Junghyun Kim
diff --git a/book/intro_ko.md b/book/intro_ko.md
index 08cdbb9..6c661a9 100644
--- a/book/intro_ko.md
+++ b/book/intro_ko.md
@@ -25,7 +25,8 @@ YouTube에서도 영상 시리즈를 볼 수 있습니다:
김정현
diff --git a/book/myst.yml b/book/myst.yml
index 617cab2..4d04517 100644
--- a/book/myst.yml
+++ b/book/myst.yml
@@ -23,6 +23,14 @@ project:
file: why_causal_inference/why_causal_inference_en.ipynb
- file: why_causal_inference/why_causal_inference_ko.ipynb
hidden: true
+ - title: RDD Basics
+ file: rdd/01_rdd_basic_en.ipynb
+ - file: rdd/01_rdd_basic_ko.ipynb
+ hidden: true
+ - title: Fuzzy RDD
+ file: rdd/02_fuzzy_rdd_en.ipynb
+ - file: rdd/02_fuzzy_rdd_ko.ipynb
+ hidden: true
site:
template: book-theme
diff --git a/book/rdd/01_rdd_basic_en.ipynb b/book/rdd/01_rdd_basic_en.ipynb
new file mode 100644
index 0000000..bfac64b
--- /dev/null
+++ b/book/rdd/01_rdd_basic_en.ipynb
@@ -0,0 +1,1027 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000001",
+ "metadata": {},
+ "source": [
+ "**🌐 Language:** **English** | [한국어 →](/rdd-basic-ko)\n",
+ "\n",
+ "# Regression Discontinuity Design (RDD)\n",
+ "\n",
+ "Written by Jiwoo Son · GitHub · LinkedIn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "a0000002",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import statsmodels.formula.api as smf\n",
+ "import warnings\n",
+ "\n",
+ "plt.rcParams['axes.unicode_minus'] = False\n",
+ "\n",
+ "np.random.seed(2026)\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000003",
+ "metadata": {},
+ "source": [
+ "## 1. What is RDD?\n",
+ "\n",
+ "The college entrance exam results are in. The scholarship cutoff is 70 points. A student who scored 69 and a student who scored 71 are just 2 points apart — a difference that likely reflects the luck of the day more than any meaningful gap in ability. Yet one receives a scholarship and the other does not.\n",
+ "\n",
+ "Now flip the question. If we want to estimate the causal effect of the scholarship on academic achievement, who is the ideal comparison group? Exactly these two students. Those just above and just below the cutoff are essentially equivalent in every way except for whether they received the scholarship.\n",
+ "\n",
+ "This is the core idea of **RDD (Regression Discontinuity Design)**.\n",
+ "\n",
+ "When treatment (a policy, benefit, etc.) is assigned based on whether a continuous variable crosses a specific threshold (cutoff), we compare units just on either side of that threshold to estimate the causal effect. Unlike RCT, there is no random assignment — but near the cutoff, the situation closely approximates one.\n",
+ "\n",
+ "RDD applies in more real-world settings than you might expect.\n",
+ "\n",
+ "| Setting | Running Variable | Cutoff | Treatment |\n",
+ "|---------|-----------------|--------|-----------|\n",
+ "| Scholarship eligibility | Exam score | 70 points | Scholarship award |\n",
+ "| Welfare benefit eligibility | Income level | 50% of median income | Cash transfer |\n",
+ "| Legal drinking age | Age | 19 years old | Alcohol purchase allowed |\n",
+ "| Drug insurance coverage | Drug price | Specific price threshold | Insurance reimbursement |\n",
+ "\n",
+ "Wherever a clear rule determines treatment, RDD is worth considering."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000004",
+ "metadata": {},
+ "source": [
+ "## 2. Types of RDD\n",
+ "\n",
+ "RDD comes in two main variants: Sharp Design and Fuzzy Design.\n",
+ "\n",
+ "* `Sharp Design`: treatment is fully determined by whether the running variable crosses the cutoff.\n",
+ "* `Fuzzy Design`: the cutoff affects the probability of treatment, but does not perfectly determine it.\n",
+ "\n",
+ "For example, if every student who scores above 70 is guaranteed a scholarship, that’s Sharp. If some eligible students don’t apply or fail a secondary review, that’s Fuzzy. This chapter focuses on Sharp RDD; Fuzzy RDD is covered in a separate chapter."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "f49a8ea9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_rdd_type():\n",
+ " c = 0\n",
+ " R = np.linspace(-3, 3, 300)\n",
+ " \n",
+ " D_sharp = np.where(R >= c, 1.0, 0.0)\n",
+ " \n",
+ " D_fuzzy = np.where(R >= c,\n",
+ " 0.65 + 0.1 * np.tanh(R * 2),\n",
+ " 0.15 + 0.1 * np.tanh(R * 2))\n",
+ " \n",
+ " fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+ " \n",
+ " # ── Sharp RDD ──\n",
+ " ax = axes[0]\n",
+ " ax.plot(R[R < c], D_sharp[R < c], 'tomato', linewidth=3, label=r'$P(D=1|R)$: $R < c$')\n",
+ " ax.plot(R[R >= c], D_sharp[R >= c], 'steelblue', linewidth=3, label=r'$P(D=1|R)$: $R \\geq c$')\n",
+ " ax.axvline(c, color='gray', linestyle=':', linewidth=1.5)\n",
+ " ax.scatter([c], [0], s=80, color='tomato', zorder=5, edgecolors='tomato', facecolors='none', linewidths=2)\n",
+ " ax.scatter([c], [1], s=80, zorder=5)\n",
+ " ax.annotate('', xy=(c, 1.0), xytext=(c, 0.0),\n",
+ " arrowprops=dict(arrowstyle='<->', color='black', lw=2))\n",
+ " ax.text(c + 0.1, 0.5, '0 → 1\\n(perfect jump)', fontsize=11, va='center', fontweight='bold')\n",
+ " ax.text(c + 0.05, -0.07, '$c$', fontsize=12, color='gray')\n",
+ " ax.set_ylim(-0.15, 1.25)\n",
+ " ax.set_title('Sharp RDD', fontsize=13, fontweight='bold')\n",
+ " ax.set_xlabel('Running Variable $R$', fontsize=11)\n",
+ " ax.set_ylabel(r'$P(D=1 \\mid R)$', fontsize=11)\n",
+ " ax.legend(fontsize=9)\n",
+ " ax.grid(alpha=0.3)\n",
+ " \n",
+ " # ── Fuzzy RDD ──\n",
+ " ax = axes[1]\n",
+ " ax.plot(R[R < c], D_fuzzy[R < c], 'tomato', linewidth=3, label=r'$P(D=1|R)$: $R < c$')\n",
+ " ax.plot(R[R >= c], D_fuzzy[R >= c], 'steelblue', linewidth=3, label=r'$P(D=1|R)$: $R \\geq c$')\n",
+ " ax.axvline(c, color='gray', linestyle=':', linewidth=1.5)\n",
+ " \n",
+ " d_l = np.interp(c - 0.009, R, D_fuzzy)\n",
+ " d_r = np.interp(c + 0.009, R, D_fuzzy)\n",
+ " ax.scatter([c], [d_l], s=80, color='tomato', zorder=5, facecolors='none', edgecolors='tomato', linewidths=2)\n",
+ " ax.scatter([c], [d_r], s=80, zorder=5)\n",
+ " ax.annotate('', xy=(c, d_r), xytext=(c, d_l),\n",
+ " arrowprops=dict(arrowstyle='<->', color='black', lw=2))\n",
+ " ax.text(c + 0.1, (d_l + d_r) / 2,\n",
+ " f'{d_l:.2f} → {d_r:.2f}\\n(partial jump)', fontsize=11, va='center', fontweight='bold')\n",
+ " ax.text(c + 0.05, -0.07, '$c$', fontsize=12, color='gray')\n",
+ " ax.set_ylim(-0.15, 1.25)\n",
+ " ax.set_title('Fuzzy RDD', fontsize=13, fontweight='bold')\n",
+ " ax.set_xlabel('Running Variable $R$', fontsize=11)\n",
+ " ax.set_ylabel(r'$P(D=1 \\mid R)$', fontsize=11)\n",
+ " ax.legend(fontsize=9)\n",
+ " ax.grid(alpha=0.3)\n",
+ " \n",
+ " fig.suptitle(r'Sharp vs Fuzzy RDD — $P(D=1 \\mid R)$', fontsize=14, fontweight='bold')\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "5fda877b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_rdd_type()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000006",
+ "metadata": {},
+ "source": [
+ "## 3. Key Assumptions\n",
+ "\n",
+ "For RDD to produce valid causal estimates, two important assumptions must hold.\n",
+ "\n",
+ "### Continuity Assumption\n",
+ "\n",
+ "The first is **continuity of potential outcomes**. Both the expected GPA without the scholarship and the expected GPA with it must vary smoothly near the cutoff — no sudden jumps.\n",
+ "\n",
+ "$$\\mathbb{E}[Y_i(0) \\mid X_i = x] \\quad \\text{and} \\quad \\mathbb{E}[Y_i(1) \\mid X_i = x] \\quad \\text{are continuous at } x = c$$\n",
+ "\n",
+ "Why does this matter? If this holds, then any **sharp discontinuity** in observed outcomes near the cutoff can only be attributed to the treatment (the scholarship).\n",
+ "\n",
+ "What if this assumption breaks down? For instance, if the school provides extra tutoring specifically to students near the 70-point threshold, then a jump in GPA near the cutoff could be due to the tutoring — not the scholarship — and we can no longer identify the causal effect.\n",
+ "\n",
+ "### Local Randomization\n",
+ "\n",
+ "The second assumption is another way of expressing continuity. Very close to the cutoff $c$, whether a student scores 69.5 or 70.5 is essentially random.\n",
+ "\n",
+ "For this logic to hold, units must be **unable to precisely manipulate** their running variable value. If a teacher inflates a student’s score to push them above 70, or if students strategically aim for exactly 70, the near-randomness near the cutoff is destroyed. This is called **manipulation**, and it must be tested before any RDD analysis."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000007",
+ "metadata": {},
+ "source": [
+ "## 4. Key Components\n",
+ "\n",
+ "A few core concepts need to be clearly defined before proceeding.\n",
+ "\n",
+ "**Running Variable** $X_i$ is the continuous variable that determines treatment. Exam scores, age, and income level are all examples — each unit has a different value, and that value determines whether they receive treatment. Crucially, units must not be able to precisely control this value.\n",
+ "\n",
+ "**Cutoff** $c$ is the threshold that determines treatment. In Sharp RDD, units with $X_i \\geq c$ receive treatment and those with $X_i < c$ do not.\n",
+ "\n",
+ "$$D_i = \\mathbf{1}[X_i \\geq c]$$\n",
+ "\n",
+ "**Potential Outcomes** follow the Rubin Causal Model introduced in the causal inference chapter.\n",
+ "\n",
+ "- $Y_i(1)$: GPA if the scholarship is received\n",
+ "- $Y_i(0)$: GPA if the scholarship is not received\n",
+ "- Observed outcome: $Y_i^{obs} = D_i \\cdot Y_i(1) + (1 - D_i) \\cdot Y_i(0)$\n",
+ "\n",
+ "We can never observe both $Y_i(1)$ and $Y_i(0)$ for the same student. A scholarship recipient reveals only $Y_i(1)$; a non-recipient reveals only $Y_i(0)$.\n",
+ "\n",
+ "The table below illustrates this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "a0000008",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
Student
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+ "
X (Exam Score)
\n",
+ "
D (Scholarship)
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+ "
Y(0) — GPA without
\n",
+ "
Y(1) — GPA with
\n",
+ "
Observed GPA
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
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+ "
A
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+ "
69
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+ "
0
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+ "
3.1
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+ "
?
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+ "
3.1
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+ "
\n",
+ "
\n",
+ "
1
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+ "
B
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+ "
70
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+ "
1
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+ "
?
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+ "
3.6
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+ "
3.6
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+ "
\n",
+ "
\n",
+ "
2
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+ "
C
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+ "
71
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+ "
1
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+ "
?
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+ "
3.7
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+ "
3.7
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+ "
\n",
+ "
\n",
+ "
3
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+ "
D
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+ "
75
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+ "
1
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+ "
?
\n",
+ "
3.9
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+ "
3.9
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+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Student X (Exam Score) D (Scholarship) Y(0) — GPA without Y(1) — GPA with \\\n",
+ "0 A 69 0 3.1 ? \n",
+ "1 B 70 1 ? 3.6 \n",
+ "2 C 71 1 ? 3.7 \n",
+ "3 D 75 1 ? 3.9 \n",
+ "\n",
+ " Observed GPA \n",
+ "0 3.1 \n",
+ "1 3.6 \n",
+ "2 3.7 \n",
+ "3 3.9 "
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.DataFrame({\n",
+ " 'Student': ['A', 'B', 'C', 'D'],\n",
+ " 'X (Exam Score)': [69, 70, 71, 75],\n",
+ " 'D (Scholarship)': [0, 1, 1, 1],\n",
+ " 'Y(0) — GPA without': [3.1, '?', '?', '?'],\n",
+ " 'Y(1) — GPA with': ['?', 3.6, 3.7, 3.9],\n",
+ " 'Observed GPA': [3.1, 3.6, 3.7, 3.9],\n",
+ "})"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000009",
+ "metadata": {},
+ "source": [
+ "The cells marked with ? are counterfactuals. RDD estimates these counterfactuals near the cutoff — for students like those at 69 and 70 points. It does not estimate the counterfactual for a student at 75 points. This is both the scope and the limitation of RDD."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000010",
+ "metadata": {},
+ "source": [
+ "## 5. Understanding RDD with Formulas\n",
+ "\n",
+ "RDD estimates the **Local Average Treatment Effect (LATE) at the cutoff**.\n",
+ "\n",
+ "$$\\tau_{SRD} = \\lim_{x \\rightarrow c+} \\mathbb{E}[Y_i \\mid X_i = x] - \\lim_{x \\rightarrow c-} \\mathbb{E}[Y_i \\mid X_i = x]$$\n",
+ "\n",
