[CLAIM]
overall job satisfaction makes anemployee less likely to leave across the board: as job satisfaction increases, employees areless likely to intend to leave their agency for another within the federal government...[Leaving Agency, Job satisfaction = –0.444, SE = 0.0163, significant at p < .01, two tailtest]

[HYPOTHESES]
Overall job satisfaction makes an employee less likely to leave across the board

14/input/replication_data/Estimation Data - Pitts (126zz).csv

Hypothesis: Overall job satisfaction makes an employee less likely to leave across the board.

The hypothesis is discussed in the Determinants of Employee Turnover: Workplace Satisfaction Factors section.

overall job satisfaction makes anemployee less likely to leave across the board: as job satisfaction increases, employees areless likely to intend to leave their agency for another within the federal government...[Leaving Agency, Job satisfaction = –0.444, SE = 0.0163, significant at p < .01, two tailtest].

more than 200,000 U.S. federal government employees (full-time, permanent)

individual - U.S. federal government employee

There is no mention how the data was accessed, only that it was collected and managed by the U.S. Office of Personnel Management. Also, no restriction is mentioned so maybe the data is open access.

the sample size is not clearly stated (only: more than 200,000 U.S. federal government employees); the data was clustered by the agency; the DV - turnover intention is used as a surrogate for actual turnover. Data limitations prevented authors from predicting actual turnover, but turnover intention and actual turnover are usually highly and positively correlated in research. Gender data would have been an appropriate demographic variable to include but was missing for approximately 140,000 respondents so it was excluded from analysis.

The authors probed associations between demographic, workplace satisfaction, and organizational/relational factors and employee turnover intention on survey data from U.S. federal employees.

1. The authors obtained Federal Human Capital Survey data.
2. They retained only respondents who intended to leave for another job and omitted those planning to retire.
3. They measured turnover intention as a dichotomous variable (1  = those who plan to leave their agency to take another job within the federal government, 0 = all others) and labelled it Leaving Agency.
4. They added independent variables.
5. Finally, they estimated logistic regression models with robust standard errors clustered by agency.

logistic regression (with two tailed tests)

turnover intention (dichotomous variable, where 1 represents those who plan to leave their agency to take another job within the federal government, and 0 represents all others)

Age, Agency tenure, Race/ethnicity, Job satisfaction, Satisfaction with pay, Satisfaction with benefits, Satisfaction with advancement, Performance culture, Empowerment, Relationship with supervisor, Relationship with coworkers; Interactions between age and satisfaction with benefits, and age and satisfaction with advancement were also included.

not stated (all variables besides the DV are marked as independent)

Summary: Higher overall job satisfaction significantly reduces employees’ intention to leave their agency for another federal job (Job satisfaction = –0.444, SE = 0.0163, p < .01, two-tailed test).

So Hard to Say Goodbye? Turnover Intention among U.S. Federal Employees

Hypothesis: Higher overall job satisfaction is associated with a lower likelihood that a federal employee intends to leave, including intentions to move to another job within the federal government.

Findings section under “Workplace Satisfaction Factors,” where the authors summarize that overall job satisfaction reduces turnover intention “across the board,” including intentions to leave one’s agency for another federal job.

In the model predicting intention to leave one’s agency for another position within the federal government, overall job satisfaction is negatively and statistically significantly associated with leaving intention. The estimated coefficient for job satisfaction is -0.444 with a clustered robust standard error of 0.0163, significant at the 1% level (two-tailed test).

Table 2 “Logit Results for Turnover Intention,” Leaving Agency column, row “Job satisfaction,” and the accompanying text in the Findings section describing job satisfaction as a consistent negative predictor.

2006 Federal Human Capital Survey (FHCS) administered by the U.S. Office of Personnel Management.

Turnover intention is measured using a survey question about whether the employee is considering leaving their organization within the next year and why. The focal outcome (“Leaving Agency”) is coded 1 if the employee intends to leave their agency for another job within the federal government and 0 otherwise. The job satisfaction measure is based on the item asking how satisfied the respondent is with their job overall on a five-point scale.

The study estimates logistic regression models to examine how overall job satisfaction and other factors relate to two forms of turnover intention among U.S. federal employees, focusing here on intention to leave one’s agency for another job within the federal government.

Use 2006 FHCS survey data on U.S. federal employees.

Construct the dependent variable “Leaving Agency” as a dichotomous indicator for intending to leave one’s agency for another federal job within the next year.

Measure overall job satisfaction using the survey item asking overall satisfaction with one’s job.

Include additional predictors spanning demographic factors, workplace satisfaction factors, and organizational/relational factors as specified in the model.

Estimate a logistic regression model for “Leaving Agency” with robust standard errors clustered by agency.

Logistic regression with robust standard errors clustered by agency.

Turnover intention: Leaving Agency (1 = intends to leave agency for another job within the federal government; 0 = otherwise).

Age category indicators; agency tenure; race/ethnicity; satisfaction with pay; satisfaction with benefits; satisfaction with advancement; performance culture; empowerment; relationship with supervisor; relationship with coworkers; and interaction terms between age categories and satisfaction with advancement and satisfaction with benefits.

Summary: Employees with higher overall job satisfaction are less likely to report an intention to leave their agency for another job within the federal government, and this association is statistically significant in the multivariate logit model.

logit coefficient (unstandardized) for job satisfaction

Hypothesis: A large body of empirical research indicates a consistent and inverse relationship between overall job satisfaction and turnover.

