Deepening Our Understanding of the Effects of US 

Foreign Assistance on Democracy Building 

Final Report 

January 28, 2008 

Steven E. Finkel, University of Pittsburgh and Hertie School of Governance (Berlin) 

Aníbal Pérez-Liñán, University of Pittsburgh 

Mitchell A. Seligson, Vanderbilt University 

C. Neal Tate, Vanderbilt University 

Executive Summary............................................................................................................ 2 

Introduction......................................................................................................................... 7 

Data and Measurement ....................................................................................................... 8 

Democracy and Governance Programs........................................................................... 9 

Dependent Variables..................................................................................................... 12 

Control Variables.......................................................................................................... 13 

Part I – Replication and Extensions.................................................................................. 19 

The “Baseline” Model................................................................................................... 19 

The “Iraq Effect” and Other Influential Cases.............................................................. 23 

Extensions: Long-Run Effects of USAID DG Assistance............................................ 27 

Extensions: The Endogeneity of USAID DG Assistance............................................. 31 

Part II – Under what Conditions Does Democracy Assistance Work Best? .................... 35 

Regional Effects............................................................................................................ 36 

Socio-Economic Conditions ......................................................................................... 39 

Domestic Political Conditions ...................................................................................... 42 

International Factors ..................................................................................................... 45 

Investment Strategies.................................................................................................... 49 

Part III – Analysis of Sub-Sectors .................................................................................... 53 

Understanding the Impact of Human Rights Assistance .............................................. 55 

Part IV – Political Culture................................................................................................. 58 

Conclusions....................................................................................................................... 66 

Appendices........................................................................................................................ 69 
1

Appendix 1 – Countries Included in the Study............................................................. 69 

Appendix 2 – Democratic Performance Indices ........................................................... 71 

Appendix 3 – Exploratory Analysis of Culture Variables............................................ 72 

Appendix 4 – Imputation Models ................................................................................. 73 

Appendix 5 – Fixed Effects and First Differences Models........................................... 75 

Appendix 6 – Models of Sub-Sectoral Effects ............................................................. 76 

Appendix 7 – Understanding the Impact of Human Rights Assistance........................ 86 

References....................................................................................................................... 100 
Executive Summary 

Does USAID’s democracy promotion program work? Although some prior studies 

have examined specific projects in individual countries, no prior effort has studied the 

question on a world-wide basis, and no prior study has encompassed the entire post ColdWar period. Vanderbilt University and the University of Pittsburgh have undertaken this 

research in a two-phased effort. In the first phase of that research, we found that the 

answer to that question was “yes.” That is, on average, in the period 1990-2003, 

USAID’s investments in democracy promotion produced significant increases in the 

national level of democracy as measured by Freedom House and Polity IV indicators. 

However, that study left many unanswered questions, and thus motivated this second 

phase of the research. 

The current report presents the results of the second phase of the project “CrossNational Research on USAID’s Democracy and Governance Programs.” This analysis 

complements and extends the study “Effects of U.S. Foreign Assistance on Democracy 

Building: Results of a Cross-National Quantitative Study,” presented in January, 2006, 

and a shortened version published in World Politics.1 The present study expands the 

initial effort in many ways, covering more years and including more variables. In 

addition, the current study responds to numerous suggestions made by readers of the prior 

report and published article, including those from academic and policy settings, as well as 

to the comments made by the expert panel convened to review the results of this work 

and to the comments made by the audience present in the public presentation of the study 

at the Center for Strategic and International Affairs (CSIS) on December 7, 2007. 

In the current effort, the data set is extended from 14 years to cover 15 years (1990-

2004) and 165 countries, yielding 2,416 observations (country-years). This expansion 

proved to be particularly important because the prior data set ended in 2003, the year of 

the U.S. invasion of Iraq, and thus did not capture the effect of the surge in democracy 

spending in that country that occurred in 2004. The main measure of democracy used in 

the study continues to be the widely used Freedom House index, complemented by the 

Polity IV index. USAID DG (Democracy and Governance) assistance is measured as 

“actual appropriated” funds (explained more fully in the text), now in constant 2000 

dollars rather than 1995 dollars as in the prior report, both as an aggregated total for each 

country, and also broken down into four main areas: 1) Elections and Political Process; 2) 

Rule of Law, 3) Civil Society; and 4) Governance. A fifth category covering regional 

and sub-regional programs was also included. 

 The revised study includes several new variables, including the percentage of 

funds invested in particular sub-sectors, the volatility of USAID DG investment, and the 

trend in USAID DG investment to determine if any of these variables influences the 

impact of DG spending on democracy. In the revised study, the impact of political 

culture is measured for the first time in order to determine if certain values can create a 

 

1

 Steven E. Finkel, Anibal Pérez-Liñán and Mitchell A. Seligson, “The Effects of U.S. Foreign Assistance 

on Democracy Building, 1990-2003”, World Politics, volume 59, (April, 2007) pp. 404-439. 

2
more receptive environment for DG dollars. The study also includes other forms of 

foreign assistance added as controls variables, including total investment in other (nonDG) programs, non-USAID assistance (including funds from the National Endowment 

for Democracy, NED), total U.S. development assistance not channeled through USAID 

or NED, bilateral non-US foreign assistance and military assistance. Additionally, in 

order to better study the problem of “endogeneity” we developed a new measure of the 

degree to which a given country was a priority for the U.S. State Department. Finally, 

the revised study includes additional improved control variables, such as a new measure 

of democratic diffusion, and an expanded set of human rights measures. 

Findings 

Replication and Extensions 

 In the first part of this report we replicate the findings of the first phase using the 

extended dataset and provide some extensions to the initial study. The main analytical 

device of the study is to calculate the democracy trend for each of the countries in the 

world that could have received U.S. DG assistance during the period 1990-2004. Those 

trend lines become part of our “baseline model” to which we add the impact of many 

variables, especially DG assistance, to determine if that assistance had an impact once all 

other factors that we could reasonably expect to influence the process of democratization 

have been taken into account. For this second phase of the research, we began with the 

baseline model from the prior phase (i.e., a “hierarchical growth model” predicting the 

country’s overall level and trends in democracy as measured by the Freedom House and 

Polity IV indicators), which included a two-year rolling average of USAID DG and nonDG appropriations and a series of other donor-related variables including funding from 

other OECD donors and the National Endowment for Democracy. 

 What we find in this second phase is that the results of the analysis for 1990-2004 

remain consistent with our previous results, namely, that DG assistance increases national 

levels of democracy among recipient countries, but the impact is smaller than the one 

documented during Phase 1 of the project. Further analysis indicates that this difference 

is mostly explained by the unusually high level of USAID DG investment in Iraq in 2004 

(the extreme levels of USAID DG assistance were not followed by an equivalent change 

in democracy scores). We propose and test alternative ways of dealing with this issue, 

each of which leads to the same conclusion, namely that once the “Iraq effect” is 

controlled for, democracy assistances has a positive effect on democracy at the same 

level as in the previous study. Specifically, the positive impact is such that $10 million of 

USAID DG funding would produce an increase of more than one-quarter of a point (.29 

units) on the 13-point Freedom House democracy index in a given year— or about a fivefold increase in the amount of democratic change that the average country would be 

expected to achieve, ceteris paribus, in any given year. 

 In the previous study, we devoted much attention to the potential problem of the 

“endogeneity” of USAID DG assistance, that is, the possibilities that either unobserved 

variables were causing both USAID DG allocations and democratic outcomes, thus 

producing a spurious relationship between the two, or that USAID DG funding 

allocations were the direct effect (and not the cause) of the democratic development that a 

country had attained. The endogeneity of USAID DG assistance is perhaps the main 

3
counter-hypothesis to the overall findings that we presented in the previous study, and the 

issue has been raised in nearly every public presentation in academic and non-academic 

settings that we have made on the project over the past several years. In addition, the

expert panel from the previous study urged us to redouble our efforts to make certain that 

the results truly were robust in the face of this potential problem. In the revised study, 

with more extensive testing, the effect of USAID DG remained consistent in models 

addressing the problem of endogeneity in much more detail. These additional tests make 

it far more likely that the findings reported in the initial report and in this follow-up 

report are valid, and that USAID DG assistance does, indeed, produce a positive impact 

on democracy in recipient countries. 

The revised study also probed more deeply the over-time impact of USAID DG 

assistance within the context of what are referred to as “lagged endogenous variable” 

models. The main finding of this section is that democracy assistance may take some 

time to “work.” The immediate impact of USAID DG assistance on Freedom House is 

estimated to be .020, so that a one-million dollar rolling average investment changes 

Freedom House scores by .020 units. If the million dollar investment was continued in 

the next year, the two-term cumulative multiplier effect would be .033. Continuing these 

calculations for a persistent one-million dollar rolling average investment over three, 

four, and five years yields cumulative impacts of .041, .047, and .050 on the Freedom 

House scale. In the revised study, then, it is found that the long-run effects of a 

permanent one million dollar investment in USAID DG investment are quite a bit higher 

than in the baseline model, and that a permanent ten million dollar investment is 

predicted to have a cumulative (equilibrium) impact of over one-half of a point on the 

Freedom House scale. 

Under What Conditions Does Democracy Assistance Work Best? 

The second part of this report analyzes the conditions under which USAID DG 

assistance is more effective. We tested for differences in the impact of DG investment 

across geographic regions. The results suggest that the effect of democracy assistance is 

hard to distinguish across regions, although investment in Africa seems to be on average 

more productive. Our limited findings in this area underscored the relevance of 

collecting retrospective data on USAID DG investment for the 1980s, and the need to 

preserve the updated data series in the future. 

Is democracy assistance more effective in some social contexts than in others? The 

answer is that the marginal effect of a million dollars invested in democracy assistance 

seems to be greater in those countries that are in greater need of external assistance (i.e., 

countries that are poorer, socially divided, and suffer from lower levels of human capital). 

Above a certain level of development (measured by the UNDP Human Development 

Index) the effect of USAID DG is statistically indistinguishable from zero. Given the 

estimates for this model, this threshold is approximately .71 (roughly the human 

development levels achieved by Brazil or Tuvalu). This finding again suggests that 

democracy assistance has a significant impact in those countries in greater socioeconomic need. 

4
Democracy assistance also makes a stronger contribution under conditions of state 

failure. Although this may be surprising, given the uncertain conditions that prevail in 

failed states, related analyses tend to support this insight. 

Democracy assistance is less effective in countries that receive a large percentage of 

U.S. military assistance. This pattern, moreover, appears to explain fully the “Iraq 

Effect” described above. Because Iraq represented a foreign policy priority mainly for 

security reasons in 2004 (e.g., it received 23 percent of all security assistance in 2004, 

vis-à-vis 0.6 percent for the average eligible country) and it was also the largest recipient 

of democracy assistance (31 percent of all USAID DG funds spent in 2004), the overall 

impact of USAID DG was depressed when compared to a model including data for 1990-

2003. In fact, once we allow the effect of USAID DG to be conditional on the U.S. 

security priority variable, the impact of the Iraq effect loses its statistical significance, 

indicating that it is in fact an extreme manifestation of a more general pattern by which 

democracy assistance is less powerful when the overall policy towards the recipient 

country is driven by security concerns. 

Our analysis also found that democracy assistance is less effective when investment is 

unstable, that is when funds are allocated to the recipient country in a volatile way. The 

findings suggest that in about half of the recipient countries the level of uncertainty in 

democracy investment may be high enough to compromise its impact. 

Analysis of Democracy Sub-Sectors 

The third part of the report explores the impact of sub-sectoral investment in the areas 

of Elections, Rule of Law (and human rights in particular), Civil Society (and free media 

in particular), and Governance on different dimensions of democracy. The results show 

that, for the models estimated on identical or virtually identical sub- or sub-sub-sectoral 

outcomes in the previous study–civil society, free media, and human rights—the addition 

of the 2004 data (and the Iraq 2004 dummy variable) leads to findings that are very 

similar to our original results. That is to say, USAID civil society and media assistance 

have a significant positive impact directly on their respective sectors, and USAID human 

rights assistance has a significant negative impact on the human rights outcome. Using 

new outcome indicators, the current study finds that elections spending has significant 

positive impact directly on the subsectoral outcome related to Elections, with some 

additional impact of the governance spending. Governance spending, in addition, 

impacts the Governance dimension, though the effect is relatively small in substantive 

magnitude. 

We collected additional data to extend our analysis of the Human Rights sub-subsector. The purpose of the extended analysis was to explore the anomalous and troubling 

negative impact of USAID DG Human Rights sub-sectoral assistance that had been found 

in our first study. Our new data allowed us to investigate a number of alternative 

hypotheses that might have accounted for this relationship. These hypotheses provided 

new insights into institutional and behavioral influences on human rights abuse. 

Unfortunately, they did not significantly ameliorate the negative impact of human rights 

assistance on respect for human integrity. 

5
The Role of Political Culture 

The fourth part of the report analyzes the role of political culture in mediating the 

impact of democracy assistance. The addition of political culture variables, 

operationalized in terms of multivariate indicators of Institutional Trust, Personal 

Satisfaction, and Social Engagement, finds that culture conditions the impact of USAID 

DG. Specifically, culture exerts a positive facilitative effect for USAID DG assistance; 

as a country’s political culture is more democratic, the impact of U.S. democracy 

assistance has stronger effects on the country’s Freedom House score. 

What appears to matter the most for facilitating USAID DG assistance is not the level 

of institutional trust in a country, nor levels of optimism or life satisfaction, but rather the 

degree to which the country’s citizens are trusting of one another, are psychologically 

engaged with politics, and are less strongly nationalistic in their political orientations. At 

these highest levels of Social Engagement, the impact of the USAID DG effect is three 

times its level in the baseline model for all eligible countries. Two culture dimensions, 

Personal Satisfaction and Social Engagement, have a significant impact on the slope of 

countries’ democratic growth trajectories as well. That is, countries with higher levels on 

these dimensions increase more rapidly on the Freedom House index, irrespective of the 

impact of USAID DG assistance. In this regard, culture appears to play a generally 

facilitative role in the development of democracy, as well as providing a more receptive 

environment for USAID DG assistance in particular to succeed. 

The data on political culture, however, were available for only about half of the 

countries in the study, thus limiting the generalizability of this finding. Moreover, since 

the availability of culture data limit the study to providing a single fixed value for each 

country over the 15-year time period, it is not possible to determine in this study if early 

investments of USAID DG assistance helped to improve the culture, which then made 

democracy assistance more effective generally. 

We conclude by noting that the evidence supporting a positive impact of USAID on 

democracy is clear. This does not mean, of course, that in the future this will continue to 

be the case. Shifts in where, when and how USAID spends its democracy assistance, and 

shifting trends in democracy world-wide could make the assistance more or less effective 

in the future. Yet, we feel that the 14 years of data we have analyzed here provide a 

robust basis for drawing the conclusion that USAID DG assistance in the post-Cold War 

period has worked. 

6
Introduction 

Under what conditions does democracy and governance (DG) assistance have its 

greatest impact? Are some investment strategies more effective than others? This study 

constitutes the second phase of the project “Cross-National Research on USAID’s 

Democracy and Governance Programs,” and attempts to answer those questions . The 

first phase of the study was conducted by our team between January and November of 

2005 under a USAID-funded subgrant from the Association Liaison Office (ALO). The 

initial study analyzed the impact of USAID’s democracy and governance programs using 

a world-wide sample of 165 countries between 1990 and 2003 (Finkel, Pérez-Liñán, and 

Seligson 2006; 2007). The results of the analysis at the time indicated that: 

1. USAID Democracy and Governance appropriations have a modest but significant 

positive impact on democracy. This effect occurs over and above the expected 

democratization trend in each country, and after controlling for a host of time-varying 

and country-level economic, social and political attributes. 

2. Using the Freedom House index as a measure of democracy, one million dollars 

(measured in constant 1995 dollars, or the equivalent of 1.2 million dollars in 2004) 

would produce an increase in democracy 50 percent greater than the improvement in 

democracy otherwise expected by the average country in the sample during any given 

year. 

3. The study uncovered lagged effects of USAID DG appropriations, suggesting that 

programs may take several years to generate full outcomes, and that the effects of 

USAID DG assistance may be cumulative. (However, long-term effects were not 

captured by the model. The estimation assumed that whenever USAID DG funds 

were withdrawn, the country’s level of democracy would return to the expected 

democratic trajectory within a year.) 

4. The research also disaggregated USAID DG assistance into four main sub-sectors: 

Elections and Political Processes, Rule of Law, Civil Society, and Governance. 

Certain models disaggregated the investment portfolio even further, exploring the 

impact of the sub-sub-sectors for Human Rights (part of Rule of Law) and Mass 

Media (part of Civil Society). The analysis suggested that, just as USAID DG 

assistance in general matters for overall levels of democratization, sub-sectoral and 

sub-sub-sectoral appropriations tend to be effective on the dimensions of democracy 

for which they are targeted. Only two exceptions seemed to defy this pattern: 

a. In our tests, Governance appropriations appeared to have no impact, yet we 

lacked appropriate measures of democratic performance in the governance area. 

b. In contrast to the other sub-sectors, investment in human rights programs was 

correlated with a decline in human rights in recipient countries. This result does 

not seem to be just the result of human rights assistance flowing to problematic 

countries. We explore some of the possible explanations for this finding below. 

The presentation of the results at the Woodrow Wilson Center in October of 2005 

elicited new questions from the Expert Panel, the audience, and the USAID team. The 

7
second phase of the study is intended to address some of those issues. The main goals of 

this study are: 

• To update the data set in order to include new indicators and longer time-series. 

• To address some remaining questions about the initial results, in particular 

questions about endogeneity (to what extent can the positive effects be explained 

by USAID DG funds flowing only to the promising cases?) and about the longterm impact of USAID DG investment. 

• To analyze the conditions under which democracy assistance has stronger effects, 

in particular the impact of different social, economic, and political characteristics 

of the recipient countries; as well as of different funding strategies adopted by 

USAID. 

• To incorporate political culture factors as control variables that might condition 

the impact of assistance. 

• To explore further the negative impact of US assistance on human rights observed 

in the first study. 

As in the first phase of the project, an expert panel was convened that helped guide 

the research at critical junctures. The team consisted of: Professor Michael Coppedge, 

Professor of Political Science, University of Notre Dame; Professor Mark Hallerberg, 

Professor of Public Management and Political Economy, Hertie School of Governance 

(Berlin); and Professor Pamela Paxton, Associate Professor of Sociology, Department of 

Sociology, Ohio State University. Without their invaluable advice, this study would have 

suffered many flaws. Any flaws in the study are, of course, the fault of the authors and 

not the review panel or those at USAID. 

Data and Measurement 

The dataset for this project comprises 195 countries for the period 1990-2004. Thirty 

countries have been excluded from the analysis because they are advanced industrial 

democracies (and therefore de facto ineligible for foreign assistance), thus the effective 

sample is constituted by 165 countries over a period of 15 years, yielding a total of 2,416 

observations.2

 Appendix 1 presents the list of countries included in the study and the 

total amount of USAID DG assistance that each country received over the period. 

Technical issues about the definition of the population of independent states, as well as 

the treatment of cases of secession and re-unification were addressed according to the 

principles established during the first phase of the study (Finkel, Pérez-Liñán, and 

Seligson 2006, 15-16). 

 

2

 All countries are observed between 1990 and 2004, with the exception of twenty-four countries that, as a 

result of geopolitical shifts, enter the sample after 1990 (Armenia, Azerbaijan, Bosnia-Herzegovina, 

Belarus, Estonia, Georgia, Croatia, Kazakhstan, Kyrgyzstan, Lithuania, Latvia, Moldova, Macedonia, 

Slovenia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan in 1991; the Czech Republic, Eritrea, and 

Slovakia in 1993; Palau and the West Bank in 1994, and East Timor in 2002), and Czechoslovakia that 

exits the sample after 1992. 

8
The dataset comprises two types of variables: time-varying factors (also referred in 

this study as Level 1 variables), and country-level characteristics (Level 2 variables). 

Variables in the first group (for instance, investment in democracy assistance and annual 

GDP growth) display variation across countries as well as within countries over time, 

while items in the second group (e.g., the size of the country) vary across countries but 

basically remain stable over time. The main variables in the analysis (democracy and 

USAID investment) are time-varying; Level 2 variables are treated as country 

characteristics that not only play a role as controls, but also may mediate the impact of 

Level 1 factors (including USAID investment) on democracy. 

Democracy and Governance Programs 

With the assistance of Andrew Green, the USAID team updated and revised the 

database on USAID DG programs. The updated database contains 44,958 entries at the 

activity level for all USAID sectors between 1990 and 2005. Each entry reports the 

purpose of the activity, the total amount appropriated in current dollars, and the recipient 

country.3

 Our analysis covers only until 2004 because information for other variables 

was not available for 2005.4

 In addition to the new information for 2004 and 2005, the 

revised database improved the coding of funds for Elections and Political Processes 

channeled through centralized mechanisms (e.g., the Consortium for Elections and 

Political Process Strengthening, CEPPS), and of funds for Civil Society related to labor 

programs channeled through the American Center for International Labor Solidarity 

(ACILS). 

We aggregated the activity-level data to measure the size of USAID sectors and subsectors in different countries and years. Because funds obligated during any given year 

may be spent the following year, we computed two-year means (corresponding to the 

current and past fiscal years) of the total amount obligated in each sector or sub-sector. 

Our indicators thus reflect two-year running means of appropriations at the country-year 

level, measured in millions of constant 2000 dollars.5 The indicators (and the respective 

variable names reported in the Codebook) are: 

1. Total investment in Democracy and Governance (henceforth USAID DG) programs 

(AID100). This sector comprises four sub-sectors, namely: 

 

3

 The amounts in the database generally reflect “actual appropriations” or the amount for which USAID is 

allowed by Congress to incur obligations for specific purposes. In our previous work we referred to these 

totals as “obligations” (Finkel et al. 2006; 2007). In this report we use the term “appropriations” as a better 

short-hand for actual appropriations, but this change in terminology does not reflect a change in the 

composition of the data. 

4

 At the time we updated our dataset, information from Polity IV, the CIRI Human Rights Project, and the 

World Bank’s Database on Political Institutions, among others, was not yet available for 2005. This lag 

between the availability of one set of data (USAID appropriations) and the other measures is inevitable, 

such that if we were to add 2006 appropriations data from USAID we would then need to wait until the 

other measures would become available for that year. 5

 In Phase 1 of the project, we used 1995 dollars, but recent versions of the World Development Indicators

(World Bank 2006) have adopted 2000 as the base year for constant dollars. We followed this practice so 

that all economic series would have a common metric.

9
1.1. Elections and Political Processes (AID110): Activities oriented towards 

electoral assistance, support for the development of political parties, and 

legislative representation. 

1.2. Rule of Law Programs (AID120): Funding for human rights programs and for 

legal and judicial development. 

1.2.1. Human Rights Programs (AID121): This sub-sub-sector is already 

captured by Rule of Law totals. When this variable is included in models 

dealing specifically with human rights outcomes, a residual category for 

Rule of Law programs (AID122) reflects only the remainder funding (mostly 

oriented towards judicial development). 

1.3. Civil Society Programs (AID130): Programs oriented towards the promotion of 

independent mass media, civic education, and labor organization. 

1.3.1. Free Media Programs (AID131): This sub-sub-sector is already captured 

by Civil Society totals. When this variable is included in models dealing 

specifically with free speech outcomes, the estimates for Civil Society 

(AID132) reflect only the remainder funding. 

1.4. Governance Programs (AID140): A very diverse category, this variable covers 

transparency and anti-corruption projects, decentralization, local government, 

and legislative assistance programs. 

1.5. Regional and Sub-Regional Programs (RSAID100): This variable captures the 

funds “available” to countries in a particular geographic area from programs 

operating at the regional or sub-regional level. The amount was calculated by 

dividing the total funding for those programs in any given year by the number of 

countries in the region (or sub-region). 

2. A new group of variables was developed to describe USAID’s patterns of 

investment in particular countries. Most of these variables were created as “Level 

2” factors: 

2.1. Percentage of funds invested in particular sub-sectors (P110, P120, P121, 

P130, P131, P140). Those variables indicate the percentage of the total USAID 

DG portfolio in a particular country that was obligated in each sub-sector in any 

given year (based on the two-year running averages). For instance a value of 55 

for AID110 indicates that fifty-five percent of the USAID DG funds invested in 

the country over the last two years were allocated to Elections and Political 

Processes. 

2.2. Volatility in USAID DG Investment (L2.V100). This “Level 2” variable 

captures the overall volatility of the democracy investment in each recipient 

country during the period 1990-2004. Volatility is defined as the average 

(positive or negative) deviation from the “expected” level of USAID DG 

funding, based on past levels of funding and a time trend (for a similar 

procedure, see Lensink and Morrissey 2000). The variable was estimated in 

three steps: 

10
(1) Investment (AID100) was predicted for each individual country as: 

AID100t=a+b1(YEARNUMt)+ b2(AID100t-1)+εt

where AID100t represents the size of the sector (or sub-sector) in the 

country in year t, and YEARNUM is a time counter (1990=1, 1991=2,...). 

(2) We computed the standard deviation of residuals εt within in each country. 

(3) Volatility was measured as the standard deviation of the residuals εt divided 

by the average AID100t for the country. This calibration of the measure 

corrected for the correlation between the gross amount of USAID DG assistance 

received by countries and the fluctuations in total spending observed in them.6

 

2.3.Trend in USAID DG Investment (L2.R100). This Level 2 variable captures the 

presence of a sustained effort (or retrenchment) in the DG sector. The values 

reflect the average yearly change in USAID DG investment in the country, 

divided by the average level of investment during the period 1990-2004. 

In addition to the USAID DG indicators, we collected information on other forms of 

foreign aid as control variables: 

3. Total investment in other (non-DG) USAID programs (AID000). This category 

includes funding devoted to Agriculture and Economic Growth, Education, 

Environment, Health, Humanitarian Assistance, Conflict Management and 

Mitigation, and Human Rights programs not managed by the DG Office (e.g., human 

trafficking programs). An additional variable (RSAID000) captured the funds 

available to countries in a particular geographic area from non-DG programs 

operating at the regional or sub-regional level. A third, Level 2 variable (L2.999A) 

captured all U.S. development assistance invested in the country between 1946 and 

1989 (measured in millions of constant 2000 dollars). 

4. Non-USAID assistance: 

4.1. Investment from the National Endowment for Democracy (AIDNED). 

Information was collected from the annual report on U.S. Overseas Loans and 

Grants, commonly known as the “Greenbook” (USAID 2006). 

4.2. Total U.S. development assistance other than USAID or NED programs

(AID_2). This value was estimated as the difference between the total loans and 

grants reported by the Greenbook as Economic Assistance, and the totals for the 

AID and AIDNED variables. For simplicity, we refer to this variable as *here 

4.3. Other Donor Assistance (DG and Non-DG: ODA100 and ODA000). Those 

variables reflect official development assistance provided by countries other than 

the United States to the particular recipient for democracy-related and non-

 

6

 In the volalitity, trend and portfolio analyses below (Section II), we exclude non-recipient countries from 

consideration, as our goal is to assess the impact of different investment strategies among countries in 

which the US actually invests. However, to verify the results of our analysis we run alternative models in 

which non-recipient countries received a score of zero for variables in the L2.V and L2.R batteries. The 

substantive findings discussed in Section II remained unchanged. 