+ "Intuitively, this is the expected outcome just to the right of the cutoff minus the expected outcome just to the left. If the continuity assumption holds, this difference is purely due to the treatment.\n",
+ "\n",
+ "In practice, we fit separate regression lines on each side of the cutoff and measure the difference in their intercepts at $c$.\n",
+ "\n",
+ "$$Y_i = \\beta_0 + \\beta_1 (X_i - c) + \\beta_2 D_i + \\beta_3 D_i (X_i - c) + \\varepsilon_i$$\n",
+ "\n",
+ "Let’s walk through each parameter. $\\beta_0$ is the expected outcome just below the cutoff — the intercept for the untreated side. $\\beta_2$ is the key parameter: the jump in outcome at the cutoff due to treatment, i.e., the **treatment effect (LATE)**. $\\beta_1$ is the slope for the untreated side, and $\\beta_3$ captures how much the slope changes on the treated side.\n",
+ "\n",
+ "The left and right limits at the cutoff are:\n",
+ "\n",
+ "$$\\lim_{x \\rightarrow c-} \\mathbb{E}[Y \\mid X=x] = \\beta_0 \\qquad \\lim_{x \\rightarrow c+} \\mathbb{E}[Y \\mid X=x] = \\beta_0 + \\beta_2$$\n",
+ "\n",
+ "Therefore $\\hat{\\tau}_{SRD} = \\hat{\\beta}_2$.\n",
+ "\n",
+ "Note that this is a **local** effect, limited to units near the cutoff. RDD cannot tell us how a scholarship would affect students who scored in the 40s."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000011",
+ "metadata": {},
+ "source": [
+ "## 6. Visualization\n",
+ "\n",
+ "The core of RDD estimation is visually confirming **how regression lines differ on each side of the cutoff**.\n",
+ "\n",
+ "The graph below contains three layers of information.\n",
+ "\n",
+ "- **Scatter plot**: each student's exam score (X-axis) and GPA (Y-axis). Students who received the scholarship (treated) and those who did not (untreated) are distinguished by color.\n",
+ "- **Regression lines**: separate lines fitted independently to the left (untreated) and right (treated) of the cutoff.\n",
+ "- **Discontinuity (Jump)**: the difference in intercepts between the two regression lines at the cutoff is the **estimated treatment effect ($\\hat{\\tau}_{SRD}$)**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "dded5318",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_rdd_regression(beta_0=2.0, beta_1=0.015, beta_2=0.5, beta_3=0.005,\n",
+ " show_line=True, show_data=True, show_labels=True, random_seed=2026):\n",
+ "\n",
+ " np.random.seed(random_seed)\n",
+ " c = 70 # cutoff\n",
+ "\n",
+ " # -----------------------------\n",
+ " # 시뮬레이션 데이터 생성\n",
+ " # -----------------------------\n",
+ " r_obs = np.random.uniform(40, 100, 200)\n",
+ " D_obs = (r_obs > c).astype(int)\n",
+ "\n",
+ " y_obs = (\n",
+ " beta_0\n",
+ " + beta_1 * (r_obs - c)\n",
+ " + beta_2 * D_obs\n",
+ " + beta_3 * D_obs * (r_obs - c)\n",
+ " + np.random.normal(0, 0.15, size=len(r_obs))\n",
+ " )\n",
+ "\n",
+ " df = pd.DataFrame({\n",
+ " \"y\": y_obs,\n",
+ " \"x\": r_obs,\n",
+ " \"D\": D_obs\n",
+ " })\n",
+ "\n",
+ " df[\"x_centered\"] = df[\"x\"] - c\n",
+ " df[\"interaction\"] = df[\"D\"] * df[\"x_centered\"]\n",
+ "\n",
+ " # -----------------------------\n",
+ " # smf 회귀\n",
+ " # -----------------------------\n",
+ " model = smf.ols(\n",
+ " \"y ~ x_centered + D + interaction\",\n",
+ " data=df\n",
+ " ).fit()\n",
+ "\n",
+ " # 추정된 계수\n",
+ " b0_hat = model.params[\"Intercept\"].round(2)\n",
+ " b1_hat = model.params[\"x_centered\"].round(2)\n",
+ " b2_hat = model.params[\"D\"].round(2)\n",
+ " b3_hat = model.params[\"interaction\"].round(2)\n",
+ "\n",
+ " # -----------------------------\n",
+ " # 회귀선 (추정치 기반)\n",
+ " # -----------------------------\n",
+ " r = np.linspace(40, 100, 200)\n",
+ " D = (r > c).astype(int)\n",
+ "\n",
+ " y = (\n",
+ " b0_hat\n",
+ " + b1_hat * (r - c)\n",
+ " + b2_hat * D\n",
+ " + b3_hat * D * (r - c)\n",
+ " )\n",
+ "\n",
+ " r_left = r[r <= c]\n",
+ " y_left = y[r <= c]\n",
+ "\n",
+ " r_right = r[r > c]\n",
+ " y_right = y[r > c]\n",
+ "\n",
+ " plt.figure(figsize=(10, 6))\n",
+ "\n",
+ " # -----------------------------\n",
+ " # 산점도\n",
+ " # -----------------------------\n",
+ " if show_data:\n",
+ " plt.scatter(\n",
+ " r_obs[r_obs <= c],\n",
+ " y_obs[r_obs <= c],\n",
+ " color='tomato',\n",
+ " alpha=0.3,\n",
+ " s=20,\n",
+ " zorder=1\n",
+ " )\n",
+ "\n",
+ " plt.scatter(\n",
+ " r_obs[r_obs > c],\n",
+ " y_obs[r_obs > c],\n",
+ " color='steelblue',\n",
+ " alpha=0.3,\n",
+ " s=20,\n",
+ " zorder=1\n",
+ " )\n",
+ "\n",
+ " # -----------------------------\n",
+ " # 회귀선\n",
+ " # -----------------------------\n",
+ " if show_line:\n",
+ "\n",
+ " plt.plot(\n",
+ " r_left,\n",
+ " y_left,\n",
+ " label=r'$X \\leq c$ (No Treatment)',\n",
+ " color='tomato',\n",
+ " linewidth=2.5,\n",
+ " zorder=2\n",
+ " )\n",
+ "\n",
+ " plt.plot(\n",
+ " r_right,\n",
+ " y_right,\n",
+ " label=r'$X > c$ (Treatment)',\n",
+ " color='steelblue',\n",
+ " linewidth=2.5,\n",
+ " zorder=2\n",
+ " )\n",
+ "\n",
+ " y_left_intercept = b0_hat\n",
+ " y_right_intercept = b0_hat + b2_hat\n",
+ "\n",
+ " plt.scatter(\n",
+ " [c], [y_left_intercept],\n",
+ " color='tomato',\n",
+ " s=80,\n",
+ " zorder=5,\n",
+ " facecolors='none',\n",
+ " edgecolors='tomato',\n",
+ " linewidths=2\n",
+ " )\n",
+ "\n",
+ " plt.scatter(\n",
+ " [c], [y_right_intercept],\n",
+ " color='steelblue',\n",
+ " s=80,\n",
+ " zorder=5\n",
+ " )\n",
+ "\n",
+ " plt.annotate(\n",
+ " '',\n",
+ " xy=(c, y_right_intercept),\n",
+ " xytext=(c, y_left_intercept),\n",
+ " arrowprops=dict(arrowstyle='<->', color='black', lw=2)\n",
+ " )\n",
+ "\n",
+ " # -----------------------------\n",
+ " # label (추정값 사용)\n",
+ " # -----------------------------\n",
+ " if show_labels:\n",
+ "\n",
+ " plt.text(\n",
+ " c - 14,\n",
+ " y_left_intercept - 0.04,\n",
+ " r\"Intercept = $\\hat{\\beta}_0$\" + f\" = {b0_hat:.2f}\",\n",
+ " color='tomato',\n",
+ " fontweight='bold',\n",
+ " fontsize=10\n",
+ " )\n",
+ "\n",
+ " plt.text(\n",
+ " c + 0.8,\n",
+ " y_right_intercept + 0.2,\n",
+ " r\"Intercept = $\\hat{\\beta}_0$ + $\\hat{\\beta}_2$\" + f\" = {(b0_hat + b2_hat):.2f}\",\n",
+ " color='steelblue',\n",
+ " fontweight='bold',\n",
+ " fontsize=10\n",
+ " )\n",
+ "\n",
+ " plt.text(\n",
+ " c + 0.8,\n",
+ " (y_left_intercept + y_right_intercept) / 2,\n",
+ " r\"Jump = $\\hat{\\beta}_2$\"\n",
+ " + f\" = {b2_hat:.2f}\",\n",
+ " color='black',\n",
+ " fontweight='bold',\n",
+ " fontsize=10\n",
+ " )\n",
+ "\n",
+ " x_sl = 52\n",
+ " y_sl = b0_hat + b1_hat * (x_sl - c)\n",
+ "\n",
+ " plt.text(\n",
+ " x_sl - 10,\n",
+ " y_sl,\n",
+ " r\"Slope = $\\hat{\\beta}_1$\"\n",
+ " + f\" = {b1_hat:.2f}\",\n",
+ " color='tomato',\n",
+ " fontsize=9\n",
+ " )\n",
+ "\n",
+ " x_sr = 86\n",
+ " y_sr = (\n",
+ " b0_hat\n",
+ " + b2_hat\n",
+ " + (b1_hat + b3_hat) * (x_sr - c)\n",
+ " )\n",
+ "\n",
+ " plt.text(\n",
+ " x_sr - 10,\n",
+ " y_sr + 0.1,\n",
+ " r\"Slope = $\\hat{\\beta}_1 + \\hat{\\beta}_3$\"\n",
+ " + f\" = {(b1_hat + b3_hat):.2f}\",\n",
+ " color='steelblue',\n",
+ " fontsize=9\n",
+ " )\n",
+ "\n",
+ " plt.axvline(\n",
+ " x=c,\n",
+ " linestyle='--',\n",
+ " color='gray',\n",
+ " linewidth=1.5\n",
+ " )\n",
+ "\n",
+ " plt.text(\n",
+ " c + 0.3,\n",
+ " plt.ylim()[0] + 0.05,\n",
+ " f'$c = {c}$',\n",
+ " fontsize=11,\n",
+ " color='gray'\n",
+ " )\n",
+ "\n",
+ " plt.title(\n",
+ " \"Regression Parameters Visualization (SAT Score → GPA)\",\n",
+ " fontsize=13,\n",
+ " fontweight='bold'\n",
+ " )\n",
+ "\n",
+ " plt.xlabel(\"SAT Score $X$\", fontsize=11)\n",
+ " plt.ylabel(\"GPA\", fontsize=11)\n",
+ "\n",
+ " if show_line:\n",
+ " plt.legend(fontsize=10)\n",
+ "\n",
+ " plt.grid(True, alpha=0.3)\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "cf85dddc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_rdd_regression(beta_0=2.0, beta_1=0.015, beta_2=0.5, beta_3=0.005)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99178d2b",
+ "metadata": {},
+ "source": [
+ "There are a few things to focus on when reading this graph.\n",
+ "\n",
+ "**Look near the cutoff.** RDD's causal inference holds only just to the left and right of the cutoff (70 points). The fact that students scoring in the 40s and 95s have different GPAs may reflect differences in ability, not the scholarship effect. Only the **jump at the cutoff where the two regression lines fail to meet** has a causal interpretation.\n",
+ "\n",
+ "**Slopes may differ.** The regression lines for the treated group (right) and untreated group (left) are allowed to have different slopes. The tendency for GPA to rise with score may vary depending on treatment status. The $\\beta_3$ term in the regression equation from Section 5 handles this slope difference.\n",
+ "\n",
+ "**A larger jump does not necessarily mean a larger effect.** In Sharp RDD, the jump is the estimated treatment effect directly. In Fuzzy RDD, the jump in Y must be divided by the jump in D. This distinction is covered in the next chapter."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000013",
+ "metadata": {},
+ "source": [
+ "## 7. Example\n",
+ "\n",
+ "Let's run a simulation directly. We set the scholarship cutoff at 70 points with a true treatment effect of 0.5 GPA points. We then check whether RDD recovers this effect.\n",
+ "\n",
+ "The key steps: fit separate OLS regression lines on the left and right of the cutoff, then estimate the treatment effect as the difference in their intercepts at the cutoff."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "a0000014",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(figsize=(10, 6))\n",
+ "\n",
+ "left = df[df['treated'] == 0]\n",
+ "right = df[df['treated'] == 1]\n",
+ "\n",
+ "ax.scatter(left['score'], left['gpa'], color='tomato', alpha=0.35, s=20, label='Untreated')\n",
+ "ax.scatter(right['score'], right['gpa'], color='steelblue', alpha=0.35, s=20, label='Treated')\n",
+ "\n",
+ "s_left = np.linspace(40, cutoff, 100)\n",
+ "s_right = np.linspace(cutoff, 100, 100)\n",
+ "ax.plot(s_left, model_left.predict(pd.DataFrame({'score_c': s_left - cutoff})), color='tomato', linewidth=2.5, label='Regression line (untreated)')\n",
+ "ax.plot(s_right, model_right.predict(pd.DataFrame({'score_c': s_right - cutoff})), color='steelblue', linewidth=2.5, label='Regression line (treated)')\n",
+ "\n",
+ "ax.scatter([cutoff], [y_at_c_left], s=100, color='tomato', zorder=6, facecolors='none', edgecolors='tomato', linewidths=2)\n",
+ "ax.scatter([cutoff], [y_at_c_right], s=100, color='steelblue', zorder=6)\n",
+ "ax.annotate('', xy=(cutoff, y_at_c_right), xytext=(cutoff, y_at_c_left),\n",
+ " arrowprops=dict(arrowstyle='<->', color='black', lw=2.5))\n",
+ "ax.text(cutoff + 0.8, (y_at_c_left + y_at_c_right) / 2,\n",
+ " f'$\\\\hat{{\\\\tau}}_{{SRD}}$ = {estimated_effect:.3f}',\n",
+ " fontsize=12, va='center', fontweight='bold')\n",
+ "\n",
+ "ax.axvline(cutoff, color='gray', linestyle=':', linewidth=1.5)\n",
+ "ax.text(cutoff + 0.3, 1.65, f'Cutoff = {cutoff}', fontsize=11, color='gray')\n",
+ "\n",
+ "ax.set_title('Sharp RDD Estimate: Effect of Scholarship on GPA', fontsize=14, fontweight='bold')\n",
+ "ax.set_xlabel('Exam Score', fontsize=12)\n",
+ "ax.set_ylabel('GPA', fontsize=12)\n",
+ "ax.legend(fontsize=10)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000017",
+ "metadata": {},
+ "source": [
+ "## ETC.\n",
+ "\n",
+ "Here we review misconceptions that often arise when applying RDD, and the checks that should be run before any analysis.\n",
+ "\n",
+ "### Common Misconceptions\n",
+ "\n",
+ "\"Just look near the cutoff\" is correct in spirit, but **how close is close enough** cannot be chosen arbitrarily. Too narrow a window leaves too few observations and inflates variance; too wide a window mixes in units far from the cutoff and introduces bias. This window is the **bandwidth**, and the optimal bandwidth is selected algorithmically (Imbens & Kalyanaraman, 2012).\n",
+ "\n",
+ "\"RDD is the same as a randomized experiment\" is also imprecise. Quasi-randomization holds only *near* the cutoff. RDD does not estimate effects for units far from the cutoff, and external validity may be limited.\n",
+ "\n",
+ "### Validity Check: McCrary Density Test\n",
+ "\n",
+ "The most important check is whether **manipulation** has occurred. The test proposed by McCrary (2008) is straightforward: verify that the distribution of the running variable is continuous at the cutoff.\n",
+ "\n",