Section; Determinants of Employee Turnover; Subsection: Workplace Satisfaction Factors; p. 752

As expected, overall job satisfaction makes an employee less likely to leave across the board: as job satisfaction increases, employees are less likely to intend to leave their agency for another within the federal government and less likely to intend to leave the federal government for outside work.

Section:Findings; Subsection: Workplace Satisfaction Factors; p. 757

Federal Human Capital Survey (FHCS) administered by the U.S. Oﬃce of Personnel Management

The exact sample size is not listed in the text.

Use logistic regressions to estimate the effect that job satisfaction has on turnover intention.

(1) Create the two dependent, dichotomous variables of turnover intention - overall turnover intention and intention to leave for a position outside the government; (2) construct regressors by combining question responses by loading weights; (3) estimate the effects using logistic regressions with robust standard errors (4) use a series of Monte Carlo estimations to show how an employee’s probability of turnover intention is inﬂuenced by two diﬀerent types of changes in our independent variables: i. moving from the minimum to the maximum possible value, and ii. increasing the value by a mean-centered standard deviation.

demographic factors: satisfaction with beneﬁts; advancement opportunity; empowerment; performance culture; coworker relationships; supervisor relationships; interactions of satisfaction with beneﬁts and satisfaction with advancement

a standard deviation increase in job satisfaction is associated with a 5 percentage point decrease in the probability of intending to leave one’s agency

Confidence intervals are not reported, but standard errors reported instead (0.0163).


[CLAIM]
Fraud increases with violence up to a certain level, but then decreases again (coefficient on Violence (election, squared) term = -13.748, SE clustered at the regional command level = 4.720, p< 0.01)

[HYPOTHESES]
The quadratic association between violence and election fraud will be negative

Table 2, Model 1, Violence (election, squared)

fraud data: computed from polling-station level data made public by the International Election Commission (IEC) on 19 September 2009 (The IEC publicly posted the data in three waves. The authors use the earliest data release - returns from 27,163 distinct polling stations on 19 September).
violence data: International Security Assistance Force (ISAF). These data, which are commonly known as ‘significant activity’ or SIGACT reports; Worldwide Incident Tracking System (WITS); Armed Conflict Location and Event Dataset (ACLED).
 development data: the 2007 National Risk and Vulnerability Assessment (NRVA) household survey, which was jointly administered by the Ministry for Rural Rehabilitation and Development (MRRD) and the Central Statistics Office (CSO) of Afghanistan.
geography data: US Geological Survey. GTOPO30 Digital Elevation Model and GIS spatial layers used for geocoding and aggregation.
 population data: LandScan (raster GIS datasets) the complete dataset is available at http://dvn.iq.harvard.edu/dvn/dv/nilsw

election data: 2009, development data: 2007, geography data: 2007, population: 2008.

The IEC publicly posted the data. WITS is available at http://wits.nctc.gov/. ACLED details are to be found in: Clionadh Raleigh et al. ‘Introducing ACLED: An Armed Conflict Location and Event Dataset’, Journal of Peace Research, 47 (2010), 651–60. US Geological Survey: Available at http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html. LandScan: Oak Ridge National Laboratory, LandScan Global Population database. http://dvn.iq.harvard.edu/dvn/dv/nilsw seems to be open access

Unlike othe work on election fraud, the authors assume (and show) that insurgent violence is not biased in favour or against a particular candidate.This assumption characterizes violence around the Afghanistan election well, but it limits the applicability of the findings to other cases where this may not hold. Also, the authors limited their view of election fraud to manipulation tactics local to the polling centre. Moreover, lacking information about the precise location of polling stations, they were unable to estimate fraud at lower levels, for example, cities. Many of the covariates used in the regression analysis are available only at the district level. All violence indicators are reported as incidents per 1,000 population. The authors created two indicators for development: first, the proportion of households supplied with electricity, and, secondly, the per capita expenditure (in 1,000 s of afghanis).

The authors test the prediction that the relationship between violence and election fraud follows an inverted U-shape. Using district-level data from Afghanistan’s 2009 presidential election, the authors measure fraud through forensic last-digit tests and validate it with recount-based evidence.

1. Collect all the data.
2. Apply the last-digit test to the total vote count and code a binary dependent variable for fraud (which takes the value of 1 if this test is significant at the 5 per cent level for a particular district).
3. Take violence data from ISAF SIGACT reports and calculate incidents per 1,000 population within five-day (20–24 August) and sixty-day windows around election day.
4. Georeference ISAF SIGACT, WITS, and ACLED incidents to districts.
5. Conduct robustness checks for the violence measure using alternative geo-referenced datasets (WITS and ACLED).
6. Compute great-circle distances from district centres to Kabul.
7. Derive average district elevation from GTOPO30 raster data.
8. Aggregate population data from LandScan grid cells.
9. Construct development indicators: proportion of households with electricity, and per capita expenditure.
10. Add the number of closed polling stations at the district level as a control.
11. Merge datasets.
12. Estimate OLS models clustering standard errors at the regional command level.

Violence (election), Violence (election, squared), Violence (2 months pre-election), Violence (2 months pre-election, squared), Electrification, Per-capita expenditure, Distance from Kabul, Elevation

number of closed stations at the district level

Summary: Fraud initially rises with violence but then falls at higher levels, following an inverted U-shaped pattern. The coefficient on the Violence (election, squared) term is -13.748 (SE clustered at the regional command level = 4.720, p < 0.01).