11
democracy related programs (measured in millions of 2000 dollars, as a two-year 

average). Data excludes multilateral cooperation. Information was compiled 

from the OECD’s Creditor Reporting System (OECD 2006). 

4.4. U.S. Military Assistance Priority (FPP01). This item was measured as the 

percentage of total U.S. military aid disbursed in any given year allocated to the 

recipient country. This indicator seeks to capture to what extent the recipient 

country constituted a geo-political strategic priority for the U.S. (USAID 2006). 

Dependent Variables 

In order to assess democratic outcomes, we employed two general measures of 

democracy (the Freedom House and Polity indices) and five composite indices. In 

general, because of its widespread universal use in democracy studies, we used Freedom 

House as our baseline measure of democracy and employed alternative indices to verify 

the robustness of our results. Put in other terms, if we began with any other measure, 

many readers might question why we did not use Freedom House as our reference point, 

even though indicator construction for national-level measures of democracy is still a 

highly contested field in contemporary political science (Munck and Verkuilen 2002). 

Using a checklist that is distributed to country experts, Freedom House rates the presence 

of political rights and civil liberties in 192 countries every year. Scores for the two items 

range from 1 to 7, with 7 being the lowest level of freedoms in each case (Freedom 

House 2004a). Following the widespread practice in the field of democracy studies, we 

inverted the scores so that the high numbers would reflect high levels of democracy, 

rather than the counter-intuitive scoring method used by Freedom House in which low 

numbers mean high democracy, and combined them into a single index of democracy, 

ranging from 1 (autocratic) to 13 (democratic). The Polity IV score ranges between -10 

(autocratic) and +10 (democratic); it reflects the competitiveness and openness of 

executive recruitment, the competitiveness and regulation of political participation, and 

the constraints on the chief executive. (For definitions of these components, see Marshall 

and Jaggers 2002). 

The five composite indices were designed to measure sub-sectoral outcomes, 

dimensions of democracy that have been specifically targeted by the programs discussed 

in the previous section of this report.7

 The indices were constructed using factor analysis 

in order to combine related indicators originating from multiple sources. (Detailed 

information on the factor analysis is available in Appendix 2). Factor scores were 

calibrated to have a mean of 50 and a standard deviation of 10, and thus can be roughly 

interpreted as scales ranging from 0 to 100.8

 For the second phase of the project, we have 

adjusted the composition of some indices following the suggestions of the Expert Panel, 

 

7

 We remain agnostic on whether these measures reflect different dimensions or whether they capture 

overlapping aspects of the democratization process. We selected component items intended to measure the 

same (or closely related) theoretical constructs, to the extent that those constructs were relevant for USAID 

funding priorities. 

8

 Because in the composite scales a value of 50 represents the average case (country-year) in the sample, 

and the standard deviation is set by construction to 10, actual values range from 24 to 78, and extreme 

values (0 or 100) do not occur. 

12
and introduced a new index of good governance that captures administrative transparency 

and efficiency. The five sub-sectoral composite indicators are 

1. Free and Fair Elections (EL15): the first factor resulting from the analysis of 

indicators of Electoral Competition (Vanhanen 2003); Electoral Competitiveness in 

Legislative Elections (Keefer 2005, 14-15); Women’s Political Rights (Cingranelli 

and Richards 2004), Competitiveness of Participation (Marshall, Jaggers, and Gurr 

2005), and Democratic Accountability (ICRG 2006).9 

2. Respect for Human Rights (RL15): the first factor resulting from the analysis of 

Political Killings, Disappearances, Torture, Political Imprisonment (Cingranelli and 

Richards 2004), and Political Terror (Gibney 2004). 

3. Conditions for Civil Society (CS08): the first factor resulting from the analysis of 

Restrictions on the Organization of Minorities (Minorities at Risk Project 2004), 

Freedom of Assembly, Religious Freedom, Respect for Worker’s Rights, Freedom of 

Movement, and Respect for Women’s Economic Rights (Cingranelli and Richards 

2004). 

4. Free Media (RL16): the first factor resulting from the analysis of Freedom of the 

Press (Freedom House 2004b, three-point and 100-point scales); Freedom of Speech 

(Cingranelli and Richards 2004), and Restrictions on Freedom of Expression 

(Minorities at Risk 2004). 

5. Good Governance (GV16): the first factor resulting from the analysis of subjective 

measures of Perceptions of Corruption (Transparency International 2005); Conditions 

for Investment; Administrative Corruption; and Bureaucratic Quality (Erb, Harvey, 

and Viskanta 1996; ICRG 2001; ICRG 2006).10

Control Variables 

The last set of variables comprises controls for social, economic, and political 

conditions in the country. Some of the control variables are what we refer to as “Level 1” 

controls, which can vary over time for a given country. Others are what we call “Level 

 

9

 Based on a network of country specialists, the International Country Risk Guide (2006) created a 

subjective measure of democratic accountability ranging from zero to six, in which values between 0 and 

2.5 correspond to autarchies; 3 to 4 to one-party states; 4.5 to “dominated” democracies; and 5 to 6 to 

“alternating democracies.” The measure is highly subjective, yet correlates well with similar indicators. 10 In Phase 1 of the project we used some of the World Bank’s Governance Matters indicators, but this 

source provides no data prior to 1996 and only bi-annual data for 1996-2004. In contrast, the International 

Country Risk Guide has developed a battery of subjective items that serve as components of its aggregate 

country-risk score since 1984. ICRG collects information from a network of 75 to 125 country specialists 

on a quarterly basis and “grades” countries based on this information. The Investment Profile, which ranges 

from 0 to 12, measures the risk resulting from contract viability and expropriation, profits repatriation, and 

delays in payments to foreign credits. The Corruption index is a subjective measure ranging from 0 (less 

transparency) to 6 (more transparency), capturing “actual or potential corruption in the form of excessive 

patronage, nepotism, job reservations, ‘favor-for-favors’, secret party funding, and suspiciously close ties 

between politics and business.” Finally, the measure of Bureaucratic Quality, ranging between 0 and 4, 

reflects subjective perceptions of whether bureaucracies are “autonomous from political pressure and have 

an established mechanism for recruitment and training,” and to what extent “a change in government tends 

to be traumatic in terms of policy formulation and day-to-day administrative functions” (ICRG 2006). 

13
2” controls, which are stable or very nearly stable characteristics of a country over the 

1990-2004 time period covered by the study.11

The time-varying, Level 1 controls include: 

1. Annual Growth in Per Capita GDP (PRF01), based on GDP figures in constant 

2000 dollars (World Bank 2006). 

2. Index of Social and Political Conflict (POL05). Banks’ index provides a weighted 

average of eight forms of conflict (each form originally coded as a yearly event count 

based on The New York Times): assassinations, general strikes, guerrilla warfare, 

government crises, purges, riots, revolutions, and anti-government demonstrations 

(Banks 2005). 

3. State Failure Indicator (POL25). This dichotomous variable indicates the 

occurrence of ethnic or revolutionary wars, genocide or politicide episodes, or violent 

regime changes in any given year (Political Instability Task Force 2006). 

4. Democratic Diffusion (DIF07). Based on our discussion of the subject with Mark 

Billera of the USAID team, we created a new measure of democratic diffusion. The 

diffusion score for any given country reflects the average Freedom House score for 

all countries in the world (excluding the case in question) during the previous year, 

with the values of the other nations’ FH scores weighted by the distance between their 

capitals and the capital of the country in question (influences closer to the country are 

weighted more heavily, based on the inverse of the distance).12 

5. We created a dummy variable that identifies the single observation corresponding to 

Iraq in 2004 (Iraq in every other year, as well as every other country, are coded as 

zero). The rationale for this ID variable is discussed in the following section. 

6. We gathered and used measures of a number of independent variables to 

operationalize a set of alternative hypotheses generated in our effort to explain the 

anomalous negative relationship between respect for human rights and USAID subsector assistance intended to promote respect for rights. These measures include 

indicators of (1) press freedom, (2) international governmental and non-governmental 

associations presence, (3) constitutional provisions designed to promote basic rights, 

establish and protect judicial independence, and regulate states of emergency, (4) a 

measure of actual judicial independence, and (5) perceived threats to leader 

 

11 The Level 2 variables are either attributes that did not change at all during the period under study (e.g., 

historical conditions that reflect the trajectory of the country prior to 1990), or because they reflect 

conditions that change slowly over time and, in the absence of detailed time-series, were assumed, 

reasonably we argue, to be considered “constants”. 

12 The diffusion measure employed in Phase 1 of the project (Finkel et al. 2006) reflected the average 

Freedom House score for all countries in the region (excluding the country in question) during the previous 

year. The new measure includes all countries in the world, but weights their influence according to the 

distance from the target country i. Let dij denote the distance between the capitals of countries i and j, the 

formula to compute the spatial lags for country i a time t is: −

DIF = ∑

J d 1

07 ij

it × DG =

∑

J 02 jt −1

j 1 d −1

ij

j =1

14
continuation in power. They are described fully in the section analyzing respect for 

human rights and in Appendix 7. 

We also computed an additional Level 1 variable capturing the number of times that a 

Secretary or Assistant Secretary of State was mentioned in relation to (i.e., in the same 

sentence with) a particular country by the New York Times in any given year. This 

variable (FPP04), conceived as a measure of the State Department’s priorities in any 

given year, does not convey any sense of direction (i.e., DOS orientation toward the 

countries may have been positive or negative, irrespective of the number of public 

references). We discuss this item separately from the list of independent variables 

because this factor was not employed in our models as a predictor of democracy, but as 

an instrument for USAID DG; that is, a factor able to predict (at least in part) the 

allocation of democracy funds in any given year, but not the level of democracy.13 We 

employ this instrument to create a proxy for democracy assistance in the models dealing 

with endogeneity presented later in the report. 

The Level 2 control variables are: 

7. Prior Democracy (L2.03). This variable captures the number of years that the country 

was rated as “Free” by Freedom House between 1972 and 1989. We employ this 

variable as an indicator of the country’s democracy “stock”. 

8. State Failure Indicator, 1960-89 (L2.12). This variable reflects the number of years 

between 1960 and 1989 that the country suffered political anarchy or foreign 

intervention according to the Polity database. 

9. Average population, measured in thousands 1990-2004 (L2.20) (World Bank 2005). 

10. Average Income per Capita, 2000-05 (L2.21). This variable captures the average per 

capita income at purchasing power parity reported by the Central Intelligence Agency 

between 2000 and 2005 (Central Intelligence Agency 2005). This indicator is highly 

correlated with PPP values reported by the World Bank, but has better coverage (195 

countries vs. 177 in WDI). 

11. Income share of top 20 percent households, 1990-2004 (L2.22) (World Bank 2006). 

12. Land area of the country, measured in square kilometers (L2.23). 

13. Ethnolinguistic Fractionalization (L2.25). This measure is an average of the Annett 

and the two Fearon indices of ethnolinguistic fractionalization, all measured using the 

same formula (Annett 2001; Fearon 2003; Fearon and Laitin 2003).14 Values close to 

zero indicate high homogeneity, and values close to one indicate extreme ethnic 

fractionalization. 

14. Human Development Index, circa 1990 (L2.28). To construct the Human 

Development Index, UNDP collects information on life expectancy at birth, adult 

 

13 For all eligible country-years, the contemporaneous correlation of the DOS variable with DG assistance 

is .30. 

14 The formula for ethnolinguistic fractionalization is: 1 - ∑n

i=1 p 2

i , where pi denotes the population share 

for each of the n ethnic groups in the country. Fearon estimated one index based on the figures of the Atlas 

Narodov Mira and a second one using the CIA’s World Factbook. 

15
literacy, combined gross primary, secondary, and tertiary enrolment ratios, and real 

GDP per capita (PPP$). The index is constructed in three steps: (1) adult literacy and 

combined gross enrolments are combined into a single index of educational 

attainment (with literacy representing two-thirds of the measure); (2) all indicators are 

re-calibrated to vary between 0 and 1; and (3) the HDI is computed as simple average 

of the life expectancy index, educational attainment index, and adjusted GDP index. 

Higher values indicate better living conditions (UNDP 2006). 

We also collected additional data on political culture using public opinion surveys. 

Because the number of surveys is limited—in many cases it was hard to find more than 

one survey per country—and because cultural traits are expected to be relatively stable 

over time, we treated public opinion data as Level 2 (we averaged individual responses 

within each country, created a country-level indicator). The main source for our culture 

data was the World Values Survey (WVS). When WVS had conducted more than one 

survey in a given country, we averaged the relevant variables across waves. For countries 

not covered by WVS, we used other sources if an equivalent survey item was available. 

As alternative sources we employed the AmericasBarometer carried out by the Latin 

American Public Opinion Project (LAPOP), the Afrobarometer, and the Asian 

Barometer. In all cases we re-scaled the items in a 0-100 scale to be consistent. The 

large number of missing values (anywhere between 50 and 64 percent of the eligible 

countries, depending on the item, lacked survey data) prevented any reliable imputation, 

and forced us to work with a very limited sub-sample of countries. Because of this 

reason, we do not include cultural variables in the baseline models, but treat them in a 

separate section. 

Based on an exploratory analysis of ten culture variables (see Appendix 3 for details), 

we selected nine of them to create three composite scales. The indices are: 

15. Institutional Trust (L2.C1). Average scores for Trust in the Government, trust in 

the Justice System, and trust in Parliament. The survey questions read: “I am going 

to name a number of organizations. For each one, could you tell me how much 

confidence you have in them: Is it a great deal of confidence, quite a lot of 

confidence, not very much confidence or none at all?” Scores for the three variables 

range from 0 (no institutional trust at all) to 100 (a great deal of institutional trust). 

16. Personal Satisfaction (L2.C2). Average score of three items 

• Satisfaction with democracy, measured as through the question: “On the whole 

are you very satisfied, rather satisfied, not very satisfied or not at all satisfied with 

the way democracy is developing in our country?” Scores range between 0 (not at 

all satisfied) and 100 (very satisfied); 

• Life satisfaction, measured through the question: “All things considered, how 

satisfied are you with your life as a whole these days?” Scores range between 0 

(dissatisfied) and 100 (very satisfied); and 

• Happiness, measured through the question: “Taking all things together, would you 

say you are: very happy, quite happy, not very happy, or not at all happy?” 

Scores range between 0 (not at all happy) and 100 (very happy). 

16
17. Social Engagement (L2.C3), the average of 

• Interpersonal trust, measured through the question: “Generally speaking, would 

you say that most people can be trusted or that you need to be very careful in 

dealing with people?” Scores range between 0 (need to be very careful) and 100 

(most people can be trusted). 

• Interest in politics, measured using the question: “How interested would you say 

you are in politics?” Scores range between 0 (not at all interested) and 100 (very 

interested). 

• National pride, based on the question: “How proud are you to be [Nationality]?” 

The factor analysis reported in Appendix 3 suggested that this item was inversely 

related to the underlying construct (social engagement), therefore we inverted the 

scores to range between 0 (very proud) and 100 (not at all proud). 

Table 1 presents the list of 64 variables included in different sections of this study. 

Twenty-six Level 1 variables and six Level 2 variables have been incorporated as new 

items in this phase of the project. 

Several sources contained incomplete information, creating a problem with missing 

values. Listwise deletion (i.e., dropping cases with missing information on any variable) 

resulted in a poor solution because it reduced the geographic coverage of the analysis 

significantly (see also King et al. 2001, 51-52). In order to minimize the number of 

missing values, we imputed a few key variables. Whenever possible, we used alternative 

sources of information to estimate the data. For instance, if GDP data from the World 

Bank database (WDI) was not available for a particular observation, we estimated the 

values using the Penn World Tables and the CIA Factbook (Heston, Summers, and Aten 

2002). In other cases, although a second measure of the same concept was not readily 

available, the high correlation among some variables in the dataset (e.g., between the 

Freedom House and the Polity indices) facilitated the imputation process. Because 

multiple imputation proved difficult in the context of our study, we adopted an 

expectation-maximization (EM) procedure for the estimation of missing data (Allison 

2001; McLachlan and Krishnan 1997).15 Appendix 4 summarizes the variables that 

required imputation, the percentage of missing values, and the variables employed to 

obtain EM estimates. 

 

15 EM is a maximum-likelihood technique that employs information from other variables to estimate 

missing data. “In simple cases, this involves running regressions to estimate β, imputing the missing values 

with a predicted value, reestimating β, and iterating until convergence” (King et al. 2001, 55). We 

considered multiple imputation (i.e., creating multiple datasets with different estimates). However, practical 

reasons (the need to impute at multiple stages of the analysis—measurement and causal modeling—and the 

difficulty to implement multiple imputation with the software for some of the models we estimated) led us 

to adopt a more parsimonious EM procedure (Allison 2001; King et al. 2001). 

17
Table 1. Variables Included in the Study 

Level 1 (Time-varying covariates) 

Outcomes (Democracy)

Freedom House score, 1-13 scale 

Polity score, –10-10 

Index of Free and Fair Elections 

Index of Human Rights 

Index of Civil Society 

Other Forms of Assistance

National Endowment for Democracy* 

US Assistance other than USAID or NED 

Other Donor Assistance on DG (bilateral) 

Other Donor Assistance on Non-DG (bilateral) 

US Military Aid Priority (% of annual worldwide) 

Index of Free Press Additional Control Variables

Index of Good Governance* Annual Growth in Per Capita GDP 

USAID Activity

Total USAID DG Assistance 

Total USAID Non-DG Aid 

Elections and Political Processes 

Rule of Law Programs 

Human Rights Programs 

Rule of Law (non-Human Rights) 

Civil Society Programs 

Free Media Programs 

Civil Society (Non-Media) 

Governance Programs 

Regional Programs, DG 

Regional Programs, non-DG 

Percentage invested in Elections* 

Percentage invested in Rule of Law* 

Percentage invested in Human Rights* 

Percentage invested in Civil Society* 

Percentage invested in Media Programs* 

Democratic Diffusion* 

Index of Social and Political Conflict 

State Failure Indicator * 

Iraq 2004 dummy* 

Threat/Protest Index* 

Civil War* 

Five Freedoms Index* 

Freedom to Strike* 

Fair Procedure Index* 

Formal Judicial Independence Index* 

Courts have Exclusive Competence* 

No Special, Military Courts* 

State of Emergency Varimax scores Factor 1* 

State of Emergency Varimax scores Factor 2* 

Actual Judicial Independence*

Intnl. Intergovernmental Orgs Memberships*

Intnl. Nongovernmental orgs count logged* 

Index of Freedom of Press* 

Percentage invested in Governance* Instruments for AID100

State Department Priorities [FPP04]* 

Level 2 (Country-level characteristics) 

USAID Activity

Total U.S. Aid 1946-89 

Volatility in USAID DG, 1990-2004* 

Trend in USAID DG, 1990-2004* 

Additional Control Variables

Years Rated “Free” by FH 1972-89 

State Failure Indicator, 1960-89 

Average Income Per Capita, 2000-05 

 Average Population, 1990-2004

Income Share of top 20%, 1990-2004 

Land Area of the Country 

 Ethnolinguistic Fractionalization

Human Development Index* 

 Institutional trust*

 Personal satisfaction*

 Social Engagement*

* New variables not included in Phase 1 of the project.

18
Part I – Replication and Extensions 

The “Baseline” Model 

We begin by replicating the “baseline model” from the previous phase of the project, 

i.e., the hierarchical growth model predicting the country’s overall level and trends in 

democracy as measured by the Freedom House (FH) and Polity IV indicators. In this 

model, FH and Polity IV are predicted from a random country-specific linear time trend, 

a series of country-level independent variables that determine the level and slope of the 

time trend, the two-year rolling average of USAID DG and non-DG appropriations and a 

series of other donor-related variables including funding from other OECD donors and 

the National Endowment for Democracy. The multi-level or “hierarchical” model can be 

expressed in two equations, one (at “Level 1”) predicting intra-country growth in FH or 

Polity scores over time, and the other (at “Level 2”) predicting the magnitude of the 

Level 1 coefficients with time-invariant country level characteristics: 

Level 1: Intra-country Growth 

(1) yti=π0i + π1iati + π2iAIDti + πkivkti + εti 

where a is a time-related variable, in this case the year of observation (1990, 1991, 

1992...2004), εti is a random error term, and π0i and π1i are regression coefficients that 

represent the individual country’s (linear) growth trajectory. Specifically, π0i is the 

“intercept” of the growth model, that is, individual county i’s “starting point” on, for 

example, the Freedom House or Polity score at the first wave of data collection (1990), 

and π1i is the linear slope of the growth trajectory, such that the individual country 

changes by π1i units on the Freedom House or Polity score for every change in one unit of 

a, in this case one year. Coefficient π2i captures the impact of foreign assistance on the 

level of democracy, while AIDti indicates the amount invested in country i during year t. 

The vkti represent additional time-varying “covariates,” i.e., factors that have potentially 

different values for a given country at each year, and which may influence the given 

democratic outcome at a specific time. The πki then represent regression coefficients 

linking the k

th time-varying covariate to yti. All USAID-related variables, including U.S. 

Democracy and Governance (DG) appropriations, non-DG appropriations, regional and 

sub-regional DG and non-DG appropriations, and non-U.S. donor appropriations are 

treated in this model as “time-varying covariates.” The set of time-varying covariates 

also includes the time-specific control variables such as economic performance, 

democratic diffusion, extent of political violence, state failure, and U.S. foreign policy 

priorities. The full set of Level 1 variables in the “baseline model” may be found in 

Table 1. 

The second portion of the growth model attempts to explain why certain countries 

have higher or lower πk coefficients, i.e., why some countries begin the period at higher 

or lower levels of democratization, why some countries change more rapidly than others, 

19
and why some countries may have higher or lower effects on democratic outcomes from 

particular time-varying covariates. In equation form, we estimate: 

Level 2: Inter-Country Differences 

(2.0) π0i = Β00 + Β0mXmi + r0i 

(2.1) π1i = Β10 + Β1mXmi + r1i 

(2.2) π2i= Β20

(2.3) πki= Βk0

where 

Β00 is the average (“fixed”) population intercept or starting point for the growth 

trajectory; 

Β0m is the average (“fixed”) effect of some country characteristic Xm on the country’s 

growth trajectory intercept; 

Β10 is the average (“fixed”) population slope for the democratization trend; 

Β1m is the average (“fixed”) effect of some country characteristic Xm on the country’s 

growth trajectory slope; 

Β20 is the average (“fixed”) population effect for DG investment; 

Βk0 is the average (“fixed”) population slope for the k

th time-varying covariate v; 

r0i is the deviation, or residual, of country i’s growth trajectory intercept from the 

value predicted by the population average Β00 and all of the Β0m Xm; and 

r1i is the deviation, or residual, of country i’s growth trajectory slope from the 

population average Β10 and all of the Β1m Xm; 

Equations (2.0) to (2.3) thus predict the magnitude of the Level 1 coefficients in 

equation (1) with country-level characteristics, which include relatively stable factors 

such as level of economic development, past political and democratic history, human 

development and the like. A full set of the Level 2 variables included in the baseline 

model can also be found in Table 1. Equations (2.0) and (2.1) express the growth curve 

intercepts and slopes as random coefficients, predicted imperfectly from the stable 

country-level characteristics with residual random variation captured in the r

disturbances. Equations (2.2) and (2.3) predict the effects of the time-varying covariates 

as fixed across countries; this is the normal specification for time-varying covariates in 

the absence of strong expectations to the contrary. We relax this assumption in the last 

section in the attempt to understand the factors that determine size of the π2i coefficient 

for USAID DG assistance, that is, the conditions under which USAID DG assistance has 

larger or smaller effects.

 The model of equations (1) and (2) can be seen as a hierarchical, or a multilevel

model because Level 1 (equation 1) represents intra-country differences in initial levels 

of democratic outcomes and growth over time, and Level 2 (equation 2) models the level 

and growth rates as functions of individual-level differences on important explanatory 

variables. The model is also called a “mixed” model that contains both “fixed” and 

“random” effects—in this case the Β coefficients are fixed, either at the level of the 

overall population of countries (Β00, Β10, Β20, and Βk0 ) or as deviations from the 

population averages that are determined by Level-2 explanatory variables (Β0m, and Β1m), 

while the r0i, r1i, and εti terms are random disturbances. Because of the complex nature of 

20
the model’s error term (in this case r0i + r1i ati+εti), the assumptions necessary for 

ordinary least squares (OLS) estimation—errors that are independent, normally 

distributed and with constant variance—are inherently inappropriate.16 In order to cope 

with this problem, the model is estimated instead via iterative maximum likelihood 

procedures, which are implemented in statistical software packages designed for 

estimating hierarchical linear models. The baseline model includes both heteroskedastic

error variances, whereby the residual democracy score may vary more at some time 

periods than others, as well as autocorrelated disturbances, which allow for the error 

term ε(t-1)i to influence its successive value εti, as is commonly the case in longitudinal 

data. The model thus captures the key features of intra-country longitudinal growth, 

inter-country differences in the growth coefficients, as well as estimating the form of the 

error term variances and covariances that is likely to obtain with over-time data on 

democratic outcomes.17

The results of the baseline model for the 1990-2004 time period are shown in Table 

2. It can be seen that the USAID DG coefficient for both FH and Polity IV are 

statistically significant, as they were in the previous grant’s analyses. However, it is also 

the case that the magnitude of the USAID DG effect is considerably smaller in both 

models than was reported earlier. The size of the coefficient in the Freedom House 

model has fallen to .018, a 28% drop from its value of .025 in the previous study, and the 

size of the DG coefficient in the Polity IV has fallen even more sharply to .023, a nearly 

48% drop from its value of .044 in the previous study (see Finkel, Pérez-Linán, and 

Seligson 2006, Table 4). We shall explore these differences in more detail below; for 

now we simply note that the inclusion of the 2004 data does not change the core finding 

that USAID DG assistance has significant impact on both Freedom House and Polity 

democracy scores within the context of the baseline hierarchical growth model, though 

the size of the effect is considerably attenuated from that reported earlier. 

The other results from the baseline model largely corroborate the findings from the 

previous study. Nearly all of the other donor variables (including the new NED variable) 

show insignificant effects, aside from the anomalous negative effect from US 

development assistance other than USAID or NED in the Freedom House model (but not 

in the Polity IV model). Economic growth and social and political conflict have similar 

effects in the FH model as before, and the new measures of democratic diffusion and 

time-specific state failure show significant effects in the expected directions in both the 

FH and Polity models. 

 

16 In this case the errors are dependent because r0i and r1i are common to each individual, and they have 

unequal variances because r0i and r1i vary across individuals and r1i ati varies across occasions of 

measurement. 

17 We estimated the models using the MIXED module in SPSS 15.0 . We specified the error term structure 

initially to be (ARH1) in order to model both the heteroskedastic and autocorrelated nature of the 

disturbances. The Polity IV models in Table 2, though, attained the best fit through an autocorrelation-only 

specification (i.e. without the heteroskedasticity option). 

