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TenureStats.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Understanding standards in rank and promotion in economics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook uses the public-use data from Heckman and Moktan (_Journal of Economic Literature,_ 2020, henceforth HM) to summarize publication records that are typical of successful tenure cases in economics departments. \n",
"\n",
"The focus of HM is to document the influence of \"top five\" publications on tenure decisions, and in the scholarly impact of academic work in economics. See, e.g., Akerlof (_JEL_ 2020) for concerns about the consequences of this emphasis. The present exercise takes those patterns as given, using the Heckman and Moktan replication data for the descriptive purpose of documenting tenure norms. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use the dataset `FinalData_tenure_clockCum` provided by HM. This presents data at the author-year level, with cumulative publication counts by type of journal, the rank of the author's affiliation, and the current title and tenure status of the author."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"use FinalData_tenure_clockCum.dta, clear "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data do not provide exact institutional affiliations for scholars, but they divide them into _rank groups,_ defined as follows:\n",
"\n",
"- Group 1: Economics departments ranked 1--5;\n",
"- Group 2: Economics departments ranked 6--15;\n",
"- Group 3: Economics departments ranked 16--25;\n",
"- Group 4: Economics departments ranked 26--35.\n",
"\n",
"For reference, the Georgetown economics department is currently ranked 25.\n",
"\n",
"Our interest will be in the publication record of successful tenure cases. To do so, we focus attention on the publication record of scholars who will go on to be tenured in the same rank group in the year prior to being granted tenure. We will focus on individuals tenured between their 5th and 8th years. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
" panel variable: authorid (unbalanced)\n",
" time variable: year, 1979 to 2017, but with gaps\n",
" delta: 1 unit\n",
"\n",
"(7,725 observations deleted)\n",
"\n",
"(118 observations deleted)\n"
]
}
],
"source": [
"sort authorid year\n",
"tsset authorid year\n",
"\n",
"keep if tenure == 0 & F.tenure == 1 & F.rankGroup == rankGroup\n",
"keep if clockCum_used>= 5 & clockCum_used <= 8"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"HM provide cumulative publication counts for four types of journals:\n",
"- Top 5 publications (`T5_cum`);\n",
"- Tier 1 field publications (`field_t1_cum`), consisting of the \"two higheest-ranked journals in fields of development, econometrics, finance, microeconomics/game theory, health economics, industrial organization, labor economics, macroeconomics and public economics\";\n",
"- Tier 2 field publications (`field_t2_cum`), consisting of \"journals ranked three to five in the same fields\"; and\n",
"- Non-Top 5 general-interest publications (`general_t1_cum`), consisting of \"the five highest ranked non-T5 general interest journals\".\n",
"\n",
"These data are the basis for HM Figure 2, reproduced here for context: ![Figure2](Figure2.png)\n",
"\n",
"From these we will also calculate a total cumulative publications variable, summing publications in these outlets, which we define as the variable `allpubs`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"global pubtypes T5_cum general_t1_cum field_t1_cum field_t2_cum\n",
"egen allpubs = rowtotal($pubtypes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What are typical publication records at time of tenure application for successful applicants?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With these data in hand, we can now summarize the publication records of successful applicants. Focusing first on totals, by rank group:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Summary for variables: allpubs\n",
" by categories of: rankGroup \n",
"\n",
" rankGroup | mean p50\n",
"------------+--------------------\n",
" 1 | 5.47619 5\n",
" 2 | 4.290698 4\n",
" 3 | 4.672414 4\n",
" 4 | 3.431818 3\n",
"------------+--------------------\n",
" Total | 4.43913 4\n",
"---------------------------------\n"
]
}
],
"source": [
"tabstat allpubs, by(rankGroup) s(mean median) c(s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that _successful_ candidates in Rank Group 3 (departments ranked 16--25) and Rank Group 4 (departments ranked 26--25) have average totals of publications in these categories of 4.67 and 3.43, respectively."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Turning to the distribution of publications among these different publication types:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Summary statistics: mean\n",
" by categories of: rankGroup \n",
"\n",
" rankGroup | T5_cum genera~m fi~1_cum fi~2_cum\n",
"------------+----------------------------------------\n",
" 1 | 3.190476 .5714286 1.333333 .3809524\n",
" 2 | 1.639535 .4069767 1.593023 .6511628\n",
" 3 | 1.068966 .4482759 2.327586 .8275862\n",
" 4 | .6136364 .4318182 1.409091 .9772727\n",
"------------+----------------------------------------\n",
" Total | 1.582609 .4521739 1.695652 .7086957\n",
"-----------------------------------------------------\n"
]
}
],
"source": [
"tabstat $pubtypes, by(rankGroup) s(mean)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This implies, for example, that _successful_ tenure candidates at Group 3 departments (ranks 16--25) have on average 1.07 Top 5 publications, 0.45 non-top-five general publications, 2.33 top field publications, and 8.3 non-top-field publications. \n",