Violence and Election Fraud: Evidence from Afghanistan.

Hypothesis: Electoral fraud initially increases as violence rises but declines once violence becomes sufficiently intense, producing a curvilinear relationship between violence and fraud.

Theory section under “Violence and Fraud,” where Hypothesis 1 is explicitly stated predicting an inverted U-shaped relationship between violence and election fraud.

The empirical results show that fraud is more likely in districts experiencing moderate levels of violence but becomes less likely in districts with very high levels of violence, consistent with an inverted U-shaped relationship between violence and fraud.

Results section, Table 2, where the linear violence term is positive and significant and the squared violence term is negative and significant.

Polling-station level election returns from the Afghan Independent Election Commission combined with geocoded violence data from ISAF SIGACT reports and auxiliary datasets.

2009 Afghanistan presidential election; violence measured in a five-day window around election day and, in alternative models, a two-month pre-election window.

Approximately 389 districts for fraud measures; up to 398 districts for violence and control variables.

Election results were publicly released by the Afghan Independent Election Commission; violence data originate from military and media-based event datasets.

Fraud is measured using a digit-based forensic test (last-digit test) aggregated to the district level and validated using data from a post-election recount of ballot boxes.

The study tests whether the relationship between violence and election fraud is nonlinear by regressing district-level fraud indicators on measures of violence and their squared terms, while controlling for development and geographic factors.

Aggregate polling-station election results to the district level.

Apply the last-digit forensic test to generate a binary indicator of fraud for each district.

Measure insurgent violence using counts of attacks per 1,000 population around election day.

Include a squared violence term to capture potential nonlinearity.

Add controls for closed polling stations, development, geography, and population characteristics.

Estimate logit models for the binary fraud measure and OLS models for the recount-based fraud share, clustering standard errors at the regional command level.

Logistic regression (for binary fraud indicator) and OLS regression (for recount-based fraud share) with linear and squared violence terms.

Violence per 1,000 population; squared violence term.

Number of planned polling stations closed; electrification; per-capita expenditure; distance from Kabul; average elevation.

Summary: The regression results support a curvilinear association between violence and fraud: fraud increases with low to moderate violence but decreases as violence becomes very high.

logit regression coefficient for violence (linear term)

logit regression coefficient for squared violence term

Hypothesis: The relationship between (in)security and election fraud should be in the form of an inverted U-shape. Fraud increases with violence up to a certain level, but then decreases again.

Section: MANIPULATION TACTICS; Subsection: Violence and Fraud; p. 58

We ﬁnd support…for the inverted U-shaped relationship between violence and fraud

Section: CONCLUSION AND POLICY IMPLICATIONS; p. 74

After removing missing, sample size drops to 22,858.

The authors discuss that they use this data, but do not describe the characteristics of the data (e.g. sample size).

Available at http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html. 2007.

This is geological raster data for Afghanistan so there is not real sample size.

They do not directly discuss access, but they leave the reference to the data: Oak Ridge National Laboratory, LandScan Global Population database, 2008. For this paper, the data is aggregated to the district level.

Use logit (for the last-digit fraud measure) and OLS models (for the recount-based fraud measure) and regress fraud on violence and its squared term to test the inverted U-shaped prediction.

(1) Clean, merge, and aggregate the data (remove missing, etc.) when necessary; (2) Group polling stations by district and apply the Beber–Scacco last-digit test to the total vote count; (3) Code a binary dependent variable for fraud; (4) Estimate the effects using logit and OLS models.

number of closed centres, economic development and geographic accessibility

Summary: Since the linear term of the violence measure receives a positive and signiﬁcant coefﬁcient, and the squared term a negative one, this implies the U-shaped prediction is credible.

Confidence intervals are not presented, but standard errors are presented instead (4.720).

Confidence intervals are not presented, but standard errors are presented instead (0.375).


[CLAIM]
Social distancing measures decreased the mobility by anadditional 23% (95% CI: 20%, 27%)

[HYPOTHESES]
The introduction of social distancing measures is associated with a decrease in mobility.

The relation is mentioned in the first paragraph and explicitly stated in third (the paper has only four paragraphs) where the results are reported.

Social distancing measures decreased the mobility by anadditional 23% (95% CI: 20%, 27%).

Citymapper Mobility Index (CMI) for mobility data; it is mentioned that the data on implementation of governmental social distancing measures was acquired from official government and media websites but specific sources are not mentioned.

1,025 observations across 41 cities (25 observations per city)

presumably that data from Citymapper is publicly available as well as the data

as no restrictions or permissions are mentioned and the authors explain that they downloaded the data.

It is explained that the data on governmental social distancing measures were obtained from official government and media websites, but the specific variables or data fields collected were not described.

The study probed the effect of governmental social distancing measures on reducing mobility in 41 cities.

1. The authors downloaded mobility data from the Citymapper Mobility Index and unspecified data on the implementation of governmental social distancing measures from unspecified official government and media websites.
2. Then, they classified each city as having instituted social distancing measures if non-essential businesses were closed
3. Further classified measures as moderate or intense based on the intensity of closure.
4. Finally, they estimated the effect of time and social distancing measures using a multilevel mixed-effects linear regression model.

time (in days), social distancing measures presence, intensity of social distancing measures (moderate, intense).

not stated; there are probably none, judgung by the results section but the authors obtained some data on governmental social distancing measures and the specific variables or data fields collected were not described.