21
Table 2. The Growth Model for Freedom House and Polity IV Democracy Scores 

Dependent Variable 2(a) – Freedom House 2(b) – Polity IV 

Coefficient S. Error Coefficient S. Error 

Level 1 

Democracy and Other Assistance 

USAID DG 0.018** 0.005 0.023* 0.012 

USAID Non-DG 0.001* 0.001 0.001 0.001 

National Endowment for Democracy 0.132 0.168 -0.172 0.327 

US Assistance other than USAID or NED -0.001** 0.000 -0.001 0.001 

Regional-Subregional DG -0.010 0.123 -0.020 0.268 

Regional-Subregional Non-DG -0.010 0.013 0.018 0.022 

Other Donor Assistance on DG 8.1E-005 0.001 0.001 0.001 

Other Donor Assistance on Non-DG -4E-006 9.1E-005 -0.000 0.000 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.009** 0.002 -0.003 0.005 

US Military Assistance Priority -0.049** 0.018 -0.052 0.043 

Democratic Diffusion 0.185** 0.088 1.151** 0.155 

Social and Political Conflict -0.001** 0.000 -0.001 0.001 

State Failure Indicator -0.696** 0.076 -1.941** 0.173 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 6.738** 0.245 1.512** 0.421 

Prior Democracy 0.280** 0.051 0.480** 0.086 

State Failure, Pre-1990 -0.499 0.751 -1.082 1.477 

Income Per Capita -0.027 0.069 -0.159 0.117 

Population -3E-006 2.5E-006 -2E-006 4.2E-006 

Income Inequality 0.075** 0.033 0.070 0.056 

Ethnic Fractionalization -0.987 1.086 -0.806 1.849 

Pre-1990 USAID 5.6E-005 4.7E-005 0.000** 0.000 

Size in Square Km -5E-005 0.0001 -8E-005 0.0003 

Human Development Index 6.938** 2.526 14.186** 4.316 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 0.054** 0.016 0.063* 0.033 

Prior Democracy -0.005 0.003 -0.009 0.006 

State Failure Indicator 0.000 0.045 -0.009 0.110 

Income per Capita 0.002 0.004 0.004 0.009 

Population 1.4E-007 1.5E-007 -2E-007 3.0E-007 

Income Inequality -0.000 0.002 0.003 0.004 

Ethnic Fractionalization 0.127** 0.064 0.179 0.135 

Size in Square Km -2E-005* 9.5E-006 1.3E-005 2.0E-005 

Pre-1990 USAID -7E-007 2.7E-006 -4E-006 5.7E-006 

Human Development Index 0.061 0.150 -0.391 0.313 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 5.807** 0.906 9.981** 3.445 

Random Variance (Slope) 0.013** 0.003 0.022 0.015 

Autocorrelation. (rho) 0.801** 0.020 0.895** 0.026 

Model Deviance/AIC 6539.983 6575.983 9986.495 9994.495 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Indented variable labels indicate interactive 

effects. 

22
The average country begins the period slightly above the midpoint of the FH and 

Polity scales, with starting points being higher for countries with stronger democratic 

traditions, for countries with higher levels of human development, and (in the Polity IV 

model) for countries that were recipients of USAID assistance before 1989. The Level 2 

results predicting the size of country growth trajectories are also largely the same as 

reported earlier. The average country changes by .054 units on the FH scale and .063 on 

the Polity index over time, approximately the same average change as was reported in the 

previous study. As in the previous study, we find that almost none of the country level 

variables significantly predict the size of the country’s growth trajectory slope. The only 

Level 2 variables to have significant impact on the growth trajectory slope in the 

Freedom House model are ethnic fractionalization and size, with larger, more 

heterogeneous countries increasing at a faster pace. Thus, this analysis leaves us with 

much the same picture we had before, with USAID DG assistance as a significant 

predictor of growth in democracy, as measured by both Freedom House and Polity IV, 

The “Iraq Effect” and Other Influential Cases 

The main difference between the 2004 results and those reported in the previous 

grant’s analyses is the attenuation of the USAID DG assistance variable. While the 

inclusion of new data, of course, will always lead to at least some minor changes in 

statistical results, the magnitude of the differences in the estimated effect (a 30-50% 

drop) for the variable that is of primary importance to this study, in our view, warranted 

further investigation. The most likely reason for such a dramatic change is the presence 

in 2004 of one or more cases with a large amount of “leverage” on the regression results, 

that is, cases that have such large (small) amounts of USAID DG assistance coupled with 

such small (large) levels of democracy that their inclusion reduces the magnitude of the 

estimated coefficients across the entire sample. We explored this possibility by 

estimating models that exclude one case at a time so that we can see how the exclusion of 

the case(s) changes the magnitude of the estimated coefficients. Specifically, we 

estimated a variant of the Freedom House and Polity IV baseline model 2,416 times, each 

time excluding one country-year from consideration and registering the value of the 

USAID DG coefficient.18 Table 3 shows the results of this estimation and the cases that 

exert that most leverage on the coefficient, that is, the cases whose exclusion changed the 

coefficient the most. 

 

18 We used the “jackknife” routine in STATA for this purpose. The baseline model in this case was 

estimated with the XTREGAR module, and was equivalent to the baseline hierarchical growth model 

without a random coefficient for “year” and without the heteroskedastic option for the error term. 

23
Table 3. The Iraq Effect: 

Cases with Unusual Leverage on the USAID DG Coefficient 

3(a) - Freedom House 

country-year Coefficient 

Average coefficient 0.018 

(standard deviation) (0.0002) 

Iraq 2004 0.026 

Iraq 2003 0.019 

Colombia 2000 0.019 

Haiti 1993 0.019 

West Bank and Gaza 1994 0.019 

Haiti 1994 0.019 

Panama 1992 0.019 

Egypt 1998 0.019 

z-score 

42.077 

7.861 

6.703 

5.749 

5.246 

4.718 

4.532 

4.417 

3(b) – Polity 

Coefficient

Average coefficient 0.036 

(standard deviation) (0.0005) 

Iraq 2004 0.056 

Serbia and Montenegro 2000 0.042 

Iraq 2003 0.040 

Haiti 1993 0.039 

Haiti 1994 0.039 

West Bank and Gaza 1994 0.038 

Colombia 2000 0.038 

Russian Federation 2000 0.037 

z-score

42.062 

13.083 

8.684 

6.905 

5.920

4.205 

3.934 

3.512 

Note: “z-score” refers to the number of standard deviations away from the average coefficient of .018 

(Freedom House) or .036 (Polity IV) that the USAID DG coefficient represents in a model without

including the given country-year in the estimation. 

The first row of the table shows that the average of the 2,416 estimated coefficients 

for the USAID DG effect on Freedom House ratings is .018, with a standard deviation of 

.0002, and the average coefficient for the USAID DG effect on Polity IV ratings is .036, 

with a standard deviation of .0005. Below these figures are the cases in each estimation 

that have the most leverage, as indicated by the size of the estimated coefficient 

excluding that case, and its “z-score,” or how many standard deviations the coefficient 

that results from excluding the case is from the average value. It can be seen that one 

case, Iraq in 2004, is exerting an extraordinary amount of downward leverage on the 

USAID DG coefficient—excluding this case would produce an estimated coefficient of 

.026 on Freedom House, and .056 on Polity, with both values being approximately the 

same magnitude as that estimated from the 1990-2003 data. That is, the exclusion of Iraq 

2004 from the analysis would completely “restore” the size of the USAID DG coefficient 

to the value found in the previous study. This is an astonishing effect for one case out of 

2,416, as confirmed by the huge z-score of 42.1 for the Iraq 2004 case in both the 

Freedom House and Polity IV models. No other case comes close to exerting this kind of 

leverage. The next highest z-scores for Freedom House belong to Iraq in 2003 (z of 7.9) 

and Colombia in 2000 (z of 6.7), with a resulting USAID DG coefficient of .019 if either 

of those cases were to be excluded. The next highest z-score for Polity IV belongs to 

Serbia-Montenegro in 2000 (z of 13.1 and a coefficient of .042 through exclusion, 

followed by Iraq in 2003 (z of 8.7 and a coefficient of .040 through exclusion). Many of 

the other cases with the highest leverage are the same for the two models, and in all other 

cases the magnitude of the changes in the USAID DG coefficient are modest by 

excluding any given country year from consideration. 

Why is this effect occurring? It is clearly the result of an extremely large amount of 

USAID DG assistance to Iraq in 2004 ($261 million in 2000 dollars), coupled with the 

very low Freedom House and Polity IV democracy ratings (3 and -5). The amount of 

USAID DG assistance to Iraq itself in 2004 is far more than the amount given to any 

other country at any time in the data set, with the next highest value being 86.5 million to 

24
Serbia-Montenegro in 2003 and 2004, and the average value for recipient countries being 

just under $5 million (in standard 2000 dollars) in any given year. In other words, Iraq 

received 52 times the average amount of democracy assistance in 2004. 

 Because of the unusual leverage that Iraq 2004 is exerting on the USAID DG 

coefficient in our models, we decided to undertake two “corrective” procedures. One is to 

include an indicator (“dummy”) variable for Iraq 2004 in subsequent models as an 

additional “control variable” predicting Freedom House and Polity IV scores. This 

strategy assumes that there is something so fundamentally different about the particular 

case that it should not be included within the general causal framework that holds for the 

other 2,415 country-year cases. We believe that this is a reasonable approach, given that 

the distribution of USAID democracy foreign assistance in Iraq that year occurred in a 

completely non-standard context, namely that of regime change brought on by largely 

U.S. military force.19 Re-estimating the hierarchical growth baseline model with an 

IRAQ 2004 dummy variable included yields significant USAID DG coefficients of .029 

for Freedom House and .044 for Polity IV, both values being somewhat higher than their 

magnitude in the original research based on data through 2003 (see Table 4).20 Further, 

the IRAQ 2004 dummy variable itself shows significant coefficients of -4.5 for Freedom 

House and -10.3 for Polity IV, indicating that, controlling for all other variables in the 

model, Iraq in 2004 is predicted to be substantially lower on both of the overall 

democratic indices than other cases in the analysis. In fact, the magnitude of the unique 

negative effect for Iraq 2004 is over one-third of the total scale distance for Freedom 

House and nearly one half of the total scale distance for Polity IV. 

The second strategy for handling the Iraq 2004 case is to attempt to explain the 

reasons for its leverage in subsequent analyses, specifically in our models that assess 

when and where USAID DG has more or less impact. It may be the case, for example, 

that USAID DG has weaker impacts in other settings where democracy is low, in other 

settings with high ethnic fractionalization, or in other settings with high US military 

assistance as well. As we pursue these models, we can then assess whether the IRAQ 

2004 dummy variable is still significant, or whether this case can be subsumed under the 

more general causal process. We shall present evidence regarding these possibilities in 

the analyses we present in Part II below. For now, though, we shall include the dummy 

variable for IRAQ 2004 from the analyses in the rest of this document unless otherwise 

noted. 21

 

19 For instance, funding amounts for democracy assistance programs in Iraq are inflated by extraordinarily 

high security costs (plus higher salaries, extra home leaves, etc.). As a result, spending $1 million in Iraq 

may not have the same impact as spending $1 million in a different setting. 

20 Note that some Level 2 predictors with no significant effect in Table 2 also have insignificant effects in 

Table 4. The elimination of four predictors (state failure for 1960-89, population, income per capita, and 

US assistance for 1946-89) does not alter the results of the baseline model. In Parts II, III, and IV we 

eliminate those predictors from the model in order to simplify the baseline equation. 

21 Another possibility would be to transform the AID variables into logarithmic form, so that the $261 

million dollars for Iraq 2004 would not be nearly as distinct from the assistance received by other countries. 

The natural logarithm of $261 is 5.56, which is not so much greater a value than 4.44, the log of the DG 

assistance to Serbia in 2003 and 2004 ($85 million). We rejected this strategy because of its fundamental 

incompatibility with all of the analyses that we have done in the project to this point. See Finkel et al. 

(2006, 59-62) for a discussion of alternative possibilities for scaling and standardizing the AID variables. 

25
Table 4. New Baseline Model with Control for Iraq 2004 

Dependent variable: 

Level 1

2(a) – Freedom House 

Coefficient S. Error 

 

2(b) – Polity IV

Coefficient S. Error 

 

Democracy and Other Assistance 

USAID DG 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

 Iraq 2004 

Level 2 

 

0.029** 0.006 

0.000 0.001 

0.071 0.168 

-0.000 0.000 

-0.018 0.122 

-0.010 0.013 

4.5E-005 0.001 

-6E-007 9.0E-005 

 

0.009** 0.002 

-0.029 0.018 

0.181** 0.088 

-0.001** 0.000 

-0.740** 0.076 

-4.509** 1.046 

 

 

0.044** 0.014 

0.000 0.001 

-0.279 0.328 

0.001 0.001 

-0.051 0.268 

0.017 0.022 

0.001 0.001 

-0.000 0.000 

 

-0.004 0.005 

-0.025 0.044 

1.156** 0.154 

-0.001 0.001 

-1.994** 0.173 

-10.309** 3.057 

 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

State Failure, Pre-1990 

Income Per Capita 

Population 

Income Inequality 

Ethnic Fractionalization 

Pre-1990 USAID 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

State Failure Indicator 

Income per Capita 

Population 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Pre-1990 USAID 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

6.776** 0.244 

0.281** 0.051 

-0.537 0.749 

-0.030 0.068 

-3E-006 2.5E-006 

0.073** 0.033 

-0.914 1.085 

5.2E-005 4.7E-005 

-4E-005 0.0002 

7.064** 2.521 

 

0.050** 0.016 

-0.005 0.003 

-0.000 0.045 

0.003 0.004 

1.7E-007 1.4E-007 

0.000 0.002 

0.117* 0.064 

-2E-005** 9.5E-006 

-9E-007 2.7E-006 

0.035 0.149 

 

5.874** 0.905 

0.013** 0.003 

0.795** 0.021 

6519.931 6555.931 

 

1.583** 0.421 

0.477** 0.086 

-1.092 1.475 

-0.159 0.117 

-2E-006 4.2E-006 

0.068 0.056 

-0.732 1.849 

0.0002** 8.1E-005 

-7E-005 0.0003 

14.252** 4.314 

 

0.056* 0.033 

-0.009 0.006 

-0.011 0.109 

0.005 0.008 

-2E-007 3.0E-007 

0.003 0.004 

0.165 0.135 

0.000 0.000 

-4E-006 5.7E-006 

-0.430 0.312 

 

10.269** 3.379 

0.022 0.014 

0.893** 0.026 

9971.079 9979.079 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

26
Extensions: Long-Run Effects of USAID DG Assistance 

One limitation of the baseline model is that, in its current form, USAID DG 

assistance is assumed to exert only a temporary impact on a country’s level of 

democracy. That is, USAID DG assistance is modeled as a time-varying covariate in the 

hierarchical growth model, so that its effect operates on Freedom House or Polity IV 

ratings at a given point in time, and the effects are assumed by dint of the choice of our 

statistical model to die out immediately thereafter. Such a formulation fails to capture the 

potential and quite realistic assumption of a longer-run impact of USAID DG 

investments, in that USAID DG programs may take several years or more to “work;” 

moreover, a permanent increase in USAID DG assistance over time may cumulate in 

ways that are not well-described by the single, time-specific USAID DG coefficient in 

the current baseline model. We have therefore devoted considerable attention in our 

current work to estimating models that can provide a more meaningful sense of the longer 

term effects of USAID DG assistance on democratic outcomes. 

In the previous study, we made some progress on assessing longer-term effects by 

adding a lagged value of USAID DG assistance to the model, so that we predict Freedom 

House or Polity IV scores with the current two-year average of USAID DG (DGit) and 

the previous year’s value as well (DG it-1). The previous findings (Table 6, p. 61 in 

Finkel et al. 2006) were that both coefficients had statistically significant values of .017. 

Re-estimating this model with the full 1990-2004 data sets yields an insignificant lagged 

USAID DG value unless the IRAQ 2004 dummy variable is included, in which case the 

coefficient for current democracy assistance is .026 (p<.001) and lagged democracy 

assistance is .012 (p<.07). In the context of this kind of statistical model, known as a 

“finite distributed lag” model because of the limited number of lags specified, the 

coefficients may be interpreted as follows: 

1) The effect of a million dollar increase in the current two-year rolling average of 

USAID DG on current Freedom House ratings is .026 (the coefficient for USAID 

DGit); 

2) The effect of each million dollar increase in the current two-year rolling average 

of democracy assistance on Freedom House ratings one year in the future is .012 

(the coefficient for USAID DGit-1); 

3) The effect of a temporary million dollar increase in the two-year rolling average 

of USAID DG assistance is therefore .026 in the first year, .012 in the second 

year, and then 0 for all subsequent years; 

4) The effect of a permanent million dollar increase in the two-year rolling average 

of USAID DG assistance is therefore .038 units on the Freedom House scale, as 

the effect in years 2 and higher will be .026 from the current year’s assistance 

added to the .012 from the previous year’s value. 

These results are suggestive of a process whereby USAID DG assistance takes some 

time to “work,” and whereby sustained increases in funding may therefore cumulate to 

some degree over time as well. However, the basic structure of a finite distributed lag 

model prevents us from saying anything more in this regard. Longer-lag lengths are 

possible to include, but we lose one year’s worth of data for each lag value of democracy 

27
assistance that is added to the model. And more generally, the specification of the model 

ensures that the effect of any given year’s assistance is a transient one, whether it is over 

one or two time periods. This is not a completely implausible specification but certainly 

a restricted one. 

A more general dynamic panel model that allows for longer-term lag effects takes the 

following form: 

(3) y y it =α + β β 1 1 it− − + 2 xit + β3 xit 1 + β 4 zit +++ ui kt ε it

where 

yit−1 is the lagged value of the Freedom House scale, or what is called the “lagged 

endogenous variable,” 

xit and xit−1 are the current and lagged values of USAID DG assistance, 

zit are all other time-varying covariates and Level 2 time-invariant independent 

variables in the model, 

ui is a country-specific effect that subsumes all unmeasured, stable country factors, 

(and which, when added to the overall population intercept α , may viewed as 

equivalent to the intercept of the country’s growth trajectory in the baseline model), 

kt is a time-specific effect, such that events in any given year affect all countries’ 

democracy ratings in that year, independent of all other variables in the model, 

ε it is the error term for a given country’s Freedom House score in a given year t, and 

β1 through β 4 are regression coefficients to be estimated. 

This model is referred in the econometric literature as an “Autoregressive Distributed 

Lag” (ADL) model, and is one of the standard models used in the field to estimate both 

short and long-run dynamic processes. Here we report the results of this model, 

estimated in the statistical package STATA 9.2. (see Table 5). The estimation of the 

model proceeds first by differencing all variables to eliminate the confounding effects of 

the ui term. It then applies relatively complex “instrumental” variable methods to 

estimate the effects of the lagged endogenous variable, which, by construction, is 

correlated with the error term of equation (3), even after the differencing process.22 

 

22 The model is estimated via STATA’s XTABOND routine, which applies the Generalized Method of 

Moments estimator developed by Arellano and Bond (1991). See Wavro (2002) for a good introduction to 

these methods for political scientists. 

28
Table 5. Dynamic Models with Lagged Freedom House Democracy, 

Generalized Method of Moments Estimation 

5(a) USAID DG and 5(b) USAID DG 

USAID DG Lagged

Coefficient St. Error Coefficient St. Error 

Democracy and Other Assistance 

USAID DG 0.018* 0.010 0.020** 0.089 

USAID DG Lagged 0.005 0.011 --- --- 

USAID Non-DG -0.000 0.001 -0.000 0.001 

National Endowment for Democracy 0.386* 0.226 0.388* 0.224 

US Assistance other than USAID or NED 0.000 0.000 0.000 0.000 

Regional-Subregional DG -0.087 0.140 -0.088 0.141 

Regional-Subregional Non-DG -0.009 0.021 -0.010 0.021 

Other Donor Assistance on DG -0.000 0.001 -0.000 0.001 

Other Donor Assistance on Non DG -0.000 0.021 -0.000 0.000 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.011* 0.006 0.011* 0.006 

US Military Assistance Priority -0.013 0.016 -0.015 0.016 

Democratic Diffusion 0.018 0.168 0.018 0.169 

Social and Political Conflict -0.001 0.001 -0.001 0.001 

State Failure Indicator -0.760** 0.197 -0.762** 0.198 

 

Freedom House Lagged 0.644** 0.063 0.648** 0.061 

Iraq 2004 -5.331** 1.297 -5.165** 1.249 

N 2086 2086

Arellano-Bond z-test for second order 1.13 1.13 

autocorrelation p=0.2572 p=0.2579 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Dependent variable is Freedom House. 

 The results in column 5(a) show that when lagged USAID DG assistance is 

included, it does not achieve statistical significance. Dropping the lagged value of 

USAID DG assistance yields the preferred model of column 5(b), with a significant 

coefficient for contemporaneous USAID DG of .020, controlling for the lagged 

differenced endogenous variable (with a significant effect of .648) and all other variables 

in the model. The model is satisfactory in other respects, with only a few meaningful 

substantive differences from the baseline model: the effects of democratic diffusion and 

social and political conflict are now insignificant, and the effect of National Endowment 

for Democracy (NED) assistance is now significant.23 We note also that the model 

satisfies the two statistical conditions for the applicability of the Arellano-Bond method: 

the instrument set used for proxying the lagged differenced endogenous variable truly is 

 

23 The insignificant results for democratic diffusion and political conflict are not unsurprising because these 

variables are relatively stable over time, and, especially in the case of diffusion, the variables come close to 

being washed out altogether in the differencing process of the model. The results for the National 

Endowment for Democracy variable are more striking, but, as will be shown in the next section, they do not 

hold up once controls for the possible “endogeneity” of the funding process are introduced. 

29
exogenous to the process (i.e., unrelated to the differenced error term), and there is no 

second-order autocorrelation in the differenced error terms.24

The USAID DG coefficients of the model may be expressed in terms of their short 

and longer-term impacts.25 In the notation of equation (3), the immediate impact of 

USAID DG assistance on Freedom House is β 2 , or in this case .020, so that a one-million 

dollar rolling average investment changes Freedom House scores in the same time period 

by .020 units. If the million dollar investment was continued in the next time period, the 

two-term cumulative multiplier effect would be β 2 *(1+ β1 ), where β1 again is the 

coefficient for the lagged differenced endogenous variable. In this case the two-term 

cumulative effect would be .020*(1+.648) or .033. Continuing these calculations for a 

persistent one-million dollar rolling average investment over three, four, and five time 

periods yields cumulative impacts of .041 after three periods, .047 after four periods, and 

.050 after five periods.26 The “long-run multiplier effect,” representing the total effect of 

USAID DG on FH at some equilibrium point as time approaches infinity, is β 2 /(1- β1 ), or 

in this case .056. Thus, we see that in this specification, the long-run effects of a 

permanent one million dollar investment in USAID DG investment are quite a bit higher 

than in the baseline model, and that a permanent ten million dollar increase is predicted to 

have a cumulative (equilibrium) impact of over one-half of a point on the Freedom House 

scale.27 This is stronger evidence than has been reported thus far in the project that 

USAID DG investment has not only immediate impact on levels of democracy in 

recipient countries, but also has impacts that cumulate to some degree over time and, 

under certain reasonable statistical assumptions, endure after funding has been 

withdrawn. 

 

24 The first condition is satisfied through the “Sargan test,” with a chi-square value of 98.28 with 90 df 

(p=.26), and the second condition is satisfied through the Arellano-Bond test for second order 

autocorrelation, with a z of 1.49, p=.14. 25 De Boef and Keele (2005) and Kaplan (2002) have good expositions of how these effects are calculated 

from the estimated coefficients in dynamic time series or panel models. 

26 The general formula for cumulative impacts is β 2 (1+ β + β 2 3

1 1 + β1 +.... β s-11 ), with each additional β1

term representing the incremental effect for the given extra time period. See Kaplan 2002, p. 88. 27 The presence of the lagged endogenous variable in this model means that the effects of even temporary

increases in AID DG funding will not immediately dissipate by next year, as is assumed in the baseline 

model. The effects of a temporary one million dollars investment at time t equals β 2 the first year, β 2 β1

the second year, β β 2 

2 1 the third year, and so on. Thus, given the coefficient of .648 for the lagged 

endogenous variable, a one-million dollar investment maintains 65% of its initial impact in the following 

year, 42% of its initial impact two years hence, and dissipates to zero a full ten years after the initial 

investment. 

30
Extensions: The Endogeneity of USAID DG Assistance 

In the previous study, we devoted much attention to the potential problem of the 

“endogeneity” of USAID DG assistance, that is, the possibilities that either unobserved 

variables were causing both USAID DG allocations and democratic outcomes, thus 

producing a spurious relationship between the two, or that USAID DG funding 

allocations were the direct effect (and not the cause) of the democratic development that a 

country had attained. The endogeneity of USAID DG assistance is perhaps the main 

counter-hypothesis to the overall findings that we presented in the previous study, and the 

issue has been raised in nearly every public presentation in academic and non-academic 

settings that we have made on the project over the past several years. In addition, the

expert panel from the previous study urged us to redouble our efforts to make certain that 

the results truly were robust in the face of this potential problem. 

One possible source of the endogeneity of USAID DG assistance is from its 

association with unobserved variables that (by definition) we were unable to include in 

the analyses and which are also related to democratic outcomes. For example, countries 

with better organized political parties may have a greater likelihood of attracting USAID 

DG funding, and better organized parties may also produce pressure for greater political 

rights and hence higher levels of overall democracy. As another example, following 

(Paxton and Morishima 2005), countries that are more peripheral than others in the global 

economic system may have a lower likelihood of receiving USAID DG funding 

compared to more integrated countries, and peripheral/integrated status may then lead to 

different levels of democracy. In both of these cases, the variable in question is not 

included in the observed data set, and thus they both represent unmeasured potential 

influences on the receipt of democracy assistance and the level of a country’s democratic 

attainment. 

An even more serious possibility, though, is that even after taking into account stable 

unobserved factors in the fixed effects or first differences models, there is still 

endogeneity in the process, such that levels of democracy cause USAID DG 

appropriations and not the reverse. Indeed, it appears to be part of the “conventional 

wisdom” regarding USAID DG assistance in the scholarly community, as evidenced by 

the claim of Knack (2004, 259) that “AID currently has an explicit policy of directing 

more aid to countries that appear to be making greater progress towards 

democratization.”28 As part of our endogeneity discussion in the previous study (pp. 62-

67), we provided ample anecdotal evidence that USAID does not necessarily fund those 

countries “trending democratic,” so we will not repeat those arguments here. For the 

skeptics, though, the issue must be settled by estimating statistical models that control for 

this possibility. 

To recap the statistical issues involved: if there are effects from democracy “causing” 

USAID expenditures, the consequence is that the assistance variable at a given time point 

will be correlated with the idiosyncratic error term of the Freedom House equation, 

leading to the inability to estimate the effect of USAID DG assistance on democracy 

 

28 We note, however, that no direct evidence was presented by Knack in making this claim. 

31
without bias. The standard approach to this problem is to utilize instrumental variables or 

Two-Stage Least Squares (2SLS) regression. In the first stage, a proxy variable is 

estimated by regressing USAID DG appropriations on all exogenous variables as well as 

on several predictors (“instrumental variables”) which are assumed to a) have no direct 

effect on the Freedom House scores in that year, and b) have some significant influence 

on the USAID DG variable. The predicted value of democracy assistance at time t from 

this equation is the best estimate of USAID DG levels, purged of their contemporaneous 

relationship with the Freedom House time t error term. In the second stage, the USAID 

DG proxy variable is used to estimate the effects of USAID DG assistance on Freedom 

House scores without bias. 

