Martyanova_304_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Влияет ли знание иностранных языков на доход?\n",
    "\n",
    "# Введение и обзор литературы\n",
    "\n",
    "Владение языками является важным элементом человеческого капитала. Изучение связи между благосостоянием индивида и его языковыми компетенциями является одним из основных направлений в economics of language. \n",
    "\n",
    "Цель данного проекта -- выявить связь между заработной индивида и его языковыми компетенциями. В данном проекте использовались данные российского мониторинга экономического положения и здоровья населения НИУ-ВШЭ за 2016 г. и 2012 г. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Economics of Linguistic Diversity: How Many Languages Make Sense? Victor Ginsburgh, Shlomo Weber, 2011\n",
    "В данной книге проведен обзор основных направлений economics of language:\n",
    "1. индексы, отражающие этно-лингвистические дистанции, в том число их применение в эмпирических экономических исследованиях:\n",
    "    * влияние языкового разнообразия на международную торговлю и миграцию\n",
    "    * литературные переводы\n",
    "    * оценка вероятности победы на Евровидении (языковые и культурные дистанции влияют и на поведение при голосовании\n",
    "    * оценка вероятности этнических конфликтов\n",
    "2. индивидуальные коммуникативные выгоды\n",
    "    * теоретико-игровое моделирование изучения иностранного языка\n",
    "    * функции спроса на языки на рынке труда\n",
    "    * выигрыш от знания дополнительного языка (непосредсвенно тема этого проекта)\n",
    "3. Diversity and Disenfranchisement Indices (индекс разнообразия и индекс, показывающий, какая доля населения не говорит на каком-либо языке) и их применение в эмпиричеких исследованиях\n",
    "4. Языковая политика, в том числе подробно рассмотрена политика Европейского Союза с точки зрения издержек и disenfranchisement indices.\n",
    "\n",
    "*Шломо Вебер является профессором Южного Методистского университета (США), а также руководит [Лабораторией изучения социальных отношений и многообразия общества РЭШ](https://diversity.nes.ru/). Однажды он читал [лекцию и на нашем факультете](https://www.m24.ru/videos/lekcii/05022014/41413).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оценка выигрыша от знания языка\n",
    "Проблема решения индивидов об изучении языка и соотношении их выгод и издержек интересует экономистов не только с точки зрения эмпирических результатов, но и с точки зрения теории игр ([Selten and Pool (1991)](http://econ.ucsb.edu/~tedb/Courses/Ec100C/seltenpool.pdf), [Church and King (1993)](https://www.jstor.org/stable/135911)).\n",
    "[Ginsburgh, Ortu˜no-Ort´ın, Weber (2007)](https://e-archivo.uc3m.es/bitstream/handle/10016/5512/learning_ortuno_JEBO_2007_ps.pdf?sequence=1) не только разработали теоретико-игровую модель, но и попытались дать эмпирические оценки.\n",
    "\n",
    "Большинство исследований посвящено влиянию знания языка на заработок мигрантов.\n",
    "\n",
    "Двуязычные страны, главным образом Канада, а также, например, Украина и Казахстан, часто выступают объектом для подобных исследований.   \n",
    "\n",
    "Во многих исследованиях были найдены значимые положительные эффекты от знания языков на заработок индивидов. Считается, что знание языка улучшает возможности трудоустройства. Однако очевидно, что далеко не все языки аналогичны в этом отношении.\n",
    "\n",
    "Про оценке выигрыша индивида от знания иностранного языка возникает проблема эндогенности, как и при оценке выигрыша от образования. Знание языка зависит может зависеть от ненаблюдаемых исследователем характеристик, например, способностей к языкам. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [\"Male-female earnings differentials and the role of language in Canada, Ontario, and Quebec\"](https://www.jstor.org/stable/pdf/134804.pdf?casa_token=N6ydUHfV2KcAAAAA:zE5pAFP1809qPCOe9agt6DfGiCM7wkNuvb3ATt4S5gKWq6IPxuogErYUf2U5ai5gGbiFiJZIUSQI9aQ8MWZ3A6BUdUs452EpFMRarmnKbckWy7Pg7lQ)\n",
    "Shapiro и Stelcner не обнаружили значимого выигрыша от знания языка другой группы для носителей английского и французского языков. В последующей работе, основанной на данных переписи 1990 года они обнаружили значимый положительный выигрыш для носителей французского языка, знающих английский."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [\"Earnings by Language Group in Quebec in 1980 and Emigration from Quebec between 1976 and 1981\", Grenier (1987)](https://www.jstor.org/stable/pdf/135415.pdf?casa_token=gPIrg9crXNoAAAAA:yvCL2kZ6vAD1EeoB0dPsyErxfkBYGyn1ElT5-Shv0vREZKCVhUkpiUNm-bA9xlr470XTJNyonyDWhoGUYukm0O4rx2sXPA77q2lwobDaKglO0ahw5FA)\n",
    "В данной статье анализируется миграция из канадской провинции Квебек в период с 1976 по 1981 год. Большую долю мигрироваших составляли носители английского языка. Так как решение мигрировать может быть связано с ожидаемым уровнем заработка, при простом сравнении средней заработной в 1976 и 1981 возникает проблема смещения из-за самоотбора. В данной работе использовалась система из двух одновременных уравнений с использованием выборки из мужчин, взятой из перепеси 1981 года. В качестве засисимой переменной в первом уравнении была выбрана бинарная переменная, равная 1, если индивид жил в Квебеке, \n",
    "регрессоры включали в себя:\n",
    "* возраст\n",
    "* бинарные переменные для знания языков и их использования\n",
    "* семейного положения\n",
    "* места происхождения и иммиграции\n",
    "Во втором уравнении оценивался логарифм общего дохода индивида, в состав независимых переенных были включены:\n",
    "* число лет образования\n",
    "* опыт работы и квадрат опыта работы\n",
    "* семейное положение\n",
    "* логарифм количества отработанных недель в году\n",
    "* бинарная перемнная для самозанятости\n",
    "* бинарная перемееная, равная 1 для живущих в Монреале \n",
    "* аналогичные бинарные переменные для знания языков и их использования\n",
    "Оценка регресии показала, что, при прочих равных, носитель английского языка, знающий французский, с большей вероятностью оставался в Квебеке. Также результаты показали, что для носителей французского нет проблемы самоотбора, в то время как для носителей английского языка она существует: наблюдалась корреляция между ошибками уравнений для веростности остаться и заработка.Таким образом, оставшиеся в Квебеке носители английского приняли такое решение, потому что они получали относительно высокий заработок по сравнению с теми, кто решил уехать. При прочих равных, билингвизм увеличивал заработную плату носителей французского на 4 п.п., носителей английского на 12 п.п. с контролем самоотбора, и на 8 п.п. -- без. \n",
    "Автор пришел к выводу, что смещение самоотбора действительно присутсвует и занижает отдачу от французского языка."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Immigrant access to work in Montreal and Toronto, Gilles Grenier and Serge Nadeau, 2011](http://www.cjrs-rcsr.org/V34/1/CJRS-RCSR-34-1-03.pdf)\n",
    "В данной статье рассматривается проблема разрыва в уровнях занятости между мигрантами и местными в Торонто и Монреале. В Монреале развыв значительно выше, так как мигранты вынуждены нести большие издержки приспособления: единственным официальным языком является французский и больше половины населения владеет в той или иной мере обоими языками. \n",
    "\n",
    "Авторы использует данные переписи 2006 года (the Statistics Canada 2006 Census Microdata Master File). Они оценивают вероятность быть занятым на полный рабочий день весь год с помощью линейной модели. В качестве регрессоров использовались:\n",
    "1. для всех наблюдений:\n",
    "    * опыт работы\n",
    "    * уровень образования \n",
    "    * язык, на котором говорят дома\n",
    "    * семейное положение\n",
    "    * знание официальных языков\n",
    "    * другие доходы в семье\n",
    "    * наличие детей\n",
    "    * бинарная переменная, если индивид посещал школу за последние 9 месяцев\n",
    "2. для иммигрантов\n",
    "    * наличие гражданства\n",
    "    * место получения наивсшей ступени образования\n",
    "    * место рождения\n",
    "    * количество лет в иммиграции\n",
    "\n",
    "В данной работе использовался метод декомпозиции [Blinder–Oaxaca](https://en.wikipedia.org/wiki/Blinder%E2%80%93Oaxaca_decomposition), поэтому авторы отдели предпочтение линейной регрессии. Всего были оценены 4 модели регрессии для обоих полов, в зависимости от города (Монреаль или Торонто) и места рождения (рожденный в Канаде или иммигрант). \n",
    "\n",
    "В конце концов, авторы пришли к следующим выводам:\n",
    "1. Знание французского языка оказывает значимое влияние на занятость мигрантов Монреале. Этот фактор в значительной степени объясняет разницу в заработной плате мигрантов в Торонто и Монреале.\n",
    "2. Страна происхождения оказывает влияние на то, насколько хорошо мигранты интегрируются в новую для них среду. Однако это не может объяснить различие в занятости мигрантов в Торонто и Монреале. \n",
    "3. Возможно, что иммигранты в Монреале подвергаются большей дискриминации, чем в Торонто. Знание французского или английского приносит меньший выигрыш мигрантам, чем местным, в Монреале и Торонто соответственно. \n",
    "4. Политика по отношению к мигрантам (больший акцент на знание французского) не является причиной для большего разрыва в занятости. Напротив, Монреаль привлекал бы таких же мигрантов, как и Торонто, и разрыв был бы еще больше."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [\"Economic Returns to Speaking the Right Language(s)? Evidence from Kazakhstan’s Shift in State Language and Language of Instruction\", Alisher Aldashev, Alexander M. Danzer, (2014)](https://www.ssrn.com/abstract=2536267) \n",
