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Created May 24, 2021 18:07
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two-stage did post draft
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index.html
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang xml:lang>
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta http-equiv="X-UA-Compatible" content="IE=Edge,chrome=1" />
<meta name="generator" content="distill" />
<style type="text/css">
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visibility: hidden;
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<title>Two-Stage Difference-in-Differences</title>
<meta property="description" itemprop="description" content="Introducing a R package to implement two-stage difference-in-differences following [@Gardner_2021]" />
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<meta property="article:published" itemprop="datePublished" content="2021-05-24" />
<meta property="article:created" itemprop="dateCreated" content="2021-05-24" />
<meta name="article:author" content="Kyle Butts" />
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<meta name="citation_reference" content="citation_title=Revisiting event study designs: Robust and efficient estimation;citation_publication_date=2021;citation_author=Kirill Borusyak;citation_author=Xavier Jaravel;citation_author=Jann Spiess" />
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};
}
var PagedTable = function (pagedTable) {
var me = this;
var source = function(pagedTable) {
var sourceElems = [].slice.call(pagedTable.children).filter(function(e) {
return e.hasAttribute("data-pagedtable-source");
});
if (sourceElems === null || sourceElems.length !== 1) {
throw("A single data-pagedtable-source was not found");
}
return JSON.parse(sourceElems[0].innerHTML);
}(pagedTable);
var options = function(source) {
var options = typeof(source.options) !== "undefined" &&
source.options !== null ? source.options : {};
var columns = typeof(options.columns) !== "undefined" ? options.columns : {};
var rows = typeof(options.rows) !== "undefined" ? options.rows : {};
var positiveIntOrNull = function(value) {
return parseInt(value) >= 0 ? parseInt(value) : null;
};
return {
pages: positiveIntOrNull(options.pages),
rows: {
min: positiveIntOrNull(rows.min),
max: positiveIntOrNull(rows.max),
total: positiveIntOrNull(rows.total)
},
columns: {
min: positiveIntOrNull(columns.min),
max: positiveIntOrNull(columns.max),
total: positiveIntOrNull(columns.total)
}
};
}(source);
var Measurer = function() {
// set some default initial values that will get adjusted in runtime
me.measures = {
padding: 12,
character: 8,
height: 15,
defaults: true
};
me.calculate = function(measuresCell) {
if (!me.measures.defaults)
return;
var measuresCellStyle = window.getComputedStyle(measuresCell, null);
var newPadding = parsePadding(measuresCellStyle.paddingLeft) +
parsePadding(measuresCellStyle.paddingRight);
var sampleString = "ABCDEFGHIJ0123456789";
var newCharacter = Math.ceil(measuresCell.clientWidth / sampleString.length);
if (newPadding <= 0 || newCharacter <= 0)
return;
me.measures.padding = newPadding;
me.measures.character = newCharacter;
me.measures.height = measuresCell.clientHeight;
me.measures.defaults = false;
};
return me;
};
var Page = function(data, options) {
var me = this;
var defaults = {
max: 7,
rows: 10
};
var totalPages = function() {
return Math.ceil(data.length / me.rows);
};
me.number = 0;
me.max = options.pages !== null ? options.pages : defaults.max;
me.visible = me.max;
me.rows = options.rows.min !== null ? options.rows.min : defaults.rows;
me.total = totalPages();
me.setRows = function(newRows) {
me.rows = newRows;
me.total = totalPages();
};
me.setPageNumber = function(newPageNumber) {
if (newPageNumber < 0) newPageNumber = 0;
if (newPageNumber >= me.total) newPageNumber = me.total - 1;
me.number = newPageNumber;
};
me.setVisiblePages = function(visiblePages) {
me.visible = Math.min(me.max, visiblePages);
me.setPageNumber(me.number);
};
me.getVisiblePageRange = function() {
var start = me.number - Math.max(Math.floor((me.visible - 1) / 2), 0);
var end = me.number + Math.floor(me.visible / 2) + 1;
var pageCount = me.total;
if (start < 0) {
var diffToStart = 0 - start;
start += diffToStart;
end += diffToStart;
}
if (end > pageCount) {
var diffToEnd = end - pageCount;
start -= diffToEnd;
end -= diffToEnd;
}
start = start < 0 ? 0 : start;
end = end >= pageCount ? pageCount : end;
var first = false;
var last = false;
if (start > 0 && me.visible > 1) {
start = start + 1;
first = true;
}
if (end < pageCount && me.visible > 2) {
end = end - 1;
last = true;
}
return {
first: first,
start: start,
end: end,
last: last
};
};
me.getRowStart = function() {
var rowStart = page.number * page.rows;
if (rowStart < 0)
rowStart = 0;
return rowStart;
};
me.getRowEnd = function() {
var rowStart = me.getRowStart();
return Math.min(rowStart + me.rows, data.length);
};
me.getPaddingRows = function() {
var rowStart = me.getRowStart();
var rowEnd = me.getRowEnd();
return data.length > me.rows ? me.rows - (rowEnd - rowStart) : 0;
};
};
var Columns = function(data, columns, options) {
var me = this;
me.defaults = {
min: 5
};
me.number = 0;
me.visible = 0;
me.total = columns.length;
me.subset = [];
me.padding = 0;
me.min = options.columns.min !== null ? options.columns.min : me.defaults.min;
me.max = options.columns.max !== null ? options.columns.max : null;
me.widths = {};
var widthsLookAhead = Math.max(100, options.rows.min);
var paddingColChars = 10;
me.emptyNames = function() {
columns.forEach(function(column) {
if (columns.label !== null && columns.label !== "")
return false;
});
return true;
};
var parsePadding = function(value) {
return parseInt(value) >= 0 ? parseInt(value) : 0;
};
me.calculateWidths = function(measures) {
columns.forEach(function(column) {
var maxChars = Math.max(
column.label.toString().length,
column.type.toString().length
);
for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) {
maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length);
}
me.widths[column.name] = {
// width in characters
chars: maxChars,
// width for the inner html columns
inner: maxChars * measures.character,
// width adding outer styles like padding
outer: maxChars * measures.character + measures.padding
};
});
};
me.getWidth = function() {
var widthOuter = 0;
for (var idxCol = 0; idxCol < me.subset.length; idxCol++) {
var columnName = me.subset[idxCol].name;
widthOuter = widthOuter + me.widths[columnName].outer;
}
widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character;
if (me.hasMoreLeftColumns()) {
widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding;
}
if (me.hasMoreRightColumns()) {
widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding;
}
return widthOuter;
};
me.updateSlice = function() {
if (me.number + me.visible >= me.total)
me.number = me.total - me.visible;
if (me.number < 0) me.number = 0;
me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total));
me.subset = me.subset.map(function(column) {
Object.keys(column).forEach(function(colKey) {
column[colKey] = column[colKey] === null ? "" : column[colKey].toString();
});
column.width = null;
return column;
});
};
me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) {
me.number = columnNumber;
me.visible = newVisibleColumns;
me.padding = paddingCount;
me.updateSlice();
};
me.incColumnNumber = function(increment) {
me.number = me.number + increment;
};
me.setColumnNumber = function(newNumber) {
me.number = newNumber;
};
me.setPaddingCount = function(newPadding) {
me.padding = newPadding;
};
me.getPaddingCount = function() {
return me.padding;
};
me.hasMoreLeftColumns = function() {
return me.number > 0;
};
me.hasMoreRightColumns = function() {
return me.number + me.visible < me.total;
};
me.updateSlice(0);
return me;
};
var data = source.data;
var page = new Page(data, options);
var measurer = new Measurer(data, options);
var columns = new Columns(data, source.columns, options);
var table = null;
var tableDiv = null;
var header = null;
var footer = null;
var tbody = null;
// Caches pagedTable.clientWidth, specially for webkit
var cachedPagedTableClientWidth = null;
var onChangeCallbacks = [];
var clearSelection = function() {
if(document.selection && document.selection.empty) {
document.selection.empty();
} else if(window.getSelection) {
var sel = window.getSelection();
sel.removeAllRanges();
}
};
var columnNavigationWidthPX = 5;
var renderColumnNavigation = function(increment, backwards) {
var arrow = document.createElement("div");
arrow.setAttribute("style",
"border-top: " + columnNavigationWidthPX + "px solid transparent;" +
"border-bottom: " + columnNavigationWidthPX + "px solid transparent;" +
"border-" + (backwards ? "right" : "left") + ": " + columnNavigationWidthPX + "px solid;");
var header = document.createElement("th");
header.appendChild(arrow);
header.setAttribute("style",
"cursor: pointer;" +
"vertical-align: middle;" +
"min-width: " + columnNavigationWidthPX + "px;" +
"width: " + columnNavigationWidthPX + "px;");
header.onclick = function() {
columns.incColumnNumber(backwards ? -1 : increment);
me.animateColumns(backwards);
renderFooter();
clearSelection();
triggerOnChange();
};
return header;
};
var maxColumnWidth = function(width) {
var padding = 80;
var columnMax = Math.max(cachedPagedTableClientWidth - padding, 0);
return parseInt(width) > 0 ?
Math.min(columnMax, parseInt(width)) + "px" :
columnMax + "px";
};
var clearHeader = function() {
var thead = pagedTable.querySelectorAll("thead")[0];
thead.innerHTML = "";
};
var renderHeader = function(clear) {
cachedPagedTableClientWidth = pagedTable.clientWidth;
var fragment = document.createDocumentFragment();
header = document.createElement("tr");
fragment.appendChild(header);
if (columns.number > 0)
header.appendChild(renderColumnNavigation(-columns.visible, true));
columns.subset = columns.subset.map(function(columnData) {
var column = document.createElement("th");
column.setAttribute("align", columnData.align);
column.style.textAlign = columnData.align;
column.style.maxWidth = maxColumnWidth(null);
if (columnData.width) {
column.style.minWidth =
column.style.maxWidth = maxColumnWidth(columnData.width);
}
var columnName = document.createElement("div");
columnName.setAttribute("class", "pagedtable-header-name");
if (columnData.label === "") {
columnName.innerHTML = "&nbsp;";
}
else {
columnName.appendChild(document.createTextNode(columnData.label));
}
column.appendChild(columnName);
var columnType = document.createElement("div");
columnType.setAttribute("class", "pagedtable-header-type");
if (columnData.type === "") {
columnType.innerHTML = "&nbsp;";
}
else {
columnType.appendChild(document.createTextNode("<" + columnData.type + ">"));
}
column.appendChild(columnType);
header.appendChild(column);
columnData.element = column;
return columnData;
});
for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
var paddingCol = document.createElement("th");
paddingCol.setAttribute("class", "pagedtable-padding-col");
header.appendChild(paddingCol);
}
if (columns.number + columns.visible < columns.total)
header.appendChild(renderColumnNavigation(columns.visible, false));
if (typeof(clear) == "undefined" || clear) clearHeader();
var thead = pagedTable.querySelectorAll("thead")[0];
thead.appendChild(fragment);
};
me.animateColumns = function(backwards) {
var thead = pagedTable.querySelectorAll("thead")[0];
var headerOld = thead.querySelectorAll("tr")[0];
var tbodyOld = table.querySelectorAll("tbody")[0];
me.fitColumns(backwards);
renderHeader(false);
header.style.opacity = "0";
header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)";
header.style.transition = "transform 200ms linear, opacity 200ms";
header.style.transitionDelay = "0";
renderBody(false);
if (headerOld) {
headerOld.style.position = "absolute";
headerOld.style.transform = "translateX(0px)";
headerOld.style.opacity = "1";
headerOld.style.transition = "transform 100ms linear, opacity 100ms";
headerOld.setAttribute("class", "pagedtable-remove-head");
if (headerOld.style.transitionEnd) {
headerOld.addEventListener("transitionend", function() {
var headerOldByClass = thead.querySelector(".pagedtable-remove-head");
if (headerOldByClass) thead.removeChild(headerOldByClass);
});
}
else {
thead.removeChild(headerOld);
}
}
if (tbodyOld) table.removeChild(tbodyOld);
tbody.style.opacity = "0";
tbody.style.transition = "transform 200ms linear, opacity 200ms";
tbody.style.transitionDelay = "0ms";
// force relayout
window.getComputedStyle(header).opacity;
window.getComputedStyle(tbody).opacity;
if (headerOld) {
headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)";
headerOld.style.opacity = "0";
}
header.style.transform = "translateX(0px)";
header.style.opacity = "1";
tbody.style.opacity = "1";
}
me.onChange = function(callback) {
onChangeCallbacks.push(callback);
};
var triggerOnChange = function() {
onChangeCallbacks.forEach(function(onChange) {
onChange();
});
};
var clearBody = function() {
if (tbody) {
table.removeChild(tbody);
tbody = null;
}
};
var renderBody = function(clear) {
cachedPagedTableClientWidth = pagedTable.clientWidth
var fragment = document.createDocumentFragment();
var pageData = data.slice(page.getRowStart(), page.getRowEnd());
pageData.forEach(function(dataRow, idxRow) {
var htmlRow = document.createElement("tr");
htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd");
if (columns.hasMoreLeftColumns())
htmlRow.appendChild(document.createElement("td"));
columns.subset.forEach(function(columnData) {
var cellName = columnData.name;
var dataCell = dataRow[cellName];
var htmlCell = document.createElement("td");
if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell");
if (dataCell === "__NA__") dataCell = "NA";
var cellText = document.createTextNode(dataCell);
htmlCell.appendChild(cellText);
if (dataCell.length > 50) {
htmlCell.setAttribute("title", dataCell);
}
htmlCell.setAttribute("align", columnData.align);
htmlCell.style.textAlign = columnData.align;
htmlCell.style.maxWidth = maxColumnWidth(null);
if (columnData.width) {
htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width);
}
htmlRow.appendChild(htmlCell);
});
for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
var paddingCol = document.createElement("td");
paddingCol.setAttribute("class", "pagedtable-padding-col");
htmlRow.appendChild(paddingCol);
}
if (columns.hasMoreRightColumns())
htmlRow.appendChild(document.createElement("td"));
fragment.appendChild(htmlRow);
});
for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) {
var paddingRow = document.createElement("tr");
var paddingCellRow = document.createElement("td");
paddingCellRow.innerHTML = "&nbsp;";
paddingCellRow.setAttribute("colspan", "100%");
paddingRow.appendChild(paddingCellRow);
fragment.appendChild(paddingRow);
}
if (typeof(clear) == "undefined" || clear) clearBody();
tbody = document.createElement("tbody");
tbody.appendChild(fragment);
table.appendChild(tbody);
};
var getLabelInfo = function() {
var pageStart = page.getRowStart();
var pageEnd = page.getRowEnd();
var totalRows = data.length;
var totalRowsLabel = options.rows.total ? options.rows.total : totalRows;
var totalRowsLabelFormat = totalRowsLabel.toString().replace(/(\d)(?=(\d\d\d)+(?!\d))/g, '$1,');
var infoText = (pageStart + 1) + "-" + pageEnd + " of " + totalRowsLabelFormat + " rows";
if (totalRows < page.rows) {
infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : "");
}
if (columns.total > columns.visible) {
var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total;
infoText = infoText + " | " + (columns.number + 1) + "-" +
(Math.min(columns.number + columns.visible, columns.total)) +
" of " + totalColumnsLabel + " columns";
}
return infoText;
};
var clearFooter = function() {
footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0];
footer.innerHTML = "";
return footer;
};
var createPageLink = function(idxPage) {
var pageLink = document.createElement("a");
pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index";
pageLink.setAttribute("class", pageLinkClass);
pageLink.setAttribute("data-page-index", idxPage);
pageLink.onclick = function() {
page.setPageNumber(parseInt(this.getAttribute("data-page-index")));
renderBody();
renderFooter();
triggerOnChange();
};
pageLink.appendChild(document.createTextNode(idxPage + 1));
return pageLink;
}
var renderFooter = function() {
footer = clearFooter();
var next = document.createElement("a");
next.appendChild(document.createTextNode("Next"));
next.onclick = function() {
page.setPageNumber(page.number + 1);
renderBody();
renderFooter();
triggerOnChange();
};
if (data.length > page.rows) footer.appendChild(next);
var pageNumbers = document.createElement("div");
pageNumbers.setAttribute("class", "pagedtable-indexes");
var pageRange = page.getVisiblePageRange();
if (pageRange.first) {
var pageLink = createPageLink(0);
pageNumbers.appendChild(pageLink);
var pageSeparator = document.createElement("div");
pageSeparator.setAttribute("class", "pagedtable-index-separator-left");
pageSeparator.appendChild(document.createTextNode("..."))
pageNumbers.appendChild(pageSeparator);
}
for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) {
var pageLink = createPageLink(idxPage);
pageNumbers.appendChild(pageLink);
}
if (pageRange.last) {
var pageSeparator = document.createElement("div");
pageSeparator.setAttribute("class", "pagedtable-index-separator-right");
pageSeparator.appendChild(document.createTextNode("..."))
pageNumbers.appendChild(pageSeparator);
var pageLink = createPageLink(page.total - 1);
pageNumbers.appendChild(pageLink);
}
if (data.length > page.rows) footer.appendChild(pageNumbers);
var previous = document.createElement("a");
previous.appendChild(document.createTextNode("Previous"));
previous.onclick = function() {
page.setPageNumber(page.number - 1);
renderBody();
renderFooter();
triggerOnChange();
};
if (data.length > page.rows) footer.appendChild(previous);
var infoLabel = document.createElement("div");
infoLabel.setAttribute("class", "pagedtable-info");
infoLabel.setAttribute("title", getLabelInfo());
infoLabel.appendChild(document.createTextNode(getLabelInfo()));
footer.appendChild(infoLabel);
var enabledClass = "pagedtable-index-nav";
var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled";
previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass);
next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass);
};
var measuresCell = null;
var renderMeasures = function() {
var measuresTable = document.createElement("table");
measuresTable.style.visibility = "hidden";
measuresTable.style.position = "absolute";
measuresTable.style.whiteSpace = "nowrap";
measuresTable.style.height = "auto";
measuresTable.style.width = "auto";
var measuresRow = document.createElement("tr");
measuresTable.appendChild(measuresRow);
measuresCell = document.createElement("td");
var sampleString = "ABCDEFGHIJ0123456789";
measuresCell.appendChild(document.createTextNode(sampleString));
measuresRow.appendChild(measuresCell);
tableDiv.appendChild(measuresTable);
}
me.init = function() {
tableDiv = document.createElement("div");
pagedTable.appendChild(tableDiv);
var pagedTableClass = data.length > 0 ?
"pagedtable pagedtable-not-empty" :
"pagedtable pagedtable-empty";
if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) {
pagedTableClass = pagedTableClass + " pagedtable-empty-columns";
}
tableDiv.setAttribute("class", pagedTableClass);
renderMeasures();
measurer.calculate(measuresCell);
columns.calculateWidths(measurer.measures);
table = document.createElement("table");
table.setAttribute("cellspacing", "0");
table.setAttribute("class", "table table-condensed");
tableDiv.appendChild(table);
table.appendChild(document.createElement("thead"));
var footerDiv = document.createElement("div");
footerDiv.setAttribute("class", "pagedtable-footer");
tableDiv.appendChild(footerDiv);
// if the host has not yet provided horizontal space, render hidden
if (tableDiv.clientWidth <= 0) {
tableDiv.style.opacity = "0";
}
me.render();
// retry seizing columns later if the host has not provided space
function retryFit() {
if (tableDiv.clientWidth <= 0) {
setTimeout(retryFit, 100);
} else {
me.render();
triggerOnChange();
}
}
if (tableDiv.clientWidth <= 0) {
retryFit();
}
};
var registerWidths = function() {
columns.subset = columns.subset.map(function(column) {
column.width = columns.widths[column.name].inner;
return column;
});
};
var parsePadding = function(value) {
return parseInt(value) >= 0 ? parseInt(value) : 0;
};
me.fixedHeight = function() {
return options.rows.max != null;
}
me.fitRows = function() {
if (me.fixedHeight())
return;
measurer.calculate(measuresCell);
var rows = options.rows.min !== null ? options.rows.min : 0;
var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0;
var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0;
if (pagedTable.offsetHeight > 0) {
var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight;
rows = Math.floor((availableHeight) / measurer.measures.height);
}
rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows;
page.setRows(rows);
}
// The goal of this function is to add as many columns as possible
// starting from left-to-right, when the right most limit is reached
// it tries to add columns from the left as well.
//
// When startBackwards is true columns are added from right-to-left
me.fitColumns = function(startBackwards) {
measurer.calculate(measuresCell);
columns.calculateWidths(measurer.measures);
if (tableDiv.clientWidth > 0) {
tableDiv.style.opacity = 1;
}
var visibleColumns = tableDiv.clientWidth <= 0 ? Math.max(columns.min, 1) : 1;
var columnNumber = columns.number;
var paddingCount = 0;
// track a list of added columns as we build the visible ones to allow us
// to remove columns when they don't fit anymore.