In the previous study, we included as instruments in the first stage regression 

predicting democracy assistance at time t the twice-lagged level of assistance (i.e. USAID 

DGt-2) and the country’s inflation and unemployment rates. This specification is 

plausible, yet there were several limitations to the analyses we conducted there. First, we 

provided no empirical verification that the set of variables indeed satisfied the exogeneity 

restrictions necessary for inclusion in the analysis, that is, that they were unrelated to the 

Freedom House error term at time t. Second, we provided no empirical verification that 

the instrument set was strongly related to time t USAID DG assistance, so that they 

would serve as good proxies for the USAID DG endogenous variable. And third, we had 

no “built-in” exogenous variables in the study in the sense that none were designed 

explicitly to serve as instruments for USAID DG assistance; our set of instruments was 

arrived at by panel analysis convention (in the case of the twice-lagged variable) or 

through examining the pattern of results in exploratory models. These limitations have 

all been overcome in our current analyses, and we present the results of three endogeneity 

models for the effect of USAID DG assistance on Freedom House ratings in Table 6

below. These models, as will be discussed, have the added statistical advantage of 

controlling for the other source of endogeneity bias discussed above, the potential 

confounding effects from unobserved stable variables. 

32
Table 6. The Effect of USAID DG Assistance: Endogeneity Models 

Democracy and Other Assistance 

USAID DG 

USAID Non-DG 

National Endowment for Democracy 

US Assistance not USAID or NED

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance DG 

Other Donor Assistance Non DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Freedom House Lagged 

Iraq 2004 

Constant 

N 

R-squared (uncentered) 

Sargan Test of Exogeneity of 

Instruments [x2

 (df)] 

Arellano-Bond z-test for second 

order autocorrelation 

6(a) 

Coefficient 

0.022** 

0.001 

0.079 

-0.000 

-0.130 

-0.022* 

0.000 

-0.000 

0.008** 

-0.011 

0.163 

-0.001* 

-0.856** 

-- 

-4.311** 

0.313** 

2251 

0.0826 

0.379 (3) 

p=0.94 

 

St. Error 

 

0.008 

0.001 

0.175 

0.001 

0.146 

0.012 

0.001 

0.000 

 

0.003 

0.024 

0.117 

0.000 

0.091 

 

-- 

1.632 

0.092 

6(b) 

Coefficient 

0.020** 

0.000 

0.325 

-0.000 

-0.061 

-0.008 

-0.000 

-0.000 

0.009 

-0.009 

-0.028 

-0.001 

-0.818** 

0.679** 

-3.591** 

0.000 

 2086 

--- 

 207.06(184) 

p=0.12 

1.44 

P=0.15 

St. Error 

 

0.009 

0.001 

0.243 

0.001 

0.136 

0.021 

0.001 

0.000 

 

0.006 

0.015 

0.150 

0.001 

0.193 

 

0.058 

1.612 

0.016 

6(c) 

Coefficient 

0.019** 

0.000 

-0.066 

-0.000 

0.020 

-0.002 

-0.001 

-0.000 

0.010* 

-0.018 

0.048 

-0.001 

-0.745** 

0.656** 

-3.930** 

-0.003 

 2086 

---- 

 381.77(364) 

p=0.25 

 1.07 

p=0.29 

St. Error 

0.008 

0.001 

0.218 

0.001 

0.138 

0.178 

0.001 

0.000 

0.006 

0.017 

0.164 

0.001 

0.177 

0.046 

1.481 

0.013 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Dependent variable is Freedom House. 

The model in Table 6a begins with all variables being expressed in first differences. 

This serves to eliminate stable unobserved country-level factors from possibly 

confounding the causal estimates. We then treat the USAID DG assistance variable as 

endogenous. The proxy variable was constructed using all exogenous time-varying 

covariates, along with differenced values of inflation and our measure of State 

Department priorities (the number of times that a Secretary or Assistant Secretary of 

State was mentioned in relation to a particular country by the New York Times). As noted 

earlier, we created the latter variable in the hopes that it might serve as a suitable 

instrumental variable, in that more State Department mentions for a given country would 

potentially be correlated with additional USAID DG assistance, but not necessarily 

correlated with democratic outcomes in that period. (This assumption was confirmed in 

the statistical tests that we will discuss). Following Lewbel (1997), we also augment this 

instrument set with the second and third moments of the USAID DG variable (for a 

recent application of this procedure, see Rudra (2005)). This is done explicitly to 

increase the explanatory power of the instrument set in the first stage, as the problem of 

“weak instruments” hampers the efficient estimation of the effects of the endogenous 

33
variables. The results of this estimation show a significant democracy assistance effect of 

.022. In addition, the model as a whole satisfies the assumptions of the instrumental 

variable procedures: the Sargan test of the model’s overidentifying restrictions supports 

the exogeneity assumptions regarding the instruments ( χ2 of .379 with 3 df, p=.94);29 the 

hypothesis that the instruments are irrelevant in the first-stage equation can be decidedly 

rejected (LR value of 1794.1 with 4 df, p<.0001); and the State Department foreign 

policy priority instrument predicts USAID DG assistance in the first stage in ways that 

make theoretical sense, as more State Department mentions lead to significantly more 

USAID DG assistance in the country’s next obligation cycle. 

In Table 6b, we show the results from estimating of an extended Arellano-Bond 

dynamic panel model that includes the lagged endogenous Freedom House variable. This 

is then the “endogenous USAID DG assistance” version of the model in Table 5b. Recall 

that the Arellano-Bond Generalized method of moments (GMM) estimator makes use of 

the panel structure of the data to include additional instruments in the form of more 

distant lags of the model’s variables. Specifically, for the endogenous variables (in this 

case both lagged Freedom House scores and USAID DG assistance), twice-lagged 

variables and, where possible, all lags further back in time are used in the instrument set, 

along with other instruments that may be specified. In this case, we augment the 

“internal” lag-based instruments with the inflation rate, the State Department variable, 

and the two higher-order moments of the assistance variable. The results again confirm 

the significance of the USAID DG assistance effect. The value of the USAID DG 

variable is .020 more than twice its standard error and nearly identical to its value in 

Table 5b. The model again passes all relevant statistical tests, as the Sargan test supports 

the exogeneity of the instruments (χ2 of 207.06 with 184 df, p=.12), and the test for 

second-order autocorrelation is negative (z=1.44 p=.15). 

In Table 6c, we show the results of an extended endogeneity model that treats all

democracy assistance --- from USAID, from the National Endowment for Democracy, 

and from other OECD donors --- as potentially endogenous. Again, the effect from 

USAID DG assistance remains significant with a value of .019, while the effects from 

NED and other donors are not statistically distinguishable from zero. The model passes 

the relevant Sargan (χ2 of 381.77 with 364 df, p=.25) and second-order autocorrelation 

tests as well (z=1.07, p=.29). 

All of this evidence shows that the USAID DG assistance effect initially 

demonstrated in the earlier hierarchical growth and dynamic panel models is robust; it 

remains after assuming that assistance is endogenously related to Freedom House scores, 

and it remains in endogeneity models that include the endogenous lagged dependent 

variable as well. We are confident that the new methods employed here provide a 

sounder statistical basis on which to make these claims.30

 

29 The Sargan test essentially tests whether the correlation between the second stage regression’s residuals 

and the set of instrumental variables is indeed 0, as required by the IV procedure. See Baltagi (2005: 141). 

30 As noted, the models in Table 6 also provide evidence that USAID DG assistance has statistically 

significant effects on Freedom House scores, controlling for the potentially confounding effects of stable 

34
Part II – Under what Conditions Does Democracy Assistance 

Work Best? 

In this section we present the findings regarding the conditional effects of democracy 

funding. The fundamental question is: Under what conditions does USAID DG 

assistance have a greater or smaller impact? We began our exploration of this problem 

by modeling USAID DG impact as a function of initial country characteristics 

(geographic location and general democratic conditions), and of USAID investment 

strategies (the trend and volatility in democracy investment). 

The analysis conducted in the following sections builds on the “baseline model” 

presented in Part I. In the baseline model, represented by equation (1), the level of 

democracy in any given country-year is estimated as a function of a country-specific 

intercept (π0i), a country-specific democratization trend (π1i), the impact of USAID DG 

assistance (π2i), and the impact of additional k covariates (πki): 

 

(1) yti=π0i + π1iati + π2iAIDti + πkivkti + εti .

Throughout Phase 1 of the project, as well as in the previous sections of this report, 

we have assumed that the effect of USAID DG assistance is the same for all recipient 

countries. Thus, Equation (2.2) presents the coefficient for USAID DG as equivalent to 

the “average” effect for the whole sample (Β20). 

 (2.2) π2i= Β20 . 

 However, it is likely that particular conditions (whether characteristics of a recipient 

nation or features of the USAID strategy towards the country) enhance or hinder the 

effects of USAID DG investment. For instance, countries with highly repressive regimes 

may be less permeable to democracy assistance. Alternatively, democracy funding may 

have greater impact in countries in which USAID has a sustained and consistent, as 

opposed to a more sporadic, presence. If this is the case, the impact of USAID DG 

assistance will vary from country to country as a function of country-level characteristics, 

just as the democratization trend (π2i ) is expected to vary from case to case. Equation 

(2.2b) reflects this extension of the baseline model, in which the impact of USAID DG 

assistance for country i (π2i) is modeled not just as a function of the “average” effect 

(Β20), but also of m country characteristics (X1, X2,..., Xm):31 

 unobserved (country-level) factors. In other models that control exclusively for this possibility (i.e., 

without also dealing with the reverse causality issue) USAID DG also shows significant effects, with 

values of .031 in a traditional “fixed effects” model, and .021 in a “first differences” model, respectively. 

These results are also shown in Appendix 5. 31 It would also be possible to include a random error term in equation (2.2b), such that the size of the DG 

assistance coefficient would depend on the included Xs, with some degree of predictive error. These 

models – with three random effects on the country level intercept, the slope of its democratic time 

trajectory, and the AID DG coefficient -- proved too complex to obtain reliable estimates. 

35
(2.2b) π2i = Β20 + Β2mXmi .

In the following sections we explore different types of “Xs”--regional characteristics, 

domestic socio-economic conditions, local political conditions, international influences, 

and investment patterns. Another way to understand equation (2.2b) is to consider Β2m as 

the coefficient for an interaction between the Level 1 predictor USAID DG assistance 

and the Level 2 variable Xm. By substitution of (2.2b) into (1), the mixed model becomes 

(1b) yti=π0i + π1iati + Β20AIDti + Β2mXmiAIDti + πkivkti + εti 

In this model, Β20 represents the impact of $1 million of USAID DG when the 

mediating variable Xm is zero (which in our sample usually indicates the value for the 

“average country,” as we have “centered” most independent variables at their means). In 

turn, Β2m represents the change in baseline coefficient Β20 for every one-unit change in the 

mediating variable Xm. Thus, when the mediating variable is different from zero, the 

overall impact of democracy assistance is conditional on the level of X, and the overall 

coefficient of interest can be estimated as Β20+Β2mXmi.

32 Following this logic we also 

explore the role of time-varying factors, such as economic performance or political and 

social conflict, that may mediate the effect of democracy investment using interaction 

terms between USAID DG and particular Level 1 independent variables. 

Because this strategy requires the introduction of multiple interaction terms in some 

models, and in order to simplify the specification of our basic equation, in the rest of the 

report we employ a “trimmed” baseline model that eliminates all independent variables 

with insignificant coefficients in Table 4a. The elimination of four Level 2 predictors 

(the state failure measure for 1960-89, population, income per capita, and total US 

assistance between 1946 and 1989) does not alter the results of the baseline model and 

simplifies the specification considerably.33 

Regional Effects 

We first analyzed regional patterns.34 Is USAID DG investment more effective in 

some regions than others? Some caveats are in order when we analyze this question. 

First, differences across regions may relate to the period covered by the study (for 

instance, democratization was steadily increasing in Eastern Europe during the 1990s, 

while most regime changes had taken place in Latin America during the 1980s). Second, 

regional locations are likely to “proxy” for other characteristics (culture, initial regime 

conditions, etc.). Thus, interpretation of regional patterns should be made with care. 

 

32 This also means that the standard error for the AID coefficient is conditional on the values of X, 

complicating the interpretation of significance levels, but we address this problem below. 33 It should be noted that the “trimmed” baseline model does include the Level 1 (time-specific) state 

failure variable, as well as the UNDP Human Development Index, which is a partial reflection of a 

country’s GDP per capita. 

34 We defined regions following standard USAID criteria. See Appendix 1 for a list of countries by region. 

36
In order to capture regional effects, we introduced regional dummy variables in 

equations (2.0) for the initial level of democracy, (2.1) for the linear time trend, and 

(2.2b) for the impact of USAID DG. The baseline category comprises countries in 

Europe and Oceania,35 and five dichotomous variables were included to indicate that 

countries were located in Africa, Asia, Latin America, Eurasia, or the Middle East. The 

resulting Level-2 equation for the USAID DG effect is presented as (2.2c) below: 

(2.2c) π2i = Β20 + Β21(Africa) + Β22(Asia) + Β23(Latin America) + Β24(Eurasia) + 

Β25(Middle East)

where Β20 now represents the USAID DG coefficient for the average country in 

Europe and Oceania, Β21 indicates the difference of the USAID DG coefficient for the 

average African country compared to the one for the reference category (Β20), and the 

remaining coefficients indicate the distance between the effect for other regions and the 

one for the reference category. 

Table 7 summarizes the results for the regional models using Freedom House as the 

dependent variable, and with unconditional effects not related to regions being omitted to 

save space. The baseline coefficient for USAID DG (i.e., in Europe and Oceania) is 

significant. The effect of democracy assistance in Asia, Latin America, Eurasia, and the 

Middle East does not differ significantly from its impact on countries in the baseline 

group but the impact of USAID DG assistance in Africa is significantly greater. An 

investment of 10 million dollars is expected to increase the Freedom House democracy 

score by 0.29 points in Europe, while the same ten millions are expected to increase 

democracy by 0.82 points in Africa. This pattern may be explained by some of the 

general conditions prevailing in Africa. We explore some of those conditions below. 

Figure 1 illustrates the effects of USAID DG by region. The dotted lines represent 

the 95 percent confidence intervals based on the conditional standard errors for each 

region (we present confidence intervals as a continuous band for graphical purposes). 

The impact of USAID democracy assistance is positive and significant (and virtually 

indistinguishable from the baseline effect) for Asia and the Middle East. The effect is not 

significant for Latin America and Eurasia, but it is also indistinguishable from the 

baseline effect. Given the historical limitations in the data discussed above (e.g., the 

democratization process occurred in Latin America mostly during the 1980s), it is hard to 

reach clear conclusions regarding Latin America and Eurasia. This fact underscores the 

need to collect retrospective data on USAID DG investment for the 1980s, and the need 

to produce comparable time series in the future. 

 

35 We lumped together Europe and Oceania because Pacific Islands are not recipients of DG funds (only 

Papua New Guinea received $0.2 million during the period under study) and thus it made little sense to 

treat Oceania as a region with a distinctive DG effect. 

37
Table 7. Summary Table of Regional Differences in USAID DG Effects, 

Levels of Democracy, and Time Trends 

 Coefficient Std. Error

Democracy assistance

Baseline coefficient for USAID DG 0.029** 0.012 

Africa 0.052** 0.023 

Asia 0.009 0.016 

Latin America -0.030 0.020 

Eurasia -0.018 0.021 

Middle East -0.009 0.014 

Latent democratization trend 

Baseline Slope for Growth Curve 0.073* 0.038 

Africa 0.084 0.058 

Asia -0.027 0.051 

Latin America -0.015 0.046 

Eurasia -0.254** 0.064 

Middle East -0.074 0.050 

Initial level of democracy (Intercept) 

Baseline Intercept 8.640** 0.589 

Africa -2.212** 0.962 

Asia -2.941** 0.833 

Latin America -1.133 0.755 

Eurasia -1.768* 1.036 

Middle East -4.388** 0.834 

Note: Dependent variable is Freedom House. Baseline coefficients correspond to the 

average country in Europe and Oceania. Coefficients for regions indicate the 

distance between the coefficient for the typical country in the region and the Average 

coefficient. ** Significant at p<.05; * Significant at p<.10

Besides the effects of the democracy assistance, Table 7 highlights the considerable

differences in the initial levels of democracy across regions. While the typical country i

Europe and Oceania entered the sample with 8.6 points in the Freedom House scale 

(similar to the average Latin American country), the average country in other regions 

entered the sample at 6.9 in Eurasia, at 6.4 in Africa, at 5.7 in Asia, and at 4.3 in the 

Middle East. The typical country in the sample displayed an upward growth curve (an 

increase of roughly 0.07 Freedom House points per year) except in the case of Eurasia 

(with an average decline of -0.18 points per year). 

n 

38
Figure 1. Impact of USAID DG, Conditional on Region

0.029

0.082

0.038

-0.001

0.012

0.020

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

Baseline

(Europe/Pacific)

Africa

Asia

Latin America

Eurasia

Middle East

Effect of USAID DG on Freedom House Index

Note: Entries indicate the conditional impact of $1 million for USAID DG; dotted lines indicate 95% 

confidence interval (estimates from Table 7). 

Socio-Economic Conditions 

Is democracy assistance more effective in some social contexts than in others? In this 

section we explore the role of domestic structural conditions as mediating variables in the 

process of democracy assistance. In this regard, two hypotheses come to mind. The first 

one is that democracy assistance has greater impact in countries that are better able to 

employ the resources—countries that are wealthier, better educated, socially cohesive, 

and with greater levels of human capital. The second, and opposite one, is that USAID 

DG will have greater impact in countries with a stronger need for development assistance 

—countries that are poorer, socially divided, and suffer from lower levels of human 

capital. We test those hypotheses by treating structural conditions in Table 4 as 

mediating factors. One Level 1 variable (annual per capita GDP growth) and four Level 

2 predictors (ethnoliguistic fractionalization, income inequality, the size of the country, 

and the Human Development Index) have been analyzed in interaction with USAID DG. 

The results presented in Table 8 tend to support the second hypothesis over the first 

one. The marginal effect of a million dollars invested in democracy assistance seems to 

be greater in those countries that are in greater need of external assistance. In this model, 

the mediating effects of economic growth, income inequality, and country size are 

insignificant (the coefficients for the interaction terms are virtually zero and have pvalues ranging from .76 to .98). However, USAID DG assistance makes a greater 

39
contribution in countries that are ethnically divided. For the average country, an 

investment of one million dollars is expected to improve Freedom House scores by .027 

points. In a country one standard deviation above the mean in terms of ethnic 

fractionalization, the same investment is expected to produce an improvement of .046 

points. Figure 2 shows the size of the USAID DG coefficient at different levels of ethnic 

fractionalization.

The coefficient for the interaction between USAID DG and the Human Development 

Index is of borderline significance (p=.108) and it has a negative sign (indicating that aid 

matters less in countries that are wealthier, more educated, and with higher levels of life 

expectancy). This pattern deserves closer examination. Because the effect of democracy 

assistance in Table 8 is conditional on human development, the standard error for the 

overall USAID DG coefficient (i.e., the estimate of uncertainty for coefficient π2i in 

equation 1) is not the standard error for USAID DG alone (i.e., for coefficient Β20 in 

equation 2.2b), but a conditional standard error, determined by the level of human 

development in each case.36 Thus, the confidence interval around the expected impact of 

USAID DG will vary according to the HDI index in each case. This will be the case in 

all of the conditional effects models we examine in this section. 

In order to illustrate these patterns, Figure 2 depicts the size of the (conditional) 

coefficient for USAID DG and the 95 percent confidence interval around this estimate at 

different levels of human development. For the average country in the sample, with an 

HDI of .619, the expected impact of one million in USAID DG investment is .027 (the 

baseline coefficient in Table 8). For the country with the largest observed HDI in the 

sample (Israel, with .857) the expected impact of one million dollars would be barely 

.001 Freedom House points (indeed, Israel has not been a recipient of democracy funds). 

The size of the confidence interval the figure indicates that, above a certain HDI 

threshold, the effect of USAID DG is statistically indistinguishable from zero. Given the 

estimates for this model, this threshold is approximately .71 (roughly the human 

development levels achieved by Brazil or Tuvalu). 

This finding again suggests that democracy assistance has a significant impact in 

those countries in greater socio-economic need. Indeed, in a model including interactions 

of USAID DG with both regional and socio-economic variables, all regional interactions 

became insignificant. This result (the table is not shown to save space) indicates that the 

greater impact of democracy assistance in Africa (reported in Table 7) may be explained 

by the prevailing socio-economic conditions in the region. 

 

36 Following the notation for equation 2.2b, the conditional standard error can be computed as 

[var(Β20)+X2

var(Β2m)+2Xcov(Β20 Β2m)]1⁄2 (see Brambor, Clark, and Golder 2006). 

40
Table 8. The Conditional Effects of USAID DG Assistance: 

Interactions with Socioeconomic Conditions 

Coefficient S. Error 

Level 1 

USAID Democracy assistance 

Baseline coefficient for USAID DG 0.027** 0.006 

Annual Growth in GDP per Capita -0.0001 0.000 

Ethnic Fractionalization 0.078** 0.021 

Income Inequality -2E-005 0.001 

Size in Square Km -3E-008 1.4E-006 

Human Development Index -0.107† 0.066 

Other Assistance

USAID Non-DG 0.000 0.001 

National Endowment for Democracy 0.075 0.171 

US Assistance other than USAID or NED -3E-006 0.0004 

Regional-Subregional DG 0.019 0.123 

Regional-Subregional Non-DG -0.008 0.013 

Other Donor Assistance on DG -2E-005 0.001 

Other Donor Assistance on Non-DG -1E-005 9.0E-005 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.009** 0.003 

US Military Assistance Priority -0.023 0.018 

Democratic Diffusion 0.182** 0.086 

Social and Political Conflict -0.001** 0.000 

State Failure Indicator -0.718** 0.077 

 Iraq 2004 -3.969** 1.100 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 6.783** 0.243 

Prior Democracy 0.284** 0.049 

Income Inequality 0.073** 0.032 

Ethnic Fractionalization -0.827 1.079 

Size in Square Km -0.0001 0.000 

Human Development Index 6.209** 1.839 

Effect on (Level-1) Time Trend 

Average Slope for Growth Curve 0.048** 0.016 

Prior Democracy -0.004 0.003 

Income Inequality -0.0002 0.002 

Ethnic Fractionalization 0.107* 0.063 

Size in Square Km -1E-005* 7.9E-006 

Human Development Index 0.107 0.108 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 5.908** 0.881 

Random Variance (Slope) 0.012** .003 

Autocorrelation (rho) 0.791** 0.021 

Model Deviance/AIC 6456.902 6492.902 

Note: **significant at p<.05; *significant at p<.10 (two tailed) †significant at p .108. 

Dependent variable is Freedom House.

41
Figure 2. Impact of USAID DG, Conditional on Ethnic Fractionalization and HDI 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.00 0.20 0.40 0.60 0.80 1.00

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.00 0.20 0.40 0.60 0.80 1.00

Ethnic Fractionalization Human Development Index 

Note: Lines indicate the size of the USAID DG coefficient (vertical axis) at different levels of the 

intervening variables (horizontal axes). Dotted lines indicate 95% confidence intervals (estimates from 

Table 8).

Domestic Political Conditions 

Can a similar argument be made with regard to local political conditions? Is 

democracy assistance more effective in some political contexts than others? In Table 9, 

we analyze the conditional effect of USAID DG according to two time-varying covariates 

(Banks’ index of social and political conflict and the indicator for whether or not the 

country experienced State Failure that year), and one country-level predictor (the prior 

“stock” of democracy, or the number of years the country was rated “free” between 1972 

and 1989). The state failure dummy captures extreme forms of political instability 

(ethnic wars, revolutionary wars, genocide, abrupt regime breakdowns) in any given year. 

The baseline coefficient for USAID DG in this model is marginally significant at the .1 

level. None of the interaction terms are significant, indicating that the effect of USAID 

DG is not significantly different from the baseline coefficient when the mediating 

variables increase by one unit. However, the direction of the coefficients hints at larger 

USAID DG effects under worse political conditions (state failure, less democratic 

experience), and the estimate for the interaction between state failure and USAID DG is 

close to the .1 level (p=.129). For these reasons, we analyzed the conditional effects in 

more detail. 

42
Table 9. The Conditional Effects of USAID DG Assistance: 

Interactions with Domestic Political Conditions 

Coefficient S. Error 

Level 1 

USAID Democracy assistance 

Baseline Coefficient for USAID DG 0.015* 0.009 

Social and Political Conflict 4.4E-005 4.3E-005 

State Failure Indicator 0.013† 0.009 

Prior Democracy -0.002 0.002 

Other Assistance 

USAID Non-DG 0.000 0.001 

National Endowment for Democracy 0.022 0.169 

US Assistance other than USAID or NED -0.000 0.000 

Regional-Subregional DG -0.011 0.122 

Regional-Subregional Non-DG -0.008 0.013 

Other Donor Assistance on DG 8.2E-005 0.001 

Other Donor Assistance on Non-DG -7E-006 0.000 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.009** 0.002 

US Military Assistance Priority -0.025 0.018 

Democratic Diffusion 0.179** 0.086 

Social and Political Conflict -0.001** 0.000 

State Failure Indicator -0.757** 0.077 

 Iraq 2004 -6.616** 1.872 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 6.752** 0.243 

Prior Democracy 0.283** 0.049 

Income Inequality 0.073** 0.032 

Ethnic Fractionalization -0.932 1.079 

Size in Square Km -0.000 0.000 

Human Development Index 6.451** 1.839 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 0.052** 0.016 

Prior Democracy -0.004 0.003 

Income Inequality -0.0002 0.002 

Ethnic Fractionalization 0.124* 0.064 

Size in Square Km -1E-005 7.9E-006 

Human Development Index 0.101 0.109 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 5.783** 0.877 

Random Variance (Slope) 0.012** 0.003 

Autocorrelation (rho) 0.798** 0.020 

Model Deviance/AIC 6442.90 6478.90 

Note: **significant at p<.05; *significant at p<.10 (two tailed) † significant at p .129. Dependent 

variable is Freedom House. 

 

43
Figure 3. Coefficient for USAID DG, 

Conditional on State Failure and Prior Democracy 

Figure 3 depicts the conditional coefficients for USAID DG at different levels of 

State Failure and Prior Democracy. In Table 9, the baseline coefficient for USAID DG 

is marginally significant at the .1 level for non-failed states (variable POL25=0) and for 

countries with an average democratic experience (rated “free” for about 3 years between 

1972 and 1989, in the sample of eligible countries). When we observe extreme political 

instability, the USAID DG coefficient grows from .015 to .028, and it becomes 

statistically significant at the .05 level. Given the size of the confidence interval 

(depicted in the graph), this difference of .013 Freedom House points is not enough to 

establish that the effect of democracy assistance is substantively different under 

conditions of state failure (thus, the interaction term in Table 9 is insignificant), but this 

difference is enough to reduce the statistical uncertainty for the estimate. In other words, 

we can be more confident that democracy assistance makes a contribution (whatever its 

size) under conditions of state failure. Although this may be surprising, given the 

uncertain conditions that prevail in failed states, the analysis of prior democracy tends to 

support this insight. The effects of USAID DG are more consistent (and thus significant 

at the .05 level) for countries with no prior democratic experience than for those with 

longer democratic traditions. As in the previous section, the analysis of conditional 

coefficients suggest that countries with a greater “need” for democracy assistance are the 

ones in which such investment is more effective. 