    "Авторы проанализировали два больших опроса, один из которых был проведен до того, как казахский стал официальным государственным языком (Living Standard Measurement Survey 1996), а второй -- уже после (Research Institute CIOM multistage random sample, 2010). В обоих случаях знающие казахский получали меньший доход. Они также использовали инструменты -- количество лет, проведенных в городских районах, где живут в основном этнически казахи. Поскольку 2МНК-оценки оказались близки к оценкам обычного МНК, авторы пришли к выводу, что обратная причинно-следственная связь не является проблемой. Затем они обратились к данным PISA и пришли к выводу, что качество обучения на казахском хуже, чем на русском, для носителей обоих языков. Авторы связали это с нехваткой учебных материалов на казахском, низкими квалификацией и мотивацией учителей."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Наиболее близкое к моему проекту исследование: [Ginsburgh, Victor A. and Prieto-Rodriguez, Juan (2011) \"Returns to Foreign Languages of Native Workers in the EU\"](http://ecares.ulb.ac.be/ecare/personal/ginsburgh/papers/170%20returns.pdf)\n",
    "\n",
    "* в исследовании использовались панельные данные The European Community Household Survey за 1994-2001 гг. для 9 стран Евросоюза: Австрия, Дания, Финляндия, Франция, Германия, Греция, Италия, Португалия и Испания.\n",
    "* использовались данные только для мужчин, чтобы уменьшить гетерогенность. \n",
    "* было использовано 2 типа \"языковых\" переменных: бинарные переменные для знания языка и disenfranchisement rate (доля населения, которая не говорит на данном языке). В первой спецификации модели использовались бинарные переменные, в двух других -- DR. \n",
    "* анализировалось Mincer-type equation (также часть используется в исследованиях по оценке отачи от образования) с помощью OLS и IV quantile regression (Chernozhukov, Hansen). Оценки для OLS оказались существенно ниже. IV quantile regression, по мнению авторов, помогает снизить ненаблюдаемую гетерогенность, так как работники из одного квантиля наверняка обладают схожими ненаблюдаемыми характеристиками.\n",
    "#### Выводы\n",
    "* Все переменные для языков во всех трех спецификациях оказались значимыми на 1%-ом уровне, кроме Австрии и Дании. Знание языков повышают заработную плату индивида при прочих равных на 5-15%, в зависимости от страны. В меньшей степени в Дании и Нидерландах, где английский язык знают от 70 до 75 процентов населения. \n",
    "* Значительная гетерогенность между странами.\n",
    "* Результаты снижаются, если ввести контроль за профессией. \n",
    "* Спецификации приводят к похожим результатам. \n",
    "* Выигрыши от знания иностранного выше для местных, чем для мигрантов в подобных исследованиях, что ожидаемо."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Williams, \"The Economic Returns to Multiple Language Usage in Western Europe\" (2006)](http://ideas.repec.org/p/irs/iriswp/2006-07.html) \n",
    "Данная работа близка к исследованию Гинсбурга, однако в ней нет контроля эндогенности, так как автор не сумел найти никаких подходящих интсрументов. Результаты показывают, что использование второго языка на рабочем месте повышают заработную плату на 5-10%, однако результаты чувствительны к спецификации."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Данные и построение моделей\n",
    "\n",
    "В данном проекте используется:\n",
    "«Российский мониторинг экономического положения и здоровья населения НИУ-ВШЭ (RLMS-HSE)», проводимый Национальным исследовательским университетом  \"Высшая школа экономики\" и ООО «Демоскоп» при участии Центра народонаселения Университета Северной Каролины в Чапел Хилле и Института социологии РАН. (Сайты обследования RLMS-HSE: http://www.cpc.unc.edu/projects/rlms и http://www.hse.ru/rlms)». \n",
    "\n",
    "## Данные 24 и 20 волны\n",
    "В данной работе изначально планировалось использовать только данные выборки 24 волны (10.2015-01.2016), так как это самые свежие данные, в которых конкретизирован иностранный язык. \n",
    "Однако нужные инcтрументы (образование и место работы родителей в то время, когда индивиду было 15 лет) есть только данных репрезентативной выборки для 20 волны (10.2011 - 02.2012). При этом в этих данных о том, каким именно языком владеет индивид, что и не особенно нужно, если я собираюсь применять инструментальные переменные. Можно было бы все-таки воспользоваться тем, что данные панельные, но после сопоставления датасетов и дропа остается меньше тысячи наблюдений, что явно недостаточно для выявления эффекта.\n",
    "Поэтому в данном проекте используется 2 волны опроса:\n",
    "* [Описание переменных для 20 волны](https://www.hse.ru/data/2016/06/07/1247611077/R20_ind_codebook.pdf)\n",
    "* [Описание переменных для 24 волны](https://www.hse.ru/data/2017/06/05/1116555090/R24_ind_codebook_21.03.17.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import statsmodels.stats.outliers_influence as sso\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from linearmodels.iv import IV2SLS\n",
    "import scipy\n",
    "import statsmodels\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def box_cox(y, data, model, model_ln):\n",
    "    y_ag = scipy.stats.gmean(data.loc[:, y])\n",
    "    data['price_BC'] = data[y].apply(lambda y: y/y_ag)\n",
    "    ESS_BC1 = statsmodels.regression.linear_model.RegressionResults.ssr(model)\n",
    "    ESS_BC_ln1 = statsmodels.regression.linear_model.RegressionResults.ssr(model_ln)\n",
    "    if ESS_BC1 > ESS_BC_ln1:\n",
    "        print('Модель с логарифмом лучше.')\n",
    "    else:\n",
    "        print('Модель без логарифма лучше.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def factor_to_binary(data, feature): # преобразует факторные переменные в бинарные\n",
    "    for i in data[feature].unique():\n",
    "        data[feature + '_' + str(i)[:-2]] = (data[feature] == i).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data20 = pd.read_csv('data20.csv')\n",
    "data24 = pd.read_csv('data24.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Описание переменных, использованных в проекте\n",
    "Каждая волна в данном опросе имеет свой код: данным 20 волны соответствует буква 'p', а данным 24 волны -- 't'.\n",
    "В данных удалены все пропуски и исключены индивиды, которые затруднялись с ответом или отказывались от него. Некоторые переменные были преобразованы или переименованы, поэтому ниже приводится описание переменных из файла. Подробности -- в дополнительном файле cleaning. \n",
    "\n",
    "#### Для данных обеих волн:\n",
    "* 'idind' - Единый идентификационный номер индивида для волн 5 и последующих\n",
    "* 'region' - код региона (факторная)\n",
    "* 'status' - статус населенного пункта, в котором проживает индивид (факторная)\n",
    "* '\\_marst' - бинарная переменная, равная 1, если индивид состоит в браке\n",
    "* '\\_age' - возраст\n",
    "* '\\_occup08' - профессиональная группа по коду по коду pj2cod08\n",
    "* '(t/p)j2cod08' - код профессии по ISCO2008 основной работы\n",
    "* '\\_educ' - количество лет образования\n",
    "* '\\_diplom' - наличие \n",
    "* 'male' - бинарнарная переменная, равная 1, если индивид - мужчина\n",
    "* 'year' - год рождения\n",
    "* 'born_here' - бинарная переменная, равная 1, если индивид родился в том же населенном пункте, где сейчас проживает\n",
    "* 'born_in_town' - бинарная переменная, равная 1, если индивид родился в городе\n",
    "* 'industry' - код для отрасли, в которой занят индивид\n",
    "* 'empl_year', 'empl_month' - год и месяц, в которых индивид начал работать на текущем месте работы\n",
    "* '\\_int_y', '\\_int_m' - год и месяц проведения интервью\n",
    "* 'work_hours', 'work_days' - количество часов и дней, которые индивид отработал за последнюю неделю\n",
    "* 'work_hours_m' - количество часов, которые индивид отработал в данном месяце\n",
    "* '\\_wage' - заработная плата\n",
    "* 'mean_wage' - средняя заработная плата за последние 12  месяцев \n",
    "* 'lang_TF' - бинарная переменная, равная 1, если индивид владеет каким-то иностранным языком\n",
    "* 'lang_level' - факторная переменная для уровня знания языка\n",
    "* 'city' - бинарная переменная, равная 1, если индивид живет в городе\n",
    "* 'msk' - бинарная переменная, равная 1 для индивидов, проживающих в Москве\n",
    "* 'spb' - бинарная переменная, равная 1 для индивидов, проживающих в СПб \n",
    "       \n",
    "Дополнительные переменные для 20 волны:\n",
    "* 'pj217a', 'pj217b' - образование отца и матери в тот момент, когда индивиду было 15 лет (+ большое количество бинарных переменных, которые были получены из этой факторной переменной)\n",
    "* 'pj216ac08', 'pj216bc08' - код профессии отца и матери, в тот момент, когда индивиду было 15 лет\n",
    "* 'father_age', 'mother_age' - возраст отца и матери в тот момент, когда индивиду было 15 лет\n",
    "\n",
    "Дополнительные переменные для 20 волны:\n",
    "* 'english', 'german', 'french', 'other' - бинарные переменные для соответствующих языков.   Под 'other' понимаются испанский, португальский, латынь и японский."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из графиков корреляции видно, что заработная плата положительно коррелирована с количеством лет образования и наличием высшего образования, с фактом рождения в городе, знанием иностранных языков. Также заработная плата положительно коррелирована с бинарными переменными male и msk. И, разумеется, чем  больше часов индивид отработает в этом месяце, тем, скорее всего, большую зарплату он получит. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d3699d748>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr20 = data20.corr()\n",
    "matplotlib.rcParams['figure.figsize']=(9,7)\n",
    "sns.heatmap(corr20, xticklabels=corr20.columns.values, yticklabels=corr20.columns.values)\n",
    "#plt.savefig('corr20.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d36eca518>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr24 = data24.corr()\n",
    "sns.heatmap(corr24, xticklabels=corr24.columns.values, yticklabels=corr24.columns.values)\n",
    "#fig, axes = plt.subplots(ncols=2, sharey=True) #plt.savefig('corr24.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На представленных ниже графиках видно, что большинство людей, владеющих иностранными языками, имеют высшее образование. Доля знающих иностранный язык стабильно выше среди людей с высшим образованием. Это в общем-то логично, во-первых, имеющие высшее образование индивиды склонны занимать должности, требущие более высокой квалификации, которая может включать в себя и знание иностранного языка. Во-вторых, опять же возникает проблема ненаблюдаемых характеристик индивидов, их таланта и способностей. Возможно, что индивиды, которые получили высшее образование, в целом обладают лучшими способностями, в том числе и к языкам. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, '')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)\n",