Summary: The mobility in general decreased in the discussed period. Social distancing measures decreased the mobility by an additional 23% (95% CI: 20%, 27%).

COVID-19 related social distancing measures and reduction in city mobility.

Hypothesis: The implementation of government social distancing measures is associated with a reduction in urban mobility.

The expectation is introduced in the background and study aim where the authors state that they analyze mobility data to assess the effect of government social distancing measures on movement in cities.

The analysis shows that government social distancing measures are associated with a substantial additional reduction in city mobility, beyond the general downward time trend in mobility.

Results para describing the multilevel mixed-effects regression results, where the authors report the estimated effect of social distancing measures on the Citymapper Mobility Index.

Citymapper Mobility Index (CMI) data combined with information on government social distancing measures compiled from official government and media sources.

Daily city-level observations from March 2, 2020 to March 26, 2020.

Citymapper Mobility Index data are publicly available via Citymapper; access procedures are not further detailed.

The CMI is based on planned trips using the Citymapper application and reflects mobility related to public transport, walking, biking, and ride-sharing, but not private automobile use. Cities are classified as having implemented social distancing measures if non-essential businesses were closed, with measures further categorized as moderate or intense.

The study estimates the association between social distancing policies and changes in urban mobility using longitudinal city-level mobility data and regression modeling.

Download daily Citymapper Mobility Index data for 41 cities from March 2 to March 26, 2020.

Compile dates and intensity of government social distancing measures for each city using official and media sources.

Classify social distancing measures as present or absent, and further as moderate or intense.

Construct a longitudinal dataset with repeated daily observations for each city.

Estimate a multilevel mixed-effects linear regression model including time trends and social distancing indicators.

Multilevel mixed-effects linear regression model with city-level repeated observations.

City mobility measured by the Citymapper Mobility Index (percentage relative to baseline).

Indicator for implementation of social distancing measures; time (day).

Summary: City mobility declined steadily over time and decreased substantially more in cities after the introduction of social distancing measures.

City mobility (Citymapper Mobility Index)

regression coefficient for social distancing measures

Hypothesis:
One approach to slowing the pandemic is reducing the contact rate in the population through social distancing. Governments the world over have instituted different measures to
increase social distancing but information on their effectiveness in reducing mobility is lacking.

Social distancing measures decreased the mobility by an additional 23%.

Data is for 41 cities. Social distancing measures are also used (as treatment) and collected from various government and media websites.

Use linear regression models to estimate the effect of social distancing measures on mobility.

(1) Download the data; (2) Tabulate data on implementation of governmental social distancing measures; (3) Estimate the effect of time and social distancing measures using a multilevel mixed-effects linear regression model.


[CLAIM]
Political polarization is strongly associatedwith smaller government in democratic countries, but there is no relationship between polarization and the size ofgovernment in undemocratic countries. When the sample is restricted to strong democracies, the estimated effect of polarization on government consumption is statistically significant (coefficient in the ‘long’ specification for the “Private” measure of polarization=-18.73, heteroscedasticity robust SE=4.79, p=.01)

[HYPOTHESIS]
Among strong democracies (countries with a Polity IV score of 9 or greater), polarization (as measured by the SD of the “private ownership” responses) will be negatively associated with the size of government

Hypothesis: Among strong democracies (countries with a Polity IV score of 9 or greater), polarization (as measured by the SD of the “private ownership” responses) will be negatively associated with the size of government.

Political polarization is strongly associatedwith smaller government in democratic countries, but there is no relationship between polarization and the size ofgovernment in undemocratic countries. When the sample is restricted to strong democracies, the estimated effect of polarization on government consumption is statistically significant (coefficient in the ‘long’ specification for the “Private” measure of polarization=-18.73, heteroscedasticity robust SE=4.79, p=.01).

polarization: World Values Surveys (WVS); government size: World Bank and International Monetary Fund; GDP, export/import, population: World Bank

polarization: primarily the 2000 wave of the WVS, but for some countries the 1995 wave is used; the appendix A shows survey years vary across countries.
government size: 2003-2005.
GDP, export/import, population: 2009.
strong democracies: Polity IV project.

polarization: European Values Study Group andWorld Values Association. 2006. European and World Values Surveys Four-wave Integrated Data File 1981−2004. Version 20060423. http://www.wvsevsdb.com/wvs/WVSData.jsp (accessed July 17, 2010).
government size: Gwartney, J., and R. Lawson. 2008. Economic Freedom of the World: 2008 Annual Report. Vancouver, Canada: Fraser Institute. http://www.freetheworld.com/release_2008.html (accessed July 17, 2010).
GDP, export/import, population: World Bank. 2009.World Development Indicators 2009.Washington, DC: World Bank. (no further access details)
strong democracies: Marshall, M.G., and K. Jaggers. 2007. Political Regime Characteristics and Transitions 1800−2007. Polity IV Project. University of Maryland. http://www.systemicpeace.org/inscr/inscr.htm (accessed May 4, 2010).Polity IV project

The WVS is based on face-to-face interviews with about 1,000 respondents in 83 different countries, but 9 of these are not included in the analysis (Northern Ireland, Puerto Rico, Taiwan, Serbia and Montenegro, Iraq, Belarus, Saudi Arabia, Israel and India). Broad measure of government size: general government consumption as a fraction of total consumption (GOVCONS), the average of the years 2003 to 2005. The control variable ASIAE is included in Table 5 but eventually omitted in strong democracy sample. Regional dummies, FEDERAL and OECD variables should be coded as per: Persson, T., and G. Tabellini. 2003. The Economic Effects of Constitutions. Cambridge, MA: MIT Press.