var columnHistory = [];
var lastTableHeight = 0;
var backwards = startBackwards;
var tableDivStyle = window.getComputedStyle(tableDiv, null);
var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) +
parsePadding(tableDivStyle.paddingRight);
var addPaddingCol = false;
var currentWidth = 0;
while (true) {
columns.setVisibleColumns(columnNumber, visibleColumns, paddingCount);
currentWidth = columns.getWidth();
if (tableDiv.clientWidth - tableDivPadding < currentWidth) {
break;
}
columnHistory.push({
columnNumber: columnNumber,
visibleColumns: visibleColumns,
paddingCount: paddingCount
});
if (columnHistory.length > 100) {
console.error("More than 100 tries to fit columns, aborting");
break;
}
if (columns.max !== null &&
columns.visible + columns.getPaddingCount() >= columns.max) {
break;
}
// if we run out of right-columns
if (!backwards && columnNumber + columns.visible >= columns.total) {
// if we started adding right-columns, try adding left-columns
if (!startBackwards && columnNumber > 0) {
backwards = true;
}
else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) {
break;
}
else {
paddingCount = paddingCount + 1;
}
}
// if we run out of left-columns
if (backwards && columnNumber == 0) {
// if we started adding left-columns, try adding right-columns
if (startBackwards && columnNumber + columns.visible < columns.total) {
backwards = false;
}
else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) {
break;
}
else {
paddingCount = paddingCount + 1;
}
}
// when moving backwards try fitting left columns first
if (backwards && columnNumber > 0) {
columnNumber = columnNumber - 1;
}
if (columnNumber + visibleColumns < columns.total) {
visibleColumns = visibleColumns + 1;
}
}
var lastRenderableColumn = {
columnNumber: columnNumber,
visibleColumns: visibleColumns,
paddingCount: paddingCount
};
if (columnHistory.length > 0) {
lastRenderableColumn = columnHistory[columnHistory.length - 1];
}
columns.setVisibleColumns(
lastRenderableColumn.columnNumber,
lastRenderableColumn.visibleColumns,
lastRenderableColumn.paddingCount);
if (pagedTable.offsetWidth > 0) {
page.setVisiblePages(Math.max(Math.ceil(1.0 * (pagedTable.offsetWidth - 250) / 40), 2));
}
registerWidths();
};
me.fit = function(startBackwards) {
me.fitRows();
me.fitColumns(startBackwards);
}
me.render = function() {
me.fitColumns(false);
// render header/footer to measure height accurately
renderHeader();
renderFooter();
me.fitRows();
renderBody();
// re-render footer to match new rows
renderFooter();
}
var resizeLastWidth = -1;
var resizeLastHeight = -1;
var resizeNewWidth = -1;
var resizeNewHeight = -1;
var resizePending = false;
me.resize = function(newWidth, newHeight) {
function resizeDelayed() {
resizePending = false;
if (
(resizeNewWidth !== resizeLastWidth) ||
(!me.fixedHeight() && resizeNewHeight !== resizeLastHeight)
) {
resizeLastWidth = resizeNewWidth;
resizeLastHeight = resizeNewHeight;
setTimeout(resizeDelayed, 200);
resizePending = true;
} else {
me.render();
triggerOnChange();
resizeLastWidth = -1;
resizeLastHeight = -1;
}
}
resizeNewWidth = newWidth;
resizeNewHeight = newHeight;
if (!resizePending) resizeDelayed();
};
};
var PagedTableDoc;
(function (PagedTableDoc) {
var allPagedTables = [];
PagedTableDoc.initAll = function() {
allPagedTables = [];
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Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-article {\n contain: layout style;\n overflow-x: hidden;\n border-top: 1px solid rgba(0, 0, 0, 0.1);\n padding-top: 2rem;\n color: rgba(0, 0, 0, 0.8);\n}\n\nd-article > * {\n grid-column: text;\n}\n\n@media(min-width: 768px) {\n d-article {\n font-size: 16px;\n }\n}\n\n@media(min-width: 1024px) {\n d-article {\n font-size: 1.06rem;\n line-height: 1.7em;\n }\n}\n\n\n/* H2 */\n\n\nd-article .marker {\n text-decoration: none;\n border: none;\n counter-reset: section;\n grid-column: kicker;\n line-height: 1.7em;\n}\n\nd-article .marker:hover {\n border: none;\n}\n\nd-article .marker span {\n padding: 0 3px 4px;\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n position: relative;\n top: 4px;\n}\n\nd-article .marker:hover span {\n color: rgba(0, 0, 0, 0.7);\n border-bottom: 1px solid rgba(0, 0, 0, 0.7);\n}\n\nd-article h2 {\n font-weight: 600;\n font-size: 24px;\n line-height: 1.25em;\n margin: 2rem 0 1.5rem 0;\n border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n padding-bottom: 1rem;\n}\n\n@media(min-width: 1024px) {\n d-article h2 {\n font-size: 36px;\n }\n}\n\n/* H3 */\n\nd-article h3 {\n font-weight: 700;\n font-size: 18px;\n line-height: 1.4em;\n margin-bottom: 1em;\n margin-top: 2em;\n}\n\n@media(min-width: 1024px) {\n d-article h3 {\n font-size: 20px;\n }\n}\n\n/* H4 */\n\nd-article h4 {\n font-weight: 600;\n text-transform: uppercase;\n font-size: 14px;\n line-height: 1.4em;\n}\n\nd-article a {\n color: inherit;\n}\n\nd-article p,\nd-article ul,\nd-article ol,\nd-article blockquote {\n margin-top: 0;\n margin-bottom: 1em;\n margin-left: 0;\n margin-right: 0;\n}\n\nd-article blockquote {\n border-left: 2px solid rgba(0, 0, 0, 0.2);\n padding-left: 2em;\n font-style: italic;\n color: rgba(0, 0, 0, 0.6);\n}\n\nd-article a {\n border-bottom: 1px solid rgba(0, 0, 0, 0.4);\n text-decoration: none;\n}\n\nd-article a:hover {\n border-bottom: 1px solid rgba(0, 0, 0, 0.8);\n}\n\nd-article .link {\n text-decoration: underline;\n cursor: pointer;\n}\n\nd-article ul,\nd-article ol {\n padding-left: 24px;\n}\n\nd-article li {\n margin-bottom: 1em;\n margin-left: 0;\n padding-left: 0;\n}\n\nd-article li:last-child {\n margin-bottom: 0;\n}\n\nd-article pre {\n font-size: 14px;\n margin-bottom: 20px;\n}\n\nd-article hr {\n grid-column: screen;\n width: 100%;\n border: none;\n border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n margin-top: 60px;\n margin-bottom: 60px;\n}\n\nd-article section {\n margin-top: 60px;\n margin-bottom: 60px;\n}\n\nd-article span.equation-mimic {\n font-family: georgia;\n font-size: 115%;\n font-style: italic;\n}\n\nd-article > d-code,\nd-article section > d-code {\n display: block;\n}\n\nd-article > d-math[block],\nd-article section > d-math[block] {\n display: block;\n}\n\n@media (max-width: 768px) {\n d-article > d-code,\n d-article section > d-code,\n d-article > d-math[block],\n d-article section > d-math[block] {\n overflow-x: scroll;\n -ms-overflow-style: none; // IE 10+\n overflow: -moz-scrollbars-none; // Firefox\n }\n\n d-article > d-code::-webkit-scrollbar,\n d-article section > d-code::-webkit-scrollbar,\n d-article > d-math[block]::-webkit-scrollbar,\n d-article section > d-math[block]::-webkit-scrollbar {\n display: none; // Safari and Chrome\n }\n}\n\nd-article .citation {\n color: #668;\n cursor: pointer;\n}\n\nd-include {\n width: auto;\n display: block;\n}\n\nd-figure {\n contain: layout style;\n}\n\n/* KaTeX */\n\n.katex, .katex-prerendered {\n contain: style;\n display: inline-block;\n}\n\n/* Tables */\n\nd-article table {\n border-collapse: collapse;\n margin-bottom: 1.5rem;\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n}\n\nd-article table th {\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n}\n\nd-article table td {\n border-bottom: 1px solid rgba(0, 0, 0, 0.05);\n}\n\nd-article table tr:last-of-type td {\n border-bottom: none;\n}\n\nd-article table th,\nd-article table td {\n font-size: 15px;\n padding: 2px 8px;\n}\n\nd-article table tbody :first-child td {\n padding-top: 2px;\n}\n'+ni+'/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@media print {\n\n @page {\n size: 8in 11in;\n @bottom-right {\n content: counter(page) " of " counter(pages);\n }\n }\n\n html {\n /* no general margins -- CSS Grid takes care of those */\n }\n\n p, code {\n page-break-inside: avoid;\n }\n\n h2, h3 {\n page-break-after: avoid;\n }\n\n d-header {\n visibility: hidden;\n }\n\n d-footer {\n display: none!important;\n }\n\n}\n',mi=[{name:'WebComponents',support:function(){return'customElements'in window&&'attachShadow'in Element.prototype&&'getRootNode'in Element.prototype&&'content'in document.createElement('template')&&'Promise'in window&&'from'in Array},url:'https://distill.pub/third-party/polyfills/webcomponents-lite.js'},{name:'IntersectionObserver',support:function(){return'IntersectionObserver'in window&&'IntersectionObserverEntry'in window},url:'https://distill.pub/third-party/polyfills/intersection-observer.js'}];class yi{static browserSupportsAllFeatures(){return mi.every((e)=>e.support())}static load(e){const t=function(t){t.loaded=!0,console.info('Runlevel 0: Polyfill has finished loading: '+t.name),yi.neededPolyfills.every((e)=>e.loaded)&&(console.info('Runlevel 0: All required polyfills have finished loading.'),console.info('Runlevel 0->1.'),window.distillRunlevel=1,e())};for(const n of yi.neededPolyfills)g(n,t)}static get neededPolyfills(){return yi._neededPolyfills||(yi._neededPolyfills=mi.filter((e)=>!e.support())),yi._neededPolyfills}}const xi=ti('d-abstract',`
<style>
:host {
font-size: 1.25rem;
line-height: 1.6em;
color: rgba(0, 0, 0, 0.7);
-webkit-font-smoothing: antialiased;
}
::slotted(p) {
margin-top: 0;
margin-bottom: 1em;
grid-column: text-start / middle-end;
}
${function(e){return`${e} {
grid-column: left / text;
}
`}('d-abstract')}
</style>
<slot></slot>
`);class ki extends xi(HTMLElement){}const vi=ti('d-appendix',`
<style>
d-appendix {
contain: layout style;
font-size: 0.8em;
line-height: 1.7em;
margin-top: 60px;
margin-bottom: 0;
border-top: 1px solid rgba(0, 0, 0, 0.1);
color: rgba(0,0,0,0.5);
padding-top: 60px;
padding-bottom: 48px;
}
d-appendix h3 {
grid-column: page-start / text-start;
font-size: 15px;
font-weight: 500;
margin-top: 1em;
margin-bottom: 0;
color: rgba(0,0,0,0.65);
}
d-appendix h3 + * {
margin-top: 1em;
}
d-appendix ol {
padding: 0 0 0 15px;
}
@media (min-width: 768px) {
d-appendix ol {
padding: 0 0 0 30px;
margin-left: -30px;
}
}
d-appendix li {
margin-bottom: 1em;
}
d-appendix a {
color: rgba(0, 0, 0, 0.6);
}
d-appendix > * {
grid-column: text;
}
d-appendix > d-footnote-list,
d-appendix > d-citation-list,
d-appendix > distill-appendix {
grid-column: screen;
}
</style>
`,!1);class wi extends vi(HTMLElement){}const Si=/^\s*$/;class Ci extends HTMLElement{static get is(){return'd-article'}constructor(){super(),new MutationObserver((e)=>{for(const t of e)for(const e of t.addedNodes)switch(e.nodeName){case'#text':{const t=e.nodeValue;if(!Si.test(t)){console.warn('Use of unwrapped text in distill articles is discouraged as it breaks layout! Please wrap any text in a <span> or <p> tag. 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this.match('@'),'@'+this.key()},this.preamble=function(){this.currentEntry={},this.currentEntry.entryType='PREAMBLE',this.currentEntry.entry=this.value_comment(),this.entries.push(this.currentEntry)},this.comment=function(){this.currentEntry={},this.currentEntry.entryType='COMMENT',this.currentEntry.entry=this.value_comment(),this.entries.push(this.currentEntry)},this.entry=function(e){this.entry_body(e)},this.bibtex=function(){for(;this.matchAt();){var e=this.directive();this.match('{'),'@STRING'==e?this.string():'@PREAMBLE'==e?this.preamble():'@COMMENT'==e?this.comment():this.entry(e),this.match('}')}}}e.toJSON=function(e){var n=new t;return n.setInput(e),n.bibtex(),n.entries},e.toBibtex=function(e){var t='';for(var n in e){if(t+='@'+e[n].entryType,t+='{',e[n].citationKey&&(t+=e[n].citationKey+', '),e[n].entry&&(t+=e[n].entry),e[n].entryTags){var i='';for(var a in e[n].entryTags)0!=i.length&&(i+=', '),i+=a+'= {'+e[n].entryTags[a]+'}';t+=i}t+='}\n\n'}return t}})(t)});class Li extends 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XMLHttpRequest;i.onload=(t)=>this.receivedBibtex(t),i.onerror=()=>console.warn(`Could not load Bibtex! (tried ${n})`),i.responseType='text',i.open('GET',n,!0),i.send()}}class Ai extends HTMLElement{static get is(){return'd-byline'}set frontMatter(e){this.innerHTML=y(e)}}const Ei=ti('d-cite',`
<style>
:host {
}
.citation {
display: inline-block;
color: hsla(206, 90%, 20%, 0.7);
}
.citation-number {
cursor: default;
white-space: nowrap;
font-family: -apple-system, BlinkMacSystemFont, "Roboto", Helvetica, sans-serif;
font-size: 75%;
color: hsla(206, 90%, 20%, 0.7);
display: inline-block;
line-height: 1.1em;
text-align: center;
position: relative;
top: -2px;
margin: 0 2px;
}
figcaption .citation-number {
font-size: 11px;
font-weight: normal;
top: -2px;
line-height: 1em;
}
ul {
margin: 0;
padding: 0;
list-style-type: none;
}
ul li {
padding: 15px 10px 15px 10px;
border-bottom: 1px solid rgba(0,0,0,0.1)
}
ul li:last-of-type {
border-bottom: none;
}
</style>
<d-hover-box id="hover-box"></d-hover-box>
<div id="citation-" class="citation">
<slot></slot>
<span class="citation-number"></span>
</div>
`);class Di extends Ei(HTMLElement){connectedCallback(){this.outerSpan=this.root.querySelector('#citation-'),this.innerSpan=this.root.querySelector('.citation-number'),this.hoverBox=this.root.querySelector('d-hover-box'),window.customElements.whenDefined('d-hover-box').then(()=>{this.hoverBox.listen(this)})}static get observedAttributes(){return['key']}attributeChangedCallback(e,t,n){const i=t?'onCiteKeyChanged':'onCiteKeyCreated',a=n.split(','),d={detail:[this,a],bubbles:!0},r=new CustomEvent(i,d);document.dispatchEvent(r)}set key(e){this.setAttribute('key',e)}get key(){return this.getAttribute('key')}get keys(){return this.getAttribute('key').split(',')}set numbers(e){const t=e.map((e)=>{return-1==e?'?':e+1+''}),n='['+t.join(', ')+']';this.innerSpan&&(this.innerSpan.textContent=n)}set entries(e){this.hoverBox&&(this.hoverBox.innerHTML=`<ul>
${e.map(l).map((e)=>`<li>${e}</li>`).join('\n')}
</ul>`)}}const Mi=`
d-citation-list {
contain: layout style;
}
d-citation-list .references {
grid-column: text;
}
d-citation-list .references .title {
font-weight: 500;
}
`;class Oi extends HTMLElement{static get is(){return'd-citation-list'}connectedCallback(){this.hasAttribute('distill-prerendered')||(this.style.display='none')}set citations(e){x(this,e)}}var Ui=f(function(e){var t='undefined'==typeof window?'undefined'!=typeof WorkerGlobalScope&&self instanceof WorkerGlobalScope?self:{}:window,n=function(){var e=/\blang(?:uage)?-(\w+)\b/i,n=0,a=t.Prism={util:{encode:function(e){return e instanceof i?new i(e.type,a.util.encode(e.content),e.alias):'Array'===a.util.type(e)?e.map(a.util.encode):e.replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/\u00a0/g,' ')},type:function(e){return Object.prototype.toString.call(e).match(/\[object (\w+)\]/)[1]},objId:function(e){return e.__id||Object.defineProperty(e,'__id',{value:++n}),e.__id},clone:function(e){var t=a.util.type(e);switch(t){case'Object':var n={};for(var i in e)e.hasOwnProperty(i)&&(n[i]=a.util.clone(e[i]));return n;case'Array':return e.map&&e.map(function(e){return a.util.clone(e)});}return 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<code id="code-container"></code>
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}
sup {
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font-size: 0.75em;
position: relative;
top: -.5em;
vertical-align: baseline;
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span {
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cursor: default;
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.footnote-container {
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<d-hover-box>
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<slot id="slot"></slot>
</div>
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<sup>
<span id="fn-" data-hover-ref=""></span>
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<style>
d-footnote-list {
contain: layout style;
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d-footnote-list > * {
grid-column: text;
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d-footnote-list a.footnote-backlink {
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<h3>Footnotes</h3>
<ol></ol>
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<style>
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z-index: 10000;
display: none;
white-space: normal
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width: 704px;
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margin: 0 auto;
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position: absolute;
font-size: 1rem;
line-height: 1.5em;
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width: 100%;
border: 1px solid rgba(0, 0, 0, 0.1);
background-color: rgba(250, 250, 250, 0.95);
box-shadow: 0 0 7px rgba(0, 0, 0, 0.1);
border-radius: 4px;
box-sizing: border-box;
backdrop-filter: blur(2px);
-webkit-backdrop-filter: blur(2px);
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<div class="container">
<div class="panel">
<slot></slot>
</div>
</div>
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d-references {
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left: 0;
background: white;
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h1 {
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-moz-box-shadow: none;
box-shadow: none;
-webkit-border-radius: none;
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border-radius: none;
outline: none;
font-size: 18px;
background: none;
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border: none;
border-bottom: solid 2px #999;
transition: border .3s;
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input[type="password"]:focus {
border-bottom: solid 2px #333;
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input[type="password"].wrong {
border-bottom: solid 2px hsl(0,100%,40%);
}
p small {
color: #888;
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.logo {
position: relative;
font-size: 1.5em;
margin-bottom: 3em;
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.logo svg {
width: 36px;
position: relative;
top: 6px;
margin-right: 2px;
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fill: none;
stroke: black;
stroke-width: 2px;
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</style>
<div class="overlay">
<div class="container">
<h1>This article is in review.</h1>
<p>Do not share this URL or the contents of this article. Thank you!</p>
<input id="interstitial-password-input" type="password" name="password" autofocus/>
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</div>
</div>
