<details>
<summary>Background terminal finished with sed -n '1,8p' wb_gdp_data.csv</summary>

Command

```bash
sed -n '1,8p' wb_gdp_data.csv
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "sed -n '1,8p' wb_gdp_data.csv"
Chunk ID: 5d97b0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 397
Output:
Data Source,World Development Indicators,

Last Updated Date,2026-02-24,

Country Name,Country Code,Indicator Name,Indicator Code,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020
Aruba,ABW,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,37524.0346018738,38729.4588237779,38098.9048329158,38086.0123762909,40000.5625183532,38828.2166342999,38566.8026759098,39462.5851547515,39881.5879733887,34986.1484622772,33951.6246182745,34837.9402869986,33972.3401082566,35621.7848546409,35746.8630679768,36660.6310808381,36832.9040242979,38116.8310992499,39278.8297271438,39652.6320700939,30347.6371851417
Africa Eastern and Southern,AFE,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,3281.92231355821,3313.42192907347,3350.14106469179,3357.24486194198,3457.12022216505,3577.58589657798,3718.02241324132,3866.72395287147,3934.08423728651,3869.99812557719,3976.01098918458,4042.84247175787,4061.64163525859,4125.18256263035,4185.23841104229,4206.25295270438,4202.23641584707,4214.39708524398,4220.91250168849,4195.86541780344,3978.93139450095
Afghanistan,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1617.82647494969,1454.11078195559,1774.30874287341,1815.92819979586,1776.91820712187,1908.11478234587,1929.72389709372,2155.35306784413,2191.50435559062,2565.02208584099,2848.58606111495,2757.05258747725,2985.31914452845,3046.57993117128,3017.94254424696,2967.692067153,2958.7853992308,2952.99891598909,2902.39211285546,2927.2451435353,2769.68574507111
```

</details>

**Assistant**

I have the headers and file shapes. Next I’m pulling a few coarse stats so the overview is specific: time coverage, entity counts, and the main categorical/value fields in each file.

<details>
<summary>Background terminal finished with awk -F, 'NR>1{years[$2]=1; countries[$1]=1; status[$5]=1} END{printf("rows=%d\nunique_countries=%d\nyear_min=%s\nyear_max=%s\nstatuses=", NR-1, length(countries), min, max)} function upd(y){if(min==""||y<min)min=y; if(max==""||y>max)max=y} NR>1{upd($2)} END{first=1; for (s in status){printf("%s%s", first?"":", ", s); first=0} printf("\n")}' fh_ratings.csv</summary>

Command

```bash
awk -F, 'NR>1{years[$2]=1; countries[$1]=1; status[$5]=1} END{printf("rows=%d\nunique_countries=%d\nyear_min=%s\nyear_max=%s\nstatuses=", NR-1, length(countries), min, max)} function upd(y){if(min==""||y<min)min=y; if(max==""||y>max)max=y} NR>1{upd($2)} END{first=1; for (s in status){printf("%s%s", first?"":", ", s); first=0} printf("\n")}' fh_ratings.csv
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "awk -F, 'NR>1{years["'$2]=1; countries[$1]=1; status[$5]=1} END{printf("rows=%d'"\\nunique_countries=%d\\nyear_min=%s\\nyear_max=%s\\nstatuses=\", NR-1, length(countries), min, max)} function upd(y){if(min==\"\"||y<min)min=y; if(max==\"\"||y>max)max=y} NR>1{upd("'$2)} END{first=1; for (s in status){printf("%s%s", first?"":", ", s); first=0} printf("'"\\n\")}' fh_ratings.csv"
Chunk ID: 7e9e15
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 25
Output:
rows=4305
unique_countries=202
year_min= E."
year_max=2020
statuses=Free, Partly Free, Not Free, -
```

</details>

<details>
<summary>Background terminal finished with awk -F, 'NR>1{count[$5]++} END{for (s in count) print s ": " count[s]}' fh_ratings.csv | sort</summary>

Command

```bash
awk -F, 'NR>1{count[$5]++} END{for (s in count) print s ": " count[s]}' fh_ratings.csv | sort
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "awk -F, 'NR>1{count["'$5]++} END{for (s in count) print s ": " count[s]}'"' fh_ratings.csv | sort"
Chunk ID: bb68cf
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 13
Output:
-: 236
Free: 1837
Not Free: 1006
Partly Free: 1226
```

</details>

<details>
<summary>Background terminal finished with sed -n '265,271p' wb_gdp_data.csv</summary>

Command

```bash
sed -n '265,271p' wb_gdp_data.csv
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "sed -n '265,271p' wb_gdp_data.csv"
Chunk ID: d9484f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 670
Output:
World,WLD,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,12769.8314917662,12904.861928295,13093.0651208582,13402.9919536458,13914.8111422905,14370.8850287562,14927.5485261449,15510.865766861,15747.7295109752,15452.5012186257,16047.9897391201,16479.4887946509,16784.9491797151,17122.0455272012,17500.1748281222,17868.0908568708,18232.7269157079,18714.9389431259,19189.0202232249,19552.0835148296,18811.4935530315
Samoa,WSM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,4638.59446502597,4941.1152254089,5183.45456244226,5425.44383557984,5568.34734622238,5914.889706043,6017.47704264273,6013.27628827659,6182.76327854773,6104.12751491364,6427.31156227699,6616.7710962495,6311.96070235073,6262.32372017912,6247.90480904724,6432.35979168526,6887.30523545286,6919.01572858501,6805.0451717529,7033.52113384547,6745.29241350438
Kosovo,XKX,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,7098.40301909042,7481.45889832785,7877.10199850556,8359.93621479336,8431.66232694657,8828.27683251638,9150.74997293883,9824.99105955319,10434.8888259268,10856.2902074239,11188.2244627966,11774.1178510908,11137.5028560404
"Yemen, Rep.",YEM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
South Africa,ZAF,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,11183.825503143,11387.492366782,11697.5657261687,11923.1211013633,12341.2102655828,12861.5633089114,13444.2332126058,14016.2444716386,14297.6887802632,13910.7017093394,14164.9958363115,14434.2611456048,14563.7624041032,14681.096638957,14643.3384782936,14541.6748613669,14501.2807823402,14573.5980840004,14553.5615668957,14352.6698709871,13250.5666594456
Zambia,ZMB,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,2220.5653712714,2268.96965564597,2299.33289188114,2383.87611741089,2471.72073328685,2564.47560965912,2673.46151928529,2796.3529332184,2908.14362510668,3065.84420108998,3266.50749293828,3335.64339500657,3474.55252844092,3535.22449043144,3585.57748507131,3576.92544834125,3598.17166023389,3612.50597664593,3646.95966481655,3591.56418883108,3391.59541156095
Zimbabwe,ZWE,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,6170.33390832202,6217.41179425199,5610.19146861437,4601.6607468251,4287.59829196083,4004.66443720945,3819.2334285389,3631.53761566256,2954.09932377926,3299.41379594873,3885.39378454343,4358.92582649309,5003.48706186748,5031.68744400391,5081.11240399225,5102.71432347607,5070.40217016051,5234.3838655748,5415.46976371689,4993.84383894752,4527.71988082724
```

</details>

**Assistant**

`fh_ratings.csv` is straightforward; `wb_gdp_data.csv` has quoted commas and metadata rows, so I’m parsing both with Node next to avoid a misleading summary.

<details>
<summary>JavaScript REPL completed</summary>

Source

```javascript
// codex-js-repl: timeout_ms=15000
const fs = await import("node:fs/promises");

function parseCsv(text) {
  const rows = [];
  let row = [];
  let field = "";
  let inQuotes = false;
  for (let i = 0; i < text.length; i++) {
    const ch = text[i];
    if (inQuotes) {
      if (ch === '"') {
        if (text[i + 1] === '"') {
          field += '"';
          i++;
        } else {
          inQuotes = false;
        }
      } else {
        field += ch;
      }
      continue;
    }
    if (ch === '"') {
      inQuotes = true;
    } else if (ch === ",") {
      row.push(field);
      field = "";
    } else if (ch === "\n") {
      row.push(field);
      rows.push(row);
      row = [];
      field = "";
    } else if (ch === "\r") {
      continue;
    } else {
      field += ch;
    }
  }
  if (field.length || row.length) {
    row.push(field);
    rows.push(row);
  }
  return rows;
}

const fhText = await fs.readFile("fh_ratings.csv", "utf8");
const wbText = await fs.readFile("wb_gdp_data.csv", "utf8");

const fhRows = parseCsv(fhText);
const fhHeader = fhRows[0];
const fhData = fhRows.slice(1);

const fhYears = fhData.map((r) => Number(r[1])).filter(Number.isFinite);
const fhCountries = new Set(fhData.map((r) => r[0]));
const fhStatuses = [...new Set(fhData.map((r) => r[4]))].sort();
const fhStatusCounts = Object.fromEntries(
  [
    ...fhData
      .reduce((m, r) => m.set(r[4], (m.get(r[4]) || 0) + 1), new Map())
      .entries(),
  ].sort(),
);
const fhRights = fhData.map((r) => Number(r[2])).filter(Number.isFinite);
const fhLibs = fhData.map((r) => Number(r[3])).filter(Number.isFinite);

const wbRows = parseCsv(wbText.replace(/^\uFEFF/, ""));
const wbHeaderRowIndex = wbRows.findIndex((r) => r[0] === "Country Name");
const wbHeader = wbRows[wbHeaderRowIndex];
const wbData = wbRows
  .slice(wbHeaderRowIndex + 1)
  .filter((r) => r.length > 1 && r[0]);
const wbYears = wbHeader.slice(4);
const wbCountryNames = new Set(wbData.map((r) => r[0]));
const wbIndicatorNames = [...new Set(wbData.map((r) => r[2]))];
const wbIndicatorCodes = [...new Set(wbData.map((r) => r[3]))];
const wbMissingCount = wbData
  .flatMap((r) => r.slice(4))
  .filter((v) => v === ".." || v === "").length;
const wbCellCount = wbData.length * (wbHeader.length - 4);
const sampleCountries = [
  wbData[0]?.[0],
  wbData[Math.floor(wbData.length / 2)]?.[0],
  wbData.at(-1)?.[0],
];

({
  fh: {
    columns: fhHeader,
    rows: fhData.length,
    countries: fhCountries.size,
    yearMin: Math.min(...fhYears),
    yearMax: Math.max(...fhYears),
    statuses: fhStatuses,
    statusCounts: fhStatusCounts,
    politicalRightsRange: [Math.min(...fhRights), Math.max(...fhRights)],
    civilLibertiesRange: [Math.min(...fhLibs), Math.max(...fhLibs)],
  },
  wb: {
    metadataRows: wbHeaderRowIndex,
    columns: wbHeader,
    rows: wbData.length,
    countries: wbCountryNames.size,
    yearMin: wbYears[0],
    yearMax: wbYears.at(-1),
    yearCount: wbYears.length,
    indicatorNames: wbIndicatorNames,
    indicatorCodes: wbIndicatorCodes,
    missingCells: wbMissingCount,
    totalValueCells: wbCellCount,
    sampleCountries,
  },
});
```

Raw tool result

```text

```

</details>

**Assistant**

The first REPL call didn’t return structured output. I’m rerunning it in the required freeform form so I can give you precise counts instead of guessing from shell snippets.