The article studies the relationship between political polarization and public spending using the dispersion of self-reported political preferences as a measure of polarization.

1.Get the government size data (government consumption as a fraction of total consumption, averaged over 2003–2005).
2. Get the political attitudes data for the 2000 wave. For countries missing in 2000, supplement with the 1995 wave.
3.Construct the polarization measure by extracting responses to the “private ownership of business” question from the WVS. Compute the SD of responses within each country.
4.Identify strong democracies by selecting only countries with Polity score ≥ 9.
5.Add control variables (long specification):
-Mean response to the PRIVATE question.
-AFRICA = 1 if in Africa.
-ASIAE = 1 if in East Asia.
-LAAM = 1 if in Latin America, Central America, or the Caribbean.
-COL_ESPA = Spanish colonial origin, weighted by years since independence.
-COL_UKA = British colonial origin, weighted by years since independence.
-COL_OTHA = Other colonial origin, weighted similarly.
-LYP = log of real GDP per capita (constant 2000 USD, year 2000).
-TRADE = (exports + imports) / GDP (year 2000).
-PROP1564 = share of population aged 15–64 (year 2000).
-PROP65 = share of population aged 65+ (year 2000).
-FEDERAL = 1 if the country has a federal structure.
-OECD = 1 if OECD member before 1993 (excluding Turkey).
6.Run an OLS of GOVCONS on PRIVATE polarization, including all control variables. Use heteroscedasticity-robust standard errors.

ordinary least squares regression with heteroscedasticity-robust standard errors

Mean of PRIVATE responses, AFRICA, ASIAE, LAAM, COL_ESPA, COL_UKA, COL_OTHA, LYP, TRADE, PROP1564, PROP65, FEDERAL, OECD

Summary: Higher polarization is associated with smaller government consumption (b = −18.73, SE = 4.79, p = .01).

Political Polarization and the Size of Government.

Hypothesis: Within countries classified as strong democracies (Polity IV score at least 9), greater economic-policy polarization—measured as the standard deviation of responses to the survey item on private versus government ownership—will be associated with a smaller government sector.

The conditional expectation that polarization matters in democracies is developed in the theory discussion and then operationalized in the empirical section where the authors estimate models for the full sample, undemocratic countries, and strong democracies, using the “Private ownership” polarization measure.

Restricting the analysis to strong democracies, the association between the private-ownership polarization measure and government consumption is negative and statistically significant.

Results table 4 for the strong-democracy subsample (Polity IV ≥ 9), long specification, using the “Private” polarization measure as the key explanatory variable for government consumption (coefficient = -18.73, robust SE = 4.79, p = .01).

World Values Survey (WVS) for individual responses used to construct polarization; Polity IV for regime type (strong democracy defined as Polity IV ≥ 9); country-level government consumption data (government size) and additional country covariates from standard international macro datasets.

Focus on the 2000 wave for most countries, but data from the 1995 wave are used for some countries so as to increase the sample size.

Face-to-face interviews with about 1,000 respondents in 83 different countries.

Polarization is operationalized as dispersion (standard deviation) in responses to the WVS ‘private versus government ownership’ item. Strong democracies are defined using Polity IV scores of 9 or greater. The focal outcome is government consumption (government size).

The study estimates cross-country regression models of government size on polarization, with regime-based sample restrictions. The focal test estimates the association between the ‘private ownership’ polarization measure and government consumption within strong democracies (Polity IV ≥ 9) using a long specification and heteroscedasticity-robust standard errors.

Construct a country-level polarization measure as the standard deviation of survey responses to the private-versus-government ownership item.

Classify countries by regime type using Polity IV and restrict the analytic sample to strong democracies (Polity IV ≥ 9).

Measure government size using government consumption.

Estimate the long-specification regression of government consumption on the polarization measure for the strong-democracy subsample.

Compute heteroscedasticity-robust standard errors and assess statistical significance.

Cross-country linear regression model (long specification) with heteroscedasticity-robust standard errors.

Government consumption (government size).

Polarization measured as the standard deviation of the ‘private ownership’ responses.

Geographic regional dummy variables: Africa (AFRICA), South and East Asia (ASIAE), and Latin and South America and the Caribbean (LAAM); colonial origin variables weighted by years of independence: British colonial origin (COL_UKA), Spanish colonial origin (COL_ESPA), and other colonial origin (COL_OTHA). Additional controls that may be endogenous: log GDP per capita in 2000 (LYP), trade openness in 2000 (TRADE), proportion of population ages 15–64 in 2000 (PROP1564), proportion of population above 65 in 2000 (PROP65), federal political structure indicator (FEDERAL), and OECD membership before 1993 (OECD; Turkey excluded).

Summary: In strong democracies, higher polarization on the private-ownership dimension is associated with significantly lower government consumption, whereas the relationship is not present in undemocratic countries.

Hypothesis: ...more recent research in economics and political science has suggested that the size of government is also determined by the dispersion or polarization of political preferences.

We ﬁnd that the relationship between polarization and size of government is substantially stronger in democratic countries, supporting the view that polarization affects public spending rather than the other way around.