    "sns.countplot(x='p_diplom', hue='lang_TF', data=data20, ax = ax[0])\n",
    "sns.countplot(x='t_diplom', hue='lang_TF', data=data24, ax = ax[1])\n",
    "\n",
    "ax[1].set(ylabel=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На графиках ниже видно, что заработная плата людей с любым образованием выше, если они владеют каким-нибудь иностранным языком. Только во первой волне разброс величины заработной платы без высшего образования довольно большой по сравнению со средними уровнями, поэтому при оценке обычным МНК коэффициент может оказаться незначимым. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21d3794bf98>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 486.975x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 486.975x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots(1,2)\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "g1 = sns.catplot(x=\"lang_TF\", y=\"p_wage\", hue=\"p_diplom\", data = data20,\n",
    "height=6, kind=\"bar\", palette=\"muted\")#, ax = ax[0])\n",
    "g1.set_titles('20я волна')\n",
    "\n",
    "g2 = sns.catplot(x=\"lang_TF\", y=\"t_wage\", hue=\"t_diplom\", data = data24,\n",
    "height=6, kind=\"bar\", palette=\"muted\")#, ax = ax[1])\n",
    "g2.set_titles('24я волна')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На графиках ниже видно, что, чем выше возраст индивида, тем скорее всего больше его заработная плата. Во многих статьях включается квадрат возраста: не исключено, что он понадобится и в наших моделях. Скорее всего в данных имеется гетероскедастичность. Люди, владеющие иностранными языками, в целом получают большую заработную плату в своей возрастной группе, причем с возрастом выигрыш, кажется, растет.\n",
    "\n",
    "При этом распределение знания языка по возрасту неоднородно: больше людей в молодых возрастах утверждают, что владеют иностранными языками. Это может быть связано с тем, что индивиды получают или недавно получали образование в школе или университете, где иностранный язык был обязательной частью программы.\n",
    "\n",
    "И к сожалению, вопрос сформулирован не совсем так, как в ECHP, поэтому достоверно неизвестно, насколько активно используется иностранный язык молодыми индивидами на работе. \n",
    "\n",
    "Кроме того, эмпирические результаты свидетельствуют, что если большая доля населения владеет иностранными языками, это перестает быть серьезным конкурентным преимуществом на рынке труда и вытесняет индивидуальные выигрыши (как, например, в Дании и Нидерландах). Возможно это имеет место быть и среди молодых индивидов: иностранные языки перестают быть серьезным преимуществом среди сверстников с небольшим количеством опыта и/или незаконченным образованием. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d37748240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data20['ln_wage'] = np.log(data20['p_wage'])\n",
    "data24['ln_wage'] = np.log(data24['t_wage'])\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "sns.despine(f, left=True, bottom=True)\n",
    "sns.scatterplot(x='p_age', y=\"p_wage\",\n",
    "                hue = 'lang_TF',\n",
    "                #size=\"depth\",\n",
    "                palette=\"ch:r=-.2,d=.3_r\",\n",
    "                sizes=(1, 8), linewidth=0,\n",
    "                data=data20)#, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d37757eb8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data20['ln_wage'] = np.log(data20['p_wage'])\n",
    "data24['ln_wage'] = np.log(data24['t_wage'])\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "sns.despine(f, left=True, bottom=True)\n",
    "sns.scatterplot(x='t_age', y=\"t_wage\",\n",
    "                hue = 'lang_TF',\n",
    "                #size=\"depth\",\n",
    "                palette=\"ch:r=-.2,d=.3_r\",\n",
    "                sizes=(1, 8), linewidth=0,\n",
    "                data=data24)#, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Модели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Модели по данным 24-ой волны"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lang = \"+\".join(lang_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Результаты оценки модели обычным МНК показывают, что имеется значимый положительный выигрыш от владения английским, немецким и другими языками, бинарная переменная для французского оказалась незначимой. Все остальные переменные также оказались значимыми на 1%-ом уровне. \n",
    "\n",
    "Однако стоит иметь в виду, что почти наверняка переменные для языков и высшего образования эндогенны. Однако вопросов, из которых можно было бы получить походящие инструменты, в 24 волне не задавалось. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 t_wage   R-squared:                       0.262\n",
      "Model:                            OLS   Adj. R-squared:                  0.260\n",
      "Method:                 Least Squares   F-statistic:                     46.68\n",
      "Date:                Mon, 01 Apr 2019   Prob (F-statistic):          2.78e-112\n",
      "Time:                        23:29:45   Log-Likelihood:                -42755.\n",
      "No. Observations:                3891   AIC:                         8.554e+04\n",
      "Df Residuals:                    3877   BIC:                         8.563e+04\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept        -6854.0088   2327.413     -2.945      0.003   -1.14e+04   -2292.363\n",
      "t_age              687.7294    101.251      6.792      0.000     489.281     886.177\n",
      "I(t_age * t_age)    -8.9373      1.142     -7.824      0.000     -11.176      -6.698\n",
      "t_marst           1546.9704    463.367      3.339      0.001     638.788    2455.153\n",
      "t_diplom          6834.9274    542.816     12.592      0.000    5771.028    7898.827\n",
      "male              6833.7548    487.444     14.020      0.000    5878.382    7789.128\n",
      "work_hours_m        46.7644      4.892      9.559      0.000      37.175      56.353\n",
      "city              1624.6257    477.716      3.401      0.001     688.319    2560.932\n",
      "msk               1.882e+04   1836.709     10.245      0.000    1.52e+04    2.24e+04\n",
      "spb               8514.4841   1062.546      8.013      0.000    6431.932    1.06e+04\n",
      "english           5997.6328    946.502      6.337      0.000    4142.523    7852.742\n",
      "german            3968.6023   1561.582      2.541      0.011     907.957    7029.247\n",
      "french            6528.9991   4185.212      1.560      0.119   -1673.865    1.47e+04\n",
      "other             4.545e+04   1.95e+04      2.327      0.020    7168.552    8.37e+04\n",
      "==============================================================================\n",
      "Omnibus:                     4953.973   Durbin-Watson:                   1.790\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          2853544.219\n",
      "Skew:                           6.443   Prob(JB):                         0.00\n",
      "Kurtosis:                     135.041   Cond. No.                     5.72e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n",
      "[2] The condition number is large, 5.72e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "ols_wave24 = smf.ols('t_wage ~ 1 + t_age + I(t_age*t_age) + t_marst + t_diplom + male + work_hours_m + city + msk + spb +' + lang, data=data24).fit(cov_type='HC0')\n",
    "print(ols_wave24.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем построить модель с другой зависимой переменной - логарифмом заработной платы - и сравним две эти модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(t_wage)   R-squared:                       0.298\n",
      "Model:                            OLS   Adj. R-squared:                  0.295\n",
      "Method:                 Least Squares   F-statistic:                     120.3\n",
      "Date:                Mon, 01 Apr 2019   Prob (F-statistic):          2.36e-273\n",
      "Time:                        23:29:45   Log-Likelihood:                -3231.0\n",
      "No. Observations:                3891   AIC:                             6490.\n",
      "Df Residuals:                    3877   BIC:                             6578.\n",
      "Df Model:                          13                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            8.4948      0.109     77.896      0.000       8.281       8.709\n",
      "t_age                0.0280      0.005      5.719      0.000       0.018       0.038\n",
      "I(t_age * t_age)    -0.0004   5.62e-05     -6.987      0.000      -0.001      -0.000\n",
      "t_marst              0.0655      0.019      3.433      0.001       0.028       0.103\n",
      "t_diplom             0.3225      0.020     16.133      0.000       0.283       0.362\n",
      "male                 0.2899      0.019     15.344      0.000       0.253       0.327\n",
      "work_hours_m         0.0029      0.000     11.320      0.000       0.002       0.003\n",
      "city                 0.0715      0.021      3.486      0.000       0.031       0.112\n",
      "msk                  0.6667      0.032     20.602      0.000       0.603       0.730\n",
      "spb                  0.4370      0.042     10.478      0.000       0.355       0.519\n",
      "english              0.2003      0.027      7.376      0.000       0.147       0.254\n",
      "german               0.1231      0.055      2.230      0.026       0.015       0.231\n",
      "french               0.1771      0.123      1.437      0.151      -0.064       0.419\n",
      "other                0.9779      0.220      4.440      0.000       0.546       1.410\n",
      "==============================================================================\n",
      "Omnibus:                      292.824   Durbin-Watson:                   1.646\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              727.269\n",