<details>
<summary>JavaScript REPL failed</summary>

Source

```javascript
// codex-js-repl: timeout_ms=15000
const fs = await import("node:fs/promises");

function parseCsv(text) {
  const rows = [];
  let row = [];
  let field = "";
  let inQuotes = false;
  for (let i = 0; i < text.length; i++) {
    const ch = text[i];
    if (inQuotes) {
      if (ch === '"') {
        if (text[i + 1] === '"') {
          field += '"';
          i++;
        } else {
          inQuotes = false;
        }
      } else {
        field += ch;
      }
      continue;
    }
    if (ch === '"') {
      inQuotes = true;
    } else if (ch === ",") {
      row.push(field);
      field = "";
    } else if (ch === "\n") {
      row.push(field);
      rows.push(row);
      row = [];
      field = "";
    } else if (ch !== "\r") {
      field += ch;
    }
  }
  if (field.length || row.length) {
    row.push(field);
    rows.push(row);
  }
  return rows;
}

const fhRows = parseCsv(await fs.readFile("fh_ratings.csv", "utf8"));
const fhData = fhRows.slice(1);
const fhYears = fhData.map((r) => Number(r[1])).filter(Number.isFinite);
const fhCountries = new Set(fhData.map((r) => r[0]));
const fhStatuses = [...new Set(fhData.map((r) => r[4]))].sort();
const fhStatusCounts = Object.fromEntries(
  [
    ...fhData
      .reduce((m, r) => m.set(r[4], (m.get(r[4]) || 0) + 1), new Map())
      .entries(),
  ].sort(),
);

const wbRows = parseCsv(
  (await fs.readFile("wb_gdp_data.csv", "utf8")).replace(/^\uFEFF/, ""),
);
const wbHeaderRowIndex = wbRows.findIndex((r) => r[0] === "Country Name");
const wbHeader = wbRows[wbHeaderRowIndex];
const wbData = wbRows.slice(wbHeaderRowIndex + 1).filter((r) => r[0]);
const wbYears = wbHeader.slice(4);
const wbCountries = new Set(wbData.map((r) => r[0]));
const wbIndicatorNames = [...new Set(wbData.map((r) => r[2]))];
const wbIndicatorCodes = [...new Set(wbData.map((r) => r[3]))];
const wbMissingCount = wbData
  .flatMap((r) => r.slice(4))
  .filter((v) => v === ".." || v === "").length;

console.log(
  JSON.stringify(
    {
      fh: {
        rows: fhData.length,
        columns: fhRows[0],
        countries: fhCountries.size,
        yearMin: Math.min(...fhYears),
        yearMax: Math.max(...fhYears),
        statuses: fhStatuses,
        statusCounts: fhStatusCounts,
      },
      wb: {
        metadataRows: wbHeaderRowIndex,
        rows: wbData.length,
        columns: wbHeader,
        countries: wbCountries.size,
        yearMin: wbYears[0],
        yearMax: wbYears.at(-1),
        yearCount: wbYears.length,
        indicatorNames: wbIndicatorNames,
        indicatorCodes: wbIndicatorCodes,
        missingCells: wbMissingCount,
      },
    },
    null,
    2,
  ),
);
```

Raw tool result

```text
Identifier 'fs' has already been declared
```

</details>

<details>
<summary>JavaScript REPL completed</summary>

Source

```javascript
// codex-js-repl: timeout_ms=15000
function parseCsv2(text) {
  const rows = [];
  let row = [];
  let field = "";
  let inQuotes = false;
  for (let i = 0; i < text.length; i++) {
    const ch = text[i];
    if (inQuotes) {
      if (ch === '"') {
        if (text[i + 1] === '"') {
          field += '"';
          i++;
        } else {
          inQuotes = false;
        }
      } else {
        field += ch;
      }
      continue;
    }
    if (ch === '"') {
      inQuotes = true;
    } else if (ch === ",") {
      row.push(field);
      field = "";
    } else if (ch === "\n") {
      row.push(field);
      rows.push(row);
      row = [];
      field = "";
    } else if (ch !== "\r") {
      field += ch;
    }
  }
  if (field.length || row.length) {
    row.push(field);
    rows.push(row);
  }
  return rows;
}

const fhRows2 = parseCsv2(await fs.readFile("fh_ratings.csv", "utf8"));
const fhData2 = fhRows2.slice(1);
const fhYears2 = fhData2.map((r) => Number(r[1])).filter(Number.isFinite);
const fhCountries2 = new Set(fhData2.map((r) => r[0]));
const fhStatuses2 = [...new Set(fhData2.map((r) => r[4]))].sort();
const fhStatusCounts2 = Object.fromEntries(
  [
    ...fhData2
      .reduce((m, r) => m.set(r[4], (m.get(r[4]) || 0) + 1), new Map())
      .entries(),
  ].sort(),
);

const wbRows2 = parseCsv2(
  (await fs.readFile("wb_gdp_data.csv", "utf8")).replace(/^\uFEFF/, ""),
);
const wbHeaderRowIndex2 = wbRows2.findIndex((r) => r[0] === "Country Name");
const wbHeader2 = wbRows2[wbHeaderRowIndex2];
const wbData2 = wbRows2.slice(wbHeaderRowIndex2 + 1).filter((r) => r[0]);
const wbYears2 = wbHeader2.slice(4);
const wbCountries2 = new Set(wbData2.map((r) => r[0]));
const wbIndicatorNames2 = [...new Set(wbData2.map((r) => r[2]))];
const wbIndicatorCodes2 = [...new Set(wbData2.map((r) => r[3]))];
const wbMissingCount2 = wbData2
  .flatMap((r) => r.slice(4))
  .filter((v) => v === ".." || v === "").length;

console.log(
  JSON.stringify(
    {
      fh: {
        rows: fhData2.length,
        columns: fhRows2[0],
        countries: fhCountries2.size,
        yearMin: Math.min(...fhYears2),
        yearMax: Math.max(...fhYears2),
        statuses: fhStatuses2,
        statusCounts: fhStatusCounts2,
      },
      wb: {
        metadataRows: wbHeaderRowIndex2,
        rows: wbData2.length,
        columns: wbHeader2,
        countries: wbCountries2.size,
        yearMin: wbYears2[0],
        yearMax: wbYears2.at(-1),
        yearCount: wbYears2.length,
        indicatorNames: wbIndicatorNames2,
        indicatorCodes: wbIndicatorCodes2,
        missingCells: wbMissingCount2,
      },
    },
    null,
    2,
  ),
);
```

Raw tool result

```text
{
  "fh": {
    "rows": 4305,
    "columns": [
      "country",
      "year",
      "political_rights_score",
      "civil_liberties_score",
      "status"
    ],
    "countries": 205,
    "yearMin": 2000,
    "yearMax": 2020,
    "statuses": [
      "-",
      "Free",
      "Not Free",
      "Partly Free"
    ],
    "statusCounts": {
      "-": 236,
      "Free": 1837,
      "Not Free": 1006,
      "Partly Free": 1226
    }
  },
  "wb": {
    "metadataRows": 4,
    "rows": 266,
    "columns": [
      "Country Name",
      "Country Code",
      "Indicator Name",
      "Indicator Code",
      "2000",
      "2001",
      "2002",
      "2003",
      "2004",
      "2005",
      "2006",
      "2007",
      "2008",
      "2009",
      "2010",
      "2011",
      "2012",
      "2013",
      "2014",
      "2015",
      "2016",
      "2017",
      "2018",
      "2019",
      "2020"
    ],
    "countries": 266,
    "yearMin": "2000",
    "yearMax": "2020",
    "yearCount": 21,
    "indicatorNames": [
      "GDP per capita, PPP (constant 2021 international $)"
    ],
    "indicatorCodes": [
      "NY.GDP.PCAP.PP.KD"
    ],
    "missingCells": 452
  }
}
```

</details>

**Assistant**

I’ve got the core counts. I’m checking one last detail: whether the freedom file has true missing classifications and whether the GDP file mixes countries with regional aggregates.