+ "If students deliberately aim to score just above 70, or if graders push borderline students over the threshold, there will be an abnormal spike in observations just above the cutoff. Conversely, if units avoid being just above the cutoff, the region just below it will be depleted. Either case appears as a sudden density change near the cutoff in a histogram."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "a0000018",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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29OhRbt6KFSt482JiYhhjjB0+fJib1qpVqxxpTEpKYh8+fOB+K2tZm9e8LG/fvuV1leHr68sYYywiIoLXBULW9ILIrWVtYfL0wYMH3LQqVarkeE07LS2NBQUF8abl1sJRWT4VtmWtoo9YLGYTJ05k6enpvGWLq2Xt+PHjuXl+fn68efKv8WffFytWrODmHT16lDfv8ePH3Lz8tqy1sLDgjmHGGKtVqxY3r2vXrtz0YcOGcdOFQiGvm4Ts25bflrWMMTZixAhuOVVVVa5Lj+x5In9+ZgkICGB79+5lq1ev5q5f8tfOHTt2KE1j165dc+xbxvI+7qKiotjp06fZhg0b2J9//smWLVvGa+nZrFkzpXnt5OTEa/U/ZMgQ3vys7hCUtVZNT0/ntWifM2cOb11Lly7l5unr6yvcvuyyn0P5+eSnZW1ERAQ3TUtLi7fdjDGWkZHB3rx5w5uW2zHLWGY3QvLXvB49evDm9+jRg5unoqLCvnz5whgr/LGb33telkePHrFdu3axlStXsmXLlrGFCxfylpd/2yD7eb1r1y5unpubG2/egQMHuDwzNTXlpk+YMEFpWgghhJCSQi1rCSGElDsjR47E0qVLkZaWhkGDBuHjx48AgKFDh+baelG+RammpiZv4LAstra2xZ7eAQMGKGwxFh4ejoEDB+LUqVO5Lv/hwwel8yZNmgRVVdUc04cMGYL58+cjOTkZT548wZ07d+Dq6spr7VlcrWqzMMYUTs8aKArIbB2mr6+vNI6bN29yrYDlmZqaolOnTtzvSpUq8eZHRkZCU1MTdevWhVQqRXJyMs6dOwcnJydUr14dDg4OqFWrFpo3b660lV9hWFpaolOnTtzgV5s2bcL69etx9OhRpKamAgCqV6+O2rVrF9s6gcLlaZUqVaCvr4/w8HA8e/YMdnZ2qFWrFhwcHFC9enV4eHjAysqqwGlxd3dXuu+Lyt3dHZMnT4ZQKCyR+OVbQis6phRRUVHBsGHDCrxcbvr37w9NTU3ut4ODAx4+fJgjvqwWjwDg7OwMJycn7ne/fv0wZMgQpKWlFXj9Xl5eWL9+PQAgKSkJBw8exKBBg3itaiUSCfr27cv9DgoKQt++fXHz5s1c487t+jVjxowC7duMjAxMmzYNK1euzLUlZm7r7NmzJ6/lc79+/bBp0ybut6+vL+zs7JQuHxAQwGv1On/+fMyfP19h2PDwcLx48QKVK1dWGl9J0tXVhZOTE/z9/RETEwMbGxvUrVsX9vb2qFatGpo3bw4bG5sCxXn37l2kp6dzvwcOHMibP3DgQOzfvx8AkJ6ejrt376JNmzbFduwqu+c9ePAAAwYM4N5cUUbZsSESiXhvDlhbW+PWrVsAALFYjC5dugDIbEltY2OD4OBgAIU73wkhhJCiKpmSMSGEEFIEZmZm8PT0BACuolYsFvMqXhSJiIjgvltYWHCvT+a2niwvXrwodIWUsgf1QYMG5VlRCwDJyckFjtvAwAC9e/fmfm/evBmfPn3iKqzFYjH69euX57oL4sWLF7zfWfknn+95CQsLUzjd2tqa9zt7pXzWaPbm5ubYvn07KlSoAAB4+vQp9u7di/nz56NLly4wNTXF3r17852e/JCvXN6zZw8SEhK4ygoAXBcNxakweaqqqor9+/dzfxy8efMGhw4dwuLFi9G7d2+YmZlh+fLlxZ7W/GrRogWWLFmCPn36QCAQAAAuXLiA5s2bIyEhIddl5c/N3M6X7OSPK2XHVHZGRka8yqL8LpffdGSPUz6+rK4KAMDY2Ji3jEgk4o77gnJxcYGjoyP3e8eOHUhPT8fu3bu5ae3bt+f9KdC5c+c8K2qBwl2/lFm1ahWWLVuW5yvzua3T0NCQ99vIyIj3Wz6PFSnIuQcov6blZtu2bWCZY4dwn8DAwALHAwC7d+/m9m1wcDCOHTuGP/74AwMHDoSlpSUmTJhQoPiyb3/2/Mv+O6sys7iOXUXHTGJiItq3b59nRS2g/NgwNDSESPS1nZJ8hb6hoSGvvCAfrjDnOyGEEFJU1LKWEEJIuTRu3Djs27eP++3p6amwf0l58n1svn//Hunp6blW2DZv3hwvX74EkPnAeezYsUL1W6uurp5jWnx8PE6ePMlb18aNG2FlZQUVFRW4uLjg3r17hYo7y5gxY7B9+3YAwN69e1GxYkXuwbJ9+/YwMDAo4JYoFx8fz9sfmpqaqFOnDgB+vpuYmORaOWBhYaFwulgs5v3OqsxTpFevXvD09MTdu3fx5MkTvHz5EpcuXcLDhw8RFxeHQYMGoX379tDQ0MjXtuWlSZMmqFatGp48eYLo6Gj8/fffXH/H2VsjFpfC5mmzZs0QGBiIBw8ewM/PD69evcLNmzdx7do1pKSkYPLkyejYsWOuLQtLSv369bnW7tWqVcP06dMBAP7+/vjzzz8xe/ZsLmz21piJiYlQU1MDAO6czQ/54yq3Y0rZMgVZrjji1NHR4b5//vyZNy8tLS1HP6cFMXDgQC7/r127xv3Bk8XLy4v7HhAQwOtTuE+fPli6dClMTU0hEAhgaGiYr0rK3K5fishfY0xNTXHkyBHUrFkTEokEU6ZMwbJly/KMI3u+hYaG8n7L57Ei2ftqHjhwIKpWrao0fPaK+NJWvXp1+Pv748mTJ3jw4AFevnyJBw8e4MyZM8jIyMBff/2FDh06oGnTpvmKL/v2Z8+/7L+z+vkurmNX0TFz9epV3rE6ceJETJs2DRUqVEBCQkK+jrPs56A8+cpZQgghpDygOxMhhJByyc3NDXXr1uUqNBW9Op9dw4YNuRaPsbGxWLZsGTdwT5a3b99yr4KPHj0amzZt4l75HDFiBGxsbFCjRg3eMqmpqfD29kbHjh1ztNpSJjo6mvcqabt27bguGAICAvD48eN8xZOb2rVro379+rh58ybi4uIwb948bl5xtvaMiYlBnz59eAO3jR49mmuZVL9+fS7fw8LC0LJlS26QoiyMMfj4+HAD2BRWREQEYmNjYWVlhQYNGqBBgwYAMivbsyoZEhISEBAQAGdn51zjyv7wnlvrzjFjxmDo0KEAMl/tzuoCoUOHDoVu7ZibwuRpUlISAgMDUaVKFdSpU4erTGeMQVdXF9HR0cjIyMCjR4+4ylr5PFC2/ZcvX+ZV9Fy6dAnu7u5F2r5JkyZhy5YtePXqFQBg+fLlGDduHLS0tADkrFC7ffs2mjVrhoyMDCxevLhI6y7P6tSpA19fXwCZXSK8evWK21e7du0qVBcIWfr3748ZM2YgPT0djDHeHwBGRkZo06YN9zs8PJy3bLdu3biW9JcvXy5Ua9L8kF9vnTp1uIEXk5KScOLEiXzFsW/fPkybNo07tuUH4AKQ53WhUqVKXHciQOYfBZMmTcoR7vPnz7hx44bSP6BKi5+fH2rWrIlq1aqhWrVq3PQaNWpw95kHDx5w53Be57yLiwtUVFS4+5e3tzfatm3Lzff29ua+Z/3xCJTssZv9eOzbty933ZV/y4EQQgj5XlBlLSGEkHJrx44deP78OcRiMdzc3PIM7+XlhUWLFnEtf6ZPnw4fHx+4ubkhISEBt2/fRoUKFXD06FEAgJOTExYsWMCN5h0SEoI6deqgffv2qFWrFgQCAV69eoVz584hNDQUHh4e+U67oaEhbwT2hQsX4vPnz0hLS8PWrVsL9Cp3bsaMGcO9qpyUlAQg8xXU1q1bFzpOf39//PHHH0hKSsKLFy9w4sQJ3iuudevW5bWC9PLywsKFC/HlyxekpaWhQYMG6N69O+zs7JCcnIyAgABcvnwZoaGhuHTpUoH7UJT34sULriK/Ro0aMDU1hUgkwtmzZ3nh8mo9ByBH37ajRo1Cq1atIBKJ0LFjRzg4OHDz+vbti6lTpyIyMpLLZ6D4+wXOUpg8jYqKgqOjI5ycnODi4gJTU1PIZDJcv34d0dHRXNzyeSOfB6dOneJaq1WoUIHX0rK4iUQiTJkyhasAj4qKwpo1a7hz0dnZGQKBgOv+oGvXrmjZsmWx/dFRXg0aNAgbN24EYwzp6elo3LgxBgwYgJiYGGzZsqVIcZuYmKBVq1Y4ffo0AH5FXb9+/XitC+3s7CAUCrmW+uPGjYOfnx/Cw8Oxbdu2IqUjN5UqVeJaTp88eRLDhg2DsbExDh48iOfPn+crDn9/f7i5uaFdu3b477//uP6mgcw+kvNqVS4UCjFhwgTMnDkTQGZl4Js3b9CiRQtoamoiJCQE9+/fx507d9CwYUOur9OyUq9ePZiamqJRo0YwNTWFlpYWHj16xDtPlJ3zvr6+GDduHCwsLCCRSDB27Fjo6+vDy8uLO97279+PqKgouLm54fbt2zh37hy3/IABA7iuM0ry2M3eb3S/fv3Qs2dPBAUF8fpdJoQQQr4bZTCoGSGEEMLJPhL0iRMn8lzGyspK6Ujid+/eZUZGRkpH2O7UqVOO+FauXMmkUmmeo3MHBgZyy8hPlx+NXt6SJUsUxlO1alXm7OyscBuy54f8OhVJSUnhjVwNgE2ePDnPPMwur23P+nTv3p0bSV7ejRs3eCOoK/tkjWLOWO6jkivLh1u3buW5jq5du+Y7P2vVqqUwjqyRweVNmjSJF8bExISlpaUVOK+zZB+pPHvaCpqnnz59yjOsi4sLS01N5dZx7NgxheGcnJyU5qH8PsxN9hHs5UeAZ4yx5ORkZmZmxs2vUKECi4+P5+b369dPYdratm2r9PzLPup8dsqWk98XVlZWuW5Hfo/h3K4RuS03bdo0hdtdu3Zt3rVt3rx5yrJeqf379yuM+8mTJznCDh8+XGHY5s2b8/ab/H7NK/+zNGnShAsjf/27du0aE4lEOdapoaHBunbtqnQfyYeVj1v+o6enx549e8Ytk9t+TU9PZ/3798/zfMq+75TJfg4pumfkdr7klta87l02Nja8a/bDhw+ZUCjMEU5dXZ0LExcXxxo3bpxrvA0aNGCxsbG8bSjMsZvfe17r1q0Vxi1/LhXkvJZfLvs8ZccnIYQQUlpogDFCCCHflbp168Lf3x/z5s1D3bp1oaWlBZFIBENDQzRr1gy9evXKsczYsWMRGBiIuXPnomHDhjAwMIBIJIKamhqqVKmCESNG4PLly1z3Cfk1depUrF27Fg4ODhCLxTA2NsaQIUNw5cqVYutPVSwWY/jw4bxpxdEFglAohEwmg6mpKdzc3DBmzBj4+vpi//790NbWzhG+fv368Pf3x+zZs+Hs7AwtLS2oqKhAR0cHzs7OGD16NC5cuIDGjRsXKV2VKlXCn3/+ia5du8LBwQHa2tpQUVGBrq4uGjRogJUrVxZogLHDhw+jS5cu0NPTy7Nv0lGjRvH6Uh0wYECeg9gVRUHzVFdXF2vWrEHv3r3h6OgIPT09qKioQEtLC3Xq1MGCBQvg4+PDa0HZsWNHrFmzBlWqVOENuFMaJBIJJk6cyP3+8uULNmzYwP3evHkzJk2aBDMzM0gkEjg4OGDp0qU4duxYqaaztC1evBgbN26Ek5MTJBIJTExMMHr0aPj4+CAmJoYLl5/W49l17NgxR5+kzs7OCvtkXb16NebPnw8rKyuIxWJYWlpi8uTJOHHiRIn18dmwYUOcO3cO9evXh1Qqhba2Ntq2bYubN2/yXvHPjZeXF06dOoUGDRpATU0N2tra6Nq1K27dupXvAc+EQiF27NiBU6dOwdPTE+bm5pBIJJBKpbCyskKHDh2wYsUK7NmzpyibWyzWr1+Pn376CdWrV+fuXRoaGqhevTqmTJmCO3fu8K7ZNWvWxJ49e1C7dm3eQHry1NXV4ePjg82bN6Np06bQ09ODSCSCrq4umjRpgr///huXL1/OcR8ryWP30KFDGD9+PExMTCCRSGBnZ4fffvutyK12CSGEkPJIwFghh74mhBBCSLmwd+9e9O7dG0DmK7G3bt0q4xR9n5KSkmBsbMx1KfD8+fMcr+cSUlSJiYmQyWQ5pp88eRIdOnTgft+4cQP169cvzaQRkis6dgkhhJDiQX3WEkIIId+gqKgo+Pn5ITQ0lOtbEcgc+IsUr9u3byMqKgo7duzgKmo9PDyoopaUiBkzZsDPzw8dOnSAjY0N0tLScP/+faxbt44LU6dOnXz1401IaaJjlxBCCCkeVFlLCCGEfIP8/Py40b2z1KtXj2thS4pPr1698PbtW+63RCLB0qVLyzBF5HvGGMPly5dx+fJlhfPt7Oxw4MCBPLvtIKS00bFLCCGEFA+qrCWEEEK+YQKBAMbGxujQoQN+++03Xp+qpHhpamqiVq1aWLhwIWrVqlXWySHfqc6dOyM0NBR37txBWFgYkpKSoKOjg6pVq6JLly4YPHgw1NTUyjqZhORAxy4hhBBSPKjPWkIIIYQQQgghhBBCCCkHqPkNIYQQQgghhBBCCCGElANUWUsIIYQQQgghhBBCCCHlAFXWEkIIIYQQQgghhBBCSDlAlbWEEEIIIYQQQgghhBBSDlBlLSGEEEIIIYQQQgghhJQDVFlLCCGEEEIIIYQQQggh5QBV1hJCCCGEEEIIIYQQQkg5QJW1hBBCCCGEEEIIIYQQUg5QZS0hhBBCCCGEEEIIIYSUA1RZSwghhBBCCCGEEEIIIeUAVdYSQgghhBBCCCGEEEJIOUCVtYQQQgghhBBCCCGEEFIOUGUtIYQQQgghhBBCCCGElANUWUsIIYQQQgghhBBCCCHlAFXWEkIIIYQQQgghhBBCSDlAlbWEEEIIIYQQQgghhBBSDlBlLSGEEEIIIYQQQgghhJQDVFlLCCGEEEIIIYQQQggh5QBV1hJCCCGEEEIIIYQQQkg5QJW1hPwgvLy8IBAIIBAIMHfu3BJbz9y5c7n1eHl5ldh6isrd3Z1L5/bt28s6Od8EyjNSHI4ePYp69epBR0dH4fG0efNm1KhRA5qamtz8y5cvl1l6CSEkv6ytrcvsuhUUFMStWyAQlOi6tm/fzq3H3d29RNdFvi2lVVYsreeaoirLa0JpULYfvpXnQQBISEjA9OnTYWdnB6lUWirX0B9Jeno6fv/9d1SpUgUymYzL36CgIACU/7kRlXUCCClJ27dvx08//cT9btGiBc6fP88LY21tjbdv3wIA1q9fj+HDh5dqGkn5EhQUxBUudXR0MH78+DJNT2mSL2SNHz8eOjo6ZZaWH8mKFSsQFRUFILPQa21tXabpyXL06FH4+fkByHz4Ko4H8vv378PT0xMZGRkK5x8+fBhDhgwp8noIId+P7GU5AHjw4AFq1arFm3bq1Cm0b9+eN+3SpUtUmVgIJXH9J2WnvJYzfmR+fn44evQogMxn0fJeofk9mzZtGlavXq1w3o/2XFgS14qVK1di2rRpSufnlv8/OqqsJT+UCxcu4MqVK2jSpElZJ4WUsdWrVyM6OhoA4ODgwE0PCgrCvHnzAABWVlbf/U1ZXtZ2A5k36OyVtcryjBTNihUruD+M3N3dy81D1NGjR+Ht7c39Lo6H9WPHjnEVtfXq1cOiRYsgkUi44+nIkSNc2I4dO+KXX36BSCRCtWrVirxuQsj3Y/Xq1di6dStv2qpVq8ooNV8dPHgQSUlJAPBNX7fyuv63bdsW165dAwBoa2uXZtJIIZTXcsaPQNk1wc/Pjyt3N2nS5JutrJ05cyYGDx4MALC0tCzj1BSOfNlz1qxZaNmyJdey80d7LiyJa4V8/g4ePBj9+/eHUCiEiYlJjvnZ8/9HR5W15IczY8YM3Lhxo1TXmZiYCKlUCqGQeh4pL77lh6iyQnlGiurjx4/c95YtW6JZs2ZK53fu3JlacxFCFNqzZw+WLVsGfX19AMDz589x4cKFMk4VUKdOnbJOQqkwNDSEoaFhWSeDkHLve78m2Nvbw97evqyTUSTyZc+ff/4ZNjY2ZZaWuLg4aGholNn6S4J8/vbp0weNGzdWOr+s87+8oZoj8sO5efMmTp06la+wsbGxWLBgAWrXrg1NTU1IpVLY2tpiyJAhePnyJS/s5cuXuT5WrK2t8eLFC3Tt2hW6urpQU1NDTExMjj6+7ty5g8aNG0NNTQ3m5uaYO3cu0tPTERISgv79+0NPTw8aGhpo164dXr9+zVvf+fPn0aNHD1SuXBn6+voQi8XQ1taGq6srli9fjtTU1HzniXwfZ1n9x+QlPj4eEyZMgKmpKWQyGVxcXPLM1/T0dPz9999o1KgRdHV1IZFIYGVlhSFDhiAwMJAXNnvfaxERERg1ahRMTEwglUpRu3ZtnDt3jrdMcnIy5s+fjxo1akBdXR0SiQTGxsZwc3PD2LFjERISwoVV1KeWtbU1mjZtyoV5+/YtLw2BgYFQUVGBQCCAhoYG4uLieOtfunQpF7Z79+75ykc/Pz94eXnB1tYWqqqq0NLSQrVq1TB58mReuJSUFKxcuRJubm7Q1taGRCKBhYUF+vTpA19f3yLlXVZ/U/JsbGxy5I+yfsiy91d1/Phx1KtXDzKZDAYGBhg2bBji4+N58Ss75rKfR9kdP34cbdu2haGhIcRiMSpUqICWLVvi4MGDOcIWZh27du3ijk+RSAR9fX1Uq1YNXl5euH37do51ZBceHo7hw4fD1dUVJiYmUFVVhUwmg52dHYYMGYI3b95wYbP688r6BxsAmjZtWqA+2DIyMuDt7Q0PDw9UqFABEokERkZGaN68OU6ePMmFU9ZnmqI+DrPyR75V1bx58xT2T5jf4zIrzm3btnHT5s+fzzueBAIBLl26xM3/+eefqd8qQgiPTCaDSCRCUlISNm3axE1fvXo1GGPQ0tJSumxBy02Fubcpu9Zmv39u3rwZ1atXh6qqKszNzTF16lSu9V2Wwt4nFZk7dy6aN28OS0tLaGhoQCKRwNTUFF26dOGlM7/X/9z6rGWMYfv27WjatCn09PQgFothbGyMTp06wcfHJ0fa5Lfz6dOnmDlzJqysrCCVSlG5cmXs2rUrX9sIAGfOnEHLli1hYGAAsVgMHR0dVK5cGX369MGZM2d4+aGsP01lZZ2PHz9i2LBhsLW1hVQqhUwmg4WFBVq0aIFff/01122aPn06LC0tIZVK4eTkhC1btihM//nz59GpUycYGxtDIpHAwMAAHTt25FoxZ/f69WuMGjUKlStXhpqaGtTV1VG5cmUMHToUycnJBSpnREREYPbs2ahRowY0NDQgk8ng5OSEuXPn5ijvAkBgYCC6desGbW1taGlpoUOHDnj+/LmyXVPoPpXDwsIwaNAgVKhQAerq6nB3d8etW7dyXSYhIQFLly6Fi4sLtLS0IJVKYW9vjwkTJiAsLIwXNvv59O7dO/Tv3x/6+vqQyWRo1KgR7t+/z1smKioKkyZNQuXKlSGTySCVSmFqaoomTZpg8uTJSEhI4MIquiYIBAJe1y5Xrlzh5Y38bxsbGzDGeOsfOXIkNz/784Ii+S3bZj8vrl+/Dnd3d2hoaEBXVxe9evXC+/fveXEXtO/g8+fPc/2WqqioYPPmzdy8u3fvonfv3rCwsIBEIoGuri48PDxw/PjxPOPNcv/+ffTr1w/VqlXjrgOampqoWbMmfv31V96xnJV2+fy1tbXlrmt5PRfKX5cLknb5a8y2bduwYsUKVKlSBRKJBLNmzcpzG6Ojo7Fw4ULUrVsX2trakEqlsLS0RLdu3RAQEAAg9/NN0fW7INeK0NBQTJ48GY6OjlBTU4NMJkPlypXxyy+/IDg4mAuXFaf8M36zZs24cy2v/CcAGCHfsW3btjEADADT19dndnZ2DACrWbMmy8jIYIwxZmVlxYVZv349t+ynT5+Yvb09Ny/7R01NjV24cIELf+nSJW6etrY2MzAw4IWPjIzkpcfc3JypqanliHfYsGHM1tY2x3RHR0eWnp7OrW/q1KlK0waAderUiZcXAwcO5Ob9+uuvvHnyywUGBuaZr+np6axp06Y51ikQCFjNmjW53wMHDuSWSUhIULhM1kdHR4fduXOHCx8YGMibr2hfSCQSFhQUxC0zYMCAXPPk1q1bXNgmTZpw07dt25bjWFD0CQwMZO3ateN+b968mZcvderU4eadOnUqz3zcuHEjE4lECtelra3NhYuLi2P16tVTmi6RSMS8vb0LnXfyx4aiT1b+KMqz7MtnnWOKjmt5yo45+fPIysqKt8yYMWNyTefQoUOLtI6tW7fmGv/ixYvz3KfPnj3LNQ5dXV32+vVrxhhjv/76a65hs5+n2SUlJbGWLVsqXX7cuHFcWPlj+9KlS9z07MdK9vxR9GnSpAljrGDHZV5xyl8bFX0IIT8u+euDkZER69GjBwPALC0tWVpaGouKimIaGhrcdU/+2iF/vStKuSm/9zZl11r5+6ejo6PCuFq3bs2VTRkr+D1M0fU8i5GRkdLtFggE7NChQznizu36L79PsqYxxlhaWhrr3LlzrnEsWrSIlzb5ecrK3Tdv3szjKGHMx8eHCQQCpeuV31fy91/5smr2fZVV1klJSWEVK1ZUGrdUKlW6Tcr292+//cZbJrfjUygU8p5RGGPs5MmTCp8jsj6RkZH5Lme8fPmSmZubKw1XtWpVFh4ezq3748ePzNjYOEc4XV1dZm1tnSP/GMv9+FQmLi5OYf5JpVLedPnyUlhYGKtatarSbTEzM2Nv3rzhwssf81paWszQ0DDHMhUqVGAxMTHcMo0bN841Xz99+sSFVXRNyG3ZrLxxcnLifl+8eJGLLz09nXc++/v755qHBSnbyh8v9vb2TCwW5whvbm7OQkNDuWWUPV8qOscuXLjAVFVVGZBZRty9ezcXfu3atUwoFCpN5/Tp03Pdzizr16/PdXudnZ1ZampqjrRn/zRp0iRfz4WFSbv8NSb7NU++7K7ImzdveOdY9s+RI0cYY7mfb4qu3/m9Vjx9+lThOSJ/rjx69CjPOK2srPLMf8IYPQGR71r2Av4///zD/d63bx9jTHllbZcuXXjLbt26lR09epQ1bNiQm25oaMji4uIYYzkLuDo6OmzFihXs/PnzbOXKlSwhISFHhUTz5s3ZiRMn2IgRI3jT9fT02NatW9nOnTuZTCbjpp89e5ZL36lTp9iqVavY0aNHmY+PD/v333/ZP//8w3uguHv3Lhe+OCtrt2/fzoUXCARs6tSp7PTp02z48OG8uOQLwFOmTOGm29jYsG3btrHz58/zlrG1teVuoNlvMhUqVGCbNm1iBw4cYGZmZtz0adOmcetQV1dnQGZF57Zt29i///7L9u7dy+bOncvq1q3LqwxWVBi/d+8eW7VqFTfd2NiYXbt2jfskJSWx06dPc/Pd3Ny4+IKCgrjpJiYmLC0tLdc89Pf3ZyoqKtwyNWvWZN7e3uzs2bNsxYoVvLh/+eUXLpyGhgZbuXIlO3nyJO+BSCqVsnfv3hUq7168eMGuXbvGW+bAgQPcdmcVyvJTWQuA9e7dm508eZJ3XItEIhYbG5vnMafsIfTYsWO8ZX755Rd2+vRpNnXqVN7D2f79+wu9DvmK+Llz5zIfHx925MgRtnz5cta6dWu2YsWKXPcpY4yFhISw+fPns3379rGzZ8+yy5cvsxMnTrB+/fpxcY8cOZIxxtjbt2/ZtWvXeA88q1at4vL97du3ua5r+vTpvPNw6NCh7Pjx4+zQoUNs/PjxbNasWVzYglTWRkVFsWvXrrE2bdpw03/66ScuXY8fP2aMFey4zCvOkJAQdu3aNd6fPTNmzODmE0J+XNnLclevXuV+Hz58mC1fvpy7Dr548YJ3TZO/3hWl3JTfe1t+KmsFAgGbMmUKO336NJswYQJvHbt27eKWKeg9LLeH88WLFzNvb2926tQpdvnyZXbu3Dn222+/cWEdHR0ZY/m//iurrF25ciU3XSwWs/nz57PTp0+zwYMH89ImXx6Tn66mpsaWL1/Ojh07xqtw69WrV57HyahRo3j32YsXL7Ljx4+zNWvWsC5durDJkydzYQtaWXvv3j1uWvXq1dmRI0fYhQsXmLe3Nxs3bhxzcnLixSG/Tdra2mzNmjXsxIkTvHKGSCTi/jSXL1vKZDK2dOlSduHCBfbnn38yqVTKhQ8ICGCMMfb582empaXFLWNra8v+/vtvdu7cObZ+/Xrm6urKIiMj813OcHV15eY3bdqUHTlyhJ04cYKXF/379+e2z8vLi7d969atY8ePH2fu7u68bS9qZe3cuXO58BKJhC1ZsoSdPHmSeXp68uKSf67J+jMHyCxb79mzh505c4a3TKNGjbjw2Z/fbGxs2O7du9m2bduYtrY2N33Dhg2MsczK4KxpFhYWbO/evczHx4ft2rWLTZ06lVWtWpWFhIRw8Su6Jly7do3NmDGDl0755w3GGFu3bh3v2pPl8uXL3PS6devmmYcFKdtmr1zr2LEjO3nyJFu9ejX3hxgANnjwYG6Z/FbW+vj4cM+0EomEq1RkjLH//vuPq+wUCoVs5syZ7Pz58+zvv/9murq6XDw+Pj55bu+NGzfYn3/+yY4cOcIuXrzILl26xA4cOMDq1q3LxZP1rJDb88/jx4/z9VxYmLTLn1dZ+XzkyBF29OhRdvLkyVy3z83NjVtOU1OTLViwgJ09e5bt3LmTde/enR0/fpwxVvDK2vxeK2rXrs3Nt7e3Z3v27GEHDhzg/XlStWpVlp6enmuc9+7dyzP/CVXWku9c9gJ+eno6q1atGgPAKlWqxNLS0hRW1kZERPD+IctqccBY5k1avgI164Kf/WafdbFUlh5VVVXuX+rPnz/zll23bh23jPxNdtWqVdz0+Ph4tnDhQubs7My0tLQUtiaQD59bZW1BtW/fnourS5cuvHkuLi45CsAZGRm8lsbLly/n3exMTEy4eVkV0tlvMvKVcEuWLOGmd+3alZtuamrKADBTU1N248YNriJdEWUVj7m17MzaFvnWFU+fPmWMMfbHH39w06ZMmZJnHk6aNIkLb25urjStGRkZTF9fnwv7559/cvOSk5O5bQbAli5dWui8YyzvSvv8VNY6OTlxLYPS09N5rT7kb7zK1qUs/+X/POnQoQMvXd26dePmtW3bttDr6NOnDzd9z549LCwsLOcOyYeTJ0+ydu3aMWNjY4Utp2vXrs0Lr+zhPjfZz6lffvkl1/AFqazNkts1ozDHZV5xMqb8GCOE/Liyl+UYY6xGjRrcg2bWPbl169aMMf61X/56V5RyU37vbfmprO3evTtv++TLVB07duSmF/Qeltv13N/fnw0YMIDZ2NhwlX/ZP/ItB/O6ViurrK1VqxY3fcyYMbxl5N8+yvrTMvt2yt8v9u7dq/S+qYh85deff/7JgoODlYYtaGWt/J8AzZs3Z/7+/iwlJUVp/PLbtHbtWm56QkJCjvIwY4xXkdi/f39eGblt27bcvKw/2desWcNN09DQYB8+fMg1b3IrZzx58oSbJxaL2blz57h1Hzx4kDcvNjaWpaen8yoxV65cycUVHh7Oe0Yq6n1cvsJevpyTkpLCa3yQdYxGRkbyGkLs3r2b25ZLly7xWoo+f/6cMZbz+U3+Dxv5BiUTJkxgjDGWmJjIraNatWrM19eXJSYmFjjvlZ1DWWJjY7kKeVVVVRYREcEYY2z06NHccvLPi8oUpGwrf16Ympqy5ORkbp78c46Ojg73tmd+KmurV6/OXTNlMhmv8RFjjE2cOJEL6+HhwTv+f/75Z25