.028

.015

-.005

.000

.005

.010

.015

.020

.025

.030

.035

.040

.045

0 1 -.005

.000

.005

.010

.015

.020

.025

.030

.035

.040

.045

0 1 2 3 4 5 6 7

State Failure (PITF dichotomous, 0 is stability) Years Rated Free (Freedom House) 

 Notes: Dotted lines indicate 95% confidence intervals (estimates from Table 9).

44
Given the results in Table 9, we also explored the role of prior democracy by looking 

at the Freedom House trichotomous classification (Free, Partially Free, and Not Free) 

during the year prior to the year under study. We created two dummy variables (one for 

Free and another for Not Free observations) and computed interactions between those 

variables and USAID DG. The reference category in this model corresponded to 

Partially Free cases. The results (not reported here to save space), indicated that 

investment in Partially Free countries makes a significant contribution to 

democratization, and that the effect of investment in Free or Not-Free countries does not 

differ from this estimated baseline impact. Thus the immediately prior level of 

democracy does not condition the effect of USAID DG assistance; what appear to matter 

are extreme conditions of state failure, and the lack of historical experience of the country 

with democratic government. 

International Factors 

In Table 10, we explore the role of two time-varying covariates, democratic diffusion 

and U.S. foreign security priorities, as international or “external” factors mediating the 

impact of democracy assistance. As described above, Democratic Diffusion is measured 

for each country using a weighted average of democracy in the world during the previous 

year, excluding the country in question, and weighting every other country’s democracy 

score according to the distance from the country’s capital city. Priority given by U.S. 

foreign policy to security concerns for each country is measured using the percentage of 

total U.S. security assistance (military and anti-narcotics aid) received by a particular 

country in any given year. Both interactions in Table 10 are significant and negatively 

signed. The democratic diffusion interaction indicates that, when a country’s external 

environment is already more democratic, USAID DG is less effective. This finding 

supports other results presented above indicating that democracy assistance makes a 

greater contribution in more difficult contexts, but we note that it should be interpreted 

cautiously, as the coefficient for diffusion is not significant in the full multivariate model 

that we present below. 

The coefficient for the interaction between U.S. Military Assistance Priority and 

democracy assistance is also negative, suggesting that democracy programs are less 

effective when the U.S. provides larger amounts of military assistance. This effect, 

moreover, appears to explain fully the “Iraq Effect” that we detected earlier in Table 3. 

Because Iraq represented a foreign policy priority mainly for security reasons in 2004 

(e.g., it received 23 percent of all security assistance in 2004, vis-à-vis 0.6 percent for the 

average eligible country) and it was also the largest recipient of democracy assistance (31 

percent of all USAID DG funds spent in 2004), the overall impact of USAID DG was 

depressed when compared to a model including data for 1990-2003. 

45
N

Fr

Table 10. The Conditional Effects of USAID DG Assistance: 

Interactions with External Factors 

Coefficient S. Error 

Level 1 

USAID Democracy assistance 

Baseline coefficient for USAID DG 0.036** 0.006 

US Military Assistance Priority -0.001** 0.000 

Democratic Diffusion -0.010* 0.005 

Other Assistance

USAID Non-DG 0.000 0.001

National Endowment for Democracy 0.016 0.169

US Assistance other than USAID or NED -0.001 0.000

Regional-Subregional DG -0.013 0.122

Regional-Subregional Non-DG -0.009 0.013

Other Donor Assistance on DG 0.000 0.000

Other Donor Assistance on Non-DG 1.1E-007 9.0E-005 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.009** 0.002 

US Military Assistance Priority -0.015 0.018

Democratic Diffusion 0.180** 0.086 

Social and Political Conflict -0.001** 0.000 

State Failure Indicator -0.731** 0.077 

 Iraq 2004 0.164 2.548

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 6.788** 0.242 

Prior Democracy 0.285** 0.048 

Income Inequality 0.073** 0.031 

Ethnic Fractionalization -0.979 1.074

Size in Square Km -0.000 0.000

Human Development Index 6.347** 1.831 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 0.050** 0.016 

Prior Democracy -0.004 0.003

Income Inequality -0.000 0.002

Ethnic Fractionalization 0.125* 0.064 

Size in Square Km -1E-005 8.0E-006 

Human Development Index 0.111 0.110

Model Statistics and Variance Parameters 

Random Variance (Intercept) 5.731** 0.874

Random Variance (Slope) 0.013** 0.003

Autocorrelation (rho) 0.797** 0.020

Model Deviance/AIC 6427.196 6463.196 

ote: **significant at p<.05; *significant at p<.10 (two tailed). Dependent variable is 

eedom House. 

46
In fact, once we allow the effect of USAID DG to be conditional on the U.S. Military 

Assistance Priority variable, the Iraq 2004 dummy loses its statistical significance, 

indicating that the “Iraq effect” is in fact an extreme manifestation of a more general 

pattern by which democracy assistance is less powerful when the overall policy towards 

the recipient country is driven by security concerns (Bueno de Mesquita and Downs 

2006). We note that none of the other interaction models presented thus far have the 

same impact on the Iraq coefficient; that is, the Iraq 2004 variable is still significant after 

including all other interactions except the military assistance variable. 

It is worth noting that the U.S. Military Assistance Priority variable does not 

distinguish between different forms of military assistance (e.g., training vs. equipment). 

It also does not measure U.S. military intervention per se, but rather reflects overall U.S. 

security or geo-strategic concerns in the bilateral relation with a given country. The 

evidence suggests that, to the extent that USAID democracy assistance is provided in 

settings where U.S. geo-strategic concerns constitute a priority for bilateral relations, the 

effectiveness of democracy programs will decline. 

In order to verify the previous findings, we estimated a model including all interactive 

terms that were significant in Tables 8, 9, and 10 (plus the few terms that approached the 

.1 level of significance in those models). This time we excluded the Iraq 2004 dummy, 

because this case is subsumed into the more general pattern captured by the security 

priority variable. The results, presented in Table 11, are consistent with the overall 

findings described above. The impact of USAID DG is greater when countries: 

• are ethnically divided, 

• have lower levels of human development, 

• face major instances of political instability, and 

• are not recipients of large amounts of U.S. military assistance. 

The interaction with the diffusion variable, as noted above, is now insignificant in the 

full model, (p=.704), indicating that we cannot reject the hypothesis that USAID DG 

works equally, regardless of the democratic nature of the country’s neighborhood.37 

Figure 4 plots these conditional coefficients for the four significant interaction 

effects. The conditional effect of foreign security assistance (depicted in the lower-right) 

is noteworthy. Because 95 percent of the countries in the sample individually receive 

less that 1 percent of the security assistance disbursed in any year, any small deviation 

from the variable’s mean (0.6 percent) tends to reflect a U.S. security priority and 

therefore affects the significance of the USAID DG coefficient. The graph indicates that 

USAID DG is insignificant (at the .05 level) for every recipient of more than 1.1 percent 

of the total security assistance in any year. This group of countries, however, represents 

only 4.3 percent of the cases in our sample. It includes countries such as Afghanistan, 

Colombia, Egypt, Iraq, Jordan, Turkey, and Pakistan. 

 

37 The results here confirm the earlier finding that diffusion has a positive impact on FH scores; the analysis 

here says only that the effect of DG assistance does not depend on the recipient country’s neighborhood. 

47
Table 11. The Conditional Effects of USAID DG Assistance: 

Multivariate Final Model 

Coefficient S. Error 

Level 1 

USAID Democracy Assistance 

Baseline coefficient for USAID DG 0.018** 0.009 

Ethnic Fractionalization 0.067** 0.022 

Human Development Index -0.124** 0.060 

State Failure Indicator 0.024** 0.010 

Military Assistance -0.001** 0.000 

Democratic Diffusion 0.002 0.007 

Other Assistance 

USAID Non-DG -7E-005 0.001 

National Endowment for Democracy 0.035 0.170 

US Assistance other than USAID or NED -0.000 0.000 

Regional-Subregional DG 0.017 0.122 

Regional-Subregional Non-DG -0.007 0.013 

Other Donor Assistance on DG 3.5E-007 0.001 

Other Donor Assistance on Non-DG 9.3E-007 9.0E-005 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.009** 0.002 

US Military Assistance Priority -0.014 0.018 

Democratic Diffusion 0.187** 0.086 

Social and Political Conflict -0.001** 0.000 

State Failure Indicator -0.734** 0.077 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 6.780** 0.243 

Prior Democracy 0.285** 0.049 

Income Inequality 0.071** 0.032 

Ethnic Fractionalization -0.872 1.082 

Size in Square Km -0.000 0.000 

Human Development Index 6.083** 1.841 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 0.049** 0.016 

Prior Democracy -0.005 0.003 

Income Inequality -0.000 0.002 

Ethnic Fractionalization 0.110* 0.063 

Size in Square Km -1E-005* 7.9E-006 

Human Development Index 0.114 0.108 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 5.917** 0.883 

Random Variance (Slope) 0.012** 0.003 

Autocorrelation (rho) 0.792** 0.021 

Model Deviance/AIC 6432.312 6468.312 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Dependent variable is Freedom 

House. 

48
Figure 4. Conditional Coefficient for USAID DG 

-.010

.000

.010

.020

.030

.040

.050

.060

.070

0.00 0.20 0.40 0.60 0.80 1.00

Ethnolinguistic Fractionalization 

-.010

.000

.010

.020

.030

.040

.050

.060

.070

0.00 0.20 0.40 0.60 0.80 1.00

Human Development Index 

.018

.042

-.010

.000

.010

.020

.030

.040

.050

.060

.070

0

State Failure (dichotomous, 0 is political stability) 

1

-.010

.000

.010

.020

.030

.040

.050

.060

.070

0.0 1.0 2.0 3.0 4.0 5.0

U.S. Military Assistance Priority (% of yearly total) 

Notes: Lines indicate the size of the USAID DG coefficient (the vertical axis) at different levels of the 

intervening variables (horizontal axis). All other intervening variables held at their means (or 0 for State 

Failure). Dotted lines indicate 95% confidence intervals (based on Table 11).

Investment Strategies 

It is reasonable to assume that USAID’s investment strategy towards each country 

may also shape the impact of democracy assistance. Levels of commitment, consistency 

in investment patterns, the composition of the USAID DG portfolio, and other aspects of 

USAID work may vary across recipient countries, with different implications for 

democracy assistance. This is one of the areas in which the growth curve models may 

49
prove more useful, yet it is also one in which prior knowledge to guide our hypotheses is 

less well developed. 

In order to assess the impact of volatility on USAID DG effects, we introduced Level 

2 volatility and trend variables as mediators for the impact of democracy assistance. As 

explained in the first section of the report, the volatility variable captures the degree of 

fluctuation in USAID investment over the period 1990-2004. This variable reflects the 

extent to which yearly funding has departed from the level that would be expected given 

the observed time trend and the level of funding during the previous year. The trend

variable, in turn, reflects the evolution of investment over time (positive values indicate a 

pattern of growing investment). Our expectations are, following Lensink and 

Morrissey’s (2000) work on foreign assistance, that more overall volatility in investment 

will decrease the impact of investment at any given point in time, and the countries with 

consistently increasing USAID DG allocations will show stronger effects from that 

investment than countries with consistently decreasing investment. 

Table 12 presents the results of two models using Freedom House as the dependent 

variable. For the purpose of this analysis, we have worked with the sub-sample of 

recipient countries (N=1,805) rather than the sample of eligible countries (N=2,416). 

That is, we have treated the values for the volatility and trend variables as missing for 

countries that never received USAID DG assistance.38 Model 12a reproduces the baseline 

specification presented in Table 4 (including the Iraq dummy, but dropping the four 

predictors with insignificant coefficients in Table 4). Model 12b reflects the revised 

specification with multiple interaction terms (and eliminating the Iraq dummy) just 

presented in Table 11. 

In both models, the coefficient for the baseline USAID DG effect (i.e., when 

assistance is at its overall average value) is similar in magnitude and significant at the .05 

level. In Model 12a, the interaction with the trend variable is significant at the .1 level, 

suggesting that democracy assistance has a greater impact when investment in the 

recipient country expands over time. This effect, however, is insignificant in Model 12b, 

once we control for other mediating factors (p=.984). The mediating effect of volatility is 

negative in both models, but it fails to achieve conventional levels of significance. 

However, the effect is close to the .1 level (p=.118) in Model 12a; because of this reason 

we analyzed the behavior of the conditional coefficient more carefully. 

 

38 We estimated alternative models including non-recipient countries assuming values of zero in the 

volatility and trend variables, and the results were consistent. Restricting an analysis of funding strategies 

to countries that actually received funds, in our view, makes greater theoretical sense. 

50
Table 12. The Conditional Effect of USAID DG Assistance: 

Interactions with Aid Volatility and Trend 

12(a) – With Iraq 12(b) – Without Iraq & 

Multivariate 

Level 1 

USAID Democracy Assistance 

Baseline coefficient for USAID DG 0.042** 0.015 0.040** 0.018 

Trend in DG Aid, 1990-2004 0.043* 0.025 0.001 0.026 

Volatility in DG Aid, 1990-2004 -0.047 0.030 -0.042 0.033 

Ethnic Fractionalization --- --- 0.054** 0.024 

Human Development Index --- --- -0.133** 0.065 

State Failure Indicator --- --- 0.030** 0.011 

Military Assistance --- --- -0.001** 0.000 

Democratic Diffusion --- --- -0.002 0.007 

Other Assistance 

USAID Non-DG 0.001 0.001 0.000 0.001 

National Endowment for Democracy 0.071 0.180 -0.006 0.183 

US Assistance other than USAID or NED -0.000 0.000 -0.000 0.000 

Regional-Subregional DG 0.035 0.143 0.041 0.143 

Regional-Subregional Non-DG -0.011 0.014 -0.009 0.014 

Other Donor Assistance on DG -0.000 0.001 -0.000 0.001 

Other Donor Assistance on Non DG -1E-005 9.5E-005 4.5E-006 9.5E-005 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.009** 0.003 0.008** 0.003 

US Military Assistance Priority -0.029 0.023 -0.020 0.025 

Democratic Diffusion 0.196** 0.099 0.215** 0.099 

Social and Political Conflict -0.000 0.000 -0.000 0.000 

State Failure Indicator -0.758** 0.086 -0.771** 0.087 

Iraq 2004 -4.867** 1.311 --- --- 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 7.629** 0.407 7.613** 0.410 

Prior Democracy 0.172** 0.059 0.169** 0.060 

Income Inequality 0.071** 0.032 0.068** 0.033 

Ethnic Fractionalization 0.573 1.180 0.595 1.192 

Size in Square Km -8E-005 0.000 -8E-005 0.000 

Human Development Index 6.121** 1.931 5.679** 1.946 

Trend in DG Aid, 1990-2004 -3.813** 1.203 -3.766** 1.212 

Volatility in DG Aid, 1990-2004 -1.104** 0.424 -1.035** 0.428 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 0.057* 0.031 0.058* 0.030 

Prior Democracy -0.006 0.004 -0.006 0.004 

Income Inequality -0.001 0.002 -0.001 0.002 

Ethnic Fractionalization 0.123 0.085 0.113 0.084 

Size in Square Km -1E-005 9.2E-006 -1E-005 9.1E-006 

Human Development Index 0.032 0.139 0.057 0.138 

Trend in DG Aid, 1990-2004 -0.082 0.088 -0.088 0.087 

Volatility in DG Aid, 1990-2004 -0.007 0.030 -0.011 0.029 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 3.622** 0.957 3.817** 0.958 

Random Variance (Slope) 0.016** 0.004 0.015** 0.004 

Autocorrelation (rho) 0.819** 0.023 0.816** 0.023 

Model Deviance/AIC 5004.710 5040.710 5028.031 5064.031 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Dependent variable is Freedom House. 

51
Figure 5. Coefficient for USAID DG, Conditional on Volatility of the Investment 

Figure 5 presents the coefficients for USAID DG conditional on the volatility of the 

investment. In Model 12a, the coefficient for USAID DG becomes insignificant above a 

level of volatility equivalent to 0.51. In Model 12b the outcome is similar: the coefficient 

for democracy assistance fails to meet the .05 level of significance above 0.43 points of 

volatility. To provide a substantive interpretation of this result, consider that a value of 

1.0 in the volatility scale would roughly indicate that the average yearly fluctuation in 

USAID DG (above and beyond any changes explained by an ongoing investment trend in 

the country) is as large as the average level of investment during the period. For instance, 

if the average democracy assistance per year is $10 million, a volatility score of 1.0 

would indicate that the expected fluctuation from year to year, once we control for any 

regular trend, would be $10 million. A volatility score of 0.5 would indicate an expected 

fluctuation of $5 million per year (i.e., average investment could be cut by half, or 

increased by 50 percent next year). About 52 percent of the recipient countries in the 

sample registered levels of USAID DG volatility above 0.5 points. This means that in 

about half of the recipient countries the level of volatility in democracy investment may 

be high enough to compromise its impact. 

 

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.00 0.20 0.40 0.60 0.80 1.00

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.00 0.20 0.40 0.60 0.80 1.00

Model 12a Model 12b 

Notes: Lines indicate the size of the USAID DG coefficient (the vertical axis) at different levels of 

volatility (the horizontal axis). Dotted lines indicate 95% confidence intervals.

The remaining interaction terms in Model 12b are consistent with the other findings 

in Table 11. The results presented throughout this section of the report indicate that 

USAID DG investment is more reliable when countries “need” it more (when they are 

less developed, ethnically divided, or undergoing major forms of political instability), 

when U.S. military assistance is low, and when assistance is deployed consistently and 

without major fluctuations. 

52
Part III – Analysis of Sub-Sectors 

In this part of the report we analyze the contribution of particular sub-sectors of 

USAID DG assistance (Elections, Civil Society, and Governance) and sub-sub-sectors 

(Human Rights, Free Media) to specific aspects of democracy. As explained in the 

introduction of the report, we have created composite measures to capture aspects of 

democratic performance targeted by USAID programs: the presence of free and fair 

elections, respect for human rights, conditions for the emergence of a vibrant civil 

society, independent media outlets, and efficient and transparent governance. 

There were several criticisms of our previous work on this subject by the expert 

panel, notably the inclusion of one of the two overall Freedom House democracy 

dimensions in our measure of Free Elections, and the lack of a good overall measure of 

governance. As noted earlier, we have undertaken a considerable review of the sub-and 

sub-subsectoral outcomes and changed the measures in several instances.39 

We show the results of the baseline hierarchical growth models for each sub- and subsubsectoral outcome in Table 13 below. The models, akin to the one presented in Table 

4a, include the Iraq 2004 dummy variable, but do not include lagged USAID DG 

variables. The results show that, for the models estimated on identical or virtually 

identical sub- or sub-sub-sectoral outcomes in the previous study–civil society, free 

media, and human rights—the addition of the 2004 data (and the Iraq 2004 dummy 

variable) leads to findings that are very similar to our original results. As in the previous 

study, Elections spending has a significant impact (.092) on the sub-sectoral outcome 

related to Elections, with some additional impact of the governance spending (.060). In 

the civil society model, civil society assistance has a significant effect of similar 

magnitude (.292) as in the previous study, with governance outlays also showing a 

significant impact of .098. The free media model shows a strong impact of USAID DG 

media outlays (.573), with an additional impact (.151) of non-media Civil Society 

spending. And the Human Rights model shows a similar pattern as in the previous study, 

with Human Rights spending having a strong negative impact (-.664) and elections 

spending having a relatively weaker positive impact (.152). We analyze the negative 

coefficient for the human rights sub-subsector in the following section. 

 

39 In the revised index of Free and Fair Elections, we have eliminated the Freedom House measure of 

political rights (to avoid overlaps with the general measure of democracy), and incorporated the World 

Bank’s indicator of Electoral Competitiveness in Legislative Elections (from the Database on Political 

Institutions) and ICRG’s index of Democratic Accountability. In the revised Civil Society index, we 

eliminated Green’s Index of Civil Society to reduce the amount of imputation (the Green index only covers 

Eastern Europe and the former Soviet Union). 

53
Table 13. Summary of Effects from Sub-Sector and Sub-Sub-Sector Analyses 

Sub-Sector and Sub-Sub-Sector 13.1 13.2 13.3 13.4 13.5 

Democracy Assistance 

Elections 

 Elections 

0.092* 

Human Rights 

0.152* 

Civil Society 

n.s. 

Free Media 

0.185** 

Governance 

n.s. 

Rule of Law (all sub-sub-sectors) 

-Human Rights 

-Non Human Rights 

Civil Society (all sub-sub-sectors) 

-Media 

n.s. 

-- 

-- 

n.s. 

-- 

-- 

-0.664** 

n.s. 

n.s. 

--- 

n.s. 

-- 

-- 

0.292** 

-- 

n.s. 

-- 

-- 

-- 

0.573** 

n.s. 

-- 

-- 

n.s. 

---

-Non-Media -- --- -- 0.151** ---

Governance 0.060** n.s. 0.098* n.s. 0.070**

 Note: Entries are coefficients for sub-sectoral and sub-sub-sectoral USAID DG variables in models using 

specific democracy dimensions as the dependent variables. General specification is otherwise equivalent to 

trimmed baseline model described in the text. Full results shown in Appendix 6. 

** Significant at p<.05; * Significant at p<.10 (two-tailed); n.s. Not significant at the .10 level. 

For the new outcome indicator, the results are also positive. Governance spending is 

the only type of assistance that impacts the new Governance dimension, though the effect 

is relatively small in substantive magnitude. The size of the coefficient indicates that 

increasing USAID governance funding by $10 million in a given year would raise the 

governance score by about 7/10 of a point on the 100-point scale of efficient governance 

and transparency. 

Are sub-sectoral outcomes driven by the absolute level of investment in an area, or by 

the relative priority placed on some issues versus others? Our next set of models 

addresses this question (Table 14). We include in the sub-sectoral models a new Level 1 

variable: the percentage of the total USAID DG portfolio invested in the particular subsector in any given year. This variable is intended to capture whether the effect of subsectoral funds results from the total level of funding, reflected by the USAID variables, or 

by the prioritization of any particular sub-sector, reflected by the portfolio indicators. In 

other terms, is it more effective to invest more in a particular sub-sector, or to expand the 

relative weight of the sub-sector in the overall portfolio? The results presented in Table 

14, indicate that, while the level of sub-sectoral investment affects particular dimensions 

of democracy (consistent with the patterns shown in Table 13), the relative emphasis 

given to particular sub-sectors does not have a direct impact on democratic outcomes. 

54
Sub-Sector Democracy Assistance 14.1 14.2 14.3 14.4 14.5 

 Elections Human Rights Civil Society Free Media Governance 

Elections 

Elections n.s. 0.155* n.s. 0.186** n.s. 

Percentage invested in Elections n.s. --- --- --- --- 

Rule of Law 

Rule of Law (all sub-sub-sectors) n.s. --- n.s. n.s. n.s. 

-Human Rights ---- -0.747** --- --- --- 

-Non Human Rights ---- n.s. --- --- --- 

-Percentage invested in Human Rights ---- n.s. --- --- --- 

Civil Society 

Civil Society (all sub-sub-sectors) n.s. n.s. 0.296** --- n.s. 

Percentage invested in Civil Society --- --- n.s. --- --- 

-Media --- --- --- 0.559** --- 

-Non Media --- --- --- 0.152** --- 

-Percentage invested in Free Media --- --- --- n.s. --- 

Governance 

Governance n.s. n.s. 0.157** n.s. 0.081** 

Percentage invested in Governance --- --- --- --- n.s. 

** Significant at p<.05; * Significant at p<.10 (two-tailed); n.s. Not significant at the .10 level. 

Table 14. Sub-Sectoral Effects According to Patterns of Investment 

Understanding the Impact of Human Rights Assistance 

As we have noted in this and previous reports, the most unanticipated result in the 

extensive analyses of the initial study of the effects of U.S. foreign assistance on 

democracy building in the period 1990-2003 is the negative impact of receiving rule of 

law funding directed at the improvement of human rights on the performance of nations 

in protecting or abusing the personal integrity rights of their populations. The finding 

persists through the models reported above. 

Our plan for investigating this anomalous and troubling finding called for an 

investigation of alternative hypotheses that might demonstrate the spuriousness of the 

anomalous relationship between democracy assistance in the human rights area and 

human integrity abuse: 

1. reexamining the "reverse causality" explanation 

2. reexamining the "measurement error" explanation 

3. investigating the effects of potential omitted independent variables 

4. exploring theory and analysis that might explain a "genuine relationship" 

We examined each of these alternative hypotheses carefully. A full report on our 

investigations is given in Appendix 7. The full statistical model for that investigation is 

given in Table A7.1 in that appendix. To avoid distraction from the principal thrust of 

our work in this report, we only summarize our procedures, analyses, and conclusions 

here. 

55
To test the reverse causality explanation -- that the relationship between 

development assistance devoted to improving human rights and respect for human 

integrity occurs because assistance is directed to countries with problematic records on 

respect for personal integrity precisely because of their suspect records, we estimated 

endogeneity models similar to those in Table 6 above. We found little change in the 

estimated negative impact of USAID DG human rights assistance on our measure of 

countries’ respect for human integrity. However, the models do not pass the relevant 

tests for the exogeneity of the instrument set, and, thus, we can't be certain that we have 

good instruments to serve as proxies for the HR AID allocation. So the possibility of 

endogeneity in the process remains, even though all of our efforts to control for this have 

yielded essentially similar results as presented previously 

The measurement or reporting error explanation contends that "more democracy 

assistance in the human rights area leads to higher levels of revealed human rights 

abuses, but not necessarily higher levels of actual abuse." If this explanation were true, it 

could mean that Rule of Law - Human Rights and other USAID DG assistance were 

achieving an important goal, increasing sensitivity to and reporting of human rights 

abuses. In the short run, such success would manifest itself in apparent -- but not real -- 

increase in abuses. We used existing data on press freedom and found interpolated 

annual data on the within country presence of international governmental and 

nongovernmental associations (IGOs/NGOs) to serve as proxies for increased reporting 

of human rights abuses. 

The omitted variables explanation suggests that the anomalous relationship between 

human rights assistance and human integrity abuse occurs because our models omit 

variables that are key to interpreting or ameliorating the relationship. After reviewing the 

human rights literature, we posited indicators of constitutional structure, including both 

formal and actual judicial independence, as omitted variables that were worthy of 

exploration. We generated cross-national time series data on these omitted variables and 

tested their effects in the model that is developed in Appendix 7. 

The genuine relationship explanation posits that leaders who find themselves under 

pressure to improve their human rights performance actually respond by becoming more 

repressive because they feel their grip on power to be threatened. Our analysis plan 

confronted this explanation more directly by including into our full statistical models of 

human integrity performance empirical indicators of events or circumstances that would 

be perceived as threatening by potentially repressive leaders. Specifically, we collected 

and analyzed comprehensive data on four threat indicators: Organized Nonviolent 

Protest, Organized Nonviolent Rebellion, Organized Violent Rebellion, and Civil War. 

Our analysis explores whether these indicators—taken individually or collectively—

serving as proxies for leaders’ perceptions that cause them to abuse rights more as they 

perceive themselves to be more threatened. 