      "Skew:                          -0.444   Prob(JB):                    1.19e-158\n",
      "Kurtosis:                       4.923   Cond. No.                     5.72e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n",
      "[2] The condition number is large, 5.72e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "ols_wave24_ln = smf.ols('np.log(t_wage) ~ 1 + t_age + I(t_age*t_age) + t_marst + t_diplom + male + work_hours_m + city + msk + spb +' + lang, data=data24).fit(cov_type='HC0')\n",
    "print(ols_wave24_ln.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Во всех изученных мною статьях использовался логарифм заработной платы. Как и ожидалось, модель с логарифмом заработной платы лучше описывает и наши данные. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Модель с логарифмом лучше.\n"
     ]
    }
   ],
   "source": [
    "box_cox('t_wage', data24, ols_wave24, ols_wave24_ln)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим модель с бинарной переменной для знания любого языка. \n",
    "\n",
    "Скорректированный R-квадрат в этой модели несколько ниже, но результаты не противоречат друг другу (они и не могли). \n",
    "При прочих равных, знание какого-нибудь иностранного языка повышает заработную плату на 20,9%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(t_wage)   R-squared:                       0.295\n",
      "Model:                            OLS   Adj. R-squared:                  0.293\n",
      "Method:                 Least Squares   F-statistic:                     153.8\n",
      "Date:                Mon, 01 Apr 2019   Prob (F-statistic):          1.61e-272\n",
      "Time:                        23:29:45   Log-Likelihood:                -3237.8\n",
      "No. Observations:                3891   AIC:                             6498.\n",
      "Df Residuals:                    3880   BIC:                             6567.\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            8.4904      0.110     77.382      0.000       8.275       8.705\n",
      "t_age                0.0285      0.005      5.789      0.000       0.019       0.038\n",
      "I(t_age * t_age)    -0.0004   5.65e-05     -7.057      0.000      -0.001      -0.000\n",
      "t_marst              0.0629      0.019      3.298      0.001       0.026       0.100\n",
      "t_diplom             0.3229      0.020     16.096      0.000       0.284       0.362\n",
      "male                 0.2893      0.019     15.319      0.000       0.252       0.326\n",
      "work_hours_m         0.0029      0.000     11.192      0.000       0.002       0.003\n",
      "city                 0.0721      0.021      3.513      0.000       0.032       0.112\n",
      "msk                  0.6764      0.033     20.497      0.000       0.612       0.741\n",
      "spb                  0.4413      0.042     10.622      0.000       0.360       0.523\n",
      "lang_TF              0.1926      0.025      7.658      0.000       0.143       0.242\n",
      "==============================================================================\n",
      "Omnibus:                      289.667   Durbin-Watson:                   1.646\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              722.340\n",
      "Skew:                          -0.438   Prob(JB):                    1.40e-157\n",
      "Kurtosis:                       4.920   Cond. No.                     2.64e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n",
      "[2] The condition number is large, 2.64e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "ols_wave24_ln_nl = smf.ols('np.log(t_wage) ~ 1 + t_age + I(t_age*t_age) + t_marst + t_diplom + male + work_hours_m + city + msk + spb + lang_TF', data=data24).fit(cov_type='HC0')\n",
    "print(ols_wave24_ln_nl.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cov_delta = (ols_wave24_ln.cov_params()).loc[['I(t_age * t_age)', 't_age'], ['I(t_age * t_age)', 't_age']]\n",
    "a, b = ols_wave24_ln.params['I(t_age * t_age)'], ols_wave24_ln.params['t_age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "jacob = np.array([-0.5/a, 0.5*b/(a*a)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197365.33180959724"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jacob.dot(cov_delta.dot(jacob))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Контроль на профессии\n",
    "Используем факторную переменную t_occup08 -- профессиональная группа по коду по коду pj2cod08:\n",
    "\n",
    "* 0 - военнослужащие\n",
    "* 1 - законодатели; крупные чиновники; руководители высш. и сред. звена\n",
    "* 2 - специалисты высшего уровня квалификации\n",
    "* 3 - специалисты среднего уровня квалификации; чиновники\n",
    "* 4 - служащие офисные и по обслуживанию клиентов\n",
    "* 5 - работники сферы торговли и услуг\n",
    "* 6 - квалифицированные работники сельского, лесного хоз-ва и рыбоводства\n",
    "* 7 - квалифицированные рабочие, занятые ручным трудом\n",
    "* 8 - квалифицированные рабочие, использующие машины и механизмы\n",
    "* 9 - неквалифицированные рабочие всех отраслей\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor_to_binary(data24, 't_occup08')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'idind', 'region', 'status', 't_marst', 't_age',\n",
       "       't_occup08', 't_educ', 't_diplom', 'male', 'year', 'born_here',\n",
       "       'born_in_town', 'tj2cod08', 'industry', 'empl_year', 'empl_month',\n",
       "       't_int_y', 't_int_m', 'work_hours', 'work_days', 'work_hours_m',\n",
       "       't_wage', 'mean_wage', 'lang_TF', 'lang_factor', 'lang_level',\n",
       "       'english', 'french', 'german', 'other', 'city', 'msk', 'spb', 'ln_wage',\n",
       "       'price_BC', 't_occup08_5', 't_occup08_8', 't_occup08_2', 't_occup08_3',\n",
       "       't_occup08_9', 't_occup08_7', 't_occup08_4', 't_occup08_1',\n",
       "       't_occup08_6', 't_occup08_0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data24.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "occup_list_24 = ['t_occup08_5', 't_occup08_8', 't_occup08_2', 't_occup08_3',\n",
    "       't_occup08_7', 't_occup08_4', 't_occup08_1',\n",
    "       't_occup08_6', 't_occup08_0']\n",
    "occup_24 = \"+\".join(occup_list_24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Включение контрольных переменных для профессий привело к снижению оценки выигрыша от знания языка и к увеличению скорректированого $R^2$. Теперь, при прочих равных, индивид, владеющий иностранным языком, получает заработную плату на 18% выше. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(t_wage)   R-squared:                       0.336\n",
      "Model:                            OLS   Adj. R-squared:                  0.332\n",
      "Method:                 Least Squares   F-statistic:                     102.6\n",
      "Date:                Mon, 01 Apr 2019   Prob (F-statistic):               0.00\n",
      "Time:                        23:29:46   Log-Likelihood:                -3122.6\n",
      "No. Observations:                3891   AIC:                             6285.\n",
      "Df Residuals:                    3871   BIC:                             6411.\n",
      "Df Model:                          19                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            8.3787      0.112     74.774      0.000       8.159       8.598\n",
      "t_age                0.0238      0.005      4.949      0.000       0.014       0.033\n",
      "I(t_age * t_age)    -0.0003    5.5e-05     -6.281      0.000      -0.000      -0.000\n",
      "t_marst              0.0392      0.019      2.105      0.035       0.003       0.076\n",
      "t_diplom             0.2255      0.022     10.171      0.000       0.182       0.269\n",
      "male                 0.2424      0.021     11.702      0.000       0.202       0.283\n",
      "work_hours_m         0.0028      0.000     10.848      0.000       0.002       0.003\n",
      "city                 0.0586      0.020      2.930      0.003       0.019       0.098\n",
      "msk                  0.6650      0.032     20.801      0.000       0.602       0.728\n",
      "spb                  0.4343      0.040     10.895      0.000       0.356       0.512\n",
      "lang_TF              0.1666      0.025      6.726      0.000       0.118       0.215\n",
      "t_occup08_5          0.1228      0.037      3.321      0.001       0.050       0.195\n",
      "t_occup08_8          0.3364      0.042      8.073      0.000       0.255       0.418\n",
      "t_occup08_2          0.3887      0.040      9.733      0.000       0.310       0.467\n",
      "t_occup08_3          0.3268      0.038      8.600      0.000       0.252       0.401\n",
      "t_occup08_7          0.3653      0.041      8.961      0.000       0.285       0.445\n",
      "t_occup08_4          0.1767      0.053      3.358      0.001       0.074       0.280\n",
      "t_occup08_1          0.5802      0.050     11.700      0.000       0.483       0.677\n",
      "t_occup08_6         -0.1489      0.176     -0.844      0.399      -0.495       0.197\n",
      "t_occup08_0          0.7964      0.096      8.297      0.000       0.608       0.985\n",
      "==============================================================================\n",
      "Omnibus:                      368.928   Durbin-Watson:                   1.622\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1093.207\n",
      "Skew:                          -0.501   Prob(JB):                    4.10e-238\n",
      "Kurtosis:                       5.396   Cond. No.                     5.78e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n",
      "[2] The condition number is large, 5.78e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "ols_wave24_ln_occ = smf.ols('np.log(t_wage) ~ 1 + t_age + I(t_age*t_age) + t_marst + t_diplom + male + work_hours_m + city + msk + spb + lang_TF + '+ occup_24, data=data24).fit(cov_type='HC0')\n",
    "print(ols_wave24_ln_occ.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Знание языков и возраст\n",
    "Попробуем оценить отдачу от иностранного языка в разных возрастах.\n",