<details>
<summary>Background terminal finished with rg -n ',-$' fh_ratings.csv | sed -n '1,8p'</summary>

Command

```bash
rg -n ',-$' fh_ratings.csv | sed -n '1,8p'
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "rg -n ',-"'$'"' fh_ratings.csv | sed -n '1,8p'"
Chunk ID: 11a96f
Wall time: 0.0003 seconds
Process exited with code 0
Original token count: 60
Output:
968:Czechoslovakia,2000,-,-,-
969:Czechoslovakia,2001,-,-,-
970:Czechoslovakia,2002,-,-,-
971:Czechoslovakia,2003,-,-,-
972:Czechoslovakia,2004,-,-,-
973:Czechoslovakia,2005,-,-,-
974:Czechoslovakia,2006,-,-,-
975:Czechoslovakia,2007,-,-,-
```

</details>

<details>
<summary>Background terminal finished with sed -n '1,20p' wb_gdp_data.csv | tail -n +5</summary>

Command

```bash
sed -n '1,20p' wb_gdp_data.csv | tail -n +5
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 2000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "sed -n '1,20p' wb_gdp_data.csv | tail -n +5"
Chunk ID: 45c1e4
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1637
Output:
Country Name,Country Code,Indicator Name,Indicator Code,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020
Aruba,ABW,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,37524.0346018738,38729.4588237779,38098.9048329158,38086.0123762909,40000.5625183532,38828.2166342999,38566.8026759098,39462.5851547515,39881.5879733887,34986.1484622772,33951.6246182745,34837.9402869986,33972.3401082566,35621.7848546409,35746.8630679768,36660.6310808381,36832.9040242979,38116.8310992499,39278.8297271438,39652.6320700939,30347.6371851417
Africa Eastern and Southern,AFE,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,3281.92231355821,3313.42192907347,3350.14106469179,3357.24486194198,3457.12022216505,3577.58589657798,3718.02241324132,3866.72395287147,3934.08423728651,3869.99812557719,3976.01098918458,4042.84247175787,4061.64163525859,4125.18256263035,4185.23841104229,4206.25295270438,4202.23641584707,4214.39708524398,4220.91250168849,4195.86541780344,3978.93139450095
Afghanistan,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1617.82647494969,1454.11078195559,1774.30874287341,1815.92819979586,1776.91820712187,1908.11478234587,1929.72389709372,2155.35306784413,2191.50435559062,2565.02208584099,2848.58606111495,2757.05258747725,2985.31914452845,3046.57993117128,3017.94254424696,2967.692067153,2958.7853992308,2952.99891598909,2902.39211285546,2927.2451435353,2769.68574507111
Africa Western and Central,AFW,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,3910.06236490428,4004.95777781768,4293.82350586776,4417.36900943689,4642.02674806122,4775.60343378638,4892.69193879293,5016.77062650533,5181.8092768247,5355.44022713777,5575.8352710514,5691.70513839694,5816.27571540301,6001.87015789534,6174.70331573076,6185.24923306943,6026.16020343528,5999.74477975389,6016.31221720046,6059.60602380843,5687.84998774233
Angola,AGO,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,6796.40633958013,6848.68325514498,7523.81291488042,7518.88948314505,8081.75707952672,8895.37660461036,9588.82238860962,10437.5899897179,11134.7047183666,10933.2712187989,11077.1301919405,11037.6388778577,11519.9933863364,11626.1877218484,11721.664415634,11392.9059344932,10805.7122533667,10414.2358842987,10000.9443772509,9648.01103767178,8960.33797242389
Albania,ALB,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,6582.01638790869,7232.99054955267,7590.49000137636,8025.28131314895,8483.29399553028,8964.31799702863,9564.02920336839,10262.9667962049,11056.3517677907,11430.6220336089,11829.0532917923,12153.1140578808,12463.5700560026,12873.4832044649,13366.5595256489,13876.81917653,14643.4892877063,15359.4611530571,16170.990493278,16761.1925261726,16457.7872245882
Andorra,AND,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,54809.1441679423,59109.0136806378,61188.6370851474,63656.1958332184,64353.4858113531,65114.8053663021,66390.293726491,65534.1168555171,60691.6051530637,57203.9708883731,58285.0577756981,60470.4662377044,58172.1323929474,57332.1585611366,59929.2444670501,62105.1542389675,64402.8092369468,63239.6577711911,63048.5985572834,63215.899791913,55488.4902991095
Arab World,ARB,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,12917.0342418817,12895.2135224156,12743.0694781131,12871.7031954571,13703.8513255052,14096.6978098463,14584.203656405,14885.4008312885,15342.6243511252,15154.7950466565,15570.139810079,15896.5397455117,16141.5972975632,16084.0540746644,16145.8898464544,16371.0585517114,16606.2469902024,16542.576023411,16723.8193619802,16782.382332277,15739.007645376
United Arab Emirates,ARE,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,95390.7013642316,90750.3469329386,87479.2448897115,89828.555395832,93166.1449767722,92750.9148604289,94872.5391985501,87180.6059048658,80285.1852028705,71488.9909603622,70913.778039508,73190.3973792075,73644.0532143257,74237.2214478496,74145.1093706462,75238.5539913477,75671.0564378596,72529.0571050174,72671.0652349751,72822.969940695,66808.6477560191
Argentina,ARG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,22281.3814299958,21066.4581290433,18571.7020471274,20005.1385113465,21591.449396517,23262.4075792816,24877.2493922296,26850.6645932771,27658.4853446831,25747.1976809995,28056.2570212342,29425.7593876084,28825.9666865361,29227.6601818437,28200.8152571318,28669.2061336796,27802.1057201498,28334.904184742,27367.115094108,26629.5529419501,23877.0931404855
Armenia,ARM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,4589.00825039977,5044.11647826468,5731.35346609213,6569.26023440631,7299.40001858866,8362.90084037119,9525.23146779778,10898.8483795634,11727.4821272949,10143.2350796867,10438.5641904169,10990.177099035,11796.2741177994,12194.0189523843,12667.3757330249,13112.7600902915,13192.9940731731,14243.8749003795,15037.0452420712,16215.3607965094,15052.9359791136
American Samoa,ASM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
Antigua and Barbuda,ATG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,25547.6120273679,24014.7754593511,23958.1638142071,25136.001691807,26303.6179046605,27692.634871178,30827.3416754916,33245.2678606765,32756.3067839753,28405.6900929523,25826.6086383235,25023.9174488179,25592.9066121735,25206.8458285339,25557.5249124716,25734.1516444171,26627.71453894,27199.1952402262,28878.6067675471,29651.8641526705,23930.6714995115
Australia,AUS,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,45099.0752097084,45428.2000837579,46688.3947760633,47581.2285345879,49061.2761503925,49997.0664953941,50693.7248649798,51663.2403953881,52473.3819160773,52407.9115378584,52739.2681556133,53257.0473237603,54403.8836902433,54895.0052588984,55489.068844146,55879.9958817103,56529.951824687,56881.8569859104,57647.8455068105,58048.6188420831,57260.4650530108
Austria,AUT,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,53534.3421055422,54032.1515612712,54565.0769851287,54919.7872512396,55980.2317146013,56890.2794433829,58460.2370782474,60470.9099906715,61157.9873105684,58810.4422020872,59730.5512005635,61272.2584397775,61376.7207131054,60863.0547666965,60845.6597729815,60951.6892510507,61572.7121353063,62535.8983176843,63778.0257051746,64609.374295614,60276.4153459141
```

</details>

**Assistant**

`fh_ratings.csv` is an annual country-level freedom dataset in long format. It has 4,305 data rows, 5 columns, and covers 2000 through 2020 for 205 country/entity names. Each row is one `country` and `year`, with two rating fields, `political_rights_score` and `civil_liberties_score`, plus an overall `status` label: `Free`, `Partly Free`, `Not Free`, or `-` when the entry is unrated/missing. At a high level, it is a panel dataset for tracking changes in political freedom and civil liberties over time.

`wb_gdp_data.csv` is a World Bank World Development Indicators extract in wide format. It has 4 metadata/header rows at the top, then 266 entity rows. Each row contains `Country Name`, `Country Code`, `Indicator Name`, `Indicator Code`, and annual values from 2000 to 2020. The indicator is the same for every row: `GDP per capita, PPP (constant 2021 international $)` with code `NY.GDP.PCAP.PP.KD`. It includes both countries and aggregate regions/groups such as `World`, `Arab World`, and `Africa Eastern and Southern`, and missing values are encoded as `..`.

High level, the two files line up well conceptually: `fh_ratings.csv` measures political/civil freedom, while `wb_gdp_data.csv` measures economic output per person. They cover the same 2000-2020 period, so they are suitable for comparative analysis, but the GDP file would need to be reshaped from wide to long format before merging cleanly on country-year.

**User**

any issues you'll run into merging them?

**Assistant**

I’m checking the practical merge risks directly: duplicate keys, country-name mismatches, and whether the GDP extract includes non-country aggregates that should be filtered out.

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv
from collections import Counter

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
rows=list(csv.DictReader(f))
keys=Counter((r['country'], r['year']) for r in rows)
dups=[k for k,v in keys.items() if v>1]
print('fh_rows', len(rows))
print('fh_duplicate_country_year_keys', len(dups))
print('fh_example_dups', dups[:10])
PY</summary>

Command

```bash
python3 - <<'PY'
import csv
from collections import Counter

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
keys=Counter((r['country'], r['year']) for r in rows)
dups=[k for k,v in keys.items() if v>1]
print('fh_rows', len(rows))
print('fh_duplicate_country_year_keys', len(dups))
print('fh_example_dups', dups[:10])
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv
from collections import Counter

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
keys=Counter((r['country'], r['year']) for r in rows)
dups=[k for k,v in keys.items() if v>1]
print('fh_rows', len(rows))
print('fh_duplicate_country_year_keys', len(dups))
print('fh_example_dups', dups[:10])
PY"
Chunk ID: 66afda
Wall time: 0.0103 seconds
Process exited with code 0
Original token count: 17
Output:
fh_rows 4305
fh_duplicate_country_year_keys 0
fh_example_dups []
```

</details>

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
data=rows[header_idx+1:]
print('wb_rows', len(data))
print('wb_unique_entities', len({r[0] for r in data if r}))
print('wb_example_aggregates', [r[0] for r in data[:15]])
PY</summary>

Command

```bash
python3 - <<'PY'
import csv
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
data=rows[header_idx+1:]
print('wb_rows', len(data))
print('wb_unique_entities', len({r[0] for r in data if r}))
print('wb_example_aggregates', [r[0] for r in data[:15]])
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
data=rows[header_idx+1:]
print('wb_rows', len(data))
print('wb_unique_entities', len({r[0] for r in data if r}))
print('wb_example_aggregates', [r[0] for r in data[:15]])
PY"
Chunk ID: b591d4
Wall time: 0.0075 seconds
Process exited with code 0
Original token count: 76
Output:
wb_rows 266
wb_unique_entities 266
wb_example_aggregates ['Aruba', 'Africa Eastern and Southern', 'Afghanistan', 'Africa Western and Central', 'Angola', 'Albania', 'Andorra', 'Arab World', 'United Arab Emirates', 'Argentina', 'Armenia', 'American Samoa', 'Antigua and Barbuda', 'Australia', 'Austria']
```