In discussing these four interpretations of the negative relationship between human 

rights assistance and human rights protection, it is important to remember that they are 

not mutually exclusive. Indeed, all four could be partial explanations for the relationship. 

56
57

The statistical model for our investigations is presented in Table A7.1 in Appendix 7. 

It shows some encouraging and interesting results. 

• Freedom of Press contributes to respect for human rights, an encouraging 

finding, but not one that supports the reporting error hypothesis. 

• Increases in IGO/NGO strength does appear to be associated with increased 

human rights abuse, thus providing some support for the reporting error 

hypothesis. 

• None of the formal measures of constitutional provisions intended to 

protect/promote human rights or judicial independence showed any 

relationship to respect for human rights, but 

• Actual judicial independence was strongly associated with greater respect for 

human rights – a most encouraging finding. 

• Increased activity that would appear threatening to political leaders were 

strongly negatively associated with respect for human rights, thus supporting 

the proposition that at least some human rights abuse may be the result of 

perceptions of threat by political leaders. 

Unfortunately for our analytical purposes, the negative relationship between Rule of 

Law - Human Rights assistance and respect for human rights persisted, despite our best 

efforts to eradicate it by testing alternative hypotheses asserting that it may be due to 

reverse causation, measurement or reporting error, the influence of important omitted 

variables, or the real reaction of leaders to perceived threat resulting from increased 

protest or rebellion activities. Our effort to untangle the web of relationships that may 

underlie this distressing and presumptively anomalous relationships and to model them 

statistically has been largely unsuccessful in its basic purpose. 
Part IV – Political Culture 

One of the weaknesses of the first phase of the project was that we did not attempt to 

include controls for political culture in the analysis, nor investigate whether culture had a 

conditioning effect on USAID DG assistance similar to the effects we have shown for 

ethnic fractionalization, human development, and other variables in Section III above. 

This absence was not for lack of interest in this subject, since all three of the authors of 

the first study have written extensively on political attitudes and their relationship to 

democratic development. Rather, the team recognized early on that there was no easy 

way to tap into political culture variables for the whole list of “potential recipient” 

countries. 

We spent considerable effort in searching for usable and reliable cross-national public 

opinion data on culture-related variables. After reviewing both the literature on political 

culture and the major surveys that have been conducted outside of ineligible countries, 

we drew up a list of variables for which we could find the largest amount of relevant data. 

These variables include: (1) Interpersonal trust; (2) Support for democracy as a form of 

government; (3) Institutional trust (government, parliament, justice system); (4) 

Satisfaction with democracy; (5) Happiness; (6) Life satisfaction; (7) Interest in politics; 

and (8) Nationalism. We then searched all data available from the most prominent crossnational data bases, including the World Values Survey (all four rounds), the 

AfroBarometer (the first two rounds and the third, as available), the Asian Barometer, 

and the AmericasBarometer (and other data bases from LAPOP, the Latin American 

Public Opinion Project at Vanderbilt Univesity).40 

This search indicated that, of the 165 eligible countries included in our study, public 

opinion data on the above items exists for approximately 60 to 80 countries (depending 

on the specific measure). That is, there are missing values for 50 to 64 percent of the 

countries in our study. Table 16 below reports the number of countries in each region for 

which there is available data on every specific item. 

The large amount of missing data made it impossible to include political culture as a 

general control variable in earlier parts of the study, as we would have had to “impute” 

values for far too many countries than we believed was defensible. We thus decided to 

restrict the culture analyses to those countries where reasonable estimates of political 

culture could be constructed, and to present the findings in a separate section to 

demarcate these more restricted analyses from the other portions of the report. 

 

40 In the exploratory phase, we also analyzed the data available through the New Democracies Barometer, 

the International Social Survey Program (rounds 1995-2004), the EuroBarometer, the European Values 

Survey, and the Korea Barometer. 

58
Table 16. Number of Countries for Which There is Public Opinion Data, by Region 

Region (Total number 

l ust 

emocracy 

tion D mocracy e

li ment 

 Gove ment rn

 e 

 Pol cs iti

of countries in the 

Interpersona Tr

 D

 Justic

i

region) 

Support forSatisfac

in Pa a Trust r

Trust inTrust inHappiness Life Satisfact onInterest in

lism Nationa

Africa (of 48) 11 5 16 16 16 16 5 5 16 10 

Asia (of 25) 13 10 12 12 12 9 11 11 13 11 

Eurasia (of 12) 8 8 8 8 8 7 8 8 8 8 

Europe (of 18) 17 17 17 17 15 17 17 17 17 17 

Latin America (of 31) 10 20 19 21 20 21 10 20 21 19 

Middle East (of 19) 8 6 5 5 6 1 8 8 8 8 

Oceania (of 12) 0 0 0 0 0 0 0 0 0 0 

 TOTAL 67 66 77 79 77 71 59 69 83 73 

Further, the paucity of culture data over time for the same country on the same 

indicators means that we cannot treat culture as a “Level 1” time-varying factor. That is, 

culture here is treated as a set of stable country characteristics, with individual items 

being measured by aggregating as many responses to survey questions on a given 

dimension as was possible to obtain during the time period covered by the study (1990-

2004). This strategy means that we cannot examine how a country’s overall democratic 

political culture may have changed over time, nor how such changes may have been 

linked to USAID DG assistance. Still, most analysts of political culture treat the concept 

as a relatively enduring country-level characteristic, and we follow this approach, to some 

degree for theoretical reasons, and to some degree due to data necessity. 

Our strategy was to construct a small number of Level 2 culture variables that would 

provide information about different aspects of key aspects of political culture on as many 

countries as possible. We then included these Level 2 variables in statistical models for 

these countries in exactly the same way as we have treated other country-level 

characteristics in the analyses thus far: as possible predictors of a country’s level and 

trajectory on Freedom House scores, and as possible conditioning factors that influence 

the size of the USAID DG coefficients. Our working hypothesis is that countries with 

cultures that promote trust and social engagement are ones in which democracy 

assistance will have a stronger impact. Conversely, in countries with less trusting and 

engaged political cultures, the impact of democracy assistance will be attenuated. 

We proceeded by first attempting to determine whether there were distinct 

dimensions of political culture for this set of variables that could be identified via factor 

analysis. These analyses would then allow us to create scales from the multiple items 

that would load on the given factors; these scales would provide more reliable 

information about the dimensions of culture than would any single indicator, and would 

also allow us to reduce the number of variables in our analysis to a more manageable 

59
level. The results of the factor analyses for all 10 items are shown in Appendix 3, and 

indicate that three distinct dimensions emerged: 

• An Institutional Trust dimension, comprising “Trust in Government,” “Trust in 

Parliament,” and “Trust in the Judicial System”; 

• A Personal Satisfaction dimension, comprising “Satisfaction with Democracy,” 

“Life Satisfaction,” and “Personal Happiness”; and 

• A Social Engagement dimension, comprising “Social Trust,” “Political Interest” 

and reversed levels of “National Pride” (i.e. less nationalist orientations). 

We then constructed scales for each of these dimensions by taking, for each country, 

the within dimension average of the non-missing indicators. That is, if a country had no 

missing data on the three Satisfaction indicators, the overall dimension score would be 

the average of the three individual scores. If the country had missing data on 

“Satisfaction with Democracy” only, then its score on the Satisfaction dimension would 

be the average of its score on the other two items, Life Satisfaction and Personal 

Happiness. If the country had missing data on two of the three indicators, the overall 

score for the dimension would be its score on the third indicator. The country was treated 

as completely missing for the Satisfaction analysis only if it was missing on all three of 

the dimension’s indicators. This strategy, again, was applied to maximize the number of 

valid cases for the analyses. It was successful in this regard, as there are 79 valid 

countries for the Institutional Trust analysis, and 82 valid countries for the analyses of the 

effects of Satisfaction and Social Engagement dimensions. 

In Tables 17, 18 and 19, we show the results of three analyses that explored the role 

of each culture dimension as a factor mediating the impact of U.S. democracy assistance, 

and as factors in their own right influencing the level and trajectory of Freedom House 

scores over time. The models are from the trimmed baseline model from Section II of 

this report for all eligible countries that also have valid scores on the given culture 

dimension. In terms of the mediation effect of political culture on the size of the USAID 

DG assistance coefficient, the results are similar across all three analyses. In all of the 

models, the effect of USAID DG assistance is negative for the countries lowest on 

political culture (i.e. when the culture dimension and the interaction term are zero), and 

the effect increases as the country exhibits higher and higher values on the given culture 

dimension. This indicates that culture exerts a positive facilitative effect for USAID DG 

assistance; as a country’s political culture is more democratic, the impact of U.S. 

democracy assistance has stronger effects on the country’s Freedom House score. While 

this pattern is common to all three culture dimensions, the interaction effect is statistically 

significant only for the Social Engagement dimension (Table 19). What appears to 

matter the most for facilitating USAID DG assistance is not the level of institutional trust 

in a country, nor levels of optimism or life satisfaction, but rather the degree to which the 

country’s citizens are trusting of one another, are psychologically engaged with politics, 

and are less strongly nationalistic in their political orientations. 

60
Fre

Table 17. The Conditional Effects of USAID DG Assistance: 

Interactions with Institutional Trust 

Coefficient S. Error 

Level 1 

USAID Democracy Assistance 

Baseline coefficient for USAID DG -0.010 0.034 

Institutional Trust 0.001 0.001 

Other Assistance 

USAID Non-DG 0.001 0.001 

National Endowment for Democracy -0.034 0.197 

US Assistance other than USAID or NED -0.001 0.000 

Regional-Subregional DG 0.113 0.160 

Regional-Subregional Non-DG -0.018 0.017 

Other Donor Assistance on DG 0.000 0.001 

Other Donor Assistance on Non-DG -9E-005 0.0001 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.003 0.004 

US Military Assistance Priority -0.046* 0.024 

Democratic Diffusion -0.070 0.156 

Social and Political Conflict -0.000 0.000 

State Failure Indicator -0.300** 0.119 

 Iraq 2004 -2.613** 1.203 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 8.879** 1.557 

Prior Democracy 0.162** 0.060 

Human Development Index 7.369** 2.514 

Institutional Trust -0.040 0.031 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 0.029 0.121 

Prior Democracy -0.006 0.005 

Human Development Index 0.032 0.191 

Institutional Trust 0.002 0.002 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 3.684** 1.010 

Random Variance (Slope) 0.023** 0.006 

Autocorrelation (rho) 0.809** 0.024 

Model Deviance/AIC 3208.142 3244.142 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Dependent variable is 

edom House. 

61
Coefficient S. Error 

Level 1 

USAID Democracy Assistance 

Baseline coefficient for USAID DG -0.006 0.037 

Overall Satisfaction 0.001 0.001 

Other Assistance 

USAID Non-DG 0.001 0.001 

National Endowment for Democracy 0.012 0.195 

US Assistance other than USAID or NED -0.001 0.000 

Regional-Subregional DG 0.114 0.155 

Regional-Subregional Non-DG -0.020 0.017 

Other Donor Assistance on DG 0.000 0.001 

Other Donor Assistance on Non-DG -7E-05 9.84E-05 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.003 0.004 

US Military Assistance Priority -0.030* 0.018 

Democratic Diffusion -0.029 0.148 

Social and Political Conflict -0.000 0.000 

State Failure Indicator -0.299** 0.116 

 Iraq 2004 -3.247** 1.096 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 10.690** 1.913 

Prior Democracy 0.231** 0.062 

Human Development Index 7.419** 2.360 

Overall Satisfaction -0.068** 0.033 

Effect on (Level-1) Trend 

Average Slope for Growth Curve -0.197 0.139 

Prior Democracy -0.009** 0.004 

Human Development Index 0.024 0.169 

Overall Satisfaction 0.005** 0.002 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 4.409** 1.081 

Random Variance (Slope) 0.022** 0.006 

Autocorrelation (rho) 0.802** 0.025 

Model Deviance/AIC 3299.400 3335.400 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Dependent variable is 

Freedom House. 

Table 18. The Conditional Effects of USAID DG Assistance: 

Interactions with Personal Satisfaction 

62
Table 19. The Conditional Effects of USAID DG Assistance: 

Interactions with Social Engagement 

Coefficient S. Error 

Level 1 

USAID Democracy Assistance 

Baseline coefficient for USAID DG -0.045 0.034 

Social Engagement 0.002** 0.001 

Other Assistance 

USAID Non-DG 0.001 0.001 

National Endowment for Democracy -0.007 0.193 

US Assistance other than USAID or NED -0.001* 0.000 

Regional-Subregional DG 0.061 0.156 

Regional-Subregional Non-DG -0.015 0.016 

Other Donor Assistance on DG 4.02E-05 0.000536 

Other Donor Assistance on Non-DG -8E-05 9.85E-05 

Economic and Political Factors 

Annual Growth in GDP per Capita 0.002 0.004 

US Military Assistance Priority -0.024 0.017 

Democratic Diffusion 0.009 0.149 

Social and Political Conflict -0.000 0.000 

State Failure Indicator -0.311** 0.116 

 Iraq 2004 -3.908** 1.116 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 7.587** 0.940 

Prior Democracy 0.174** 0.060 

Human Development Index 7.779** 2.369 

Social Engagement -0.022 0.028 

Effect on (Level-1) Trend 

Average Slope for Growth Curve -0.035 0.067 

Prior Democracy -0.003 0.004 

Human Development Index -0.017 0.165 

Social Engagement 0.004** 0.002 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 4.547** 1.101 

Random Variance (Slope) 0.021** 0.005 

Autocorrelation (rho) 0.804** 0.024 

Model Deviance/AIC 3296.598 3332.598 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Dependent variable is 

Freedom House. 

63
We show these patterns in graph form in Figure 6. It can be seen in all three graphs 

that the effect of USAID DG assistance has a steadily increasing influence on democracy 

from the lowest to highest levels of political culture. The slope of this line is steepest, 

however, for the Social Engagement dimension, reflecting the stronger effect of this 

interaction term. For example, when Social Engagement is at its lowest level (14.11 for 

El Salvador), the impact of USAID DG is predicted to be -.014, which is statistically 

indistinguishable from zero. The effect of USAID DG begins to be positive at the Social 

Engagement value of 20.5 (roughly at the level of Guyana and Brazil), and begins to be 

both positive and statistically significant at the level of 27.2 (roughly at the level of 

Nigeria and Albania). This is also approximately the median level on this dimension for 

all 82 valid countries, so in this model USAID DG is exerting a potentially statistically 

significant impact for about half of the countries in the analysis. The highest observed 

levels of Social Engagement among eligible countries are for Thailand, Cape Verde and 

Kenya, all with values over 60 and a concomitant USAID DG effect of approximately 

.09, nearly three times its level in the baseline model for all eligible countries reported in 

Table 4a previously. 

The evidence, of course, is tentative due to the missing data, but the results are 

suggestive that culture, especially in the form of social trust and engagement, can be a 

potentially important mediator of the impact of foreign democracy assistance. We also 

note that two culture dimensions, Personal Satisfaction and Social Engagement, have a 

significant impact on the slope of countries’ democratic growth trajectories as well. That 

is, countries with higher levels on these dimensions increase more rapidly on the 

Freedom House index during the 1990-2004 period, irrespective of the impact of 

democracy assistance. In this regard, culture appears to play a generally facilitative role 

in the development of democracy, as well as providing a more receptive environment for 

USAID DG assistance in particular to succeed.

64
Figure 6. Coefficient for USAID DG, Conditional on Cultural Characteristics 

-.005

.015

.035

.055

.075

.095

.115

.135

22 42 62 82

-.005

.015

.035

.055

.075

.095

.115

.135

27 37 47 57 67

Institutional Trust Satisfaction 

-.005

.015

.035

.055

.075

.095

.115

.135

14 24 34 44 54 64 74

Social Engagement 

Lines indicate the size of the USAID DG 

coefficient (the vertical axis) at different levels 

of the intervening variables (horizontal axes). 

Range for intervening variables reflects the 

observed distribution. Dotted lines indicate 95% 

confidence intervals (based on Tables 17-19).

 

65
Conclusions 

The United States Agency for International Development’s Strategic and Operational 

Research Agenda (SORA) project took a bold risk when it commissioned the research on 

which this paper and its companion piece are based. With a total of $8.5 billion spent 

between 1990 and 2005, there was always the risk that the study would have shown that 

the funds on average had no positive impact, regardless of evidence about the success of 

individual projects in particular countries in particular years. The results could also have 

found a systematic negative effect, such that U.S. efforts to promote democracy actually 

slowed progress toward democracy in some countries and/or “caused” reversals in others. 

Certainly the limited research that existed prior to our effort suggested exactly those 

kinds of no-impact, negative impact, giving support to those who have been critical of 

U.S. foreign assistance in general, or democracy assistance in particular. 

The findings from the first report suggested that U.S. democracy assistance in the 

period 1990-2003 had a positive impact on national levels of democracy, even after 

controlling for all other plausible impacts. It also found that in several subsectors of 

democracy (e.g., elections, civil rights), funding targeted in those subsectors produced 

results in precisely those areas, though the findings were ambivalent regarding other sub 

or sub-subsectors (governance) and negative in another (human rights) 

The current research project extended the first by covering the period up through 2004. 

The revised study includes several new variables, including the percentage of funds 

invested in particular sub-sectors, the volatility of USAID DG investment, and the trend 

in such investment. The study also includes other forms of foreign assistance added as 

controls variables, including total investment in other (non-DG) programs, non-USAID 

assistance (including funds from the National Endowment for Democracy, NED), total 

U.S. development assistance not channeled through USAID or NED, bilateral non-US 

foreign assistance and military assistance. This new effort found results that were very 

similar to those reported in the first study, once the huge impact of spending in Iraq in 

2004 was controlled. Thus, with a larger data base covering a longer time frame, we find 

that when USAID provides funds to promote democracy, on average, the effort achieves 

the same degree of impact as we showed previously. Specifically, the positive impact is 

such that $10 million of USAID DG funding would produce an increase of more than 

one-quarter of a point (.29 units) on the 13-point Freedom House democracy index in a 

given year -- or about a five-fold increase in the amount of democratic change that the 

average country would be expected to achieve, ceteris paribus, in any given year. 

 

The extended analysis also showed that the impact of USAID democracy assistance 

cumulates over time and, under certain reasonable statistical assumptions, it may endure 

after funding has been withdrawn. The new study worked even harder to uncover 

possible “endogeneity effects,” such that it is not that U.S. aid fosters democracy, but that 

growth in democracy spurs more aid. In fact, in applying what we believe to be the most 

thorough, reasonable and plausible statistical tests to study this possible flaw in our 

analysis, we find no evidence to support it. 

66
The revised study looked at regional effects, suggesting that the contribution of 

democracy assistance is statistically similar across regions, with the exception of Africa, 

where USAID DG investment has had a larger impact. Certain limitations in the data for 

regional analysis underscored the need to collect data on democracy assistance for the 

1980s and the importance of compiling comparable time series into the future. 

In addition, it looked at the impact of country context on democracy assistance, and 

concluded that countries that needed the assistance the most had the greatest impact from 

it. That is, countries with lower levels of human development, greater levels of ethnic 

fractionalization, and experiencing contemporaneous failure of state institutions, appear 

to benefit the most in terms of overall democratization from a given amount of USAID 

DG assistance. 

Democracy assistance, however, is less effective when the U.S. provides larger 

amounts of military assistance. Our model suggests that, as countries receive larger 

amounts of US military aid, the impact of USAID democracy assistance matters less and 

less, and among the few countries that receive larger than 1.1% of US military outlays, 

the effect of USAID DG assistance is statistically indistinguishable from zero. This 

pattern warrants further investigation, as countries with larger military investments from 

the United States sometimes receive significant amounts of USAID DG assistance as 

well. Thus, substantial amounts of USAID DG outlays appear to be targeted toward 

countries where their effects are more limited. 

This process, moreover, appears to explain fully the “Iraq 2004 Effect,” whereby the 

attenuated effect of USAID DG assistance that we originally found in the 1990-2004 

analysis was caused solely by the addition of Iraq in 2004 to the data set. Because Iraq 

represented a foreign policy priority mainly for security reasons in 2004 (e.g., it received 

23 percent of all security assistance in 2004, vis-à-vis 0.6 percent for the average eligible 

country) and it was also the largest recipient of democracy assistance (31 percent of all 

funds spent in 2004), the overall impact of USAID DG was depressed when compared to 

a model including data for 1990-2003. 

We investigated the impact of different funding strategies by USAID in terms of the 

consistency or volatility of democracy investment over time, and in terms of a general 

positive or negative linear trend in such investment during the 1990-2004 time period. 

We found limited evidence for an effect of investment trend, but suggestive evidence that 

volatility in investment does have a negative impact on overall democracy. That is, a 

given amount of USAID DG investment showed more impact on Freedom House scores 

when the overall investment pattern was consistent over time than when investment 

changed considerably from one year to the next. This suggests that investment decisions 

need to be considered over the relatively long-term so as to avoid the negative impact of 

large-scale year-to-year changes in the outlays for a given country. 

We also added variables to measure the underlying political culture of recipient 

countries. Although we are missing data on about half of our sample, we found that 

culture exerts a positive facilitative effect for USAID DG assistance; as a country’s 

political culture is more democratic, the impact of U.S. democracy assistance has 

67
stronger effects on the country’s Freedom House score. What appears to matter the most 

for facilitating democracy assistance is not the level of institutional trust in a country, nor 

levels of optimism or life satisfaction, but rather the degree to which the country’s 

citizens are trusting of one another, are psychologically engaged with politics, and are 

less strongly nationalistic in their political orientations. 

The new study was unable to explain or “wash out” the only important negative effect 

of aid on democracy, namely, the impact in the human rights area. We continue to find 

that spending in this area worsens human rights. We remain concerned about difficulties 

in measuring human rights abuses, given the extreme sensitivity in this area. Yet, we 

take note of the fact that human rights spending has been limited in terms of dollars spent 

(the average investment per year in human rights programs among the USAID DG 

recipient countries was $130,000), and has also been restricted to 72 of the 165 countries 

in our study. This counter-intuitive finding remains an unanswered puzzle for us, having 

resisted all our efforts to explain it. 

We conclude by noting that the evidence supporting a positive impact of USAID on 

democracy is clear. This does not mean, of course, that in the future this will continue to 

be the case. Shifts in where, when and how USAID spends its democracy assistance, and 

shifting trends in democracy world-wide could make the assistance more or less effective 

in the future. Yet, we feel that the 14 years of data we have analyzed here provide a 

robust basis for drawing the conclusion that USAID DG assistance in the post-Cold War 

period has worked. 

68
Appendices 

Appendix 1 – Countries Included in the Study 

1.1. Countries that Received USAID DG Assistance during 1990-2004 

Africa: Angola ($43.1 million in 10 years), Benin ($19.4 million in 10 years), Botswana 

($0.1 million in 3 years), Burkina Faso ($0.5 million in 4 years), Burundi ($26.2 million 

in 11 years), Cameroon ($0.1 million in 2 years), Cape Verde ($0.03 million in 1 year), 

Central African Republic ($0.2 million in 2 years), Congo, DR (Zaire) ($35.2 million in 

10 years), Congo, Republic of ($1.0 million in 2 years), Djibouti ($0.4 million in 1 year), 

Eritrea ($4.7 million in 9 years), Ethiopia ($41.3 million in 13 years), Gambia ($7.8 

million in 6 years), Ghana ($25.2 million in 11 years), Guinea-Bissau ($7.7 million in 6 

years), Guinea ($20.2 million in 12 years), Ivory Coast ($4.9 million in 10 years), Kenya 

($36.1 million in 10 years), Lesotho ($0.7 million in 4 years), Liberia ($45.6 million in 11 

years), Madagascar ($13.5 million in 12 years), Malawi ($23.0 million in 12 years), Mali 

($27.4 million in 12 years), Mauritania ($0.2 million in 2 years), Mozambique ($55.6 

million in 14 years), Namibia ($11.8 million in 13 years), Niger ($1.7 million in 4 years), 

Nigeria ($81.6 million in 12 years), Rwanda ($42.6 million in 11 years), Sao Tome and 

Principe ($0.3 million in 1 year), Senegal ($21.8 million in 13 years), Sierra Leone ($31.0 

million in 10 years), Somalia ($15.6 million in 8 years), South Africa ($273.9 million in 

15 years), Sudan ($31.8 million in 4 years), Swaziland ($0.4 million in 2 years), Tanzania 

($21.5 million in 12 years), Togo ($0.9 million in 4 years), Uganda ($16.8 million in 11 

years), Zambia ($25.8 million in 13 years), Zimbabwe ($30.4 million in 10 years). 

Asia: Afghanistan ($214.9 million in 7 years), Bangladesh ($40.1 million in 15 years), 

Cambodia ($97.6 million in 14 years), China ($12.9 million in 3 years), East Timor 

($44.8 million in 4 years), India ($20.1 million in 8 years), Indonesia ($224.2 million in 

15 years), Korea, Republic of ($0.8 million in 2 years), Malaysia ($0.3 million in 1 year), 

Mongolia ($28.0 million in 9 years), Myanmar (Burma) ($27.5 million in 7 years), Nepal 

($23.3 million in 13 years), Pakistan ($21.5 million in 8 years), Philippines ($56.9 million 

in 15 years), Sri Lanka ($20.8 million in 15 years), Thailand ($4.6 million in 8 years), 

Vietnam ($0.2 million in 1 year). 

Eurasia: Armenia ($91.6 million in 13 years), Azerbaijan ($70.4 million in 13 years), 

Belarus ($31.8 million in 13 years), Georgia ($92.8 million in 13 years), Kazakhstan 

($98.9 million in 13 years), Kyrgyzstan ($70.4 million in 13 years), Moldova ($25.5 

million in 13 years), Russian Federation ($346.7 million in 13 years), Tajikistan ($31.3 

million in 13 years), Turkmenistan ($11.6 million in 13 years), Ukraine ($238.3 million 

in 13 years), Uzbekistan ($46.2 million in 13 years). 

Europe: Albania ($95.7 million in 14 years), Bosnia-Herzegovina ($164.0 million in 12 

years), Bulgaria ($113.2 million in 15 years), Croatia ($85.7 million in 13 years), Czech 

Republic ($2.9 million in 4 years), Czechoslovakia ($4.8 million in 3 years), Estonia 

($3.2 million in 5 years), Hungary ($20.8 million in 9 years), Latvia ($6.2 million in 7 

years), Lithuania ($9.7 million in 9 years), Macedonia ($73.8 million in 13 years), Poland 

($79.3 million in 10 years), Romania ($78.8 million in 15 years), Serbia and Montenegro 

69
($372.5 million in 11 years), Slovakia ($28.2 million in 9 years), Slovenia ($0.9 million 

in 4 years). 

Latin America and the Caribbean: Belize ($1.5 million in 4 years), Bolivia ($78.6 

million in 15 years), Brazil ($6.5 million in 9 years), Chile ($4.5 million in 6 years), 

Colombia ($101.8 million in 11 years), Costa Rica ($10.5 million in 7 years), Cuba 

($29.0 million in 5 years), Dominican Republic ($55.3 million in 14 years), Ecuador 

($57.1 million in 15 years), El Salvador ($188.2 million in 15 years), Guatemala ($76.0 

million in 15 years), Guyana ($18.2 million in 14 years), Haiti ($237.9 million in 15 

years), Honduras ($62.2 million in 15 years), Jamaica ($9.7 million in 11 years), Mexico 

($50.9 million in 10 years), Nicaragua ($100.0 million in 15 years), Panama ($76.4 

million in 15 years), Paraguay ($38.2 million in 10 years), Peru ($100.0 million in 15 

years), Uruguay ($1.2 million in 1 year), Venezuela ($1.5 million in 2 years). 