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{"title":"Two-Stage Difference-in-Differences","description":"Introducing a R package to implement two-stage difference-in-differences following [@Gardner_2021]","authors":[{"author":"Kyle Butts","authorURL":"https://www.kylebutts.com/","affiliation":"CU Boulder Economics","affiliationURL":"https://www.colorado.edu/economics/","orcidID":""}],"publishedDate":"2021-05-24T00:00:00.000+00:00","citationText":"Butts, 2021"}
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<h1>Two-Stage Difference-in-Differences</h1>
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<p><p>Introducing a R package to implement two-stage difference-in-differences following <span class="citation" data-cites="Gardner_2021">(Gardner <a href="#ref-Gardner_2021" role="doc-biblioref">2021</a>)</span></p></p>
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<div class="d-byline">
Kyle Butts <a href="https://www.kylebutts.com/" class="uri">https://www.kylebutts.com/</a> (CU Boulder Economics)<a href="https://www.colorado.edu/economics/" class="uri">https://www.colorado.edu/economics/</a>
<br />2021-05-24
</div>
<div class="d-article">
<h2 id="two-stage-difference-in-differences-gardner_2021">Two-stage Difference-in-differences <span class="citation" data-cites="Gardner_2021">(Gardner <a href="#ref-Gardner_2021" role="doc-biblioref">2021</a>)</span></h2>
<p>Researchers often want to estimate either a static TWFE model,</p>
<p><span class="math display">\[\begin{equation}
y_{it} = \mu_i + \mu_t + \tau D_{it} + \varepsilon_{it},
\end{equation}\]</span></p>
<p>where <span class="math inline">\(\mu_i\)</span> are unit fixed effects, <span class="math inline">\(\mu_t\)</span> are time fixed effects, and <span class="math inline">\(D_{it}\)</span> is an indicator for receiving treatment, or an event-study TWFE model</p>
<p><span class="math display">\[\begin{equation}
y_{it} = \mu_i + \mu_t + \sum_{k = -L}^{-2} \tau^k D_{it}^k + \sum_{k = 1}^{K} \tau^k D_{it}^k + \varepsilon_{it},
\end{equation}\]</span></p>
<p>where <span class="math inline">\(D_{it}^k\)</span> are lag/leads of treatment (k periods from initial treatment date).</p>
<aside>
Sometimes researches use variants of this model where they bin or drop leads and lags
</aside>
<p>However, running OLS to estimate either model has been shown to not recover an average treatment effect and has the potential to be severely misleading in cases of treatment effect heterogeneity <span class="citation" data-cites="Borusyak_Jaravel_Spiess_2021 Callaway_SantAnna_2018 deChaisemartin_DHaultfoeuille_2019 Goodman-Bacon_2018 Sun_Abraham_2020">(Borusyak, Jaravel, and Spiess <a href="#ref-Borusyak_Jaravel_Spiess_2021" role="doc-biblioref">2021</a>; Callaway and Sant’Anna <a href="#ref-Callaway_SantAnna_2018" role="doc-biblioref">2018</a>; Chaisemartin and D’Haultfoeuille <a href="#ref-deChaisemartin_DHaultfoeuille_2019" role="doc-biblioref">2019</a>; Goodman-Bacon <a href="#ref-Goodman-Bacon_2018" role="doc-biblioref">2018</a>; Sun and Abraham <a href="#ref-Sun_Abraham_2020" role="doc-biblioref">2020</a>)</span>.</p>
<p>One way of thinking about this problem is through the FWL theorem. When estimating the unit and time fixed effects, you create a residualized <span class="math inline">\(\tilde{Y}_{it}\)</span> which is commonly said to be “the outcome variable after removing time shocks and fixed units characteristics”, but you also create a residulaized <span class="math inline">\(\tilde{D}_{it}\)</span> or <span class="math inline">\(\tilde{D}_{it}^k\)</span>. To simplify the literature, this residualized treatment indicators is what creates the problem of interpreting <span class="math inline">\(\tau\)</span> or <span class="math inline">\(\tau^k\)</span>, especially when treatment effects are heterogeneous.</p>
<p>That’s where <span class="citation" data-cites="Gardner_2021">Gardner (<a href="#ref-Gardner_2021" role="doc-biblioref">2021</a>)</span> comes in. What Gardner does to fix the problem is quite simple: estimate <span class="math inline">\(\mu_i\)</span> and <span class="math inline">\(\mu_t\)</span> seperately so you don’t residualize the treatment indicators. In the absence of treatment, the TWFE model gives you a model for (potentially unobserved) untreated outcomes</p>
<p><span class="math display">\[y_{it}(0) = \mu_i + \mu_t + \varepsilon_{it}.\]</span></p>
<p>Therefore, if you can <strong><em>consistently</em></strong> estimate <span class="math inline">\(y_{it}(0)\)</span>, you can impute the untreated outcome and remove that from the observed outcome <span class="math inline">\(y_{it}\)</span>. The value of <span class="math inline">\(y_{it} - \hat{y}_{it}(0)\)</span> should be close to zero for control units and should be close to <span class="math inline">\(\tau_{it}\)</span> for treated observations. Then, regressing <span class="math inline">\(y_{it} - \hat{y}_{it}(0)\)</span> on the treatment variables should give unbiased estimates of treatment effects (either static or dynamic/event-study).</p>
<aside>
This is the same logic as the new paper <span class="citation" data-cites="Borusyak_Jaravel_Spiess_2021">Borusyak, Jaravel, and Spiess (<a href="#ref-Borusyak_Jaravel_Spiess_2021" role="doc-biblioref">2021</a>)</span>
</aside>
<p>The steps of the two-step estimator are:</p>
<ol type="1">
<li><p>First estimate <span class="math inline">\(\mu_i\)</span> and <span class="math inline">\(\mu_t\)</span> using untreated/not-yet-treated observations, i.e. the subsample with <span class="math inline">\(D_{it}=0\)</span>. Residualize outcomes <span class="math inline">\(\tilde{y}_{it} = y_{it} - \hat{\mu}_i - \hat{\mu}_t\)</span>.</p></li>
<li><p>Regress <span class="math inline">\(\tilde{y}_{it}\)</span> on <span class="math inline">\(D_{it}\)</span> or <span class="math inline">\(D_{it}^k\)</span>’s to estimate the treatment effect <span class="math inline">\(\tau\)</span> or <span class="math inline">\(\tau^k\)</span>’s.</p></li>
</ol>
<p>Some notes:</p>
<h3 id="standard-errors">Standard Errors</h3>
<p>First, the standard errors on <span class="math inline">\(\tau\)</span> or <span class="math inline">\(\tau^k\)</span>’s will be incorrect as the dependent variable is itself an estimate. This is referred to the generated regressor problem in econometrics parlance. Therefore, <span class="citation" data-cites="Gardner_2021">Gardner (<a href="#ref-Gardner_2021" role="doc-biblioref">2021</a>)</span> has to develop a GMM estimator that will give asymptotically correct standard errors.</p>
<aside>
Details are left to the paper, but are implemented in the R package
</aside>
<h3 id="anticipation">Anticipation</h3>
<p>Second, this procedure works so long as <span class="math inline">\(\mu_i\)</span> and <span class="math inline">\(\mu_t\)</span> are <strong><em>consistently</em></strong> estimated. The key is to use only untreated/not-yet-treated observations to estimate the fixed effects. For example, if you used observations with <span class="math inline">\(D_{it} = 1\)</span>, you would attribute treatment effects <span class="math inline">\(\tau\)</span> as “fixed characteristics” and would combine <span class="math inline">\(\mu_i\)</span> with the treatment effects.</p>
<p>The fixed effects could be biased/inconsistent if there are anticipation effects, i.e. units respond before treatment starts. The fix is fairly simple, simply “shift” treatment date earlier by as many years as you suspect anticipation to occur (e.g. 2 years before treatment starts) and estimate on the subsample where the shifted treatment equals zero.</p>
<aside>
The R package allows you to specify the variable <span class="math inline">\(D_{it}\)</span>, if you suspect anticipation, provide the shifted variable to this option.
</aside>
<h3 id="covariates">Covariates</h3>
<p>This method works with pre-determined covariates as well. Augment the above step 1. to include <span class="math inline">\(X_i\)</span> and remove that from <span class="math inline">\(y_{it}\)</span> along with the fixed effects to get <span class="math inline">\(\tilde{y}_{it}\)</span>.</p>
<h2 id="r-package">R Package</h2>
<p>I have created an R package with the help of John Gardner to estimate the two-stage procedure. To install the package, run the following:</p>
<div class="layout-chunk" data-layout="l-body">
<div class="sourceCode">
<pre class="sourceCode r"><code class="sourceCode r"><span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://devtools.r-lib.org//reference/remote-reexports.html">install_github</a></span><span class="op">(</span><span class="st">&quot;kylebutts/did2s&quot;</span><span class="op">)</span>
</code></pre>
</div>
</div>
<p>To view the documentation, type <code>?did2s</code> into the console.</p>
<p>The main function is <code>did2s</code> which estimates the two-stage did procedure. This function requires the following options:</p>
<ul>
<li><code>yname</code>: the outcome variable</li>
<li><code>first_stage_formula</code>: formula for first stage, can include fixed effects and covariates, but do not include treatment variable(s)!</li>
<li><code>treat_formula</code>: This should be the treatment variable or in the case of event studies, treatment variables.</li>
<li><code>treat_var</code>: This has to be the 0/1 treatment variable that marks when treatment turns on for a unit. If you suspect anticipation, see note above for accounting for this.</li>
<li><code>cluster_vars</code>: Optional, this tells which variables to cluster on</li>
</ul>
<p>did2s returns a list with two objects:</p>
<ol type="1">
<li>fixest estimate for the second stage with corrected standard errors.</li>
</ol>
<h3 id="twfe-vs.-two-stage-did-example">TWFE vs. Two-Stage DID Example</h3>
<p>I will load example data from the package and plot the average outcome among the groups. Here is one unit’s data:</p>
<div class="layout-chunk" data-layout="l-body">
<details>
<summary>
Show code
</summary>
<div class="sourceCode">
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://tidyverse.tidyverse.org">tidyverse</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">did2s</span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://lrberge.github.io/fixest/">fixest</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/rstudio/rmarkdown">rmarkdown</a></span><span class="op">)</span>
<span class="co"># Load theme</span>
<span class="kw"><a href="https://rdrr.io/r/base/source.html">source</a></span><span class="op">(</span><span class="st">&quot;https://raw.githubusercontent.com/kylebutts/templates/master/ggplot_theme/theme_kyle.R&quot;</span><span class="op">)</span>
<span class="co"># Load Data from R package</span>
<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span><span class="op">(</span><span class="st">&quot;df_het&quot;</span><span class="op">)</span>
<span class="co"># One observation</span>
<span class="va">df_het</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span>n <span class="op">=</span> <span class="fl">31</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu">rmarkdown</span><span class="fu">::</span><span class="fu"><a href="https://pkgs.rstudio.com/rmarkdown/reference/paged_table.html">paged_table</a></span><span class="op">(</span><span class="op">)</span>
</code></pre>
</div>
</details>
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":["unit"],"name":[1],"type":["int"],"align":["right"]},{"label":["state"],"name":[2],"type":["int"],"align":["right"]},{"label":["unit_fe"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["group"],"name":[4],"type":["chr"],"align":["left"]},{"label":["g"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["year"],"name":[6],"type":["int"],"align":["right"]},{"label":["year_fe"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["treat"],"name":[8],"type":["lgl"],"align":["right"]},{"label":["rel_year"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["rel_year_binned"],"name":[10],"type":["dbl"],"align":["right"]},{"label":["error"],"name":[11],"type":["dbl"],"align":["right"]},{"label":["te"],"name":[12],"type":["dbl"],"align":["right"]},{"label":["te_dynamic"],"name":[13],"type":["dbl"],"align":["right"]},{"label":["dep_var"],"name":[14],"type":["dbl"],"align":["right"]}],"data":[{"1":"1","2":"29","3":"5.523901","4":"Group 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1","5":"2000","6":"1995","7":"-0.07528443","8":"FALSE","9":"-5","10":"-5","11":"0.8811842","12":"0","13":"0.00","14":"6.329800"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"1996","7":"1.06317582","8":"FALSE","9":"-4","10":"-4","11":"-0.8575802","12":"0","13":"0.00","14":"5.729496"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"1997","7":"0.75422147","8":"FALSE","9":"-3","10":"-3","11":"0.3724619","12":"0","13":"0.00","14":"6.650584"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"1998","7":"0.70147644","8":"FALSE","9":"-2","10":"-2","11":"3.0376125","12":"0","13":"0.00","14":"9.262990"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"1999","7":"0.64828085","8":"FALSE","9":"-1","10":"-1","11":"-0.4348458","12":"0","13":"0.00","14":"5.737336"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2000","7":"-0.86491184","8":"TRUE","9":"0","10":"0","11":"-0.6782561","12":"2","13":"0.00","14":"5.980733"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2001","7":"-0.68147084","8":"TRUE","9":"1","10":"1","11":"0.6084212","12":"2","13":"0.05","14":"7.500851"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2002","7":"-0.40851703","8":"TRUE","9":"2","10":"2","11":"-0.6967064","12":"2","13":"0.10","14":"6.518677"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2003","7":"0.44116551","8":"TRUE","9":"3","10":"3","11":"-0.3903801","12":"2","13":"0.15","14":"7.724686"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2004","7":"-1.89630806","8":"TRUE","9":"4","10":"4","11":"0.2949940","12":"2","13":"0.20","14":"6.122587"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2005","7":"-0.60094668","8":"TRUE","9":"5","10":"5","11":"-0.2986756","12":"2","13":"0.25","14":"6.874278"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2006","7":"-0.69171484","8":"TRUE","9":"6","10":"6","11":"2.2347539","12":"2","13":"0.30","14":"9.366940"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2007","7":"0.45735807","8":"TRUE","9":"7","10":"6","11":"1.4796393","12":"2","13":"0.35","14":"9.810898"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2008","7":"0.09452109","8":"TRUE","9":"8","10":"6","11":"1.1175902","12":"2","13":"0.40","14":"9.136012"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2009","7":"1.15085141","8":"TRUE","9":"9","10":"6","11":"1.5450265","12":"2","13":"0.45","14":"10.669779"},{"1":"1","2":"29","3":"5.523901","4":"Group 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1","5":"2000","6":"2015","7":"0.09296431","8":"TRUE","9":"15","10":"6","11":"-0.9842208","12":"2","13":"0.75","14":"7.382644"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2016","7":"0.25950820","8":"TRUE","9":"16","10":"6","11":"-1.7970712","12":"2","13":"0.80","14":"6.786338"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2017","7":"0.23363751","8":"TRUE","9":"17","10":"6","11":"0.5173953","12":"2","13":"0.85","14":"9.124934"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2018","7":"0.60420642","8":"TRUE","9":"18","10":"6","11":"-0.5704375","12":"2","13":"0.90","14":"8.457670"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2019","7":"-0.83127757","8":"TRUE","9":"19","10":"6","11":"-1.2716152","12":"2","13":"0.95","14":"6.371008"},{"1":"1","2":"29","3":"5.523901","4":"Group 1","5":"2000","6":"2020","7":"-0.75278479","8":"TRUE","9":"20","10":"6","11":"-1.8627070","12":"2","13":"1.00","14":"5.908409"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
</script>
</div>
</div>
<p>Here is a plot of the average outcome variable for each of the groups:</p>
<div class="layout-chunk" data-layout="l-body-outset">
<details>
<summary>
Show code
</summary>
<div class="sourceCode">
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Plot Data </span>
<span class="va">df_avg</span> <span class="op">&lt;-</span> <span class="va">df_het</span> <span class="op">%&gt;%</span>
<span class="fu">group_by</span><span class="op">(</span><span class="va">group</span>, <span class="va">year</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu">summarize</span><span class="op">(</span>dep_var <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">dep_var</span><span class="op">)</span>, .groups <span class="op">=</span> <span class="st">&#39;drop&#39;</span><span class="op">)</span>
<span class="co"># Get treatment years for plotting</span>
<span class="va">gs</span> <span class="op">&lt;-</span> <span class="va">df_het</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">treat</span> <span class="op">==</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu">pull</span><span class="op">(</span><span class="va">g</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span><span class="op">(</span><span class="op">)</span>
<span class="fu">ggplot</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">geom_line</span><span class="op">(</span>data <span class="op">=</span> <span class="va">df_avg</span>, mapping <span class="op">=</span> <span class="fu">aes</span><span class="op">(</span>y <span class="op">=</span> <span class="va">dep_var</span>, x <span class="op">=</span> <span class="va">year</span>, color <span class="op">=</span> <span class="va">group</span><span class="op">)</span>, size <span class="op">=</span> <span class="fl">1.5</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">geom_vline</span><span class="op">(</span>xintercept <span class="op">=</span> <span class="va">gs</span> <span class="op">-</span> <span class="fl">0.5</span>, linetype <span class="op">=</span> <span class="st">&quot;dashed&quot;</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">theme_kyle</span><span class="op">(</span>base_size <span class="op">=</span> <span class="fl">16</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">theme</span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">&quot;bottom&quot;</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">&quot;Outcome&quot;</span>, x <span class="op">=</span> <span class="st">&quot;Year&quot;</span>, color <span class="op">=</span> <span class="st">&quot;Treatment Cohort&quot;</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">scale_y_continuous</span><span class="op">(</span>expand <span class="op">=</span> <span class="fu">expansion</span><span class="op">(</span>add <span class="op">=</span> <span class="fl">.5</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">scale_color_manual</span><span class="op">(</span>values <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">&quot;Group 1&quot;</span> <span class="op">=</span> <span class="st">&quot;#d2382c&quot;</span>, <span class="st">&quot;Group 2&quot;</span> <span class="op">=</span> <span class="st">&quot;#497eb3&quot;</span>, <span class="st">&quot;Group 3&quot;</span> <span class="op">=</span> <span class="st">&quot;#8e549f&quot;</span><span class="op">)</span><span class="op">)</span>