Primarily the 2000 wave, but use the 1995 wave for some countries and analysis. The access is not stated, but there is a reference to the dataset: http://www.wvsevsdb.com/wvs/WVSData.jsp.

Authors use standard regression analysis to test the effect of polarization on government size

(1) Clean the data (e.g. remove unused countries); (2) construct polarization measure using standard errors and also using the Esteban and Ray method; (3) construct the measure of government size as the general government consumption as a fraction of total consumption; (4)

geographic dummy variables; colonial origin; logarithm of GDP per capita in 2000; openness to trade in 2000; proportion of population between 15 and 64 in 2000; proportion of population above 65 in 2000; dummy variable indicating whether the country has a federal political structure; indicator variable for Organisation for Economic Cooperation and Development membership before 1993

Summary: Political polarization has a negative and statistically signiﬁcant relationship with government consumption in the speciﬁcations with controls for the mean response and exogenous set of control variables…When the sample is restricted to strong democracies, the estimated effect of polarization on government consumption is statistically signiﬁcant and robust to the different sets of control variables.

The confidence intervals are not presented, but the standard error is (4.79).


[CLAIM]
A  one standard deviation decrease in median age (equal to 3.5 years in 2010) results in a 2.5 percentage point increase in the entrepreneurship rate, which is over 40 percent of the mean entrepreneurship rate across countries (equal to 0.061 in 2010).

[HYPOTHESIS]
The entrepreneurship rate in a country is negatively associated with the country’s median age.

p. 154 Corollary 3; p.157 empirical implications: implication 5; also discussed in the abstract.

p. S167, section V. Empirical Implementation (B. Country-Level Analysis); also discussed in the abstract.

entrepreneurship: Global Entrepreneurship Monitor; the GEM data is checked against: Flash Eurobarometer Survey on Entrepreneurship.
demographics: US Census Bureau’s International Data Base

GEM: Bosma, Niels, Alicia Coduras, Yana Litovsky, and Jeff Seaman. 2012. “Global Entrepreneurship Monitor Manual.” Version 2012-9 (May). Global Entrepreneurship Res. Assoc., London. no further access details are provided;
FESE: https://dbk.gesis.org
IDB: http://www.census.gov/population/international/data/idb/index.php

The total number of observations is 1.3 million, the number of country-year cells is 393, and the number of country-age-year cells is 17,554. The entrepreneurship rate can be constructed at each cell level. China was excluded from the study.

The study examines how demographic structure influences entrepreneurship, regressing country-level entrepreneurship rates on median age

1.Get entrepreneurship data defined as managing and owning a business up to 42 months old that pays wages. Aggregate the data to construct entrepreneurship rates at the country-year level.
2.Get population data and calculate median age for each country-year.
3.Estimate a regression and weight observations are weighted by the number of individuals who make up each country-year cell. Include year dummies as controls. Standard errors clustered at the country level are in brackets.

Entrepreneurship Rate (Early Stage, Pays Wages)

Summary: A one–standard deviation (3.5-year) reduction in median age raises the entrepreneurship rate by 2.5 percentage points, equivalent to roughly 40% of the sample mean (0.061) across countries.

not stated; p was not stated explicitly, but the result is calculated using the estimates from column 3 of table 2, and regression coefficients on median age there are significant at 1 percent level

Hypothesis: Countries with younger populations tend to exhibit higher rates of entrepreneurial activity, implying an inverse relationship between median age and entrepreneurship.

The Effect of Age on Entrepreneurship & Country-Level Aggregation and Summary of Empirical Predictions sections, where the authors argue that population age structure shapes aggregate entrepreneurship.

The empirical estimates indicate that countries with lower median ages have higher entrepreneurship rates. A decline in median age is associated with a sizable increase in entrepreneurship.

Results (Table 2) section discussing the baseline cross-country regressions and the standardized effect of median age on entrepreneurship rates.

Global Entrepreneurship Monitor (GEM) individual-level survey data collected by the Global Entrepreneurship Research Association (GERA); Flash Eurobarometer Survey on Entrepreneurship (FESE) for robustness checks; population statistics from the US Census Bureau’s International Data Base (IDB); macroeconomic and institutional controls from the Penn World Table (version 7.1), World Bank databases, Barro and Lee (2010) education attainment data, and the Property Rights Alliance.

GEM surveys conducted in multiple waves between 2001 and 2010 across 82 countries; FESE surveys conducted in overlapping years starting in 2002; demographic data matched annually to country-year observations.

More than 1.3 million individuals aged 15–60 from the GEM; 82 countries; 393 country-year cells; and 17,554 country-age-year cells. FESE sample sizes vary by country-year and are reported separately in the appendix.

Individual respondents in the GEM and FESE surveys, aggregated to country-year or country-age-year cells for analysis.

Entrepreneurship is primarily defined as owning and managing a business up to 42 months old that pays wages. Alternative definitions include all new businesses (including recent failures), start-ups without paid wages, and total early-stage entrepreneurship; results are robust across definitions.

The study estimates cross-country regression models to assess how demographic structure, particularly median age, is associated with national entrepreneurship rates.

Assemble country-level measures of entrepreneurship rates.

Collect demographic indicators including median age.

Standardize median age in selected specifications.

Estimate regression models relating entrepreneurship rates to median age.

Interpret coefficients in both raw and standardized terms to assess economic magnitude.

Linear regression models estimated at the country level.