efv60SU1NZatXr2YNGjRgurq6Clu8Zu3LLMqutYzl/VxYmLTLX2OcnZ3z3KYs//33Hy+tx44dUxq2oJW1WXK7Vjx69IgXp6+vr9K0yZ9HeT3n5Jb/Pzrqs5b8UIRCIRYsWAAACAgIwI4dOxSGe/nyJTdiOQA0bNiQ+16hQgVUqlSJ+62ofyapVIr27dvnmpbKlStDT08PALgBMrK4ubnx1pclIiICAMAYQ9u2bTFr1iz4+voiJiYmR39GABAZGZlrGgrr1atXCtMKAA0aNMgRPiwsjNdH1IQJE9CoUSPu8+nTJ27ef//9p3CdzZs3577L51dWngDA8OHDAQDBwcFo0KABNDQ0YG5ujk6dOmHfvn353bxcCQQCjBgxgvud1e+YfJ+p+RnR9enTp9z3Vq1aQV1dXWG4sLAwhIeHc7/lj0WJRAIXFxfut7K+wvKTd8Ulqy8iIPN809XVLZb1yW+bfB5k/51bf2l5GTJkCFRUVAAAvXv3hoGBAfT09ODu7o4//viD1weZMlu3bkX79u1x6tQphISEIC0tLUeY4jgvv3z5wjununbtWuQ4C6I4jktCCCmsMWPGAMjs6zGrT/+saYoUtdxUnPe23O5h2cdDKA5PnjyBi4sLduzYgcDAQCQnJysMVxz3pqLeq4tSXunfvz9Xlpo4cSJMTU2hqakJNzc3zJ07t0hlEDs7O3h4eAAAfHx84OTkBJlMhkqVKuXZp738dstkMjg7O3O/s/a3fJlw586dvDLy6dOnuXlZZWT58K6urjAzMyv0tsnHlZqailatWnHr7tatG29eQEAAwsLCEB0dzU2Xfw7Q09ND5cqVC52W7JQ9b4jFYl45I8uLFy+Qnp7O/e7Tpw+3LU2bNuX1S63oeUNTUxN169blfis6BlVVVTFw4EAAmeeWs7Mz1NXVYWNjg169euUYT6OwNDQ0MGDAAABAUlIS/vnnHzDGcPjwYQCZz5q9e/fOM57Clm1dXV0hkUi43/LHcVRUFL58+ZLvbXn8+DG3nk2bNqFVq1a8+fLH4MWLF3nH/9atW7l5yp4R5f38888YM2YMbty4gcjISN7zfJbifD4uatoLUn6XX5dUKkW7du0KkeLCk79uy2Qy1K5dm/vt5OQEHR0dhWFJ4VFlLfnhdOrUibvBz5s3DykpKcW+DiMjozw7z9fW1ua+C4X8U1H+Yicv68Hi1q1buHLlCgBARUUFCxYsgI+PD65du4YWLVpw4RXdoMo7RYMYAOAqtgFAJBJx3+UftmbPno3jx49j4MCBqFWrFjQ0NPDx40ccP34cvXr1wsqVK4sljT/99BNkMhmAzEJ1YGAg7ty5AyCzcFOlSpViWU9xyU/elcS68rs++QrN7AM/FJf8rMPd3R3379/HuHHj0KBBA1SoUAGRkZG4cuUKJk+ejL59++a5niVLlnDfW7dujePHj+PatWv466+/uOllcV7KX49KI78JIaQk9enTh3e/sbOzQ5s2bZSGL2q5qTD3tuJUlOv26tWruYHQ7O3t8c8//+Dq1au8gcWA8lFmLEp5pXLlyvDz88P06dPRpEkTmJiYIC4uDrdv38a8efPQqlUrriJP2T0RUJy/AoEAJ06cwIYNG9ClSxdUqlQJQqEQL168gLe3Nxo3bpxjEKqSoKyMXFrKev3FSdG25Pc837hxI3bt2oVevXqhatWqkEgkCAoKwr59+9C6dWscO3asWNI4cuRI7vvWrVtx8+ZNbgCnzp07K31elFccZduiyqosBjIHnZJvpFMQeR1/Hz9+xM6dO7nf48ePx/nz53Ht2jWu4hsom2udsrSbmJiUyPqy10NQ2f/bRJW15Ie0aNEiAJmjOiq6Ydjb2/MqUG/cuMF9Dw8P50ZaBKDwH+SSHr383bt33PeaNWti1qxZaNasGerVq8ebV1IqVqzIfc/emuDmzZs5whsYGPBaCJ87dw4ssxsW3icuLi7HiLoFwRhDhw4dsH37djx48AAxMTHYv38/N3/Pnj15xiG/33N7aMv6N/vz588YNGgQV4jLT6taAHB0dOS+nz9/PseI0lnxGRgY8P7Zlz8WU1NTce/ePe53UVszyB+3JV2QkW+V9OHDB+77iRMnFIaX3zb5PMj+Wz5cQdfBGEPNmjWxYsUKXL9+HWFhYXj16hU0NDQAAMeOHcuzda38+bds2TJ06NABDRs2zLWAmZ9jLrsKFSrAwMCA+33kyBGF25OloHmRV7pK87gkhJDsZDIZBg8ezP0eNWpUrmWvsi43ycvtHmZnZ8d9L8x1WxH57Rs7dizX2lC+AiW7wtyXgMLdq4sLYwx2dnb47bffcPnyZQQHB+PTp0+wtrYGkDlKfFZLVmV5+/LlS14ZXz5uVVVVDBs2DIcPH8bz588RHx+PsWPHAsi878m/YSVPfruTkpLw4MED7nfW/pb/k3/69OkKy8jp6ek4c+YMAH4Z8s6dO7wR2BXJbX/Kr1smkyEqKkppGb1JkyYwMDCAlpYWt4z8c0BERESxtqhT9ryRlpbGK2dkcXBw4B3XAQEBSrclq3VsYQiFQvTt2xd79uzBkydPEB8fj2XLlnHzi+t5o0qVKmjWrBkA4OHDh5g9ezY3L7/PG4Ut2969e5fXEln+ONbW1uY91+WlR48eXMv0V69eoXnz5rwKQ/ljsHfv3gr3GWMsz5a179+/577r6+vjr7/+QosWLdCwYUN8/Pgx3+mVl9d+KmraC1JnIH/eJycn81rdZ8kq+8tf4wD+de7kyZNK15Hb9spftxMTE/Hw4UPu99OnTxEVFaUwLCk8Ud5BCPn+eHh4oGnTprh06ZLC+bq6uujUqRNXATJq1ChER0dDT08Pf/75JxITEwFkVli0bdu21NKdxdbWlvv++PFjrFu3DjY2Nti4caPCQmZe5G8UgYGBXMFWmW7duuHUqVMAgKNHj2LmzJlo1KgRTpw4wbUwzR7/Tz/9xBVkBgwYgGnTpqFq1aqIi4vDu3fvcPv2bZw8eRIxMTEFTn+Whg0bonLlynBzc4OpqSnEYjHOnj3LzU9KSsozDvkKqODgYOzYsQO2trY5Xl0bNWoU93pL1nGkqqqKXr165SutXl5e+Ouvv5Ceno7379+jSZMmGDduHIyMjPDixQvs3bsX169fh0AgwIABA7iWmb/++ivEYjFsbW2xZcsWrvAhlUrzve7ctj3rtaYNGzagffv2EAqFcHFx4b0KVRwcHBy4Y2XUqFEYNWoUfH19ef+Iy/Py8uLOxxMnTmDSpEnw8PDA1atXcejQIV64wq7jl19+wevXr9GyZUtYWFhAW1sbDx484AqxjDEkJydDTU1N6XbZ2tri2bNnAICFCxdi0KBB8PX15f4gUkRfXx+BgYEAAG9vbwiFQohEIlSvXp33QCRPIBBg0KBBXEveFStWICEhAe3atUNaWhpu3LgBVVVVrtsXBwcHrlA1a9YsxMbGIjAwEKtWrco1XVlOnz6Nhg0bQk1NDVZWVrCwsCi145IQQhQZO3Ys95bLTz/9lGvY4i43FcXBgwe51p///vsv78G5R48e3PeC3sOUkd/2zZs3w9raGhEREZg1a5bSZfK6/ivj5eXF3Ws2bNgAQ0NDODs748iRI7zKtaJUlCnz559/4syZM2jXrh2srKygp6eHly9f8iqFssqBDg4O3LSrV69iwoQJsLS0xKpVq3iv0WcJDQ1FgwYN4OnpiWrVqsHExAQJCQm81rTKypgzZswAAFhaWuLvv//G58+fAWS2Nsx6BXrQoEHc6+3Lli1DRkYGGjduDKFQiHfv3uHx48c4duwYdu7cCXd3d/To0QPTp09HbGwsV4k6ZcoUWFtbIygoCNu2bcPp06e5lpe5lTOqVauGunXr4t69e0hMTESzZs0wduxYWFhYICwsDIGBgfj333+RkZGBixcvQigUokuXLvD29gYAzJkzBxKJBGZmZli+fDn3jJRdUFAQbGxsuN/5aS3drVs3+Pv7AwDWrl0LY2NjODk5Yfv27bzKpyw6Ojro2rUrDhw4AABo27YtJk+eDDs7O0RFReHt27e4evUqnj9/XqRKZTs7O7Rt2xbOzs4wNTVFeno6rl69ys0v6PPG48ePcfjwYRgaGkJHRwdVq1bl5o0aNQr//vsvgK/PG2ZmZrw3AnJT2LLtx48f0aNHDwwePBhBQUGYN28eN69bt2453grNjUQiwZEjR9C8eXPcvXsXz549Q4sWLXDp0iXo6urCy8sLK1asQEZGBvbs2QNNTU20b98eUqkUHz58wNOnT3H8+HHMmDEj10pq+WtdeHg4Fi1ahDp16uDgwYPw8fHJd3rl5fVcWFxpzw8nJyfUq1eP++OiX79+mDp1KpydnREeHo6TJ0+id+/e6NChA7S0tGBkZITQ0FAubN++fXH+/Hnesapoe5VdK6pXr47atWtzfzj17t0b8+bNg4qKCu/4qFq1Ku+ZmRRB8XV/S0j5o2hQiiw3b97M0eF41gBjjDH26dMnZm9vnyNM1kdNTY2dP3+eC59XB+TZ05O9Q2/5uOU711bUcXt6ejqrX79+jjSpq6vzRruU7+g9t8EilK1bmbS0NNa4cWOF+eLg4MB9lx+0ISEhIccosYo+WQrTMXqlSpVyjTtrIAfGlA9klJaWxszNzXMsW7FixRz5UK9ePV6Y/HR8L2/9+vW8gRDkP9ra2ly4uLi4HOuS/4hEIubt7V2kvGOMsd69eyuM//3797nmWW7HlrJO5f/55x+F65IfTCL7eSQ/qIKiz5AhQ3jhC7qOYcOG5Rq//MAvymzYsEHhsvLHfvbtmj59usJlskYEViYxMZE1b95caXrHjRvHhb1+/XqeeZH9WDl37pzCZRYsWMAYK/hxyRgNMEYIKbjcynKKyF+Hsu47xV1uUnZvy88AY/KDcMl/WrRowQ3aw1jB72HK7v2PHz/mDayk6L4E8Mt/eV3/lZUl0tLSWOfOnXO9ly5cuFDp/srPQGrKLF68ONf11qpVi8vftLQ0Vrly5RxhtLW1mYWFRY770KdPn3KNWyQSsTt37ijcJmX7Oysvs0yZMiXXdWQ/po4dO8YbzCv7JzIykgubVznjxYsXCsu+8h/5/fzhwwdmZGSk8HySH/hL/j6eW9lUmdjYWIX7SUVFhTfYr/z5+fnz5xxlm+wf+eMpt+NM2UB0ygbpy/rID0yt7JoQERHBG6gw69O8eXNeGhQ9l2QNNJcfBSnbym+vo6Ojwu00MzNjISEh3DL5GWAsK+++fPnCqlSpwk13cXHhBjZcs2aNwsHA5D/5KRf26tVL4fHSqFEjhfuSsdyfg/PzXFjQtBelrPv69WveNSr758iRI1zYRYsWKQwjf35kfxbM61rh7+/PDA0Nla5fX1+fPXr0iBcnDTBWeNQNAvlhubm55ToImLGxMe7fv4958+ahZs2aUFNTg0QigbW1NQYNGoSHDx/m+1/N4iYUCnHs2DF4eXnByMgI6urqaNq0KS5fvsx7RaKkqKio4NSpUxg/fjyMjIwglUpRs2ZN7NmzR2ln9zKZDBcvXsTGjRvh7u4OPT09iEQiGBkZwdnZGb/88kuO/tMKatq0aejevTvs7OygpaUFFRUVrhP9nTt34pdffsnXth05cgSNGzfOtQUlkPlvt7y8WvZkN3z4cNy5cwf9+/eHtbU1JBIJNDQ0ULVqVQwZMoQLp66ujitXruCvv/6Cq6srNDU1IRKJYGpqil69euHWrVu8vpgKa+XKlejZsyf09PRKvCuPPn36YNmyZbCysoJYLIa9vT2WL1+ea7/Cq1evxpEjR9C6dWtUqFABIpEIenp68PDwwP79+7Fx48YiraN3794YPHgwqlWrBn19faioqEBdXR21atXC/Pnz8zVI3bBhw7B+/XpUrlwZqqqqsLe3x4oVKzBnzhyly8yaNQvDhg2DoaFhgfJdVVUV58+fx5YtW9C0aVPunDIwMECzZs24V86AzIH/vL29UalSJYjFYlhaWmL27Nlc6xNFWrZsieXLl6NixYoKX5ctreOSEEKKqqzLTfLGjh0Lb29vVK9eHVKpFKamppg0aRKOHTvGa61WmPukItWqVcOFCxfg5uYGdXV1GBsbY/To0bl2p5DX9V8ZFRUVHD58GFu3bkWTJk2go6MDkUgEQ0NDdOzYERcvXsTMmTMLlP78at26NUaPHo3atWvD0NAQIpEIMpkMjo6OmDx5Mnx8fLj8VVFRwbFjx9C6dWuoqalBU1MTnTp1wu3bt3mt87Lo6OhgwYIFaNmyJSwtLSGTybj7XdeuXXHt2jWFA14Bma/Ez5o1C5aWlpBIJKhSpQr+/vvvHC2bf//9d5w/fx5dunSBiYkJxGIxdHV14ejoiAEDBuDgwYOoV68eF75jx47w8/PD8OHDYW9vD1VVVaipqcHBwQGDBw/mWp0DeZcz7O3t8fjxY8yZM4cb80EqlcLS0hKNGzfGokWLsGHDBi68mZkZbt68iS5dukBTUxMaGhpo0aIFrl69yuvKo6g0NDRw5coVeHl5QU9PDzKZDG5ubjh79myOAeyyGBgY4O7du/jjjz9Qr149aGtrQywWw9TUFPXq1cPMmTN5b2QVxuLFi9GxY0dYW1tDQ0MDKioqMDAwQOvWrXH69Ol8DRqlq6uLw4cPo06dOpBKpUrDqaioYNiwYbxpBWmhWdiybd26dXHlyhU0a9YM6urq0NbWRo8ePXDjxg0YGRnle/3y9PX1cf78eVhaWgLI7GqhXbt2SEhIwKhRo3D79m307duXO1e0tLRQqVIldO/eHTt27MhXvm7evBnjx4+Hubk5ZDIZXF1dcfr0aa47iYLKz3NhcaU9P2xtbfH48WPMmzcPtWvXhoaGBiQSCSwsLODp6cnrlmHKlCmYOnUqjI2NIZFIUL16dezYsQMTJ05UGn9e1wpHR0c8fvwYEydO5J51VFVV4eDggHHjxuHx48eoXr16sWwrAQSMlUKv+IQQ8h2Ki4uDlpYWGGMwNzfH27dvC/RaECGEEEK+f+7u7twAZ9u2bSvy67CkfCto92KE5Mbf35/rGsHNzU3h+CDFYe7cudzr7AMHDsT27dtLZD2EkPyhPmsJIaSAEhMTkZCQgJUrV3J9bg0ePJgqagkhhBBCCCFFlpCQgNjYWG58AAA5WtkSQr5fVFlLCCEF1KZNG66FDACYmJhg3LhxZZgiQgghhBBCyPfC0dERb9++5X5Xq1YNffv2LcMUEUJKEzUDI4SQQtLS0kLr1q1x4cIFbsRdQgghhBBCCCkOenp66N69O06fPg2RiNraEfKjoD5rCSGEEEIIIYQQQgghpByglrWEEEIIIYQQQgghhBBSDlBlLSGEEEIIIYQQQgghhJQDP0SnJxkZGQgODoampiYEAkFZJ4cQQgghhCjBGENsbCxMTU0hFFK7AmWofEsIIYQQ8u0oSBn3h6isDQ4OhoWFRVkngxBCCCGE5NP79+9hbm5e1skot6h8SwghhBDy7clPGfeHqKzV1NQEkJkhWlpaZZwaQggpWZUrV8anT59gYmKC58+fl3VyCCGkQGJiYmBhYcGV34hiVL4lhJRnVB4lhBC+gpRxf4jK2qxXw7S0tKgwSwj57s2dOxdxcXHQ0NCgax4h5JtFr/bnjsq3hJDyjMqjhBCiWH7KuGVeWbt+/XqsX78eQUFBAAAnJyfMmTMHbdq0AQC4u7vjypUrvGWGDRuGDRs2lHZSCywhIQFfvnxBcnJyWSeFlAPq6uowNDSESFTmpx35zg0dOrSsk0AIIeQ7lZGRgc+fPyMuLg6MsbJODiEkn2QyGQwMDCCVSktlfVQeJYSQwivzWiNzc3MsWbIE9vb2YIzB29sbnTp1wsOHD+Hk5AQAGDJkCObPn88to6amVlbJzRNjDKdPn8aZM2dw9+5dZGRklHWSSDmipaUFd3d3dOvWDY6OjmWdHEIIIYSQfAkMDMSBAwfg4+OD8PDwsk4OIaQQJBIJGjZsiI4dO6Jhw4ZlnRxCCCFKlHllbYcOHXi/Fy1ahPXr1+P27dtcZa2amhqMjY3LInkFwhjDqlWrsHPnTjg7O2Py5MmoWLFiqf17ScqvjIwMxMfH48GDBzh37hzOnTuHv/76Cy4uLmWdNEIIIYSQXAUEBGDEiBGQSqVo3bo1XFxcoKWlledIxoSQ8oExhsTERDx79gwXLlzA+PHjMW3aNHTr1q2sk0YIIUQBAStH7y+lp6fjwIEDGDhwIB4+fAhHR0e4u7vD398fjDEYGxujQ4cOmD17doFa18bExEBbWxvR0dEl2l/OsWPHsGDBAkyePBk9e/YssfWQb1tKSgomTpyIBw8e4OjRozAwMCjrJJHvzKdPn5Ceng4VFRWYmJiUdXIIyZeMjAykpKSUdTJIKZFIJEor+kqr3PatK618Sk5ORtu2bWFmZoa1a9fSwG+EfOMYY1i+fDn27NmDv//+G87OziWyHiqPEpIpPT0dqampZZ0MUgrEYjFUVFSUzi9I2a3MW9YCwJMnT+Dm5oakpCRoaGjgyJEj3Cviffr0gZWVFUxNTfH48WNMnToVAQEBOHz4sNL4kpOTef3ExsTElPg2AMCpU6dQv359qqgluZJIJFi0aBFatmyJf//9l44XUuzq1q2Ljx8/wszMDB8+fCjr5BCSp5SUFAQGBlLXQT8QoVAIGxsbSCSSsk4KycONGzcQHR2Nbdu2UUUtId8BgUCACRMm4Pr16zh9+nSJVdZSeZT86BhjCAkJQVRUVFknhZQiHR0dGBsbF3mg3HJRWVupUiX4+fkhOjoaBw8exMCBA3HlyhU4OjryOiavVq0aTExM0Lx5c7x+/RoVK1ZUGN/ixYsxb9680ko+ACAyMhIPHz7E7NmzS3W95NukpaUFFxcXXLp0iSprCSE/NMYYPn36BBUVFVhYWNBr1T+AjIwMBAcH49OnT7C0tCxyYZaUrEuXLsHBwQGWlpZlnRRCSDERCARo3rw5Dh8+jFmzZtF1mJASkFVRa2hoCDU1NTrPvnOMMSQkJODz588AUOQ3CspFZa1EIoGdnR0AwNnZGffu3cPKlSvx999/5wjr6uoKAHj16pXSytrp06djwoQJ3O+YmBhYWFiUQMq/Cg0NBWOM2w5C8mJvb4+LFy+WdTIIIaRMpaWlISEhAaampuV6AFFSvAwMDBAcHIy0tDSIxeKyTg7JxadPn6h8S8h3yN7eHjExMUhMTKT7LyHFLD09nauo1dfXL+vkkFIik8kAAJ8/f4ahoWGuXSLkpVxU1maXkZHB68ZAnp+fH4Dca6mlUmmpD+qV1c+eqqpqqa6XfLtUVVWVHueEEPKjSE9PBwB6Hf4Hk7W/09PTqbK2nEtJSaHBcgn5DmU9tyYnJ1NlLSHFLKuPWjq3fjxZ+zw1NbVIlbVl/q7h9OnTcfXqVQQFBeHJkyeYPn06Ll++jL59++L169dYsGABfH19ERQUhOPHj2PAgAFo3LgxqlevXtZJLxD37e4QzBNAME+AGhtq8OaFJ4RDtkjGzZ92cVqB4k5KS8LyW8vhutkVWou1oLZIDQ6rHTDsxDC8iXxTnJtRrC4HXea2OSgqqETWYb3CGoJ5Asy9PLdE4i8Keg2CEEK+omvij4X297dF2f762GkMXhs0wmuDRnjv7sWblx4RjTcWzbn54fM3FGidGUnJiFq/Fx9aDcUbm1Z4Y+mBd669ETZxGVKDggu7KSUu8cZDbptT330qkXW8rd0drw0aIWLp1hKJn/wY6DpMSMmj8+zHU1z7vMxb1n7+/BkDBgzAp0+foK2tjerVq+PcuXNo0aIF3r9/j4sXL2LFihWIj4+HhYUFPD09MWvWrLJOdpE8Dn2Mq2+vorFVYwDA5gebkZSWVKi4IhMj0XxHczwMeQgA0JRooqJeRbyLfoeNDzbCzcINtrq2CpdNSU+BRKVoLZmKIw5CCCGEEPJtS/F/jcSbfpDVrwkAiNl1EiwppVBxpUfFIrjrOKQ8eQkAEGioQWxthrQPoYjZcRzSOk4QW5sqXJalpEIgKVpr7eKIgxBCCCGksMq8snbLli1K51lYWODKlSulmJqSJxaKkZqRitV3V6OxVWOkZ6Rj3f113PTsYpNjMe/KPBx5fgTvo99DU6qJuqZ1caTnEcjEMow+M5qrqJ1cfzJ+a/4bRMLM3Xr17VWIhZkFTa+jXvB+5I0mVk3Q1r4tVt1ZheDYYGT8moF/Hv+DlXdW4k3kG0QnR0NDogEXMxcsaLoALmYuADJbwTb1bgoA2N9tP5bdXAa/ED+sa7cOQ08MBQPDhf4X4GHrAQA4EXACHfd2hFAgxLvx72CmZcbbrrmX52Lela+DwNmstAEADKwxENs7b0cGy8DqO6ux6cEmvIp4BZlYBg9bDyz1WAob3cywcSlxmHx+Mk6+PInP8Z+hJdWCg74DhtYeiibWTbg4AWDelXnc+tivrIh7kRBCCCGEcMQiIDUN0ZsPQVa/Jlh6OmK2HeGmZ5cRl4CIZdsQf/oq0j5+hlBDDdKalWHs/RuEMim+TPuLq6jVGd0bejOHQiDKLN8m3vSDQJz5/fPoRYjddxaq9WtCzaMeojcdQnrIF1T8fBWxB88jeuNBpL4NRkZMHITqMkhrVYHe9MFQre2YGdeNhwjuPBYAYLR5PqLW7kHyfy9h8PsEhE1cBjAGk4N/Qa1JHQBA/LkbCOk3DRAKYeV3ECITA952RSzdishl27jf75x7AAA0e7aG4ZqZYBkZiN50CDG7TiAt8CMEqhLIGteB/q8jILYy5fImfN46xJ+/hfQvkRBqqkFiawHNAR0hq1+TixMAIpdt49ZXMexaEXciIYQQQsqLMu8G4UdT07gmbHVtcfT5UXyI+YDjAcfxLvodujl2yxE2JT0F7t7u+PPWn3gT+QammqbQk+nh/OvzSE5PRnRSNPb77wcA1DCqgd89fucqagGgsVVjuFm48eK89eEWpvtMh5ZUC3oyPQDAveB7ePL5CfTV9OFk4ITE1EScf30eHjs8EBIXkiNd/Y70w4eYD7DRtYFYKOYqaLc+/Poq1qFnhwAAHrYeOSpqAcBcyxxVKlTh5YurmSsq6mYOGjf69GiMPzce/mH+sNOzg4pABQefHkT9rfXxOT5zdL05l+Zgg+8GhMWHwcnACZoSTdz5cAeXgi5BqiKFq5kr1+rXTNMMrmaucDVzzWMPEUIIKReSEoHY6NL7JCUWKHlhYWEYMWIELC0tIZVKYWxsjFatWuHGjRsllCFlx8vLCwKBIMfHycmJF27t2rWwtraGqqoqXF1dcffu3TJKMSlt0qr2EFmbIv7MNaQFf0b82RtI+xAKjQ7uOcKylFR87DQG0ev2Ii0oGCLjClDR0ULi5XtgKSlIj4lD3LF/AQASJzvozRnBVdQCgKx+TajWrcqLM+m+PyIWboRQUw1CXS0AQPLD50h59hoqulqQVLIBS0pB4uV7CPb8BWmh4TnSFTpyAdKCP0NsaQKIRZD9v4I2dvcpLkz8icuZaWjsnKOiFgBEJgYQO1hxvyVV7SF1doTIJrMs/GXaXwiftQqpzwMzp6moIP7EZXxsOwJpYZEAgIjftyBm+zFkhEdBUskaQg01JD14hqTrDyCQiCF1dgT+3+pXxcQAUmfHzGmEEEK+DaVZxi1g+RagMm55KeOWecvaH41QIMSouqMw8fxErL+3Hrc+3AIAjHEZgz3/7eGF3fvfXjz49AAAsNRjKSY3mAwA8P/sDzWxGh6FPEJaRmZrhUaWjfLVN0ZKegpO9zmNNvZtkJ6ROajLqLqj8Fvz36AmzuwI+VXEK9ivtkdsSixOvTiFQbUH8eLo5tgNO7vshFAgRHpGOrSkWrjw5gKOPD+CqKQoqIvVcTzgOABgQPUBCtMxuPZg2OnZca11j/Q8AmsdawBAYGQgNtzP7NfMu7M3BtQYgLiUOFRZWwUfYj5g9Z3VWNBsAV5GZLa4mN14NmY2ngkAiEiMwPvo9zDRNMHtwbdhvcIab6PfYnDtwZjrPjfP/CGEEFJOpKYAJ3YDUTkrVYqdjj7QoQ+gKsv3Ip6enkhJSYG3tzdsbW0RGhoKHx8fhIeXXHpTUlJKZCC2lJQUREREwNjYWOH8lStXYsmSJdzvtLQ01KhRA927d+em7du3DxMmTMCGDRvg6uqKFStWoFWrVggICIChoWGxp5mUM0IBtH/ugvA5axG97SiS7vsDALQHeyLu8EVe0LgjPkh5/AIAoPfrCOiO7gMASHkeCKFMFcn+r4C0zDKqar3q+ev7LSUVxnuWQd2jHtj/By3U+rkL9GYOhVAtcxCl1Dcf8M61N1hcAhIu3IJWv/a8KDQ6uMNw3SwIhEKw9HQINdWRePke4k9fRXp0LIRqMsSfy3xQ1ezZWmEytPp3gNjWnGuta+y9KLPyF0Dq22DEbD8GADBcMxOaPVsjIy4B7xr0R3rwZ8RsPgS96YOR+uYDAEB3wkDoTsgsR6dHxiDt42eIjCvA/OzfeFu7O9Leh0CrX3voTfk57/whhBBSfpRWGbcQ5VuAyrjlpYxLLWvLwM+1foa6WB2r767GpaBLcDZxztECFgDufLgDAJCqSDHBbQI33cnQCRIVCRi+vs6f306MK+lXQhv7NgAAFWHmyHSRSZHotLcT9H7Xg3CeEPar7bnwwbE5B3AY4zIGQoGQi6NDpQ4w0zRDUloSdj/ZjUtBlxCZFAlNiSa6VOmSr3TJux98n9u2gUcHQjBPAM3FmvgQk1l4vf3xNgCgg0MHAMDsS7NhtcIKrXa1wuo7q2GkYVTgdRJCCCmHosKB8NCS/xSwsBwVFYVr167h999/R9OmTWFlZQUXFxdMnz4dHTt25IUbNmwYjIyMoKqqiqpVq+LkyZPc/EOHDsHJyQlSqRTW1tb4888/eeuxtrbGggULMGDAAGhpaWHo0KEAgOvXr6NRo0aQyWSwsLDA2LFjER8fX+Ds9fX1xZgxY2Bqaop9+/YpDaetrQ1jY2Puc//+fURGRuKnn37iwixfvhxDhgzBTz/9BEdHR2zYsAFqamrYupUGQPpRaPZpB4GaDNGbDyHp+gNIa1TK0QIWAJJ8nwIABFIJdEb05KZLKttk9hPLCl6+FdtZQt2jXuYy/x95OSM6FiEDpiPQvi1eGzbGO9feXPi00C854tAe7AmBUMjFod6qPlRMDMCSUhB36CISrz9ARlQsBBpqUG/bOF/pkpfsF8Bt2+fRi/DaoBECbVohPTjzjbEk38wKbvVWDQAAEUs2422tbgjuPgHRmw9BxUC3wOskhBBSTpVGGbcQlcFUxi0/ZVyqrC0DOqo66Fe9H2JTYgFkVn7mRVFhtZJ+Ja7bg+vvroOxvPtizV6RGZcSh1a7WuHim4tITEtELZNavK4C0ll6zjjU+XGIhCIMdc48ubb5bcOhp5ldIHR37M611i2srO4R5D9W2pmvlw11HoorXlcwwW0CKleoDN9gX8y9MhceOzyKtE5CvnU+Pj7477//4OPjU9ZJIeS7pKGhAQ0NDRw9ehTJyckKw2RkZKBNmza4ceMGdu3ahadPn2LJkiVQ+X9Fkq+vL3r06IFevXrhyZMnmDt3LmbPno3t27fz4vnjjz9Qo0YNPHz4ELNnz8br16/RunVreHp64vHjx9i3bx+uX7+O0aNH5yvtnz59wrJly1C1alXUr18fHz9+xObNmzFy5Mh8b/+WLVvg4eEBK6vM+3FKSgp8fX3h4fH1/isUCuHh4YFbt27lO17ybVPR1oRm95ZgcQkAAK3BnnkvpKB8K7azBESZ50nincf5Kt9mr8jMiEvApx4TkXjlPlhSMiTV7PldBaRn5BmHQCSC1oDMhgGxe04j/mTmOBoaHZtyrXULK6t7BPmPyDyzfK01oCNMj62G9oieENtZIvnxC0Qu3Yrgbr8UaZ2ElDYqjxLy7aEybvkp41I3CGVktMto/O37NwzUDNCrai+FYVzNXbHu/jokpydjxe0VXOvaZ2HPUFGvIrRVtdHDqQd2P9mNhyEPMcNnBhY0W8BV4F58cxFqYjXUt6jPxSkAv1Ac8CUAUUlRAICtHbeid7XeuP3hNty25Gzpy8WhoGA9uPZgLLi6APeD7+NZ2DMAwIAairtAyCJfkRuf8vXfEmdTZwggAAODVw0vjKs3DgDAGMP1d9ehraoNALj78S6cDJzQ2CqzdUNWuv3D/BGeEA59NX1uHfLxE/K9q1SpEhJT0pCSmo7oeMU3WWUkYhXIJHRrICQ3IpEI27dvx5AhQ7BhwwbUrl0bTZo0Qa9evVC9enUAwMWLF3H37l08e/YMDg4OAABbW1sujuXLl6N58+aYPXs2AMDBwQFPnz7FsmXL4OXlxYVr1qwZJk6cyP0ePHgw+vbti/HjxwMA7O3tsWrVKjRp0gTr16+HqmrOSqSUlBQcOXIE3t7euHDhAurUqYNRo0ahV69e0NUtWGu94OBgnDlzBrt37+amffnyBenp6TAy4v+Za2RkhOfPnxcofvJt0xrUFTHexyCsoAPNLs0VhlF1dkTMtiNgySmI/ns/dEZkloNTXgRBbG0GFS0NaHRqhrhDF5Dy5CUiFv4NvemDuX5rE67ch1AmhapLta+RZiubpr5+j4zoOACAwcrp0OzqgaT7/vjYZrjyxCso32r164DIP72R7PccKS/eAgA0e7TKNQ8EMin3nSUkcd+lNSplroMxaPZqA51hma9YMsaQdPsxhFrqAICkB08hqWwDWf2amb//n+7U54FIj4iGip42t46MhIL3RUhIaalUqVKRls8qyxYGlWcJKRwq45afMi5dwcpIVcOqCJ8SDpFQBKlIqjBMr6q9sPLOSjz49AATz0/E6rurIRaK8TryNcKnhEOiIsHqNqvxNOwp/EL8sOTGEqy7vw7WOtZ4H/0ekUmR2NZpG6+yNjtbXVuoi9URnxqPQccHYfH1xdwAXgVhqmmKTpU64dCzQ4hPjYe1jjVXiapMRd2KEAvFSM1IhcdOD1hpW2FS/Uno5tgNQ2oPwcYHGzH+3HisvLMSGhINvI1+i5jkGGzrtA3Vjapj1Z1V2Oe/D+Za5tCT6eFVxCsAmYOJZQ2eVrlCZTz78gyr7q7C5beXUdWwKrZ12pZbsgj5LqSkpuOfa68QHpuUd+D/09dURd9GdiVeuL1x4waGDx+O58+fo127djh69KjCaYSUZ56enmjXrh2uXbuG27dv48yZM1i6dCk2b94MLy8v+Pn5wdzcnCvEZvfs2TN06tSJN61BgwZYsWIF0tPTudYJderU4YV59OgRHj9+jH/++YebxhhDRkYGAgMDUaVKFWR38+ZN9OrVCxYWFvj333/RqFGjQm+3t7c3dHR00Llz50LHQb5f0iq2sH5xCgKRCgRSxX3PaXRpjqiNB5Dy+EVmH7ebD0MgUkFqUDCsX5yEikSMCovHIyUgCCn/vUTUqn8Qs+0oRJbGSPv4GRlRsTBYNZ1fWZuNyMoUAjUZWEIiwsYvQdTKnUgPiyrw9oiMK0C9dUPEn7wClpAIkaUJVP9fiaqM2NoMEIuA1DQEe46HyMIYOiN7QaNjU2j174CYHccRPmsVojcdgFBdhrT3ociIjYfBqumQOtkhetNBxB39FyJTQwh1NJEW+BFA5mBiWYOnSeytkPriLaI3HULSDT9IKtvAcPWMAm8fIeVZYcqyQP7Ls1QeJUQxKuN2LnQcxYm6QShDejI9aEm1lM6XqEhwaeAlTHSbCBsdG3yM+YjwxHB42HpAqiLl4rg16Bb+aPEH6prWRQbLQMCXAOjKdDG41uA8K0x1Zbo40P0AHA0ckcEyIFGR4ETvE4XanpF1vzYv71+9f579jOmr6WNVm1Ww0LJAaFwo7ny8g5C4EADA+vbr8Verv1DNsBqCY4PxNvotrHWsMaHeBLhbuwMA2tm3QyPLRkhMTcST0CdQFamig0MHnO57mlv3wmYLUc+8HoQCIe4H38eT0CeF2jZCvkXhsUkIjU7M8Xnz7gMOrP8Nf4zsgLl93LB0WFtsXTgWd29ezXfcly9fhkAgQFRUVIHTNWHCBNSsWROBgYHc6zCKphU3RSN9CgQCLFu2jAsTERGBvn37QktLCzo6Ohg0aBDi4uJKJD3k26eqqooWLVpg9uzZuHnzJry8vPDrr78CAGSygg3moIy6ujrvd1xcHIYNGwY/Pz/u8+jRI7x8+RIVK1ZUGIeLiws2bdoEKysrNGvWDG3atMHu3buRkJBQoLQwxrB161b079+fNwhEhQoVoKKigtDQUF740NBQpQM6kO+Xiq4WhJrqSucLJGKYHV0F7ZG9ILIyQdqnMKRHxkDW2BmC/x9XKrpaMDuzHvrzRkFaqwpYRgZSX72HUEcTmv3aQ+ZWM/c06GjCaMt8iCtZAxkMArEYxv8syXUZZbR+/jr+gmb3lnmWb1X0tFHht3EQmRkiPSwSyb5Pkf45AgBQYdlE6C8YA4mjLdJDwpH2PhQiS2Noj+gJWYNaAAC1FvWhWq8GWGIyUp69gUBVArVWDWCyZym3br3pgyGt4wSBUJDZ6vfZm0JtGyFlKSQkBGPGjIGtrS2kUiksLCzQoUMHXrcJysqyWZ8Hd29iTo86ePfpMzctv5W7VB4lRDkq42YqyzIutawtJZe9LucZhv2as08uLakW/mj5B/5o+YfS5VRFqphYfyIm1p+oNMz2ztuxvfN2hfPa2LfhBh1TlhZ3a3eF6ZPnaJDZF5gAAgysMTDXsFmG1xmO4XVyvpImFAgxvt54jK83Xumyvav1Ru9qvZXOz0rTrUHUXx75sezevRvhkdF4/CICRtWa8ObFfPmEw7+PhESmgfrdRkLfzBbp6el4738Hp7b8jpVTS35U6devX2P48OEwNzfPdVpx+/TpE+/3mTNnMGjQIHh6fu1XsW/fvvj06RMuXLiA1NRU/PTTTxg6dCjvdRhClHF0dORa4VSvXh0fPnzAixcvFLY8qFKlCm7cuMGbduPGDTg4OHAtDhSpXbs2nj59Cjs7u3ynS01NDYMHD8bgwYPx+vVreHt7Y+bMmRg+fDg8PT3Rv39/uLu7QyjM/T/8K1eu4NWrVxg0aBBvukQigbOzM3x8fLjWCBkZGfDx8cl3P2Pk22R2bHWeYSqGXcsxTaipjgrzRqHCvFFKlxOqSqEzshd0RiruLgwADNfMhOGamQrnqXvU4wYdU5YWWYNaCtMnT+JgnflFIIBmzza5hs2i7dUZ2l6dc0wXCIXQGd4DOsN7KF1Ws6sHNLvmPv6CpJINzM9syFdaCCkrWRUmampq6NOnD29eUFAQGjRoAB0dHSxbtgzVqlVDamoqzp07h1GjRpVKFzpUHiUk/6iMW/plXGpZS4rsS8IX9D3cF022Z1YKdanSBRX1FP/zQQgpeVOmTMHY0SNxfteqHPOu/LMcgADdZ25ERWd36BhbQt/MBjVb9sKQRdsBZBagBQIB/Pz8uOWioqIgEAhw+fJlBAUFoWnTpgAAXV1dCAQCrv+h5ORkjB07FoaGhlBVVUXDhg1x7949Xrzh4eH4+eefIRAIsH37doXTSoL8SJ/GxsY4duwYmjZtyvWx9OzZM5w9exabN2+Gq6srGjZsiNWrV2Pv3r0IDg4ukTSRb1N4eDiaNWuGXbt24fHjxwgMDMSBAwewdOlS7rWvJk2aoHHjxvD09MSFCxcQGBiIM2fO4OzZswCAiRMnwsfHBwsWLMCLFy/g7e2NNWvWYNKkSbmue+rUqbh58yZGjx4NPz8/vHz5EseOHct3gbFixYqYP38+3rx5g+PHj4Mxhk6dOmHt2rV5Lrtlyxa4urqiatWqOeZNmDABmzZtgre3N549e4YRI0YgPj6eN5ouId+S9PAohA6fj+BOmQMBq7dtDLGNWRmnipBvx5QpUzBkyBBMmTIlx7yRI0dCIBDg7t278PT0hIODA5ycnDBhwgTcvn0bAPD2bRDm9KiDsHcvueWSE2KxdkgjfAx4iJgvn3D0j7EAgM3j2mLtkEbw2booMxyVRwkpFCrjlp8yLrWsJUUWlxKH3U92Q1WkitZ2rbGhHf3TT0h5lBQfg3f+d1Cv8xCIpTlfX5Gpa+YrHgsLCxw6dAienp4ICAiAlpYW9zrMlClTcOjQIXh7e8PKygpLly5Fq1at8OrVK1hYWODTp0+oVKkS5s+fj549e0JTUxOtW7fmTdPW1la43uHDh2PXrl25pi2/r4iFhobi1KlT8Pb25qbdunULOjo6vP6TPDw8IBQKcefOHXTp0kVRVKQk6eiXy/VoaGjA1dUVf/31F16/fo3U1FRYWFhgyJAhmDHja7+Rhw4dwqRJk9C7d2/Ex8fDzs4OS5Zkvopdu3Zt7N+/H3PmzMGCBQtgYmKC+fPn8wZeUKR69eq4cuUKZs6ciUaNGoExhooVK6Jnz54F2gaBQAB3d3e4u7tj7dq1iIiIyDV8dHQ0Dh06hJUrVyqc37NnT4SFhWHOnDkICQlBzZo1cfbs2RwDMvworK2t8fbt2xzTR44cibVr1yIpKQkTJ07E3r17kZycjFatWmHdunU/bH6VRxnxiYg7dAECVQlkzVxh8IfyN9gIIcoxBt6At5ERETh79ixm/zoPaRDlGAxXIJYhNiEFub/TCWjoGaL1iIU4u34W+i74B2KZOkTizK4C58yagRPHjlB5lJRfpVHGLcQ6qIybU1mVcamylhSZtY51nl0kEELKXvTnDwBj0DW2KlI8Kioq0NPLHMTP0NAQOjo6AID4+HisX78e27dvR5s2ma+Kbtq0CRcuXMCWLVswefJkGBsbQyAQQFtbm+vnR11dPcc0RebPn5/nP7L55e3tDU1NTXTt2pWbFhISAkNDQ144kUgEPT09hISEFMt6SQGIJUCHPnmHK8715ZNUKsXixYuxePHiXMPp6elh69atSud7enryXnvMLigoSOH0unXr4vz58/lKa36oq6vn6DcsO21t7Tz7/xo9ejR1e/B/9+7dQ3r611HM//vvP7Ro0QLdu3cHAPzyyy84deoUDhw4AG1tbYwePRpdu3bN8dogKTtiS5M8u0gghOQtITkNq07/x/3+8Oo/MMbwIk7Gmy6vorEWnPRyj1coVIGqeub4LzItXUjVMhsdpCQlYuvmjVQeJeVXaZZxC1C+BaiMq0xZlHGpspYQQn4QjJXsnypZ/742aNCAmyYWi+Hi4oJnz54VOX5DQ8MchdfC2rp1K/r27QtVVdViiY+UAFVZ5oeQb5CBgQHv95IlS1CxYkU0adIE0dHR2LJlC3bv3o1mzZoBALZt24YqVarg9u3bqFevnqIoCSHkm5TBGEKjE7nfWQOARcen8KbL09eUAnriQq0vIvQDlUdJ+UZlXJIP1GctgddRLwjmCeC+3b1E4r8cdBmCeQII5gkQFBXETd/5aCccVjtAvEAMwTwBtvttL/G0EPIj0zGyAAQCRIbkfDVXXlYH7PKVu6mpqSWatvwYPnw4NDQ0cv3kx7Vr1xAQEIDBgwfzphsbG+Pz58+8aWlpaYiIiKAR7QkhhZaSkoJdu3Zx/SD6+voiNTUVHh5fB5GqXLkyLC0tcevW9zUo6ufRi/DaoBE+/r/f1+KWeOMhXhs0wmuDRkh993XQntj9Z/HOtTdem7jjtUEjxOw5XeJpIUWX+u4Ttz8Tbzws6+R8Ez52GvPNHdf5L48K/v/ta3k0Iz2tBFOWP1QeJYSUBmpZ+w0KigqCzUobAMClgZfgbu1epPgq6laEq5krHA0ciyF1OWlJteBq5goAkKpk9iP0Of4zBh0fhNSMVJhpmsFcyxwGagYlnpa8FHfeElKeqKprwdLJBU8uHUH15t1y9FubGB8L4GuLsE+fPqFWrVoAwBtsDMgcGRMA7zXfihUrQiKR4MaNG7CyyuxqITU1Fffu3cP48eOLnP7ieu1sy5YtcHZ2Ro0aNXjT3dzcEBUVBV9fXzg7OwMA/v33X2RkZMDV1bXI6yWE/JiOHj2KqKgorq+2kJAQSCQSrguZLEZGRrm+4pqcnIzk5K99O8bExJREcouVyMYMUmdHSCpZl0j8Qg01SJ0zy4wCSWYrvLSwSHwe/zuQmgYVEwOITA2goq9T4mnJS+q7T3jn3AMAYHp0FWQNapXo+hJvPERw57G8aQI1VYhtzKHVrz20B399PTVi6VZELtv2/0ACCFQlUNHXgcSxIjT7tINGu8a8eF4bNPr6Q6QCoYYaRBbGUHN3gfbQbhAZV8g1bVnrE1kYw+rBgaJtaAkq7X1WXOTTbbBqOrR6ty3jFPHlVR5NTogFoAP9Cpnl0fjocGS9q/Dl/SteWKFKZnUGy8jgpukZmVN5lBDyzaPKWoLZTWZjdpPZJRZ/bZPauD34Nm/ai/AXSM3IbKnnM8AHlSpUAgC0c2hXomkpLe+j32PdvXWYWH8iKqjlXmAlpDQ17jMBh38fgQOLhsK10yDom1dERkY63j+9j+fXjmFq9+eQyWSoV68elixZAhsbG3z+/BmzZs3ixWNlZQWBQICTJ0+ibdu2kMlk0NDQwIgRIzB58mTo6enB0tISS5cuRUJCAgYNGlTktBfHa2cxMTE4cOAA/vzzzxzzqlSpgtatW2PIkCHYsGEDUlNTMXr0aPTq1QumpqZFWi8h5Me1ZcsWtGnTpsjXkcWLF2PevHnFlKrSoTfRC3oTvUosfmmNSjA/+zdvWurr90BqZus708MrILGzBACot6xfomkpz0TWplDR0ULKq3dI8X+FL9NXQMVQDxodm+YIK3GyA0tIROq7T0j7EIqE8zeR0KsNDFZNh0Ag4IUV6mtDbG6M1HefkPLkJVKevETM7pMw2fsHVGtWLq3N47CUVK7SnpRvuZVH/S8fRa1tJyGTyWBuXw0PzvwDrQomSIyJxO2jm3jxaOobAwIBgh7fhFW1elARSyHR1sfPg4dSeZQQ8k2jbhBKifUKawjmCTD1wlSMPj0a+kv1YbjMEOPOjENaxtfXORJTEzHTZybsVtlBskACvd/10HlvZzwJfQIA2O63nWv5CQBNvZvm2W3A6Zen4bbFDTpLdKC2SA12q+zQ82BPRCZGAlDcDcK76HdotasVVBeqwmG1Aw4/O8xtg9dRLwCZrVCzujfY7rcd7Xe3h9oiNdistMGWB1u4uLJ3gzD38lw02vb1H/nKaytDME+Ay0GXFaYlJT0Fi64uQpW1VaC6UBU6S3TQZHsTfIj5AAD45/E/cNnkggpLK0C8QAzd33XRalcr3P14V2Eajj0/hsbbGkO2SIbKayrj5IuThc5bRSITI9HUuylikmOoopaUO9oGpugxawvMK9fGjQNrsWfuQBxfPgEfnvmiw+BpXLitW7ciLS0Nzs7OGD9+PBYuXMiLx8zMDPPmzcO0adNgZGTEdbi+ZMkSeHp6on///qhduzZevXqFc+fOQVdXt1S3U5m9e/eCMYbevXsrnP/PP/+gcuXKaN68Odq2bYuGDRti48aNpZxKQsj34u3bt7h48SLvNVdjY2OkpKQgKiqKFzY0NDTXV1ynT5+O6Oho7vP+/fuSSna+xV+4hQ9thiOwYhu8sfTA27q9EDL4V6RHZb6poajrgdQPoQjuPgFvzJvjnWtvxJ28gre1u+O1QSN8Hr0oM4zc6/Axe07jU58pmfE790DMrpNcXNm7QYhYuhXBHUZx89+79eVeqVeUFpaSisjlO/Cufj+8MW+OwIpt8LHjaKQFZ76CHHvwPD60HIrASu3x2sQdgXZtENx9ApIePFWYhvgz1/Cxw2i8sWiOd259EX8+c8C4mD2nuZaOABDceWyhX11njHHpyy/dCQNhfmETLC5t+5rum34Kwxp7L4LlnT2wengQsiaZo9HH7j2DWLl8z6Lu4Qbzi5th8+IUDDfMAcQiZIRHI3TwHLAUxd0nfew0hmvFm/Y+hLef5aV9CkPIwBl4Y9Ui1/0ed+wSPrQcitemTRF76AIAIOXlW4T8PBuBldvjtWlTvKvfD9HbjvDij9qwD+/df0KgfdvMfVu5PUK8ZiLl9TsA+dtnsQfO4UOLIXhj6YE31i0R3GMikp+8VJhOZcdGlvykOT0qFiGD5mSeCzU9Eb39qMI8/hbkVh5t0m8iF67LiDlgGek4sHAwru9bjXqdh/Di0dA1gEvHn3Hr8AZsndgJ13b/BQCYO38hlUcJId80allbyv66/Rc0pZqQiWT4GPsRq+6uQlXDqhjinHnj6bi3Iy6+uQgBBKhUoRI+xHzAsYBj8An0wb0h92CgZoCaxjXhF+IHAKhSoQq0pFpKuw0Iiw9Dl31dkJKeAkttS+io6uBd9Dvs99+P3z1+h64s5w2LMYau+7rC95MvhAIhREIR+h/pj/SMdAVryDT0xFCYaZlBrCJGUFQQhp4cigaWDVC5Qs5/1c21zFGlQhU8+5LZwXtN45qQqkihJdVSGLfnfk+uQtVEwwTaqtq48e4GviR8gbmWOe4F38OTz09gqW0Jcy1zPP/yHOdfn8et97fwYswLGGvwH3y6H+gOax1rCCBAQHgA+hzqg6DxQQXOW2UGHh0IfTV9/NX6rwItR0hpUdepgMZ9fkHjPr/wphtpf30NrUqVKrh58yZvfvYBymbPno3Zs/kt4VVVVbFq1SqsWrVK6fqzV1Aom1YShg4diqFDhyqdr6enh927d5dKWggh379t27bB0NAQ7dq146Y5OztDLBbDx8eHGyk5ICAA7969g5ubm9K4pFIppFJpiac5v9K/RCLEayaQkgqRuRGE2hpI+xCK+GP/Qn/OcKjoaOZYhjGGUK+ZSH4UAAiFgEgFn0ctBNIzFKwhU9jEZRCZGEAgEiHt3SeETVwGVddqkNhb5QgrMjGA2MEKqS8y+8KUVLWHQCqGUENNYdwhP81CwvnMe52KkT6EWhpIuvsf0sOjITI1RPLD50h59hoiMyOITAyQ+uodEi/fQ/B9f1je3g2RkT4/vkFzILbIbOmX+uodQofNh9WDA5ldClS1R8p/mRV5YgcrCDXVC9UlQ9Tq3YjesA+mh1dCUtkm7wWUEJkb5T7fuAIM183Gu9rdwZJTELPjOLT6d1AaXtOzBZIfPkP03weQ9vYTEi7fg3rL+jnCSSpZIzXwI9I/hQESMaTV7AEAKvo6vHBhE5ZCxUAPAhWVXPd76MgFUNHVgtjSBBAIkPL6PT62Ho6MmDgIdbUgsbNEyvNAfJmyHOnhUdCb9BOAzMrq1MCPEJkbQmRigJQXbxF/6iqSHj6H5Z3dee6zyNX/IGL+hsx5FS2QEZ+IxEt38fHOE5hf2AiJgzUvnUqPDV2tfKc5bPzviD91BQAgkKki/Nd1ue7D8k5ZeVSegbkNPKet500bteka73fd9l6o296LN43Ko4SQbx21rC1l5lrmeDP2DV6NfQVTzczXGHwCfQAAlwIv4eKbiwCA5a2W49moZ3g26hk0JBqIS4nD4uuL0c6hHY70/Pov67p263B78G2sa6f4Zv0u+h1S0lOgKdHE81HP8Wj4I0RMicDdwXdhoGagcJl/A/+F7ydfAMCq1qvwdNRTHO91HMnpyQrDA0Cnyp3wZuwbXPsp8+aZwTJwOeiywrCDaw/mpfdIzyO4Pfg2apvUzhH26turXEXt6Lqj8WHCBzwb9Qyvx76GpXbma22j6o5C+JRwBIwOgN9wP/w38j8AQGxKLE69OJUjzjEuY/BizAvs7baXC3f3490C560i6+6tw833N3Gg+wFIVCT5Xo6Q4mRsbAxTUzNo6OjnHZgQQkiJyMjIwLZt2zBw4ECIRF/bR2hra2PQoEGYMGECLl26BF9fX/z0009wc3NDvXr1yjDFBZP28TOQkgqBhhosbv4Di8vbYf3yNMzOb8xR8ZYl8ZpvZkUtgAq/jYPljV0w3rkYLDlF6XrUWzeE5f19MD2xJnNCRobSwae0+neAwdKvrfKMvRfB/OzfkNaolDMtN/24ilqtQV1h9fgwLG/uguW9vVxFptbPXWAdcAqWt3fD4vI2WFz1BgCwuAQkXMg5GJz2YE9Y3tkDo41zuXDJD55BvWV9GHsv4sIZLJ0I87N/89KaX1r9O0DFUB/BXccj5UVQvpaJXO6NDy2G4H3TzEo/tZb1oe3VOc/lRIZ6EFc0B4B8rUu13te+N5WFN1g6EVr92mfGb6QP87N/w/zs3zkqdtU83PK13zU6uGfuu9u7odm9JaJW7ERGTBwkVWxh5XcIFle9ob8gszVs1Kp/kBGXAADQnzkUNq9Ow/LGLlhc9YbJvj8AAOnBn5F090mu+ywjIQmRf2wHAOhO/RmWt3fD6uEBSGtWBktIROSKnTnSqezYAJCvNKcGfuQqanXG9IXlrX9g7rMZLCXnuSPUVP/6XSt/g12VBCqPEkJI4VHL2lLWsVJHaKtqAwBsdGwQHBuM0PhQAMC94HtcuD7V+gDIrNxtZNkIZ16dwf3g+wVen5OhE2x1bfEm8g0M/zCEvZ49qhlVg2cVT9Q1q6twGf8wf+57D6fM13+a2zaHnkwPEYkRCpfpW60vBAIBrxVqaFxogdOb3Z0Pd7jv0xpOg1CQ+f+Clc7Xf9UjkyIx8vRI+Ab7IiopCkxuxNDg2OAccfav0R8Aii2t4QnhUBGqICIxAlMuTMEezz1cRTIhZeH+/fuIjk/GqtP/ITQ6sayTQwghP6SLFy/i3bt3+Pnnn3PM++uvvyAUCuHp6Ynk5GS0atUK69Z9W63kxJWsIbI2RVpQMIKqdIDY1hySKhWh0aEJhLWqKFwm5XkQ912jU2Z/qWqN60Coq4WMSMUDpml2awGBQMBrhZoeFlnk9Mt3ZaA7rh8Ewswyptji6xtZGdGxCJm6HMmPApARHQfIvWGSFvolZ1q7twIAXqvKtDDFZefcKBocTJGQATNgeTvv1ndpQcFIQ2aZWKAmg3rrBkpbG2fHMljegbJkKG8hXVD53e/agz25fSdQUUHSw8wK0JRnbxBo1YIXliUmI9n/FWSu1ZH6PhRhE5Yh+elrsPhE3r5ND8m5b+WlBASCJSQBACJ/34rI37fy5ifff5pjmdyOjfykWf78UO/QJDMuO0tIHCsi5fEL3jIquloQqMvA4hMhzqMFdUmi8ighhBQeVdaWMh1VHe67SPj/0StZAQpBBaQqUoXvUF/sfLQTdz7ewdOwp9j5aCd2PNqB/d32o7tT92JZT9Z2ZW0TAF6laUmJS4lDq12tEJUUBVWRKmqZ1IJYKMadj5mVvOksZ9cNxZ3Wxtsbo6FFQwRGBaK7U3d0qKT8FTFCCCGE/BhatmyptIynqqqKtWvXYu3ataWcquIjVJXC/OJmxO0/h6QHT5ESEIS4A+cQt/8sjDbP5ypji7we7czuFARyrZNRgmXnLBlxCfjUYyIyouMgUJVAUs0eArEIyb7/r4hT0HWDUPv/rRhFKkVKq4qBLjS6tVA6P9kvAKmv3kFS1T5f8Rmsmg71No3weexvSDhzHWGT/oSkmkOeg4ClhYYjLfAjAOR4rV+RpNuPue/5CZ+b/O53FQPFfZAK9bUhtjbLMV2gooLUoGCEDJzBtQyX1qgElpbOdXnAcumWI7us7hF4adLN2bVbfo6N3NJcUCILY6Q+D4TIQnk/2IQQQsovqqwtR+qafm3puvvJboyvNx4fYj7g2rvMrgXqmGZ28q8m/vpPeHxKfK5xxiTH4FnYM4x2GY0xgszXaVrvao1zr8/h6turCitrqxpW5b4feX4EQ52HwueNj9JWtSXJ1dyV+77s5jL81eovCAQCvI9+D3WJOgIjAxGVFAUA2NpxK3pX643bH27DbYvyPt9yU5C8zVLDqAY2PtgIcy1zHOh+oFDrJYQQQgj5lmTExiP1xVtoDfaEtqAbACC4x0QkXrqLxFt+CitrJVW+9rEaf/oatAZ0RMLV+0pb1ZYk1dpf37CKWrMH+gvHQCAQIO1jKARqMqS9+5TZmhaAwcrp0OzqgaT7/vjYZnih1ieQqXLfM+Jzb2UocbCG0fo5Cucl/HsH8SeuQK11Qxitn60wjCIqOpow+GMy3l2+B5aYjMjft8BkzzKl4dNCvuDz6EVcFxW59VcLALGHL3IDXoksTaDmrvgNPgAQyDL7Xs5ITAJjDAKBIN/bkTMy/rKqNSsjNSAIQk0NmOxZxlWcpodHIfGqL1TrOCHuxGXg/wOgme7/E6p1qyL2yEV8HjovWzoV7zNJJRsIZFKwxGSoNXOF/vzR3DYkP34BlqS86zhF8pPm1DcfuPDxp65CtVYVpLx+h5Snr3PEx1LTYLx1QeYPUcEregkhhJQ9qqwtR5raNIWHrQcuvrmICecmYKPvRnyI+YC4lDhoSDQwveF0AICBmgH0ZfoITwxH/yP9Ya9vj37V+mGMa84RZT/Hf0b9rfWhq6oLcy1zpKSnICA8s6+w6kbVFafDuimcTZzh+8kXI0+NxMo7KxEYGQipijTXfmtLQmOrxmjv0B4nX5zEyjsrsd9/P3RUdfAy4iXuDbkHW11bqIvVEZ8aj0HHB2Hx9cX4HF+wEXLlFSRvs4yoMwJ+IX5Y3WY118UFIYSQoklMSUNKqvKBLYubRKwCmST/xaKwsDDMmTMHp06dQmhoKHR1dVGjRg3MmTMHDRo0KMGUlj4vLy94e3vnmO7o6Ah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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+ "\n",