</code></pre>
</div>
</details>
<div class="figure"><span id="fig:plot-df-het"></span>
<img 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" alt="Example data with heterogeneous treatment effects" width="768" />
<p class="caption">
Figure 1: Example data with heterogeneous treatment effects
</p>
</div>
</div>
<h3 id="estimate-two-stage-difference-in-differences">Estimate Two-stage Difference-in-Differences</h3>
<p>First, lets estimate a static did:</p>
<div class="layout-chunk" data-layout="l-body">
<div class="sourceCode">
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Static</span>
<span class="va">static</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/did2s/man/did2s.html">did2s</a></span><span class="op">(</span><span class="va">df_het</span>,
yname <span class="op">=</span> <span class="st">&quot;dep_var&quot;</span>, first_stage_formula <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="https://lrberge.github.io/fixest/reference/i.html">i</a></span><span class="op">(</span><span class="va">state</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://lrberge.github.io/fixest/reference/i.html">i</a></span><span class="op">(</span><span class="va">year</span><span class="op">)</span>,
treat_formula <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="https://lrberge.github.io/fixest/reference/i.html">i</a></span><span class="op">(</span><span class="va">treat</span><span class="op">)</span>, treat_var <span class="op">=</span> <span class="st">&quot;treat&quot;</span>,
cluster_vars <span class="op">=</span> <span class="st">&quot;state&quot;</span><span class="op">)</span>
<span class="fu">fixest</span><span class="fu">::</span><span class="fu"><a href="https://lrberge.github.io/fixest/reference/etable.html">esttable</a></span><span class="op">(</span><span class="va">static</span><span class="op">)</span>
</code></pre>
</div>
<pre><code> static
Dependent Var.: adj
(Intercept) 1.78e-15 (4.59e-10)
treat = TRUE 2.380*** (0.0504)
_______________ ___________________
S.E. type Custom
Observations 31,000
R2 0.28957
Adj. R2 0.28955</code></pre>
</div>
<p>Then, let’s estimate an event study did:</p>
<div class="layout-chunk" data-layout="l-body">
<div class="sourceCode">
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Event Study</span>
<span class="va">es</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/did2s/man/did2s.html">did2s</a></span><span class="op">(</span><span class="va">df_het</span>,
yname <span class="op">=</span> <span class="st">&quot;dep_var&quot;</span>, first_stage_formula <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="https://lrberge.github.io/fixest/reference/i.html">i</a></span><span class="op">(</span><span class="va">state</span><span class="op">)</span> <span class="op">+</span> <span class="fu"><a href="https://lrberge.github.io/fixest/reference/i.html">i</a></span><span class="op">(</span><span class="va">year</span><span class="op">)</span>,
treat_formula <span class="op">=</span> <span class="op">~</span><span class="fu"><a href="https://lrberge.github.io/fixest/reference/i.html">i</a></span><span class="op">(</span><span class="va">rel_year</span><span class="op">)</span>, treat_var <span class="op">=</span> <span class="st">&quot;treat&quot;</span>,
cluster_vars <span class="op">=</span> <span class="st">&quot;state&quot;</span><span class="op">)</span>
<span class="fu">fixest</span><span class="fu">::</span><span class="fu"><a href="https://lrberge.github.io/fixest/reference/etable.html">esttable</a></span><span class="op">(</span><span class="va">es</span><span class="op">)</span>
</code></pre>
</div>
<pre><code> es
Dependent Var.: adj
(Intercept) 0.0495 (0.1094)
rel_year = -19 0.1055 (0.1079)
rel_year = -18 -0.0065 (0.1351)
rel_year = -17 0.0303 (0.1089)
rel_year = -16 0.0529 (0.1247)
rel_year = -15 0.1670 (0.1354)
rel_year = -14 0.1213 (0.1179)
rel_year = -13 0.0445 (0.1042)
rel_year = -12 0.0404 (0.1267)
rel_year = -11 0.1482 (0.1230)
rel_year = -10 0.0458 (0.1299)
rel_year = -9 0.0018 (0.1227)
rel_year = -8 0.0383 (0.1083)
rel_year = -7 0.1048 (0.0966)
rel_year = -6 -0.0274 (0.1088)
rel_year = -5 -0.0143 (0.1115)
rel_year = -4 -0.1003 (0.1202)
rel_year = -3 -0.0589 (0.1126)
rel_year = -2 -0.0406 (0.1101)
rel_year = -1 0.0684 (0.1158)
rel_year = 0 1.678*** (0.1240)
rel_year = 1 1.703*** (0.1161)
rel_year = 2 1.822*** (0.1262)
rel_year = 3 1.869*** (0.1191)
rel_year = 4 1.890*** (0.1257)
rel_year = 5 2.096*** (0.1374)
rel_year = 6 2.131*** (0.1173)
rel_year = 7 2.298*** (0.1117)
rel_year = 8 2.363*** (0.1091)
rel_year = 9 2.570*** (0.1141)
rel_year = 10 2.631*** (0.1219)
rel_year = 11 2.663*** (0.1549)
rel_year = 12 2.622*** (0.1551)
rel_year = 13 2.606*** (0.1647)
rel_year = 14 2.705*** (0.1746)
rel_year = 15 2.774*** (0.1704)
rel_year = 16 2.645*** (0.1608)
rel_year = 17 2.847*** (0.1490)
rel_year = 18 3.081*** (0.1669)
rel_year = 19 3.181*** (0.1601)
rel_year = 20 3.259*** (0.1509)
rel_year = Inf -0.1104 (0.1334)
_______________ _________________
S.E. type Custom
Observations 31,000
R2 0.30629
Adj. R2 0.30537</code></pre>
</div>
<p>And plot the results:</p>
<div class="layout-chunk" data-layout="l-body-outset">
<details>
<summary>
Show code
</summary>
<div class="sourceCode">
<pre class="sourceCode r"><code class="sourceCode r"><span class="va">pts</span> <span class="op">&lt;-</span> <span class="fu">broom</span><span class="fu">::</span><span class="fu"><a href="https://generics.r-lib.org/reference/tidy.html">tidy</a></span><span class="op">(</span><span class="va">es</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu">str_detect</span><span class="op">(</span><span class="va">term</span>, <span class="st">&quot;rel_year::&quot;</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu">select</span><span class="op">(</span>rel_year <span class="op">=</span> <span class="va">term</span>, <span class="va">estimate</span>, se <span class="op">=</span> <span class="va">std.error</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu">mutate</span><span class="op">(</span>
rel_year <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span><span class="op">(</span><span class="fu">str_remove</span><span class="op">(</span><span class="va">rel_year</span>, <span class="st">&quot;rel_year::&quot;</span><span class="op">)</span><span class="op">)</span>,
ci_lower <span class="op">=</span> <span class="va">estimate</span> <span class="op">-</span> <span class="fl">1.96</span> <span class="op">*</span> <span class="va">se</span>,
ci_upper <span class="op">=</span> <span class="va">estimate</span> <span class="op">+</span> <span class="fl">1.96</span> <span class="op">*</span> <span class="va">se</span>,
group <span class="op">=</span> <span class="st">&quot;Estimated Effect&quot;</span>
<span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rel_year</span> <span class="op">&lt;=</span> <span class="fl">8</span> <span class="op">&amp;</span> <span class="va">rel_year</span> <span class="op">&gt;=</span> <span class="op">-</span><span class="fl">8</span><span class="op">)</span>
<span class="va">te_true</span> <span class="op">&lt;-</span> <span class="va">df_het</span> <span class="op">%&gt;%</span>
<span class="co"># Keep only treated units</span>
<span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">g</span> <span class="op">&gt;</span> <span class="fl">0</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu">group_by</span><span class="op">(</span><span class="va">rel_year</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu">summarize</span><span class="op">(</span>estimate <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">te</span> <span class="op">+</span> <span class="va">te_dynamic</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu">mutate</span><span class="op">(</span>group <span class="op">=</span> <span class="st">&quot;True Effect&quot;</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="va">rel_year</span> <span class="op">&gt;=</span> <span class="op">-</span><span class="fl">8</span> <span class="op">&amp;</span> <span class="va">rel_year</span> <span class="op">&lt;=</span> <span class="fl">8</span><span class="op">)</span>
<span class="va">pts</span> <span class="op">&lt;-</span> <span class="fu">bind_rows</span><span class="op">(</span><span class="va">pts</span>, <span class="va">te_true</span><span class="op">)</span>
<span class="co"># Stagger true and estimate</span>
<span class="co"># es &lt;- es %&gt;%</span>
<span class="co"># mutate(rel_year_stagg = if_else(group == &quot;True Effect&quot;, rel_year - 0.1, rel_year + 0.1))</span>
<span class="va">max_y</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">pts</span><span class="op">$</span><span class="va">estimate</span><span class="op">)</span>
<span class="fu">ggplot</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span>
<span class="co"># 0 effect</span>
<span class="fu">geom_hline</span><span class="op">(</span>yintercept <span class="op">=</span> <span class="fl">0</span>, linetype <span class="op">=</span> <span class="st">&quot;dashed&quot;</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">geom_vline</span><span class="op">(</span>xintercept <span class="op">=</span> <span class="op">-</span><span class="fl">0.5</span>, linetype <span class="op">=</span> <span class="st">&quot;dashed&quot;</span><span class="op">)</span> <span class="op">+</span>
<span class="co"># Confidence Intervals</span>
<span class="fu">geom_linerange</span><span class="op">(</span>data <span class="op">=</span> <span class="va">pts</span>, mapping <span class="op">=</span> <span class="fu">aes</span><span class="op">(</span>x <span class="op">=</span> <span class="va">rel_year</span>, ymin <span class="op">=</span> <span class="va">ci_lower</span>, ymax <span class="op">=</span> <span class="va">ci_upper</span><span class="op">)</span>, color <span class="op">=</span> <span class="st">&quot;grey30&quot;</span><span class="op">)</span> <span class="op">+</span>
<span class="co"># Estimates</span>
<span class="fu">geom_point</span><span class="op">(</span>data <span class="op">=</span> <span class="va">pts</span>, mapping <span class="op">=</span> <span class="fu">aes</span><span class="op">(</span>x <span class="op">=</span> <span class="va">rel_year</span>, y <span class="op">=</span> <span class="va">estimate</span>, color <span class="op">=</span> <span class="va">group</span><span class="op">)</span>, size <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span>
<span class="co"># Label</span>
<span class="fu">geom_label</span><span class="op">(</span>data <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>x <span class="op">=</span> <span class="op">-</span><span class="fl">0.5</span> <span class="op">-</span> <span class="fl">0.1</span>, y <span class="op">=</span> <span class="va">max_y</span> <span class="op">+</span> <span class="fl">0.25</span>, label <span class="op">=</span> <span class="st">&quot;Treatment Starts ▶&quot;</span><span class="op">)</span>, label.size<span class="op">=</span><span class="cn">NA</span>,
mapping <span class="op">=</span> <span class="fu">aes</span><span class="op">(</span>x <span class="op">=</span> <span class="va">x</span>, y <span class="op">=</span> <span class="va">y</span>, label <span class="op">=</span> <span class="va">label</span><span class="op">)</span>, size <span class="op">=</span> <span class="fl">5.5</span>, hjust <span class="op">=</span> <span class="fl">1</span>, fontface <span class="op">=</span> <span class="fl">2</span>, inherit.aes <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">scale_x_continuous</span><span class="op">(</span>breaks <span class="op">=</span> <span class="op">-</span><span class="fl">5</span><span class="op">:</span><span class="fl">5</span>, minor_breaks <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">scale_y_continuous</span><span class="op">(</span>minor_breaks <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">scale_color_manual</span><span class="op">(</span>values <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">&quot;Estimated Effect&quot;</span> <span class="op">=</span> <span class="st">&quot;#013ef5&quot;</span>, <span class="st">&quot;True Effect&quot;</span> <span class="op">=</span> <span class="st">&quot;#eb3f25&quot;</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">labs</span><span class="op">(</span>x <span class="op">=</span> <span class="st">&quot;Relative Time&quot;</span>, y <span class="op">=</span> <span class="st">&quot;Estimate&quot;</span>, color <span class="op">=</span> <span class="cn">NULL</span>, title <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">theme_kyle</span><span class="op">(</span>base_size <span class="op">=</span> <span class="fl">16</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">theme</span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">&quot;bottom&quot;</span><span class="op">)</span>
</code></pre>
</div>
</details>
<div class="figure"><span id="fig:plot-es"></span>
<img 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nn++eelRYsW5lw2B7ewVJkC2uivI+01ffDBB9KqVStzubz+ueaaa2Tp0qX27vQpxz59+sh5550nrVu3luTkZNEnBFauXGl+j9HwQNu3bzcHRNx00012OX8L2vA/bNgw88nIwYMH+8viunUvvfSSPP3006JPhZZ3CDLXYXDCCCCAAAIIIIAAAggggMBfAq7vANA/8jQMkMbvz8vLk7S0NK+Lm5mZKZ06dZL58+fLokWLvLZZH3QuACtpiAk6ACwN3hFAAIHIBHQulokTJwYtfNddd3l1AGgYtzPOOCNoGTaKOXfN+++/D0UUCLz33nsVdhYa8//jjz+291+jRg359NNPRTvKAiWdDHjGjBny448/mh0EgfLp+nCfKgi2r2jYpnM6VeT1iIY6cg4IIIAAAggggAACCCCAQCQCrg8BpI37mnTCyUmTJvk1sPKsX79e9u7dWyKPdhxYSR+bJyGAAAIIIBCtAhqubsOGDdF6enF1XhXZiK5PFDjTs88+G7TxX/PqEwI6CfDQoUOdRUssL1u2TH755ZcS6928Qp/kzMrKclUV9OnVXbt2ueqcOVkEEEAAAQQQQAABBBBwn4DrOwDOP/98W33kyJHmyDd7xV8LzrARf/zxh+9m+fXXX+11tWrVspdZQAABBBBAINoEKrLROdrqGs3no98ndKR9RSXndxOd1HfgwIHldqhYvIfcOPp/48aN5pMa//73v82BLOV2gdkRAggggAACCCCAAAIIIOAQcH0IoKOOOsoc7TZt2jQpLCyUO++8U3r37i1DhgyRRo0amVU95JBD7CrrBHGdO3e2P+tTA9ZIyvT0dGnatKm9jQUEEEAAgegV0JGzGiZFU+3ataVLly72yerTXBo+5euvv5adO3eK9f/7448/bucJtDB37lz55ptv5PfffzfnmKlZs6YceeSRZtz1ww47LFCxgOv1XHR08g8//CDaaGyN+NUOZ43j3r17d/nb3/4m2sgbLOnvuLfeekvefvttO5s+wfbtt9/an60F3a++nEnjw2uYFE06Stw6nu73yy+/NDvQ161bZ27XuRiOO+448/ep2vpLav/VV1+ZIfh0Lh51at++vZx77rnmu78yoaybN2+e7a9P9+l+O3ToYPo7O/SD7UuvudU473tv6D4///xz83poA2xCQoJoKMBjjz3WPEaDBg2C7drcpo3zd9xxh1e+xYsXm+fqXKkx+k855RTnqpCX9+3bZ+fVOug9XNakIYK0ofyVV16xd6Xr/N1DOr9EMG+d/0C/e6nF5s2bJT8/3/450ycvNZxX3bp17eMEW9B7yfq50O9oznJ6nHHjxplzIej9W6dOHbnkkkvkpJNOMnepP1+6fezYsfYhAv1cHHroodKmTRs7n3NB54DS+1nDRep9ofvQUGYaalLnitLr2Lx5c2eRclnWORz+9a9/yQsvvCAPPPCA6LwPOocKCQEEEEAAAQQQQAABBBAoNwHjjynXpz179ngGDBjgOf744+3XCSec4Lnllls8xig3j/GHpb3emCDOY/xR5zEeffeMGDHCXq9ljT+8XG9R3hX4z3/+4+nfv7/9euedd8r7EOwPAQTiVMDosNXWaPtlNNaHJWE0aNtljYZnu6zRcO855phj7G3WMYzGPzuPv4WZM2d6jEbgEuWs8vpuPHXmMead8Ve8xDo9j9tuu81jNC4H3afu12hs9RgNsyX2Ya144403PEaDfqn7sc7V3+8zo6HbLm9MLGvu2mj49aiLVc73PSMjw2NMqmqdhvluNJB6TjzxxIBldB8XXHCBx5iM1qtcaR++//57j9H4G3S/xuS3HqMTpbRdeYwOHHs/amul0aNHe+rXr29v862vMY+Qx5ibwmM0ZltFvN6N+YY8F154ocdp6bsP52ej88KrfDgftK7WvlJTUz1GR004xUvkNeaN8BgdKfY+rX0Hev/nP/9ZYh9G54nnv//9r8foHCp1P3rOxkTEHiPMTYn9+K4wOsDs/en3Nis9+uijHqMx3N5mnavREWZmMRr9PW3bti2x3crn+240tFu7tt+NjgfPdddd59Hz9c3v+1m/KxqdJ56CggK7fFkWVqxYUeKY+nNudPJ5jI6Zsuy6zGX1Z8V4stajxtGS9GfA6JzxelW1U7TYBDoPzALJBF6PWWCbQFv0/0R+NgPp+F/vz8x/TtZaAphZErwjgAACkQm4/gkA448zc8Sd8QepGA0tYvwxpatER4TpaEt9OZPvKDFrm/GHnwwePNj6yDsCCCCAgIsEdI4XTTpauGfPnuaIZN/Tb9eune8q+/PLL78sRmOl+bvDXuln4bPPPhONwa/vRiO4nxx/rlq5cqUcccQRpe7P2oGOvDcalsXodDXPw1qv78avd7nqqqucq8q8rPHf9aVhZfT3ZaCkMcr/7//+T4yOdhk+fLg5ct7ocJfc3NxARcz1H374ofkEhf4ONjoRgubVjUZjoxgNzkHPRfPpyH31nzx5sj0CXNcHS2qr5//ggw+ao6yD5dVR308++aQ5ObU+QaJPBzjTqFGjKm2i2VatWtmH1tH1+hRJsHvOzhxg4frrrxcd5V6WdPbZZ8t3330X0i70nI1BF+aIen36RL9nhZKsn+V77rlHnnjiCb9FjEZ/c71+bwt2//ot7Fi5bds28z5avny5Y23gxfnz54uO2L/22msDZyrjFqODUS699FKz7o899pj5VEoZdxlRcX0SgYQAAggggAACCCCAAAKxIRATHQB6KYwRffL666+bj7VPnDjRDAfk7xLpo+6+ScMgPPTQQ+IMFeSbh88IIIAAAtEloA3jVtJwKRr25aKLLrIb/zMzM82wLho6ZcuWLWJNCG+Vsd41FNwNN9xgh8fRRt9zzjnHbBisUaOGaAOyMXpatFFfkzYA6vwzGmJGQ4r4SxpmRBtrrcZSPZfTTjvNPAcNN6ONltphrcfW8ClW0rAy2shqNXDqej0fDRFiJQ3fonXVpOFxbrzxRmuT/a6dIMGS1ueLL74wz0PDyxgj9kVD6mkjrdbTeNrL67yMkdhmiCXtMLAa/0899VQzLIr+/tXwLRpGyHiKwj6sdjA88sgjovHNg6WPPvpIjBHYXv5qcPLJJ5v1s/ytOXy0Mf/vf/+7Gf4pUDgX572hx7799tvFeIrCPA0N8dO3b18zTJEx4l+0wVnPwdkIrB0Mr732WomG3tNPP90OxaPXX0PGWEkNfUPm6P4jTRriRuc2spI2iE+dOjXi8DA6SELDH2nSwRBWQ7uGKdLwib7JeJLSd5VcccUV9j2t96UxIt68z/X7k947+nOmYbecgy+0w0Y7VYYNG1Zif9YK5/XS81J/Z+O/ht7SnzUNNaUT/VqdeWpifa/TTiTjqRNzl/rzdvPNN1u7t981jI8zaaeT87prGMh+/fqZ+9f66P5++uknM0SSFZJJOwp9O4ac+yyvZQ0ppf/P6P8jGrqstJ/p8jou+0EAAQQQQAABBBBAAIEYFDD+6Iq5ZDSmeIxRembYGmdYIH/LxlwBniVLlsScQXlViBBA5SXJfhBAwFegrCGANHyN8WvZfl155ZXmcrVq1cywNRqupLRkNFh6jAZwex/16tXzGA2WJYppSBhjRKydT49rxLovkc+5wmhgN0MRvfnmmwHDoOzdu9djNPR67dcYce/cTYllo5HZzu8Mb1Mio88Ko9HSLme5nXnmmR5jVLhPTo/H6OQwQ+RZ+fTdKq9GM2bMKFFGVzzzzDNexzDitQcMp6P5jRHYHiPeu11Gl41OE93klfSxbw3T4jwfPfdAyRkCyFnGGDHu91poyAf9PuDMq+FygiUNhePM/8EHHwTLHvY2o7HbY8xl5HUMI66+Z9OmTWHvy7eA0ahs79eIc++7OeBnDaFozIfhufvuu4OGwnr++eft/auR1sPo9Aq4X2cIIKOTxdOsWTOzvJ6n0QAfsJxzg56XdT2MuP3OTX6XjY4l+57WckbngN97QwsbnV4evb59+vTxaNjJ8kr+QgBZdfB91/9vNPxWPCdCs4R/9THDLHyB8Ev4C81CeK7gjv7MgpdgK2bcAwgggEDZBGLmCQDjDyU76eS/OqpLXzoazYh1bI7g1BGKxhdhc/SeTuSmE/5VxIRu9omwgAACCCBQYQK+o3CNhnbR0cwankdH24eSdHS6jui3kj5BpiNufZNOyqlhghYsWGCGNNHtOoL+559/FmO+Ad/s5md9ikBHsvuepzOzPmGg560j2Y1f5+YmHeVdEUnPwzqG7r+VEWJGQ/X4C9GjExTrCHhn3ayy+nSANQGr73nqEwxGQ6nMnj3b3KS/d9VMJzr2l3RkuPU0g26fMGGC35HOel11NLzuy5r4WUea6yh8nTQ2lHTWWWeZdfJ3PfRJQA2/pCP6165da+5OvzvoaPOWLVuGsvtyz2N0ZInRoSKDBg2y962TU2toKX2iQUeiG50x9rbKWNBR8fpkhz9D5/FvvfVW896ynoDR72LLli0zJ9N25vO3bE1IrNfr008/jfiJB3/7dq7T+8i6p3W9PmUTaKJlfZJDn/DQV1Ul/f/mf//7n1x88cXmkzWBnn6pqvPjuAgggAACCCCAAAIIIBC9AonRe2rlc2baGdCrVy+5/PLLRf8gHTp0qNkx0Lt3bxr/y4eYvSCAAAJRI6Dx6kNt/Nd471ZYGK2AltMQL4GSNhLr7xFn0gbrYKm0hlItq6FNnA3tVqibYPstj236+9Bf47+176OPPtoMk2N91nftHDFGoTtXlVjWMEzOZDxl5/xoL2uMeA3dZyUN+aONvoFSJP7OfWk89WDXQxu3dR4GZ9LG7qpMGgv+/vvv9zoFDYH0kBG20Hj6w5wbYu7cuV7bK/pDMEPnsTWUjjOFc19raCsNU6QdbxWVrHBI1v7LEq7J2kdFv2uHhXbAGZOem99ljadBKvqQ7B8BBBBAAAEEEEAAAQRiQMD1HQCrV6/2Gj0YA9eEKiCAAAIIRCCgDbjaqB1q0pH2ztH/vo2//vajce+dqbxG6ztH81qxxp3HqYhl3wZaf8cwwqp4rTZCoHh99vfBt4yO/vaXpk+f7vX7uyL91TeUJwW008OZAp27M09FL+vky/qUiDaKO5PGw9f1GqtfXxo3P5qS857W8zLCXYV8ejoBrc7VUJHJCBPktXudF8MtyQiDYD6R1K1bN/uJFbecO+eJAAIIIIAAAggggAAClS/g+g4A/YNXQyzoCH9rYsJIGHVkmv5BRUIAAQQQcKeAEUfcnBA+1LN3TlirZbp27VpqUQ0H4xwprGFiyiM59+kMS1Ie+/a3D21cNeKs+9vktU5DATmT80kF53rnsk4q7Ez6pIW/pJPDOlMo/nrOzjAt6h+Kl05WG0oy5izwylaW7xVeOyrjB518VyeN1rA/Rsz+EnvTpwC0c0ZDM/32228ltlfFCuc9rce3JusN5VxC6ZwKZT/B8minifN6a4gpnQw5Wq55sHPXSY71yZBff/1VdAJmEgIIIIAAAggggAACCCAQTCBm5gBYvny5Gd8/WGX9bduwYYM89dRTZrxijV2ssVVJCCCAAALuE+jUqVNYJ+3beG9MbCvGhKOl7kPj0VuN2hpGZP/+/aINcqEkjYmvv3c0jIuWtV6rVq0KpXi55dHwMaGkxETvcQKhdBr4hogJ1EDvGxpIO2RCCbmjoYCspNdBLX07Hazt1rvOdxBKCvXcQ9lXeefRkIY6T4GG/9HQVTonxcqVK70Oo4ZdunQx52EwJkn22lZRH/QarF+/XozJpM2fBX0yQe/r+fPnR3zIcH+WIzmQzrGgYaF0vigrPf300/Luu++aHQFXXXVVyD/XVvmKftennK6//nq57777pGHDhhV9OPaPAAIIIIAAAggggAACMSIQMx0AkV4PHUk4b948s7hOhkgHQKSSlEMAAQSqVkAbSMNJ2hDvTLfddpvzY8jL2gAaqANARxNrg+LHH39sdjRv3bo15P1WZMbSGswDHds5+j5QnlDX+/prJ3wkSecSKC05R3qXljfat+vEvzrXhXp98sknoiGCFi1aZJ+2dkjpCHr9buMbjsnOVMaFH374QcaPHy/Tpk0znzgIZ3R/aYfWJxwC/TyVVjbc7dqYvm3bNnnwwQftJ0nWrVsnt9xyiznC/sorrzTn/dB5OqoyaUfcZZddJg8//DAj/qvyQnBsBBBAAAEEEEAAAQRcKuA9tC/KK6GNBZ999pn8/PPPsn379nI5W20U0NFymtasWVNu+y2Xk2MnCCCAAAIhC+jI/HBSRcfa/+ijj6R169aijYjaARAtjf9qFOnkqr4j5MPx9s1b0f7O40VaX+c+om1ZG4W1of/HH3+UV155xSs0kI7Aj7RDK1g9s7KyzEmge/ToIaNGjZJly5aFFdon2L6tbeH+HFvlIn3XUDrakeH71IE+WfLCCy9Iu3btzMmW9SmHqkh9+/Y1n4zR+R4I91MVV4BjIoAAAggggAACCCDgfoHwWkuquL76x5iOdLOSPr7t/EPx888/F510rnnz5uaj0b6hC6xyvu/OUaM64V/9+vV9s/AZAQQQQCDGBJyhabRhe9CgQRHV0DfWue5k9OjRMmTIEK/96SSup59+ujkZrYbg0Q5oHelco0YNueuuu8xGSK8CFfihPBvyfU8z0n3rCOdIkn4XKK8U6bmX1/Ej2Y+es95rRxxxhHl/WfMZffvtt+bAhlDDH5V27LVr10r37t1l06ZNdlY9tsbSP/HEE6Vt27bmxL16P+t9PWfOHPMpBTtzkIWqdj/55JPNjhQdZKJhIZ3zgxQVFZmTLetTotoIXxnzEyhVr1695PHHHxed6LcqkoZD07pr2CHfybGr4nw4JgIIIIAAAggggAACCEQu4KoOAN94p74TtT3zzDO2hI72a9q0qWgji8Ys1nftGNCXrrc6DgoLC80RbFbBUDsNrPy8I4AAAgi4U0AbKq2knQGvv/56xCPjrf3ou8Zlv/nmm52r5F//+pcZtzsjI8NrvfVBOwfiLfmGefEdxR5vHmWtr04APHjwYPNpAGtf33//vZRXB4Du29n4r50BY8aMMTserOM53zdv3uz8GPXL2gnRu3dv87Vw4UJ58skn5f3337dDA+3du1cGDBhghl06//zzK6w+xx13nNnwf8YZZ1TYMULZsd5PGk5Kv0NX1dMPoZwneRBAAAEEEEAAAQSqVkC/K27cuNE8CX16NpbCr1atbPke3VUdABr3Vhv5Na7t3LlzzZFtgTh0BJyOVtOXb9JGfh31rzeljvjXies06fpQJ0b03SefEUAAAQTcJaANW9rQZyUNM1cejaX//e9/7UmCdd+33367OdmodRx/7+UZQ93f/qNxne+EwvqlUZ/iI0UuoBP/akeKlcqr4VYnZ546daq1WzMUzddffx00Vr+b72lthNe5O+655x6zU8WaY0HrpPMGrF69ulw6C21QY+Hwww+XESNGSP/+/aWqn4hwnhfLCCCAAAIIIIAAAggEE/jiiy9k3LhxZhb9PqtP15KiT8BVcwDoH0Q9e/aUoUOHynvvvSeffvqp140V6uSE+gecjmJbunSp3fivl0ZvUt8RidF3yTgjBBBAAIHyEDjqqKO8drN48WKvz5F+mDFjhlfRW2+91euzvw86EWm8JV9/nd+HVDaBunXreu2gvBrhp0+f7rVffRqgtO9LsXBPH3vssaJPUXTt2tWuv3YUzpo1y/5c1gV9MvW1116TJUuWmE8Y0PhfVlHKI4AAAggggAACCCCAgK+AqzoAfE9eR/HrH05W0titX331lRnG4eGHH5ZrrrlGzj77bNFGhlq1alnZ/L4fdthhZgxmvxtZiQACCCAQcwIaY9uZ/ve//zk/RrxsPf6oO9CO6dIm7szLyzMn+Yz4gC4t6Ov/5ZdfurQm0XPavhNN+4ZOtM403EZm5z2t++jQoYO1q4Dv8+fPD7itIjaEW6dQz0F/hjWElzNpmK/ySPrU6e+//y5XX321JCUllccu2QcCCCCAAAIIIIAAAgggUELAVSGASpy9scI5iaNu17A++urYsWOJ7BrLdN26dWYsU30sfteuXaKTB+ofsn/729/seQFKFGQFAggggEDMCZxyyinSuHFjsWKVv/322/LII4+UeSJ451wyOs+M/p4K1jj5wQcfyIEDB0L2dU56u2/fvpDLRVtGjTGuc/JYjcvvvPOOGQIlUKN1tJ2/8zrouUXDtZg8ebIXk4ay8ZecE1frfEoaNlHnTgqUfBunrYmGA+XX71sfffRRoM0Vst5Zp/K+Fg0aNKiQc/a9hyrkIOwUAQQQQAABBBBAAAEE4l7A1U8A6NXTR9B9/zANdFU1b/v27UUnVrvqqqvkjjvukBtuuEG0EciaFDhQWdYjgAACCMSWgP6/f8stt9iV0kZD/d1QWuOmVWD37t1+8zrnksnPz5effvrJKlLifefOnXLvvfeWWB9shTaaW0nPQTu23Zj0d/dtt91mn7o2Gl955ZV+Te1MjoU9e/aEnNdRrNwWnddBd1peIaR0XzpAIdwwMxoWUTtRrKQx5Tt16mR99Hp3nrt2UP36669e230/OO9p3abzMAVLd999t1mHYHnKe5uzTnovaZz+YMma/ylYHmvb7NmzrUXzvXXr1l6f+YAAAggggAACCCCAAAIIRLOA6zsANMzPzJkzRUdQVq9ePZqtOTcEEEAAgSgT0Abodu3a2WeloeS0U1jjfvtL2dnZopOfXnvttWYIur1795bI5jvpkc4B4C+fxrzXvP4mqy+xU8eKzp07Oz6JPPPMM16f3fTh5ptvliOOOMI+ZQ3DpE8GBGr8Vv9vvvlGrrvuOtNfO1CqKml8eOeTHePHjxffEDyRnps2TuucR2qjE9Fqnf3dQ7r/ZcuWmR0p/fr1E2fM/+HDhwc8fLj3kF4TZxo9erT4Norrdj1HHVgxatQoZ/ZKWfZ92qG0nwsrTKTOKRXsiQGd1EyfDLKSzrNw4oknWh95RwABBBBAAAEEEEAAAQSiXsD1IYBUWEdx+o5Oi3p5ThABBBBAoMoFtOP4ww8/NBudddS1Jm3Y1LBwGopG54fRPNrwrKFqtLHe2cjqrwLaOfDss8+Kjv7XpI3Z2smgDbTNmjUTHbWvI6j1ODr6un79+vLggw+KNoaHki688EJ59NFH7awvvPCCOYfA8ccfb56nnmPLli3lP//5j50nWhc0BIp24Gtjt9WYP2fOHPOzhl3RUezh+ldWXfX8dB6DqVOnmofUxv9jjjlG+vfvb879sGXLFnNiVx2Z36RJk4hO67fffpMnnnjCfGlng94/GrYqNTXVvNZZWVm2m/MAQ4YMEb1PAqULLrhA7rzzTtEQVZo0/JWOmNeGbb1vdb/61ORbb71lbtenJ0877TSZMmWK+Vl/HrRT4LzzzjOfMtD7WDsidB4mqzF95MiRcv/99/s9P3Mn5fzPP/7xD9H5n/RcNOnxdWJdncBXwxzpz4U+JWB1Tmg+PV99afgj7dDR8JH686jb9PrpvajXwJn0GM5wQ85tLCOAAAIIIIAAAggggAAC0SgQEx0A0QjLOSGAAAIIuENAJ4r/7rvvzIbbFStW2CetDbqRjOjW8CBPPvmkV3gb3c8rr7xi79ta0Anq9akD7WjQcERW46W13d/70UcfbYbKefPNN+3N2ghtNUTrygEDBtjbon1B5+HRJ/m0UdrZ2Lpt2zbRVzSnf//732ajuRU2SueT0IZnZyqtw8iZ11p2PllgrdN7Q+cv0legpPNP6BMDzhHr/vJqB5E+/fL000/bm7VDyjmqX5+EcaaXX35ZTjjhBLFC5xQVFYnOOeA774CW0bBW+iSAPi3zySefOHdTYct6H+lkuq+99pp9jOnTp4u+rNS3b19r0evpDb1+8+bNM192Bj8LGjrypptu8rOFVQgggAACCCCAAAKxImD9TebvO3ms1JF6xJ9AYixUeeHChfLuu++af7hqLGRrRFss1I06IIAAAghUvIB2AixatEi0Qbd58+ZBD6ij1rUhUUOD1KtXz29eDfujIWGCjfw+88wzZcGCBdKtW7eAk9f73bmxUhtjNYRJoC+lOkLcTUkbb9VfR7uX9kSfjr7u06ePfP755+ZTGlVZT33qQhu4dVS+v6QN8qHOU+Qsrw302mGkT0aEMkdRenq6XHrppeZ8E/p0iHMiaud+nct6r2uDdqDz850UuG3btvLDDz+YTz049+Nc1ms3YcIE+wkV39BBzrwVsaydLxoeKlD9nXUaNmyY6FMDpYWP1J8xvQ76pEBpYYUqok7sEwEEEEAAAQQQQKDyBDTUpX6H1de0adMq78AcCYEKFkgwerb+fFa6gg9UkbsfNGiQOEdt6h+z+pi3/iGqL/1D2lrWP9J9/9jVEYczZswwG3Q05APpoMBLL73kNXpOR5VefPHFBzOwhAACCEQooPHCrXAhugsNvRFOaI28vDzZvn27fXQdTa9hS8qa9NeiTtz7448/yqZNm8yJZrXRX38/aEiaLl26lNpoaJ2Ddkjr6Hbdn4b+0frp7yMNMeQ7kaiGwMnJyTGLaqiXUJKGNdEvphqyRc9bf8dpGBMNe+LbcLxhwwZ7l1qfQJ0XdiZjQc/5wIED9ip/v0PtjX8t6Ghq55MTNWrUkJo1a/pmC/hZ66GdAeqvYZd0f3q+GnJHY+JrrHdt8A6WIr03fMvVrl1bMjIygh3K3Kbl9Dr88ssv5j2t9W3Tpo306NFDGjVqVGr5YBn0ntBJev/44w/zftSfGb2vtOFa963hedQknJ8d5/HUWJ8e0RBAOqpf7/MjjzxSunfvboYacua1lvU7lz41Yz2NoNdGwx9pGed3LA29Yz0xUKdOnYDXTX+O1VCTNt4H6zizziHYu/486PXQnw+9n9TJ+rlwdgLoPvQc9V7T74Ia9kfDG+k56LXXn1HtoCvrNQx2rtG6TX9udTJl/b/Ius5Vfa56fzr/b9Hz0f+TAnWEVvX5RsPxMQv/KmAWvpn+TvJ9Yo+fzeCO/szK+rsv+BHdvxUz919Dt9RAOwCsMJg6p5aG/CQFF3j11Vdl3LhxZqYRI0aY89wFL8HWqhBwfQeA/sF22WWXhWynDSL6x4w2wOgoTx0l+eWXX5p/UOgfrToZnG4j/SlABwB3AgIIIIAAAgggEF8CS5cuNTuEtMPEOVF3VSrQMBu+PmaYhS8Qfgl/DbN0AAR39GdGBwBmwQXYWlkCdACEL00HQPhmVVHC9XMA6Ai0cJL+stURYfryTTraS58cICGAAAIIIIAAAgggEK8CGpaLhAACCCCAAAIIIIAAArEh4PoOAA0TYKWJEyeaj3rrPADWSx9b1mV9ZFgfAw+U9HHvYLFwA5VjPQIIIIAAAggggAACCCCAAAIIIIAAAggggAAC0Sjg+g4AayS/xvm34ilr3F3flJ+fLxr/2OoQ+PDDD82YyZqvd+/ecskll4hOcEdCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAWBBLdXoldu3aZVShtwkSN9X/ooYdKz549ZeDAgfLmm2+aEzlq4YULF5bLxJFut+T8EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGJHwPUdAMXFxebVSEhICOuqZGZmynPPPSfHHHOMbNy4UW6//XbJzc0Nax9kRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgWgVc3wFQs2ZN01ZD+4Sb0tLS5PHHH5fq1avLypUr5a233gp3F+RHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAqBVzfAaBhfTRlZWXJb7/9FjZy/fr15bTTTjPLTZo0SawnCsLeEQUQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIgiAdd3AJx44ok2p47mz8vLsz+HutCqVSsz6+7du+2JgUMtSz4EEEAAgcoR8Hg8Zqg2DddmvXQdKbAAZoFtAm3BLJBM4PWYBbYJtAWzQDKsRwABBBBAAAEEEEAAgfIWcH0HQO/evSU9Pd10Wbp0qdx6661iTQwcKtbOnTvtrPv27bOXWUAAAQQQiB4BbTDT/9+dL11HCiyAWWCbQFswCyQTeL0+Pen8udRlfjYDe+kWzIL7sBUBBBBAAAEEEEAAAQTKT8D1HQC1a9eWm266yRb58ccf5aKLLhIN5xPKpL7bt2+XL774wi5fp04de5kFBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTcKpDs1hN3nveAAQNk9erV8v7775urNZTPk08+KSNHjhQNEdS5c2dp27atNG7c2HxaoKioSLZu3SoLFiyQcePGiebXpPMBNGvWzFzmHwQQQAABBBBAAAEE4lFAv0Pn5+dLZmamXHvttfFIQJ0RQAABBBBAAAEEShHYsq1Y5v16iKzfM0ASEopk1sJaclwXj2RmJJRSks2VLRATHQCKduedd0qtWrXktddesw0PHDggX3/9tfmyVwZZGDRokHHDcpMGIWITAggggAACCCCAQIwL3HPPPbJ//35zYAwdADF+sakeAggggAACCCAQpsDqrGK5+/E8mfRFoRH6s4dRWl8iw54XeWTUfrnh8hR58LY0qVWTNlYTJgr+cX0IIKfhkCFD5OWXX5Y2bdo4V4e0fMYZZ8jFF18cUl4yIYAAAggggAACCCCAAAIIIIAAAggggAAC8SQwbXahdD73gLz/uTb+l6x5Xr7Ic68VyPG9D8iqtcUlM7CmSgRi5gkAS0/D/UyYMEFmzZoln3/+ufzwww+Sk5NjbS7xrmF/Bg8eLP3792f0fwkdViCAAAIIIIAAAggggAACCCCAAAIIIIBAvAss+71I+lydI/sOlC7x+2qPnHNFtsybnMGTAKVzVXiOmOsAUDEN49OzZ0/zVVhYKCtWrJCVK1fKli1bJDs7W5KSksx4/0cccYQcddRR5ucKl+YACCCAAAIIIIAAAggggAACCCCAAAIIIICACwVuuC8vpMZ/q2orVnlkxIt58tSwatYq3qtIICY7AJyWycnJ0qFDB/PlXM8yAggggAACCCCAAAIIIIAAAggggAACCCCAQHCBhYuLZPqcouCZ/Gwd+VaBPDw0TdKrMx+AH55KWxVTcwBUmhoHQgABBBBAAAEEEEAAAQQQQAABBBBAAAEE4kDgi2mFEdUyJ1dk+g/hdxxEdDAKBRSI+ScAAtacDQgggAACCCCAAAIIIIAAAggggAACUS+wc+dO2bFjh3meTZo0ifrz5QQRiDWBVVmRT+hblrKx5lhV9Ym5DgCPMQW1zgFAQgABBBBAAAEEEEAAAQQQQAABBBBwv8CUKVPk/fffNyty6623Srt27dxfqQquwZw5c2Tu3LnmUc4991zMKtg71ndvTLEaMCVJoRRLoniMl79UxAMA/lgqdV1MdADs2rVLXn/9dZkxY4Y50W9iYqI0atRIWrRoYb6aN29uvzdr1kzS0tIqFZmDIYAAAggggAACCCCAAAIIIIAAAgggUFkCS5YskUmTJpmH69ixIx0ApcBv2Fwss39uJVm7LzYGFhvx7ufVkWM7e6R2LQYZK13LZt6N+50SF8vlyROlZ9JsaZSwTYokSdZ5msmUopNlTMEgY7m5Ld6yGYY2RhUtuL4DYOvWrXLNNdfI5s2bbcLi4mLZtGmT+Zo3b5693lqwOgecHQPaWaCfq1VjZmrLiXcEEEAAAQQQQAABBBBAAAEEEEAAAQRiVWDlmmL5vxF58vHXOsT9hL9eIsNHijz2yn4ZckmKPHJnmtStHd+N2GeelCSPvSSSKvnySOoIGZjygXlLGIFYjA4TkWSjC8DoPpFrUsbJFcnvyBMFt8krBVdJUpJIr+6ub352/e3v+ivw0ksveTX+h3JFtmzZYj4psGDBAq/sHTp0kDfffNNrHR8QQAABBBBAAAEEEIgngVq1ahl/rCVJzZo146na1BUBBBBAAAEE4kxgyqxCuWBIjuzd77/iGvZm1LgC+d/0QvlyXLoc1tp7FLz/UrG5tmfXJOl0uEfuWHOz9EqeJVbDvzMKu7WcbIQEGpb6tNSR3bKpz508RREFt4TrOwBmz55tM2oD/vnnn2+O4tfJYdavXy9ZWVnm+7Zt2+x8gRZ0/gASAggggAACCCCAAALxLKDfoUkIIIAAAggggEAsCyxdUSR9r8mR/dml13L1Oo+cc3m2LPgsQ+rE6ZMAiYkJ8lbX16XO+lkmmNXY709Pt2kT642pr0nCST2MLKf7y8a6ShRwfQdAdvafP6ka11+fBsjMzPTLl5ubKxs2bJB169Z5dQzoZw0jROO/XzZWIoAAAggggAACCCCAAAIIIIAAAgggEFMCN9yXF1Ljv1XpVVkeefj5PHn+ofgMHV68e7fU/d8rxmS/RsgfCyXIu9VBkDTmCZG+dAAEoaqUTa7vAGjatKk5yr9hw4YBG/9VUmP7t2nTxnz5yubn55udA/v27fPdxGcEEEAAAQQQQAABBBBAAAEEEEAAAQQQiBGBhYuLZMbcorBr8/L4AhlhzAeQmRFKE3jYu4/qAjnTvhFPTnZIjf/OihT+/psUGK+Udoc7V7NcyQKuD17Vs2dPk2zjxo2ya9euiPhSU1Pl0EMPlaOPPjqi8hRCAAEEEEAAAQQQQAABBBBAAAEEEEAAgegX0Jj+kaS8fJFps8PvOIjkWNFWJn/J4ohPKf/XnyMuS8HyEXB9B8Cll14qGRkZUlRUJE899RShfMrnvmAvCCCAAAIIIIAAAggggAACCCCAAAIIxJzA6iwNZBNZWr0u8rKRHTE6ShXv2hnxiRRHOGA74gNSsISA6zsA6tevLw888IAkGMGlvv32WxkxYoQU6jTdJAQQQAABBBBAAAEEEEAAAQQQQAABBBBAwCFQVIY2fGP8cVymxFq1I653Yu06EZelYPkIuL4DQBl69eol999/vyQlJcmnn34q1113nWhIIBICCCCAAAIIIIAAAggggAACCCCAAAIIIGAJtGwaeXPoIc3jL/6/uqV26Gjxhf2e0v7IsMtQoHwFIr/jy/c8yry3U045Rfr27Wvu55dffpFLLrlEJkyYIHl5eWXeNztAAAEEEEAAAQQQQAABBBBAAAEEEEAAAfcLnHVyUkSVMMYdS6/uyRGVdXuhaqeeIZKWFnY1klu1llQ6AMJ2K+8CMXHXfvTRR/L8889LTk6O7aPLuu6FF16Qhg0bSosWLaR58+bmuy7rq1mzZlKtWjW7DAsIIIAAAggggAACCCCAAAIIIIAAAgggELsCPbokSeejEuXHX8OLBXTZBclSp3Z8PgGQVLee1LjiGtn36siwboyat98dVn4yV4yA6zsAvvvuO3n88ccD6ng8HtmyZYv5WrBgQYl8DRo0MDsGWrZsKUcffbT07t27RB5WIIAAAggggAACCCCAAAIIIIAAAggggID7BXQe0ZEjqslJA7KlIMRpRBvWS5BH7wx/BLz7tQ7WoOZNd0jej/Mlf8G8gyuDLGVceoWkn3lOkBxsqiwB14cAGjNmjJdVSkqKZGRkeK0L9mHbtm3y008/ySeffCKTJk0KlpVtCCCAAAIIIIAAAggggAACCCCAAAIIIOBygRM6J8m456tJasqfFcmQA3Jq0gy5LHmiXJI8SXokzpEUyTc31jPmsP3szerStLHrm1HLdNUSjDbX+q+8JdXPOs/ej8deErGXjQ6WGtffLLXvG+7YymJVCrj+CYDff//d9rvrrrukX79+5mTAubm5sn79elm3bp3Xe1ZWlmzdutUuwwICCCCAAAIIIIAAAggcFLj00kvN0Jr16tWT0aNHH9zAEgIIIIAAAgggEEMCF/09RdrW3C6/3v2k9NzziaQmeD8OsM+TIbNaXCFnjrpJDmkX2bwBMcRlViUxPV3qPf9fyZ09U/ZPGCv7v5smyQUF5raizBpS84yzpcZVQySl3eGxVnVX18f1HQCpqalSYNxoGspnwIAB9sXQ2P5t27Y1X/bKvxZ0YuANGzaU6BgI58kB333yGQEEEEAAAQQQQACBWBCYPHmy7N+/35wvKxbqQx0QQAABBBBAAAF/AvmLF0mT+66SRnt3iMdPaP/MhANyzvpRkvJ/U6Xw5TcluUlTf7uJy3XVevQUfX346qvy4dg3pdAY9X//o4/JySefHJce0V5p13cAdOjQQebPny8a6z/UlGbMWt26dWvzFWoZ8iGAAAIIIIAAAggggAACCCCAAAIIIICA+wUK12fJ9iGXS/Ge3WZl/LT/i7WuYMVy2X7dFdLwnY8lMYyw4+5XCq0GBxJ5OiI0qarL5frgVf379zf1tm/fLnPnzq06SY6MAAIIIIAAAggggAACCCCAAAIIIIAAAlEvsPuR++3G/1BOtvD332Tfqy+FkpU8CESdgOs7AE499VQ55ZRTTNiHHnrIjPkfdcqcEAIIIIAAAggggAACCCCAAAIIIIAAAghUuUDBit8k14hdH27a/9YY8RhzjpIQcJuA6zsAFHz48OHSuXNn2bFjh1x99dWycOFCt10HzhcBBBBAAAEEEEAAAQQQQAABBBBAAAEEKlggZ/q3ER3Bk5sjufN+iKgshRCoSgHXdwDs3r1bsrOz5bnnnpNzzz1X9PMNN9wgL774ouTSK1eV9xbHRgABBBBAAAEEEEAAAQQQQAABBBBAIKoECtetjfh8irIiLxvxQSmIQBkFXD8J8NixY+Xtt982GWrWrCkJxqzTOiHw+PHj5dtvv5ULLrhAWrRoIc2aNZPmzZtLZmZmGckojgACCCCAAAIIIIAAAggggAACCCCAAAKuFMjPj/i0PQWRl434oBREoIwCru8AcNZ/7969zo+yefNmGTVqlNe6WrVq2Z0B2iGgHQNW50CDBg3MDgSvAnxAAAEEEEAAAQQQQAABBBBAAAEEEEAAgZgQSGrcNOJ6JDVuEnFZCiJQVQKu7wBo2bKltGrVSjZs2CAFBQWlOu7Zs0f0tXTp0hJ5O3bsKK+//nqJ9axAAAEEEEAAAQQQQAABBBBAAAEEEEDADQIr1xTL9AVtZM2uK4yBrkXy5cx60uGoYmnc0PWRwMuFP63H32Tfqy+Fvy8j6khatx7hl6MEAlUs4PoOgH79+om+iouLZevWrbJu3TpZv359ife8vLxSqQsLC0vNQwYEEEAAAQQQQAABBBBAAAEEEEAAAQSiTWDZ70Vy+8N58tV3RcapHffXS+QpY6zr02MOyOX9k+Xf96TFfUdAWpduktymnRSu/D2sS1j9rPMkqW69sMqQGYFoEHB9B4CFmJiYKI0bNzZfxx9/vLXaft+2bVuJTgHtLNAnBw4cOGDnYwEBBBBAAAEEEEAAgXgWOOeccyQnJ0fq1eMP3Hi+D6g7AggggIC7BP43rVD+cX2OHMjxf97GdJkydlKhfDPTeCJgXHXpeESS/4xxsDYhKUlqDxsu26++VIwRxSHVOCGzhtQaek9IecmEQLQJxEwHQGmwGt9fX507dy6RddeuXWbnQHZ2doltrEAAAQQQQAABBBBAIJ4E3nvvvXiqLnVFAAEEEEDA9QKLlxXJAKPxPztA47+zghu3eOTcK3Lkxy/SpUG9+A0JVO2EE6X2AyNk9/BhpXYCJKSnS72XRkty85ZOSpYRcI1A3HQABLsiderUEX2REEAAAQQQQAABBBBAAAEEEEAAAQQQcJPAjcPy7Mb/2rJb+iZ/LiclzZZmCZukQJIly9NcphadJJ8WniN5kibrN3nk/qfz5eXHq7mpmuV+rpkXDZLkFofI7kful8I1q/zuP7Xz8VLnocckpd3hfrezEgE3CER9B4DG9s/Pz5dq1eL7PyU33EycIwIIIIAAAggggAACCCCAAAIIIFBeAnv3eWTCJ4UyZnIvWbmphzmh7Ytv15aClALpf06ypKYmlNehXLufBT8Xyaz5GvNfZGDye3Jf6jNSM2G/V32OkSXSO/kruTPlRbk7/2GZXtRTXn+3QJ74V5rUqhnfhtV69JRGn34reXO+l/kvj5QtvyyWIoOkfa/T5fArBktap5KRRLxw4/zDiSeeaIeNbNeuXZxrRG/1o7oD4Oeff5a7775bdu7cKd26dZPHHntMatSoEb2anBkCCCCAAAIIIIAAAggggAACCCCAQJkF3ppUIHc8kis7dumuWtn7m7lQZObCXGnVPEFGP1FNTu8Z1U1b9nlX1MKXMwrNXd+T8pzcmPqaaKz/QKlp4hYZm/ZPGZo/QiYV9pWpswul39kpgbLHzfqE5GSp9reTZfmyFfLW2k1mvYf/vT+N/yHcAUceeaToixTdAlEd7GvUqFFm478Szp07V0aPHl1Cc+nSpTJv3jzZtGmTMW9HaBN3lNgJKxBAAAEEEEAAAQQQQAABBBBAAIFKEJg6darcc8895mvRokWVcET3HeLh5/Lkijusxn//579mvUfOuixHxn9Y4D9DnKxds84jf0/6wm78TyhlQH+CeOTJ1AelY+IS0bIkBBCIfYGo7ibNy8vzugJ//PGH12f98M0338jbb79trk9JSZGmTZtKixYtpHnz5l7vjRs3lmSjR4+EAAL58Xf+AABAAElEQVQIIIAAAggggAACCCCAAAIIVJWARjlYterPeOP79u2rqtOI2uN+9GWBPPRcfkjnp+NAB/9frhx5WKIce1RSSGViLVNiYZ4R9udps1qlNf5rJs2TIoVyf+pTss7zTqxxUB8EEPAjENUt4n369BEd4W+ljh07Wot+3wsKCmTt2rXmyzdDUlKSNGnSxKtToFmzZuZnfdfOAxICCCCAAAIIIIAAAggggAACCCCAQNUIFBR45Pbh3oNBSzuTAiMCztBH8mTqu+mlZY3J7SfID6KhfcJN3ZPmS7X0DUaxQ8MtSn4EEHCZQFR3APTr18+M+f/DDz/IoYceKhdffHEJXm3YDyUVFRXJ+vXrzZdv/gSj+7NRo0bSo0cP8xE83+18RgABBBBAAAEEEEAAAQQQQAABBBCoWIGps4tkrRHaJ9w07YciI5xNsbRqEdWRrsOtVkj5u6cYkyJEmE5I0rJ0AETIRzEEXCMQ1R0Aqnj66aebr0CiN910kwwaNMhs2F+3bl2J9z179gQqaq/3GDOkbN68WZYtW2avYwEBBBBAAAEEEEAAAQQQQAABBIILFBYWSn7+n+FaUlNTg2dmKwKlCMyYU1RKjsCbtWw8dgA0Stgm2YFZgm5J27816HY2IoBAbAhEfQdAKMy1a9cWfR111FElsu/fv198Owb0SQBdt2PHjhL5WYEAAggggAACCCCAAAIIIIAAAqEJTJ48WSZOnGhm1gF6F154YWgFyYWAH4FNW42g/hGmjVsiLxvhIaOiWEJ6RsTnkViGshEflIIIIFDpAjHRARBMLTMzU9q3b2++fPPl5OTYYYG0Q4CEAAIIIIAAAggggAACCCCAAAIIIFA1AhnVjRlqI0yZGZGXjfCQUVEspU3biM8juQxlIz4oBRFAoNIFYr4DIJho9erVpV27duYrWD62IYAAAggggAACCCAQLwI695Y+RdukSRNZvHhxvFSbeiKAAAIIRIHAEW0jj+F/eOvIy0ZB1SM+hWq9zhB57CERI7x1OCmxVm1JO65rOEXIiwACLhVwfQdAQUGBpKSkuJQ/stNetWqVZGVlSXFxsfmH2eGHHy6JifH5iy4yQUohgAACCCCAAAIIBBLYvn272QGQlpYWKAvrEUAAAQQQqBCB3qcnyy0P5oXbli21aoic0j2pQs4p2nea3Ky5pPcdINkfvR/Wqda47iZJiLP2tNKABg4cKH369DGz1apVq7TsbEfANQKu7wAYNWqUzJkzR7p37y433HCDJCdHViWduCjSspVxtfX83njjDRk9erQZtsh5zPr168t1111nvqK5Ds5zZhkBBBBAAAEEEEAAAQQQiDaB5cuX20++dO3aVRo3bhxtp8j5IBDTAoc0T5TL+yfL2EmFYdXz7n+mSmpqfIYAUqjad98veQvnSVHW2pDc0rr1kMzLBoeUN54yZWRkiL5ICMSaQEwMG1+5cqWMHz9ecnNzw74+2dnZZlnt4Zs5c2bY5SujgE5W3K9fP3nwwQdLNP7r8XWU1qOPPiqDBg2S/Pz8yjgljoEAAggggAACCCCAAAIIxJyAdgBMmjTJfK1Zsybm6keFEHCDwDP3V5NDW3g35jdM2CadEhdLx8QlUlt2e1Xjb8cnydAhqV7r4u2DhvNpMOYdSTnsiFKrXq3nKVLvpdGSEOEA2lIPQAYEEIg6gZjoACiL6qZNm+TFF1+Ubdu2ydtvv12WXVVIWR35f/HFF8vChQvN/ffq1cv8MqpfTH/99VcZOXKk6BMAmr777jt55plnzGX+QQABBBBAAAEEEEAAAQQQQAABBNwmUK9Ogkx5J106tiuWS5PflW+q95GF6afIp9UvkS+qXyiL00+Uj6sNlHOSvpFTeyTJ5Nerx/Xof+v6aiighu9Nllp33idJTZtbq+335HaHS51/Pyf1Xn5TEjONmEkkBBCIGwFXdQDs27dPtmzZYsSCC29ik2BXs02bNtKhQwczy6JFi+TAgQPBslf6Ng3pM3ToUMnMzJQ777zT7KTo0aOH1KxZU+rWrWs+GfDOO+9IQsKfveOvv/665OTkVPp5ckAEEEAAAQQQQAABBBBAAAEEEECgPARaVt8uX9e7XP6dNlyOSPyjxC6PS/pZXq12m7zb4DaplUobiAWUkFZNagy+TppMmS0zr75JRtRsKMNrNZJVI56RxpO/kYw+/SWBOSQtLt4RiBuByALmVxHPH3/8Yca510l/mzVrJs2bN5fNmzfbZ7Nx40Zp3bp12LH8tRNg6dKl5qS669evF51UN5rS2WefLT/88IPUq1fP72kdeeSRctxxx8mCBQtEQxotWbJEunTp4jcvKxFAAAEEEEAAAQQQQAABBBBAAIFoFSg+sF+2XT1QCn9f4fcU/xr/aG7L/fZL2XHrdVLfGNWekBSfkwD7RTJWZtepJ0tSq5ubC41lEgIIxK+AqzoAGjVqZF6pgoIC0XiMvjEZNQZ+kvEfvk7U1KJFC7OTQN+1o0Bf2mmQlpZW4mprDH0r6b6jMQVq/LfOtWHDhtai7N+/315mAQEEEEAAAQQQQAABBBBAAAEEEHCLwJ6nHw/Y+O+vDnmzZsj+t9+UGpdf7W8z6xBAAIG4F3BVB4A2cl9yySUyd+5cWbVqld+LV1RUJBs2bDBf/jLoPqynBzSEjubV0fVWsjoZrM9ued+1a5d9qtacAPYKFhBAAAEEEEAAAQQQQAABBBBAAIEoFyjaulkOvD8h7LPc9/J/JHPgFUxsG7YcBRBAIB4EXNUBoPHwb7/9dvO66Kh97QiYOHGi/PbbbyFfq61bt4q+fvrppxJl2rdvLw0aNCixPtpX5Ofnyy+//GKeZnp6esghjHSCYWfHgb966pwI+lSEPlmRaMSJKy4uFu1kISGAAAKVLaD///gm/f+oPOeF8d2/2z9jFv4VxAyz8AXCL+HvPtN10fL/mXUe+h4t3/v8nYeus+bBCv8qxH4JzMK/xpiFb+bv/zN+NoM7BjILXip+th6Y8rUYv3zCrnDxrp2SM2+OpHXrHnbZWC3gvNd02d//cbFad+qFQGUIaDupW76LuqoDwHnxdJT7eeedJzovgNUB8PLLL8uOHTtE4/ivW7fOftd1paXq1avLXXfdVVq2qNz+6aef2mF/+vTpIzpHQihJ7TR/KKlJkyai5jrHgHagkBBAAIFoEHCGcIuG83HDOWAW/lXCDLPwBcIvsW3btvALVVAJqwNAGwui+XtfNJlV0KUo991iFj4pZpiFLxB+iWj+vzb82pStRPEvP0e8g12LFkrioW0iLh9rBbX9xkp79uyJ6t/p1nnyjoCbBDRce2pqqitO2bUdAJau9QeKfj7ssMMkMzPT2mS/5+bm2p0B2jlgvXT0e7Vq1aRDhw5maCENDeS2pP+hP/HEE+Zpa8P/zTff7LYqcL4IIIAAAggggAACUSSgg2L0CVB/36uj6DQ5FQQQQACBWBQwohBEnLLLUDbig1IQAQQQiH4B13cAhPKohTbyt23b1nxF/yUJ7wwfeughs0NDS91yyy3SqlWr8HZAbgQQQAABBBBAAAEEHAJDhgxxfGIRAQQQQACBShSoWy/yg9UpQ9nIj0pJBBBAIOoFXN8BcPnll0uPHj3MRvC0tLSoBy/PE3z33Xdl/Pjx5i67desmt956a3nunn0hgAACCCCAAAIIIIAAAggggAAClSaQ0LGTeCaOi+h4WpaEAAIIIFBSwPUdAHXq1JHjjz/efJWsXuyumTVrlj1nQYsWLWT06NGikySHkzRkkr/JkJ37eOWVV2TmzJn2pBYZGRnSqFEjZxaWEUAAgUoR0FjUvnF4deJ2nXiH5F8AM/8uwdZiFkzH/zbM/LsEW6uT8PnOLdGwYUP7+1awsvG6DbPwrzxmkZn5luJn01fE+7O/SUUx8zby/VRYWOi7ir+xHSKec86TLc8/IcXbwpt7MOXoY6VBl+Mde2JR22+sVKtWLe4zC4N3BMpJIJSoNOV0qDLvJrwW4zIfruJ2oJPm6BeNeEi//PKLDB482IzNWrduXZkwYYI5QW+4dddGs/T09KDFdF4BZ+Oa3tzOz0ELsxEBBBCoYAH9/4j/k8JDxiw8L82NWXAz53xMVk7MLAn/7/7M+I7