Middle East and the Mediterranean: Algeria ($3.7 million in 8 years), Bahrain ($1.3 

million in 2 years), Egypt ($334.3 million in 14 years), Iraq ($523.6 million in 3 years), 

Jordan ($28.3 million in 5 years), Lebanon ($28.5 million in 11 years), Morocco ($3.6 

million in 7 years), Oman ($0.6 million in 2 years), Qatar ($0.8 million in 1 year), Saudi 

Arabia ($0.4 million in 1 year), Tunisia ($11.2 million in 5 years), Turkey ($0.9 million 

in 4 years), West Bank and Gaza ($155.4 million in 11 years), Yemen ($6.6 million in 8 

years). 

Oceania: Papua New Guinea ($0.2 million in 1 year). 

In addition, programs covering multiple countries involved $982.3 million in 1990-2004. 

1.2. Countries in the Sample that Did Not Receive USAID DG Assistance 

Africa: Chad, Comoros, Equatorial Guinea, Gabon, Mauritius, Seychelles. 

Asia: Bhutan, Brunei Darussalam, Iran, Korea, DPR (N), Laos, Maldives, Singapore, 

Taiwan. 

Europe: Portugal 

Latin America and the Caribbean: Antigua and Barbuda, Argentina, Dominica, 

Grenada, Saint Lucia, St. Kitts and Nevis, St. Vincent and the Grenadines, Suriname, 

Trinidad and Tobago. 

Middle East and the Mediterranean: Cyprus, Israel, Kuwait, Libya, Syria, United Arab 

Emirates. 

Oceania: Fiji, Kiribati, Marshall Islands, Micronesia, Federated States, Nauru, Palau, 

Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu. 

1.3. Countries Not Included in the Sample 

Asia: Japan. Europe: Andorra, Austria, Belgium, Denmark, Finland, France, Germany, 

Greece, Holy See (Vatican City State), Iceland, Ireland, Italy, Liechtenstein, 

Luxembourg, Malta, Monaco, Netherlands, Norway, San Marino, Spain, Sweden, 

Switzerland, United Kingdom. Latin America and the Caribbean: Bahamas, Barbados 

North America: Canada (and the U.S., by definition). Oceania: Australia, New Zealand. 

70
71

Appendix 2 – Democratic Performance Indices 

The creation of the indices followed three steps: 1) we minimized missing values 

by conducting EM imputation among the components; 2) we performed an exploratory 

factor analysis; 3) for presentation purposes we re-calibrated the score (to have a mean of 

50, standard deviation of 10). The following table presents the factor loadings, the 

communalities for the component items, and Cronbach’s alpha for the unweighted items. 

Item Loadings Communalities 

1. Free and Fair Elections 

Index of Competition (Vanhanen) .902 .814 

Legislative Index Electoral Competitiveness (DPI) .845 .715 

Women's Political Rights (CIRI) .608 .370 

Competitiveness of Participation (Polity) .927 .859 

Accountability (ICRG) .889 .790 

Total variance explained (%) 80.0 

Cronbach’s alpha (standardized, non-imputed items) .890 

2. Respect for Human Integrity 

Political and Extrajudicial Killings (CIRI) .856 .732 

Disappearances (CIRI) .771 .595 

Torture (CIRI) .787 .620 

Political Imprisonment (CIRI) .761 .579 

Political Terror Scale (Gibney) -.906 .822 

Total variance explained (%) 66.9 

Cronbach’s alpha (standardized, non-imputed items) .850 

3. Conditions for Civil Society 

Restrictions on Organization of Minorities (MAR) -.550 .302 

Freedom of Assembly and Association (CIRI) .828 .686 

Freedom of Religion (CIRI) .766 .587 

Worker Rights (CIRI) .755 .570 

Freedom of Movement (CIRI) .772 .596 

Women's Economic Rights (CIRI) .606 .368 

Total variance explained (%) 51.8 

Cronbach’s alpha (standardized, non-imputed items) .768 

4. Free Media 

Freedom of the Press (FH, Ordinal) .934 .872 

Freedom of the Press (FH, Interval, inverted) .960 .922 

Freedom of Speech & Press (CIRI) .880 .775 

Freedom of Expression for Minorities (MAR, inverted) .643 .413 

Total variance explained (%) 74.5 

Cronbach’s alpha (standardized, non-imputed items) .845 

5. Good Governance 

Corruption Perceptions Index (TI) .931 .866 

Investment Conditions (ICRG) .645 .416 

Corruption (ICRG) .838 .702 

Bureaucracy Quality (ICRG) .897 .805 

Total variance explained (%) 69.7 

Cronbach’s alpha (standardized, non-imputed items) .851 
72

Appendix 3 – Exploratory Analysis of Culture Variables 

Personal 

Item Institutional Trust Satisfaction Social Engagement 

Interpersonal Trust .165 .158 -.884 

Support for Democracy .401 .055 .370 

Satisfaction with Democracy .578 .705 .106 

Interest in Politics .292 -.264 -.509 

Nationalism .253 .398 .760 

Life Satisfaction -.112 .940 -.022 

Happiness .157 .696 .493 

Trust in Government .854 .096 -.070 

Trust in Justice .777 .065 -.088 

Trust in Parliament .945 -.016 .017 

Note: This analysis represents a varimax rotation of the factor analysis solution. These analyses were, 

restricted to only the 43 countries that had non-missing values on all 10 culture indicators. The eigenvalues 

and percentage of variance explained for the three culture factors were, respectively, 3.39 (33.9%), 2.41 

(24.1%), and 1.29 (12.9%). We take note of the distributed loadings present for some of the variables. For 

example, Happiness loaded most strongly on “Personal Satisfaction,” but also loaded moderately high on 

what we call “Social Engagement.” We also note a similar problem with the Satisfaction with Democracy 

item (loading on both “Personal Satisfaction” and “Institutional Trust.” These distributed loadings indicate 

that some of items that comprise a dimension of the culture variables share some of their variance with 

other items, a common finding in most factor analysis of public opinion data. Since, however, we 

incorporate each of items into one of our three factors, the analysis we perform on them in our regressions 

is sensitive to the impact of each of these variables. We did not include the “Support for Democracy” item 

in any of the three dimensions since its loading fell below the threshold we set (.5) and because of its weak 

contribution to the “Institutional Trust” dimension on which it loaded the highest.
Appendix 4 – Imputation Models 

4.1. Imputation for Measurement Models 

Item % Missing EM Predictors 

1. Free and Fair Elections 

Index of Competition (Vanhanen) 

Index Electoral Competitiveness (DPI) 

Women's Political Rights (CIRI) 

Competitiveness of Participation (Polity) 

Accountability (ICRG) 

17.1 

11.7 

17.8 

22.7 

30.7 

Index of Competition (EL02) 

Electoral Competitiveness (EL04) 

Women's Political Rights (EL08) 

Competitive Participation (EL12) 

Accountability (DG07) 

Political Rights (EL01) 

Sub-regional dummies 

2. Respect for Human Integrity 

Political and Extrajudicial Killings 

Disappearances (CIRI) 

Torture (CIRI) 

Political Imprisonment (CIRI) 

Political Terror Scale (Gibney) 

(CIRI) 17.8 

17.6 

17.6 

17.6 

10.3 

Political-Extrajudicial Killings (RL08) 

Disappearances (RL09) 

Torture (RL10) 

Political Imprisonment (RL11) 

Political Terror Scale (RL12) 

Freedom House Index (DG02) 

Sub-regional dummies 

3. Conditions for Civil Society 

Restrictions Organization (MAR) 

Freedom Assembly-Association (CIRI) 

Freedom of Religion (CIRI) 

Worker Rights (CIRI) 

Freedom of Movement (CIRI) 

Women's Economic Rights (CIRI) 

46.0 

15.4 

17.4 

17.5 

17.4 

18.4 

Restrictions Org. Minorities (CS01) 

Freedom of Association (CS02) 

Freedom of Religion (CS04) 

Worker Rights (CS05) 

Freedom of Movement (CS06) 

Women's Economic Rights (CS07) 

Freedom House Index (DG02) 

Sub-regional dummies 

4. Free Media 

Freedom of the Press (FH, Ordinal) 

Freedom of the Press (FH, Interval) 

Freedom of Speech & Press (CIRI) 

Freedom Expression Minorities (MAR) 

11.1 

28.4 

17.4 

46.2 

Freedom of the Press (RL02) 

Freedom of the Press, 3-point (RL03) 

Freedom of Speech & Press (RL04) 

Freedom Expression Minorities (RL14) 

Freedom House Index (DG02) 

Sub-regional dummies 

5. Good Governance 

Corruption Perceptions Index (TI) 

Investment Conditions (ICRG) 

Corruption (ICRG) 

Bureaucracy Quality (ICRG) 

69.2 

30.7 

30.7 

30.7 

Corruption Perceptions Index (GV01) 

Investment Conditions (GV13) 

Corruption (GV14) 

Bureaucracy Quality (GV15) 

Country means for World Bank’s: 

Government Effectiveness (GV07) 

Regulatory Quality (GV08) 

Control of Corruption (GV09) 

Sub-regional dummies 

73
4.2. Imputation for Causal Models 

Item % Missing EM Predictors 

Polity Index 

Freedom House Index 

GDP Growth 

Income Inequality 

18.5 

1.1 

9.8 

34.4 

Polity IV Score (DG01) 

Freedom House Index (DG02) 

Coups d’état (POL01) T 

Number of Legislative Elections (POL02) 

Religious Fragmentation (SOC09) 

Ethnolinguistic Fractionalization (SOC10) 

GDP, current U.S. dollars (DEV01) T 

GDP per capita, 2000 U.S. dollars (DEV03) T 

GDP per capita (sub-regional mean DEV03) 

GDP per capita, 1996 U.S. dollars (PWT) S 

GDP per capita, PPP (DEV04) T 

GDP, PPP (sub-regional mean DEV04) 

Telephone Lines PTI (DEV05) T 

Growth in GDP Per Capita (PRF01) 

Growth (yearly sub-regional mean PRF01) 

Annual Growth in GDP Per Capita (PWT) S 

Inflation, Consumer Prices (PRF02) T 

Inflation, based on GDP Deflator (PRF04) T 

Inflation (yearly sub-regional mean PRF04) 

Income Distribution, Share of top 20% 

(SOC06) T 

Unemployment (SOC07) T 

Unemployment (sub-regional mean SOC07) 

Merchandise Exports (DEP01) T 

Exports/ GDP (sub-regional mean, DEP02) 

Population (SOC01) 

Non-US Assistance (ODA03) 

Democracy in International System (DIF01) 

Democracy in the Region (DIF02) 

Application for EU Membership (DIF03) 

FH among neighboring countries (DIF04) 

Polity among neighboring countries (DIF05) 

Diffusion - Brinks-Coppedge (DIF06) 

Diffusion - Spatial lags, FH (DIF07) 

Diffusion - Spatial lags, Polity (DIF08) 

Time trend (YEARNUM) 

T Transformed during imputation to correct for truncation; S Substitutive data from Penn World Tables 

74
Appendix 5 – Fixed Effects and First Differences Models 

Controlling for Omitted Variables: Fixed Effects and First Difference Models 

Democracy and Other Assistance 

USAID DG 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or 

NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non DG

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Constant 

N 

R-squared (within) 

5(a) Fixed Effects 

Coefficient St. Error 

 

0.031** 0.007 

0.000 0.001 

0.065 0.185 

-0.000 0.001 

-0.196 0.146 

0.007 0.016 

0.001* 0.001 

-0.000 0.000 

 

0.007** 0.003 

-0.016 0.024 

0.065 0.126 

-0.001* 0.000 

-0.797** 0.093 

-4.769** 1.590 

7.530 4.27E11 

2251 

0.0868 

5(b) First Difference 

Coefficient St. Error 

 

0.021** 0.007 

0.001 0.001 

0.192 0.179 

-0.000 0.001 

-0.035 0.149 

-0.038** 0.018 

-0.000 0.001 

-0.000 0.000 

 

0.011** 0.003 

-0.017 0.023 

0.040 0.116 

-0.000 0.000 

-0.605** 0.089 

-3.835** 1.500 

0.044** 0.021 

2086 

0.0665 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

75
Appendix 6 – Models of Sub-Sectoral Effects 

Table A6.1 

Effects for Elections 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law 

Civil Society 

Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

 Iraq 2004 

Level 2 

 

0.092* 

0.056 

0.037 

0.060** 

 

0.003* 

-0.659 

-0.001 

-0.143 

-0.037 

-0.001 

0.000 

 

0.001 

0.016 

0.945** 

-0.003** 

-1.138** 

-15.193** 

 

0.047 

0.047 

0.030 

0.028 

0.001 

0.418 

0.001 

0.329 

0.031 

0.002 

0.000 

0.007 

0.049 

0.212 

0.001 

0.217 

4.704 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

46.300** 

0.588** 

0.019 

-0.760 

-7E-005 

18.952** 

 

0.257** 

-0.008 

-0.002 

0.453** 

-4E-006 

-0.617** 

 

38.723** 

0.121** 

0.678** 

11509.827

0.564 

0.115 

0.075 

2.551 

0.0003 

4.343 

0.038 

0.007 

0.005 

0.161 

0.000 

0.274 

4.851 

0.018 

0.016 

 11545.827 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

76
Table A6.2 

Effects for Human Rights 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law: Human Rights 

Rule of Law: Non Human Rights 

Civil Society 

Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Level 2 

 

0.152* 

-0.664** 

0.139 

0.020 

0.037 

 

-0.003 

-0.969 

0.003 

-0.390 

0.024 

0.005* 

-1E-005 

 

0.039** 

-0.007 

0.410 

-0.005** 

-5.266** 

-9.880 

 

0.088 

0.231 

0.099 

0.059 

0.062 

0.003 

0.738 

0.003 

0.649 

0.048 

0.003 

0.000 

0.015 

0.098 

0.332 

0.002 

0.430 

9.876 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

49.689** 

0.205* 

0.011 

-3.195 

-0.001** 

15.853** 

 

-0.061 

-0.015** 

0.015** 

0.043 

-4E-005* 

0.183 

 

39.245** 

0.049 

0.397** 

14299.190

0.620 

0.120 

0.078 

2.631 

0.000 

4.563 

0.046 

0.007 

0.005 

0.158 

2.0E-005 

0.276 

5.340 

0.023 

0.034 

 14307.190 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

77
Table A6.3 

Effects for Civil Society 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law 

Civil Society 

Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

 Iraq 2004 

Level 2 

 

0.047 

-0.007 

0.292** 

0.098* 

 

-0.004* 

-0.024 

-0.004 

-0.163 

-0.023 

-0.004 

9.3E-005 

 

-0.001 

0.019 

1.630** 

-0.002 

-1.754** 

-4.291 

 

0.078 

0.083 

0.055 

0.055 

0.002 

0.672 

0.003 

0.583 

0.043 

0.003 

0.0004 

0.012 

0.090 

0.310 

0.002 

0.382 

8.408 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

49.714** 

0.661** 

0.067 

2.322 

-0.001** 

4.140 

 

-0.167** 

-0.015** 

0.013** 

-0.024 

1.5E-006 

0.309 

 

43.399** 

0.027 

0.551** 

13589.299

0.643 

0.126 

0.082 

2.774 

0.000 

4.783 

0.044 

0.007 

0.005 

0.154 

2.0E-005 

0.270 

5.581 

0.020 

0.029 

 13606.299 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

78
Table A6.4 

Effects for Free Media 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law 

Civil Society: Media 

Civil Society: Non-Media 

Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Level 2 

 

0.185** 

0.021 

0.573** 

0.151** 

0.048 

 

-0.004* 

-0.251 

0.002 

-0.701 

0.004 

0.003 

-7E-005 

 

-0.000 

-0.001 

1.066** 

-0.002 

-1.650** 

-10.323 

 

0.060 

0.059 

0.228 

0.042 

0.037 

0.002 

0.545 

0.002 

0.427 

0.039 

0.002 

0.000 

0.009 

0.068 

0.266 

0.001 

0.279 

6.322 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

49.234** 

0.693** 

0.190** 

1.566 

-0.0003 

11.620** 

 

-0.115** 

-0.009 

0.002 

0.051 

-5E-005** 

0.240 

 

37.504** 

0.089** 

0.506** 

12301.284

0.572 

0.114 

0.074 

2.517 

0.000 

4.318 

0.041 

0.007 

0.005 

0.159 

2.0E-005 

0.273 

5.003 

0.021 

0.030 

 12337.284 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

79
Table A6.5 

Effects for Governance 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law 

Civil Society 

Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Level 2 

 

-0.014 

-0.013 

0.042 

0.070** 

 

-0.001 

-1.062** 

-0.000 

-0.123 

-0.014 

0.001 

-4E-005 

 

0.003 

-0.004 

0.137 

-0.003** 

-0.122 

-9.568** 

 

0.037 

0.039 

0.029 

0.025 

0.001 

0.334 

0.001 

0.272 

0.022 

0.001 

0.0002 

0.005 

0.045 

0.159 

0.001 

0.176 

3.687 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

46.573** 

0.194* 

0.014 

0.896 

0.000 

20.490** 

 

0.030 

-0.004 

0.002 

-0.010 

-4E-005** 

0.550** 

 

11.994 

0.941** 

10031.374

0.519 

0.104 

0.068 

2.316 

0.000 

3.941 

0.035 

0.006 

0.004 

0.143 

1.8E-005 

0.245 

6.523 

0.015 

 10037.374 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

80
Table A6.6 

Effects for Elections According to Portfolio Patterns 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Percentage invested in Elections 

Rule of Law 

Civil Society 

Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Level 2 

 

0.068 

-0.001 

0.020 

0.019 

0.038 

 

0.004** 

-0.959** 

-0.001 

0.001 

-0.051 

-0.000 

0.000 

 

-0.003 

0.033 

0.837** 

-0.003** 

-1.388** 

-13.581** 

 

0.049 

0.003 

0.052 

0.036 

0.031 

0.002 

0.467 

0.001 

0.362 

0.034 

0.002 

0.000 

0.007 

0.057 

0.229 

0.001 

0.231 

4.711 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

46.361** 

0.592** 

0.032 

-0.001 

2.0E-005 

19.657** 

 

0.256** 

-0.008 

-0.004 

0.383** 

-3E-006 

-0.630** 

 

41.676** 

0.029 

0.844** 

11379.681

0.676 

0.136 

0.089 

3.026 

0.000 

5.152 

0.044 

0.008 

0.005 

0.176 

2.2E-005 

0.301 

6.796 

0.026 

0.020 

 11415.681 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

81
Table A6.7 

Effects for Human Rights According to Portfolio Patterns 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law: Human Rights 

Rule of Law: Non Human Rights 

Percentage invested in Human Rights 

Civil Society 

Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Level 2 

 

0.155* 

-0.747** 

0.143 

0.009 

0.022 

0.037 

 

-0.004 

-0.957 

0.003 

-0.377 

0.024 

0.005* 

-7E-006 

 

0.039** 

-0.006 

0.405 

-0.004** 

-5.263** 

-10.101 

 

0.089 

0.278 

0.099 

0.017 

0.059 

0.062 

0.003 

0.738 

0.003 

0.649 

0.048 

0.003 

0.000 

0.015 

0.098 

0.332 

0.002 

0.430 

9.881 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

49.663** 

0.202* 

0.009 

-3.230 

-0.001** 

15.828** 

 

-0.060 

-0.014** 

0.015** 

0.046 

-4E-005* 

0.189 

 

39.476** 

0.050 

0.396** 

14305.208

0.622 

0.120 

0.078 

2.637 

0.000 

4.571 

0.046 

0.007 

0.005 

0.158 

0.000 

0.277 

5.395 

0.024 

0.034 

 14313.208 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

82
Table A6.8 

Effects for Civil Society According to Portfolio Patterns 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law 

Civil Society 

Percentage invested in Civil Society 

Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Level 2 

 

0.076 

-0.018 

0.296** 

0.001 

0.157** 

 

-0.006** 

-0.142 

-0.003 

0.400 

-0.006 

-0.004 

0.000 

 

0.000 

0.013 

1.241** 

-0.001 

-1.387** 

-15.702* 

 

0.080 

0.073 

0.054 

0.004 

0.052 

0.002 

0.656 

0.002 

0.548 

0.047 

0.003 

0.000 

0.011 

0.088 

0.306 

0.002 

0.351 

9.521 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

49.359** 

0.700** 

0.053 

1.107 

-0.001** 

2.326 

 

-0.077* 

-0.016** 

0.016** 

0.096 

-2E-006 

0.570** 

 

49.485** 

0.033 

0.524** 

13424.785

0.665 

0.133 

0.086 

2.931 

0.000 

5.022 

0.043 

0.007 

0.005 

0.162 

2.0E-005 

0.279 

6.322 

0.023 

0.028 

 13460.785 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

83
Table A6.9 

Effects for Free Media According to Portfolio Patterns 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law 

Civil Society: Media 

Civil Society: Non-Media 

Percentage invested in Free Media 

DG Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Level 2 

 

0.186** 

0.021 

0.559** 

0.152** 

0.002 

0.049 

 

-0.004* 

-0.252 

0.002 

-0.689 

0.004 

0.003 

-7E-005 

 

-0.000 

-0.002 

1.062** 

-0.002 

-1.648** 

-10.371 

 

0.060 

0.059 

0.237 

0.042 

0.010 

0.037 

0.002 

0.545 

0.002 

0.430 

0.039 

0.002 

0.000 

0.009 

0.068 

0.267 

0.001 

0.279 

6.326 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Random Variance (Slope) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

49.220** 

0.694** 

0.191** 

1.557 

-0.000 

11.599** 

 

-0.114** 

-0.009 

0.002 

0.051 

-5E-005** 

0.242 

 

37.446** 

0.089** 

0.506** 

12308.668

0.574 

0.114 

0.074 

2.516 

0.000 

4.317 

0.041 

0.007 

0.005 

0.159 

2.0E-005 

0.273 

5.004 

0.022 

0.030 

 12344.668 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

84
Table A6.10 

Effects for Governance According to Portfolio Patterns 

Level 1

Coefficient 

 

S. Error 

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law 

Civil Society 

Governance 

Percentage invested in Governance 

Other Assistance 

USAID Non-DG 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assistance on Non-DG 

Economic and Political Factors 

Annual Growth in GDP per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq 2004 

Level 2 

 

-0.016 

-0.013 

0.037 

0.081** 

-0.003 

 

-0.001 

-1.075** 

-0.001 

-0.095 

-0.014 

0.001 

-4E-005 

 

0.003 

-0.006 

0.132 

-0.003** 

-0.121 

-10.493** 

 

0.037 

0.039 

0.030 

0.028 

0.003 

0.001 

0.334 

0.001 

0.274 

0.022 

0.001 

0.0002 

0.005 

0.045 

0.160 

0.001 

0.176 

3.814 

Effect on (Level-1) Intercept 

Average Intercept 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Effect on (Level-1) Trend 

Average Slope for Growth Curve 

Prior Democracy 

Income Inequality 

Ethnic Fractionalization 

Size in Square Km 

Human Development Index 

Model Statistics and Variance Parameters 

Random Variance (Intercept) 

Autocorrelation (rho) 

Model Deviance/AIC 

 

46.579** 

0.193* 

0.015 

0.889 

0.000 

20.531** 

 

0.033 

-0.004 

0.002 

-0.007 

-4E-005** 

0.544** 

 

11.718 

0.941** 

10040.377

0.519 

0.104 

0.068 

2.316 

0.000 

3.942 

0.036 

0.006 

0.004 

0.143 

1.8E-005 

0.246 

6.631 

0.015 

 10046.377 

Note: **significant at p<.05; *significant at p<.10 (two tailed) 

85
Appendix 7 – Understanding the Impact of Human Rights Assistance 

 As we have noted in this and previous reports, the most unanticipated result in the 

extensive analyses of the initial study of the effects of U.S. foreign assistance on 

democracy building in the period 1990-2003 is the negative impact of receiving rule of 

law funding directed at the improvement of human rights on the performance of nations 

in protecting or abusing the personal integrity rights of their populations. The finding 

persists through the models reported above. 

Candidate Explanations 

Our plan for investigating this anomalous and troubling finding called for an 

investigation of alternative hypotheses that might demonstrate the spuriousness of the 

anomalous relationship between democracy assistance in the human rights area and 

human integrity abuse:41

1. reexamining the "reverse causality" explanation 

2. reexamining the "measurement error" explanation 

3. investigating the effects of potential omitted independent variables 

4. exploring theory and analysis that might explain a "genuine relationship" 

Reverse Causality 

 This hypothesis suggests the possibility that the relationship between 

development assistance devoted to improving human rights and respect for human 

integrity is the result of reverse causation—assistance is directed to countries with 

problematic records on respect for personal integrity precisely because of their suspect 

records—continues to receive little support in our analyses. We estimated endogeneity 

models similar to those in Table 6 above, and found little change in the estimated 

negative impact of USAID DG human rights assistance on our measure of countries’ 

respect for human integrity. Recall that these models employ variables of USAID DG 

Human Rights assistance that are lagged by at least two years as proxies for current HR 

outlays, or employ variables such as the State Department foreign policy priority measure 

that are assumed to be unrelated to the indicator of respect for human integrity, once 

other variables in the model are controlled. However, the models do not pass the 

relevant tests for the exogeneity of the instrument set, and, thus, we can't be certain that 

we have good instruments to serve as proxies for the HR AID allocation. So the 

possibility of endogeneity in the process remains, even though all of our efforts to control 

for this have yielded essentially similar results as presented previously. 

 

41 We should note that, while we continue to use the Respect for Human Integrity Index (RL15 in our 

codebook) as our dependent variable, replications of these models with the most frequently used measure 

of personal integrity rights abuse, the Political Terror Scale, produce nearly identical results. 

86
Measurement Error 

 The measurement or reporting error explanation contends that "more democracy 

assistance in the human rights area leads to higher levels of revealed human rights 

abuses, but not necessarily higher levels of actual abuse." If this explanation were true, it 

could mean that USAID DG Rule of Law - Human Rights and other democracy 

assistance were achieving an important goal, increasing sensitivity to and reporting of 

human rights abuses. In the short run, such success would manifest itself in apparent – 

but not real -- increase in abuses. In the longer run, increasing sensitivity to and 

promoting more vigorous reporting of human rights abuses could lead to reduction in 

actual abuse. 

Directly assessing the validity of the measurement error explanation would 

require information on the reliability of reporting/non-reporting of human rights abuses 

that does not exist. We know of no valid method to directly distinguish between 

reporting of abuses and the unknown undercounting of occurrences of abuses. It is thus 

not only necessary but perhaps preferable to seek proxy measures that plausibly could 

create the conditions under which increased reporting would take place. 

Press Freedom 

 Through what mechanism could increases or improvements in reporting of rights 

abuses take place? One possibility is that increased abuse reporting would accompany 

increased press freedom and initiative. If increases in human rights USAID DG funding 

are associated with increases in press freedom, it may be that the increases in the latter 

enable better reporting of human rights abuses and thus apparent increases in abuses. 