</details>

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
fh={r['country'] for r in csv.DictReader(f)}
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb={r[0] for r in rows[header_idx+1:] if r}

only_fh=sorted(fh-wb)
only_wb=sorted(wb-fh)
print('fh_not_in_wb_count', len(only_fh))
print('fh_not_in_wb_examples', only_fh[:40])
print('wb_not_in_fh_count', len(only_wb))
print('wb_not_in_fh_examples', only_wb[:60])
PY</summary>

Command

```bash
python3 - <<'PY'
import csv

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh={r['country'] for r in csv.DictReader(f)}
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb={r[0] for r in rows[header_idx+1:] if r}

only_fh=sorted(fh-wb)
only_wb=sorted(wb-fh)
print('fh_not_in_wb_count', len(only_fh))
print('fh_not_in_wb_examples', only_fh[:40])
print('wb_not_in_fh_count', len(only_wb))
print('wb_not_in_fh_examples', only_wb[:60])
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 4000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh={r['country'] for r in csv.DictReader(f)}
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb={r[0] for r in rows[header_idx+1:] if r}

only_fh=sorted(fh-wb)
only_wb=sorted(wb-fh)
print('fh_not_in_wb_count', len(only_fh))
print('fh_not_in_wb_examples', only_fh[:40])
print('wb_not_in_fh_count', len(only_wb))
print('wb_not_in_fh_examples', only_wb[:60])
PY"
Chunk ID: 411461
Wall time: 0.0091 seconds
Process exited with code 0
Original token count: 499
Output:
fh_not_in_wb_count 32
fh_not_in_wb_examples ['Bahamas', 'Brunei', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Czech Republic', 'Czechoslovakia', 'Egypt', 'Germany, E.', 'Germany, W.', 'Iran', 'Kyrgyzstan', 'Laos', 'Micronesia', 'North Korea', 'Russia', 'Serbia and Montenegro', 'Slovakia', 'Somalia', 'South Korea', 'Syria', 'Taiwan', 'The Gambia', 'Turkey', 'USSR', 'Venezuela', 'Vietnam', 'Vietnam, N.', 'Vietnam, S.', 'Yemen', 'Yemen, N.', 'Yemen, S.', 'Yugoslavia']
wb_not_in_fh_count 93
wb_not_in_fh_examples ['Africa Eastern and Southern', 'Africa Western and Central', 'American Samoa', 'Arab World', 'Aruba', 'Bahamas, The', 'Bermuda', 'British Virgin Islands', 'Brunei Darussalam', 'Caribbean small states', 'Cayman Islands', 'Central Europe and the Baltics', 'Channel Islands', 'Congo, Dem. Rep.', 'Congo, Rep.', 'Curacao', 'Czechia', 'Early-demographic dividend', 'East Asia & Pacific', 'East Asia & Pacific (IDA & IBRD countries)', 'East Asia & Pacific (excluding high income)', 'Egypt, Arab Rep.', 'Euro area', 'Europe & Central Asia', 'Europe & Central Asia (IDA & IBRD countries)', 'Europe & Central Asia (excluding high income)', 'European Union', 'Faroe Islands', 'Fragile and conflict affected situations', 'French Polynesia', 'Gambia, The', 'Gibraltar', 'Greenland', 'Guam', 'Heavily indebted poor countries (HIPC)', 'High income', 'Hong Kong SAR, China', 'IBRD only', 'IDA & IBRD total', 'IDA blend', 'IDA only', 'IDA total', 'Iran, Islamic Rep.', 'Isle of Man', "Korea, Dem. People's Rep.", 'Korea, Rep.', 'Kyrgyz Republic', 'Lao PDR', 'Late-demographic dividend', 'Latin America & Caribbean', 'Latin America & Caribbean (excluding high income)', 'Latin America & the Caribbean (IDA & IBRD countries)', 'Least developed countries: UN classification', 'Low & middle income', 'Low income', 'Lower middle income', 'Macao SAR, China', 'Micronesia, Fed. Sts.', 'Middle East, North Africa, Afghanistan & Pakistan', 'Middle East, North Africa, Afghanistan & Pakistan (IDA & IBRD)']
```

</details>

**Assistant**

Yes. The main merge issues are structural and naming-related, not duplicate-key problems.

From [fh_ratings.csv](/Users/christopherlee/Documents/Github/ucsb-workshop/fh_ratings.csv) and [wb_gdp_data.csv](/Users/christopherlee/Documents/Github/ucsb-workshop/wb_gdp_data.csv):

- `wb_gdp_data.csv` is wide, not long. You need to pivot years `2000` through `2020` into rows before you can merge on `country` + `year`.
- The GDP file includes many non-country aggregates, such as `World`, `Arab World`, and `Africa Eastern and Southern`. Those should usually be filtered out before merging.
- Country names don't line up cleanly. Examples:
  - `Bahamas` vs `Bahamas, The`
  - `Brunei` vs `Brunei Darussalam`
  - `Czech Republic` vs `Czechia`
  - `Egypt` vs `Egypt, Arab Rep.`
  - `Iran` vs `Iran, Islamic Rep.`
  - `Laos` vs `Lao PDR`
  - `South Korea` vs `Korea, Rep.`
  - `North Korea` vs `Korea, Dem. People's Rep.`
  - `Turkey` vs `Turkiye`
- `fh_ratings.csv` includes historical/legacy entities that are not really mergeable to modern World Bank rows, such as `USSR`, `Yugoslavia`, `Czechoslovakia`, `Germany, E.`, `Germany, W.`, and `Serbia and Montenegro`.
- `fh_ratings.csv` has unrated rows with `-` in the score/status fields. Those need to be converted to missing values before analysis.
- `wb_gdp_data.csv` uses `..` for missing GDP values, so those also need cleaning.
- Coverage is not identical. I found 32 entity names in Freedom House not present verbatim in the World Bank file, and 93 in the World Bank file not present verbatim in Freedom House, though many of those 93 are regional aggregates or naming variants.

The good news is the key structure is otherwise clean:

- `fh_ratings.csv` has no duplicate `country` + `year` rows.
- `wb_gdp_data.csv` has one row per entity before reshaping.

So the workable path is: reshape GDP to long, drop aggregates, standardize country names with a mapping table, convert `-` and `..` to nulls, then merge on normalized `country` + `year`.

If you want, I can do that cleanup and produce a merged CSV next.

**User**

can you provide this mapping table for standardizing country names?

**Assistant**

I’m deriving the name crosswalk from the actual unmatched entities in both files, then I’ll separate true aliases from legacy entities that should stay unmapped.

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv, difflib
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_fh=[x for x in fh if x not in wb]
for name in only_fh:
    matches=difflib.get_close_matches(name, wb, n=5, cutoff=0.45)
    print(name, '=>', matches)
PY</summary>

Command

```bash
python3 - <<'PY'
import csv, difflib
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_fh=[x for x in fh if x not in wb]
for name in only_fh:
    matches=difflib.get_close_matches(name, wb, n=5, cutoff=0.45)
    print(name, '=>', matches)
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 4000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv, difflib
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_fh=[x for x in fh if x not in wb]
for name in only_fh:
    matches=difflib.get_close_matches(name, wb, n=5, cutoff=0.45)
    print(name, '=>', matches)
PY"
Chunk ID: ffe0d2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 535
Output:
Bahamas => ['Bahamas, The', 'Panama', 'Bahrain', 'Barbados', 'Samoa']
Brunei => ['Burundi', 'Benin', 'Brunei Darussalam', 'Guinea', 'France']
Congo (Brazzaville) => ['Brazil', 'Congo, Rep.']
Congo (Kinshasa) => ['Mongolia']
Czech Republic => ['Kyrgyz Republic', 'Central African Republic', 'Dominican Republic', 'Slovak Republic', 'Czechia']
Czechoslovakia => ['Czechia', 'Colombia', 'Costa Rica', 'Moldova', 'Croatia']
Egypt => ['Egypt, Arab Rep.']
Germany, E. => ['Germany', 'Yemen, Rep.', 'Korea, Rep.']
Germany, W. => ['Germany', 'Yemen, Rep.', 'Korea, Rep.']
Iran => ['Iraq', 'Ireland', 'Jordan', 'Israel', 'France']
Kyrgyzstan => ['Kazakhstan', 'Uzbekistan', 'Kyrgyz Republic', 'Afghanistan', 'Turkmenistan']
Laos => ['Lebanon', 'Lao PDR', 'Barbados']
Micronesia => ['Micronesia, Fed. Sts.', 'Indonesia', 'Mongolia', 'French Polynesia', 'Monaco']
North Korea => ['North America', 'Eritrea', 'North Macedonia', 'South Africa', 'Euro area']
Russia => ['Tunisia', 'Austria', 'Australia', 'South Asia', 'Russian Federation']
Serbia and Montenegro => ['Montenegro', 'Trinidad and Tobago', 'Bosnia and Herzegovina', 'Sierra Leone', 'Africa Eastern and Southern']
Slovakia => ['Slovenia', 'Slovak Republic', 'Romania', 'Moldova', 'Croatia']
Somalia => ['Romania', 'Jamaica', 'Croatia', 'Bolivia', 'Somalia, Fed. Rep.']
South Korea => ['South Africa', 'South Asia', 'South Sudan', 'North America', 'Euro area']
Syria => ['Serbia', 'Suriname', 'Sri Lanka', 'Nigeria', 'Liberia']
Taiwan => ['Thailand', 'Tajikistan', 'Tanzania', 'Pakistan', 'Spain']
The Gambia => ['Zambia', 'Namibia', 'Gambia, The', 'Thailand', 'Cambodia']
Turkey => ['Turkiye', 'Turkmenistan', 'Hungary']
USSR => []
Venezuela => ['Venezuela, RB', 'Senegal']
Vietnam => ['Viet Nam', 'Suriname', 'Kenya', 'China', 'Argentina']
Vietnam, N. => ['Viet Nam', 'Venezuela, RB', 'Yemen, Rep.', 'Korea, Rep.']
Vietnam, S. => ['Viet Nam', 'Venezuela, RB', 'Yemen, Rep.', 'Korea, Rep.']
Yemen => ['Yemen, Rep.', 'Sweden', 'Germany', 'Armenia', 'Cameroon']
Yemen, N. => ['Yemen, Rep.']
Yemen, S. => ['Yemen, Rep.']
Yugoslavia => ['Bolivia', 'Mongolia', 'Bulgaria', 'Australia', 'Latvia']
```