    "\n",
    "Бинарная переменная lang_TF оказалась незначимой ни на каком допустимом уровне. Зато ее произведение с переменной возраста оказалось значимым на 10% уровне. При прочих равных, с увеличением возраста на год, отдача от знания иностранного языка увеличивается на 0,4%.\n",
    "\n",
    "Этот результат сложно однозначно объяснить. Во-первых, солидная доля молодых индивидов владеет иностранным языком, что могло привести к вытеснению выигрыша. Во-вторых, возможно их знания иностранного языка не используется на их основной работе. \n",
    "\n",
    "Это приводит к мысли, что оценки для менее молодых индивидов больше страдают от смещения самоотбора. Наверняка многие учили какой-то иностранный язык в школе, однако постепенно забыли его, так как не использовали."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(t_wage)   R-squared:                       0.336\n",
      "Model:                            OLS   Adj. R-squared:                  0.333\n",
      "Method:                 Least Squares   F-statistic:                     98.18\n",
      "Date:                Mon, 01 Apr 2019   Prob (F-statistic):               0.00\n",
      "Time:                        23:29:46   Log-Likelihood:                -3120.8\n",
      "No. Observations:                3891   AIC:                             6284.\n",
      "Df Residuals:                    3870   BIC:                             6415.\n",
      "Df Model:                          20                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept              8.4365      0.118     71.344      0.000       8.205       8.668\n",
      "t_age                  0.0218      0.005      4.320      0.000       0.012       0.032\n",
      "I(t_age * t_age)      -0.0003   5.68e-05     -5.802      0.000      -0.000      -0.000\n",
      "t_marst                0.0399      0.019      2.139      0.032       0.003       0.076\n",
      "t_diplom               0.2225      0.022     10.027      0.000       0.179       0.266\n",
      "male                   0.2419      0.021     11.688      0.000       0.201       0.282\n",
      "work_hours_m           0.0028      0.000     10.844      0.000       0.002       0.003\n",
      "city                   0.0585      0.020      2.929      0.003       0.019       0.098\n",
      "msk                    0.6598      0.032     20.405      0.000       0.596       0.723\n",
      "spb                    0.4322      0.040     10.828      0.000       0.354       0.510\n",
      "I(t_age * lang_TF)     0.0035      0.002      1.834      0.067      -0.000       0.007\n",
      "lang_TF                0.0358      0.074      0.480      0.631      -0.110       0.182\n",
      "t_occup08_5            0.1230      0.037      3.332      0.001       0.051       0.195\n",
      "t_occup08_8            0.3360      0.042      8.067      0.000       0.254       0.418\n",
      "t_occup08_2            0.3867      0.040      9.674      0.000       0.308       0.465\n",
      "t_occup08_3            0.3274      0.038      8.630      0.000       0.253       0.402\n",
      "t_occup08_7            0.3636      0.041      8.912      0.000       0.284       0.444\n",
      "t_occup08_4            0.1756      0.053      3.337      0.001       0.072       0.279\n",
      "t_occup08_1            0.5795      0.050     11.699      0.000       0.482       0.677\n",
      "t_occup08_6           -0.1473      0.174     -0.846      0.397      -0.489       0.194\n",
      "t_occup08_0            0.7914      0.096      8.208      0.000       0.602       0.980\n",
      "==============================================================================\n",
      "Omnibus:                      370.473   Durbin-Watson:                   1.621\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1106.462\n",
      "Skew:                          -0.500   Prob(JB):                    5.43e-241\n",
      "Kurtosis:                       5.413   Cond. No.                     5.78e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n",
      "[2] The condition number is large, 5.78e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "ols_wave24_la = smf.ols('np.log(t_wage) ~ 1 + t_age + I(t_age*t_age) + t_marst + t_diplom + male + work_hours_m + city + msk + spb + I(t_age*lang_TF) + lang_TF + '+ occup_24, data=data24).fit(cov_type='HC0')\n",
    "print(ols_wave24_la.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Модели с данными 20-ой волны\n",
    "В данной волне опроса уже отсутствуют данные о конкретных языках, но имеются данные о образовании, профессии, годе рождения родителей, что можно использовать в качестве инструментов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Контроль на профессии\n",
    "Используем факторную переменную p_occup08 --профессиональная группа по коду по коду pj2cod08:\n",
    "\n",
    "* 0 - военнослужащие\n",
    "* 1 - законодатели; крупные чиновники; руководители высш. и сред. звена\n",
    "* 2 - специалисты высшего уровня квалификации\n",
    "* 3 - специалисты среднего уровня квалификации; чиновники\n",
    "* 4 - служащие офисные и по обслуживанию клиентов\n",
    "* 5 - работники сферы торговли и услуг\n",
    "* 6 - квалифицированные работники сельского, лесного хоз-ва и рыбоводства\n",
    "* 7 - квалифицированные рабочие, занятые ручным трудом\n",
    "* 8 - квалифицированные рабочие, использующие машины и механизмы\n",
    "* 9 - неквалифицированные рабочие всех отраслей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor_to_binary(data20, 'p_occup08')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'idind', 'region', 'status', 'p_marst', 'p_age',\n",
       "       'p_occup08', 'p_educ', 'p_diplom', 'male', 'year', 'born_here',\n",
       "       'born_in_town', 'pj2cod08', 'industry', 'empl_year', 'empl_month',\n",
       "       'p_int_y', 'p_int_m', 'work_hours', 'work_days', 'work_hours_m',\n",
       "       'p_wage', 'mean_wage', 'lang_TF', 'lang_level', 'pj217a', 'pj217b',\n",
       "       'pj216ac08', 'pj216bc08', 'father_age', 'mother_age', 'pj217a=4.0',\n",
       "       'pj217a=5.0', 'pj217a=6.0', 'pj217a=12.0', 'pj217a=7.0', 'pj217a=9.0',\n",
       "       'pj217a=8.0', 'pj217a=11.0', 'pj217a=10.0', 'pj217b=4.0', 'pj217b=12.0',\n",
       "       'pj217b=6.0', 'pj217b=7.0', 'pj217b=9.0', 'pj217b=8.0', 'pj217b=5.0',\n",
       "       'pj217b=10.0', 'pj217b=11.0', 'city', 'msk', 'spb', 'ln_wage',\n",
       "       'p_occup08_8', 'p_occup08_5', 'p_occup08_1', 'p_occup08_3',\n",
       "       'p_occup08_9', 'p_occup08_7', 'p_occup08_2', 'p_occup08_4',\n",
       "       'p_occup08_6', 'p_occup08_0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data20.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "occup_list_20 = ['p_occup08_8', 'p_occup08_5', 'p_occup08_1',\n",
    "       'p_occup08_3', 'p_occup08_7', 'p_occup08_2',\n",
    "       'p_occup08_4', 'p_occup08_6', 'p_occup08_0']\n",
    "occup_20 = \"+\".join(occup_list_20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим обычную МНК-модель, аналогичную представленным выше моделям для данных 24 волны.\n",
    "\n",
    "Выигрыш индивидов от знания языка для данных 20 волны несколько ниже, чем для данных 24 волны. При прочих равных, знание какого-либо языка увелчивает заработную плату индивида на 11%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(p_wage)   R-squared:                       0.336\n",
      "Model:                            OLS   Adj. R-squared:                  0.333\n",
      "Method:                 Least Squares   F-statistic:                     113.6\n",
      "Date:                Mon, 01 Apr 2019   Prob (F-statistic):               0.00\n",
      "Time:                        23:29:46   Log-Likelihood:                -3540.4\n",
      "No. Observations:                4175   AIC:                             7121.\n",
      "Df Residuals:                    4155   BIC:                             7248.\n",
      "Df Model:                          19                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            7.6625      0.128     59.907      0.000       7.412       7.913\n",
      "p_age                0.0408      0.006      6.477      0.000       0.028       0.053\n",
      "I(p_age * p_age)    -0.0005   7.84e-05     -6.948      0.000      -0.001      -0.000\n",
      "p_marst              0.0091      0.018      0.495      0.621      -0.027       0.045\n",
      "p_diplom             0.2771      0.023     12.045      0.000       0.232       0.322\n",
      "male                 0.2633      0.022     12.099      0.000       0.221       0.306\n",
      "work_hours_m         0.0025      0.000     10.603      0.000       0.002       0.003\n",
      "city                 0.0257      0.020      1.270      0.204      -0.014       0.065\n",
      "msk                  0.7925      0.032     24.852      0.000       0.730       0.855\n",
      "spb                  0.5306      0.046     11.626      0.000       0.441       0.620\n",
      "lang_TF              0.1019      0.025      4.136      0.000       0.054       0.150\n",
      "p_occup08_8          0.4190      0.045      9.388      0.000       0.332       0.506\n",
      "p_occup08_5          0.2131      0.042      5.106      0.000       0.131       0.295\n",
      "p_occup08_1          0.6743      0.052     12.852      0.000       0.572       0.777\n",
      "p_occup08_3          0.3711      0.043      8.676      0.000       0.287       0.455\n",
      "p_occup08_7          0.5125      0.042     12.088      0.000       0.429       0.596\n",
      "p_occup08_2          0.3928      0.043      9.032      0.000       0.308       0.478\n",
      "p_occup08_4          0.3716      0.053      7.060      0.000       0.268       0.475\n",
      "p_occup08_6          0.1663      0.102      1.635      0.102      -0.033       0.366\n",
      "p_occup08_0          0.6395      0.144      4.442      0.000       0.357       0.922\n",
      "==============================================================================\n",
      "Omnibus:                      349.078   Durbin-Watson:                   1.603\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1169.721\n",