GDP growth rate, college enrolment rate, start-up cost, property rights index, military service, percentage of agriculture in GDP.

Summary: Countries with younger median ages have higher entrepreneurship rates. The magnitude of the association implies that demographic differences can account for a substantial share of cross-country variation in entrepreneurship.

percentage-point change in entrepreneurship rate associated with a one-standard-deviation decrease in median age

Hypothesis: ... this factor [the advantages of youth] has a positive effect on entrepreneurship and the importance of that factor declines with age.

The estimates imply that a median age that is one standard deviation lower is associated with a 2.5 percentage point higher country rate of entrepreneurship, which is about 40 percent of the mean rate.

individual level who are 15 to 60 years of age

The authors aggregate the data up to the country level.

Used for population data to construct sampling weights.

international property rights index from the Property Rights Alliance

Limited details as it is only used for control variable. Access is not discussed, but a reference is included (Strokova and Mitra 2010). Data is for 2010 only.

The authors use reduced-form regressions at the country level of aggregation to estimate the effect that the age distribution in a country has on the rate of entrepreneurship.

(1) Construct sampling weights to aggregate data; (2) Clean data and aggregate to the country level using sampling weights; (3) run reduced-form, country-level regressions.

age of the particular group; share of population younger

year dummies, log of per capita GDP, the 5-year per capita average growth rate, the share of agriculture sector in GDP, the tertiary education completion rate, an estimate of start-up costs as a percentage of gross national income, an index of intellectual property, and a dummy for whether the country has more than 1 year of compulsory military service.

Confidence intervals are not presented, but standard errors are (0.001). Discussed positively but written negatively because these results are discussed in terms of a decrease in median age.


[CLAIM]
Nations with efficient governments and tight cultures have been most effectiveat limiting COVID-19’s infection rate and mortality likelihood (a significant interaction betweentightness and efficiency, b = -.17, SE = .07, t(41) = -2.23, p = .031).

[HYPOTHESIS]
The interaction between cultural tightness and government efficiency will be negative in its association with the COVID-19 infection rate.

Nations with efficient governments and tight cultures have been most effective at limiting COVID-19’s infection rate and mortality likelihood (a significant interaction between tightness and efficiency, b = -.17, SE = .07, t(41) = -2.23, p = .031).

COVID-19: European Center for Disease Control; government efficiency: the World Bank’s Government Efficiency Index; cultural tightness: index from Gelfand, M. J., Raver, J. L., Nishii, L., Leslie, L. M., Lun, J., Lim, B. C., ... & Aycan, Z. (2011). Differences between tight and loose cultures: A 33-nation study. science, 332(6033), 1100-1104; economic development: GDP per capita retrieved  from the International Monetary Fund; inequality: nations’ Gini coefficients retrieved from the World Bank; median ages: CIA World Factbook.

World Bank: 2017(for government efficiency), and unspecified for inequality (the authors mention most recent release for each nation); Covid: 2020; International Monetary Fund: 2019; CIA World Factbook: 2018

all the data is available on the OSF (https://osf.io/pc4ef/); it is not certain if the original data collected by the authors is open access or not but no restrictions are mentioned.

The authors downloaded data on cases per million citizens, and indexed death rate through the number of mortalities divided by the number of total cases. According to the government efficiency metric, efficient governments score highly on 5 dimensions: they are efficient in spending public revenue, they do not place strong compliance burdens on the private sector, they are able to efficiently settle legal and judicial disputes in the private sector, they are receptive to challenges from the private sector, and they offer transparent information about changes in government policies and regulations affecting private sector 5 activities. The mortality likelihood was measured through the number of deaths from COVID-19 divided by the number of COVID-19 cases in a nation. Economic development was indexed through GDP per capita, Inequality was indexed through the nations’ Gini coefficients.

The authors tested the hypothesis that nations with efficient governments and tight cultures have been most effective at limiting COVID-19’s infection rate and mortality likelihood.

1. Retrieve and filter the virus data to focus on the period after each nation surpassed 1 case per million people.
2. Calculate the infection rate per million citizens for each day.
3. Capture infection rate by fitting regression equations for each nation, log-transforming the outcome variable (cases per million people) and the predictor variable (days) to account for the exponential growth rate of the virus.
4. Get government efficiency and cultural tightness data.
5. Combine nation-level growth rate estimates with government efficiency and cultural tightness scores.
6. Add and standardize control variables.
7. Examine the distribution of the rate of cases and mortality likelihood before fitting the models like the authors did. They discovered that the growth rate of cases was normally distributed, but the mortality likelihood was highly skewed. If the distribution is similar, proceed to steps 8 and 9. If not, choose a model that better fits the data’s distributional characteristics.
8. Conduct weighted ordinary least squares regression predicting log-transformed COVID-19 infection rate per million, including both main effects of cultural tightness and government efficiency and their interaction.
9. Weight each nation’s observation by the number of days of data available to account for reliability.

government efficiency, cultural tightness

economic development, inequality, median age

Summary: The authors tested the interaction of cultural tightness and government efficiency on growth rates of COVID-19 and found a significant interaction betweentightness and efficiency, b = -.17, SE = .07, t(41) = -2.23, p = .031.

Cultural and Institutional Factors Predicting the Infection Rate and Mortality Likelihood of the COVID-19 Pandemic.

Hypothesis: Countries with stronger social norms (greater cultural tightness) and more capable public institutions (greater government efficiency) will experience slower COVID-19 infection growth, with the slowest growth occurring where both tightness and efficiency are high.