</details>

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_wb=[x for x in wb if x not in fh]
for name in only_wb[:120]:
    print(name)
PY</summary>

Command

```bash
python3 - <<'PY'
import csv
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_wb=[x for x in wb if x not in fh]
for name in only_wb[:120]:
    print(name)
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 5000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_wb=[x for x in wb if x not in fh]
for name in only_wb[:120]:
    print(name)
PY"
Chunk ID: 046afa
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 500
Output:
Africa Eastern and Southern
Africa Western and Central
American Samoa
Arab World
Aruba
Bahamas, The
Bermuda
British Virgin Islands
Brunei Darussalam
Caribbean small states
Cayman Islands
Central Europe and the Baltics
Channel Islands
Congo, Dem. Rep.
Congo, Rep.
Curacao
Czechia
Early-demographic dividend
East Asia & Pacific
East Asia & Pacific (IDA & IBRD countries)
East Asia & Pacific (excluding high income)
Egypt, Arab Rep.
Euro area
Europe & Central Asia
Europe & Central Asia (IDA & IBRD countries)
Europe & Central Asia (excluding high income)
European Union
Faroe Islands
Fragile and conflict affected situations
French Polynesia
Gambia, The
Gibraltar
Greenland
Guam
Heavily indebted poor countries (HIPC)
High income
Hong Kong SAR, China
IBRD only
IDA & IBRD total
IDA blend
IDA only
IDA total
Iran, Islamic Rep.
Isle of Man
Korea, Dem. People's Rep.
Korea, Rep.
Kyrgyz Republic
Lao PDR
Late-demographic dividend
Latin America & Caribbean
Latin America & Caribbean (excluding high income)
Latin America & the Caribbean (IDA & IBRD countries)
Least developed countries: UN classification
Low & middle income
Low income
Lower middle income
Macao SAR, China
Micronesia, Fed. Sts.
Middle East, North Africa, Afghanistan & Pakistan
Middle East, North Africa, Afghanistan & Pakistan (IDA & IBRD)
Middle East, North Africa, Afghanistan & Pakistan (excluding high income)
Middle income
New Caledonia
North America
Northern Mariana Islands
Not classified
OECD members
Other small states
Pacific island small states
Post-demographic dividend
Pre-demographic dividend
Puerto Rico (US)
Russian Federation
Sint Maarten (Dutch part)
Slovak Republic
Small states
Somalia, Fed. Rep.
South Asia
South Asia (IDA & IBRD)
St. Martin (French part)
Sub-Saharan Africa
Sub-Saharan Africa (IDA & IBRD countries)
Sub-Saharan Africa (excluding high income)
Syrian Arab Republic
Turkiye
Turks and Caicos Islands
Upper middle income
Venezuela, RB
Viet Nam
Virgin Islands (U.S.)
West Bank and Gaza
World
Yemen, Rep.
```

</details>

**Assistant**

I’ve got the unmatched names. I’m verifying a few ambiguous cases like Taiwan and Congo so the table distinguishes direct aliases from cases with no safe one-to-one match.

<details>
<summary>Background terminal finished with rg -n '^Tai|Taiwan|Korea, Dem|Korea, Rep.|Russian Federation|Turkiye|Syrian Arab Republic|Venezuela, RB|Viet Nam|Yemen, Rep.|Somalia, Fed. Rep.|Congo, Dem. Rep.|Congo, Rep.' wb_gdp_data.csv</summary>

Command

```bash
rg -n '^Tai|Taiwan|Korea, Dem|Korea, Rep.|Russian Federation|Turkiye|Syrian Arab Republic|Venezuela, RB|Viet Nam|Yemen, Rep.|Somalia, Fed. Rep.|Congo, Dem. Rep.|Congo, Rep.' wb_gdp_data.csv
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 3000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "rg -n '"'^Tai|Taiwan|Korea, Dem|Korea, Rep.|Russian Federation|Turkiye|Syrian Arab Republic|Venezuela, RB|Viet Nam|Yemen, Rep.|Somalia, Fed. Rep.|Congo, Dem. Rep.|Congo, Rep.'"' wb_gdp_data.csv"
Chunk ID: 275017
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1012
Output:
49:"Congo, Dem. Rep.",COD,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,981.422080026496,930.8578260745,929.452807092197,953.044643237975,987.746920877671,1016.6338907666,1036.13329616563,1091.0040733455,1136.9467210438,1108.57506967795,1158.01422439658,1205.73984565432,1267.23827814087,1342.74156112016,1392.75841334673,1433.21579814709,1388.80163211633,1395.71961761712,1431.7285269693,1394.76461472893,1361.02791099008
50:"Congo, Rep.",COG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,7570.58279297342,7567.43916817104,7729.3889014189,7577.45630456352,7577.44597274153,7876.11806658285,8192.57979719023,7375.82454100145,7587.28826163492,8138.45033818822,8583.66179834288,8492.42098392103,9079.68656400891,8798.49382506314,9159.25664414045,8977.70681641417,8001.8255194505,7372.77046737599,7029.33651566257,6939.36913397466,6350.50913409722
132:"Korea, Rep.",KOR,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,26404.1588575745,27440.6564915847,29395.2766036796,30142.4605585814,31579.5283048867,32886.0472450743,34429.6222013137,36257.3401689702,37064.1518484106,37176.1939270969,39574.0500732947,40719.2273771671,41537.6728611117,42709.9751857887,43806.9250985661,44849.106767089,46088.2430785383,47536.7898568851,48834.4471157733,49790.8679884821,49374.0926148025
199:"Korea, Dem. People's Rep.",PRK,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
208:Russian Federation,RUS,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,20104.75,21219.904296875,22319.677734375,24057.927734375,25894.146484375,27656.658203125,30022.671875,32630.369140625,34342.2265625,31654.00390625,33063.5859375,34444.64453125,35740.69921875,36260.06640625,35853.24609375,35037.15234375,35005.44921875,35570.390625,36536.6953125,37319.3203125,36376.3359375
219:"Somalia, Fed. Rep.",SOM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,702.850071117611,723.37973295051,749.913550912115,777.884552733299,803.059780580571,830.807245939909,857.993655901941,887.36935223579,919.132083452828,956.220325392405,995.225555512318,1054.36214979106,1114.80414892809,1197.72166459747,1259.27432882615,1343.92517439606,1384.15517410135,1527.66029071672,1504.97908203481,1491.29493918405,1396.05544674978
233:Syrian Arab Republic,SYR,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,7807.92995548742,7698.3969367866,7811.81601910972,8177.54506854394,8528.65604597154,8828.94590461523,8885.80539630373,8829.30906367425,8903.77225264131,9284.10162126587,9546.56048805399,9648.82426718361,7144.37244952745,5530.97472757035,5301.62535257941,5302.07413721497,5022.1127178437,4977.79210063798,4956.09296464777,4825.40602053761,4633.34951847728
250:Turkiye,TUR,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,14856.914625257,13872.1287284566,14587.894621656,15255.7177960689,16583.8837094062,17902.5966004487,18972.0089563632,19909.083519675,19839.5845462269,18612.1925231905,19897.4464836887,21760.4291923634,22517.7148287875,24117.3121099839,24888.8521742576,25984.8027306177,26489.6436816046,28194.688309381,28780.3036458151,28741.0535082701,28976.7179161611
260:"Venezuela, RB",VEN,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
263:Viet Nam,VNM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,4349.22348751462,4570.26846577385,4809.62151118535,5090.28990869885,5421.09702829121,5776.36304451268,6098.24495436514,6418.54381402878,6659.13561432065,6914.33863589545,7274.73310698016,7652.61895672271,7979.86828926858,8324.19871250047,8751.88280634375,9248.02396069622,9743.1882231455,10290.5487142244,10936.8937859558,11628.6139147451,11851.3965685649
268:"Yemen, Rep.",YEM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
```

</details>

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv
from collections import defaultdict
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
by_country=defaultdict(lambda: [9999, -9999])
for r in rows:
    y=int(r['year'])
    by_country[r['country']][0]=min(by_country[r['country']][0], y)
    by_country[r['country']][1]=max(by_country[r['country']][1], y)
for name in ['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']:
    if name in by_country:
        print(name, by_country[name][0], by_country[name][1])
PY</summary>

Command

```bash
python3 - <<'PY'
import csv
from collections import defaultdict
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
by_country=defaultdict(lambda: [9999, -9999])
for r in rows:
    y=int(r['year'])
    by_country[r['country']][0]=min(by_country[r['country']][0], y)
    by_country[r['country']][1]=max(by_country[r['country']][1], y)
for name in ['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']:
    if name in by_country:
        print(name, by_country[name][0], by_country[name][1])
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv
from collections import defaultdict
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
by_country=defaultdict(lambda: [9999, -9999])
for r in rows:
    y=int(r['year'])
    by_country[r['country']][0]=min(by_country[r['country']][0], y)
    by_country[r['country']][1]=max(by_country[r['country']][1], y)
for name in ['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']:
    if name in by_country:
        print(name, by_country[name][0], by_country[name][1])
PY"
Chunk ID: af7c69
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 60
Output:
USSR 2000 2020
Yugoslavia 2000 2020
Czechoslovakia 2000 2020
Germany, E. 2000 2020
Germany, W. 2000 2020
Serbia and Montenegro 2000 2020
Vietnam, N. 2000 2020
Vietnam, S. 2000 2020
Yemen, N. 2000 2020
Yemen, S. 2000 2020
Taiwan 2000 2020
```

</details>

**Assistant**

Some legacy names appear in Freedom House for all years, which is a signal they’re placeholder series rather than mergeable country observations. I’m checking their status values before finalizing the crosswalk.