"\n",
"Successful candidates at Group 4 institutions have an average of 0.61 Top 5s, 0.43 non-top-five general publications, 1.4 top fields, and 0.98 Tier B field publications."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Relative ranking among candidates who received tenure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above descriptives do not directly tell us how publications of each measured type are associated with tenure rates. In their paper, HM undertake this issue by estimating a hazard model for the transition to tenure by untenured, tenure-track scholars in top-35 economics departments. They estimate the following parameters on the contribution of publication outcomes to that hazard rate:\n",
"\n",
"| | No heterogeneity | Individual heterogeneity | Heterogeneity, multiple spells |\n",
"| :- | --- | --- | --- |\n",
"| $\\mathrm{1}[T5\\geq 1]$ | 1.68 | 1.74 | 1.77 |\n",
"| $\\mathrm{1}[T5\\geq 2]$ | 1.76 | 1.83 | 1.89 |\n",
"| $\\mathrm{1}[T5\\geq 3]$ | 1.17 | 1.20 | 1.22 |\n",
"| | \n",
"| $\\mathrm{1}[Tier A\\geq 1]$ | 0.97 | 0.93 | 0.91 |\n",
"| $\\mathrm{1}[Tier A\\geq 2]$ | 1.13 | 1.14 | 1.15 |\n",
"| $\\mathrm{1}[Tier A\\geq 3]$ | 1.01 | 1.04 | 1.05 |\n",
"| |\n",
"| $\\mathrm{1}[Tier B\\geq 1]$ | 1.24 | 1.25 | 1.27 |\n",
"| $\\mathrm{1}[Tier B\\geq 2]$ | 0.66 | 0.69 | 0.70 |\n",
"| $\\mathrm{1}[Tier B\\geq 3]$ | 1.19 | 1.24 | 1.26 |\n",
"| | \n",
"| $\\mathrm{1}[General\\geq 1]$ | 0.93 | 0.95 | 0.96 |\n",
"| $\\mathrm{1}[General\\geq 2]$ | 0.84 | 0.79 | 0.75 |\n",
"| $\\mathrm{1}[General\\geq 3]$ | 0.78 | 0.82 | 0.83 |\n",
"| |\n",
"| $\\ln(Total Pubs + 1)$ | 3.53 | 3.60 | 3.65 |\n",
"\n",
"\n",
"We can use these estimated coefficients to score the publication records of _successful_ tenure cases. Doing so with their base model as follows"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"gen pubscore = 1.68 * (T5_cum >= 1) ///\n",
" + 1.76 * (T5_cum >= 2 ) ///\n",
" + 1.17 * (T5_cum >= 3 ) ///\n",
" + 0.97 * (field_t1_cum >= 1 ) ///\n",
" + 1.13 * (field_t1_cum >= 2 ) ///\n",
" + 1.01 * (field_t1_cum >= 3 ) ///\n",
" + 1.24 * (field_t2_cum >= 1) ///\n",
" + 0.66 * (field_t2_cum >= 2) ///\n",
" + 1.19 * (field_t2_cum >= 3) ///\n",
" + 0.93 * (general_t1_cum >= 1 ) ///\n",
" + 0.84 * (general_t1_cum >= 2 ) ///\n",
" + 0.78 * (general_t1_cum >= 2 ) ///\n",
" + 3.53 * ln(allpubs + 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"yields the following distribution of publication records among individuals who receive tenure the subsequent year:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"// Create CDF values\n",
"forvalues r=1/4 {\n",
" cumul pubscore if rankGroup == `r', generate(pubcdf`r')\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"\t&lt;text x=&quot;902.88&quot; y=&quot;1138.17&quot; style=&quot;font-family:&#x27;Helvetica&#x27;;font-size:99.99px;fill:#000000&quot;&gt;Depts 26-35&lt;/text&gt;\n",
"\t&lt;text x=&quot;408.00&quot; y=&quot;2737.89&quot; style=&quot;font-family:&#x27;Helvetica&#x27;;font-size:79.94px;fill:#000000&quot;&gt;Source: Calculated from Heckman and Mokhir (2020) replication data.&lt;/text&gt;\n",
"\t&lt;text x=&quot;2124.91&quot; y=&quot;215.98&quot; style=&quot;font-family:&#x27;Helvetica&#x27;;font-size:139.96px;fill:#000000&quot; text-anchor=&quot;middle&quot;&gt;Cumulative distributions of publication scores&lt;/text&gt;\n",
"\t&lt;text x=&quot;2124.91&quot; y=&quot;356.06&quot; style=&quot;font-family:&#x27;Helvetica&#x27;;font-size:139.96px;fill:#000000&quot; text-anchor=&quot;middle&quot;&gt;among successful candidates&lt;/text&gt;\n",
"\t&lt;text x=&quot;2124.91&quot; y=&quot;496.03&quot; style=&quot;font-family:&#x27;Helvetica&#x27;;font-size:139.96px;fill:#000000&quot; text-anchor=&quot;middle&quot;&gt;by department ranking groups&lt;/text&gt;\n",
"&lt;/svg&gt;\n",
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"This front-end cannot display the desired image type."
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"text": [
"\n",
"\n",
"\n",
"(file PublicationScoreDistributions.png written in PNG format)\n"
]
}
],
"source": [
"// Plot CDFs by departmental rank group\n",
"twoway (line pubcdf1 pubscore if rankGroup == 1, sort lcolor(gs6) lpattern(shortdash)) ///\n",
" (line pubcdf2 pubscore if rankGroup == 2, sort color(green) lpattern(dash)) ///\n",
" (line pubcdf3 pubscore if rankGroup == 3, sort color(blue) lpattern(dash_dot)) ///\n",
" (line pubcdf4 pubscore if rankGroup == 4, sort color(red)) ///\n",
" , title(\"Cumulative distributions of publication scores\" \"among successful candidates\" \"by department ranking groups\") ///\n",
" xtitle(\"Publication score\") ytitle(\"Cumulative distribution\") ///\n",
" legend(order(1 \"Depts 1-5\" 2 \"Depts 6-15\" 3 \"Depts 16-25\" 4 \"Depts 26-35\") cols(1) ring(0) pos(11) region(lwidth(none))) ///\n",
" note(\"Source: Calculated from Heckman and Mokhir (2020) replication data.\")\n",
" \n",
"\n",
"gr export \"PublicationScoreDistributions.png\", replace"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Without implying an endorsement of the _normative_ judgments implied by these decisions, this can be used as a point of comparison for candidates, to see where they fit in relative to typical standards among successful tenure candidates. "
]
}
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This file has been truncated, but you can view the full file.
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