      "Skew:                          -0.398   Prob(JB):                    9.96e-255\n",
      "Kurtosis:                       5.468   Cond. No.                     3.27e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n",
      "[2] The condition number is large, 3.27e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "ols_wave20_ln = smf.ols('np.log(p_wage) ~ 1 + p_age + I(p_age*p_age) + p_marst + p_diplom + male + work_hours_m + city + msk + spb + lang_TF + '+ occup_20, data=data20).fit(cov_type='HC0')\n",
    "print(ols_wave20_ln.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим такую же модель для только для тех индивидов, которые имею высшее образование (понадобится в дальнейшем). \n",
    "\n",
    "При прочих равных, знание какого-либо иностранного языка увеличивает заработную плату индивида на 12.6%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(p_wage)   R-squared:                       0.302\n",
      "Model:                            OLS   Adj. R-squared:                  0.292\n",
      "Method:                 Least Squares   F-statistic:                     29.39\n",
      "Date:                Mon, 01 Apr 2019   Prob (F-statistic):           6.30e-83\n",
      "Time:                        23:29:47   Log-Likelihood:                -1098.7\n",
      "No. Observations:                1269   AIC:                             2235.\n",
      "Df Residuals:                    1250   BIC:                             2333.\n",
      "Df Model:                          18                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            7.4010      0.307     24.071      0.000       6.798       8.004\n",
      "p_age                0.0638      0.013      4.851      0.000       0.038       0.090\n",
      "I(p_age * p_age)    -0.0008      0.000     -4.948      0.000      -0.001      -0.000\n",
      "p_marst              0.0030      0.034      0.087      0.931      -0.064       0.070\n",
      "male                 0.2355      0.037      6.313      0.000       0.162       0.309\n",
      "work_hours_m         0.0023      0.000      4.957      0.000       0.001       0.003\n",
      "city                 0.0017      0.040      0.042      0.967      -0.077       0.080\n",
      "msk                  0.7839      0.052     15.196      0.000       0.683       0.885\n",
      "spb                  0.4773      0.072      6.650      0.000       0.337       0.618\n",
      "lang_TF              0.1187      0.035      3.382      0.001       0.050       0.188\n",
      "p_occup08_8          0.5479      0.210      2.605      0.009       0.136       0.960\n",
      "p_occup08_5          0.2939      0.195      1.509      0.131      -0.088       0.676\n",
      "p_occup08_1          0.8178      0.191      4.272      0.000       0.443       1.193\n",
      "p_occup08_3          0.4975      0.189      2.632      0.008       0.127       0.868\n",
      "p_occup08_7          0.5346      0.196      2.732      0.006       0.151       0.918\n",
      "p_occup08_2          0.5050      0.187      2.704      0.007       0.139       0.871\n",
      "p_occup08_4          0.4401      0.197      2.231      0.026       0.054       0.827\n",
      "p_occup08_6          0.1327      0.315      0.421      0.674      -0.486       0.751\n",
      "p_occup08_0          0.8694      0.321      2.705      0.007       0.239       1.499\n",
      "==============================================================================\n",
      "Omnibus:                       85.594   Durbin-Watson:                   1.660\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              196.597\n",
      "Skew:                          -0.402   Prob(JB):                     2.04e-43\n",
      "Kurtosis:                       4.753   Cond. No.                     9.48e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n",
      "[2] The condition number is large, 9.48e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "ols_wave20_ln1 = smf.ols('np.log(p_wage) ~ 1 + p_age + I(p_age*p_age) + p_marst+ male + work_hours_m + city + msk + spb + lang_TF + '+ occup_20, data=data20[data20['p_diplom'] == 1]).fit(cov_type='HC0')\n",
    "print(ols_wave20_ln1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2МНК"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data20['father_degree'] = data20['pj217a=10.0'] + data20['pj217a=11.0']\n",
    "data20['father_ptu'] = data20['pj217a=5.0'] + data20['pj217a=6.0']\n",
    "data20['father_low_ed'] = data20['pj217a=12.0'] + data20['pj217a=4.0']\n",
    "data20 = data20.rename(columns={'pj217a=7.0': 'father_school'})\n",
    "data20 = data20.rename(columns={'pj217a=8.0': 'father_tech'})\n",
    "data20 = data20.rename(columns={'pj217a=9.0': 'father_univer'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data20['mother_degree'] = data20['pj217b=10.0'] + data20['pj217b=11.0']\n",
    "data20['mother_ptu'] = data20['pj217b=5.0'] + data20['pj217b=6.0']\n",
    "data20['mother_low_ed'] = data20['pj217b=12.0'] + data20['pj217b=4.0']\n",
    "data20 = data20.rename(columns={'pj217b=7.0': 'mother_school'})\n",
    "data20 = data20.rename(columns={'pj217b=8.0': 'mother_tech'})\n",
    "data20 = data20.rename(columns={'pj217b=9.0': 'mother_univer'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем оценить влияние переменной h_diplom на уровень заработной платы. В качестве интрументов выберем образование матери и отца на тот момент, когда индивиду было 15 лет. С одной стороны, эти инструменты релеванты: чем выше уровень образования был в то время у родителей, тем вероятнее, что ребенок пойдет по их стопам и получит высшее образование или даже степень. С другой стороны, все-таки хочется надеяться, что образование родителей непостредственно не влияет на заработок их детей, то есть инструменты экзогенны. \n",
    "Эти инструменты релевантны и для языка, дети более образованных родителей на их примере понимают важность вложения сил и средств в человеческий капитал -> учат иностранные языки. \n",
    "\n",
    "Попробуем оценить модель с помощью двухшагового МНК, используя инструменты только для переменной p_diplom. Представленная ниже модель не дает значимой оценки для переменной lang_TF, поэтому как-то проинтерпретировать ее нельзя."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          IV-2SLS Estimation Summary                          \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(p_wage)   R-squared:                      0.2520\n",
      "Estimator:                    IV-2SLS   Adj. R-squared:                 0.2486\n",
      "No. Observations:                4175   F-statistic:                    1822.5\n",
      "Date:                Mon, Apr 01 2019   P-value (F-stat)                0.0000\n",
      "Time:                        23:29:47   Distribution:                 chi2(19)\n",
      "Cov. Estimator:                robust                                         \n",
      "                                                                              \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            7.7928     0.1372     56.782     0.0000      7.5238      8.0618\n",
      "p_age                0.0341     0.0068     5.0115     0.0000      0.0208      0.0475\n",
      "p_marst             -0.0027     0.0198    -0.1355     0.8922     -0.0416      0.0362\n",
      "I(p_age * p_age)    -0.0005  8.517e-05    -5.3468     0.0000     -0.0006     -0.0003\n",
      "male                 0.2449     0.0239     10.256     0.0000      0.1981      0.2917\n",
      "work_hours_m         0.0024     0.0002     9.9189     0.0000      0.0020      0.0029\n",
      "city                 0.0242     0.0214     1.1302     0.2584     -0.0177      0.0661\n",
      "msk                  0.7763     0.0346     22.467     0.0000      0.7086      0.8440\n",
      "spb                  0.4942     0.0516     9.5860     0.0000      0.3931      0.5952\n",
      "lang_TF             -0.0297     0.0415    -0.7146     0.4749     -0.1111      0.0517\n",
      "p_occup08_8          0.4170     0.0457     9.1307     0.0000      0.3275      0.5065\n",
      "p_occup08_5          0.1566     0.0450     3.4772     0.0005      0.0683      0.2449\n",
      "p_occup08_1          0.4072     0.0856     4.7579     0.0000      0.2394      0.5749\n",
      "p_occup08_3          0.2062     0.0593     3.4756     0.0005      0.0899      0.3224\n",
      "p_occup08_7          0.4889     0.0441     11.081     0.0000      0.4024      0.5754\n",
      "p_occup08_2          0.0629     0.0917     0.6858     0.4928     -0.1169      0.2427\n",
      "p_occup08_4          0.2667     0.0611     4.3644     0.0000      0.1469      0.3864\n",
      "p_occup08_6          0.0595     0.1262     0.4713     0.6374     -0.1878      0.3067\n",
      "p_occup08_0          0.4644     0.1534     3.0276     0.0025      0.1638      0.7651\n",
      "p_diplom             0.8166     0.1328     6.1490     0.0000      0.5563      1.0769\n",
      "====================================================================================\n",
      "\n",
      "Endogenous: p_diplom\n",
      "Instruments: father_degree, mother_degree, mother_univer, father_univer\n",
      "Robust Covariance (Heteroskedastic)\n",
      "Debiased: False\n"
     ]
    }
   ],
   "source": [
    "ols_wave20_inst = IV2SLS.from_formula('np.log(p_wage) ~ 1 + p_age + p_marst + I(p_age*p_age) + + male + work_hours_m + city + msk + spb + lang_TF + [p_diplom ~  father_degree + mother_degree +  mother_univer + father_univer] + ' + occup_20, data=data20).fit()\n",
    "print(ols_wave20_inst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wu-Hausman test of exogeneity\n",
       "H0: All endogenous variables are exogenous\n",
       "Statistic: 20.6858\n",
       "P-value: 0.0000\n",
       "Distributed: F(1,4154)\n",
       "WaldTestStatistic, id: 0x21d389b1be0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst.wu_hausman()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sargan's test of overidentification\n",
       "H0: The model is not overidentified.\n",
       "Statistic: 1.5898\n",
       "P-value: 0.6617\n",
       "Distributed: chi2(3)\n",