Our cross country, cross time dataset includes several measures of press freedom. These 

are summarized in an Index of Freedom of the Press (RL16) created by factor analyzing 

Freedom House’s ordinal and interval measures of press freedom (RL02 RL03), 

Cingranelli and Richards’ measure of freedom of speech and press (RL04), and the 

freedom of expression indicator from the Minorities at Risk Project (RL14). We use this 

Index of Freedom of Press to operationalize changing press freedom across our sample of 

countries and years for subsequent analyses of the impact of USAID DG human rights 

assistance on respect for human rights. 

Organizational Growth 

 Another possible mechanism through which increased reporting of human rights 

abuses could occur would be through the growth of international or nongovernmental 

organizations (IGOs, NGOs) concerned with human rights issues. If increased USAID 

DG for human rights promotes IGO/NGO growth and if those IGOs/NGOs then do a 

better job of reporting human rights abuse that been occurring all along but going 

unreported, USAID DG could appear to be responsible for an increase in human right 

abuses. 

Sadly, finding adequate annual indicators of the number or membership of 

IGOs/NGOs across all countries has proved to be an insurmountable obstacle, and we are 

87
quite confident that there is no effective way to generate such data. Nevertheless, 

important work on human rights abuses by Landman (2005; 2006) reports on measures 

of the number of intergovernmental organizations (IGOs) of which countries are 

members, as well as the number of international nongovernmental organizations (INGOs) 

with a registered office in each country. These data series were generated from the 

statistical yearbooks published by the Union of International Associations (UIA).42 

Unfortunately, these data are not generated annually by UIA. The INGO data are 

reported every few years; the IGO data less systematically. Because he needed annual 

data for his analyses, Landman imputed scores for the missing years by linear 

interpolation (2005, 177-180) and created two relevant variables, measuring national IGO 

memberships and the number of international NGO registered within a country. These 

data are available on Landman’s web site and cover the period through 2000. 

Initially, we were optimistic about the use of the Landman data. However, the fact 

that Landman’s data, when annualized, exhibit more than 80% of their values imputed 

was a cause for serious concern. This very large proportion of imputed values grew even 

larger when we imputed values for the end years of the series, 2001-2004, to make it 

match our time period.43 Nevertheless, since (1) Landman’s interpolated series appear to 

be the best measures of IGO/NGO activity available and (2) measuring IGO/INGO 

activity is so important to assessing the measurement/reporting error alternative 

hypothesis, we decided to use the two Landman series and have incorporated them in the 

models we report below. Yet, we urge strong caution in the interpretation of these data. 

Omitted Variables 

 A major part of our plan to investigate the anomalous relationship between human 

rights assistance and human integrity abuse focuses on identifying, measuring, and 

assessing the effects of variables that had previously been omitted from our explanatory 

models. We posited indicators of constitutional structure, including formal and actual 

judicial independence, as omitted variables that were worthy of exploration. 

Omitted Variable: Constitutional Protections for Rights 

 Beginning around 1990, the initial year of our period of analysis, there was a 

major increase in efforts by nations to create new constitutions or to revise existing ones 

to provide greater formal protection for human rights. The publically stated justifications 

for these constitution writing exercises were clear enough, but the motivations underlying 

them were no doubt more complex. 

Some constitution creators and rulers may have acted in good faith, if perhaps 

also with some naiveté, believing that writing down formal rules to protect human rights 

would make it so. Others may have created constitutional protections to give the 

 

42 The IGO data were generated initially and supplied to Landman by Bruce Russett of Yale University and 

were used in Russett and O’Neal (2001). Landman’s data are available at 

http://privatewww.essex.ac.uk/~todd/ 

43 Since the series had no end year toward which to interpolate, we incremented last year’s value by the 

average of the four year to year increases to create the values for 2001-2004. 

88
impression they were doing something serious to protect human rights -- perhaps to 

divert international pressure to improve their human rights performance, for example, but 

with no intention of acting on or obeying the formal rules thus created. Less cynically, 

constitutional protections might tend to be adopted disproportionately by nations with 

serious human rights problems in the hope that adopting formal provisions would help 

improve their human rights situation. It is easy to see that in these circumstances, such 

hopes, no matter how sincere, could easily prove futile, so that poor human rights 

performance came to be associated with efforts to add constitutional protections to 

produce good human rights performance. 

How efforts to provide formal constitutional protections for human rights are 

related to grants of democracy assistance also could be complicated. There has been 

since the 1970s a Congressional mandate that the granting of foreign assistance be based 

in part on a country’s human rights record – this is indeed the reason the Department of 

State began the publication of its annual Human Rights Reports. If constitutional 

provisions to protect rights are seen as evidence of acceptable human rights performance 

when they are not, assistance could end up being negatively related to actual human 

rights performance. We will thus include measures of formal constitutional provisions 

designed to protect rights in our model of respect for human rights. 

As a result of work on another project, we have available for use in our analysis 

full data for 1990-2005 on constitutional provisions related to basic freedoms, judicial 

independence, and regulation of states of emergency of the constitutions of all the world's 

nations for the entire period of our analysis. Our analyses will not be naïve about the 

likelihood that purely formal measures of government such as constitutional provisions 

will have an impact on human integrity abuse or mediate the relationship between 

assistance and such abuse. But we do believe that the fact that nations spend 

considerable time and resources creating the constitutions that include such provisions 

makes their potential effects worthy of the investigation we will conduct. Furthermore, 

preliminary analyses of the relationships between the constitutional right provisions and 

available measures that purport to be more empirically grounded (drawn from the 

Cingranelli and Richards dataset) support the conclusion that the constitutional measures 

are in fact valid measures of rights protection. 

Our data on constitutional provisions are those collected by Keith and Tate (see 

Keith 2002; Keith and Poe 2004; Keith, Tate, and Poe 2007). They include ten measures 

of the extent to which constitutions protect basic political freedoms and rights (namely, 

the freedoms of speech, association, assembly, press, religion, the freedom to strike, and 

the rights to a fair and a public trial, to the writ of habeas corpus, and to be free from 

torture or cruel and unusual punishment), nine protections for judicial independence 

(guaranteed terms for judges, the provisions that judges’ decisions are final, that judges 

have exclusive authority to decide issues of their own competence, that there are no 

exceptional courts, financial autonomy for the judiciary, a separate judicial branch, 

established qualifications for judges, a hierarchical judicial system, and for the power of 

judicial review), and four provisions to regulate the declaration and exercise of 

emergency powers in a state of emergency (a provision for legislative declaration of a 

state of emergency, limitations on the duration of states of emergency, a provision that 

89
the executive cannot dissolved the legislature, and a provision identifying non-derogable 

rights during states of emergency).44 

Omitted Variable: Judicial Independence 

 USAID's concerns with the rule of law are broader than the human rights 

dimension that is our principal focus in this section. 45 Another indicator of rule of law 

that is of considerable interest to USAID is judicial independence. Judicial independence 

would thus join human rights performance as another important indicator of rule of law; 

we will analyze its existence and determinants separately. 

However, judicial independence is also important as a possible intervening 

variable that may modify or mitigate the discovered negative relationships between 

democracy aid and human rights. It is possible that countries that already have wellestablished traditions and systems of judicial independence would disproportionately be 

countries that did not receive high levels of USAID DG human rights assistance. If that 

were so, and if judicial independence did indeed promote respect for human rights, as one 

would expect it to do, then USAID DG human rights assistance might end up going 

disproportionately to countries with lesser judicial independence and, consequently, less 

respect for human rights. Thus we need to include measures of judicial independence in 

our revised model of human rights respect. 

Judicial independence is almost always insisted upon as essential for the 

promotion of stable and effective democracy and the protection of human rights.46 

Unfortunately, it is a difficult concept to operationalize and measure on a reliable, valid, 

and cross-national basis. There have been efforts to tackle the concept empirically. 

Because of the difficulty of measuring actual judicial independence, these efforts have 

heretofore concentrated on formal legal or constitutional provisions intended to promote 

or guarantee judicial independence. None, so far as we know, has succeeded in 

producing a face valid cross-national measure of actual judicial independence that exists 

across a long period of time. 

One of the most significant of the efforts to generate formal indicators of judicial 

independence is in the work done by Keith and Tate, described above. As we noted, 

Keith and Tate coded the presence or absence of nine discrete provisions designed to 

promote judicial independence in the texts of the world's constitutions. These indicators 

exist for the period 1976-2005 and we shall use them in our analysis. 

As it is trite to note, formal or constitutional provisions are not the same as actual 

behavior. So to strengthen our analysis, we use a new measure intended to operationalize 

 

44 For full details on these measures, see the codebook. 

45 "Judicial independence lies at the heart of a well functioning judiciary and is the cornerstone of a 

democratic, market-based society based on the rule of law" (Gail Lecce, "USAID Preface" in USAID 2002) 46 "In democratic, market-based societies, independent and impartial judiciaries contribute to the equitable 

and stable balance of power within the government. They protect individual rights and preserve the security 

of person and property" (USAID 2002, 6). 

90
actual levels of judicial independence. The measure is coded from the U.S. State 

Department’s annual Country Reports on Human Rights. Since the 1980s, the raw 

materials for generating this measure of judicial independence have appeared in the 

Country Reports. Near the beginning of each report there is now a brief summary 

judgment assessing the actual state of judicial independence in that country for that year. 

Before the current project had begun, Tate and Keith had initiated the work necessary to 

produce from these judgment paragraphs a standards based measure47 of actual judicial 

independence that is directly analogous to the Political Terror Scale measures that are a 

staple of empirical studies of personal integrity rights abuse, including this one.48 That 

work is now completed and an "actual judicial independence" measure has been 

integrated into our dataset. 

Descriptive analysis of the distribution of our discrete measure of judicial 

independence over time shows a pattern of decline across the period 1990-2005. (Figure 

A7.1 graphs our judicial independence measure over time.) 

 

47 Standards-based measures are derived by coding the content of standard textual materials describing a 

particular phenomenon (judicial independence in this case) into the categories of a rigorous classification 

scheme. Coders are extensively trained for their tasks. Such measures can be demonstrated to have 

considerable face validity and reliability. 

48 David Cingranelli of the University of Binghamton and David Richards of Memphis State University 

have also coded but not yet completed or released a judicial independence measure from the material in 

these paragraphs over time. Cingranelli and Richards kindly shared a preliminary version of their measure 

with us. Analyses of the two measures showed that, when completed, the Cingranelli and Richards 

indicator is likely to correlate moderately highly with the Tate-Keith measure. This is reassuring evidence 

of the measure’s validity, since the coding rules for the Cingranelli and Richards measure are significantly 

different from those used to create the Tate-Keith measure. 

91
Figure A7.1 

Genuine Relationship 

The genuine relationship explanation posits that leaders who find themselves 

under pressure to improve their human rights performance actually respond by becoming 

more repressive because they feel their grip on power to be threatened. 

Threat Perception 

 Our analysis plan called for confronting this explanation more directly by 

including into our full statistical models of human integrity performance empirical 

indicators of events or circumstances that would be perceived as threatening by 

potentially repressive leaders. We have collected and cleaned for the period 1990-2004 

comprehensive data on four threat indicators: Organized Nonviolent Protest, Organized 

Nonviolent Rebellion, Organized Violent Rebellion, and Civil War. The analysis 

explores whether these indicators—taken individually or collectively—serving as proxies 

for leaders’ perceptions of threats to their continuation in power, perceptions that then 

cause them to abuse rights more as they perceive themselves to be more threatened. 

In discussing these four interpretations of the negative relationship between human 

rights assistance and human rights protection, it is important to remember that they are 

not mutually exclusive. Indeed, all four could be partial explanations for the relationship. 

92
The analysis we perform attempts insofar as possible to identify the extent to which each 

is valid. 

The Model 

 We begin with the baseline model for human rights that underlies Table 13 (see 

Tables A6.2 and A6.7), which reports a negative coefficient of -.664 for the impact of 

USAID DG Rule of Law/Human Rights Aid on the Respect for Human Rights Index. To 

the baseline model, we add the several variables discussed above that are intended to test 

the measurement/reporting error, omitted variables, and genuine relationships alternative 

hypotheses. To recapitulate, these new variables added to our model are: 

1. The Freedom of Press Index 

2. Landman’s indicators of International Governmental Organization (IGO) 

membership and the number of International Non-Governmental Organizations 

(INGOs) registered in each country. 

3. The 23 formal indicators of constitutional provisions created by Tate and Keith to 

measure protections for rights and freedoms. Preliminary analyses using these 

measures tested their impacts individually. This model was, not unexpectedly, 

cumbersome and confusing, with 23 separate constitutional provisions impact 

coefficients to interpret. It also provided little evidence that any particular 

indicator had an impact that required representing in the model. Consequently, 

as we have done with many other variables represented by multiple indicators, 

we factor analyzed the three subsets of variables operationalizing guarantees of 

rights and liberties, promoting judicial independence, and limiting declarations of 

emergency rule. A few indicators did not load to any substantial degree on the 

first principal components representing rights and freedoms, formal judicial 

independence, and limiting emergency rule. To allow for the possibility that 

these maverick indicators could have different effects, we retained them in our 

multivariate model, using them along with the indexes derived from the factor 

analyses. 

4. The indicator of actual (behavioral) judicial independence derived from the TateKeith coding of the U.S. State Department reports. 

5. The four indicators of domestic unrest intended to serve as proxies for leader’s 

perceptions of threat, namely organized nonviolent protest, organized nonviolent 

rebellion, organized violent rebellion, and civil war. 

Table A7.1 gives the results of the mixed models analysis for the expanded model 

that includes these variables in addition to the base model indicators. They document 

considerable success for some of the measures we are using to test the alternate 

hypotheses we are considering as possible mediating variables that would explain away 

or at least reduce the negative relationship between USAID DG human rights assistance 

and human rights performance (respect for human rights index, RL 15). 

93
Table A7.1: Final Multivariate Human Rights Model

Level 1

 Coefficient

 

S. Error Sig.

Sub-Sector USAID Democracy Assistance 

Elections 

Rule of Law: Human Rights 

Rule of Law: Non-Human Rights 

Civil Society 

Governance 

Other Assistance 

USAID 

National Endowment for Democracy 

US Assistance other than USAID or NED 

Regional-Subregional DG 

Regional-Subregional Non-DG 

Other Donor Assistance on DG 

Other Donor Assitance on Non-DG 

Economic and Political Factors 

Annual Growth GDP Per Capita 

US Military Assistance Priority 

Democratic Diffusion 

Social and Political Conflict 

State Failure Indicator 

Iraq2004

 

.019 

-.589** 

.164* 

-.133** 

.015 

 

.000 

-.195 

.005**

-.004 

-.051 

.004 

.000 

 

.010 

-.047 

-.225 

-.002 

-3.633**

-2.156 

.090 

.226 

.092 

.065 

.056 

.002 

.776 

 .003 

.659 

.055 

.003 

.000 

.015 

.091 

.384 

.002 

 .450 

9.072 

.830 

.009 

.077 

.041 

.788 

.929 

.801 

.050 

.996 

.351 

.191 

.809 

.498 

.603 

.558 

.411 

1.42E-015 

.812 

Table A7.1: Final Multivariate Human Rights Model (Continued)

ALTERNATE HYPOTHESES 

Real Effects: Threat Indicators 

Threat/Protest Index -.476** .106 7.54E-006 

Civil War -4.369** .529 3.19E-016 

Omitted Variables: Constitutional Provisions Indicators 

Five Freedoms Index .255 .367 .488 

Freedom to Strike -.037 .611 .951 

Fair Procedure Index .092 .390 .814 

Formal Judicial Independence Index -.124 .591 .834 

Courts have Exclusive Competence -.381 .583 .514 

No Special, Military Courts -.153 .433 .724 

State of Emergency Varimax scores Factor 1 .359 .357 .315 

State of Emergency Varimax scores Factor 2 .513 .337 .128 

Omitted Variables Actual Judicial Independence 

Actual Judicial Independence .545** .216 .012 

Measurement/Reporting Error 

Intergovernmental Organizations Membership -.043** .016 .007 

94
International Nongovernmental organizations count -.274 .437 .531 

Index of Freedom of Press .231** .030 2.57E-014 

Level 2 

Effect on (Level-1) Intercept 

Average Intercept 49.077** .927 7.41E-165 

Prior Democracy -.033 .111 .764 

Income per Capita -.035 .072 .628 

Ethnic Fractionalization -3.405 2.502 .175 

Size in Square Km -.001** .000 .012 

Human Development Index 13.406** 4.311 .002 

Effect on (Level 1) Trend 

Average Slope for Growth Curve .058 .052 .268 

Prior Democracy -.010 .007 .142 

Income per Capita .012** .005 .020 

Ethnic Fractionalization -.053 .168 .754 

Size in Square Km -2.01E-005 2.08E-005 .335 

Human Development Index .055 .287 .849

Model Statistics and Variance Parameters 

Random Variance (Intercept) 27.787 4.523 

Random Variance (Slope) .0562 .022 

Autocorrelation. (rho) .338 .034 

Model Deviance/AIC 12155.49 12191.49 

Note: **significant at p<.05; *significant at p<.10 (two tailed). Dependent Variable: RL 15 - Respect for 

Human Integrity 

Measurement Error/Reporting Increase Results 

Two of the three indicators we used to test the possibility that the negative 

relationship between USAID DG Human Rights aid and human rights performance were 

due to measurement error have relationships with our dependent variable that are 

statistically significant beyond p = .01. However, they run in different directions. 

Increases in press freedom produce increases in respect for integrity rights. This is not an 

illogical or necessarily unexpected relationship. Indeed, proponents of human rights 

would not doubt be gratified to see that greater freedom of the press, something that they 

would probably regard as a good thing in and of itself, also produces increases in respect 

for personal integrity. But this positive relationship is not consistent with the 

measurement error hypothesis, which posits that greater press freedom would lead to 

more reporting of human rights abuses, even if the increases were not in fact occurring, 

and thus produce a negative effect on respect for human rights. Thus the good news is 

that increasing press freedom does reduce human rights abuse while the bad news is that 

it does not ameliorate the anomalous negative impact of USAID DG human rights aid on 

human rights performance. 

95
 

49 This was not the case for the intergovernmental organizations membership measure. Its frequency 

distribution was quite normal and its logged distribution strongly peaked and compressed. 

50 We should stress again, however, the fragile nature of the IGO/NGO variables: A very substantial 

portion of their scores have been imputed by linear interpolation and thus represent presumed and 

unnaturally smoothed, rather than measured, patterns of change. 

51 We don’t want to push this too far. It is still possible, for example, that assessments of judicial 

independence are colored by assessments of human rights performance and vice versa. 

96

Increases in intergovernmental organization memberships, in contrast, produce 

decreases in human rights performance, an effect that is consistent with the measurement 

error hypothesis. The greater the number of intergovernmental organization 

memberships a country has, the greater the ability to discover and report rights abuses 

and, thus, plausibly, the less its apparent tendency to respect human rights, even if there 

were no actual increases in human rights abuse. 

The organizational membership results would provide greater support for the 

measurement error/reporting hypothesis if the relationship for international nongovernmental registrations were also in the expected negative direction and statistically 

significant. In fact, the relationship for the logged version of international nongovernmental registrations is in the expected negative direction, but is not statistically 

significant. Following Landman, we logged this variable because its frequency 

distribution was skewed to the right, while its logged distribution naturally was not.49 In 

fact, in an alternative version of the model reported in Table A7.1, the coefficient for the 

raw (unlogged) version of the international non-governmental memberships variable was 

not only negative but highly statistically significant. Since our decision to use the logged 

version of nongovernmental memberships is something of a judgment call, the 

relationships for the IGO/NGO variables provide consistent support for the hypothesis 

that measurement error due to more effective reporting of rights abuses is part of the 

explanation for the negative relationship between human rights aid and human rights 

performance.50

Omitted Variables 

 The results for the indicators we included in the model to test the hypothesis that 

omitted variables might be partially responsible suggest that there is indeed one omitted 

variable that significantly affects human rights abuse. Judicial independence is positively 

indeed related to respect for human rights and its coefficient is statistically significant 

beyond the .01 level. This is, once again, a gratifying result for proponents of both 

human rights and judicial independence, and is consistent with the speculation that if 

human rights specific assistance goes disproportionately to countries with weak traditions 

of judicial independence, it might appear to promote abuse of human rights. These 

results are further gratifying because of the uncertainty surrounding the validity of a 

measure of actual judicial independence based on the narrative summaries of judicial 

independence in the State Department Human Rights reports.51 
The predictive success of actual judicial independence does not extend to the 

formal constitutional provisions measures, however, including the formal measures of 

judicial independence. None of the factor analysis-based indexes or the individual 

provisions retained in Table A7.1 is accompanied by a coefficient that comes even 

remotely close to being statistically significant. We obtained the same non-result no 

matter how we configured the constitutional provisions measures: their coefficients were 

insignificant when calculated for their 23 individual indicators or for various 

combinations of the indexes we created to summarize them into more manageable 

categories. Thus, whatever their utility in other contexts, the constitutional provisions 

(see Keith, Tate, and Poe 2007, for example), play no role in explaining respect for 

human rights in the context of our mixed model for the period 1990-2004. 

Genuine Relationship 

 Our attempt to get at the possibility that the relationship between USAID DG 

human rights assistance and respect for human rights reflects a real relationship, posits 

that assistance leads to greater activity promoting human rights which is seen as 

threatening by leaders, who then resort to greater rights abuse to try to secure their hold 

on power. To test this insight, we operationalized and tested the effects of four varieties 

of domestic protest or opposition to leaders on human rights abuse. The three less 

threatening varieties of protest/opposition, Nonviolent Protest, Nonviolent Rebellion, 

Violent Rebellion, were well-summarized by the first principal component of a factor 

analysis of the four items, but the fourth item, Civil War, was not. Thus our model 

includes a Threat/Protest index based on the protest/rebellion items and civil war as a 

separate independence variable. 

Both threat indicators are strongly negatively related to respect for human rights 

and are statistically significant beyond the .001 level. There is clear evidence, based on 

these indicators, at least, that the anomalous negative relationship that concerns us in this 

section may reflect a real abusive reaction by leaders who are threatened by the human 

rights assistance their countries receive. 

AID Assistance and Other Relationships 

 Five Level 1 and three Level 2 variables in Table A7.1 that were in our base 

model demonstrate coefficients that are statistically significant at or beyond any 

conventional level. Four of the Level 1 variables are for various measures of USAID DG 

and other assistance, including the principal one of concern to us here, that between 

USAID DG Human Rights assistance and respect for human rights. We will return to 

these measures below. 

Non-AID DG related variable effects

 The fourth Level 1 (and only non-assistance related) variable is the “State 

Failure” indicator, which has a highly statistically significant negative impact on respect 

for human rights. It seems logical and obvious that state failure would create conditions 

97
under which human rights abuse would increase. We included the state failure indicator 

because it has been in our base models all along. However, we realized, on second 

thought, there is also a measurement problem with the state failure indicator as we have 

operationalized it. State failure is operationalized as having occurred in states that 

experience ethnic wars or “genocide and politicide” events, among other events. Clearly 

the latter and probably the former must overlap significantly with human rights abuse and 

thus are not independent of our dependent variable.52

As in our base model, two Level 2 variables, Country Size (in sq. km) and the 

Human Development Index demonstrate statistically significant effects on the Level 1 

intercept and one, Income per capita, affects the level one trend. Country Size decreases 

and the Human Development Index increases respect for human rights (by increasing the 

Level 1 intercept). Per capita income also increases respect for human rights by 

increasing the slope of the Level 1 trend. All these effects were present in our base 

model. 

Assistance related variable effects

 There is, perhaps, some irony in the fact that USAID DG Rule of Law NonHuman Rights assistance has a positive impact on human rights performance (significant 

at .08) while Rule of Law Human Rights assistance continues to exhibit its negative 

impact, even after controlling for indicators intended to operationalize various alternate 

hypotheses. The irony lessens when one realizes that most of the funding in this category 

is related to promoting judicial development. Our model has demonstrated that a 

previously omitted variable, actual judicial independence, promoted respect for human 

rights. That the same is true for expenditures intended to promote judicial development 

is not just consistent with this finding, but also would be gratifying to proponents of rule 

of law more generally.53

The negative impact of USAID DG Civil Society assistance (significant at .05) 

shows up in our current model for the first time. In previous base line analyses, its 

relationship with respect for human rights was not significant. That the negative 

relationship shows up in a model in which we have tried explicitly to represent the effect 

of organizational growth (through the IGO/INGO variables, however weakly measured) 

is important, as USAID DG Civil Society assistance is granted for the purpose of 

supporting “non-governmental associations (including civic groups, professional 

associations, and labor unions).” 

 

52 “Social and Political Conflict” had a statistically significant negative coefficient for its effect on respect 

for human rights in our base model, but does not have such an effect here, no doubt because we have 

included more directly relevant measures of threat and protest. 

53 The positive impact of Rule of Law Non-Human Rights assistance is also exhibited by total Non-AID or 

NED United States development assistance (the grand mean centered version of variable AID_2). We view 

this as a positive result. Beyond that, it is difficult to know what beyond that to make of this relationship 

since AID_2 is a residual variable that our codebook notes is “Estimated as the difference between total 

Economic Assistance (loans and grants reported by the Greenbook) and the AID and AIDNED variables.” 

98
All the measures intended to capture as directly as possible the effects of 

associational/organizational support and growth are depicted by their coefficients as 

promoting human rights abuse, rather than respect for human integrity. Perhaps they all 

fit into our conception that decreases in respect for human rights may reflect better 

reporting of abuses made possible by stronger and more numerous organizations, both 

non-governmental and governmental but international. On the other hand, we can also 

easily conceptualize increasing financial support for and growth of such organizations as 

linking to greater perceived threat by governmental leaders who are uneasy about 

retaining their positions and inclined to respond to such threats with greater repression. 

 

Bottom Line

We are left with a bottom line that leaves USAID DG Rule of Law assistance for 

Human Rights (and to a lesser extent, assistance for Civil society) producing less, rather 

than as one would expect and hope, more respect for human integrity. Our efforts to 

untangle the web of relationships that may underlie these distressing and presumptively 

anomalous relationships and to model them statistically have led to some important 

findings. But it has been largely unsuccessful in its basic purpose. We are left with the 

conclusion that, as least as measured in our data or as administered across the period 

1990-2004, USAID DG assistance in support of human rights is associated with effects 

opposite to those for which it is presumably given. 

Certainly this is not the end of this story. Our perception of the anomalous nature 

of the negative relationship that has been our focus in this section is so strong that we 

find it very difficult to simply accept it. Even less do we find it comfortable to 

recommend that human rights aid from USAID Rule of Law programs be re-examined or 

even terminated, especially given (1) the positive link between respect for human 

integrity and Rule of Law assistance directed not at promotion of human rights 

specifically but more generally at development of judicial independence, (2) the strong 

positive impact of judicial independence itself on respect for rights, and (3) the otherwise 

quite positive statistical demonstrations that USAID democracy assistance is generally 

successful in producing more democracy. 

An appropriate further step to take in examining the effects of human rights aid 

would be to design and execute a set of country specific studies employing both 

qualitative and quantitative techniques to focus on the giving, receiving, administration, 

and putative effects of that aid. Such research might produce insights into how such an 

anomalous effect results might be produced or, possibly, into what other mediating or 

intervening variables need to be taken into consideration to account for the relationship 

that persists at the end of this study. 

99
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