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv
from collections import defaultdict
names=['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']
vals=defaultdict(set)
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add((r['political_rights_score'], r['civil_liberties_score'], r['status']))
for n in names:
    print(n, sorted(vals[n]))
PY</summary>

Command

```bash
python3 - <<'PY'
import csv
from collections import defaultdict
names=['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']
vals=defaultdict(set)
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add((r['political_rights_score'], r['civil_liberties_score'], r['status']))
for n in names:
    print(n, sorted(vals[n]))
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 3000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv
from collections import defaultdict
names=['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']
vals=defaultdict(set)
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add((r['political_rights_score'], r['civil_liberties_score'], r['status']))
for n in names:
    print(n, sorted(vals[n]))
PY"
Chunk ID: 30b493
Wall time: 0.1207 seconds
Process exited with code 0
Original token count: 121
Output:
USSR [('-', '-', '-')]
Yugoslavia [('-', '-', '-'), ('3', '2', 'Free'), ('3', '3', 'Partly Free'), ('4', '4', 'Partly Free')]
Czechoslovakia [('-', '-', '-')]
Germany, E. [('-', '-', '-')]
Germany, W. [('-', '-', '-')]
Serbia and Montenegro [('-', '-', '-'), ('3', '2', 'Free')]
Vietnam, N. [('-', '-', '-')]
Vietnam, S. [('-', '-', '-')]
Yemen, N. [('-', '-', '-')]
Yemen, S. [('-', '-', '-')]
Taiwan [('1', '1', 'Free'), ('1', '2', 'Free'), ('2', '1', 'Free'), ('2', '2', 'Free')]
```

</details>

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv
names=['Congo (Brazzaville)','Congo (Kinshasa)','North Korea','South Korea','Russia','Turkey','Venezuela','Vietnam','Syria','Somalia','Yemen']
vals={n:set() for n in names}
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add(r['status'])
for n in names:
    print(n, sorted(vals[n]))
PY</summary>

Command

```bash
python3 - <<'PY'
import csv
names=['Congo (Brazzaville)','Congo (Kinshasa)','North Korea','South Korea','Russia','Turkey','Venezuela','Vietnam','Syria','Somalia','Yemen']
vals={n:set() for n in names}
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add(r['status'])
for n in names:
    print(n, sorted(vals[n]))
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv
names=['Congo (Brazzaville)','Congo (Kinshasa)','North Korea','South Korea','Russia','Turkey','Venezuela','Vietnam','Syria','Somalia','Yemen']
vals={n:set() for n in names}
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add(r['status'])
for n in names:
    print(n, sorted(vals[n]))
PY"
Chunk ID: 519cde
Wall time: 0.1215 seconds
Process exited with code 0
Original token count: 82
Output:
Congo (Brazzaville) ['Not Free', 'Partly Free']
Congo (Kinshasa) ['Not Free']
North Korea ['Not Free']
South Korea ['Free']
Russia ['Not Free', 'Partly Free']
Turkey ['Not Free', 'Partly Free']
Venezuela ['Not Free', 'Partly Free']
Vietnam ['Not Free']
Syria ['Not Free']
Somalia ['Not Free']
Yemen ['Not Free', 'Partly Free']
```

</details>

**Assistant**

I’ve separated the clean aliases from the cases that should be dropped or handled manually. I’m writing the crosswalk as a small CSV so you can use it directly in a merge step.

<details>
<summary>Applied patch to add /Users/christopherlee/Documents/Github/ucsb-workshop/country_name_mapping.csv</summary>

Patch

```diff
*** Begin Patch
*** Add File: /Users/christopherlee/Documents/Github/ucsb-workshop/country_name_mapping.csv
+fh_country,wb_country,action,notes
+Bahamas,"Bahamas, The",map,Direct naming variant
+Brunei,Brunei Darussalam,map,Direct naming variant
+"Congo (Brazzaville)","Congo, Rep.",map,Republic of the Congo
+"Congo (Kinshasa)","Congo, Dem. Rep.",map,Democratic Republic of the Congo
+"Czech Republic",Czechia,map,Renamed country label in World Bank data
+Czechoslovakia,,drop,Historical entity with no direct 2000-2020 World Bank country row
+Egypt,"Egypt, Arab Rep.",map,Direct naming variant
+"Germany, E.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+"Germany, W.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+Iran,"Iran, Islamic Rep.",map,Direct naming variant
+Kyrgyzstan,"Kyrgyz Republic",map,Direct naming variant
+Laos,"Lao PDR",map,Direct naming variant
+Micronesia,"Micronesia, Fed. Sts.",map,Direct naming variant
+"North Korea","Korea, Dem. People's Rep.",map,Direct naming variant
+Russia,"Russian Federation",map,Direct naming variant
+"Serbia and Montenegro",,drop,Historical composite entity with no direct single-country World Bank row
+Slovakia,"Slovak Republic",map,Direct naming variant
+Somalia,"Somalia, Fed. Rep.",map,Direct naming variant
+"South Korea","Korea, Rep.",map,Direct naming variant
+Syria,"Syrian Arab Republic",map,Direct naming variant
+Taiwan,,manual,No Taiwan row appears in this World Bank extract
+"The Gambia","Gambia, The",map,Direct naming variant
+Turkey,Turkiye,map,Renamed country label in World Bank data
+USSR,,drop,Historical entity with no direct 2000-2020 World Bank country row
+Venezuela,"Venezuela, RB",map,Direct naming variant
+Vietnam,"Viet Nam",map,Direct naming variant
+"Vietnam, N.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+"Vietnam, S.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+Yemen,"Yemen, Rep.",map,Direct naming variant
+"Yemen, N.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+"Yemen, S.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+Yugoslavia,,drop,Historical entity with no direct 2000-2020 World Bank country row
*** End Patch
```

Raw tool result

```text
{"output":"Success. Updated the following files:\nA /Users/christopherlee/Documents/Github/ucsb-workshop/country_name_mapping.csv\n","metadata":{"exit_code":0,"duration_seconds":0.1}}
```

</details>

**Assistant**

I created a crosswalk at [country_name_mapping.csv](/Users/christopherlee/Documents/Github/ucsb-workshop/country_name_mapping.csv).

It has four columns:

- `fh_country`: name from Freedom House
- `wb_country`: matching name in the World Bank GDP extract
- `action`: `map`, `drop`, or `manual`
- `notes`: why that row is handled that way

The clean aliases in the file include cases like:

- `Bahamas` -> `Bahamas, The`
- `Congo (Kinshasa)` -> `Congo, Dem. Rep.`
- `South Korea` -> `Korea, Rep.`
- `North Korea` -> `Korea, Dem. People's Rep.`
- `Turkey` -> `Turkiye`
- `Vietnam` -> `Viet Nam`

Rows marked `drop` are historical or composite entities with no safe one-to-one World Bank country row in this extract, such as `USSR`, `Czechoslovakia`, `Yugoslavia`, and `Serbia and Montenegro`.

`Taiwan` is marked `manual` because this specific World Bank extract does not include a Taiwan row, so there is no direct target to merge onto.

This table only handles naming mismatches. You still need to:

- reshape [wb_gdp_data.csv](/Users/christopherlee/Documents/Github/ucsb-workshop/wb_gdp_data.csv) from wide to long
- drop World Bank aggregates like `World` and `Arab World`
- convert `-` and `..` to nulls before analysis

If you want, I can use this crosswalk next and produce a merge-ready country-year dataset.

**User**

ok please merge, and please retain country_fh and country_wb columns so we can see the country aliases you resolved

**Assistant**

I’m building the merged country-year panel now. The output will keep both `country_fh` and `country_wb`, use the crosswalk for renamed countries, and leave GDP null where a Freedom House row has no safe World Bank match.

<details>
<summary>Background terminal finished with sed -n '1,40p' country_name_mapping.csv</summary>

Command

```bash
sed -n '1,40p' country_name_mapping.csv
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 2000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "sed -n '1,40p' country_name_mapping.csv"
Chunk ID: bf343d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 531
Output:
fh_country,wb_country,action,notes
Bahamas,"Bahamas, The",map,Direct naming variant
Brunei,Brunei Darussalam,map,Direct naming variant
"Congo (Brazzaville)","Congo, Rep.",map,Republic of the Congo
"Congo (Kinshasa)","Congo, Dem. Rep.",map,Democratic Republic of the Congo
"Czech Republic",Czechia,map,Renamed country label in World Bank data
Czechoslovakia,,drop,Historical entity with no direct 2000-2020 World Bank country row
Egypt,"Egypt, Arab Rep.",map,Direct naming variant
"Germany, E.",,drop,Historical entity with no direct 2000-2020 World Bank country row
"Germany, W.",,drop,Historical entity with no direct 2000-2020 World Bank country row
Iran,"Iran, Islamic Rep.",map,Direct naming variant
Kyrgyzstan,"Kyrgyz Republic",map,Direct naming variant
Laos,"Lao PDR",map,Direct naming variant
Micronesia,"Micronesia, Fed. Sts.",map,Direct naming variant
"North Korea","Korea, Dem. People's Rep.",map,Direct naming variant
Russia,"Russian Federation",map,Direct naming variant
"Serbia and Montenegro",,drop,Historical composite entity with no direct single-country World Bank row
Slovakia,"Slovak Republic",map,Direct naming variant
Somalia,"Somalia, Fed. Rep.",map,Direct naming variant
"South Korea","Korea, Rep.",map,Direct naming variant
Syria,"Syrian Arab Republic",map,Direct naming variant
Taiwan,,manual,No Taiwan row appears in this World Bank extract
"The Gambia","Gambia, The",map,Direct naming variant
Turkey,Turkiye,map,Renamed country label in World Bank data
USSR,,drop,Historical entity with no direct 2000-2020 World Bank country row
Venezuela,"Venezuela, RB",map,Direct naming variant
Vietnam,"Viet Nam",map,Direct naming variant
"Vietnam, N.",,drop,Historical entity with no direct 2000-2020 World Bank country row
"Vietnam, S.",,drop,Historical entity with no direct 2000-2020 World Bank country row
Yemen,"Yemen, Rep.",map,Direct naming variant
"Yemen, N.",,drop,Historical entity with no direct 2000-2020 World Bank country row
"Yemen, S.",,drop,Historical entity with no direct 2000-2020 World Bank country row
Yugoslavia,,drop,Historical entity with no direct 2000-2020 World Bank country row
```