l38pa689M7zM3/eFl1aWy3jELXxqz8M20E9g38bPpK+L9WX18k791vnni5nP1dKl1xz2y6193hF5lw7T23cP4u8BHzPk7Uu8x7jMfID4iEEcCJX/zuLDy8+fPl379+smkSZOCnr2OTvjxxx/NfJp33rx54q/3PehOqnjjypUrZeDAgbJ//36pUaOGvPPOO9KmDbPcV/Fl4fAIIIAAAggggAACCCCAAAIIIFAOAhl9B0jmoKtC3lPtex+WtM6M/g8ZjIwIIBB3Aq5/AkAb8B9//HFzNPyTTz4pRx99tGhoG9+0fPlyGTZsmGRlZXlt0qcG7r33XnMeAa8NUfhh/fr1ctFFF8mOHTvMkfsa/79jx45ReKacEgIIIIAAAggggAACCCCAAAIIOAX0KZP8RT/KEfO/lyH7tkuxJEizmVMl/5iOknp4e2fWuF+ufd/DktS8hex9/inx5Ob49UisU1dqP/iopJ91nt/trEQAAQQQ+FPA9U8ATJs2zZwAWKtzzjnn+G3837Bhg9x4440lGv+1jIYOGjp0qBnnXj9Ha9LzvPDCC2Xjxo1SvXp1GTduXNzNexCt14bzQgABBBBAAAEEEEAAAQQQQCCYQN7PP8nWf/SWnZcNkPbzZ0uvvANyWt5+OeTbL2Rr37Nk29WXSsHqVcF2EXfbalxxjTT+eqZk3nqnbGvaQrYkJsvGpGTZ1e4IqX3fcGn8zSwa/+PurqDCCCAQiYDrOwCmTJli1/uKK66wl50LTzzxhOzbt8+5ymtZQwM9/PDDsnv3bq/10fJBR/xr4/+aNWukWrVqMnbsWOnevXu0nB7ngQACCCCAAAIIIIAAAggggAACAQSyv5gs2wb1l4IliwPkEMmbPVO2Xni+5M2fGzBPPG5IatBQMq/5p8zsc5HcVreZDK3TTH67fIgRIuhKSczIjEcS6owAAgiELeD6EEDLli0zK920aVNp3bp1CYAlS5bInDlz7PWnnHKKXHvttVK7dm1ZsGCBPP/887Jr1y7Zu3evOTfANddcY+eNloUHHnhAVqxYYZ6OdgAMHz486Kl16dJFHnvssaB52IgAAggggAACCCCAAAIIIIAAAhUrkL94key8+zbxGOGLE0o5lMeY62/7TddIow+/kORmLUrJzWYEEEAAAQRCE3B9B8D27dvNmrZs2dJvjSdPnmyv1zyPPvqopKSkmOs0ZJBOoHv55ZdLcXGxfPvttxKNHQAHDhyw66BPKZT2pMKxxx5r52cBAQQQQAABBBBAAAEE4lPgjz/+EGvAVKdOnaRx48bxCUGtEagiAY35v+uRYSIhNP5bp+jZu0f2PP241HtulLWKdwQQQAABBMok4PoOAG2415SYWDKakW6bPn26uV3/GTRokN34b63UCYN1xPy8efNk1apVst/occ/MjK7HyM4880w55JBDrFMu9f20004rNQ8ZEEAAAQQQQAABBBDwJ6BPnmqjVXJysjlYxl+eqliXk5Mj1sCYaPu+XhUeoRxTn4Z+++23zaxqdvzxx4dSjDwIhCyw7I9imbbgCFm18xpJSCiWr2e3lK7di+XQlkkh7yOWM+ro/4JfA4f98Vd3j7Ey56vPpWj7Nkmq38BfFtYhgAACCCAQloDrOwA0lI/GyNfJcX3T/PnzzfA+uj41NVXOOOMM3yzmZ+0E0A4ATZs3b5a2bduay9Hyz8CBA6PlVDgPBBBAAAEEEEAAgRgXOO6448xBMc2aNZP169dHTW2/+eYbGT9+vHk+119/vbRq1Spqzo0TQSDeBJb8ViS3PpQnU74vMqre9a+XyJiP9JUt/c9JlmcfSJOWzUoO1IsnK43rH24ywwQZnbB5c76X9PP7hluc/AgggAACCJQQcP1vY6uxXifIteLkW7WcOHGitSgnnHCCZGRk2J+dC+np6fbH7Oxse5kFBBBAAAEEEEAAAQQQQAABBBA4KPDF1ELp+vfsvxr/D653Ln3wv0I57twDMm+RdhDEb9q6NPJO1H2rNsQvHDVHAAEEEChXAdd3AJx00kk2yL333is//fSTbNmyRcaMGSPff/+9vU3j/QdKOgGwldLS0qxF3hFAAAEEEEAAAQQQQAABBBBA4C+BX5YXyT/+mSPZOaWTbN8l0vuqHNmw+c+wvaWXiL0cWZsjb3L5Pau0KYNjz4sala9A+/btpW/fvuZLn+ojIYBA/Aoku73q559/vrzxxhuikwFnZWXJddddV6JKTZs2lZNPPrnEemuF88kBJsayVHhHAAEEEEAAAQQQQAABBBBA4KDATcPyQmr8t0ps3eGRex7Pk3EvVLdWxdV7ljSXwyKs8TppEWFJiiHwp0CPHj1EXyQEEEAg8u7oKLGrXr26PPTQQ+YkZYFO6a677gq4XTsOfv75Z7No8+bNpVatWoF2w3oEEEAAAQQQQAABBBBAAAEE4lJg0ZIi+W5e+CF93v64ULbtiM+nAH5vcIp5rxgh/UNOmjffkyybmncPuQwZEUAAAQQQCCbg+g4ArVzXrl1l5MiRJSYC08b8Rx55JGiP59ixY6Wo6M8vMaeeemowK7YhgAACCCCAAAIIIIAAAgjEsIDZ+FpUSwqKako4jbYxTGJX7asZhfZyOAvq+O2s8DsOwjlGtObNaN9Ophb2lIQwovlo3rcLL5SmrRmcGK3XlfNCAAEE3Cbg+hBAFvixxx4r7777rixfvlw2b94stWvXliOPPFJSU1OtLCXeFy9eLO+99565XvP94x//KJGHFQgggAACCCCAAAIIIIAAArEt8O3MQnnxjXz5cvrFUlA4yKzs1Q8Uysczc2XokFTpdmxSbAOEULs168MYxu6zvzXr4vMJgLNOTpb+T98rnZMuktoJB+ce9OHx+ri2uLk8V3ijrPgb95wXDB8QQAABBCIWiIknAKzaJxhd5TrJSa9evUQ7BII1/muZ1q1by1FHHWUWHzJkiDRq1MjaFe8IIIAAAggggAACCCCAAAIxLpCd45FLbsyRMy7NkU+/LTIa/w82uubmJ8n7nxfKCX2y5aZhuVJYGHkDeCwwBhvFniwFkiiBR/kHKxsLNoHqcNzRSdL8uEPlytxRstNT28zm78kSa90ao/H/stxX5Py+9aV+3ZhqrglExHoEEEAAgUoQcM1vlK1bt0pubm65kmRmZsqLL74o119/vVx22WXlum92hgACCCCAAAIIIIAAAgggEL0C2qD/98E5MvFTZ2gbZ6yWg8sj3yqQy28r379Ho1fG/5kd2sK7+eDoxF/lmdT7ZH71U2RVeidZnX6MzK5+hoxIfURaJ6z22olvWa+NMf7hpUfSZEnKsXJ2ziT5sPB8KTa6SnxTgaTImIJL5fyc92R/nUPl8XvSfLPwGQEEEEAAgYgFSv7miXhXFVtw8uTJctppp8mNN94oq1atKreDZWRkyODBg42YfAe/3JXbztkRAggggAACCCCAAAIIIIBAVAo8PjJfpnwfeNS670m/M7lQXp+Y77s6bj6ffcqfT0foaP9HU4fLZ9UukgtTPpZGCdvMGPeJCR5pnrBRrkiZKFOq95HbU0YaNh5JNFodTu8ZM9GHw77enY5MkndHVpfdaU3k1rwn5Njs7+Sfuc/Io/lD5eG8u+Sa3Bfl6Ozv5cH8eyWlTi357M3q0qyxa5pqwvagAAIIIIBA5Qu46rdwQUGBzJ8/XzZu3GiG76l8Lo6IAAIIIIAAAggggAACCCDgdoG9+zzyxH/Db8y//+l8ufIfKZKUFH8DyDoekSSn9UiUixfeIWcnT7UnSXaOpbOWk4xwQHekjpK6CbtkVZ+HpF6d+PNy/oz8/cxkmfNJutzyQJ7MmFtHPis6W3wjJvU7K1meezBNDmlO47/TjmUEEEAAgbILuKoDINTqFhUVGV/IDsZuLK3cV199JS1atJCWLVuKhgUiIYAAAggggAACCCAQrwJTpkwR/T5d2nxa8epDvWND4ItphXIgO/y6bNrqkVnzi+TkE2LyT+lSQV7uPE6q/fxn47/V2O+vkG7TuPZXprwjiV3/ZmTp7S9bXK07un2STH8/XRYtyZeHn5gp8xZuNOpfLGeffrjcd0dXaX0IDf9xdUNQWQQQQKASBWLyW8vEiRNlzJgx0qxZM7ntttukc+fOQUnvv/9+c7tOIDx27NigedmIAAIIIIAAAggggEAsC3Tt2jWWq0fdEDAFfvq1OGKJRUuKjQ6AiIu7tmDxgQNS/f3/GE3WYob8Ka0iVgdBwutPiOfC8wm7+xfYUYcnyqnHL5Xtq98z15x14s1G438c3lCl3UA+2xs3biydOnUy19apU8dnKx8RQAABBIIJxGQHgFZ43759snz5ctmxY0ew+rMNAQQQQAABBBBAAAEEEEAgzgR27TGGp0eYdu6OvGyEh4yKYrkzp4ln/z4JN5hP0bosyV+8SNKOOTYq6sFJuFOgZ8+eoi8SAggggED4AjxjFr4ZJRBAAAEEEEAAAQQQQAABBFwsUL9uuM3YByvboF7kZQ/uxX1L+b8ujvikC8pQNuKDUhABBBBAAAEETAE6ALgREEAAAQQQQAABBBBAAAEE4kqga6fI/xTu2in0+eZiCbV4186Iq1OWshEflIIIIIAAAgggYApE/q0HQAQQQAABBBBAAAEEEEAAAQRcKHDmSclSL4Iw4m1bJcjxx8Tnn9GJtSMA++veKEtZF95enDICCCCAAAJRJRCf31yi6hJwMggggAACCCCAAAIIIIAAApUpkF49QR6+Iy3sQz7xr7S4ncw2tUPHsL2sAilHHmUt8o4AAggggAAClSxAB0Alg3M4BBBAAAEEEEAAAQQQQACBqhe48YpUueTvySGfyB3XpsgF56SEnD/WMlY7uZckZGSEXa2kJs0k9ZjOYZejAAIIIIAAAgiUj0Do33bK53jlspdRo0bJ1KlTpUWLFvarefPmkhHBl5FyOSF2ggACCCCAAAIIIIAAAggg4DqBcS9Uk2aN8+TZ1wqkuFhP32O8vCf5TTH+an7k/1Ll7hvCf2LAdSBBTjgxs4bUuPZG2fv8k0FyldxU6/a7JCGRsYclZViDAAIIIIBA5Qi4sgPgjz/+EH35prp164p2BOTm5tqbNmzYIDt37hTdRkIAAQQQQAABBBBAAAEEEEDAEkhKSpCnhlWTK/+RIi+NzZdJn+2SHburGZs90qRBvlzUp47cfGWqHNqSBmw1q3HNPyVvwVzJmzXDIgz6nt7/Iknv3S9oHjYigAACCCCAQMUKuLIDIBCJNvTry5n0aQF9paenm50D2kGgTw5Y782aNXNmZxkBBBBAAAEEEEAAAQQQQCDOBI48PElevCdfTi98Wn41njbX1LVPXznrzrslsRqN/9btkJCUJPVHvia7HrxXsj9+31ztfGbCXk5IkMzB10mtO+6xivKOAAIIIIAAAlUk4JoOgLPOOkvq1asn69evl3Xr1pnvuuwc7R/MMDs7W1asWGG+guVjGwIIIIAAAggggAAC8SwwduxYKSgoMAfQDBw4MJ4pXFv33FyPfPhlobz12QmydMuRRkAbj7z2QV2pVqdQ/n5GsiQne4e4cW1Fy+nEi3Zsl70vPiMHPnpPuhn3fjdrvxPGyKYP35GMgZdLzX/eIhoCh2QESEpNk7qPPyMZ/S+UfePfkH3TpkhKfp5JU5CeIbXPPEcyr7xWUg9vDxcCCCCAAAIIRIGAazoArHj/vmbbt2+3OwSsjgHr/cCBA77Z+YwAAggggAACCCCAAAJBBG666SbZv3+/6JOydAAEgYrSTZO/LpQb78+V9Zt0LHYb+yynzReZNj9X2rZKkFf/XU169XDNn4J2HSpiIX/pr7L9n1dJ8dYtfnfvyc2R/WNekdwpX0v9V8dKcstWfvPF48q0Lt0kqdNx8u4rr8gnEyZIsdGvdO3Nt8iFF14oCcYTACQEEEAAAQQQiA4B13/rq1+/vujr2GOPLSG6a9eugJ0De/fuLZGfFQgggAACCCCAAAIIIICAWwX+80a+3PLgnyOxA9XhjzUeOePSHHnruWoysG9KoGxxsb5w8ybZfu1lUrxzR6n1LVy7WrZde7k0ev9TSaxZq9T88ZYhh0l+4+2SU18EEEAAARcJuL4DIJh1nTp1RF9HH310iWzaAWCFE7KeGEhNTS2RjxUIIIAAAggggAACCCBQ9QIbt9WUnTldjHA2xbJxW0bVn1CUncGUWYVy60PBG/+tUy4qErlqaK4c0SZROndMslbH3fuexx4KqfHfginKWiN7jFBBdYYNt1bxjgACCCCAAAIIRL1ATHcABNOvWbOmdOjQwXwFy8c2BBBAAAEEEEAAAQQQqBoBjWX/nzcL5EVjZPv6TecbJ6EvkbueFfnvpANyxzWpct2lKZKSEt/hRoqLPWbjv0ej/oSY8gtEbn84T2ZMSg+xRGxlKzQa83O++V/YlTrw7nipdev/SWKNmmGXpQACCCCAAAIIIFAVAolVcVCOiQACCCCAAAIIIIAAAggEE1i3sVi6982Wux7L+yuevXfu1VkeufmBPDnlwmzZur3Ye2OcfZrzY7EsWRG+wXfziuT31eGXiwXenBlTI6tGYaHkzp4ZWVlKIYAAAggggAACVSBAB0AVoHNIBBBAAAEEEEAAAQQQCCywZ6/Gqc+WRUtLb5yevbBYzr0iR3KMpwXiNU2bXRhx1ctSNuKDRkHBoqy1EZ9FYRnKRnxQCiKAAAIIIIAAAhEK0AEQIRzFEEAAAQQQQAABBBBAoGIE/vVEnvy2MvQG/YW/FMuIF/Mr5mRcsNcNW0K38q3Ohs2Rl/Xdl5s+ewqMGEiRprKUjfSYlEMAAQQQQAABBCIUoAMgQjiKIYAAAggggAACCCCAQPkLbNlWLK9OCL9x9rnX8mX/gfhszE6vFvl1SK8eeVk3l0xq0iTi0y9L2YgPSkEEEEAAAQQQQCBCAToAIoSjGAIIIIAAAggggAACCJS/wOdTi6SoKPz95uSKfDMz8lA44R/x/9m7Dzi5qnpx4L/d7CabnhB6UJo8QIr0ItJBBEFABQUFBEXaQ6xPBBQFRaSDFCkCUnxSVERUFBVUQB8C/hUF6YIB6UnIpm022f+cgd3sJrO7d7bOzP2ez2eTO/eec+4533Pnzsz93VI5Jf5rjb7/rOtP2coRKL8lTVtvW36hN0uM2updfS6rIAECBAgQIEBgqAX6/k1xqFtqfQQIECBAgAABAgQI1LzAo0/2ft//7hD6U7a7Oqth/nt3boj6PvyyaxoVseu2DdXQxQFvY+MG74jG9TYsu96mHXaOhpVWLrucAgQIECBAgACB4RLow9fE4Wqq9RIgQIAAAQIECBAgMNgCI0aMiPa/wV5Xqfpnz+n7bXya55SqsfbnTV2xPj72wfIP5B932MgYN7au9oFK9LCuri4mnXRKFDb2EktLz6obMyYm/s+XSy80lwABAgQIECBQoQICABU6MJpFgAABAgQIECBAYDgEZsyYEa2trfHMM88Mx+pjpeX7fkB65RX6XnZYOjuAKz3jxKZY/S3Z+/+Odevjy8eNHMAWVF9VozbaJCafdnamIEBd0+iYct53onH1Naqvo1pMgAABAgQI5Fqg/NNEcs2l8wQIECBAgAABAgQIDKbADltnPyN7yXbssFXfyy5ZV7W9njK5Lu64fkzsediceP6JmbFvw22x/Yh7YpX652NRW3082zY17ly4bdzS+t5Yb8Nxcet3R8fYMdkDBtXmkbW9Y9/3/miYukpM/9qJ0fr4oyWLjSwECiadfFqMXOftJZebSYAAAQIECBCoZAEBgEoeHW0jQIAAAQIECBAgkDOBrTcdEeu+rT4eeaK8ZwFsuVF9vP2/8hsASJvJmqvVx92f/HnM+OYpMarl9S5bznrxz9i94TfxtckXxgpHfyMmrLB7l+V5fjFq0y1ihVt+GXP/dG/cd/EF8eJDf4u2wi2C3rrFlrHJUf8dTRtvlmcefSdAgAABAgSqXMAtgKp8ADWfAAECBAgQIECAQC0J1NfXxbknF55OW0ZKt3E/9+SmMkrUZtbXLzw35n7t80sd/O/c29FzXonXP3dENF//vc6zcz9dV3iK8qgtt45HttkxLpywXFw0ftl4ZsfdYtRGm+beBsDAC4wfPz5WXHHF4t/o0aMHfgVqJECAAAECnQRcAdAJwyQBAgQIECBAgAABAsMvsNv2DXHmiaPiC9+Y32tjCidqx3dOGxXpyoE8p7m/+WW8ftG5mQlmnHZyNK6zbqSz3yUCBIZWYLfddov0JxEgQIAAgaEQEAAYCmXrIECAAAECBAgQIECgLIHPHzEyVp1aF8d+ZX68+EpbybJvWbmucPC/KfbYKd8/a9oWLowZ3zq1pFG3MxctihmnnxIr3HRbt1ksIJBFYN9994299967mLW+cCWFRIAAAQIECFSWgE/nyhoPrSFAgAABAgQIECBA4E2B/fZsjCfvHhtXnDEyNl33mRg/8p8xftTDsfU7psW1542Kx+4am/uD/4lq/v3/Fwv//WzZ282Cv/8tFnTz4NuyK1MgtwJ1hctwRhTuw5X+0rREgAABAgQIVJZAvk+Vqayx0BoCBAgQIECAAAECBJYQGDumLj62X0O0zrwnrrvuuuLSIz98ZBy47zoONr5p1XL/fUuoZX+ZggeNa62dvYCcBAgQIECAAAECVSXgCoCqGi6NJUCAAAECBAgQIECAQFeBhS+/1HVGGa/6U7aM1chKgAABAgQIECAwTAICAMMEb7UECBAgQIAAAQIECBAYCIG6sWP7XE392HF9LqsgAQIECBAgQIBA5QsIAFT+GGkhAQIECBAgQIAAAQIEuhVoXHOtbpf1tqBhjbf1lsVyAgQIECBAgACBKhbwDIAqHjxNJ0CAAAECBAgQIDDQAkcccUTMmzcvJk+eHOedd95AV6++QRBo2n6niBGFn3YLW8uqvW7M2Gja+l1llZGZAAECBAgQIECgugRcAVBd46W1BAgQIECAAAECBAZV4Pvf/35cc801cfPNNw/qelQ+cAIjpiwbYz/80bIrHP/xI6OuqanscgoQIECAAAECBAhUj4ArAKpnrLSUAAECBAgQIECAAAECJQUmfuaLMf++e6P18cdKLl9y5siNN4vxnzhqydm5f73RRhvFuHFvPBdh7bXXzr0HAAIECBAgQKD6BQQAqn8M9YAAAQIECBAgQIBAzQuMmj0r3rZgfrQVeto4u7nm+1tuB+sLDwJe7orr45X//kQseOivPRYftdU2MeW8S6Ju5Mge8+Vx4WqrrRbpTyJAgAABAgQI1IqAAECtjKR+ECBAgAABAgQIEKgxgbaFC2POj2+KWdd8N3Z5/NHYpb1/534jXvrlrTH+0MNj9Hv3jrq6uvYluf5/xPIrxPLX/yhm33B9zLr2ylj47L+6eDS87b8KZp+MMft8MOrq3Q22C44XBAgQIECAAIEaFRAAqNGB1S0CBAgQIECAAAEC1Syw8LVX49VPHREtD9xXshsLHn4oXvvCp2LUT34YU865KOrHTyiZL28z6xobY9xHPxajDzgorjvrzLjnxz+MRQWE3Q85NN5/5FGCJXnbIPSXAAECBAgQyL2A0z5yvwkAIECAAAECBAgQIFBZAovmzo1XDj+o24P/nVs7/+7fxStHHxZtCxZ0nm26IDB34qR4pLEpHi38zS9MSwQIECBAgAABAvkTEADI35jrMQECBAgQIECAAIGKFnj9grNiwcN/z9zGlvvvi1nfvSRzfhkJECBAgAABAgQI5EVAACAvI62fBAgQIECAAAECBKpAYOH06dF8/dVlt3TWFZdE27x5ZZdTgAABAgQIECBAgEAtCwgA1PLo6hsBAgQIECBAgACBKhOY9/vfRvThdj5ts2fHvD/eXWW91VwCBAgQIECAAAECgysgADC4vmonQIAAAQIECBAgQKAMgdYnHisjd9esC/pRtmtNXhEgQIAAAQIECBCoDYGG2uiGXhAgQIAAAQIECBAgMBAC2267bcwtPIR32WWXHYjqyq5j0ezmssu0F0hXAUgECBAgQIAAAQIECCwWEABYbGGKAAECBAgQIECAQO4Ffv7znw+rwYhll+/z+kcsu1yfyypIgAABAgQIECBAoBYF3AKoFkdVnwgQIECAAAECBAhUqcDITTfvc8v7U7bPK1WQAAECBAgQIECAQAULCABU8OBoGgECBAgQIECAAIG8CYzadIsYscpby+5249rrxsh11yu7nAIECBAgQIAAAQIEallAAKCWR1ffCBAgQIAAAQIECFSZQF1DQ0z6n5PKa3VdXUw8/ivllZGbAAECBAgQIECAQA4EPAMgB4OsiwQIECBAgAABAgSqSWD0ru+J8UccG7Mu/XamZk/8wonRtNU2mfLmKdOee+4Zu+++e7HLDYXAikSAAAECBAgQIJA/Ad8C8zfmekyAAAECBAgQIECg4gUmfvoLMWLllWPmmd+Itubmku2tnzgpJp10SozZc5+Sy/M+c8SIEZH+JAIECBAgQIAAgfwKCADkd+z1nAABAgQIECBAgEBFC4zb/yMxetc9ovnHN8ZTN3w/Fjz372grtLhptTVijQ8dGGP3+WDUj59Q0X3QOAIECBAgQIAAAQLDKSAAMJz61k2AAAECBAgQIECAQI8CIyZPjnGHfCLuWxBx3XXXFfMeecChseGBB0Zd4d7/EgECBAgQIECAAAEC3Qt4CHD3NpYQIECAAAECBAgQIECAAAECBAgQIECAAIGqFXAFQNUOnYYTIECAAAECBAgQyI/AqquuGjvttFOxwysXng0gESBAgAABAgQIECDQu4AAQO9GchAgQIAAAQIECBAgMMwC73jHOyL9SQQIECBAgAABAgQIZBdwC6DsVnISIECAAAECBAgQqHmBDTbYINZYY4145zvfWfN91UECBAgQIECAAAECtS7gCoBaH2H9I0CAAAECBAgQIFCGwL/+9a9obm6OlpaWMkrJSoAAAQIECBAgQIBAJQq4AqASR0WbCBAgQIAAAQIECBAgQIAAAQIECBAgQIBAPwUEAPoJqDgBAgQIECBAgAABAgQIECBAgAABAgQIEKhEAQGAShwVbSJAgAABAgQIECBAgAABAgQIECBAgAABAv0UEADoJ6DiBAgQIECAAAECBAgQIECAAAECBAgQIECgEgUEACpxVLSJAAECBAgQIECAAAECBAgQIECAAAECBAj0U0AAoJ+AihMgQIAAAQIECBAgQIAAAQIECBAgQIAAgUoUEACoxFHRJgIECBAgQIAAAQIECBAgQIAAAQIECBAg0E8BAYB+AipOgAABAgQIECBAgAABAgQIECBAgAABAgQqUUAAoBJHRZsIECBAgAABAgQIECBAgAABAgQIECBAgEA/BRr6WV5xAgQIECBAgAABAgRqSOCLX/xizJ8/PyZMmFBDvdIVAgQIECBAgAABAvkUEADI57jrNQECBAgQIECAAIGSAieddFLJ+WYSIECAAAECBAgQIFB9Am4BVH1jpsUECBAgQIAAAQIECBAgQIAAAQIECBAgQKBXAQGAXolkIECAAAECBAgQIECAAAECBAgQIECAAAEC1ScgAFB9Y6bFBAgQIECAAAECBAgQIECAAAECBAgQIECgVwEBgF6JZCBAgAABAgQIECBAgAABAgQIECBAgAABAtUnIABQfWOmxQQIECBAgAABAgQIECBAgAABAgQIECBAoFcBAYBeiWQgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLVJyAAUH1jpsUECBAgQIAAAQIECBAgQIAAAQIECBAgQKBXAQGAXolkIECAAAECBAgQIECAAAECBAgQIECAAAEC1ScgAFB9Y6bFBAgQIECAAAECBAgQIECAAAECBAgQIECgV4GGXnPIQIAAAQIECBAgQIBAbgSef/75aGtrixEjRsSKK66Ym37rKAECBAgQIECAAIFaFBAAqMVR1ScCBAgQIECAAAECfRRYe+21o7m5OaZOnRrTpk3rYy2KESBAgAABAgQIECBQCQJuAVQJo6ANBAgQIECAAAECBAgQIECAAAECBAgQIEBggAUEAAYYVHUECBAgQIAAAQIECBAgQIAAAQIECBAgQKASBAQAKmEUtIEAAQIECBAgQIAAAQIECBAgQIAAAQIECAywgADAAIOqjgABAgQIECBAgAABAgQIECBAgAABAgQIVIKAAEAljII2ECBAgAABAgQIECBAgAABAgQIECBAgACBARYQABhgUNURIECAAAECBAgQIECAAAECBAgQIECAAIFKEBAAqIRR0AYCBAgQIECAAAECBAgQIECAAAECBAgQIDDAAgIAAwyqOgIECBAgQIAAAQIECBAgQIAAAQIECBAgUAkCAgCVMAraQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBlhAAGCAQVVHgAABAgQIECBAgAABAgQIECBAgAABAgQqQaChEhqhDQQIECBAgAABAgQIVIbAT37yk2htbY2mpqbKaJBWECBAgAABAgQIECDQZwEBgD7TKUiAAAECBAgQIECg9gR22mmn2uuUHhEgQIAAAQIECBDIqYBbAOV04HWbAAECBAgQIECAAAECBAgQIECAAAECBGpbQACgtsdX7wgQIECAAAECBAgQIECAAAECBAgQIEAgpwICADkdeN0mQIAAAQIECBAgQIAAAQIECBAgQIAAgdoWEACo7fHVOwIECBAgQIAAAQIECBAgQIAAAQIECBDIqYAAQE4HXrcJECBAgAABAgQIECBAgAABAgQIECBAoLYFBABqe3z1jgABAgQIECBAgAABAgQIECBAgAABAgRyKiAAkNOB120CBAgQIECAAAECBAgQIECAAAECBAgQqG0BAYDaHl+9I0CAAAECBAgQIECAAAECBAgQIECAAIGcCggA5HTgdZsAAQIECBAgQIAAAQIECBAgQIAAAQIEalugoba7p3cECBAgQIAAAQIECJQjcPPNN0dra2uMGTMm3ve+95VTVF4CBAgQIECAAAECBCpMQACgwgZEcwgQIECAAAECBAgMp8Chhx4azc3NMXXqVAGA4RwI6yZAgAABAgQIECAwAAJuATQAiKogQIAAAQIECBAgQIAAAQIECBAgQIAAAQKVJuAKgEobkTLb8+STT8Y555wTM2fOjLFjx8all15aZg2yEyBAgAABAgQIECBAgAABAgQIECBAgEAtCggAVOmozp8/Py644IK46KKLoqWlpdiLCRMmVGlvNJsAAQIECBAgQIAAAQIECBAgQIAAAQIEBlpAAGCgRYegvt/97nfxpS99Kf71r38NwdqsggABAgQIECBAgAABAgQIECBAgAABAgSqUcAzAKpo1F566aU4+uij44ADDug4+L/HHnvEpEmTqqgXmkqAAAECBAgQIECAAAECBAgQIECAAAECQyEgADAUygO0juOPPz5uueWWYm2rrbZaXH/99XHFFVdEU1PTAK1BNQQIECBAgAABAgQIECBAgAABAgQIECBQKwICAFU0kieccEKMGzcuPve5z8Wdd94ZO+64YxW1XlMJECBAgAABAgQIECBAgAABAgQIECBAYCgFPANgKLX7ua63ve1t8eCDDxaDAP2sSnECBAgQIECAAAECBAgQIECAAAECBAgQqHEBVwBU2QCnKwAkAgQIECBAgAABAgQIECBAgAABAgQIECDQm4ArAHoTquHlTz/9dHz605/usYevvPJKNDc3x7LLLhuTJ0+OOXPmxMsvv9xjGQsJECAwGAJtbW1LVZv2UXV1dUvNN+MNAWblbwnMmJUvUH6JSt/O2tu3cOHCivne196mztrpO6nPgM4iXaeZdfXI8opZFqWueZh19cjyqjuzLGXzmodZXkdevwlUtsCkSZOisbGxshv5ZusEAKpimAankfPnz49//vOfmSpPPwBTWrRoUbS2tmYqIxMBAgQGW6B93zTY66ml+pmVP5rMmJUvUH6JStrOHnrooUgHW9LB9Ur+3ldJZuWP+PCUYFa+OzNm5QuUX6KS97Xl92ZoSjAbGmdrIUCge4FSwcnucw/vEgGA4fW3dgIECBAgQIAAAQIVJdDU1FRR7dEYAgQIECBAgAABAgT6LuAZAH23U5IAAQIECBAgQIAAAQIECBAgQIAAAQIECFSsgCsAKnZoBr9hU6dOjfPPP7/HFd1+++3xj3/8I9rPBBs1alSke1xJBAgQGGqBdHndzJkzu6x2woQJUV8vlt0FpdOLdNu2119/vdOcCGZdOJZ6Ucps4sSJ7jO+lNTiGcwWW2SdYpZVanE+Zostsk4xyyq1OB+zxRZZp9ItkmbNmtUlu8/NLhxLvShl5jf2UkxdZjDrwuEFAQIVItDQUD2H1aunpRUyuLXUjPHjx8d73vOeHrv0xBNPxLRp0zrypIdbjB49uuO1CQIECAyVQPpRvmQAIO2PBAC6H4FSBzKYde+VlpQyS0Fw21n3bulH+ZKBJmbde6UlzHr2KbW0lFnan3kIcCmtN+Yx696muyXMupPpfn66D/uSAQDvze690pLuzHoule+lzPI9/npPgED/BZw22X9DNRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYoTEACouCHRIAIECBAgQIAAAQIECBAgQIAAAQIECBAg0H8BAYD+G6qBAAECBAgQIECAAAECBAgQIECAAAECBAhUnIAAQMUNiQYRIECAAAECBAgQIECAAAECBAgQIECAAIH+CwgA9N9QDQQIECBAgAABAgQIECBAgAABAgQIECBAoOIEBAAqbkg0iAABAgQIECBAgAABAgQIECBAgAABAgQI9F+gof9VqGG4BVZbbbUYN25c8W+422L9BAgQIECAAAEC1S3wuc99LubNmxeTJk2Kb3zjG9XdGa0nQIAAAQIECBAgkHMBAYAa2AB+9KMf1UAvdIEAAQIECBAgQKASBC677LJobm6OqVOnCgBUwoBoAwECBAgQIECAAIF+CLgFUD/wFCVAgAABAgQIECBAgAABAgQIECBAgAABApUqIABQqSOjXQQIECBAgAABAgQIECBAgAABAgQIECBAoB8CAgD9wFOUAAECBAgQIECAAAECBAgQIECAAAECBAhUqoAAQKWOjHYRIECAAAECBAgQIECAAAECBAgQIECAAIF+CAgA9ANPUQIECBAgQIAAAQIECBAgQIAAAQIECBAgUKkCAgCVOjLaRYAAAQIECBAgQIAAAQIECBAgQIAAAQIE+iEgANAPPEUJECBAgAABAgQIECBAgAABAgQIECBAgEClCggAVOrIaBcBAgQIECBAgAABAgQIECBAgAABAgQIEOiHgABAP/AUJUCAAAECBAgQIECAAAECBAgQIECAAAEClSrQUKkN0y4CBAgQIECAAAECBIZeYOONN445c+bE8ssvP/Qrt0YCBAgQIECAAAECBAZUQABgQDlVRoAAAQIECBAgQKC6BX7/+99Xdwe0ngABAgQIECBAgACBDgG3AOqgMEGAAAECBAgQIECAAAECBAgQIECAAAECBGpHQACgdsZSTwgQIECAAAECBAgQIECAAAECBAgQIECAQIeAAEAHhQkCBAgQIECAAAECBAgQIECAAAECBAgQIFA7AgIAtTOWekKAAAECBAgQIECAAAECBAgQIECAAAECBDoEBAA6KEwQIECAAAECBAgQIECAAAECBAgQIECAAIHaERAAqJ2x1BMCBAgQIECAAAECBAgQIECAAAECBAgQINAhIADQQWGCAAECBAgQIECAAAECBAgQIECAAAECBAjUjoAAQO2MpZ4QIECAAAECBAgQIECAAAECBAgQIECAAIEOAQGADgoTBAgQIECAAAECBAgQIECAAAECBAgQIECgdgQEAGpnLPWEAAECBAgQIECAAAECBAgQIECAAAECBAh0CDR0TJkgQIAAAQIECBAgQCD3AltvvXXMnj07Vlhhhbjjjjty7wGAAAECBAgQIECAQDULCABU8+hpOwECBAgQIECAAIEBFvj73/8ezc3N8dprrw1wzaojQIAAAQIECBAgQGCoBdwCaKjFrY8AAQIECBAgQIAAAQIECBAgQIAAAQIECAyBgADAECBbBQECBAgQIECAAAECBAgQIECAAAECBAgQGGoBAYChFrc+AgQIECBAgAABAgQIECBAgAABAgQIECAwBAICAEOAbBUECBAgQIAAAQIECBAgQIAAAQIECBAgQGCoBQQAhlrc+ggQIECAAAECBAgQIECAAAECBAgQIECAwBAICAAMAbJVECBAgAABAgQIECBAgAABAgQIECBAgACBoRYQABhqcesjQIAAAQIECBAgQIAAAQIECBAgQIAAAQJDICAAMATIVkGAAAECBAgQIECAAAECBAgQIECAAAECBIZaQABgqMWtjwABAgQIECBAgAABAgQIECBAgAABAgQIDIFAwxCswyoIECBAgAABAgQIEKgSgWOOOSbmz58fEydOrJIWayYBAgQIECBAgAABAt0JCAB0J2M+AQIECBAgQIAAgRwKnH766TnstS4TIECAAAECBAgQqE0BtwCqzXHVKwIECBAgQIAAAQIECBAgQIAAAQIECBDIuYAAQM43AN0nQIAAAQIECBAgQIAAAQIECBAgQIAAgdoUEACozXHVKwIECBAgQIAAAQIECBAgQIAAAQIECBDIuYAAQM43AN0nQIAAAQIECBAgQIAAAQIECBAgQIAAgdoUEACozXHVKwIECBAgQIAAAQIECBAgQIAAAQIECBDIuYAAQM43AN0nQIAAAQIECBAgQIAAAQIECBAgQIAAgdoUEACozXHVKwIECBAgQIAAAQIECBAgQIAAAQIECBDIuYAAQM43AN0nQIAAAQIECBAgQIAAAQIECBAgQIAAgdoUEACozXHVKwIECBAgQIAAAQIECBAgQIAAAQIECBDIuUBDzvuv+wQIECBAgAABAgQIdBJ4/fXXi6/q6upi/PjxnZaYJECAAAECBAgQIECg2gRcAVBtI6a9BAgQIECAAAECBAZRYOrUqTFx4sRYd911B3EtqiZAgAABAgQIECBAYCgEBACGQtk6CBAgQIAAAQIECBAgQIAAAQIECBAgQIDAEAsIAAwxuNURIECAAAECBAgQIECAAAECBAgQIECAAIGhEBAAGApl6yBAgAABAgQIECBAgAABAgQIECBAgAABAkMsIAAwxOBWR4AAAQIECBAgQIAAAQIECBAgQIAAAQIEhkJAAGAolK2DAAECBAgQIECAAAECBAgQIECAAAECBAgMsYAAwBCDWx0BAgQIECBAgAABAgQIECBAgAABAgQIEBgKAQGAoVC2DgIECBAgQIAAAQIECBAgQIAAAQIECBAgMMQCAgBDDG51BAgQIECAAAECBAgQIECAAAECBAgQIEBgKAQEAIZC2ToIECBAgAABAgQIECBAgAABAgQIECBAgMAQCwgADDG41REgQIAAAQIECBAgQIAAAQIECBAgQIAAgaEQaBiKlVgHAQIECBAgQIAAAQLVIXD99ddHa2trjB49ujoarJUECBAgQIAAAQIECHQrIADQLY0FBAgQIECAAAECBPIn8L73vS9/ndZjAgQIECBAgAABAjUq4BZANTqwukWAAAECBAgQIECAAAECBAgQIECAAAEC+RYQAMj3+Os9AQIECBAgQIAAAQIECBAgQIAAAQIECNSogABAjQ6sbhEgQIAAAQIECBAgQIAAAQIECBAgQIBAvgUEAPI9/npPgAABAgQIECBAgAABAgQIECBAgAABAjUqIABQowOrWwQIECBAgAABAgQIECBAgAABAgQIECCQbwEBgHyPv94TIECAAAECBAgQIECAAAECBAgQIECAQI0KCADU6MDqFgEC4x+W6AAAQABJREFUBAgQIECAAAECBAgQIECAAAECBAjkW0AAIN/jr/cECBAgQIAAAQIECBAgQIAAAQIECBAgUKMCAgA1OrC6RYAAAQIECBAgQIAAAQIECBAgQIAAAQL5FmjId/f1ngABAgQIECBAgACBzgK33357LFy4MJqammLnnXfuvMg0AQIECBAgQIAAAQJVJiAAUGUDprkECBAgQIAAAQIEBlNgv/32i+bm5pg6dWpMmzZtMFelbgIECBAgQIAAAQIEBlnALYAGGVj1BAgQIECAAAECBAgQIECAAAECBAgQIEBgOAQEAIZD3ToJECBAgAABAgQIECBAgAABAgQIECBAgMAgCwgADDKw6gkQIECAAAECBAgQIECAAAECBAgQIECAwHAICAAMh7p1EiBAgAABAgQIECBAgAABAgQIECBAgACBQRYQABhkYNUTIECAAAECBAgQIECAAAECBAgQIECAAIHhEBAAGA516yRAgAABAgQIECBAgAABAgQIECBAgAABAoMsIAAwyMCqJ0CAAAECBAgQIECAAAECBAgQIECAAAECwyEgADAc6tZJgAABAgQIECBAgAABAgQIECBAgAABAgQGWUAAYJCBVU+AAAECBAgQIECAAAECBAgQIECAAAECBIZDQABgONStkwABAgQIECBAgAABAgQIECBAgAABAgQIDLJAwyDXr3oCBAgQIECAAAECBKpI4Omnn462traor3euUBUNm6YSIECAAAECBAgQKCkgAFCSxUwCBAgQIECAAAEC+RRYdtll89lxvSZAgAABAgQIECBQgwJO66nBQdUlAgQIECBAgAABAgQIECBAgAABAgQIECAgAGAbIECAAAECBAgQIECAAAECBAgQIECAAAECNSggAFCDg6pLBAgQIECAAAECBAgQIECAAAECBAgQIEBAAMA2QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEalBAAKAGB1WXCBAgQIAAAQIECBAgQIAAAQIECBAgQICAAIBtgACBARG466674uCDDy7+3X777QNSp0oIECBAgECtCtx2222x//77F/9+/etf12o39YsAAQIECBAgQIAAgWEWaBjm9Vs9AQI1IvD666/H008/XezNjBkzaqRXukGAAAECBAgQIECAAAECBAgQIECgegVcAVC9Y6flBAgQIECAAAECBAgQIECAAAECBAgQIECgWwEBgG5pLCBAgAABAgQIECBAgAABAgQIECBAgAABAtUr4BZA1Tt2Wk6AAAECBAgQIEBgwAW+8pWvREtLS0yYMCFOOOGEAa9fhQQIECBAgAABAgQIDJ2AAMDQWVsTAQIECBAgQIAAgYoXOPfcc6O5uTmmTp0qAFDxo6WBBAgQIECAAAECBHoWcAugnn0szanAnXfeGQcccEDx76c//WlOFXSbAAECBAgQIECAAAECBAgQIECAAIFqFnAFQDWPnrYPmsDs2bNj2rRpxfpnzZo1aOtRMQECBAgQIECAAAECBAgQIECAAAECBAZLwBUAgyWrXgIECBAgQIAAAQIECBAgQIAAAQIECBAgMIwCAgDDiG/VBAgQIECAAAECBAgQIECAAAECBAgQIEBgsAQEAAZLVr0ECBAgQIAAAQIECBAgQIAAAQIECBAgQGAYBQQAhhHfqgkQIECAAAECBAgQIECAAAECBAgQIECAwGAJeAjwYMmqlwABAgQGXOCpp56KBx98sFjvRhttFCussMKAr0OFBAgQIECAAAECBAgQIECAAIFaERAAqJWR1A8CBAjkQOCJJ56IG2+8sdjTMWPGxDbbbJODXusiAQIECBAgQIAAAQIECBAgQKBvAm4B1Dc3pQgQIECAAAECBAgQIECAAAECBAgQIECAQEULCABU9PBoHAECtSxw6623xvbbb1/8u+mmm2q5q/pGgAABAlUksNZaa8Xaa68da665ZhW1WlMJECBAgAABAgQIECgl4BZApVTMI0CAwBAItLW1xaJFi4prStMSAQIEqlXg0Ucf7Xg+x+abb+75HNU6kG+2u/1ZK1XeDc0nQIAAAQIECBAgQKAgIABgMyBAgAABAgQIEOiXQHo+x49//ONiHVOmTPF8jn5pKkyAAAECBAgQIECAAIGBExAAGDhLNRHIrcCCBW3xjycmxkvNO0Vb1MXDTy4TLS1tMXJkXW5NdJwAgeoVSGc///a3vy12YJdddonddtutejuj5QQIECBAgAABAgQIECCQawEBgFwPv84T6J9A8+y2OOOSlrjgqpaYOWuTQmXpL+LUSyPOva45jvpoY5x47KiYOEEgoAjjHwIEqkLghRdeiPvuu6/Y1g022KAq2qyRBAgQIECAAAECBAgQIECglICHAJdSMY8AgV4Fnn52UWz5vjlx6gXp4P/S2ZtnR5x56YLY9L2z49En37jP/dK5zCFAgAABAgQIECBAgAABAgQIECBAYLAEBAAGS1a9BGpYYMbMtnjPwXPi4cd7P7D/5DNt8e6PzomXX+09bw2T6RqBYRNID5pubW0t/rU/dHrYGmPFBAgQIECAAAECBAgQIECAwJAKCAAMKbeVEagNga+cPT8ee6otc2eefa4tvvCN+Znzy0iAwMAJ/OpXv4oDDzyw+JemJQIECBAgQIAAAQIECBAgQCA/AgIA+RlrPSUwIALTZ7TFd65bUHZd1/ywNZ57wVUAZcMpQIAAAQI1JzB/flv88+nl44XmXePF5l3ikaeWjZaW7IH1mgPRIQIECBAgQIAAAQIEBk1AAGDQaFVMoDYFfvn71ljQWn7f2grHNX7+2z4ULH9VShAgQIAAgYoUSLfQ+/yp82L5TebGOdftFE+8+pl4/NXPxje/+65YfuM5ccK35sesZoGAihw8jSJAgAABAgQIECBQpQICAFU6cJpNYLgEnvhX38/if/zpvpcdrv5aLwECBAgQGAiBRx5fGJvsMTvOvnxBzJ6zdI2zZkd886KW2Hyv2fHUMz4vlxYyhwABAgQIECBAgACBvggIAPRFTRkCORaY149b+c9vyTGcrhMgQIBAbgVeemVR7HbQ3Hj6372f3f/ok22FvHMi3XJPIkCAAAECBAgQIECAQH8FBAD6K6h8TQq8Mn1kzJy3XuFv/Xh5+uia7GNfO7XKinV9LRpT+1G2zytVkAABAgQIDLPAF74xP/79fPYD+k/8qy2+fFY/Iu797O+uu+4a22yzTeyzzz79rElxAgQIECBAgAABAgSGW6BhuBtg/QQqRWDRorb43s2tcc5lLfH3x7YuNCv9RXz6WxEX39gcx318ZBx+QGM0Nvb9AHixwir/Z+d3pd1G3w5K7LyNXU6VD7/mEyBAgECZAs+9sCiu/VH5z8C59PsL4pTPj4plJg39944//elP0dzcHFOnTi2zt7ITIECAAAECBAgQIFBpAq4AqLQR0Z5hEUiX2b/7I3PjsM/PKxz8X/q+u4893RbHnDQ/tvvgnHjhpaWXD0ujh2mla61eHztsNaLstW+2YX1sumH55cpekQIECBAgQKCCBH7+29Zoy37yf0fLWwsxg1/+rvzAQUcFJggQIECAAAECBAgQIFAQcDpuFW0G8+fPj9tuuy3uvvvuePHFF6OpqSnWWWed2HfffWOttdaqop5UVlNbWtpiz0PnxL0P9H5g/09/KdzD96Nz454fj4lxY4f+jLxKkTv35FGx1d5zIus9/RsKx/3P/2pTpTRfOwgQIECAwJAJPP50798vumvME//qe9nu6jSfAAECBAgQIECAAIF8CbgCoErG+x//+EfssMMOceyxx8YNN9wQd911V9x+++1x3nnnFeeffvrphbPL+nB6WZX0fzCbefrFLZkO/re34W//XBQnndm3W+C011Ht/2+03oi49rymaMwQQqwv7GWuOKMp3rmZs/+rfdy1nwABAgTKF8gaLC9Vc3/KlqrPPAIECBAgQIAAAQIE8icgAFAFYz5t2rTYf//945lnnim2dsstt4xjjjkmDjjggBg3blzxwP8FF1wQZ511VhX0prKaOHdeW5x5aUs3jUoBldJBlYuvWRCvvJbvs/L227Mx7rpxTGywdufdSFeztdeoizuuHx2H7NfYjbHZxftCCN6VtyEkL2bMyhMoP7ftjFn5AiVLrLJS58/JJbN0/dxccukqK+b3asMlLbwmQIAAAQIECBAgQKBvAhnO3+1bxUoNnMCJJ54Y06dPL1b45S9/OY466qiOyj/zmc/E3nvvHf/5z3/i/PPPj/e9732x9tprdyw30bPAHb9fGM2zF+dZre6ZOLTx+thxxB9ilbrno64QAHi+bcX43cJt4uoFH4nH2t5WzLygcEven/1mYeHAdk8/6hfXW6tT6az++y97Mf55xpXR+sc7Ypl5Lxa7On3U8lG3xc6x9uc+FqPXsT0uOf4Lnn4qmq+/Ota7/ba49tVXiotbzzstpv/zrzHuwEOi8W3/tWSR3L9e8PST0XzdVbH1z2+L7We8VvRoufyCmDHtqRj/0Y9F45pug7bkRrLgqSdiVsFsm1/cFjvOeOMzpOWy82LGv58omB0ajWu8sT9bslyeX7ebbVvYznae+abZpefGzILZuGS2+pp55inZ9wVPPl7czrYrbGe7zJxRzNPynYLZs48XzA4rmK1RslyeZu7yrq5XwK1V90R8rPH7sf2Ie2Jq3X8K3zTqYlrbynHnwm3jqsJ3jX+1rdrBs/O7fFXvwDBBgAABAgQIECBAgECfBPJ99LJPZENb6PHHH4877rijuNLNN9+8y8H/NHOVVVaJU045pbh80aJFcfHFFxen/ZNN4JEnFr6ZsS2Oa7wkfjt6rzisEABYre7ZaKxrjYa6hfGWuufioMYb447R+8SJI8+M+nijzMOP5/sKgHTLqdcvPDde2mvnWOaua2K5+f8peC0q/i3b8kIse/f18eoHdosZZ50WbYVtU0onrbfFzAvPiRcLZrOvvzpGFQ7+p0M76W/U9Ndi9v9eGy/u/e6YeU7hll7MiptM0eyCswpmu8Ts718TowsH/9vNRhcO0M75QcHsfbvGzHO/xawo9uZ2dt6ZRbM5hW1qdOHgf4dZYTrNS54zC3ncOu4NtPR+m3nuGYvNCttWZ7O07RXNCtsis05mhX1Vev/N+cF1Mbpw8H+xWWF/VjTbOWZ+++zcm228/ojY4h31hcP8i+JLjWcXvk/sGwc33hBvrZtW/J6Rvm+k7x3p+8edhe8hn2m8qIDcFju9c0Sstbqv6m/u2vxHgAABAgQIECBAgEAfBfyq6CPcUBX76U9/2rGqD3/4wx3TnSfe8573xDLLLFOc9fOf/zwWLFjQebHpHgRmvXn2/6kjvxGfH3lh4eDFGwf36zpdcd8+na4GOLLx6jhv1JeKNTbPTpft5zfNOOWkeP2icyMWvmnWiaKDr3BQrfm734npx3+m09L8Ts746gkx66LzejWbdfnFMf2Ez+UXqlPPZ5x8fMy65IJo62E7SwdvZ112UUw/8QudSuZ3cvqX/ydmXfrtiDeDSB3vxwJJx3QyK+SZfhKztKUkh1mXXdizWWEbTNvi9K98Mb8bV6eeTz/x85H2VT1uZ8ns4vMj7fvyns7/WlOc13RCHD3yysKJBG8Exdu/XySb9ukRhe8hnx15cZw++tQ49+RReWfTfwIECBAgQIAAAQIEBkBAAGAAEAezij/+8Y8d1ad7/5dKI0aMiPZls2fPjoceeqhUNvNKCKy0fF3sNeIXhUvx/7dwhuLiH+AlshZ/nKc8+zb8LA5q+EGsWCib1zTntltiduHM66xpzk9/HM03XJc1e03mm33rj2L2jddn7tucn/wwmm/8fub8tZhx9i03x+ybCu/NQud6erelZSnPnFtuitk3/6AWKTL3afaPb4w5P7whc/45P7oxUpk8p9kFrzk/vikzwZzCNpa2zTyn9L6cU4ZB2velfWCe0/qP/2+8f8RPM3/X+Ej9DfG2JxefBJJnO30nQIAAAQIECBAgQKB/AgIA/fMb9NKPPPJIcR3pIP+qq67a7frWWmvx/a8ffvjhbvNZ0FVgu83r4viRhbPYC6n97LuuObq+SnlSEOBzhasFtt+k8CCAHKbirTLO+VbZPX893TqjZX7Z5WqhQDp7/fXCLWrKTW+YdfeQ6nJrq678ba2thVvUnFFsdE8H/9t71R4EmHl+4bY2Ob0KqmhWuI1NuSnd+ia3ZoVtpX07K8ctlUneeUxtLS2R3mflprQPbL+Sp9yy1Z4/ffa9fsHZxW5k/a6RMs8sfNbm1azax1z7CRAgQIAAAQIECFSSQLpdq1ShAvPmzYvXXnvjYZfLLrtspCBAd2nFFVfsWPTcc891TPc08fLLL8c111zTU5Z44IEH4oUXXojx48fH2LFjY/78wo/Y11/vsUw1LZz66oMxpz6bV3u/0o/3KTE9Vp7xm4LFDu2zc/N/618eiIX/Kc8s4SwqbMuv/eaOaNxmu9xYtXe09cE/x8IX/tP+MvP/iwrPCJh+56+jYet3ZS5TKxlb7/+/WPTiC2V1JwUBFr3y8htmW21TVtlayNz65z/FopdfKrsrqcz03/02GrbYuuyy1V6g9U/3FLeZcvuRts3pv7szGjYvfWVeufVVU/7WP94dad9Ubkr7wOl/uCsaNtm83KJVn3/BPb8vfAa+WnY/0mft9Lt/Hw0bb1p22f4WOOCAAyJ9D500aVLFfO9Lz7paMqXvpHVZoipLFszJa2blDzQzZuULlF+iu+2s/JryU4JZfsZaTwlUk8CYMWOioaE6Dq1XRyurafQHsK0zZszoqC0dgO8pTZgwoWPx9OnTO6Z7mnj11Vfjsssu6ylLx7K0QacAwCWXXBInnnhix/xSE+lqhO9/P/utSw455JDIetXCBz/4wfjiF7PdfzkFK971rp4PnH6oMeIzTX27EObi446Jy1vSjUd6Tz/72c9i+eWX7z1jIcell14aV1xxRaa8U6dOjVtuuSVT3pTpyCOPLAZ1shTYY4894mtf+9pSWRf99cGl5mWd8e1jjoorCmYnnHBC7LvvvpmK/e1vf4uPf/zjmfKmTDfddFOsttpqmfJfe+21ccEFF2TKO3ny5PjVr36VKW/K9NnPfjb+8Ic/FPN/pLCdHdvH7Wzu/3sg6jfcuMt6d9ppp5g1a1aXed29+MxnPhMHHnhgd4u7zH/iiSciHfTJmr73ve/F29/+9kzZf/jDH8bpp5+eKe+oUaPizqM+WbhPdt/SOYXt/OqW9gd8d60j3S7twgsL93rPmPbcc8948cUXM+U+4ogj4hOf+ESmvM8//3zsvffemfKmTGnfu9lmm/WYf1EhONfXNPf/PRj1623Ya/HTTjstfvzjH/eaL2XYYIMN4sorr8yUN2Xaf//94+mnn86U/6CDDopPfepTmfKmz9Jdd921ZN6PjSzsF0f1bUubW9gX/v6lV+ILX8j+HIXf/OY30fnzumSj3px53nnnxfXXX99Tlo5la665ZvzgB9lvf/Wxj30s/vGPf3SU72niAx/4QBx//PEdWRYVtpW+pm8dekhc3+kxRaeeemqk5xhlSffdd18cc8wxWbIW86RnKHU+OaKngum70OWXX95Tlo5lK620Utx6660dr3ubOProo2PDv94fh49KIcryU/Ez4L/WKVlw6623jtaMV6J86Utfive///0l61ly5t///vdIz5RqTz/60Y/aJ0v+f+ONN8bqq69ectmSM6+77ro4//zzl5xd8vXEiRPj17/+dcll7TPnzJnTPhmf//zn43e/+13H654mdthhhzjzzOxXsuy8886ZAyHHHXdcfPSjH+1p9R3LnnrqqfjQhz7U8bq3iauvvjrWW2+93rIVl5fzudvY2Bj33ntvpnpTpq985Svxi1/8IlP+LbbYIi666KJMeVOmvfbaq3jyUZYChx9+eHzyk5/MkrVYZ6o7a7r44otj883fCFh23s5KlU8WySRrStbJPEtK353SWGZJ66+/flx11VVZshbzpG0vbYNZUtqm07adJaXAXLtdlvxnnXVWbL/99lmyFt/j6b2eNaV9SNqXZElp35T2UVnSGmusETfckP2Wi4ceemikfWuWlPbVaZ+dJaXPgPRZkDWdcsopsfvuu2fK/uc//znSZ1jWlD4b02dklpQ+c7Meh0if5Z2fi9i5/nT74yVT+q6QvjNkSek7SPoukjVts8020VK4EjJLSt+d0neoLCl9J0vfzbKm9J0vfffLktJ3yfSdMktK31HTd9WsKX0HvuuuuzJl32677eLss8/OlDdlSt/dOx8P66lgOZ+76bdG+s2RNaXfMuk3TZaUfiOl30pZUjq+1vl2372VOfnkk7t8N+spf/rNmH47Zk3pN2n6bZollfO5m35Dp9/SWVP6jd5+a/Peyvzyl7+Mk046qbdsHcvvueeeGDlyZMfrnibK+dxNx0Juu+22nqqrqGUCABU1HF0b0/nDpbeNtfPyzuW61tj/V+mgY29XGPQWrFiyFWnH0Fud7WWyfgik/G2Fe/X0Vm/DsoUvhE0T2qsv6//GObPjuRenZyqT9Qd6qmzmzJm9trt9peWe9Zau+ujNpL3u9qtP2l93/D8jW5878neaaJjTXDCbEaW+rHXK1mUybc9Z25wKlvMQ7Czbc3tj5s6d2z6Z6f8UYGtv94jl+r6dxczFgcD2FacP6KxX4jQ3N7cX6/X/tJ22t7nXzIUM5exrUjuy1p32Z/95bGZMzdKIEnka50+M555/qMSSiPQeKCf95z//ifSXJWUdk1TXwsJtobJ6pPwpoNlrKrGt9FqmPUPGsmkfnLXdK6ywQnvtmf5PV5tlrTvtJ7OmdMZYd/U2Lj8pYtT4rFV1zVfYF86dtFy3dXfN/MarUmevlcqX5pVjnQL05aRyPneXOqlgZt8/A0bMLuwHXl48dr0dVOvcp/Qe6G4cO+drn07vsawpvXfLqTtrvSlf2uc0Fj77+ryd9fDeTG3O+v2iGj93y2lzsn7llVcyj2PKW05Kn7tZ9zuV8rmb3l9Zt+usB6PbzdJ3xKx1l/u5W85nQTmfu+V+x0lXwGRN5VinOtNvlKypHOvlllsua7XFfOVYl/MbrKfP3VINLOc7dhqXrNteWlc5n7vl/AZLZ3yWk1566aXM7V7qc7eHFWX5vdu5eDV+7pbzfkl9HZDfu53ROk2nbS/r759yPsPS79dytuusn/2p6eX8Bivn8yvV3fn3bnrdU+rL527W98JgfhZkHe/U9zTmWcexp7t7lHIsx7qnW4eXqjv91s3a7qzfhdJ6BuX37psdGMzP3XJ+g02ZMqUUacXOEwCo2KFJ96RffLZYbx98nZd3LjfQ3Us7qnR2bk+pczCip3zty1L+3upsz1vuD5Te6m3uZNy+jqz/zyqU7a3+9rrKGZMUDc5ab1NTU/sqMv1fjnW34zi+bwGT1MDmwjndqW/lfODV179RJlMHC5nKsc6yPbevN+uYtOfvPI7NdT2/Z9rLlPp/3siJseRPjNSWrO1JfllTsstab6qznLrLsU7b3r+bJ/U5ADCz/q2FfjxWstt92YdkNSlnux4U6wmFQFNfU8b3defturdVDaZ1akfW1JP16/34DIiCdznbdWpvOfun5Jd128uar90s5c9aZqlxHN/37ay5ruv+vJz3zGB+FpSzXWd1a7dOfumzr8+ph/dmaktWw6z5UjsH07qc90xfrLOWWWq77mWAUr1Z6y7Huqf9U6kmlfO5W844lutRznum3LrL+a5aTt3lWpczjuVs16XGtad5qY9Zt72s+drXV852XY51qr+ctgzWdt3ez6z/l7Ndl9O/tP7B2q5T3eW0hXUSW5zK+T6ZSiXrrN/lyrEud/+UtQ2pzeXsn7r9/Z8qKpHK2T/1ZR+SddvuyziW6E7JWeWMYznW5XzGpIaVsw8pdxyT82BYl7tdD5Z1yYHtYWY523VWtx5WN6SL6goHjrOfgjCkTbOyFCXdcMMNixDpMsO77767W5Sbb76543YIhx12WHz961/vNm/7gnTWR2+3P0m35kn50qWT6cz+dAnPPvvs015F1f+f7jM+79jD+9SPptPOjoYdd+1T2Wou1Hrfn2LecZ/sUxeavnluNOywc5/KVnOhe75zb7zje0f2qQsP7Hd+bP/ZHftUtpoLXfmFe2L/u4/qUxdu3PLbcdh52S4l79MKKrRQ671/iHmfO6ZPrWs668JoyOPzOe7+Xcz7wrF9MzvnosLzObbtU9lqLtT6+9/GvC9+uk9daDrvO9Gw5Tv7VLaaC7Xe9ZuY96XP9KkLTedfVng+x1Z9KltrhdJPliXPyk4nQpRzIKTWTHrrD7PehJZens4YX/KqO9vZ0k6d5zDrrJFtupTZ6NGjsxXOaS5mOR143SZQ4QLjxo3zDIAKH6OqaF568Fr6UZO+vPd22WXny6PSA4OzpHQ/vd7uUZbuw3XXXXd1VJe+AKd21UpqKxyM/s+yy5X9EMi6wpt8mXfvEfVl3nahFtzadiyYLTOl7Aca1o0bXzDbPerLvGS2Fsz+NmrbmNo2ufjw6KwnHKfQ7OsxPv4yesfYu4bec1nHs3WD7eLVP0yOZQoP3C7HbGZMiHkb7FDYT/X9LOWsbay0fG27vDv+M3FSLOrhliGl2lxfKDOlULZuVHlXFJWqq9rmte26WzxfOJN/0eszY/E1dz33Ip01MWJS4f28c8FsZN+v7ul5LZW7dNo73hMz28YX3mmzynpvvhaTY6V37FR4b+ZvO1u02x7xn298Jdqasz27pX306wuftVMKn7l1Ge8V3l6uVv9Pl5IvGQBo/65cq33ub7+YlS+YzNLtWjon21lnjaWn0+1IlrzVE7OlnTrP6c6scx7TXQWYdfXwigABAuUK9OOa5HJXJX+5AulSppVXXrlYLN3/cckfPZ3r6/zQjre+9a2dF5nuQaCuYDzh2M/1kKP0ovGf/O9cHvxPGulAxIRjP1sapoe5E448NpcH/xPJzHkj49yWY4oHy7Jcc5XypIPeF7QcEdPn5e9gWTLb6B2j47yWo8o2O7/lyNho4/Luh57WVwspHYwef0z5Z2anMnk8+J/GPPX76e3/u3jwP/N7s1Du6Z0+lcuD/8nsF/c0RHqfpX1UZrNC3nMK+8Bf3jMiVZG7VF84qzN9Bpab0metg//lqslPgAABAgQIECBAgMCSAgIAS4pU2OvOTxx/7LHS97ROTX7kkUc6Wt65TMdME90KjNv/wBi9x17dLl9ywah3bR/jDztiydm5ej32Qx+N0e/J/kT3pu12jHGH9u22QbUAu+JydXFN64fjZ62FM4Z7OWjWfvD/163bx+Wth0Qqm8e04ztHxM8nfCR+0bpLZrNfte4Yt4w7KHbeJp8HGdN2Mu6jh0ZT4cz0rCnlTWXymhYubIuD/3BApG0n63szbZMH37V/4cGC6VqA/KUnn2mLK1oPjrSPymqW9n1pH/jkM4vyB/Zmj9NnYNP2O2Xuf/qMTZ+1EgECBAgQIECAAAECBPorIADQX8FBLr/ttovvL/yHP/yh5NrSE7D//Oc/F5ettNJKsdZaa5XMZ2b3Ast86/wYe+DBHRk6H9bpPD1m7w/EshdeHnWFhyHnOaVbUy1zRsHswwd1MHR26jw9Zp8PxpRvXxZ1ZTyMtqPSGpnYdou0vdTFf88/I65dsH/xoFnqWuezZ9un0wG1GxbsG0fMPy/aCg+OfKNsjUCU0Y3Gxro45fOj4uj5Z8X1C/br1ewHBbMj558TJ392dOEhRvkMmiTe9N6ccs7FMeaDH+7Q7vx+7Dyd8qS8eb5/9t1/XhhPP1dX3Hb+d8H7e93O0raYtsmn/h3xxwcWdhjnaaJlQVtx35T2UWlflfZZKbXvwzpPp2XXFfZ5ad+X9oEtC9LSfKb0GTjlgktjzD77dQB0fj92nk6frekzNs/vzQ4kEwQIECBAgAABAgQI9FtAAKDfhINbwV577VV84nday7XXXrvUQ6nS/Ouuuy7mzp2bJmO//Rb/sCzO8E8mgXQroMlf/nos94OfxOjd94pFhWcdtKdFhVveNO2yWyz7vRtimdPPze2tMto92v9PtyWYfPI3Yrn/vaV4NcCiwtPj29OixpExetf3xHLX3BTLfPOc3N4qo91j0w1HxAZr10drNMYJLSfH3nOvj5+27hbNsfhWNXNidPy8ddf4wNxr4vMtX4+WGBlrrV4X79wsv8GmIz46Mj641+g4vuWrsc/c6+K2gtnsGNPOWpxOZu8vmH2hYLb3HmPimEMaO5bndaJu5MiY9LVvxQOfuCHuHvPumN3Wyawwnebd//EbinlS3jyne+9/4yD+gsL77X9aTi1uS+ls9SW3s7TtpW0wbYvpfZzSvQ/k82z2t6z0xlfHtI9K+6q0z0rvw7QPa09p35b2cWlf96XCPq/d7C0r5Tc4l2zSbbqW+ebZxc/G9L1iYad7+y8sLGva7b3Fz9T02erWP+1bk/8JECBAgAABAgQIEOivQEN/K1B+cAXSA30PPvjguOKKK+LZZ5+NT33qU3HOOefE2DcfPvvrX/86Tj/99GIjJkyYEIcffvjgNqjGax/1jo1j1DkXxR9vuy0u/OZpxftCH3TMf8eBBx5Y4z3ve/dGbbRJjNro4rjn1lvj4m+dXjQ75NhPxYc/vPgM5L7XXjslz/nKqNj1I28E6h5ctFHhLOKNip2bFDOK/8+IpR+ufc6Xm6K+Pt8HzK49vymWX3Z+XHDVxvHA/I0LVm0FqZlvmqUH/b7hc/RBjXH+10Y5Y7Yg8tqMttj/qLnxm3vWL7w6t2jVZTubU5h1QcRO98+Nm74zOpaZlN9t7KVXO593HfHnRZvGn+dvurRZcU7Xf156JZ8BgF227RqUvK9gdl9Gs1229bUzbUWjNt8yGjbZLH585ZXxo6uvKuzVIj5y5FHxkY98pLDPd25O13eaVwQIECBAgAABAgQI9FfAr4z+Cg5B+eOPPz7WXz8dyIn46U9/GhtttFHsvffekW4PlIID6eHA6TLxs88+O6ZMmTIELcrHKmbXj4jmwp+UXYBZ91bpwNcZJyy+SqI9ZzrwX+rg/ymfGxl77uJgWUNDXeHAflPcf9uY+Oi+DTF+TEvRK5mNG90SH9lnRNz30zFx0TeaIuXNe5o/vy3ee8icwsH/rrenKbWd/fbehcW8qUxe06QJ3W8zpcw6O03soWznfLU2vcE6I+Jdm5f+bOzJbJd3jShc1eRr55LbQ/qekT47Ky0tWrSo8JyLN/4qrW3aQ4AAAQIECBAgQIBAeQJ+iZXnNSy5x4wZEzfddFPxoH9qwOzZs4v3/H/yySeL7Un3/b/66qvjve9977C0z0oJEMgm8IUjR8b1FzTFlMnd5580IeKqs5riy8ctHSzovlTtL0m3UfreuaPi21/6WWz9ln2Lf+f9z0/imvOaYvN3VN7Bs+Eaka9f0BJ/+kv2M9NT3lMLZfKaNlin71+D0m298prO/+qoGFXG3aNGF+6qd+7J9mnVtL1MnDgxRhSed/TWt761mpqtrQQIECBAgAABAgQIlBBwemkJlEqclX6IXXLJJZGuBrj33nvjpZdeKjzoclSss846sc0220Rjp/vIVmL7tYkAgTcEDtynMfbcuSGu/dGCuOoHz8Ujj80u3P6hLtZec3Qc+uFV4qD3F56tkONbsmTZTkbUz8+SLXd5ZjW3xdmXl38w/5xCmS8eNTLGj+v+bPhaxXz3dg0xrvAojubZ5fVwwriIXQtl85o22WBEXFe4PddHPjWv1wf7pkDBDy4cHeuvLVCX1+1FvwkQIECAAAECBAgQGF6B/P56HV73Pq991VVXjfQnEag0gZ122ql4e6rUrsmTezjFvdIaPgztmTC+rvCw2pGxyqS/xVlnnVVswbGfODb233+NYWiNVdaKwO13tcbceeX3JpVJZffbM38PUB43ti7+p3BlzlfOLi9wcvzRI2PM6PwFTDpvXR98b2O8ZeX6OOqEefGXf5S+6mTzDevjktOaIl3BIxEgQIAAAQIECBAgQIDA8AgIAAyPu7USqDmBcePGRfqTCBAYHoGHHy99EDZLa/pTNkv9lZwnHcz/beGZCXf9qetzE7pr887bjIh0Oy8pYsuNR8QDPx8Td/1xQZz57b/E/X95sciy1eYrxef/e6PYdosGD+a2oRAgQIAAAQIECBAgQGCYBfJ7A9thhrd6AgQIECAwkALpFkB9Ta/3o2xf11kp5Rob6+LWK0fH+3btfJZ6Z8vF0/vu1hA/+e5oD5zuNHh1dXWx3ZYj4v07PxRrL3dm8e8DuzxcfFBwWiYRIECAAAECBAgQIECAwPAKCAAMr7+1EyBAgACBARFYYbm+f6Sv2I+yA9L4Ya4kPf/gJ98dEz+6rKlwMLu+cNb64gal6e23qo9bLm+KH10+OsaO6bRwcTZTBAgQIECAAAECBAgQIECgIgXcAqgih0WjCBAgQIBAeQLbbtH5DPbyyr5r876XLW9NlZ173/c0xt7vHhGXXHptXPm92wuNbYtPHvbeOPwTH4n6+r4HWCq711pHgAABAgQIECBAgAABArUs4NdsLY+uvhEgQIBAbgS23Lg+1lmz/I/1VGarTcovV8uwTSNbY0zjtMLfczFqZLZnA9Syh74RIECAAAECBAgQIECAQPUK+MVfvWOn5QQIECBAoEMg3W/97C+P6niddSKVca/2rFryESBAgAABAgQIECBAgACB6hIQAKiu8dJaAgQIECDQrcAeOzXE1z8/stvlSy44tZA3lZEIECBAgAABAgQIECBAgACB2hQQAKjNcdUrAgQIEMipwImfGhVXndUUkyZ0D5CWpTwnFfJKBAgQIECAAAECBAgQIECAQO0KOO2vdsdWzwgQIEAgpwIf2z89zLYhrrqpJa78/rPx1DOtRYk1Vm2Iww58axy638iYPKkupzq6TYBAbwKXXnppLFiwIMaMGdNbVssJECBAgAABAgQIEKhwAQGACh8gzSNAgAABAn0RSAf4P/3xxpjccHdceeWVxSoOO/iwOOSQQ6K+3sH/vpgqQ2AgBTbffPNYccUVi1WuttpqA1l1v+s68MAD+12HCggQIECAAAECBAgQqAwBAYDKGAetIECAAAECBAgQyJHACiusEOlPIkCAAAECBAgQIECAwGAKCAAMpq66q1ZgvfXWi+OOO67Y/g022KBq+6HhBAgQIECAAAECBAgQIECAAAECBAjkV0AAIL9jr+c9CKy++uqR/iQCBAgQIECAAAECBAgQIECAAAECBAhUq0B9tTZcuwkQIECAAAECBAgQIECAAAECBAgQIECAAIHuBQQAurexhAABAgQIECBAgAABAgQIECBAgAABAgQIVK2AAEDVDp2GEyBAgAABAgQIECBAgAABAgQIECBAgACB7gUEALq3sYQAAQIECBAgQIAAAQIECBAgQIAAAQIECFStgABA1Q6dhhMgQIAAAQIECBAgQIAAAQIECBAgQIAAge4FBAC6t7GEAAECBAgQIECAAAECBAgQIECAAAECBAhUrUBD1bZcwwkQIEAgdwJrrLFG7LvvvsV+v+1tb8td/3WYAAECQyFw9913x8KFC2PUqFGx1VZbDcUqrYMAAQIECBAgQIAAgUESEAAYJFjVEiBAoDeB1VdfPfbee+9itjXXXLO37JYXBNJBfwf+bQoECBAYXIHdd989mpubY+rUqTFt2rTBXZnaCRAgQIAAAQIECBAYVAEBgEHlVTkBAgS6F9hwww0j/UkECBAgQIAAAQIECBAgQIAAAQIEBkNAAGAwVNVJgAABAgQIEMiRwJQpU+Ltb397scfLLLNMjnquqwQIECBAgAABAgQIEKhsAQGAyh4frSNAgAABAgQIVLxAuk+8e8VX/DBpIAECBAgQIECAAAECORSoz2GfdZkAAQIECBAgQIAAAQIECBAgQIAAAQIECNS8gCsAan6IdZAAAQIECBAoR6CxsTHGjBlTLJKmJQIECBAgQIAAAQIECBAgUK0CAgDVOnLaTYAAAQIECAyKwK677hrpTyJAgAABAgQIECBAgAABAtUu4BZA1T6C2k+AAAECBAgQIECAAAECBAgQIECAAAECBEoICACUQDGLAAECBAgQIECAAAECBAgQIECAAAECBAhUu4AAQLWPoPYTIECAAAECBAgQIECAAAECBAgQIECAAIESAgIAJVDMIkCAAAECBAgQIECAAAECBAgQIECAAAEC1S7gIcDVPoLaT4AAAQIECBAgQGAABR566KFYtGhRNDT4qTCArKoiQIAAAQIECBAgMCwCvtUPC7uVEiBAgAABAgQIEKhMgdVWW60yG6ZVBAgQIECAAAECBAiULSAAUDaZAgQIECBAoHoEVl555dhmm22KDV5ppZWqp+FaSoAAAQIECBAgQIAAAQIECPRbQACg34QqIECAAAEClSuw4YYbRvqTCBAgQIAAAQIECBAgQIAAgfwJeAhw/sZcjwkQIECAAAECBAgQIECAAAECBAgQIEAgBwICADkYZF0kQIAAAQIECBAgQIAAAQIECBAgQIAAgfwJCADkb8z1mAABAgQIECBAgAABAgQIECBAgAABAgRyICAAkINB1kUCBAgQIECAAAECBAgQIECAAAECBAgQyJ+AAED+xlyPCRAgQIAAAQIECBAgQIAAAQIECBAgQCAHAgIAORhkXSRAgAABAgQIECBAgAABAgQIECBAgACB/AkIAORvzPWYAAECBAgQIECAAAECBAgQIECAAAECBHIg0JCDPuoiAQIECBAgQIAAAQIZBc4444xoaWmJ8ePHx3HHHZexlGwECBAgQIAAAQIECFSigABAJY6KNhEgQIAAAQIECBAYJoFTTz01mpubY+rUqQIAwzQGVkuAAAECBAgQIEBgoATcAmigJNVDgAABAgQIECBAgAABAgQIECBAgAABAgQqSEAAoIIGQ1MIECBAgAABAgQIECBAgAABAgQIECBAgMBACQgADJSkeggQIECAAAECBAgQIECAAAECBAgQIECAQAUJCABU0GBoCgECBAgQIECAAAECBAgQIECAAAECBAgQGCgBAYCBklQPAQIECBAgQIAAAQIECBAgQIAAAQIECBCoIAEBgAoaDE0hQIAAAQIECBAgQIAAAQIECBAgQIAAAQIDJSAAMFCS6iFAgAABAgQIECBAgAABAgQIECBAgAABAhUk0FBBbdGUKhCYOXNmPPvss1XQUk0kQKDWBBYtWhSvvvpql27Nnz8/6uvFsrugdHrBrBNGxklmGaE6ZVu4cGG89tprneZEeG924VjqRaWbjRs3rrhvHT16dMV87ytl1tLSEnV1dUv5mvGGALPytwRmzMoXKL9Eqe1swYIF5VeUoxLMcjTYukqgigQmTpwY6a8aUl1bIVVDQ7VxeAQuvPDCuOuuu4Zn5dZKgAABAgQIECBAgAABAgQIECBAgACBChP40Ic+FPvtt1+Ftap0c1wBUNrF3DcFxo4dG5MnT445c+YUz+YDQ4AAgeESSPHq1tbWLqtvaGhw9mcXka4vmHX1yPKKWRalrnmYdfXI8opZFqWueUqZNTY2ds3kVRcBZl04Mr1glompSyZmXTgyvWCWialLJmZdOLwgQIBA2QKuACibLJ8Frr766rjtttvy2Xm9JkCgIgTSpb8PP/xwl7asu+66kYIAUmmBUmZvf/vbY8SIEaULmFsMMj3yyCNdJJh14VjqRQrMMVuKpccZ6VYP//znP7vkWW+99dzSrItI1xfMunpkecUsi1LXPOm2Uo8++miXmeuvv76TDbqIdH3BrKtHllelzDbYYIMsRXObJ91a8LHHHuvSf2ZdOLwgQGAYBFwBMAzoVjm4AulgkbOsBtdY7QQI9CxQ6l7/ab8kANC9W6l7YycvZuWZpe1M0KR7s1JLmJVS6Xleel/azro3Smd/LpnSdlbqs2HJfHl9zaz8ke/OrNTnafm112aJ9OycJVN6bzJbUmXx6+7MFucwtaRAOqllyZS2M4kAAQLDKVBN30NdATCcW4p1EyBAgEBmgVmzZsVmm23WJf8f//jHWGaZZbrM82KxQHpw+xZbbLF4RmHqvvvuq5oHFXVp+BC9mD59emy11VZd1nb//ffH+PHju8zzYrFAegDw1ltvvXhGYeqBBx6I9CBZqbTAK6+8Ettss02XhX/5y19izJgxXeZ5sVjgxRdfjO22227xjMLUX//612hqauoyz4vFAi+88EJsv/32i2cUph566KEYOXJkl3leLBZ4/vnnY8cdd1w8ozD1j3/8Q+C8i0jXF9OmTYudd965y8x0VVg1HRTp0vghePHvf/87dtllly5rWvLKky4LvYhnnnkm3v3ud3eRYNaFwwsCBAj0KFDf41ILCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaoUEACoymHTaAIECBAgQIAAAQIECBAgQIAAAQIECBAg0LOAAEDPPpYSIECAAAECBAgQIECAAAECBAgQIECAAIGqFBAAqMph02gCBAgQIECAAAECBAgQIECAAAECBAgQINCzgABAzz6WEiBAgAABAgQIECBAgAABAgQIECBAgACBqhQQAKjKYdNoAgQIECBAgAABAgQIECBAgAABAgQIECDQs4AAQM8+lhIgQIAAAQIECBAgQIAAAQIECBAgQIAAgaoUaKjKVms0AQIECOROYNSoUXHEEUd06ffo0aO7vPaiq0BTU9NSZmme1L1A2qaW3M7Stid1L1DKbOTIkd0XsCTGjBmz1HbW2NhIpgeBsWPHLmXW0OCnTA9kMW7cuKXMRowY0VOR3C8bP378Umb19c6Z62nDmDBhwlJmdXV1PRXJ/bJSZrlH6QVg4sSJXbYz21gvYBYTIEBgCYG6tkJaYp6XBAgQIECAAAECBAgQIECAAAECBAgQIECAQJULOJ2hygdQ8wkQIECAAAECBAgQIECAAAECBAgQIECAQCkBAYBSKuYRIECAAAECBAgQIECAAAECBAgQIECAAIEqFxAAqPIB1HwCBAgQIECAAAECBAgQIECAAAECBAgQIFBKQACglIp5BAgQIECAAAECBAgQIECAAAECBAgQIECgygUEAKp8ADWfAAECBAgQIECAAAECBAgQIECAAAECBAiUEhAAKKViHgECBAgQIECAAAECBAgQIECAAAECBAgQqHIBAYAqH0DNJ0CAAAECBAgQIECAAAECBAgQIECAAAECpQQaSs00jwABAgQIVLLAc889F3/6058yNXGZZZaJHXfcMVPevGW66qqrYsMNN4xNN900b10vq7///ve/45e//GU88sgj0dzcHBMnToz1118/3vve98aUKVPKqisvmV9++eX4xS9+EX//+99j5syZMW7cuHj7298ee+65Z6ywwgp5YehXP6dNmxb/93//V6wjvU/XWmutftVX7YX/8pe/xK9+9at49tlno66uLlZbbbXYfffdY7311qv2rg1J+//2t7/F448/XlzXDjvsYN9VQj35pG0s/T937txI3x/S5+Mee+wRY8aMKVHCrL/+9a9x5513xlNPPRUtLS2x3HLLxWabbRa77bZbNDU1AepBYMGCBfHtb3879t9//1hllVV6yJnfRWm/n7atLGndddctfs/IklceAgQI5FGgrq2Q8thxfSZAgACB6hVIBxY//vGPZ+rAPvvsExdffHGmvHnKlA4ubr311rFw4cL4xCc+Eaecckqeup+pry+99FJ89atfjZ/85CdR6uvS6NGj44tf/GJ88pOfzFRfHjKlA0BnnnlmXH755cWDQUv2eeTIkXHcccfFpz/96eJB3CWXex2RDgpddtllcc455xQPQiaTtB3mdTubNWtWcXtJ+/1S6YMf/GCcccYZDjaWwinMSwG40047La699tqOHLfccktsscUWHa/zPvHKK6/El770pfjZz35WkiIFAr71rW8Vg74lM+RwZgoonXjiifHAAw+U7P2KK64Y5557bmy//fYll5sZ8cMf/jCOPfbY4mdh+sxMgSapq8Dxxx8f11xzTdeZ3bxK29uHPvShbpaaTYAAAQKuALANECBAgEBVCyy77LIxYcKEbvuw5ZZbdrsszwuuuOKK4sH/ZLDrrrvmmaJk3x977LH4wAc+EK+++mpx+dSpU2OTTTYpngWazg598MEHiwdn04HZ1tbWOProo0vWk6eZs2fPjoMOOqjj6px0sD+ZTZ48OZ555pl4+OGHi0GBFCB4/fXX4+STT84TT6a+3nfffcWg0qOPPpopf61nSu+tgw8+uONKiHRQMR1QTIHL3/72t/Haa6/FzTffHGnb++53v1vrHGX3Lx1g/NrXvhbpALdUWuDpp58unoGdrixMKV3htfHGGxcDSg899FCk+Wk7O/zww+PSSy+Nvfbaq3RFOZqbguLpwHV6f6aUrsJZZ511itPpszGZvvDCC8XPgxtvvDG22mqr4jL/dBVI21NK6eqSbbfdtutCr5YSeMtb3hKNjY1LzW+fka6UkwgQIECgewEBgO5tLCFAgACBKhBIZwcdeOCBVdDSymliOqP2+9//frFB6ZJpPzyXHptVV1011lxzzeKZ/+ns2XTQJ912pD2lWx4cdthhMX/+/OIZ7/vtt1/x1gfty/P4/6233tpx8D/dHumb3/xmpABde7rjjjuKZ7Ens3Tg4/+3dyfwVk3//8cXpTQZiihDqYhI9KVIhCL5SyKSIRlS5rmMifLL/NVkHpNCZmVMNClDVKakQYakpAiRhv99L9+1rbPvPueeO3S7Z+/Xejxyzh7PXs+9zymfzxo6duxoh6By25P8umzZMtO/f38zcuTIgKFmzZpGvVCSXB5++OEg+N+6dWvbM0I9b1TUsr1Lly5m+vTpdrgptWpXjy+KMXPnzrUt2idNmmQ59Nul4cpIBOR/OoYOHWqD/DJSzyQFtt3QNWvXrrVDtKj1v4p6Ceg5TPpwQErsbrHFFka/UXfccYdp0qRJAKvecoMGDbI9JpQgUC+Bt956K9jOm38E9N3UEHkq+jdstWrV/tnAf9MKPPfcc0aNMSgIIIAAAkUTYBLgorlxFAIIIIAAAjkroOC/xrJXSeqwIgXdvIoVKxrNkaBhR44++uiU4L+O1bwSp59+uj2NAtoaNzrpRcFYBX569uxphwDyg/+yUU8TbXNFLbcpxqi1v5JwLvhfq1Yt69erV69E86iV/z333GMNqlSpYgYOHGhc8F8r1VJ7yJAhZuON//nfGW2nGDuMjYLULvivVrEa2uaII46AJ0JAwf1zzjnH/nZdccUVQfBfu+rZ0pBlLVu2tEeqJ8DYsWMjzpKsVWqJrd4lSvr6wX8pKJEis7322suiaO6cOXPmJAsoi9q61v/yynZIyyxOyy4IIIAAAgikFSABkJaGDQgggAACCMRPQC3yNPyPigK0tJhNf481dI0CHemKholw5YcffnBvE/2q8dj79OmT1sAfD9pNSJp254RsUE8TtaQtV66cTZBMmDCBscbz7r0mQP7xxx/tU6DJfjUOe7jUq1cv6MGkRMqsWbPCuyRuWROwavgtDY130003mVdeeSUIxiYOI4sK63t33XXX2SHf0u1+0EEHBZs0PBzF2EnJM/WE4O/H9E+J/u5zvSKUmNtxxx3T78wWBBBAAAEESkiAIYBKCJLTIIAAAgggkAsCo0ePtsMd6Fq7detm1NKdUjQB14tCR0cFJ4t21ngflWn83njXPH3typcvb1u3K2jbsGHD9DsmbMu7774b1FgTlqcrBxxwgBk/frzdPHny5GAs8nT7x339NttsY4dK0rjsW2+9ddyrWyr103eTUjgB/+9HDT9F+VdAk7y7Qi9MJ8ErAggggMD6FiABsL6FOT8CCCCAAAJlSODee++1V6PA/2mnnVaGriz3LkXDaqioC/+hhx6aexXYAFf87bffBp9Kq8eAwjRu3PjfBd5ZAQ0d4srOO+/s3uZ79ZMmmmiaYszBBx8MQwkK+L9bdevWLcEzxz8OlrUAAEAASURBVPNUf/zxh9E8OSoas91NEBzP2hauVkuXLrUTl+soDZ/UvHnzwp2AvRFAAAEEECiiAAmAIsJxGAIIIIDAhhPQJHOuzJs3z9xwww1mypQpZsmSJUZjRWvM41NPPZX/sXJI/3udOnWqmTlzpl067rjj7KSQbpe7777bTkinITUuv/xyt5rXCAENo3T77bebcePG2a2aNJKgUARUxCoXFNImfzigiF1ZlXCB7777LhDYdtttg/fhN/42/5jwfiwjUFQB97ulZK96nFDSC2hCc/2dqAmnNYfCzTffHMzTkf6o5GzR3EKaN0gl3Ppfcyf8/fffRkOetW/fPjkoGWrq/3t/4sSJRj3DZsyYYeex0jCW6h121llnme233z7DWdiEAAIIICABEgA8BwgggAACOSeg/wl3RYHrcNGEc88995w577zzzDXXXBPenNhlN+mcALp3757ioACHhs/Yd999SQCkyPyzoFZ7119/vVm5cqWZNm2aWbx4sZ2EVMkSJvCLAItYtXDhQjtppDbVrl3bTgocsRurELACCiS6Uq1aNfc236smA3bFP8at4xWB4ghognc1NFDReO2apJuSKvD++++bYcOGmeXLlxs1NFAPgJ122skMGDDA+PMnpB6VvKU///zTPProo7bieo78IL8C3aNGjbLb1KDA35Y8qegaX3rppSkbNPfSJ598Yh5//HE7Yfzhhx+esp0FBBBAAIFUARIAqR4sIYAAAgiUsoBaUxfU4lyTyYWHq3FJALXGO+yww4z+h0n/06mxoJ9++mmzdu1aM3ToUDuJa9euXUu5Vuv34zSpo4ISmUqvXr1skNXtowCGO0Ytr/1hM7TP77//7naN5aueh/D/PIYrqu74p59+eni1XdYEo0oquVKpUiVz7LHHmmbNmrlVsXy94oorbIvEdJXT0A49e/ZMtzll/bXXXhu0fLzyyitNXOcDWLNmjbnssstS6h5e2GuvvewcHOH1LP8roGCZK5meFX+bf4w7llcEiiqgf1O4Sc01V0fv3r2LeqpYHzdp0qSUvx/VK+f444+3EwXHuuKFrNwzzzxjfv75Z3uU/q2hZ8oVNS6g5BfQv/X1Z9NNN7U9I1q2bGnU8l+NMPRvMvUIkF2PHj3M66+/bnbZZZf8J2ENAggggIAV+PdvHUAQQAABBBDYAAJKAChgn6noH/d+AkDdo7///vvIQzp06GDUCuiMM86w22+99VbTuXPnWE12q2F8CjLT/wyplbUrmnTOdaUOdzvXPgp0xLkoAVCQmSYtTJcAqFq1qg10K7irZ08BD3Xl1x95qneAS0rFyVEBCzdcQVS91LozmwSAWoe+9tpr9hSaL6FTp05Rp4vFumyeNSXcNAk3Jb1AHL9P6WvLlrIooETlN998Yy9NCWSCi9F3SXN06O+BVatWGfXAVFBW//YaPHiwHS6vY8eO0QcmaK3+/eUm/1UDglNOOSWl9nH/N1hKZQuxoCGk9CeqnHTSSebqq6+2vSr07xQ9cw8++GDUrqxDAAEEEMgTIAHAY4AAAgggsEEFNEbsPvvsk/Ea6tevn3F7eKO66bdo0cL+T6haW2l+gDhNiqixTgsyq1y5csAiA9e1XP+jHmUR9//5VDCxILMGDRoEZuE3mldCf1xRskBDTL355pv2f+o10WF4WCW3by6//uc//7FBnXR1CPckidpPQ0Jcd911dpOe3UGDBkXtFpt16+M3LTY4haiIgmSuKLjj/6a59Xr1E1Tp9vH35z0C2QgoWKsEqIr+zrzwwguzOSyR+2i4Gn/ImgULFpguXbqYr7/+2s4HoOGA1OspyeWtt96yyREZqFHKFltskcIR93+DpVS2BBeUABg5cqT9e2Ds2LH2tWLFiiX4CZwKAQQQiI8ACYD43EtqggACCOSkQIUKFYJxwUuyAhqaRa3QVDR8S1TQuyQ/rzTPpZZj4dZjmT5fra9d93L1kIjqPeGGAFIwzU2kqf9BVcv3OJRy5cqV6HMmlzvuuMMGNdTi+5577ollAsAFwIr6DMyePdv2qtDEhhqrffjw4aZ69epFPV1OHFfSz1pOVHo9XKSeE9f6+tdffzVbbrll5Kf88ssvwfq4P1tBRXmzXgVGjx5tbrjhBvsZu+22m9H8OUrsUbITqFOnjk36an4c/f1477332j/ZHR3PvWTgihqpuH9nuXXz5893b82KFSuC7dtss01sh8sLKlyMN/q3mL6j06dPt40VlHTKpmFCMT6SQxFAAIGcFSABkLO3jgtHAAEEEMgk4LeuUvAoyWXixIlB9W+//XbbJT9YEXqj4YXcuPZ9+/a1w9uEdmHxfwIah7ZevXq2Vd+iRYuMJqRjgsh/Hw8FOE488USjAK1acz/22GMMofEvD+8KENhhhx1sUEe76buloGJU0eTSruy4447uLa8IFElAf1+qd5eGbNHzNGLECJNpEuoifUgCDnL/jlBVFZxNclESxDVIkYP+XsxUHnroIaM/KmrV3qhRo0y7J36b/+99PyGceBgAEEAAgZAATRlCICwigAACCMRDwA/6J31YCDf2fzzubNmqhSamc8X1snDLSX5dsmSJHeZAiRFN0qpghh8QSrINdc9OYPfddw92VC+udGXWrFnBpj322CN4zxsECivw0UcfBT2WNJGt5o1RC2xK4QX4u7HwZhxRNAH1mHClSpUq7i2vCCCAAAIhAXoAhEBYRAABBBCIh4D+R96VXXfd1b1N5KuGL/DHyY5CaNmype0+rXHu3SRqfquqqGOSvk4TWGusYxXNMUCg6J8nYvny5baFo4Y0KF++vJ0jIU5DcP1TS/67vgX0m+TK5MmTUyaCd+v1OmHChGDRPyZYyRsEshD4/PPPzcknn2w0Frt6dyn4T4+SLODS7DJ37txgS9J7xmn4qPfffz/wiHqj3pdnnXWW3aShk3r06GHf8++KKK1/16nhhb67Kvr3huaboCCAAAIIRAuQAIh2YS0CCCCAQBkWUBd9DSmSblJX/Y/U+PHjbQ007rgmBE5y2XrrrQusvgLYKpo8TRO1Jr2sWbPGjvGvuRZq164dyaEAkWt5ptbttDwzRpMjn3TSSeaLL74wGgtfcyO0bds20o+VCGQS2HvvvW0wR4mkN954w85dosm2/fLZZ5+Z9957z67ShNV169b1N/MegawE5syZY3ssafgQzSMxatQok2lS+KxOGuOdfv75Zzum/6WXXmr8lv5+ld0QNlpHAtgU+O8q9ZZzRUNO8e+wfzQ0b1C7du1MjRo1HE/Kq54z1/vy0EMPTTtZfMpBLCCAAAIJFWAIoITeeKqNAAII5KrAO++8Y7p27Wo6depkBg0aZAOOfl0mTZpkt7thby688EKbLPD34T0CBQmce+655q677rLB62effdZoEltXlBxQ8P/aa691q4yes6QXtZw99dRTg/GeL7jgAlO/fn2jIVrS/dExFASiBJSUdN+rVatW2RaxS5cuDXbVHBPnnHNOsKxgJAWBwgpooukTTjjB6NnScGX9+vWz4/+n+83yh5wq7GfFYX/18DrmmGPMkCFDTPv27c3UqVNTqvX7779bQyVRVBTMPuOMM1L2YQGBbARuueUW06tXL3P44YebMWPGGP3byxX9m0y9VW+++Wa7Sg0O+DvA6fCKAAIIRAvQAyDahbUIIIAAAmVUQK3z1Kp/8eLF9h/+d955p2nYsKFt9aOA0Pfffx9ceYcOHYJu1MFK3iCQhYD+h/PVV1+1QSEFsq+66io7ga3+J1OtRZctWxac5aKLLjKHHHJIsJzUN59++mnQGlsGSqDoT6aihJ0mUqYgECXQuXNn2/pf30UN66aeNvvuu68NBH344YfB0GZKPPEdjBJkXUECCiy61tcKKmoC4ExF/wbRb11Si/79ddBBB9m/B9UD59hjjzU1a9a0vXX+/PNPo/k69KqihIoSBdpOQaCwAurxpX9zaRL47t2723/7q2eOksNfffWV8Sf8HTBggNEQlhQEEEAAgfQC9ABIb8MWBBBAAIEyKKB/4I8dO9Ycd9xxRuOqqmXoJ598YgOPLvi/2WabmT59+pihQ4fafcpgNbikMi6g5+vll182GlZERUPbKAD5wQcfBMF/TRI5ePBg07t37zJem7J5efofe8bYLpv3pixdlYaRUgti/d5rqAeN+a85ATSvicZ8Vi8BBX8oCJSGQNKHmVLwtX///ubuu+8OhqlRgwwNxTVjxowg+K9/q73wwgvmsMMOK43bwmfEUEBDMI4ePdo0bdrU1k4B/2nTphklf13wX70MR4wYYbQvBQEEEEAgs8BGeUMkrMu8C1sRQAABBBAomwJqtaf5ADTZnFqcqWVao0aNzIEHHsg4oIW8ZWrJp38SVK5cmRbZIbvZs2ebKVOmGPUw0cS/miCySZMmZr/99rMByNDuiV3UcD7z5s3Luv4K3iZ9gu5MWBpne+HChXYXTaKZbgzkTOeI0zZ9/95++2078baSAXXq1DFt2rRh8u0sb7IS5K7nknrd6LeeYsxPP/0U9ADIxkMNDEhc/iO1du1aO7nt9OnTba9MfS81Z4566uyxxx7ZcLLP/wT8vz/VY4JeE6mPhib61XBT+jtRz518lBhQjzA3h1XqESwhgAACCIQFSACERVhGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAGAgwBFIObSBUQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAgLkAAIi7CMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAMBEgAxOAmUgUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMICJADCIiwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIBADARIAMbiJVAEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgbAACYCwCMsIIIAAAggggAACCCCAAAIIIIAAAggggAACCMRAgARADG4iVUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAICxAAiAswjICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAjEQIAEQg5tIFRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCAuQAAiLsIwAAggggAACCCCAAAIIIIAAAggggAACCCCAQAwESADE4CZSBQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEwgIkAMIiLCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEAMBEgAxuIlUAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBsAAJgLAIywgggAACCCCAAAIIIIAAAggggAACCCCAAAIIxECABEAMbiJVQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgLEACICzCMgIIIIAAAggggAACCCCAAAIIIIAAAggggAACMRAgARCDm0gVEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAIC5AACIuwjAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBADARIAMTgJlIFBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTCAiQAwiIsI4AAAggggAACCCCAAAIIIIAAAggggAACCCAQAwESADG4iVQBAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGwAAmAsAjLCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEQIAEQAxuIlVAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAsQAIgLMIyAggggAACCCCAAAIIIIAAAggggAACCCCAAAIxECABEIObSBUQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAgLkAAIi7CMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAMBEgAxOAmUgUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMICJADCIiwjUEYE/v777zJyJWX/MlavXm3WrVtX9i+UK0QAAQQQQAABBBBAAAEEEEAAAQQQQKAUBUgAlCI2H4VANgJz5swxJ554ojnggAPMySefbBYtWpTNYYncZ+XKlaZ3797W6vDDDzcTJkxIpAOVRgABBBBAAAEEEEAAAQQQQAABBBBAIEpgo7xWszSbjZJhXWwE7rnnHqOgugLpHTt2NJ06dSrTdevcubOZP39+cI263l69egXLvPlXYNCgQWb48OHBilq1apkXX3wxWOYNAggggAACCCCAAAIIIIAAAggggAACSRYon+TKU/f4C6xZs8YMGzbM6FWlevXqZbrSGsrGD/7rYhkKKP0t++qrr1I2rlq1KmWZBQQQQAABBBBAAAEEEEAAAQQQQAABBJIsQAIgyXe/BOo+cOBAs2DBgsgzVahQwVSuXNmoVfauu+5qmjZtaqpUqRK57/pa+cMPPwTBf33G9ttvv74+qkTOW758ebP33nubjz/+2J5Pyx06dCiRc2/ok1x77bXmjz/+KNZl6Hnq379/cI5mzZqZ9957L1gu6707ggvlDQIIIIAAAggggAACCCCAAAIIIIAAAqUgQAKgFJDj/BGvv/66+emnn7KqYsWKFc3RRx9tevbsaapVq5bVMcXd6dtvv005xQ477JCyXBoLM2bMMN988439qPbt2xf4kbfffrt5/vnnjca3b926tWnQoEGBx5T1HX777TfzxhtvFPsyNS+CX0455RSz1VZbmblz55rGjRubVq1a+Zt5jwACCCCAAAIIIIAAAggggAACCCCAQKIFSAAk+vYXr/J//vln1sF/fdJff/1lRo0aZVts33fffaZGjRrFu4Asjv7uu++CvfR5lSpVCpZL442Mzj33XDuMj4LT2SQAlBzp2rVraVxeqX1GOBFT1A9u3rx5yqEbbbSRadeuXco6FhBAAAEEEEAAAQQQQAABBBBAAAEEEEDgHwESADwJRRYIB3WHDBlih/lxJ9R47EuWLDFqAT9y5EjbSlvb1Bp+wIABRi3d13dxLe/1ORti+J+PPvooGMN/Q3z++vbN9vwaBmrw4MGRu2scf03m64omPE7XU0NDSVEQQAABBBBAAAEEEEAAAQQQQAABBBBAIDsBEgDZObFXhIDful6b69SpYzRmvSt6r3X6c8QRR5gePXqYzz77zG6eMGGC0fj8Cgyvz+JfY7qg8vr8/Pfffz84fZITAFtssYUJt953MEoS+aVNmzZG+1MQQAABBBBAAAEEEEAAAQQQQAABBBBAoHgCGxfvcI5OsoAfXNeEvzVr1kzLoe0nnXRSyvZPP/00ZTnTwu+//27mz59vZs+ebRYtWmTWrVuXafdgm99LoSgB+OXLl9ueC/rchQsXmtWrVwfnzuaNP0FtUT4/m88o6X1k++OPP1rrefPmGdmvz+I/R5okurSD/7qnX3/9tVFdC7q/eh70LMinOEVDQ+kzv/zyS/tcrV27tjin41gEEEAAAQQQQAABBBBAAAEEEEAAAQQiBf5trh25mZUIpBfwg+vbbbed0XjsmUrt2rVTNv/yyy8py+EFBZ6feeYZ8+qrr9rgrL99s802M4cddpjp3r27qV69ur8peL9mzRobXHUrsu0BoEmNNWTRW2+9lXK8zlOuXDmzyy67mBYtWpi2bduaunXrutPb16eeesqMGzcuWKfJaV0ZOnSoefjhh92iadmypTn11FODZb2ZPHmyGTZsmF2nSZP9oXHkpeFxXNHY98ccc4xbTPt69dVXm6VLl9rt9erVM717947cd+bMmebJJ580U6ZMyRf0b9Sokb1WTUpc0sV/jrK9R7qGX3/91VxxxRXB5Vx66aWmYcOGwbL/pk+fPkHQXkNVbbLJJkbBfM1F8corr9gJl7W/5l8466yzTJcuXfzDzfjx4+29++KLL4L1Go7o+uuvN/Xr1w/WZXqjOTBeeOEFM2bMGBv495NYSnwccsgh5uyzzzbbbrttptOwDQEEEEAAAQQQQAABBBBAAAEEEEAAgawFSABkTcWOYQG/5XY2rdt/++23lFNkmpBX8wYocB0eHsadQMHfZ5991mgooXvuucfsuOOOblPwqp4CfovubK5RyYZbbrnF/PHHH8F5/DdKKigIrD8PPfSQOfzww03//v2DXT744APz8ccfB8v+m1mzZvmLkcF79Ypwx++0004p+2+++eY2EeISJ5rUuKAEgIYgGjt2bHCeqP1lpPkYnnvuuWC/8JvPP//cXHXVVbYXx8UXXxzeXKzlwj5H7sOUOHBWWqfgfVRRoF1JGc1JoZ4oCv5r3gHVI/x8rVixwvz3v/81us+nnHKKfX5uvfVWG7gPn1v385xzzjEjRowwW221VXhzyrI+78orrzR+ssPfQcmu0aNH20SDEhS77babv5n3CCCAAAIIIIAAAggggAACCCCAAAIIFEmABECR2DhIAn4wM5uW25988kkKXIMGDVKW3YJaoF9++eXB5LnqWfCf//zHqBW6AriaR0D7qCiAq0TB448/nq8Hgh9Y1r4FXeMTTzxhBg4cqF1t0efus88+9nPVGv/nn382aiWvIWBc0Tq/aCgXl4xYtmyZUUBZRdcdbtkdNSa+f81RCQsZuLpryJqCinoyuKIeGEpY+EXB/8suuyw4p7YpsaAeDrpeXf+bb74Z9CBQsHvPPfc0hx56qH+aYr3361zQPfI/yH/+NN/ENtts428O3mu4HgX/VTS80IIFC8y5555rlEhRy/tmzZoZDcmjZIkC/yoPPvigOfbYY21yxyVQdt55Z6MeFEr+uMml1YtAz81FF11kj4v6j57X888/P6VXhQwbN25sKleubJMR77zzjj1U3koUjBo1yj4zUedjHQIIIIAAAggggAACCCCAAAIIIIAAAtkKkADIVor9UgQUUF28eHGwLipYHWzMe6MAq4Y/cUVDBmkonXBRcFYB0L///ttuUkB4wIAB+fa94447jIbbUVFAXskFBVX94geI1Xq+atWq/uaU9xruxw/+q/X9//3f/0UO73LeeecZtfRXCQes77zzzuC8ffv2tcPLaIUSCXfddVewLd0b/5rD59YxGnbGJQC0rwLWGpYoqshSQwq50rVr13z7ytGdT/uddtppdhgatZJ35eSTTzYnnnhiEMBWcLqkEgAKwrskiT6voOfIXZNe/cSBkhvpHPz9lMhRwkOfqyTHDTfcYPRsqKj3h4b0UVEPEO03bdo0O7G1kkxHHXWU3aakiSa0dgktf6Jnu4P3Hw0npaGJ3DwK6img52qvvfby9jLm0UcfNXfffbddp8mxdd80JBAFAQQQQAABBBBAAAEEEEAAAQQQQACB4ggwCXBx9BJ87Pfff58yEW+mwK2GYNEwKn7C4IwzzsjXYl+c/fr1C8ZjV2BWQdGoREHHjh1T9DWharj4gd+oYLrbX0Ham266yS0aJSc0Nny6sd1Vd1cynbegYL47h//qX3OUqT80jJIk/v7+efRe4/m7osCzC2C7dR9++KEdRskta5JmJTf84L+2qWX9AQcc4HazLeiDhWK+CV9/VJ3TfUS2vv5+eq/W+02bNjW33XZbEPzXZ2hOBr8o+K+iJIFvp94G++23X7BruBdIsCHvjZ579QRRUfJh8ODB+YL/2hYemknJGwoCCCCAAAIIIIAAAggggAACCCCAAALFFaAHQHEFE3p8OHAbFQjXcDhqJa2x8qdOnRpIKdDqB1TdhnfffdcOseOWNUxLumFd/LH9tb+Cq+HihmnR+kyB5QceeMC4+Qk07I/G9NdQMVFFn6u5BVxxw/24Zf/VTxQoqVBQ0bwG+uNKlKmfANB+SnzUqVPHHRK86jwaU94VteLXMER+0dwJrmi4H41nn664HhnaHmWd7riC1mfzHKU7h39spvvr76dzaYgj9SoJJzqiJrFWjwhNNh0u/r7pPDRUkBvaR8d369YtbVIpm+c5fA0sI4AAAggggAACCCCAAAIIIIAAAgggUJAACYCChNgeKeC3qtYOV1xxhdl44387lPz1119GY69r6B+/tGrVyrby9wOobrsm9XVl6623Nu3bt3eL+V41+a9fND57uPiB36hguvbXUDB+oLx169Zm9913D58qWF64cKFRYsOVdOfVEDJ+y/B0+7nz6NW/Xi1HBbWVEFFyQmPPq8yfP9/INFw0oa/ugYp6Umg8e79oUlo3hI3Wa8LbdIFs1cW1hte+UdZaX5TiP0ebbrppgZPp+p/he2Xy9T9Dx2s4ny233NI/lX2v59UvqqeG+okqfm+WdMmd559/PjhUY/1rGKV0ZeLEiSmbStI45cQsIIAAAggggAACCCCAAAIIIIAAAggkSoAEQKJud8lVNhxUnTNnTsaT161b15x55pmmbdu2kfspUeD3EtBktRpqJaq899575rHHHgs2aWJcTc7qFwXp/Rb4UcF07T9u3LhgvgEtd+rUSS9pix90VhIjXfA37JPu8/0P8o/RePbhSYPdvuoF4MbtVwIgXNSaXOP0u9K5c2dTqVIlt2hf/ZbpStxkui8aF9/vmZApMZPyIVks+J7ZGLlTakx9N7SO1mU61v8MJUsOPPBAd5qUV99fGzQXRbpn0O9dEpV80LBXvrE+VxMORxVNEjxkyJBgk+Yz2HvvvYNl3iCAAAIIIIAAAggggAACCCCAAAIIIFBUgegIa1HPxnGJEfCDqmq5Xb169aDuCub7rd816WmbNm2C7VFvZs6cmRKIb968ecpuCmp/9NFHdiLhsWPHBtvUsvqaa64Jlt0btdD2h62JCtJqXxdI1/vNNtsscnx2bXPFDxIrQB8eVsft5/soUaCgbkHFP7cSC+kmtVXCw113VAJAPkuWLLEfJ58TTjgh30dr/H9XNLGwmwjXrdPxb775phk5cqTtyeHWH3HEEWkD6G6fwrz6dc4UxA+f0/fVtkzH+vumS3ToHP5+NWvWNI0bN9bqyOLvG/XZ8+bNC3pp6ATh51kJKj3zL730kp18WJM5qyjh0KdPn7T33u7EfxBAAAEEEEAAAQQQQAABBBBAAAEEEMhSgARAllDslirgB241nn+vXr2CHdQyW+tcAH7GjBkFJgBmz54dHK83SiKMGTPGjnE/a9YsO1yNhqLxy0477WRuvPHGyCFp/BbaOiYqSKv1/jA4Cvj6wxhpe7gUFPh1+/v7adie8Hjzbj//1T8m3fVqfwXsXdEcAGpt7g+ppKC9K+rRoMRGuPjeGhNfQ9Donqonh1qkhxMLSkZokmDNy1CSJZteGlGf5z9/umfpEiya4NkfhiqTq3/OdAkjXYuGVvKHAIra98svv0y5bAX4X331VTuBsrYp+L9ixYqUfWrVqmX69u1L6/8UFRYQQAABBBBAAAEEEEAAAQQQQAABBIojQAKgOHoJPTY8EW44AKrx1dXS2o2t/+KLL5ru3btHBqIdoR8I1jo/oeD2ca8aHqVjx452ctZ0reT9YHrVqlUjJ/VVAHbp0qXutPmGEQo2eG/8IHGmCYD9zw/7eKdLeeufO9MxfgJAwW1NSqzgscr06dONJp9VUe+ELl262Pf+fzTvgYbQcUXB//AY9G6begboXmoYoUzX5PYvzGtR5klw5/d91RMj3VA9/n46NlMCwN83U139/dKdU3NF+KVfv37+Ysp7Demk5/nII49M26Mk5QAWEEAAAQQQQAABBBBAAAEEEEAAAQQQyFKABECWUOz2r0B4ItyooKomPHUJAAWpNSltt27d/j1J6J0/xry/Sa27FYzVxLxNmzY1+++/v9EEwQWVbILpbpgcd65szusHfzMFif3PTzdPgPtc9+qfO8rU7aceBRpyyQ2zpNb6LgEwYsQIt5s5+uijjVr3h0s6a+2nZIkSDHvuuadp1qyZfU0XXA+ft7DLvpGOzVTn8Ln9Y7O9D0pmqH7pin/OTNfi3yedK+r+KsmSrujcGsZJiSw9z+l6L6Q7nvUIIIAAAggggAACCCCAAAIIIIAAAghkK0ACIFsp9gsEwgHQqADsLrvsYgP2Grdf5amnnjInn3xy2qFwNIyNKwrUXnvttTawqnNXrFjRbcr61b/GdMFcDeXiF81lkKloGBe/p0JUvd3x/udn2s/tH57UtqBjFKR/99137eFKALRo0cJe24QJE+w69Yzo2rWrO33Kq8af98sxxxxjNLa/PjObJIh/bHHe+0Y6T7r7FPUZ/rGZjvP3yxRoX7VqVcpcB5n8/USB5gqIem7851n3QvNg6PPVayQ8IXNU/ViHAAIIIIAAAggggAACCCCAAAIIIIBASQhsXBIn4RzJEvADoJkmuFUvAFc01I7GQE9XqlWrFmzS3AGtWrUyDRo0KFLwXyfyrzFdMDc8ga+CwJmKgv9uslbtl24IoPAY8ZkC1O7z/EC11hV0jIaNcUXzAKgoyeKC++3atTMaGieqhOcEUAt29a4ozeC/rsuvs+ZIUM+GbEs291fn8vfLZKp76wftM+3rX3e6/fznWc/MfvvtZxo2bEjwP9sbzH4IIIAAAggggAACCCCAAAIIIIAAAiUiQAKgRBiTdRI/AKoW0OFAutM46KCDUoY3eeKJJ1KCrG4/vdapUydY1Njwc+fODZYL+0aBXL+lfrogbTjg7Ncr/JkK4t5+++3Bag1NFDX0i3bwP1vLmVqea7uK/9mZJrX9Z29j/ATAvHnzzG+//WZefvllu1lJmdNOO83tmu9VczT4SYBPP/003z6lscKvs4xU72yKhpTS5L6upEvwaLv/Gemeg/B+Ws60r59USPfZ/vOs8/mTTWuZggACCCCAAAIIIIAAAggggAACCCCAQGkIZBdxK40r4TNyRiCbAKgqo4Du8ccfH9RLQ9W4YWuClf9707x585RVShZkKgp6z5w5M3IX9Tbwh/dJF6TVePB169YNzjF58uSgBX2wMu+NWtX379/fTJ06NVitxEe6sfEVjPeL3xrcX++/900zTWrrjvETAOoB8NJLLwUT+x566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" alt="Event-study plot with example data" width="768" />
<p class="caption">
Figure 2: Event-study plot with example data
</p>
</div>
</div>
<div class="sourceCode" id="cb3"><pre class="sourceCode r distill-force-highlighting-css"><code class="sourceCode r"></code></pre></div>
<div id="refs" class="references hanging-indent" role="doc-bibliography">
<div id="ref-Borusyak_Jaravel_Spiess_2021">
<p>Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. 2021. “Revisiting Event Study Designs: Robust and Efficient Estimation,” 48.</p>
</div>
<div id="ref-Callaway_SantAnna_2018">
<p>Callaway, Brantly, and Pedro H. C. Sant’Anna. 2018. “Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment.” <em>arXiv:1803.09015 [Econ, Math, Stat]</em>, August. <a href="http://arxiv.org/abs/1803.09015">http://arxiv.org/abs/1803.09015</a>.</p>
</div>
<div id="ref-deChaisemartin_DHaultfoeuille_2019">
<p>Chaisemartin, Clement de, and Xavier D’Haultfoeuille. 2019. <em>Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects</em>. w25904. National Bureau of Economic Research. <a href="https://doi.org/10.3386/w25904">https://doi.org/10.3386/w25904</a>.</p>
</div>
<div id="ref-Gardner_2021">
<p>Gardner, John. 2021. “Two-Stage Difference-in-Differences.” Working Paper. <a href="https://jrgcmu.github.io/2sdd_current.pdf">https://jrgcmu.github.io/2sdd_current.pdf</a>.</p>
</div>
<div id="ref-Goodman-Bacon_2018">
<p>Goodman-Bacon, Andrew. 2018. <em>Difference-in-Differences with Variation in Treatment Timing</em>. w25018. National Bureau of Economic Research. <a href="https://doi.org/10.3386/w25018">https://doi.org/10.3386/w25018</a>.</p>
</div>
<div id="ref-Sun_Abraham_2020">
<p>Sun, Liyang, and Sarah Abraham. 2020. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects,” 53.</p>
</div>
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<h3 id="references">References</h3>
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