       "WaldTestStatistic, id: 0x21d389d00f0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst.sargan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rsquared</th>\n",
       "      <th>partial.rsquared</th>\n",
       "      <th>shea.rsquared</th>\n",
       "      <th>f.stat</th>\n",
       "      <th>f.pval</th>\n",
       "      <th>f.dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p_diplom</th>\n",
       "      <td>0.36897</td>\n",
       "      <td>0.037681</td>\n",
       "      <td>0.037681</td>\n",
       "      <td>140.51721</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chi2(4)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          rsquared  partial.rsquared  shea.rsquared     f.stat  f.pval  \\\n",
       "p_diplom   0.36897          0.037681       0.037681  140.51721     0.0   \n",
       "\n",
       "           f.dist  \n",
       "p_diplom  chi2(4)  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst.first_stage.diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Согласно результатам теста Хаусмана, проблема эндогенности переменной p_diplom все-таки имеется. Подобранный набор инструментов релевантен и экзогеген.\n",
    "\n",
    "В следующей модели попробуем оценить обе переменные: lang_TF и p_diplom. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          IV-2SLS Estimation Summary                          \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(p_wage)   R-squared:                      0.2283\n",
      "Estimator:                    IV-2SLS   Adj. R-squared:                 0.2247\n",
      "No. Observations:                4175   F-statistic:                    1733.5\n",
      "Date:                Mon, Apr 01 2019   P-value (F-stat)                0.0000\n",
      "Time:                        23:29:48   Distribution:                 chi2(19)\n",
      "Cov. Estimator:                robust                                         \n",
      "                                                                              \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            7.8689     0.4879     16.127     0.0000      6.9126      8.8253\n",
      "p_age                0.0311     0.0197     1.5833     0.1134     -0.0074      0.0697\n",
      "p_marst             -0.0047     0.0232    -0.2011     0.8406     -0.0502      0.0408\n",
      "I(p_age * p_age)    -0.0004     0.0002    -2.0029     0.0452     -0.0008  -9.096e-06\n",
      "male                 0.2459     0.0251     9.7934     0.0000      0.1967      0.2952\n",
      "work_hours_m         0.0024     0.0003     8.0348     0.0000      0.0018      0.0030\n",
      "city                 0.0201     0.0331     0.6084     0.5429     -0.0447      0.0850\n",
      "msk                  0.7889     0.0847     9.3146     0.0000      0.6229      0.9549\n",
      "spb                  0.4974     0.0566     8.7860     0.0000      0.3864      0.6083\n",
      "p_occup08_8          0.4161     0.0465     8.9528     0.0000      0.3250      0.5072\n",
      "p_occup08_5          0.1536     0.0485     3.1663     0.0015      0.0585      0.2488\n",
      "p_occup08_1          0.3950     0.1126     3.5095     0.0004      0.1744      0.6156\n",
      "p_occup08_3          0.2002     0.0695     2.8803     0.0040      0.0640      0.3364\n",
      "p_occup08_7          0.4876     0.0452     10.796     0.0000      0.3991      0.5761\n",
      "p_occup08_2          0.0455     0.1379     0.3295     0.7418     -0.2249      0.3158\n",
      "p_occup08_4          0.2579     0.0812     3.1773     0.0015      0.0988      0.4170\n",
      "p_occup08_6          0.0451     0.1565     0.2883     0.7731     -0.2617      0.3519\n",
      "p_occup08_0          0.4523     0.1689     2.6777     0.0074      0.1212      0.7833\n",
      "p_diplom             0.8832     0.4256     2.0750     0.0380      0.0490      1.7174\n",
      "lang_TF             -0.1236     0.5813    -0.2126     0.8317     -1.2629      1.0158\n",
      "====================================================================================\n",
      "\n",
      "Endogenous: p_diplom, lang_TF\n",
      "Instruments: father_degree, mother_degree, mother_univer, father_univer\n",
      "Robust Covariance (Heteroskedastic)\n",
      "Debiased: False\n"
     ]
    }
   ],
   "source": [
    "ols_wave20_inst2 = IV2SLS.from_formula('np.log(p_wage) ~ 1 + p_age + p_marst + I(p_age*p_age) + + male + work_hours_m + city + msk + spb + [p_diplom + lang_TF ~  father_degree + mother_degree + mother_univer + father_univer]  + ' + occup_20, data= data20).fit()\n",
    "print(ols_wave20_inst2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wu-Hausman test of exogeneity\n",
       "H0: All endogenous variables are exogenous\n",
       "Statistic: 10.3613\n",
       "P-value: 0.0000\n",
       "Distributed: F(2,4153)\n",
       "WaldTestStatistic, id: 0x21d38b40c18"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst2.wu_hausman()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sargan's test of overidentification\n",
       "H0: The model is not overidentified.\n",
       "Statistic: 1.5039\n",
       "P-value: 0.4714\n",
       "Distributed: chi2(2)\n",
       "WaldTestStatistic, id: 0x21d36a595c0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst2.sargan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rsquared</th>\n",
       "      <th>partial.rsquared</th>\n",
       "      <th>shea.rsquared</th>\n",
       "      <th>f.stat</th>\n",
       "      <th>f.pval</th>\n",
       "      <th>f.dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p_diplom</th>\n",
       "      <td>0.341920</td>\n",
       "      <td>0.050812</td>\n",
       "      <td>0.004688</td>\n",
       "      <td>344.351569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chi2(4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lang_TF</th>\n",
       "      <td>0.187665</td>\n",
       "      <td>0.031843</td>\n",
       "      <td>0.002938</td>\n",
       "      <td>98.331643</td>\n",
       "      <td>0.0</td>\n",
       "      <td>chi2(4)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          rsquared  partial.rsquared  shea.rsquared      f.stat  f.pval  \\\n",
       "p_diplom  0.341920          0.050812       0.004688  344.351569     0.0   \n",
       "lang_TF   0.187665          0.031843       0.002938   98.331643     0.0   \n",
       "\n",
       "           f.dist  \n",
       "p_diplom  chi2(4)  \n",
       "lang_TF   chi2(4)  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst2.first_stage.diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Предыдущие модели показывали, что переменная знания языка незначима и дает аж отрицательную отдачу. Возможно, что проблема в высокой гетерогенности. Попробуем построить модель на данных только для тех индивидов, которые получили высшее образование. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          IV-2SLS Estimation Summary                          \n",
      "==============================================================================\n",
      "Dep. Variable:         np.log(p_wage)   R-squared:                      0.1662\n",
      "Estimator:                    IV-2SLS   Adj. R-squared:                 0.1542\n",
      "No. Observations:                1269   F-statistic:                    425.64\n",
      "Date:                Mon, Apr 01 2019   P-value (F-stat)                0.0000\n",
      "Time:                        23:29:49   Distribution:                 chi2(18)\n",
      "Cov. Estimator:                robust                                         \n",
      "                                                                              \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            6.6476     0.5099     13.037     0.0000      5.6482      7.6470\n",
      "p_age                0.0851     0.0174     4.8883     0.0000      0.0510      0.1192\n",
      "p_marst              0.0313     0.0391     0.7988     0.4244     -0.0455      0.1080\n",
      "I(p_age * p_age)    -0.0010     0.0002    -4.9949     0.0000     -0.0014     -0.0006\n",
      "male                 0.1747     0.0484     3.6094     0.0003      0.0798      0.2696\n",
      "work_hours_m         0.0027     0.0005     5.1666     0.0000      0.0017      0.0038\n",
      "city                 0.0451     0.0473     0.9527     0.3407     -0.0477      0.1378\n",
      "msk                  0.6563     0.0805     8.1568     0.0000      0.4986      0.8140\n",
      "spb                  0.3819     0.0871     4.3859     0.0000      0.2112      0.5525\n",
      "p_occup08_8          0.6020     0.2889     2.0834     0.0372      0.0357      1.1683\n",
      "p_occup08_5          0.3134     0.2703     1.1597     0.2462     -0.2163      0.8431\n",
      "p_occup08_1          0.7575     0.2680     2.8268     0.0047      0.2323      1.2827\n",
      "p_occup08_3          0.4834     0.2654     1.8212     0.0686     -0.0368      1.0036\n",
      "p_occup08_7          0.6017     0.2730     2.2041     0.0275      0.0667      1.1367\n",
      "p_occup08_2          0.4548     0.2643     1.7207     0.0853     -0.0633      0.9729\n",
      "p_occup08_4          0.4820     0.2730     1.7658     0.0774     -0.0530      1.0171\n",
      "p_occup08_6          0.3273     0.3800     0.8614     0.3890     -0.4174      1.0721\n",
      "p_occup08_0          0.8113     0.3748     2.1647     0.0304      0.0767      1.5460\n",
      "lang_TF              0.6664     0.2462     2.7064     0.0068      0.1838      1.1490\n",
      "====================================================================================\n",
      "\n",
      "Endogenous: lang_TF\n",
      "Instruments: father_degree, mother_degree, father_univer, mother_univer\n",
      "Robust Covariance (Heteroskedastic)\n",
      "Debiased: False\n"
     ]
    }
   ],
   "source": [
    "ols_wave20_inst3 = IV2SLS.from_formula('np.log(p_wage) ~ 1 + p_age + p_marst + I(p_age*p_age) + male + work_hours_m + city + msk + spb + [lang_TF ~  father_degree + mother_degree + father_univer + mother_univer] +' + occup_20, data=data20[data20['p_diplom'] == 1]).fit()\n",
    "print(ols_wave20_inst3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wu-Hausman test of exogeneity\n",