Early theory and prediction statement (p. 1-2) where the authors propose that cultural tightness and government efficiency should combine to predict slower COVID-19 growth rates.

The regression results (p. 7) support the interactive prediction for infection growth: the tightness-by-efficiency interaction is negative and statistically significant, indicating that tightness is linked to slower infection growth particularly in countries with more efficient governments (and efficiency is likewise linked to slower growth particularly in tighter cultures).

Results paragraph reporting the interaction model for infection rate and the coefficient for the tightness × government efficiency interaction (reported with b, SE, t, and p).

European Centre for Disease Prevention and Control (ECDC) daily country-level COVID-19 cases and deaths; World Bank Government Efficiency Index (2017); cultural tightness index (Gelfand et al. 2011) expanded to additional nations (Eriksson et al. 2020); covariates from IMF (GDP per capita, 2019 release), World Bank (Gini coefficient; most recent estimate per nation), and CIA World Factbook (median age, 2018 release).

COVID-19 case/death data downloaded for 161 nations and updated between March 21 and March 30, 2020. Infection-growth modeling begins after each country exceeds 1 case per million people. Government efficiency data available for 126 nations (2017). Cultural tightness data available for 57 nations.

The paper reports 528,019 confirmed cases (including 23,672 deaths) across 141 nations as the global case/death totals used in the study framing.

The paper states that all data and code associated with the analyses are available on OSF.

Infection rate is operationalized as a country-specific growth-rate estimate obtained by fitting a log–log model of cases-per-million over days (after exceeding 1 case per million). Mortality likelihood is measured as deaths divided by total cases. In the second-stage country-level regressions predicting infection rate, cases are weighted by the number of observations (days) used to estimate each country’s growth curve.

The study first estimates country-specific COVID-19 infection growth rates from daily case-per-million time series and then uses country-level regressions to test whether cultural tightness and government efficiency—individually and interactively—predict cross-national variation in infection growth (and separately mortality likelihood), controlling for economic development, inequality, and median age.

Download daily COVID-19 cases and deaths by country and convert cases to cases per million people.

For each country, start the growth-rate series once the country exceeds 1 case per million people.

Estimate each country’s infection growth rate by fitting a regression with log(cases per million) as the outcome and log(days) as the predictor.

Compute mortality likelihood as deaths divided by total cases for each country.

Assemble country-level predictors: government efficiency and cultural tightness, plus GDP per capita, Gini, and median age.

Standardize the covariates (GDP per capita, Gini, median age) prior to estimation.

Estimate an OLS regression (Gaussian) predicting infection rate and include the tightness × government efficiency interaction; weight the country-level regression by the number of observations contributing to each country’s growth estimate.

Country-level OLS regression predicting infection rate (Gaussian) with an interaction term between cultural tightness and government efficiency; weighted by the number of daily observations used to estimate each nation’s growth curve.

Infection rate (country-specific log-transformed growth-rate estimate of COVID-19 cases per million).

Cultural tightness; government efficiency; cultural tightness × government efficiency interaction.

GDP per capita; Gini coefficient; median age (all included in the regressions; covariates standardized prior to estimation).

Summary: The interaction between cultural tightness and government efficiency is negative and statistically significant for infection growth, consistent with the idea that countries that are both tighter and more institutionally efficient show especially slow growth in COVID-19 cases per million during the early phase of the pandemic.

Infection rate (log-transformed growth rate of cases per million)

regression coefficient units (on the modeled infection-rate scale)

interaction-term coefficient (cultural tightness × government efficiency)

Hypothesis: We predicted that cultural tightness and government efficiency would predict slower growth rates of COVID-19 and lower mortality likelihoods, and that nations with high cultural tightness and high government efficiency would show especially slow growth rate and mortality likelihood.

Culturally tight nations and 20 nations with higher levels of government efficiency each had significantly slower COVID-19 infection rates.

For government efficiency data. World Bank data was also used for Gini coefficient data.

For cultural tightness data. No access discussed but a reference is included (Gelfand et al., 2011).

For economic development, proxied by GDP per capita, data.

The authors run a OLS regression to test the effect of the interaction between government efficiency and cultural tightness on the infection rate of COVID-19 and a logistic regression to test the effect of this interaction on mortality likelihood.

(1) Download the data and construct the variables, including constructing death rate by dividing the number of mortalities by the number of cases; (2) Standardize relevant covariates; (3) Take the log of the outcome (cases per million people) and explanatory variable (days) and run a country-level, logged regression of COVID cases on days; (4)  conducted a second set of regressions using the estimates from the initial 10 general linear models to predict cross-cultural variation in the infection rate of COVID-19, weighting cases by the number of observations across nations; (5) Run regressions using interacted terms as well.

ordinary least squares regression with gaussian distribution and logistic regression with exponential distribution

infection rate of COVID-19 and mortality likelihood

government efficiency interacted with cultural tightness

economic development; inequality; median age

Summary: ...nations with high cultural tightness and high government efficiency would have a much lower log-transformed rate of 1.06 new cases per million. Culturally tight nations and nations with higher levels of government efficiency each had significantly lower death rates of COVID-19.

log-transformed rate of 1.41 new cases per million people per day or 103.21 fewer cases per million people

No confidence intervals presented, but standard errors are presented instead (0.07).

No confidence intervals presented, but standard errors are presented instead (0.03).