+ "# ── Left: No manipulation ──\n",
+ "ax = axes[0]\n",
+ "score_clean = np.random.normal(70, 12, 600)\n",
+ "score_clean = score_clean[(score_clean >= 40) & (score_clean <= 100)]\n",
+ "\n",
+ "ax.hist(score_clean[score_clean < cutoff], bins=20, color='tomato', edgecolor='white', alpha=0.8, label='Score < 70')\n",
+ "ax.hist(score_clean[score_clean >= cutoff], bins=20, color='steelblue', edgecolor='white', alpha=0.8, label='Score ≥ 70')\n",
+ "ax.axvline(cutoff, color='black', linestyle='--', linewidth=2)\n",
+ "ax.text(cutoff + 0.5, ax.get_ylim()[1] * 0.92, f'Cutoff = {cutoff}', fontsize=10, color='black')\n",
+ "ax.set_title('Normal: density continuous at cutoff', fontsize=13, fontweight='bold')\n",
+ "ax.set_xlabel('Exam Score', fontsize=11)\n",
+ "ax.set_ylabel('Count', fontsize=11)\n",
+ "ax.legend(fontsize=10)\n",
+ "ax.text(0.05, 0.90, 'McCrary test\\nnot significant ✓', transform=ax.transAxes,\n",
+ " fontsize=10, color='green', fontweight='bold',\n",
+ " bbox=dict(boxstyle='round,pad=0.4', facecolor='white', alpha=0.8))\n",
+ "\n",
+ "# ── Right: Manipulation suspected ──\n",
+ "ax2 = axes[1]\n",
+ "score_below = np.random.normal(62, 8, 300)\n",
+ "score_below = score_below[(score_below >= 40) & (score_below < cutoff)]\n",
+ "score_above = np.concatenate([\n",
+ " np.random.normal(78, 7, 200),\n",
+ " np.random.uniform(70, 73, 120) # units abnormally clustered just above cutoff\n",
+ "])\n",
+ "score_above = score_above[(score_above >= cutoff) & (score_above <= 100)]\n",
+ "\n",
+ "ax2.hist(score_below, bins=20, color='tomato', edgecolor='white', alpha=0.8, label='Score < 70')\n",
+ "ax2.hist(score_above, bins=20, color='steelblue', edgecolor='white', alpha=0.8, label='Score ≥ 70')\n",
+ "ax2.axvline(cutoff, color='black', linestyle='--', linewidth=2)\n",
+ "ax2.text(cutoff + 0.5, ax2.get_ylim()[1] * 0.92, f'Cutoff = {cutoff}', fontsize=10, color='black')\n",
+ "ax2.set_title('Manipulation suspected: density spike after cutoff', fontsize=13, fontweight='bold')\n",
+ "ax2.set_xlabel('Exam Score', fontsize=11)\n",
+ "ax2.set_ylabel('Count', fontsize=11)\n",
+ "ax2.legend(fontsize=10)\n",
+ "ax2.text(0.05, 0.90, 'McCrary test\\nsignificant → RDD threatened!', transform=ax2.transAxes,\n",
+ " fontsize=10, color='crimson', fontweight='bold',\n",
+ " bbox=dict(boxstyle='round,pad=0.4', facecolor='white', alpha=0.8))\n",
+ "\n",
+ "plt.suptitle('McCrary Density Test: Running Variable Histogram', fontsize=14, fontweight='bold')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000019",
+ "metadata": {},
+ "source": [
+ "### Other Validity Checks\n",
+ "\n",
+ "Beyond the McCrary test, several other checks are important.\n",
+ "\n",
+ "**Covariate smoothness test**: run RDD using predetermined covariates (gender, parental income, etc.) as the outcome. If a covariate is discontinuous at the cutoff, it suggests confounding. These checks should show no jump.\n",
+ "\n",
+ "**Placebo cutoff test**: run RDD at a fake cutoff (e.g., 60 or 80 points). There should be no discontinuity at a non-threshold value.\n",
+ "\n",
+ "**Donut hole test**: exclude observations very close to the cutoff (e.g., within ±2 points) and re-run the analysis. If the estimate changes substantially, something suspicious is happening near the cutoff.\n",
+ "\n",
+ "| Test | Purpose | Expected Result |\n",
+ "|------|---------|------------------|\n",
+ "| **McCrary density test** | Check for running variable manipulation | Density continuous at cutoff |\n",
+ "| **Covariate smoothness** | Verify other factors are unrelated to cutoff | Covariate jump = 0 |\n",
+ "| **Placebo cutoff** | No discontinuity at non-threshold values | Effect at fake cutoff = 0 |\n",
+ "| **Donut hole** | Robustness to near-cutoff manipulation | Estimates stable after exclusion |"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a0000020",
+ "metadata": {},
+ "source": [
+ "## References\n",
+ "\n",
+ "- [Causal Inference for the Brave and True (Youtube)](https://www.youtube.com/watch?v=8SIoMJTmO3A)\n",
+ "- Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. *Journal of Econometrics*, 142(2), 615–635.\n",
+ "- McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity design: A density test. *Journal of Econometrics*, 142(2), 698–714.\n",
+ "- Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019). *A Practical Introduction to Regression Discontinuity Designs*. Cambridge University Press."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/book/rdd/01_rdd_basic_ko.ipynb b/book/rdd/01_rdd_basic_ko.ipynb
new file mode 100644
index 0000000..a691883
--- /dev/null
+++ b/book/rdd/01_rdd_basic_ko.ipynb
@@ -0,0 +1,1002 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b0248633",
+ "metadata": {},
+ "source": [
+ "**🌐 Language:** [English →](/rdd-basic-en) | **한국어**\n",
+ "\n",
+ "# Regression Discontinuity Design (RDD)\n",
+ "\n",
+ "작성자 손지우 · GitHub · LinkedIn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "58cf8b6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import statsmodels.formula.api as smf\n",
+ "import warnings\n",
+ "\n",
+ "plt.rcParams['font.family'] = 'AppleGothic'\n",
+ "plt.rcParams['axes.unicode_minus'] = False\n",
+ "\n",
+ "np.random.seed(2026)\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "236f5080",
+ "metadata": {},
+ "source": [
+ "## 1. RDD란 무엇인가?\n",
+ "\n",
+ "수능이 끝나고 결과가 나왔습니다. 장학금 기준은 70점. 69점을 받은 학생과 71점을 받은 학생은 딱 2점 차이밖에 나지 않습니다. 실력 차이라기보다는 그날의 컨디션이나 운이 결과를 갈라놓은 것에 가깝습니다. 그런데 한 명은 장학금을 받고, 다른 한 명은 받지 못합니다.\n",
+ "\n",
+ "이 상황을 거꾸로 생각해봅시다. 장학금이 이후 학업 성취도에 미치는 인과 효과를 알고 싶다면, 가장 이상적인 비교 대상은 누구일까요? 바로 이 두 학생입니다. 기준 점수 바로 위와 아래에 있는 학생들은 장학금 수혜 여부를 제외하면 사실상 동등한 조건에 있습니다.\n",
+ "\n",
+ "이것이 **RDD(Regression Discontinuity Design)**의 핵심 아이디어입니다.\n",
+ "\n",
+ "어떤 특정 기준점(cutoff)을 넘는지 여부로 처치(정책, 혜택 등)가 결정될 때, 그 기준점 바로 근방의 개체들을 비교해 인과 효과를 추정하는 방법입니다. RCT처럼 무작위 배정을 하지 않았지만, 기준점 근방에서는 마치 무작위 배정에 가까운 상황이 만들어집니다.\n",
+ "\n",
+ "현실에서 RDD를 적용할 수 있는 상황은 생각보다 많습니다.\n",
+ "\n",
+ "| 상황 | Running Variable | Cutoff | Treatment |\n",
+ "|------|-----------------|--------|-----------|\n",
+ "| 장학금 기준 점수 | 수능 점수 | 70점 | 장학금 수혜 |\n",
+ "| 복지 수급 자격 | 소득 수준 | 기준 중위소득 50% | 급여 지원 |\n",
+ "| 음주 허용 나이 | 나이 | 만 19세 | 주류 구매 허용 |\n",
+ "| 의약품 보험 적용 | 약가 | 특정 기준 가격 | 보험 급여 적용 |\n",
+ "\n",
+ "이처럼 RDD는 \"명확한 규칙으로 처치가 결정되는\" 상황이라면 어디서든 적용을 고려해볼 수 있습니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8d273a62",
+ "metadata": {},
+ "source": [
+ "## 2. RDD 종류\n",
+ "\n",
+ "RDD는 크게 Sharp Design과 Fuzzy Design으로 나뉩니다.\n",
+ "\n",
+ "* `Sharp Design`: 임계값을 기준으로 처치 여부가 완전히 결정되는 경우\n",
+ "* `Fuzzy Design`: 임계값이 처치 확률에는 영향을 주지만 실제 처치 여부가 완전히 일치하지는 않는 경우\n",
+ "\n",
+ "예를 들어 장학금 기준 점수를 넘으면 반드시 장학금을 받는다면 Sharp, 넘어도 신청하지 않거나 심사에서 탈락할 수 있다면 Fuzzy입니다. 이 장에서는 Sharp RDD를 중심으로 다루며, Fuzzy RDD는 별도 챕터에서 자세히 설명합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "d759fe99",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_rdd_type():\n",
+ " c = 0\n",
+ " R = np.linspace(-3, 3, 300)\n",
+ " \n",
+ " D_sharp = np.where(R >= c, 1.0, 0.0)\n",
+ " \n",
+ " D_fuzzy = np.where(R >= c,\n",
+ " 0.65 + 0.1 * np.tanh(R * 2),\n",
+ " 0.15 + 0.1 * np.tanh(R * 2))\n",
+ " \n",
+ " fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+ " \n",
+ " # ── Sharp RDD ──\n",
+ " ax = axes[0]\n",
+ " ax.plot(R[R < c], D_sharp[R < c], 'tomato', linewidth=3, label=r'$P(D=1|R)$: $R < c$')\n",
+ " ax.plot(R[R >= c], D_sharp[R >= c], 'steelblue', linewidth=3, label=r'$P(D=1|R)$: $R \\geq c$')\n",
+ " ax.axvline(c, color='gray', linestyle=':', linewidth=1.5)\n",
+ " ax.scatter([c], [0], s=80, color='tomato', zorder=5, edgecolors='tomato', facecolors='none', linewidths=2)\n",
+ " ax.scatter([c], [1], s=80, zorder=5)\n",
+ " ax.annotate('', xy=(c, 1.0), xytext=(c, 0.0),\n",
+ " arrowprops=dict(arrowstyle='<->', color='black', lw=2))\n",
+ " ax.text(c + 0.1, 0.5, '0 → 1\\n(perfect jump)', fontsize=11, va='center', fontweight='bold')\n",
+ " ax.text(c + 0.05, -0.07, '$c$', fontsize=12, color='gray')\n",
+ " ax.set_ylim(-0.15, 1.25)\n",
+ " ax.set_title('Sharp RDD', fontsize=13, fontweight='bold')\n",
+ " ax.set_xlabel('Running Variable $R$', fontsize=11)\n",
+ " ax.set_ylabel(r'$P(D=1 \\mid R)$', fontsize=11)\n",
+ " ax.legend(fontsize=9)\n",
+ " ax.grid(alpha=0.3)\n",
+ " \n",
+ " # ── Fuzzy RDD ──\n",
+ " ax = axes[1]\n",
+ " ax.plot(R[R < c], D_fuzzy[R < c], 'tomato', linewidth=3, label=r'$P(D=1|R)$: $R < c$')\n",
+ " ax.plot(R[R >= c], D_fuzzy[R >= c], 'steelblue', linewidth=3, label=r'$P(D=1|R)$: $R \\geq c$')\n",
+ " ax.axvline(c, color='gray', linestyle=':', linewidth=1.5)\n",
+ " \n",
+ " d_l = np.interp(c - 0.009, R, D_fuzzy)\n",
+ " d_r = np.interp(c + 0.009, R, D_fuzzy)\n",
+ " ax.scatter([c], [d_l], s=80, color='tomato', zorder=5, facecolors='none', edgecolors='tomato', linewidths=2)\n",
+ " ax.scatter([c], [d_r], s=80, zorder=5)\n",
+ " ax.annotate('', xy=(c, d_r), xytext=(c, d_l),\n",
+ " arrowprops=dict(arrowstyle='<->', color='black', lw=2))\n",
+ " ax.text(c + 0.1, (d_l + d_r) / 2,\n",
+ " f'{d_l:.2f} → {d_r:.2f}\\n(partial jump)', fontsize=11, va='center', fontweight='bold')\n",
+ " ax.text(c + 0.05, -0.07, '$c$', fontsize=12, color='gray')\n",
+ " ax.set_ylim(-0.15, 1.25)\n",
+ " ax.set_title('Fuzzy RDD', fontsize=13, fontweight='bold')\n",
+ " ax.set_xlabel('Running Variable $R$', fontsize=11)\n",
+ " ax.set_ylabel(r'$P(D=1 \\mid R)$', fontsize=11)\n",
+ " ax.legend(fontsize=9)\n",
+ " ax.grid(alpha=0.3)\n",
+ " \n",
+ " fig.suptitle(r'Sharp vs Fuzzy RDD — $P(D=1 \\mid R)$', fontsize=14, fontweight='bold')\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "94f73d8a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_rdd_type()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10000001",
+ "metadata": {},
+ "source": [
+ "## 3. 핵심 가정\n",
+ "\n",
+ "RDD가 인과 효과를 제대로 추정하려면 중요한 가정 두 가지가 필요합니다.\n",
+ "\n",
+ "### 연속성 가정 (Continuity Assumption)\n",
+ "\n",
+ "첫 번째는 **잠재적 결과의 연속성**입니다. 장학금을 받지 않았을 때의 기대 GPA와, 받았을 때의 기대 GPA 모두 기준 점수 근방에서 매끄럽게 이어져야 한다는 뜻입니다.\n",
+ "\n",
+ "$$\\mathbb{E}[Y_i(0) \\mid X_i = x] \\quad \\text{and} \\quad \\mathbb{E}[Y_i(1) \\mid X_i = x] \\quad \\text{는 } x = c \\text{ 에서 연속}$$\n",
+ "\n",
+ "왜 이게 중요할까요? 만약 이 조건이 충족된다면, 기준 점수 근방에서 관측 결과에 나타나는 **갑작스러운 점프(discontinuity)** 는 오직 처치(장학금) 때문이라고 해석할 수 있습니다.\n",
+ "\n",
+ "반대로 이 조건이 깨지면 어떻게 될까요? 예를 들어 학교에서 70점 전후 학생들만 선별해서 특별 지도를 제공한다면, 그 근방에서 GPA가 점프하는 것이 장학금 때문인지 특별 지도 때문인지 알 수 없게 됩니다.\n",
+ "\n",
+ "### 국소 무작위화 (Local Randomization)\n",
+ "\n",
+ "두 번째는 연속성 가정의 또 다른 표현입니다. 기준 점수 $c$ 아주 근방에서는, 69.5점을 받을지 70.5점을 받을지가 실질적으로 무작위에 가깝다는 것입니다.\n",
+ "\n",
+ "이 논리가 성립하려면 개체가 기준점을 **의도적으로 넘을 수 없어야** 합니다. 만약 선생님이 특정 학생을 70점 이상으로 밀어주거나, 학생 스스로 70점을 겨냥해 점수를 관리한다면, 기준점 근방의 \"준무작위성\"은 사라지게 됩니다. 이것을 **조작(manipulation)** 이라 하며, 타당성 검정 과정에서 반드시 확인해야 합니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20000001",
+ "metadata": {},
+ "source": [
+ "## 4. 구성 요소\n",
+ "\n",
+ "RDD를 이해하려면 몇 가지 핵심 개념을 명확히 해야 합니다.\n",
+ "\n",
+ "**Running Variable (실행 변수)** $X_i$는 처치 여부를 결정하는 연속형 변수입니다. 수능 점수, 나이, 소득 수준처럼 개체마다 값이 다르고, 이 값에 따라 처치를 받는지 여부가 결정됩니다. 중요한 것은 개체가 이 값을 정밀하게 조작할 수 없어야 한다는 점입니다.\n",
+ "\n",
+ "**Cutoff (임계값)** $c$는 처치를 받는 기준입니다. Sharp RDD에서는 $X_i \\geq c$면 반드시 처치를 받고, $X_i < c$면 받지 못합니다.\n",
+ "\n",
+ "$$D_i = \\mathbf{1}[X_i \\geq c]$$\n",
+ "\n",
+ "**잠재적 결과(Potential Outcomes)** 는 앞서 인과추론 챕터에서 다룬 그 개념 그대로입니다.\n",
+ "\n",
+ "- $Y_i(1)$: 장학금을 받았을 때의 GPA\n",
+ "- $Y_i(0)$: 장학금을 받지 않았을 때의 GPA\n",
+ "- 실제 관측값: $Y_i^{obs} = D_i \\cdot Y_i(1) + (1 - D_i) \\cdot Y_i(0)$\n",
+ "\n",
+ "한 학생의 $Y_i(1)$과 $Y_i(0)$을 동시에 볼 수는 없습니다. 장학금을 받은 학생은 $Y_i(1)$만, 받지 못한 학생은 $Y_i(0)$만 관측됩니다.\n",
+ "\n",
+ "아래 표가 이 상황을 정리해줍니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "20000002",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_rdd_regression(beta_0=2.0, beta_1=0.015, beta_2=0.5, beta_3=0.005)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b9ae9c1d",
+ "metadata": {},
+ "source": [
+ "그래프를 볼 때 주목해야 할 것들이 있습니다.\n",
+ "\n",
+ "**기준점 근방을 보세요.** RDD의 인과적 추론은 기준점(70점) 바로 좌우에만 성립합니다. 기준점에서 멀리 떨어진 40점대와 95점대 학생의 GPA가 다른 것은 장학금 효과가 아니라 학생 간 능력 차이일 수 있습니다. 두 집단의 **회귀선이 기준점에서 만나지 않고 뛰어오르는 것**, 그 점프만이 인과적 해석이 가능합니다.\n",
+ "\n",
+ "**기울기가 다를 수 있습니다.** 처치 집단(오른쪽)과 미처치 집단(왼쪽)의 회귀선 기울기가 달라도 됩니다. 점수가 높을수록 GPA가 오르는 경향이 처치 여부에 따라 다를 수 있기 때문입니다. 섹션 5의 회귀식에서 $\\beta_3$ 항이 이 기울기 차이를 처리합니다.\n",
+ "\n",
+ "**점프가 클수록 효과가 크다는 뜻은 아닙니다.** Sharp RDD에서 점프가 곧 처치 효과 추정값이지만, Fuzzy RDD에서는 Y의 점프를 D의 점프로 나눠야 합니다. 이 차이는 다음 챕터에서 다룹니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50000001",
+ "metadata": {},
+ "source": [
+ "## 7. 코드 예시 (Python)\n",
+ "\n",
+ "시뮬레이션을 직접 실행해보겠습니다. 수능 점수 70점을 기준으로 장학금이 주어지고, 장학금의 진짜 효과는 GPA 0.5점이라고 설정합니다. 이 설정 아래에서 RDD가 효과를 제대로 복원해내는지 확인해봅시다.\n",
+ "\n",
+ "핵심 과정은 이렇습니다. 기준점을 중심으로 왼쪽과 오른쪽에 각각 회귀선을 적합하고, 기준점에서의 두 절편 차이를 처치 효과로 추정합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "50000002",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_rdd_type()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe200001",
+ "metadata": {},
+ "source": [
+ "## 1. What is Fuzzy RDD?\n",
+ "\n",