       "H0: All endogenous variables are exogenous\n",
       "Statistic: 6.1591\n",
       "P-value: 0.0132\n",
       "Distributed: F(1,1249)\n",
       "WaldTestStatistic, id: 0x21d39c6b080"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst3.wu_hausman()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sargan's test of overidentification\n",
       "H0: The model is not overidentified.\n",
       "Statistic: 4.1135\n",
       "P-value: 0.2495\n",
       "Distributed: chi2(3)\n",
       "WaldTestStatistic, id: 0x21d39d2f4a8"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst3.sargan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rsquared</th>\n",
       "      <th>partial.rsquared</th>\n",
       "      <th>shea.rsquared</th>\n",
       "      <th>f.stat</th>\n",
       "      <th>f.pval</th>\n",
       "      <th>f.dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lang_TF</th>\n",
       "      <td>0.138447</td>\n",
       "      <td>0.023344</td>\n",
       "      <td>0.023344</td>\n",
       "      <td>30.677127</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>chi2(4)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rsquared  partial.rsquared  shea.rsquared     f.stat    f.pval  \\\n",
       "lang_TF  0.138447          0.023344       0.023344  30.677127  0.000004   \n",
       "\n",
       "          f.dist  \n",
       "lang_TF  chi2(4)  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_wave20_inst3.first_stage.diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          Model Comparison                                         \n",
      "===================================================================================================\n",
      "                          IV for diplom IV for languages (p_diplom = 1) IV for languages and diplom\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Dep. Variable            np.log(p_wage)                  np.log(p_wage)              np.log(p_wage)\n",
      "Estimator                       IV-2SLS                         IV-2SLS                     IV-2SLS\n",
      "No. Observations                   4175                            1269                        4175\n",
      "Cov. Est.                        robust                          robust                      robust\n",
      "R-squared                        0.2520                          0.1662                      0.2283\n",
      "Adj. R-squared                   0.2486                          0.1542                      0.2247\n",
      "F-statistic                      1822.5                          425.64                      1733.5\n",
      "P-value (F-stat)                 0.0000                          0.0000                      0.0000\n",
      "==================     ================                ================            ================\n",
      "Intercept                        7.7928                          6.6476                      7.8689\n",
      "                               (56.782)                        (13.037)                    (16.127)\n",
      "p_age                            0.0341                          0.0851                      0.0311\n",
      "                               (5.0115)                        (4.8883)                    (1.5833)\n",
      "p_marst                         -0.0027                          0.0313                     -0.0047\n",
      "                              (-0.1355)                        (0.7988)                   (-0.2011)\n",
      "I(p_age * p_age)                -0.0005                         -0.0010                     -0.0004\n",
      "                              (-5.3468)                       (-4.9949)                   (-2.0029)\n",
      "male                             0.2449                          0.1747                      0.2459\n",
      "                               (10.256)                        (3.6094)                    (9.7934)\n",
      "work_hours_m                     0.0024                          0.0027                      0.0024\n",
      "                               (9.9189)                        (5.1666)                    (8.0348)\n",
      "city                             0.0242                          0.0451                      0.0201\n",
      "                               (1.1302)                        (0.9527)                    (0.6084)\n",
      "msk                              0.7763                          0.6563                      0.7889\n",
      "                               (22.467)                        (8.1568)                    (9.3146)\n",
      "spb                              0.4942                          0.3819                      0.4974\n",
      "                               (9.5860)                        (4.3859)                    (8.7860)\n",
      "lang_TF                         -0.0297                          0.6664                     -0.1236\n",
      "                              (-0.7146)                        (2.7064)                   (-0.2126)\n",
      "p_occup08_8                      0.4170                          0.6020                      0.4161\n",
      "                               (9.1307)                        (2.0834)                    (8.9528)\n",
      "p_occup08_5                      0.1566                          0.3134                      0.1536\n",
      "                               (3.4772)                        (1.1597)                    (3.1663)\n",
      "p_occup08_1                      0.4072                          0.7575                      0.3950\n",
      "                               (4.7579)                        (2.8268)                    (3.5095)\n",
      "p_occup08_3                      0.2062                          0.4834                      0.2002\n",
      "                               (3.4756)                        (1.8212)                    (2.8803)\n",
      "p_occup08_7                      0.4889                          0.6017                      0.4876\n",
      "                               (11.081)                        (2.2041)                    (10.796)\n",
      "p_occup08_2                      0.0629                          0.4548                      0.0455\n",
      "                               (0.6858)                        (1.7207)                    (0.3295)\n",
      "p_occup08_4                      0.2667                          0.4820                      0.2579\n",
      "                               (4.3644)                        (1.7658)                    (3.1773)\n",
      "p_occup08_6                      0.0595                          0.3273                      0.0451\n",
      "                               (0.4713)                        (0.8614)                    (0.2883)\n",
      "p_occup08_0                      0.4644                          0.8113                      0.4523\n",
      "                               (3.0276)                        (2.1647)                    (2.6777)\n",
      "p_diplom                         0.8166                                                      0.8832\n",
      "                               (6.1490)                                                    (2.0750)\n",
      "==================== ==================              ==================          ==================\n",
      "Instruments               father_degree                   father_degree               father_degree\n",
      "                          mother_degree                   mother_degree               mother_degree\n",
      "                          mother_univer                   father_univer               mother_univer\n",
      "                          father_univer                   mother_univer               father_univer\n",
      "---------------------------------------------------------------------------------------------------\n",
      "\n",
      "T-stats reported in parentheses\n"
     ]
    }
   ],
   "source": [
    "from linearmodels.iv import compare\n",
    "print(compare({'IV for diplom':ols_wave20_inst,'IV for languages and diplom': ols_wave20_inst2, 'IV for languages (p_diplom = 1)': ols_wave20_inst3}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Заключение\n",
    "В данном проекте были проанализированы данные RLMS для работающих индивидов 20 и 24 волны. \n",
    "\n",
    "Для индивидов, участвовавших в 24 волне, были получены значимые положительные МНК-оценки для английского, немецкого и \"других\" (испанский, португальский, латынь и японкий) языков. Однако с большой долей уверенности можно утверждать, что оценки в данных моделях страдают от смещения из-за ненаблюдаемых характеристик, а также ошибок измерения.   \n",
    "\n",
    "Для данных 20 волны удалось подобрать набор релевантных и экзогенных инструментов для оценки влияния наличия высшего образования и знания иностранных языков. Оказалось, что эндогенность действительно присуствует: оценка для высшего образования оказалась завышенной, оценка для знания языков -- заниженной на части выборки, имеющей высшее образование. Если первый вывод кажется логичным, то насчет второго возникают вопросы. Такой результат (оценки с помощью инструментов значительно превышают оценки с помощью обычного МНК) был получены и в изученной мной литературе; ответ на данный вопрос дан, например, в [этой статье (стр. 601, Econometric Issues)](http://ecares.ulb.ac.be/ecare/personal/ginsburgh/papers/170%20returns.pdf). С одной стороны, при наличии ошибок измерения (сведения о владении языками дают сами индивиды без какого либо тестирования или проверки) стоит ожидать занижения оценок при МНК-оценке. С другой стороны, при оценке возникает проблема эндогенности из-за ability bias, то стоит ожидать завышения оценок обычным МНК. \n",
    "\n",
    "МНК-оценки для владения языками оказались выше для 24 волны, чем для 20. Однако из этого нельзя сделать однозначный вывод о том, что лингвистические способности стали цениться выше, так как 24 волна не была оценена с помощью инструментов. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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