{"part":"task","text":"# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"dwiputri_the_corruption\",\n \"rows\": 945,\n \"description\": \"The existence of ambiguity in the study of the impact of corruption on economic growth, namely the grease the wheel hypothesis and sand the wheel hypothesis, triggered this research to look at the impact of corruption through another perspective, i.e. income inequality. This study seeks to understand the mutual influence between corruption and income inequality in Asian countries. The data is collected from a variety of sources, including the World Bank, Transparency International, and The Institute Worldwide Governance Index.\\n\\nDataset variables: \\n\\n- trade: Merchandise trade (% of GDP)\\n- id: Unique country identifier\\n- gini: GINI index (World Bank estimate)\\n- corr: corruption indicator\\n- country (str): Country name\\n- year: Year collected\\n- popgrow: Population growth (annual %)\\n- unemptot: Unemployment, total (% of total labor force) (national estimate)\\n- healthtot: Health expenditure, total (% of GDP)\\n- educsec: Gross enrolment ratio, secondary, both sexes (%)\\n- educprim: Gross enrollment ratio, primary, both sexes (%)\\n- gdpcapgrow: GDP per capita growth (annual %)\\n- gdpcapcons: GDP per capita (constant 2010 US$)\\n- cap: Gross fixed capital formation (% of GDP)\\n- capgrow: Gross fixed capital formation (annual % growth)\\n- expense: Expense (% of GDP)\\n- taxrev: Tax revenue (% of GDP)\\n- fdi: Foreign direct investment, net inflows (% of GDP)\\n- al_ethnic: Ethnic Fractionalization\\n- al_language: Language Fractionalization\\n- al_religion: Religion Fractionalization\\n- p_democ: Institutionalized Democracy\\n- ln_gdpcapcons: natural log of gdp per capita growth\\n- gdpcapcor: interaction between corruption and gdp per capita growth\\n- gov: Governance indicator\\\"\",\n \"columns\": {\n  \"trade\": \"float64\",\n  \"id\": \"float64\",\n  \"corr\": \"float64\",\n  \"gini\": \"float64\",\n  \"country\": \"str\",\n  \"year\": \"float64\",\n  \"popgrow\": \"float64\",\n  \"unemptot\": \"float64\",\n  \"healthtot\": \"float64\",\n  \"educsec\": \"float64\",\n  \"educprim\": \"float64\",\n  \"gdpcapgrow\": \"float64\",\n  \"gdpcapcons\": \"float64\",\n  \"cap\": \"float64\",\n  \"capgrow\": \"float64\",\n  \"expense\": \"float64\",\n  \"taxrev\": \"float64\",\n  \"fdi\": \"float64\",\n  \"al_ethnic\": \"float64\",\n  \"al_language\": \"float64\",\n  \"al_religion\": \"float64\",\n  \"p_democ\": \"float64\",\n  \"gov\": \"float64\",\n  \"ln_gdpcapcons\": \"float64\",\n  \"gdpcapcorr\": \"float64\"\n }\n}"}