+ "In one sentence, Fuzzy RDD is RDD for situations where the cutoff does not \"force\" treatment.\n",
+ "\n",
+ "For example, suppose a student becomes eligible to **apply** for a scholarship if they score 70 on the exam. Under Sharp RDD, everyone who becomes eligible would receive the scholarship. But in reality:\n",
+ "\n",
+ "- A student who scored 71 and became eligible but missed the application window\n",
+ "- A student who scored 72 but was disqualified for incomplete documents\n",
+ "- A student who scored 75 but did not apply because their family is wealthy\n",
+ "\n",
+ "Conversely, someone scoring 68 might still receive it through a special review or appeals process.\n",
+ "\n",
+ "The cutoff determines **eligibility**, but actual treatment does not necessarily follow from that eligibility. This is the world of Fuzzy RDD.\n",
+ "\n",
+ "In this situation, we must distinguish the key variables:\n",
+ "\n",
+ "- $X_i$: Exam score (Running variable)\n",
+ "- $Z_i = \\mathbf{1}[X_i \\geq 70]$: Scholarship **eligibility** (Instrumental variable)\n",
+ "- $D_i$: Actual scholarship **receipt** (Treatment variable)\n",
+ "- $Y_i$: GPA (Outcome variable)\n",
+ "\n",
+ "In Sharp RDD, $Z_i = D_i$. In Fuzzy RDD, $Z_i \\neq D_i$. $Z_i$ influences $D_i$ but does not fully determine it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe300001",
+ "metadata": {},
+ "source": [
+ "## 2. Estimation Target\n",
+ "\n",
+ "Fuzzy RDD estimates the LATE restricted to **Compliers**.\n",
+ "\n",
+ "Based on the value of the instrumental variable $Z_i$ (eligibility), individuals can be classified into four types."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "fe300002",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
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+ " \n",
+ "
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+ "
\n",
+ "
Type
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+ "
D when Z=0
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+ "
D when Z=1
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+ "
Description
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+ "
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+ " \n",
+ " \n",
+ "
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+ "
0
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+ "
Complier
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+ "
0
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+ "
1
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+ "
Takes treatment only when eligible ← estimatio...
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+ "
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+ "
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+ "
1
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+ "
Always-taker
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+ "
1
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+ "
1
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+ "
Always takes treatment regardless of eligibility
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+ "
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+ "
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+ "
2
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+ "
Never-taker
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+ "
0
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+ "
0
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+ "
Never takes treatment even when eligible
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+ "
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+ "
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+ "
3
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+ "
Defier
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+ "
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+ "
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+ "
Refuses treatment when eligible (excluded by m...
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+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " Type D when Z=0 D when Z=1 \\\n",
+ "0 Complier 0 1 \n",
+ "1 Always-taker 1 1 \n",
+ "2 Never-taker 0 0 \n",
+ "3 Defier 1 0 \n",
+ "\n",
+ " Description \n",
+ "0 Takes treatment only when eligible ← estimatio... \n",
+ "1 Always takes treatment regardless of eligibility \n",
+ "2 Never takes treatment even when eligible \n",
+ "3 Refuses treatment when eligible (excluded by m... "
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.DataFrame({\n",
+ " 'Type': ['Complier', 'Always-taker', 'Never-taker', 'Defier'],\n",
+ " 'D when Z=0': [0, 1, 0, 1],\n",
+ " 'D when Z=1': [1, 1, 0, 0],\n",
+ " 'Description': [\n",
+ " 'Takes treatment only when eligible \\u2190 estimation target',\n",
+ " 'Always takes treatment regardless of eligibility',\n",
+ " 'Never takes treatment even when eligible',\n",
+ " 'Refuses treatment when eligible (excluded by monotonicity assumption)',\n",
+ " ]\n",
+ "})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe300003",
+ "metadata": {},
+ "source": [
+ "Under the Monotonicity assumption, we assume there are no Defiers. The effect estimated near the cutoff is then:\n",
+ "\n",
+ "$$\\tau_{Fuzzy} = \\mathbb{E}[Y_i(1) - Y_i(0) \\mid \\text{Complier},\\; X_i = c]$$\n",
+ "\n",
+ "That is, this is the treatment effect — near the cutoff — for those who received treatment because they became eligible (Compliers).\n",
+ "\n",
+ "**Difference from ATE (Average Treatment Effect)**: Always-takers and Never-takers are not included. We cannot estimate the effect for those who always receive (or never receive) treatment regardless of eligibility. This is both a fundamental limitation of Fuzzy RDD and, at the same time, an honest estimate."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe400001",
+ "metadata": {},
+ "source": [
+ "## 3. Key Assumptions (IV)\n",
+ "\n",
+ "Fuzzy RDD is fundamentally an **Instrumental Variable (IV)** estimation problem.\n",
+ "\n",
+ "We use $Z_i = \\mathbf{1}[X_i \\geq c]$ as the instrumental variable. For this instrument to be valid, three conditions must hold.\n",
+ "\n",
+ "### Relevance\n",
+ "\n",
+ "$Z_i$ must have a meaningful effect on $D_i$. Eligibility must raise the probability of treatment.\n",
+ "\n",
+ "$$\\text{Cov}(Z_i,\\, D_i) \\neq 0$$\n",
+ "\n",
+ "This is verified using the F-statistic from the first-stage regression. Conventionally, $F > 10$ is considered a strong instrument.\n",
+ "\n",
+ "### Exclusion Restriction\n",
+ "\n",
+ "$Z_i$ must affect $Y_i$ only through $D_i$. That is, the mere fact of crossing the 70-point threshold should not directly change GPA. GPA should rise because the scholarship was received, not simply because eligibility was granted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "fe400002",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n",
+ "\n",
+ "pos = {'Z': (0.12, 0.50), 'D': (0.50, 0.50), 'Y': (0.88, 0.50), 'U': (0.50, 0.85)}\n",
+ "node_colors = {'Z': '#AED6F1', 'D': '#A9DFBF', 'Y': '#F9E79F', 'U': '#D7DBDD'}\n",
+ "node_labels = {'Z': 'Z\\n(Eligibility)', 'D': 'D\\n(Treatment)', 'Y': 'Y\\n(Outcome)', 'U': 'U\\n(Confounder)'}\n",
+ "R = 0.09\n",
+ "\n",
+ "def draw_nodes(ax):\n",
+ " for name, (x, y) in pos.items():\n",
+ " circle = plt.Circle((x, y), R, color=node_colors[name], ec='#555', lw=1.8, zorder=3)\n",
+ " ax.add_patch(circle)\n",
+ " ax.text(x, y, node_labels[name], ha='center', va='center', fontsize=11,\n",
+ " fontweight='bold', zorder=4, linespacing=1.3)\n",
+ "\n",
+ "def arrow(ax, a, b, color='#333', lw=1.8, rad=0.0, ls='-'):\n",
+ " x1, y1 = pos[a]; x2, y2 = pos[b]\n",
+ " dx, dy = x2 - x1, y2 - y1\n",
+ " dist = (dx**2 + dy**2) ** 0.5\n",
+ " sx = x1 + R * dx / dist; sy = y1 + R * dy / dist\n",
+ " ex = x2 - R * dx / dist; ey = y2 - R * dy / dist\n",
+ " style = f'arc3,rad={rad}' if rad != 0 else 'arc3,rad=0'\n",
+ " ax.annotate('', xy=(ex, ey), xytext=(sx, sy),\n",
+ " arrowprops=dict(arrowstyle='->', color=color, lw=lw,\n",
+ " connectionstyle=style,\n",
+ " linestyle=ls))\n",
+ "\n",
+ "# ── Left: Exclusion Restriction Violated ──\n",
+ "ax = axes[0]\n",
+ "ax.set_xlim(0, 1); ax.set_ylim(0.1, 1.05); ax.axis('off')\n",
+ "ax.set_title('Exclusion Restriction Violated\\n(Z directly affects Y)', fontsize=13, fontweight='bold', color='#C0392B')\n",
+ "draw_nodes(ax)\n",
+ "arrow(ax, 'Z', 'D')\n",
+ "arrow(ax, 'D', 'Y')\n",
+ "arrow(ax, 'U', 'D', color='#888', lw=1.4)\n",
+ "arrow(ax, 'U', 'Y', color='#888', lw=1.4)\n",
+ "arrow(ax, 'Z', 'Y', color='#E74C3C', lw=2.2, rad=-0.38) # direct path (violation)\n",
+ "# ✗ annotation\n",
+ "mx = (pos['Z'][0] + pos['Y'][0]) / 2\n",
+ "my = min(pos['Z'][1], pos['Y'][1]) - 0.20\n",
+ "ax.text(mx, my, '\\u2717 Direct path exists', color='#E74C3C', fontsize=11,\n",
+ " ha='center', fontweight='bold')\n",
+ "ax.text(mx, my - 0.10, 'Z \\u2192 Y \\u2192 IV invalid', color='#E74C3C', fontsize=10, ha='center')\n",
+ "\n",
+ "# ── Right: Exclusion Restriction Holds ──\n",
+ "ax2 = axes[1]\n",
+ "ax2.set_xlim(0, 1); ax2.set_ylim(0.1, 1.05); ax2.axis('off')\n",
+ "ax2.set_title('Exclusion Restriction Holds\\n(Z affects Y only through D)', fontsize=13, fontweight='bold', color='#1A5276')\n",
+ "draw_nodes(ax2)\n",
+ "arrow(ax2, 'Z', 'D')\n",
+ "arrow(ax2, 'D', 'Y')\n",
+ "arrow(ax2, 'U', 'D', color='#888', lw=1.4)\n",
+ "arrow(ax2, 'U', 'Y', color='#888', lw=1.4)\n",
+ "mx2 = (pos['Z'][0] + pos['Y'][0]) / 2\n",
+ "my2 = min(pos['Z'][1], pos['Y'][1]) - 0.20\n",
+ "ax2.text(mx2, my2, '\\u2713 Unique path: Z \\u2192 D \\u2192 Y', color='#1A5276',\n",
+ " fontsize=11, ha='center', fontweight='bold')\n",
+ "ax2.text(mx2, my2 - 0.10, 'Z affects Y only through D', color='#1A5276',\n",
+ " fontsize=10, ha='center')\n",
+ "\n",
+ "plt.suptitle('Exclusion Restriction', fontsize=14, fontweight='bold', y=1.01)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe400003",
+ "metadata": {},
+ "source": [
+ "\n",
+ "### Monotonicity\n",
+ "\n",
+ "There must be no Defiers. No one should refuse treatment when they become eligible.\n",
+ "\n",
+ "$$D_i(Z=1) \\geq D_i(Z=0) \\quad \\forall i$$\n",
+ "\n",
+ "When these three assumptions hold, the Fuzzy LATE can be expressed cleanly as the **Wald estimator**:\n",
+ "\n",
+ "$$\\hat{\\tau}_{Fuzzy} = \\frac{\\text{ITT}_Y}{\\text{ITT}_D} = \\frac{\\text{Jump in Y}}{\\text{Jump in D}}$$\n",
+ "\n",
+ "$\\text{ITT}_Y$ is the effect of eligibility on the outcome (Reduced form), and $\\text{ITT}_D$ is the effect of eligibility on actual treatment (First stage). Intuitively, dividing \"how much Y jumped\" by \"how much D jumped\" gives the per-unit effect for the Compliers who actually received treatment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe500001",
+ "metadata": {},
+ "source": [
+ "## 4. Understanding Fuzzy RDD with Formulas\n",
+ "\n",
+ "The ultimate goal of Fuzzy RDD is to obtain the LATE via the **Wald estimator**.\n",
+ "\n",
+ "$$\\hat{\\tau}_{Fuzzy} = \\frac{\\widehat{\\text{ITT}}_Y}{\\widehat{\\text{ITT}}_D} = \\frac{\\text{Jump in Y}}{\\text{Jump in D}}$$\n",
+ "\n",
+ "To do this, we must distinguish **three regressions**.\n",
+ "\n",
+ "| Name | Path | Estimation Target | Role |\n",
+ "|------|------|-----------|------|\n",
+ "| **Reduced Form** | $Z_i \\rightarrow Y_i$ | $\\widehat{\\text{ITT}}_Y$ | Wald numerator / pre-check |\n",
+ "| **2SLS First Stage** | $Z_i \\rightarrow D_i$ | $\\widehat{\\text{ITT}}_D$ | Wald denominator / instrument strength |\n",
+ "| **2SLS Second Stage** | $\\hat{D}_i \\rightarrow Y_i$ | $\\hat{\\beta}_1 = \\text{LATE}$ | Final estimate (= Wald equivalent) |\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### Reduced Form: $Z_i \\rightarrow Y_i$\n",
+ "\n",
+ "$$Y_i = \\gamma_0 + \\gamma_1 Z_i + \\gamma_2(X_i - c) + \\gamma_3 Z_i(X_i - c) + \\varepsilon_i$$\n",
+ "\n",
+ "- **Purpose**\n",
+ " - **Pre-check role**: Before running 2SLS, we first verify whether \"the instrument $Z_i$ actually reaches the outcome $Y_i$.\" If $\\hat{\\gamma}_1 \\approx 0$, running 2SLS will also yield a LATE estimate near zero, signaling that the analysis design itself needs to be reconsidered.\n",
+ " - **Wald numerator role**: At the same time, $\\hat{\\gamma}_1$ is used directly as the Wald numerator ($\\widehat{\\text{ITT}}_Y$). Thus, the Reduced Form serves simultaneously as a pre-check and as an ingredient for the final estimate.\n",
+ "- **Key coefficient**: $\\hat{\\gamma}_1 = \\widehat{\\text{ITT}}_Y$ (jump in Y at the cutoff)\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### 2SLS (Two-Stage Least Squares)\n",
+ "\n",
+ "$D_i$ (actual treatment) is an **endogenous variable**. People who received the scholarship may have differed from those who did not from the start (in ability, motivation, etc.), so regressing $Y_i$ directly on $D_i$ produces bias.\n",
+ "\n",
+ "2SLS extracts from $D_i$ only **the part exogenously driven by instrument $Z_i$**, and uses that part to explain $Y_i$.\n",
+ "\n",
+ "#### First Stage: $Z_i \\rightarrow D_i$\n",
+ "\n",
+ "$$D_i = \\alpha_0 + \\alpha_1 Z_i + \\alpha_2(X_i - c) + \\alpha_3 Z_i(X_i - c) + \\varepsilon_i^{(1)}$$\n",
+ "\n",
+ "- **Purpose**: Confirms how much eligibility ($Z_i$) changes actual treatment ($D_i$).\n",