</details>

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv
from pathlib import Path

root = Path('.')
fh_path = root / 'fh_ratings.csv'
wb_path = root / 'wb_gdp_data.csv'
map_path = root / 'country_name_mapping.csv'
out_path = root / 'fh_wb_merged.csv'
summary_path = root / 'fh_wb_merge_summary.txt'

# Load mapping table

name_map = {}
map_action = {}
with map_path.open(newline='', encoding='utf-8') as f:
for row in csv.DictReader(f):
name_map[row['fh_country']] = row['wb_country']
map_action[row['fh_country']] = row['action']

# Load WB wide extract and reshape to long

with wb_path.open(newline='', encoding='utf-8-sig') as f:
rows = list(csv.reader(f))
header_idx = next(i for i, r in enumerate(rows) if r and r[0] == 'Country Name')
header = rows[header_idx]
years = header[4:]
wb_long = {}
for r in rows[header_idx + 1:]:
if not r or not r[0]:
continue
country_name, country_code, indicator_name, indicator_code = r[:4]
for year, value in zip(years, r[4:]):
wb_long[(country_name, year)] = {
'country_wb': country_name,
'country_code_wb': country_code,
'indicator_name_wb': indicator_name,
'indicator_code_wb': indicator_code,
'year': year,
'gdp_per_capita_ppp_constant_2021_intl_dollars': None if value in ('', '..') else value,
}

# Merge onto FH rows

fieldnames = [
'country_fh',
'country_wb',
'year',
'political_rights_score',
'civil_liberties_score',
'status',
'country_code_wb',
'indicator_name_wb',
'indicator_code_wb',
'gdp_per_capita_ppp_constant_2021_intl_dollars',
'merge_status',
'merge_note',
]

merged_rows = []
stats = {
'exact_match': 0,
'mapped_alias': 0,
'dropped_historical': 0,
'manual_unresolved': 0,
'no_wb_row_after_mapping': 0,
'matched_with_gdp_value': 0,
'matched_but_gdp_missing': 0,
}

with fh_path.open(newline='', encoding='utf-8') as f:
for row in csv.DictReader(f):
country_fh = row['country']
year = row['year']
pr = None if row['political_rights_score'] == '-' else row['political_rights_score']
cl = None if row['civil_liberties_score'] == '-' else row['civil_liberties_score']
status = None if row['status'] == '-' else row['status']

        action = map_action.get(country_fh)
        if action == 'map':
            country_wb = name_map[country_fh]
            merge_status = 'mapped_alias'
            merge_note = 'Matched using country_name_mapping.csv'
            stats['mapped_alias'] += 1
        elif action == 'drop':
            country_wb = None
            merge_status = 'dropped_historical'
            merge_note = 'Historical/composite entity intentionally left unmatched'
            stats['dropped_historical'] += 1
        elif action == 'manual':
            country_wb = None
            merge_status = 'manual_unresolved'
            merge_note = 'Requires manual handling; no safe WB row in this extract'
            stats['manual_unresolved'] += 1
        else:
            country_wb = country_fh
            merge_status = 'exact_match'
            merge_note = 'Exact country-name match'
            stats['exact_match'] += 1

        wb = wb_long.get((country_wb, year)) if country_wb else None
        if wb is None and country_wb and merge_status in ('exact_match', 'mapped_alias'):
            stats['no_wb_row_after_mapping'] += 1

        gdp_value = wb['gdp_per_capita_ppp_constant_2021_intl_dollars'] if wb else None
        if wb is not None:
            if gdp_value is None:
                stats['matched_but_gdp_missing'] += 1
            else:
                stats['matched_with_gdp_value'] += 1

        merged_rows.append({
            'country_fh': country_fh,
            'country_wb': country_wb,
            'year': year,
            'political_rights_score': pr,
            'civil_liberties_score': cl,
            'status': status,
            'country_code_wb': wb['country_code_wb'] if wb else None,
            'indicator_name_wb': wb['indicator_name_wb'] if wb else None,
            'indicator_code_wb': wb['indicator_code_wb'] if wb else None,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': gdp_value,
            'merge_status': merge_status,
            'merge_note': merge_note,
        })

with out_path.open('w', newline='', encoding='utf-8') as f:
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(merged_rows)

with summary_path.open('w', encoding='utf-8') as f:
f.write(f"rows={len(merged_rows)}\n")
for key, value in stats.items():
f.write(f"{key}={value}\n")

print(out_path)
print(summary_path)
print(stats)
PY</summary>

Command

```bash
python3 - <<'PY'
import csv
from pathlib import Path

root = Path('.')
fh_path = root / 'fh_ratings.csv'
wb_path = root / 'wb_gdp_data.csv'
map_path = root / 'country_name_mapping.csv'
out_path = root / 'fh_wb_merged.csv'
summary_path = root / 'fh_wb_merge_summary.txt'

# Load mapping table
name_map = {}
map_action = {}
with map_path.open(newline='', encoding='utf-8') as f:
    for row in csv.DictReader(f):
        name_map[row['fh_country']] = row['wb_country']
        map_action[row['fh_country']] = row['action']

# Load WB wide extract and reshape to long
with wb_path.open(newline='', encoding='utf-8-sig') as f:
    rows = list(csv.reader(f))
header_idx = next(i for i, r in enumerate(rows) if r and r[0] == 'Country Name')
header = rows[header_idx]
years = header[4:]
wb_long = {}
for r in rows[header_idx + 1:]:
    if not r or not r[0]:
        continue
    country_name, country_code, indicator_name, indicator_code = r[:4]
    for year, value in zip(years, r[4:]):
        wb_long[(country_name, year)] = {
            'country_wb': country_name,
            'country_code_wb': country_code,
            'indicator_name_wb': indicator_name,
            'indicator_code_wb': indicator_code,
            'year': year,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': None if value in ('', '..') else value,
        }

# Merge onto FH rows
fieldnames = [
    'country_fh',
    'country_wb',
    'year',
    'political_rights_score',
    'civil_liberties_score',
    'status',
    'country_code_wb',
    'indicator_name_wb',
    'indicator_code_wb',
    'gdp_per_capita_ppp_constant_2021_intl_dollars',
    'merge_status',
    'merge_note',
]

merged_rows = []
stats = {
    'exact_match': 0,
    'mapped_alias': 0,
    'dropped_historical': 0,
    'manual_unresolved': 0,
    'no_wb_row_after_mapping': 0,
    'matched_with_gdp_value': 0,
    'matched_but_gdp_missing': 0,
}

with fh_path.open(newline='', encoding='utf-8') as f:
    for row in csv.DictReader(f):
        country_fh = row['country']
        year = row['year']
        pr = None if row['political_rights_score'] == '-' else row['political_rights_score']
        cl = None if row['civil_liberties_score'] == '-' else row['civil_liberties_score']
        status = None if row['status'] == '-' else row['status']

        action = map_action.get(country_fh)
        if action == 'map':
            country_wb = name_map[country_fh]
            merge_status = 'mapped_alias'
            merge_note = 'Matched using country_name_mapping.csv'
            stats['mapped_alias'] += 1
        elif action == 'drop':
            country_wb = None
            merge_status = 'dropped_historical'
            merge_note = 'Historical/composite entity intentionally left unmatched'
            stats['dropped_historical'] += 1
        elif action == 'manual':
            country_wb = None
            merge_status = 'manual_unresolved'
            merge_note = 'Requires manual handling; no safe WB row in this extract'
            stats['manual_unresolved'] += 1
        else:
            country_wb = country_fh
            merge_status = 'exact_match'
            merge_note = 'Exact country-name match'
            stats['exact_match'] += 1

        wb = wb_long.get((country_wb, year)) if country_wb else None
        if wb is None and country_wb and merge_status in ('exact_match', 'mapped_alias'):
            stats['no_wb_row_after_mapping'] += 1

        gdp_value = wb['gdp_per_capita_ppp_constant_2021_intl_dollars'] if wb else None
        if wb is not None:
            if gdp_value is None:
                stats['matched_but_gdp_missing'] += 1
            else:
                stats['matched_with_gdp_value'] += 1

        merged_rows.append({
            'country_fh': country_fh,
            'country_wb': country_wb,
            'year': year,
            'political_rights_score': pr,
            'civil_liberties_score': cl,
            'status': status,
            'country_code_wb': wb['country_code_wb'] if wb else None,
            'indicator_name_wb': wb['indicator_name_wb'] if wb else None,
            'indicator_code_wb': wb['indicator_code_wb'] if wb else None,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': gdp_value,
            'merge_status': merge_status,
            'merge_note': merge_note,
        })

with out_path.open('w', newline='', encoding='utf-8') as f:
    writer = csv.DictWriter(f, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerows(merged_rows)

with summary_path.open('w', encoding='utf-8') as f:
    f.write(f"rows={len(merged_rows)}\n")
    for key, value in stats.items():
        f.write(f"{key}={value}\n")

print(out_path)
print(summary_path)
print(stats)
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 3000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv
from pathlib import Path

root = Path('.')
fh_path = root / 'fh_ratings.csv'
wb_path = root / 'wb_gdp_data.csv'
map_path = root / 'country_name_mapping.csv'
out_path = root / 'fh_wb_merged.csv'
summary_path = root / 'fh_wb_merge_summary.txt'

# Load mapping table
name_map = {}
map_action = {}
with map_path.open(newline='', encoding='utf-8') as f:
    for row in csv.DictReader(f):
        name_map[row['fh_country']] = row['wb_country']
        map_action[row['fh_country']] = row['action']

# Load WB wide extract and reshape to long
with wb_path.open(newline='', encoding='utf-8-sig') as f:
    rows = list(csv.reader(f))
header_idx = next(i for i, r in enumerate(rows) if r and r[0] == 'Country Name')
header = rows[header_idx]
years = header[4:]
wb_long = {}
for r in rows[header_idx + 1:]:
    if not r or not r[0]:
        continue
    country_name, country_code, indicator_name, indicator_code = r[:4]
    for year, value in zip(years, r[4:]):
        wb_long[(country_name, year)] = {
            'country_wb': country_name,
            'country_code_wb': country_code,
            'indicator_name_wb': indicator_name,
            'indicator_code_wb': indicator_code,
            'year': year,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': None if value in ('', '..') else value,
        }

# Merge onto FH rows
fieldnames = [
    'country_fh',
    'country_wb',
    'year',
    'political_rights_score',
    'civil_liberties_score',
    'status',
    'country_code_wb',
    'indicator_name_wb',
    'indicator_code_wb',
    'gdp_per_capita_ppp_constant_2021_intl_dollars',
    'merge_status',
    'merge_note',
]

merged_rows = []
stats = {
    'exact_match': 0,
    'mapped_alias': 0,
    'dropped_historical': 0,
    'manual_unresolved': 0,
    'no_wb_row_after_mapping': 0,
    'matched_with_gdp_value': 0,
    'matched_but_gdp_missing': 0,
}

with fh_path.open(newline='', encoding='utf-8') as f:
    for row in csv.DictReader(f):
        country_fh = row['country']
        year = row['year']
        pr = None if row['political_rights_score'] == '-' else row['political_rights_score']
        cl = None if row['civil_liberties_score'] == '-' else row['civil_liberties_score']
        status = None if row['status'] == '-' else row['status']

        action = map_action.get(country_fh)
        if action == 'map':
            country_wb = name_map[country_fh]
            merge_status = 'mapped_alias'
            merge_note = 'Matched using country_name_mapping.csv'
            stats['mapped_alias'] += 1
        elif action == 'drop':
            country_wb = None
            merge_status = 'dropped_historical'
            merge_note = 'Historical/composite entity intentionally left unmatched'
            stats['dropped_historical'] += 1
        elif action == 'manual':
            country_wb = None
            merge_status = 'manual_unresolved'
            merge_note = 'Requires manual handling; no safe WB row in this extract'
            stats['manual_unresolved'] += 1
        else:
            country_wb = country_fh
            merge_status = 'exact_match'
            merge_note = 'Exact country-name match'
            stats['exact_match'] += 1

        wb = wb_long.get((country_wb, year)) if country_wb else None
        if wb is None and country_wb and merge_status in ('exact_match', 'mapped_alias'):
            stats['no_wb_row_after_mapping'] += 1

        gdp_value = wb['gdp_per_capita_ppp_constant_2021_intl_dollars'] if wb else None
        if wb is not None:
            if gdp_value is None:
                stats['matched_but_gdp_missing'] += 1
            else:
                stats['matched_with_gdp_value'] += 1

        merged_rows.append({
            'country_fh': country_fh,
            'country_wb': country_wb,
            'year': year,
            'political_rights_score': pr,
            'civil_liberties_score': cl,
            'status': status,
            'country_code_wb': wb['country_code_wb'] if wb else None,
            'indicator_name_wb': wb['indicator_name_wb'] if wb else None,
            'indicator_code_wb': wb['indicator_code_wb'] if wb else None,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': gdp_value,
            'merge_status': merge_status,
            'merge_note': merge_note,
        })

with out_path.open('w', newline='', encoding='utf-8') as f:
    writer = csv.DictWriter(f, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerows(merged_rows)

with summary_path.open('w', encoding='utf-8') as f:
    f.write(f\"rows={len(merged_rows)}\\n\")
    for key, value in stats.items():
        f.write(f\"{key}={value}\\n\")

print(out_path)
print(summary_path)
print(stats)
PY"
Chunk ID: 4f4e59
Wall time: 0.2099 seconds
Process exited with code 0
Original token count: 58
Output:
fh_wb_merged.csv
fh_wb_merge_summary.txt
{'exact_match': 3633, 'mapped_alias': 441, 'dropped_historical': 210, 'manual_unresolved': 21, 'no_wb_row_after_mapping': 0, 'matched_with_gdp_value': 3885, 'matched_but_gdp_missing': 189}
```

</details>

**Assistant**

The merge is written. I’m spot-checking alias cases and the unmatched categories so I can tell you exactly what ended up in the output.

<details>
<summary>Background terminal finished with sed -n '1,8p' fh_wb_merged.csv</summary>

Command

```bash
sed -n '1,8p' fh_wb_merged.csv
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 2000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "sed -n '1,8p' fh_wb_merged.csv"
Chunk ID: 657c89
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 355
Output:
country_fh,country_wb,year,political_rights_score,civil_liberties_score,status,country_code_wb,indicator_name_wb,indicator_code_wb,gdp_per_capita_ppp_constant_2021_intl_dollars,merge_status,merge_note
Afghanistan,Afghanistan,2000,7,7,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1617.82647494969,exact_match,Exact country-name match
Afghanistan,Afghanistan,2001,7,7,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1454.11078195559,exact_match,Exact country-name match
Afghanistan,Afghanistan,2002,6,6,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1774.30874287341,exact_match,Exact country-name match
Afghanistan,Afghanistan,2003,6,6,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1815.92819979586,exact_match,Exact country-name match
Afghanistan,Afghanistan,2004,5,6,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1776.91820712187,exact_match,Exact country-name match
Afghanistan,Afghanistan,2005,5,5,Partly Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1908.11478234587,exact_match,Exact country-name match
Afghanistan,Afghanistan,2006,5,5,Partly Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1929.72389709372,exact_match,Exact country-name match
```

</details>

<details>
<summary>Background terminal finished with python3 - <<'PY'
import csv
wanted=['Bahamas','South Korea','North Korea','Taiwan','USSR','Turkey','Vietnam']
with open('fh_wb_merged.csv', newline='', encoding='utf-8') as f:
    rows=csv.DictReader(f)
    seen=set()
    for r in rows:
        c=r['country_fh']
        if c in wanted and c not in seen:
            print(c, '|', r['country_wb'], '|', r['year'], '|', r['merge_status'], '|', r['gdp_per_capita_ppp_constant_2021_intl_dollars'])
            seen.add(c)
PY</summary>

Command

```bash
python3 - <<'PY'
import csv
wanted=['Bahamas','South Korea','North Korea','Taiwan','USSR','Turkey','Vietnam']
with open('fh_wb_merged.csv', newline='', encoding='utf-8') as f:
    rows=csv.DictReader(f)
    seen=set()
    for r in rows:
        c=r['country_fh']
        if c in wanted and c not in seen:
            print(c, '|', r['country_wb'], '|', r['year'], '|', r['merge_status'], '|', r['gdp_per_capita_ppp_constant_2021_intl_dollars'])
            seen.add(c)
PY
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 2000
}
```

Raw tool result

```text
Command: /bin/zsh -lc "python3 - <<'PY'
import csv
wanted=['Bahamas','South Korea','North Korea','Taiwan','USSR','Turkey','Vietnam']
with open('fh_wb_merged.csv', newline='', encoding='utf-8') as f:
    rows=csv.DictReader(f)
    seen=set()
    for r in rows:
        c=r['country_fh']
        if c in wanted and c not in seen:
            print(c, '|', r['country_wb'], '|', r['year'], '|', r['merge_status'], '|', r['gdp_per_capita_ppp_constant_2021_intl_dollars'])
            seen.add(c)
PY"
Chunk ID: ec2faa
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 98
Output:
Bahamas | Bahamas, The | 2000 | mapped_alias | 34597.1621145274
North Korea | Korea, Dem. People's Rep. | 2000 | mapped_alias |
South Korea | Korea, Rep. | 2000 | mapped_alias | 26404.1588575745
Taiwan |  | 2000 | manual_unresolved |
Turkey | Turkiye | 2000 | mapped_alias | 14856.914625257
USSR |  | 2000 | dropped_historical |
Vietnam | Viet Nam | 2000 | mapped_alias | 4349.22348751462
```

</details>

<details>
<summary>Background terminal finished with cat fh_wb_merge_summary.txt</summary>

Command

```bash
cat fh_wb_merge_summary.txt
```

Invocation metadata

```json
{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}
```

Raw tool result

```text
Command: /bin/zsh -lc 'cat fh_wb_merge_summary.txt'
Chunk ID: d7466f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 43
Output:
rows=4305
exact_match=3633
mapped_alias=441
dropped_historical=210
manual_unresolved=21
no_wb_row_after_mapping=0
matched_with_gdp_value=3885
matched_but_gdp_missing=189
```

</details>

**Assistant**

The merged file is [fh_wb_merged.csv](/Users/christopherlee/Documents/Github/ucsb-workshop/fh_wb_merged.csv). I also wrote a small summary at [fh_wb_merge_summary.txt](/Users/christopherlee/Documents/Github/ucsb-workshop/fh_wb_merge_summary.txt).

The merge keeps both name columns:

- `country_fh`: original Freedom House name
- `country_wb`: resolved World Bank name used for the join

It also includes:

- `year`
- Freedom House fields: `political_rights_score`, `civil_liberties_score`, `status`
- World Bank fields: `country_code_wb`, `indicator_name_wb`, `indicator_code_wb`, `gdp_per_capita_ppp_constant_2021_intl_dollars`
- merge metadata: `merge_status`, `merge_note`

High-level result:

- 4,305 total rows
- 3,633 exact country-name matches
- 441 alias-based matches via the crosswalk
- 210 historical/composite Freedom House rows left unmatched
- 21 manual unresolved rows, which are `Taiwan`
- 3,885 rows matched to a non-missing GDP value
- 189 rows matched a World Bank country-year row but GDP is missing there

A few examples in the output:

- `Bahamas` -> `Bahamas, The`
- `South Korea` -> `Korea, Rep.`
- `Turkey` -> `Turkiye`
- `Vietnam` -> `Viet Nam`
- `Taiwan` stays unmatched with `merge_status=manual_unresolved`
- `USSR` stays unmatched with `merge_status=dropped_historical`

I converted Freedom House `-` values and World Bank `..` values to blank cells in the merged CSV.