+ "- **Key coefficient**: $\\hat{\\alpha}_1 = \\widehat{\\text{ITT}}_D$ (jump in treatment probability at the cutoff)\n",
+ "- **Diagnostic**: F-statistic $\\geq 10$ indicates a strong instrument; below 10 signals a weak instrument problem.\n",
+ "- **Output**: Fitted values $\\hat{D}_i$ — the exogenous part of treatment explained by the instrument\n",
+ "\n",
+ "#### Second Stage: $\\hat{D}_i \\rightarrow Y_i$\n",
+ "\n",
+ "$$Y_i = \\beta_0 + \\beta_1 \\hat{D}_i + \\beta_2(X_i - c) + \\beta_3 \\hat{D}_i(X_i - c) + \\varepsilon_i^{(2)}$$\n",
+ "\n",
+ "- **Purpose**: By plugging the first-stage fitted values $\\hat{D}_i$ in place of the treatment variable, we estimate the treatment effect with endogeneity removed.\n",
+ "- **Key coefficient**: $\\hat{\\beta}_1$ = **LATE estimator**\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### Final Calculation: Wald Estimator\n",
+ "\n",
+ "The $\\hat{\\beta}_1$ from the second stage of 2SLS is exactly equal to the Wald estimator.\n",
+ "\n",
+ "$$\\hat{\\beta}_1 = \\frac{\\hat{\\gamma}_1}{\\hat{\\alpha}_1} = \\frac{\\widehat{\\text{ITT}}_Y}{\\widehat{\\text{ITT}}_D}$$\n",
+ "\n",
+ "For example, if GPA increases by 0.3 points when eligibility is granted ($\\text{ITT}_Y$) and the treatment probability increases by 50 percentage points ($\\text{ITT}_D$), then the per-Complier effect is $0.3 \\div 0.5 = 0.6$ points.\n",
+ "\n",
+ "> **Note**: Implementing the second stage manually yields the correct point estimate ($\\hat{\\beta}_1$), but **standard errors are underestimated**. In practice, an IV estimation package such as `linearmodels` should be used."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe510001",
+ "metadata": {},
+ "source": [
+ "## 5. Visualization\n",
+ "\n",
+ "We visualize the three stages of Fuzzy RDD estimation on one screen. Before looking at the graphs, make sure you understand what each panel contains.\n",
+ "\n",
+ "- **Left — Reduced Form (Z → Y)**: A scatter plot of exam score (X) and GPA (Y). Regression lines are fitted on each side of the cutoff, showing how much GPA jumps at the cutoff ($\\widehat{\\text{ITT}}_Y$). This serves as a pre-check to confirm that the instrument actually reaches the outcome before running 2SLS.\n",
+ "- **Center — 2SLS First Stage (Z → D)**: A scatter plot of exam score (X) and actual treatment status (D). This shows how much treatment probability jumps at the cutoff ($\\widehat{\\text{ITT}}_D$) and visually confirms the strength of the instrument.\n",
+ "- **Right — 2SLS Second Stage Result**: The LATE estimated by 2SLS and its 95% confidence interval. The diamond (◆) is the point estimate, the horizontal line is the confidence interval, and the green dashed line is the True LATE set in the simulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "fe500002",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n = 1000\n",
+ "cutoff = 70\n",
+ "true_late = 0.6\n",
+ "\n",
+ "score = np.random.uniform(40, 100, n)\n",
+ "elig = (score >= cutoff).astype(int)\n",
+ "treated = np.where(elig == 1,\n",
+ " np.random.binomial(1, 0.65, n),\n",
+ " np.random.binomial(1, 0.15, n))\n",
+ "gpa = (2.0 + 0.012*(score - cutoff)\n",
+ " + true_late * treated\n",
+ " + np.random.normal(0, 0.3, n))\n",
+ "\n",
+ "df_viz = pd.DataFrame({'score': score, 'elig': elig,\n",
+ " 'treated': treated, 'gpa': gpa,\n",
+ " 'score_c': score - cutoff})\n",
+ "\n",
+ "# First stage → D_hat\n",
+ "fs_viz = smf.ols('treated ~ elig + score_c + elig:score_c', data=df_viz).fit()\n",
+ "df_viz['D_hat'] = fs_viz.fittedvalues\n",
+ "\n",
+ "# Second stage → LATE\n",
+ "ss_viz = smf.ols('gpa ~ D_hat + score_c + D_hat:score_c', data=df_viz).fit()\n",
+ "LATE = ss_viz.params['D_hat']\n",
+ "LATE_se = ss_viz.bse['D_hat']\n",
+ "LATE_ci = ss_viz.conf_int().loc['D_hat'].values # [lower, upper]\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
+ "left = df_viz[df_viz['elig'] == 0]\n",
+ "right = df_viz[df_viz['elig'] == 1]\n",
+ "xs_l = np.linspace(40, cutoff, 100)\n",
+ "xs_r = np.linspace(cutoff, 100, 100)\n",
+ "\n",
+ "ACCENT = 'steelblue'\n",
+ "GREEN = '#27ae60'\n",
+ "GRAY = '#555555'\n",
+ "\n",
+ "# ── Left: Reduced Form (Z → Y) ──\n",
+ "ax = axes[0]\n",
+ "ax.scatter(left['score'], left['gpa'], color='tomato', alpha=0.2, s=10, label='Ineligible (Z=0)')\n",
+ "ax.scatter(right['score'], right['gpa'], color='steelblue', alpha=0.2, s=10, label='Eligible (Z=1)')\n",
+ "m_y_l = smf.ols('gpa ~ score_c', data=left).fit()\n",
+ "m_y_r = smf.ols('gpa ~ score_c', data=right).fit()\n",
+ "ax.plot(xs_l, m_y_l.predict(pd.DataFrame({'score_c': xs_l - cutoff})), color='tomato', lw=2.5)\n",
+ "ax.plot(xs_r, m_y_r.predict(pd.DataFrame({'score_c': xs_r - cutoff})), color='steelblue', lw=2.5)\n",
+ "y_l_gpa = m_y_l.params['Intercept']\n",
+ "y_r_gpa = m_y_r.params['Intercept']\n",
+ "ax.scatter([cutoff], [y_l_gpa], s=90, color='tomato', zorder=6, facecolors='none', edgecolors='tomato', linewidths=2)\n",
+ "ax.scatter([cutoff], [y_r_gpa], s=90, color='steelblue', zorder=6)\n",
+ "ax.annotate('', xy=(cutoff, y_r_gpa), xytext=(cutoff, y_l_gpa),\n",
+ " arrowprops=dict(arrowstyle='<->', color='black', lw=2))\n",
+ "ax.text(cutoff + 1, (y_l_gpa + y_r_gpa) / 2,\n",
+ " f'ITT_Y\\n= {y_r_gpa - y_l_gpa:.3f}', fontsize=10, fontweight='bold', va='center')\n",
+ "ax.axvline(cutoff, color='gray', ls=':', lw=1.5)\n",
+ "ax.set_title('Reduced Form\\n(Z \\u2192 Y)', fontsize=13, fontweight='bold')\n",
+ "ax.set_xlabel('Exam Score (X)', fontsize=11)\n",
+ "ax.set_ylabel('GPA (Y)', fontsize=11)\n",
+ "ax.legend(fontsize=9)\n",
+ "\n",
+ "# ── Center: 2SLS First Stage (Z → D) ──\n",
+ "ax2 = axes[1]\n",
+ "ax2.scatter(left['score'], left['treated'] + np.random.normal(0, 0.012, len(left)),\n",
+ " color='tomato', alpha=0.2, s=10)\n",
+ "ax2.scatter(right['score'], right['treated'] + np.random.normal(0, 0.012, len(right)),\n",
+ " color='steelblue', alpha=0.2, s=10)\n",
+ "m_d_l = smf.ols('treated ~ score_c', data=left).fit()\n",
+ "m_d_r = smf.ols('treated ~ score_c', data=right).fit()\n",
+ "ax2.plot(xs_l, m_d_l.predict(pd.DataFrame({'score_c': xs_l - cutoff})), color='tomato', lw=2.5)\n",
+ "ax2.plot(xs_r, m_d_r.predict(pd.DataFrame({'score_c': xs_r - cutoff})), color='steelblue', lw=2.5)\n",
+ "y_l_d = m_d_l.params['Intercept']\n",
+ "y_r_d = m_d_r.params['Intercept']\n",
+ "ax2.scatter([cutoff], [y_l_d], s=90, color='tomato', zorder=6, facecolors='none', edgecolors='tomato', linewidths=2)\n",
+ "ax2.scatter([cutoff], [y_r_d], s=90, color='steelblue', zorder=6)\n",
+ "ax2.annotate('', xy=(cutoff, y_r_d), xytext=(cutoff, y_l_d),\n",
+ " arrowprops=dict(arrowstyle='<->', color='black', lw=2))\n",
+ "ax2.text(cutoff + 1, (y_l_d + y_r_d) / 2,\n",
+ " f'ITT_D\\n= {y_r_d - y_l_d:.3f}', fontsize=10, fontweight='bold', va='center')\n",
+ "ax2.axvline(cutoff, color='gray', ls=':', lw=1.5)\n",
+ "ax2.set_title('2SLS First Stage\\n(Z \\u2192 D)', fontsize=13, fontweight='bold')\n",
+ "ax2.set_xlabel('Exam Score (X)', fontsize=11)\n",
+ "ax2.set_ylabel('Treatment (D)', fontsize=11)\n",
+ "\n",
+ "# ── Right: Forest Plot (LATE + 95% CI) ──\n",
+ "ax3 = axes[2]\n",
+ "y_pos = 0\n",
+ "ax3.barh(y_pos, LATE_ci[1] - LATE_ci[0], left=LATE_ci[0],\n",
+ " height=0.25, color=ACCENT, alpha=0.25, zorder=2) # CI shading\n",
+ "ax3.hlines(y_pos, LATE_ci[0], LATE_ci[1],\n",
+ " colors=ACCENT, lw=2.5, zorder=3) # CI horizontal line\n",
+ "ax3.vlines([LATE_ci[0], LATE_ci[1]], y_pos - 0.07, y_pos + 0.07,\n",
+ " colors=ACCENT, lw=2.5, zorder=3) # end caps\n",
+ "ax3.scatter([LATE], [y_pos], color='white', s=120,\n",
+ " marker='D', zorder=5, edgecolors=ACCENT, linewidths=1.8) # point estimate (diamond)\n",
+ "ax3.axvline(true_late, color=GREEN, lw=1.5, ls=':',\n",
+ " label=f'True LATE = {true_late}', zorder=1) # True effect vertical line\n",
+ "ax3.axvline(0, color=GRAY, lw=1, ls='--', alpha=0.5, zorder=1) # zero reference line\n",
+ "ax3.text(LATE, y_pos + 0.18, f'{LATE:.3f}',\n",
+ " ha='center', color=ACCENT, fontsize=13, fontweight='bold')\n",
+ "ax3.text(LATE, y_pos - 0.22,\n",
+ " f'95% CI: [{LATE_ci[0]:.3f}, {LATE_ci[1]:.3f}]',\n",
+ " ha='center', color=GRAY, fontsize=9)\n",
+ "ax3.text(LATE, y_pos - 0.35, f'SE = {LATE_se:.3f}',\n",
+ " ha='center', color=GRAY, fontsize=9)\n",
+ "\n",
+ "ax3.set_xlim(min(LATE_ci[0] - 0.3, -0.1), max(LATE_ci[1] + 0.3, true_late + 0.2))\n",
+ "ax3.set_ylim(-0.6, 0.6)\n",
+ "ax3.set_yticks([0])\n",
+ "ax3.set_title('2SLS Second Stage Result\\n(LATE estimator)', fontsize=13, fontweight='bold')\n",
+ "ax3.set_xlabel('Effect on GPA', fontsize=11)\n",
+ "ax3.set_ylabel('LATE (2SLS)', fontsize=10)\n",
+ "ax3.legend(fontsize=9, loc='upper left')\n",
+ "ax3.spines[['top', 'right', 'left']].set_visible(False)\n",
+ "\n",
+ "plt.suptitle(\n",
+ " f'Reduced Form (ITT_Y = {y_r_gpa - y_l_gpa:.3f}) \\u2192 '\n",
+ " f'First Stage (ITT_D = {y_r_d - y_l_d:.3f}) \\u2192 '\n",
+ " f'LATE = {LATE:.3f}',\n",
+ " fontsize=13, fontweight='bold'\n",
+ ")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe510002",
+ "metadata": {},
+ "source": [
+ "Here are the key points to check when reading the graphs.\n",
+ "\n",
+ "**Left and center: Compare the size of the jumps.** The Y jump in the Reduced Form ($\\widehat{\\text{ITT}}_Y$) should be smaller than the D jump in the First Stage ($\\widehat{\\text{ITT}}_D$) in the center panel. In Fuzzy RDD, only a fraction of those who become eligible actually receive treatment. If the Y jump is larger than the D jump, that is a signal of a problem with the design or the data.\n",
+ "\n",
+ "**Right: Check whether the confidence interval includes zero.** If the confidence interval includes zero, we fail to reject the hypothesis that \"there is no treatment effect.\" Also check how close the True LATE (green dashed line) is to the estimate (◆). The larger the sample, the closer the estimate converges to the True LATE.\n",
+ "\n",
+ "**Verify by computing the Wald estimator directly.** Dividing $\\widehat{\\text{ITT}}_Y$ from the left graph by $\\widehat{\\text{ITT}}_D$ from the center graph yields the same value as the LATE in the Forest Plot. This relationship is also summarized in the overall title."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe600001",
+ "metadata": {},
+ "source": [
+ "## 6. Examples\n",
+ "\n",
+ "### Example 1: Scholarship Eligibility\n",
+ "\n",
+ "| Item | Content |\n",
+ "|------|------|\n",
+ "| **Running Variable** | Exam score |\n",
+ "| **Cutoff** | 70 points |\n",
+ "| **Z (Eligibility)** | Score ≥ 70 → eligible to apply for scholarship |\n",
+ "| **D (Treatment)** | Actual scholarship receipt |\n",
+ "| **Y (Outcome)** | GPA |\n",
+ "\n",
+ "Crossing 70 points grants application eligibility, but students may not apply or may be disqualified for missing documents. Conversely, some may receive the scholarship through a special review even below the cutoff. Since D does not fully follow Z, this is a Fuzzy design.\n",
+ "\n",
+ "Wald estimator example:\n",
+ "\n",
+ "| | Below cutoff (Z=0) | Above cutoff (Z=1) |\n",
+ "|---|---|---|\n",
+ "| Actual receipt rate (ITT_D) | 10% | 65% |\n",
+ "| Average GPA (ITT_Y) | 2.0 | 2.3 |\n",
+ "\n",
+ "$$\\hat{\\tau}_{Fuzzy} = \\frac{2.3 - 2.0}{0.65 - 0.10} = \\frac{0.3}{0.55} \\approx 0.55$$\n",
+ "\n",
+ "→ The effect on GPA for students who received the scholarship because they became eligible (Compliers) is approximately 0.55 points.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### Example 2: Health Insurance Eligibility (Medicare at 65)\n",
+ "\n",
+ "| Item | Content |\n",
+ "|------|------|\n",
+ "| **Running Variable** | Age |\n",
+ "| **Cutoff** | Age 65 |\n",
+ "| **Z (Eligibility)** | Age ≥ 65 → eligible to enroll in Medicare |\n",
+ "| **D (Treatment)** | Actual Medicare enrollment |\n",
+ "| **Y (Outcome)** | Healthcare utilization, mortality, etc. |\n",
+ "\n",
+ "Turning 65 grants Medicare eligibility, but not everyone enrolls immediately. Those with employer-sponsored insurance or low-income individuals already on Medicaid may delay enrollment. Card et al. (2008) used this design to estimate the causal effect of Medicare enrollment on healthcare utilization and mortality.\n",
+ "\n",
+ "In this way, Fuzzy RDD naturally arises whenever \"eligibility\" and \"actual receipt\" are separated."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe700001",
+ "metadata": {},
+ "source": [
+ "## 7. Simulation Code\n",
+ "\n",
+ "Let us run a simulation directly. We set up a scenario where scholarship application eligibility is granted at an exam score of 70, with a **compliance rate** of 50%.\n",
+ "\n",
+ "- Treatment rate among the ineligible group: 10% (Always-takers)\n",
+ "- Treatment rate among the eligible group: 60% (Always-takers + Compliers)\n",
+ "- Complier proportion: 60% - 10% = 50%\n",
+ "- True treatment effect (True LATE): GPA +0.5 points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "fe700002",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total sample: 1000\n",
+ "Ineligible (Z=0) - treatment rate: 11.1%\n",
+ "Eligible (Z=1) - treatment rate: 56.9%\n",
+ "Treatment probability jump (estimated compliance rate): 45.8%\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "