Arrangements by which politically connected firms receive economic favors are a common feature around the world, but little is known of the form or effects of influence in business-government relationships. We argue that influence not only brings significant privileges for selected firms, but requires firms to relinquish certain control rights in exchange for subsidies and protection. We show that, under these conditions, political influence can actually harm firm performance. Enterprise surveys from approximately 8,000 firms in 40 developing countries indicate that influential firms benefit from lower administrative and regulatory barriers (including bribe taxes), greater pricing power, and easier access to credit. But these firms also provide politically valuable benefits to incumbents through bloated payrolls and greater tax payments. These firms are also less likely to invest and innovate, and suffer from lower productivity than their non-influential counterparts. Our results highlight a potential channel by which cronyism leads to persistent underdevelopment.

1. The Costs of Political In‡uence
Firm-Level Evidence from Developing Countries1
Raj M. Desai2
Georgetown University
Anders Olofsgård3
Georgetown University
June 2010
1
We are grateful for comments from Torbjörn Becker, Lael Brainard, Marc Busch, Tore Ellingsen,
Garance Genicot, James Habyarimana, Homi Kharas, Chloé Le Coq, Johannes Linn, Rod Ludema,
Lars Persson, Dennis Quinn, Vijaya Ramachandran, Anna Sjögren, and David Strömberg. Previous
drafts were presented at the Georgetown Public Policy Institute, the Inter-American Development
Bank, the Research Institute for Industrial Studies, the Delhi School of Economics, Stockholm Uni-
versity, the Stockholm School of Economics, the Brookings Institution, and the annual meeting of the
American Political Science Association.
2
Address: Edmund A. Walsh School of Foreign Service and Department of Government, George-
town University; Brookings Institution. E-mail: desair@georgetown.edu.
3
Address: Edmund A. Walsh School of Foreign Service and Department of Economics, Georgetown
University; Stockholm Institute of Transition Economics, Stockholm School of Economics. E-mail:
afo2@georgetown.edu.

2. Abstract
Arrangements by which politically connected …rms receive economic favors are a common
feature around the world, but little is known of the form or e¤ects of in‡uence in business-
government relationships. We argue that in‡uence not only brings signi…cant privileges for
selected …rms, but requires …rms to relinquish certain control rights in exchange for subsi-
dies and protection. We show that, under these conditions, political in‡uence can actually
harm …rm performance. Enterprise surveys from approximately 8,000 …rms in 40 develop-
ing countries indicate that in‡uential …rms bene…t from lower administrative and regulatory
barriers (including bribe taxes), greater pricing power, and easier access to credit. But these
…rms also provide politically valuable bene…ts to incumbents through bloated payrolls and
greater tax payments. These …rms are also less likely to invest and innovate, and su¤er from
lower productivity than their non-in‡uential counterparts. Our results highlight a potential
channel by which cronyism leads to persistent underdevelopment.

3. Introduction
Arrangements by which …rms with close ties to incumbent political authorities receive favors
that have economic value are a pervasive feature of business-government relationships in
countries around the world. These favors typically take the form of privileges enshrined
in formal policies, discriminatory enforcement of formal rules, or rewards and/or sanctions
granted informally. The merits of these privileges have featured prominently in recent debates
on industrial policy in developing economies. For defenders of industrial policy a state role
in promoting technological "upgrading" remains vital in helping the private sector exploit
(or defy) its comparative advantage (Rodrik 2004; Lin 2009; Lin and Chang 2009). For
its detractors, industrial policy invites corruption by encouraging …rms to devote greater
resources to securing political connections (Pei 2006). Leaving aside the theoretical disputes
at the heart of these debates, the critical question relates to how political connections a¤ect
…rm-level decisions, and ultimately, enterprise productivity and competitiveness.
Despite the prevalence of these arrangements around the world, however, relatively little
is known about the precise form political in‡uence takes between …rms and politicians, or its
consequences. What characterizes the bargain between in‡uential …rms and governments?
How do in‡uential …rms compensate governments, if at all, for any bene…ts they receive?
Recent …rm-level analyses have examined various determinants of political in‡uence, and
how these connections a¤ect market valuation. Others have detailed the channels through
which the bene…ts accrue. Still other, …nally, have explained how “systems”of in‡uence come
into being, and why they survive. Much less is known, however, about how these political
connections a¤ect decisions within …rms. Moreover, we know little of the strings, if any, that
may come attached with political in‡uence.
We investigate both the characteristics that de…ne political in‡uence among …rms in
1

4. developing countries as well as the e¤ects of that in‡uence on company behavior and pefor-
mance. We argue, as other have, that in‡uential …rms are likely to receive numerous bene…ts
through industrial or quasi-industrial policies, but also that these favors can be costly. In
particular, in‡uential …rms may have to relinquish a portion of their control rights in order
to provide rents of political value to public o¢ cials. Based on these assertions, we argue that
these arrangements, despite carrying some known bene…ts to the …rms, may tend to weaken
incentives to invest or innovate, and ultimately, worsen peformance.
We draw on …rm-level surveys in over 40 developing countries, consisting of over 8,000
…rms. We …nd that politically in‡uential …rms do indeed face a more favorable business
environment than their non-in‡uential counterparts across several dimensions. However, in-
‡uential …rms also tend to carry bloated payrolls and report more (hide less?) of their sales
to tax authorities, suggesting two mechanisms by which they o¤er political compensation:
employment levels and tax revenues. In‡uential …rms are also less likely to open new prod-
uct lines or production facilities, or to close obsolete ones, they report lower real growth in
sales and shorter investment horizons. Finally, in‡uential …rms also su¤er from lower pro-
ductivity than non-in‡uential …rms. These results hold up to di¤erent speci…cations of our
empirical model, including propensity-score matching to adjust for the fact that in‡uence is
not randomly assigned. Taken together, our results explain why crony capitalism persists in
countries despite it’s adverse e¤ects on long term economic peformance; it may, at least in
the short run, be in the interest of economic and political elites. Our …ndings also o¤er some
con…rmation for the view that politically-devised restrictions that block access to technologies
and preserve rents for elites are at the heart of prolonged economic under-development.
2

5. Political In‡uence in Business-State Relations
Three sets of questions must be addressed in order to assess the characteristics and e¤ects of
…rm-level political in‡uence: (i) what bene…ts do in‡uential …rms receive? (ii) what bene…ts
do politicians receive?; and (iii) what are the economic consequences of political in‡uence?
For the …rst question, arrangements by which political authorities grant favors to in‡uential
economic agents that allow these agents to earn above-market returns has been documented
in case studies of countries around the world. On the other hand, little empirical investigation
has been conducted regarding the last two questions.
Bene…ts to Firms
The speci…c nature of relationships of in‡uence varies from country to country. Studies of US
campaign …nance, political action committees, and the revolving door between lobbying …rms
and congressional sta¤ o¢ ces, have typically identi…ed the ties that politically-in‡uential US
…rms can forge with speci…c political …gures (Agrawal and Knoeber 2001; Ang and Boyer 2000,
Krozner and Stratmann 1998). An analysis of the bene…ts to …rms from the seniority system
in the US Senate, for example, showed that the sudden death of Senator Henry "Scoop"
Jackson (D-WA) in 1983 led to an abnormal drop in stock prices of …rms contributing to
his re-election campaign (Roberts 1990). At the same time, …rms connected to his successor
as ranking member of the Senate Armed Services Committee, Senator Sam Nunn (D-GA),
bene…ted from an unexpected rise in stock prices. Similarly, in Brazil, Claessens, Feijen, and
Laeven (2007) found that …rms that provided contributions to federal deputies experienced
higher stock returns around election time.
In other developing nations, political in‡uence is usually obtained through a combination
of kinship ties, political alliances, ethnic solidarity, or …nancial dealings between owners and
3

6. political elites. Fisman (2001) showed that …rms connected to the Suharto family in Indonesia
experienced a negative shock to their stock values when rumors circulated that Suharto was
experiencing serious health problems. Johnson and Mitton (2003), looking at the introduction
of capital controls in Malaysia in the aftermath of the Asian …nancial crisis, argue that these
controls were designed speci…cally to bene…t …rms with political connections. The political
in‡uence of economic actors is often supported by the absence of con‡ict-of-interest laws.
Sitting in the Ukrainian Duma in the 1990s, for example, were the heads of several major
(formerly state-owned) privatized companies (Åslund et all. 2001).
Favors granted to in‡uential …rms have large economic value. In Pakistan, politically
connected …rms borrow 45 percent more and have a 50 percent higher default rate than other
…rms (Khwaja and Mian 2005). These di¤erences in access to credit are all driven by lending
practices from government banks, and bene…ts increase with the strength of the political
connections. Cross-national evidence also shows that …rms whose controlling shareholders
or top managers are members of legislatures or national governments enjoy easier access to
debt …nancing, as well as also lower taxation and greater market power (Faccio 2006). Chong
and Gradstein (2007) …nd that managers who rate their own …rm’s in‡uence over laws and
regulations highly also consider the judicial system and tax regulations to be less constraining
for the …rm’s growth, suggesting that in‡uential …rms face lower costs of regulation, and more
reliable contract enforcement and more secure property rights. Conversely, …rms excluded
from these privileges may be forced to rely on bribery and subterfuge in order to compete
with more in‡uential …rms. In the formerly centrally-planned economies of Eastern Europe
and the former Soviet Union, for example, Hellman and Kaufmann (2002) …nd that …rms
that perceive a strong “crony bias” (i.e., are less in‡uential than crony …rms) tend to pay
more bribes.
4

7. We characterize two privileges granted to selected …rms: protection and subsidy. Both can
be provided directly or indirectly. Protection from competition can involve direct instruments
such as tari¤s, monopoly rights, etc. Subsidies can be provided via budgetary or o¤-budget
(though still public) funding. But protection can also involve mechanisms that hinder new
entrants such as high-cost, burdensome, or time-consuming licensing procedures, barriers to
accessing capital and land, or predatory behavior by public o¢ cials targeted against potential
competitors. Similarly, indirect subsidies can be provided in the form of tax breaks, cheap
credit, dual exchange rates, or greater depreciation allowances. It should be recognized
that, in all cases, what matters is that …rms that are the bene…ciaries receive preferential
treatment— their start-up costs must be lower than other …rm’s costs, their loan interest
payments must be relatively smaller than that of other …rms, their access to land must be
easier than for other …rms, and so on.
In‡uence as Mutual Exchange
A common perspective, then, is that politically-powerful …rms manipulate policies and shape
legislation in order to give themselves long-term material bene…ts (e.g. Hellman et al. 2003,
Slinko et al. 2005). But these "state-capture" models convey the mistaken impression that
governments are unwitting victims of this behavior rather than willing participants in a
relationship that is mutually bene…cial to politicians and …rms alike. Two facts seem to reject
this perspective. First, politically-in‡uential …rms typically owe their wealth (and in many
cases, their origin) to the state’s intervention in, and regulation of, the economy. Russian
…nancial-industrial groups prospered through arbitrage between world market prices (mainly
for natural resources) and domestic prices for outputs that remained controlled (Johnston
2000). In countries such as Mexico and Thailand, companies that acquired concessions during
5

8. the privatization of state telecoms companies were able to …x prices, restrict the supply of
connections, or engage in predatory pricing against would-be competitors while anti-trust
authorities looked the other way (Winter 2007; Phongpaichit and Baker 2004). In all cases,
speci…c political parties or public o¢ cials bene…ted directly as a result of elevating these …rms
to positions of political in‡uence.
Second, substantial evidence from around the world suggests that political in‡uence is
better characterized as an “elite exchange”between …rms and politicians, whereby economic
rewards are transferred to …rms that, in return, provide politicians with politically-valuable
services. While much attention has previously focused on how …rms bene…t from these
rewards, more is now known of the types of rents politicians extract from …rms. In the 1990s
in‡uential Russian businesses, for example, were more likely to be subject to price controls
and more frequent inspections— both being bene…cial to politicians (Frye 2003). Meanwhile,
Russian regional governments maintained control over local privatized …rms, even as these
…rms also obtained signi…cant rewards through tax breaks, investment credits, subsidized
loans, and direct subsidies through regional legislation (Stoner-Weiss 2006; World Bank 2005).
More generally, Choi and Thum (2007) argue that the provision of rent streams from …rms
to governments is a fundamental part of the in‡uence “bargain”allowing …rms to invest in
stabilizing the political regime because, in case of a changeover, the …rm will lose politically
granted bene…ts.1
One channel by which powerful …rms can reward politicians is through employment.
Shleifer and Vishny (1994) argue that in‡uential …rms receiving public subsidies will, in re-
turn, cede some control rights over employment decisions to politicians (who bene…t from low
unemployment rates). Robinson and Verdier (2002) also emphasize the advantage of control
1
For this reason, crony capitalism is sometimes considered a second-best solution to the government’s
commitment problem, since politicians share in the above-market returns that economic actors receive over
time (Haber 2006).
6

9. over employment decisions, suggesting that politicians can generate support through selec-
tive job o¤ers that are contingent on government survival. As long as these jobs pay better
than the market rate, potential supporters have a joint stake in keeping incumbents in o¢ ce.
Politicians facing unemployment can also design and implement a range of “hidden”(implicit,
o¤-budget) subsidies or other forms of preferential treatment to keep up employment levels
in private …rms in order to avoid signaling economic mismanagement (Desai and Olofsgård
2006). Bertrand, et al. (2004), …nd that politically connected business leaders in France
generate "re-election favors" to incumbent politicians by creating more jobs, particularly in
more electorally contested areas and around election years.
Although less investigated, a second channel of politically-valuable bene…ts is the revenue
stream from …rms to the state. Examining the tax compliance of …rms in Eastern Europe and
in the former Soviet Union, Gelbach (2006) …nds that the ability of …rms to hide revenues from
tax authorities accounts for di¤erences in …rm-level satisfaction with state-provided goods and
services, and in particular, that larger …rms are less likely to hide tax revenues and tend to be
happier with public goods. In formerly state-socialist economies, the ability of …rms to provide
revenues is often associated with privileges. In Russia, for example, …nancial companies that
…nanced the de…cit were, in turn, given shares in natural resource companies under the loans-
for-shares program in the mid 1990s (Shleifer and Treisman 2000). Alternatively, leaders in
Latin America have often targeted tax hikes at politically-powerful businesses, especially
during election cycles (Weyland 2002). These examples raise the possibility that in‡uential
…rms may be more taxable making them, at once, the recipients of tax breaks as well as targets
of more stringent monitoring by tax authorities— a possibility suggested by the observation
that cronyistic ties between corporations and governments can actually reduce monitoring
costs (Kang 2003).
7

10. We therefore consider political in‡uence to be a form of mutual exchange between se-
lected …rms and incumbent political authorities by which …rms receive economic privileges.
In return, in‡uential …rms provide various rents of political value. First, politicians grant
in‡uential …rms economic privileges that e¤ectively shield these …rms from market forces or
lower their cost of doing business. Second, in‡uential …rms relinquish some control rights to
politicians who can extract bene…ts from …rms that are politically valuable. Note that the
…rm-level e¤ects of ceding of control rights is not the same as that of providing direct transfers
to politicians. Contributions to incumbents’electoral campaigns, for example, constitute a
direct transfer but do not a¤ect investment or production decisions since marginal costs or
marginal revenues remain una¤ected. By contrast, if politicians impose restrictions on …ring
(to limit local unemployment) or impose ad hoc taxes (to access revenues that can then be
showered on potential voters), …rm performance on the margin will be a¤ected.2
It is important to emphasize that, although political connectedness is often considered a
form of corruption, there are two important di¤erences. First, unlike "administrative" corrup-
tion, in‡uence does not necessarily involve bribe-taking by public o¢ cials. In fact, in‡uential
enterprises or individuals may actually be shielded from predatory public o¢ cials. Second,
unlike corruption, in‡uence can be perfectly legal— obtained through political …nancing or
lobbying, through favoritism on the part of regulators, through industrial policies, laws or
statutes granting special favors, or simply through selective enforcement of existing rules.
The e¤ects of preferential treatment on enterprise behavior and overall economic perfor-
mance of countries where selected …rms are supported via industrial policies have long been
debated, not least of all in the East Asian context. Prior to the East Asian Crisis of the
late 1990s, for example, arguments had been made that preferential treatment to selected
2
Or, political appointees in management positions may be less skilled, driving down productivity both in
the aggregate and at the margin.
8

11. industries contributed to export-led growth (Wade 1990), high levels of productivity (Jomo
and Edwards 1993), and rapid capital accumulation (Woo-Cummings 1999). To some, how-
ever, the crisis revealed industrial policy’s dark side: a system of crony capitalism by which
politically-connected …rms were temporarily able to earn above-market returns while social-
izing their risk (Krugman 1998; Corsetti 1999).3 In the next section we provide a simple
formal illustration of the e¤ect of political in‡uence on …rm investment incentives, and by
extension, productivity, when political in‡uence requires …rms to cede control rights in return
for preferential treatment.
The Investment Decision
A continuum of …rms of size one uses capital (k) and labor(l) in a Leontie¤ production tech-
nology, yielding quantity Q = minfk; lg. Some selected …rms are protected from competition
through monopoly rights, regulatory forebearance, or bureaucratic predation against com-
petitors, and/or subsidized via budgetary transfers, tax breaks, access to cheap credit, etc.
The e¢ cient per-unit labor and capital costs to …rm i are, respectively
wi + I , and
r I ,
where I is an indicator function taking on the value 1 if the …rm is a recipient of privileges
that have economic value (protection and subsidies), 0 otherwise. We will refer to these as
in‡uential …rms and non-in‡uential …rms, respectively. E¢ cient per-unit labor costs depend
on wages wi and worker e¤ort; parameterizes the negative e¤ect on worker e¤ort due to
3
We focus exclusively on …rm-level e¤ects. Arguments have been made, however, that selective protection
and subsidy can harm aggregate welfare, e.g., by distorting competition and leading to production of goods of
inferior quality, or by increasing costs to state budgets. Khwaja and Mian (2005) estimate that the distortion
on …nancial markets from politically-motivated lending incurs a cost of 0.3- 1.9 percent of GDP.
9

12. the absence of competition.4 Additionally, represents the capital cost reduction due to
subsidization.
In addition to capital and labor, …rms face two additional costs. The …rst, denoted c,
corresponds to the administrative barriers that …rms must overcome, including onerous and
costly start-up procedures, bribes paid, as well as the cost-equivalent of delays in being
granted licenses and permits, harassment by police or inspectors, and other methods poten-
tially used by public o¢ cials to extract rents. We normalize this cost to zero for in‡uential
…rms. The second cost consists of any direct payments or transfers to politicians (including
campaign contributions), denoted by .
Firms are price takers, but bene…t from higher prices when protected from competition.
We denote the price as p ( ) = 1+I , where represents the bene…t from protection. Demand
for …rms’products is uncertain. With probability that demand is high, …rms sell Q at price
p ( ); with probability (1 ) that demand is low, …rms sell Q at price p ( ), and Q >Q.
Firms (i) make an investment decision (i.e., whether to augment their capital stock), and
(ii) set employment levels. Finally nature draws demand conditions. The initial employment
decision is, therefore, made under uncertainty regarding Q. We assume that …rms bene…t-
ting from industrial policies have partially ceded control rights over employment decisions,
and are prevented from shedding labor. Consequently, non-in‡uential …rms can …re workers
without cost once Q is realized, whereas in‡uential …rms cannot. It follows from the Leon-
tie¤ production function that once Q is realized, the pro…t-maximizing employment level is
min fk; Qg. In‡uential …rms, unable to follow this rule, will have to retain the number of
workers decided under uncertainty.
4
X-e¢ ciency losses due to weak competitive pressures (Leibenstein 1966) typically form the analytical core
of microeconomic models that examine how economic agents’e¤orts are in‡uenced by competitors. Where
principals compare the outcome of agents’ e¤orts across competing …rms, compensation contracts can be
designed with stronger incentives, and agents will thus expend greater e¤ort (Vickers 1995). But without
competition, the ability to use such yardsticks is severely limited. Here we follow Vickers’approach.
10

13. Firms start with capital and employment at level k, the optimal level under low-demand
conditions. Each …rm decides whether to increase its capital stock to k as well as the number
of additional workers to hire. It follows from the production technology that l = k for
non-in‡uential …rms for all k and for in‡uential …rms when k =k: When k = k, in‡uential
…rms will only employ extra employees such that l = k if the bene…t of being able to meet
the extra demand in good times exceeds the risk of ending up with bloated payrolls in bad
times. This condition is given by
wi < p ( ) I :5 (1)
When this condition is not satis…ed, l =k even though k = k.
We can now compare expected utility with and without investment. The representative
…rm’s expected pro…t if it does not invest can be written as
p ( ) k (r I ) k (wi + I ) k (1 I) c :
The same …rm’s expected pro…t, if it invests6 , will be
p ( ) k (r I ) k (wi + I ) k (1 I) c
+ (1 ) p ( ) k (r I ) k (wi + I ) l (1 I) c :
It follows that the …rm will choose to invest if and only if
p ( ) k k (r I ) k k + (wi + I ) l k + k lj : (2)
5
This condition comes from the …rst derivative of the expected pro…t function with respect to labour. The
linearity of the problem leads to a corner solution at l =k or l = k (there is never any reason to go below k
or above k). The higher level of employment is the solution when the …rst derivative is positive. This is the
condition in equation 1.
6
An in‡uential …rm will have no incentives to invest in capital in the …rst stage if it doesn’t also employ
more people in the second stage, i.e. the condition in equation 1 must hold. We therefore presume when
stating this condition that l = k.
11

14. Rearranging terms we can rewrite the investment condition (2) as a wage threshold
wi ~w
( p ( ) (r I ))
~l
I ; (3)
where ~l is de…ned by
~l
l k + k l
k k
;
i.e., the cost of ceding control rights over employment decisions. It follows from the restric-
tions against …ring that ~l = 1 for an in‡uential …rm (l = k), whereas ~l = for a non-in‡uential
…rm (l = k). Firms with a labor unit cost below the wage threshold in (3) will invest, the
others will not. An increase in the wage threshold, therefore, increases the likelihood that a
randomly-selected …rm will invest. If subsidized credits were provided to …rms without cost,
then the threshold will increase by a factor of , suggesting that …rms bene…ting from indus-
trial policies should be more likely to invest. On the other hand, if these …rms are required to
cede control over employment decisions, they will be faced with excessive employment (and
wage expenditures) in the event of low demand (~l), reducing their likelihood of investing.
Additionally, protection from competition has two contrasting e¤ects: a price e¤ect and
an e¢ ciency e¤ect. On the one hand protection means that the …rm can charge higher prices,
making new investments more attractive. The size of this e¤ect is captured by the parameter
. On the other hand, the absence of competition increases the wage needed to obtain an
e¤ective unit of labor input, captured by , suggesting that in‡uential …rms may be less likely
to make new investments because of lower labor e¤ort. Note that lower …xed operating costs
(c) have no impact on relative incentives to invest, but only in‡uence …rm pro…ts. Similarly,
direct payments ( ) a¤ect pro…ts but not investment (although the e¤ect on pro…ts of larger
direct payments, of course, is the opposite of that of lower operating costs). Finally, expected
labor productivity (Q=l) will be lower in in‡uential …rms, who will retain excess employees
if they invest when demand turns out to be low.
12

15. Data and Methodology
In the preceding sections we hypothesized that political in‡uence involves the following ex-
change between certain …rms and their political patrons: …xed operating costs are lowered
for these in‡uential …rms through protection and subsidy, but their ability to …re workers is
restricted. If this is the case, then although these privileged …rms may earn higher pro…ts
than non-in‡uential …rms, they will be less likely to invest and will have lower productivity.
In sum, in‡uential …rms may be supported by policies that ease the constraints they face in
doing business, but if they serve as a source of politically-valuable bene…ts, these …rms will
be less dynamic and less e¢ cient than their non-in‡uential counterparts.
To test these implications, we rely on the World Bank’s Enterprise Surveys (formerly the
Productivity and Investment Climate Surveys), which, since its inception in 2000 has col-
lected data from approximately 50,000 manufacturing and service …rms in over 60 developing
countries. These data, although expansive in their cross-country coverage, do not contain the
type of information that would allow us to measure actual political connections, namely, de-
tailed information on owners or o¢ cers that could be used to assess their political identities.
Instead, the Enterprise Surveys contain several perception-based questions about the political
in‡uence of …rms in shaping national policies a¤ecting their businesses. Moreover, questions
on political in‡uence were dropped from the core questionnaire after 2005. The subset of this
total sample of …rms who have coded responses for questions of political in‡uence, therefore,
is smaller— but still leaves us with over 8,000 …rms surveyed in approximately 40 developing
countries between 2000 and 2005.
13

16. Measuring Firm-Level Characteristics with Subjective Data
The problems of comparability when respondents are asked to use ordinal response categories
are well known. Di¤erent respondents may interpret concepts such as “in‡uence”in di¤erent
ways based on unobservable characteristics (“culture,” socialization, etc.). Ordinal scales
may mean di¤erent things to di¤erent respondents based on idiosyncratic factors such as
mood or overall optimism. Sometimes referred to in educational testing as “di¤erential item
functioning” (DIF), the problem is particularly acute in measurements of political e¢ cacy,
where the actual level of e¢ cacy may di¤er from the reported level due to individual-speci…c
proclivities (King and Wand 2007). Firm-level perceptions of in‡uence would similarly be
a¤ected by DIF where identical …rms may have unequal probabilities of answering questions
about their own political in‡uence in the same way.
Explicit “anchoring vignettes”or other hypothetical questions to establish baselines that
could normally correct survey responses for inter-…rm incomparability, however, are not in-
cluded in the Enterprise Surveys core questionnaire. We rely, then, on two corrections. First,
to measure in‡uence we use four related categories to a perception-based question from the
core questionnaire:
How much in‡uence do you think the following groups actually had on recently
enacted national laws and regulations that have a substantial impact on your busi-
ness? A: your …rm; B: other domestic …rms; C: dominant …rms or conglomerates
in key sectors of the economy; D: individuals or …rms with close personal ties to
political leaders.
Each answer ranges from 0 (no impact) to 4 (decisive in‡uence). We take the sum of the
di¤erences between the self-assessment A and the assessments of other groups, i.e., A –(B +
C + D)/3, which yields a measure of the perceived in‡uence “gap”between the responding
…rm and other types of …rms.7 Our measure of in‡uence ranges from -4 to +4. As with survey
7
We di¤erence …rms’self perceptions with their average perceptions regarding three other groups (other
14

17. “anchors”, assessments of others are subject to less inter-…rm variation than self-assessments,
and thus we use responses to questions about other groups to subtract o¤ the DIF from the
self-assessment question.
This is, of course, an imperfect solution to the problem of potential incomparability
when other variables with ordinal-response categories are being regressed on our measure of
in‡uence. In cases where our outcome of interest is perception-based, our second solution is
to include among the regressors a proxy for …rm-speci…c systematic bias. Previous analyses of
business environment constraints using Enterprise Surveys have shown that the interpretation
of (subjective) outcomes is complicated by the fact that some managers simply tend to have
a high propensity to complain, regardless of the actual constraints their businesses may face
(Carlin, Sha¤er, and Seabright 2006). Inclusion of a variable among regressors that proxies
this propensity, then, can correct for incomparability in perception-based outcomes. For this
purpose we use responses by managers to questions about the degree to which their …rms’
activity is constrained by two things: the macroeconomic environment and economic policy
uncertainty. We assume that …rms within the same country and the same industry are likely
to face, objectively, a very similar macroeconomic environment as well as policy uncertainty.
The distribution of responses to these questions, in equations including country and industry
…xed e¤ects, should therefore closely proxy the distribution of the propensity to complain
among the management within our sample. The range for each question is 0 (no obstacle)
to 4 (very severe obstacle). Our proxy for …rm-speci…c systemic bias is the sum of these
responses.
…rms, other conglomerates, and other politically-connected …rms) rather than simply “other domestic …rms”
to reduce the e¤ect of biased perceptions towards any particular category of …rms.
15

18. Speci…cation and Methods
Our basic speci…cations take the following form:
Ri = f ( !!i; i; xxi) (4)
where R is the hypothesized outcome for …rm i speci…ed in the preceding section (…rm i
faces better business environment; …rm i provides politically valuable bene…ts; …rm i invest
less), ! is our measure of the relative in‡uence of …rm i, is the …rm-speci…c systematic bias
of …rm i as described above, x is a vector of …rm-speci…c control variables, and !, , and x
are vectors of coe¢ cients. The …rm-speci…c characteristics we include are: the age of the …rm
(in years), the size of the …rm (number of permanent employees, log scale, lagged one year), a
legal-status e¤ect (identifying whether the …rm is publicly listed, privately held, a cooperative,
partnership, or sole proprietorship), a location e¤ect (identifying whether the …rm is located
in the capital city, in a city with more than 1 million, 250,000 to 1 million, 50,000 to 250,000,
or less than 50,000 in resident population), dummy variables identifying whether the …rm
is an exporter, whether the …rm is majority-owned by a domestic company or individual
(vs. a foreign entity), and whether the …rm is a state-owned enterprise. In addition, we
include the following sets of dummies in all speci…cations (results are not reported): industry
dummies (ISIC 2-digit), survey-year dummies, and country dummies. Summary statistics for
all variables used in our analysis are in table 1.8
Our basic speci…cations are estimated using OLS or logit regressions depending on whether
the outcome of interest is continuous or binary. Estimates of the causal e¤ects of …rm-level
political in‡uence, however, may be a¤ected by selection bias due to the non-random character
of “in‡uential”vs. “non-in‡uential”…rms, whereby the distribution of covariates !, , and
8
We also included a dummy specifying whether the …rms have ever been state-owned, given that newly
privatized …rms may maintain close political connections while struggling with legacies of state ownership
(bloated payrolls and ine¢ cient business practices). The inclusion of this dummy is without consequence for
our results.
16

19. x, may be very di¤erent for …rms depending on their level of political in‡uence. We therefore
correct for observable di¤erences between in‡uential/non-in‡uential …rms by pre-processing
our data with matching methods, then re-running our parametric analyses on the matched
sub-sample of the data as recommended by Ho et all. (2007), and similar to the parametric
bias-adjustment for matching by Abadie and Imbens (2006). We compute coe¢ cients on all
independent variables after matching rather than reporting the simple di¤erence in means
without controlling for potential confounding variables. The purpose of matching here, of
course, is to ensure that in‡uential …rms are as close as possible to non-in‡uential …rms in
terms of relevant covariates, a method analogous to severing the links between explanatory
covariates and likelihood of “treatment”in observational data.
We rely on exact matching based on the following model:
Pr (Influencei = 1) = ^ i + ^
xxi + ^
lLobbyi ; (5)
where Influence = 1 [Influence = 0] occurs when a …rm is [is not] able to in‡uence national
policies a¤ecting its business. We designate …rms as in‡uential if their in‡uence score as
calculated above is greater than zero. is the standard normal distribution function, is the
…rm-speci…c bias, and x is a vector of …rm-speci…c indicators–age of the …rm, number of per-
manent workers, legal-, location-, sector-, year-, and country-dummies, as well as additional
dummies specifying whether the …rm is an exporter, domestically-owned, or state-owned. To
this we add an additional dummy: whether, in the past two years, the …rm has sought to
lobby the government or otherwise in‡uence the content of laws or regulations a¤ecting the
…rm’s business.
Tests of matching balance are shown in table 2. We perform exact matching without
replacement using a propensity score derived from a logit regression of (5), which generates
a conditional “treatment”probability— in this case, the conditional probability of being an
17

20. in‡uential …rm9 . As mentioned above, propensity score matching adjusts for pre-treatment
observable di¤erences between treated and control samples. Without matching, the means
of non-in‡uential and in‡uential samples of …rms are distinct as seen in T-tests (we see, e.g.,
that in‡uential …rms are older, larger, more likely to be SOEs, more likely to be exporters,
and more likely to lobby than non-in‡uential …rms). After matching, however, we can no
longer reject the null hypothesis of equality of means between in‡uential and non-in‡uential
…rms, suggesting that propensity-score matching reduces imbalance between these samples.
As an alternative to matching, for dependent variables that are continuous, we also use
three-stage least squares (3SLS) to estimate the selection model (5) and the basic speci…cation
(4) as a system of simultaneous equations.10 3SLS, a systems equivalent of two-stage least
squares (2SLS) but which takes into account covariances across equation disturbances, is
asymptotically more e¢ cient than 2SLS when cross-equation disturbances are correlated.
3SLS also allows us to deal with potential endogeneity between some of the outcomes in (4)
and in‡uence, given the possibility than the costs that …rms face or the bene…ts they obtain
may boost their in‡uence rather than the other way around. For example, it is possible that
…rms with bloated payrolls are more likely to have the ear of politicians, or that …rms who are
able to reduce the costs of navigating regulatory barriers are also better at bringing pressure
to bear on lawmakers. The critical assumption for identifying causality is that lobbying
should only have an impact on the hypothesized outcome (R) if it has an impact on the
probability of a …rm being in‡uential— a reasonable assumption, given that the purpose of
lobbying is to in‡uence political decisions.
9
Exact matching matches each in‡uential …rm to all possible non-in‡uential …rms with the same values on
all the covariates, forming sub-groups such that within each sub-group all …rms (in‡uential and non-in‡uential)
have the same covariate values.
10
In the 3SLS setup, instead of the logit format in (5) with a binary outcome, we use the regular in‡uence
measure.
18

21. Results
Is Life Easier for In‡uential Firms?
Table 3 examines three costs typically imposed on businesses in developing countries: bribes,
non-payment, and theft. Columns (1) to (6) examine bribes, both in total as a percentage
of sales, and speci…cally for government contracts as a percentage of procurement contract
value. For each outcome we present, in order, the results from basic OLS regressions, 3SLS
regressions in which equations (4) and (5) are estimated as a system (we do not report results
from equation 5), and basic OLS regression re-run on the matched sample of observations.
In each speci…cation, in‡uential …rms pay less in bribes both generally and for government
contracts.11 With less consistency, we also …nd that older …rms, state-owned companies, and
foreign companies are better protected from bribe-collectors. We also include workers (our
measure of …rm size) in quadratic form, and …nd that …rms with more employees pay more
in bribes (for government contracts) but the e¤ect is diminishing. We include, but do not
report, legal status, location, industry, time, and country dummies. From a simple stochastic
simulation of columns (3) and (6), setting all variables at their sample means, an average …rm
pays 1.8% of sales in bribes, and 2.5% of the value of a government contract in bribes. But
for the most in‡uential …rms, the amounts drop to 1% and 0.7%, respectively. Meanwhile
…rms that score below the bottom quintile in in‡uence pay 2% of sales and 3% of contract
value in bribes to public o¢ cials.12
11
Using our basic estimation for the matched sub-sample of …rms, we further computed the probabilities
that in‡uential vs. non-in‡uential …rms are forced to pay bribes to various types of inspectors and o¢ cials
(these results are not reported here). With statistical con…dence (p < 0.01), we …nd that the likelihood
that non-in‡uential …rms will have to pay bribes to building inspectors, health inspectors, and environmental
inspectors is, respectively 27%, 29%, and 24% greater than for in‡uential …rms. With lower con…dence (p
< 0.1), non-in‡uential …rms were also found to be 17% and 24% more likely to have to bribe tax collectors
and local police, respectively. Notably, no signi…cant di¤erence in bribe propensity between in‡uential and
non-in‡uential …rms is found for labor inspectors— perhaps a re‡ection that, if labor regulations might a¤ect
non-in‡uential …rms more adversely while labor costs are a problem for in‡uential …rms, the bribe tax paid
to labor inspectors may be equivalent.
12
It is always possible that in‡uential …rms pay less bribes because they have less extensive dealings with
public o¢ cials than non-in‡uential …rms. We …nd no evidence for this disparity. We estimated the percentage
19

22. These results argue against the view that bribes are an instrument of in‡uence-peddling
by private sector elites. Rather, our …ndings suggest that bribe taxes are used by the public
sector to extort payments from weak or vulnerable enterprises. This is consistent with a
bargaining framework for bribe-paying in which political connectedness can increase …rms’
relative bargaining power in dealing with public o¢ cials (Svensson 2003). High-level connec-
tions shield …rms from predatory behavior by rank-and-…le administrators, indicating that
the prevalence of corruption and cronyism in an economy are, for non-in‡uential …rms, rein-
forcing.
Columns (7) to (9) estimate the percent of sales that are left unpaid. Firms were asked
to report the percent of sales to private customers that involve overdue payments. Firms in
developing nations— particularly in the former Soviet-bloc countries— typically su¤er from
signi…cant unpaid bills from customers, and have often responded by non-payments of their
own to creditors, suppliers, tax collectors, and even workers. In all three equations, we …nd
that politically in‡uential …rms are less likely to be trapped in these cycles of non-payment.
Finally, in columns (10) to (12) we examine the e¤ect of political in‡uence on losses from theft,
robbery, arson, or vandalism. Although in simultaneous regressions and in regression on the
matched sub-sample, political in‡uence does reduce sales losses from crime, the di¤erences
are relatively small, indicating that crime take a toll on the politically-connected and ordinary
in roughly similar ways. For in‡uential forms, governments have less control over crime than
they have over bribes and debt collection.
In table 4 we turn to …rms’subjective rankings of business constraints. Our dependent
variables are averages of responses to questions about the severity of …ve categories of con-
straints: infrastructure (telecommunications, electricity, and transportation), taxation (both
of "senior management’s time spent in dealing with requirements imposed by government regulations" based
on the benchmark speci…cation in table 3, and …nd that in‡uence has no statistically signi…cant e¤ect.
20

23. rates and the administration of), regulations (including customs, licensing, and permits),
…nance (cost and access), and the legal system (anti-competitive practices, crime, and the
e¢ cacy of the legal system). In each case we code these variables 1 if the obstacle was consid-
ered “major”or “severe,”0 otherwise. To these …ve indicators we add a sixth, based on …rm
responses to a question of how customers would respond were the …rm to raise prices of their
main product or service by 10%. We code this outcome 1 if …rms state that there would be no
change in customer behavior, 0 otherwise.13 The results of logit regressions are summarized
in table 4. For simplicity we only report the coe¢ cient on in‡uence across estimations. All
outcomes, however, were estimated using the full speci…cation in (4), including …rm-speci…c
systematic bias, on both unmatched and matched samples. We also report pseudo R2 and
prob: > 2 values from the full estimations. As with crime, poor infrastructure does not
discriminate between in‡uential and non-in‡uential …rms. But all other constraints are de-
cidedly more severe for non-in‡uential …rms, which are …ve to eight times more likely to
consider tax, regulatory, …nancial, and legal constraints to be major or severe obstacles than
in‡uential …rms. These results also con…rm the absence of competition for in‡uential …rms,
for whom price hikes are less likely to a¤ect customers behavior.
As mentioned previously, the allocation of credit to privileged …rms on “soft”terms is con-
sidered one of the central instruments of crony capitalism. In table 5 we investigate whether
in‡uential …rms have easier access to credit. As with the previous table, we estimated our
full model on both unmatched and matched samples, but we only report the coe¢ cients and
standard errors for the in‡uence variable. Here we examine four outcomes: (i) whether collat-
eral was required for the most recent loan (for …rms that obtained loans); (ii) the cost of the
collateral (as a percentage of loan value); and the percentage of (iii) working capital and (iv)
13
Firms were given four choices of responses: A: customers would continue to buy at the same quantities;
B: customers would continue to buy but at slightly lower quantities; C: customers would continue to buy but
at much lower quantities; and D: customers would stop buying.
21

24. new investments …nanced by “informal sources” (money-lenders or other informal …nancial
institutions). For the …rst outcome— the collateral requirement— we use logit regressions,
while for all others we rely on OLS. Consistently and unsurprisingly, in‡uential …rms have
easier access to credit. In‡uential …rms are less likely to be asked to collateralize loans by
lenders. Among …rms that do provide collateral or a deposit for their …nancing, the more
in‡uential …rms typically have to cover less of their loans than less in‡uential …rms. And
in‡uential …rms are less entwined in the informal …nancial sector than less in‡uential …rms.
Can In‡uential Firms Bene…t Politicians?
In table 6 we examine evidence of high-employment guarantees we have suggested as a source
of political rents. Columns (1) and (2) present logit results for unmatched and matched sam-
ples, respectively, of estimating the e¤ect of political in‡uence on excess employment. Firms
were asked, if they could change the number of full-time workers without restriction or pun-
ishment, whether they would shrink their payrolls. We code responses 1 or 0 depending on
whether …rms reported they would lay o¤ workers. In columns (1) and (2), in addition to
the variables included in the basic speci…cation, we also include …rms’capacity utilization,
on the assumption that use of installed productive capacity can a¤ect …rm managers’prefer-
ences regarding optimal employment levels. We …nd that in‡uential …rms are more likely to
maintain excess labor than non-in‡uential …rms.14
A second source of potential rents are tax payments, since public expenditures may be
used to bolster public support. Evidence on tax payments by in‡uential …rms, however,
14
One might expect that …rms with long-term labor contracts will be more resistant to labor shedding during
economic downturns. Firms with strong unions, for example, would hold onto their sta¤ even during recessions,
especially where those recessions are expected to be transitory. It is possible, therefore, that in‡uential …rms
are more likely to be unionized than non-in‡uential …rms, and that the channel by which in‡uence leads to
excess labor is through the bargaining strength of the company union. A very small percentage of …rms in the
Enterprise Survey responded to the question of the level of unionization of their workforce. We …nd, however,
that unionized …rms are less likely to have excess labor than non-unionized …rms, and we …nd no di¤erence in
unionization levels between in‡uential and non-in‡uential …rms.
22

25. is divided. In Russia, it it the in‡uential, politically-powerful …rms that tend to be tax
sco- aws (Frye 2002). Others have found that …rms with "strong" political connections pay
a smaller share of taxes relative to income, but that …rms connected to the head of state
pay higher taxes (Faccio 2006). Tax compliance, of course, is often endemically weak in
countries where tax administrations su¤er from limited capacity, or where the interpretation
of tax rules is inconsistently applied. In columns (3) and (4) the dependent variable is
percentage of sales reported for tax purposes. We con…rm Gelbach’s result, namely, that
…rms with more employees hide less revenue (Gelbach 2006). Even when controlling for …rm
size, unmatched and matched results show that in‡uential …rms comply with tax reporting
rules to a greater extent than non-in‡uential …rms.15 We do not, in this instance, report
3SLS results, although these are similar to our OLS results. Together with the results in
the aforementioned papers, our results suggest that in‡uential …rms may have a harder time
evading taxes, maybe because their connections put them under closer scrutiny, but also at
times get compensated by explicit exemptions.
Does In‡uence a¤ect Investment and Innovation?
Rewards in the form of lowered costs of business, monopoly rents, and other bene…ts are
often justi…ed by developing country governments as a de facto form of targeted industrial
policy, on the assumption that most politically-connected …rms use these bene…ts to invest
and innovate, and that these in‡uential …rms, therefore, are also the most dynamic. However,
our model suggests that the opposite could be true, more in‡uential …rms are less likely to
invest and innovate if the costs of bloated payrolls and x-ine¢ ciency due to lack of competitive
pressure dominate the bene…cial e¤ects. We examine this relationship in tables 7 and 8.
15
Gehlbach (2006), looking at so called "transition" countries, also shows that …rms reporting a larger share
of their sales revenues for tax purposes are more satis…ed with several di¤erent indicators of public good
provision.
23

26. Firms were asked a series of questions on their restructuring activities and innovation.
Table 7 shows the results of estimations in which the dependent variables are a set of in-
novation/restructuring outcomes: whether, in the past three years, the …rm opened a new
plant, introduced a new product line, closed an old plant, or closed an obsolete product line.
While there are valid concerns regarding the comparability of “newness” or “obsolescence”
across …rms in di¤erent countries and in di¤erent industries, the inclusion of industry and
country dummies should correct for these di¤erences. In addition to these outcomes, we also
examined whether …rms engaged in R & D activities in the past year. As in tables 4 and 5,
we only report coe¢ cients and standard errors for the in‡uence variable, for logit estimations
using both unmatched and matched samples. Once again, despite the reductions in sample
size from matching, in‡uential …rms display a certain consistency: they are less likely to open
or close facilities, introduce or close out product lines, or engage in R&D.
In table 8 we examine real growth in sales over the past three years (log scale), total
investment, and the investment planning horizon in months (estimated with a Poisson event-
count model). In‡uential …rms su¤er from lower real growth in sales over the three-year
period. Columns (3) and (4) represent a log-form estimation of investment per worker.
Political in‡uence lowers …rm-level investment (although the coe¢ cient is signi…cant only in
the matched-sample regression). Finally, in‡uential …rms have a more myopic investment-
planning horizon than non-in‡uential …rms.
Political In‡uence and Productivity
In columns (7) - (10) in table 8, …nally, we show results from basic productivity estimations.
A Cobb-Douglas production function for …rm i in country c can be written in log form as:
log Yic = 0 + c + L log Lic + K log Kic + ic; (6)
24

27. where Y is output, L and K are labor and capital inputs, and 0 and c are common and
country-speci…c intercepts, respectively. The error term, ic, can be interpreted as within-
country total-factor productivity (TFP), i.e., productivity after measured inputs have been
accounted for. Using equation (6) we estimate …rm productivity in two ways. First we es-
timate an augmented production function where we regress output on L (workers) and K
(capital stock) in addition to the variables in our basic speci…cation (4), including in‡uence
and other …rm-speci…c characteristics. The augmented production function allows us to esti-
mate the independent e¤ect of political in‡uence on …rm productivity. Secondly, we generate
the residuals (TFP) from equation (6) and regress the result using our basic speci…cation,
allowing us to gauge the e¤ect of political in‡uence on …rm-level TFP. Incorporating these
measures shrinks our sample size signi…cantly, but we do see that the e¤ect of political in-
‡uence seems to support the notion that connected …rms su¤er from lower e¢ ciency. In the
augmented production functions, political in‡uence is associated with less output (although
the signi…cance of the coe¢ cient in the matched sample drops below the 10% level), and
in‡uence is also associated with lower TFP. Firms that bene…t from preferential treatment
are less productive than those that do not.
Conclusion
We have examined the content of …rm-state relationships characterized by special privileges
granted to favored …rms— something that is prevalent across the developing world. Theoret-
ical and empirical analyses of these relationships have generally focused exclusively on the
bene…ts or costs to …rms (and to some extent to politicians), without assessing the impact
on …rm-level decision making. We argue that economic privileges often come with a price,
and use a simple framework to show how company performance varies between in‡uential
25

28. and non-in‡uential …rms.
We have characterized political in‡uence as a bargain between …rms and politicians
whereby the former relinquish a portion of control rights in exchange for subsidies and pro-
tection. We model how this bargain a¤ects …rm decisions, arguing that protection from
competition, combined with the tendency to oversta¤ dampens incentives to invest, innovate,
and lowers productivity. Data from more than 8,000 …rms in over 40 developing countries
show that in‡uential …rms do have easier access to credits, face lower demand elasticity, en-
joy regulatory forbearance, pay smaller bribes, and generally face fewer obstacles to doing
business than less-in‡uential …rms. In exchange, in‡uential …rms provide politicians with
politically-valuable bene…ts in the form of higher employment and revenue.
These constraints— the costs of political in‡uence— mean that in‡uential …rms are less
likely to restructure operations, invest less in R&D, rely on shorter investment-planning
horizons, and report lower real sales growth, investment rates, and productivity than less-
in‡uential …rms. Despite their access to privileges of economic value, in‡uential …rms su¤er
from sharp disincentives to innovation and long-term investment.
Our …ndings, …nally, suggest some …rm-level answers to three separate but related ques-
tions on the political-economy of development. First, when does industrial policy lead to
adverse economic outcomes and when does it work? There are examples where industrial
policy has played an important role in promoting development, just as there are examples
where industrial policy has had the opposite e¤ect. The di¤erence may be attributable to
the nature of the political institutions implementing these policies (Robinson 2009). Our
…ndings highlight a particular channel: if industrial policy, by picking winners, also endows
selected …rms with greater in‡uence in public a¤airs, it is likely that those …rms will also
provide bene…ts to incumbent politicians. The underlying political motives for industrial
26

29. policies are often opaque and the temptation to secure political favors (employment, rev-
enue) in return for selective, targeted supports can be irresistible to political leaders, and
is ultimately harmful to the dynamism and e¢ ciency of bene…ciary …rms.16 We show that
…rms bene…ting from distortionary industrial policies are often precisely those that are po-
litically valuable, and that this bargain with the state reduces incentives for investment and
innovation, and harms productivity. Non-distortionary interventions, by contrast, such as
those that enhance infrastructure or support skill-acquisition by workers, would not directly
bene…t speci…c …rms. We can speculate, consequently, that governments relying on these
broader interventions might avoid cronyism in business-state relations since there would be
little grounds for the direct exchange of favors between …rms and politicians.17
Second, why do some economies remain chronically under-developed? Several authors
have argued that di¤erences in barriers to adopting technological innovations account for
di¤ering rates of development (e.g., Rosenberg and Birdzell 1986; Mokyr 1992). Others have
suggested that that these barriers, far from being exogenously-determined, are deliberately
fashioned through restrictive labor practices and restrictions on the import of productivity-
enhancing equipment (Parente and Prescott 2002). Our results suggest an additional source
of these barriers, namely, the bargains associated with …rm-level political in‡uence whereby
incentives to invest in advanced equipment and technology (even if available domestically) are
weakened, while at the same time, the variable costs of business are raised for non-in‡uential
…rms.
Finally, if these …rm-state in‡uence relationships are dependent on political incumbents,
why do they persist even as political regimes change? For Haber (2006), these bargains are
16
Even some advocates of experimentation in industrial policy acknowledge that the risks of cronyism can
be substantial (e.g. Mukand and Rodrik 2005).
17
Harrison and Rodriguez-Clare (2010), for example, have argued these types of "soft" industrial policies,
while they are more di¢ cult to implement than tari¤s, subsidies, tax breaks, etc., are less vulnerable to
political manipulation.
27

30. a solution to the government’s commitment problem: by sharing a stream of rents with a
small group of elites, the bargain is made more credible to income holders and the cronyistic
arrangement more durable. For others, limited access to privileges among certain favored
groups is a mechanism for maintaining order under conditions of fragile state capacity (North,
Wallis, and Weingast 2009). Our results suggest a slightly di¤erent logic: both …rms and
politicians have a strong interest in ensuring that enterprises remain a permanent source of
mutual rents. For politicians, control rights in critical sectors of the economy are highly
desirable. Meanwhile …rms risk losing a series of privileges should politicians be replaced,
and thus those that have privileges are encouraged to perpetuate their in‡uence-peddling
activities regardless of who is in power.
28

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34

37. 35
Table 1: Summary statistics
Variable N Mean Std. Dev. Min. Max.
Influence 8,501 -1.02 1.243 -4 4
Age of firm (years) 8,501 19.54 17.70 3 206
Exporter* 8,501 0.20 0.40 0 1
Domestically-owned firm* 8,501 0.86 0.35 0 1
State-owned firm* 8,501 0.07 0.25 0 1
Firm-specific systematic bias 8,501 4.23 2.29 0 8
Lobbied government* 6,968 0.23 0.42 0 1
Permanent workers (log, t – 1) 8,501 3.44 1.64 0 9.21
Capacity utilization (% of total capacity) 8,110 76.53 20.27 3 120
Total bribes (% sales) 6,272 1.82 3.79 0 50
Bribes for govt. contracts (% of value) 6,630 3.96 8.40 0 100
Overdue receivables (% of sales) 3,022 15.51 22.22 0 100
Losses due to crime (% sales) 7,871 0.94 3.79 0 95
Infrastructure* 8,423 0.09 0.28 0 1
Taxation* 8,501 0.43 0.49 0 1
Regulation* 7,742 0.18 0.39 0 1
Finance* 8,119 0.38 0.48 0 1
Legal system* 7,571 0.24 0.43 0 1
Monopoly pricing* 7,860 0.17 0.37 0 1
Excess labor* 8,283 0.22 0.41 0 1
Tax compliance (% of sales reported) 7,581 77.51 27.73 0 100
Collateral requirement* 3,326 0.77 0.42 0 1
Cost of collateral (% of loan value) 2,417 136.49 86.56 1 1000
Informal finance (% of working capital) 8,358 1.42 8.34 0 100
Informal finance (% of new investments) 6,124 1.00 7.54 0 100
Opened new plant or facility (past 3 years)* 8,000 0.15 0.35 0 1
Opened new product line (past 3 years)* 8,008 0.46 0.50 0 1
Closed old plant or facility (past 3 years)* 7,995 0.10 0.30 0 1
Closed obsolete product line (past 3 years)* 8,001 0.26 0.44 0 1
Conducted R&D activities (past year)* 2,482 0.48 0.50 0 1
Output (US$, log) 3,104 7.15 2.63 4.83 18.71
Capital inputs (US$, log) 2,581 5.07 4.09 -9.38 18.24
TFP (US$, log) 2,560 0.01 0.82 -4.21 5.54
Real sales growth (US$, log, 3-year) 2,577 0.24 0.58 -5.99 7.15
Investment (US$, log) 1,220 3.33 3.31 -5.29 18.45
Investment horizon (months) 2,653 9.21 11.13 0 120
Notes: All summary statistics taken from full (unmatched) sample. * denotes dichotomous
variable.

38. 36
Table 2: Balance-testing for unmatched and matched covariates
Unmatched Matched
Mean T-test Mean T-test
Age Non-influential → 19.432
Influential → 22.485
0.000 22.330
22.633
0.554
Exporter 0.188
0.221
0.010 0.248
0.260
0.624
Domestic 0.872
0.839
0.002 0.793
0.803
0.296
State Owned 0.063
0.113
0.000 0.151
0.155
0.824
Bias 4.218
4.032
0.014 3.542
3.438
0.374
Lobby 0.187
0.531
0.000 0.518
0.522
0.873
Workers 3.330
3.862
0.000 3.960
3.924
0.722
Notes: Results generated from exact propensity score matching using logit regression with a trend
and with legal-status, location, industry, time, and country dummies (not reported). Figures in
italics are for influential firms. T-tests are of equality of means between non-influential and
influential firms.

39. 37
Table 3: Firm influence and the costs of doing business
Notes: Results from OLS and 3SLS regressions, with legal-status, location, industry, time, and country dummies (not reported). Columns (1), (4),
(7), and (10) are OLS regressions on the unmatched sample. Columns (2), (5), (8), and (11), are 3SLS with each equation estimated
simultaneously with an equation estimating influence (results not reported). Columns (3), (6), (9), and (12) are OLS regressions on the matched
sample of observations. Exact matching is performed using a conditional propensity score.
*** p < 0.01
** p < 0.05
* p < 0.10.
Total bribe payments
(% of sales)
Bribes for government
procurement
(% of contract value)
Unpaid receivables
(% sales)
Losses from crime
(% of sales)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Influence -0.184***
(0.039)
-0.710***
(0.186)
-0.182***
(0.040)
-0.384***
(0.076)
-0.963***
(0.329)
-0.407***
(0.067)
-0.724***
(0.276)
-6.897**
(3.005)
-1.342**
(0.524)
-0.057
(0.035)
-0.387*
(0.207)
-0.075*
(0.045)
Age -0.006*
(0.003)
-0.004
(0.003)
-0.005*
(0.003)
-0.013**
(0.006)
-0.009*
(0.005)
-0.010**
(0.005)
0.024
(0.023)
0.055
(0.044)
0.021
(0.042)
-0.003
(0.003)
-0.003
(0.003)
-0.003
(0.003)
Exporter -0.042
(0.128)
-0.068
(0.131)
-0.047
(0.129)
-0.015
(0.259)
0.038
(0.219)
0.058
(0.219)
-1.779*
(1.003)
-1.782
(1.831)
-3.000*
(1.684)
0.009
(0.119)
0.052
(0.143)
0.057
(0.143)
Domestic 0.327**
(0.134)
0.320**
(0.138)
0.339**
(0.136)
0.266
(0.279)
0.143
(0.227)
0.151
(0.226)
1.908
(1.254)
0.659
(1.927)
1.058
(1.857)
0.237*
(0.133)
0.217
(0.151)
0.229
(0.151)
State Owned -0.643***
(0.243)
-0.525**
(0.251)
-0.714***
(0.248)
-0.699
(0.536)
-0.628
(0.430)
-0.841*
(0.429)
4.491
(4.837)
8.939
(6.463)
6.741
(6.244)
0.070
(0.251)
0.181
(0.279)
0.064
(0.278)
Workers (log L) 0.062
(0.107)
0.061
(0.108)
0.033
(0.109)
0.719***
(0.226)
0.528***
(0.182)
0.495***
(0.183)
2.661**
(1.189)
3.263*
(1.684)
2.952*
(1.697)
-0.044
(0.108)
-0.056
(0.122)
-0.073
(0.122)
Workers2
-0.021
(0.013)
-0.017
(0.013)
-0.018
(0.013)
-0.114***
(0.028)
-0.068***
(0.023)
-0.069***
(0.023)
-0.377***
(0.138)
-0.500**
(0.199)
-0.485**
(0.201)
-0.001
(0.013)
0.004
(0.015)
0.004
(0.015)
N 6,531 6,377 6,371 6,879 5,747 5,742 3,090 1,618 1,618 8,136 6,464 6,457
R2
, Adj. R2
0.113 0.097 0.124 0.232 0.104 0.116 0.262 0.175 0.237 0.033 0.026 0.034
p > F, χ2
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

40. 38
Table 4: Political influence and business constraints, logit regressions
Constraint Eq. Coeff. S.E. N Pseudo R2
p > χ2
Infrastructure (1) 0.017*** 0.034 8,290 0.161 0.000
(2) -0.006*** 0.043 6,753 0.193 0.000
Taxation (3) -0.186*** 0.023 8,501 0.249 0.000
(4) -0.197*** 0.026 6,968 0.180 0.000
Regulation (5) -0.218*** 0.028 7,660 0.188 0.000
(6) -0.257*** 0.035 6,464 0.179 0.000
Finance (7) -0.103*** 0.024 8,213 0.248 0.000
(8) -0.102*** 0.027 6,702 0.174 0.000
Legal system (9) -0.161*** 0.027 7,586 0.278 0.000
(10) -0.181*** 0.037 6,068 0.240 0.000
Monopoly pricing (11) 0.113*** 0.027 7,975 0.091 0.000
(12) 0.128*** 0.030 6,462 0.077 0.000
Notes: Coefficients and standard errors on “influence” are reported. All regressions include, in
addition to influence, the following variables: age of firm, exporter dummy, domestic dummy,
workers (linear and quadratic), firm-specific bias, time trend, legal-status, location, industry,
time, and country dummies. Figures in italics are for matched data using propensity-score
matching based on logit model.
*** p < 0.01
** p < 0.05
* p < 0.10.

41. 39
Table 5: Political influence and access to finance
Constraint Eq. Coeff. S.E. N Pseudo R2
p > χ2
Collateral required (1) -0.158*** 0.037 3,413 0.128 0.000
(2) -0.158*** 0.044 2,823 0.139 0.000
Cost of collateral
(% of loan value)
(3) -3.502*** 1.400 2,481 0.192 0.000
(4) -3.124*** 1.593 2,096 0.211 0.000
Informal finance
(% of working capital)
(5) -0.187*** 0.076 8,641 0.025 0.000
(6) -0.146*** 0.078 6,947 0.022 0.000
Informal finance
(% of new investments)
(7) -0.178*** 0.080 6,312 0.014 0.000
(8) -0.220*** 0.096 4,937 0.016 0.000
Notes: Coefficients and standard errors on “influence” are reported. All regressions include, in
addition to influence, the following variables: age of firm, exporter dummy, domestic dummy,
workers (linear and quadratic), firm-specific bias, time trend, legal-status, location, industry,
time, and country dummies. Figures in italics are for matched data using propensity-score
matching based on logit model. Equations (1) and (2) show results from logit regressions, all
other equations are OLS.
*** p < 0.01
** p < 0.05
* p < 0.10.

42. 40
Table 6: Political influence, excess labor, and tax compliance
Excess labor
Tax compliance
(% of sales)
(1) (2) (3) (4)
Influence 0.091***
(0.024)
0.088***
(0.027)
0.616***
(0.231)
0.679**
(0.269)
Age -0.003
(0.002)
-0.001
(0.002)
0.052***
(0.018)
0.037*
(0.020)
Exporter 0.059
(0.079)
0.061
(0.087)
0.407
(0.778)
-0.705
(0.865)
Domestic 0.181**
(0.085)
0.116
(0.089)
-3.777***
(0.856)
-3.404***
(0.901)
State-owned 0.214
(0.179)
0.175
(0.182)
3.622**
(1.631)
4.032**
(1.673)
Bias 0.027*
(0.014)
0.014
(0.015)
Workers (log L) 0.188***
(0.071)
0.117
(0.073)
2.106***
(0.696)
2.615***
(0.728)
Workers2
-0.030***
(0.009)
-0.018*
(0.009)
-0.042
(0.085)
-0.122
(0.090)
Capacity utilization -0.002*
(0.001)
-0.001
(0.002)
N 6,422 5,075 7,831 6,271
Pseudo R2
, R2
0.109 0.089 0.276 0.281
p > χ2
, F 0.000 0.000 0.000 0.000
Notes: Results from logit (1 – 2) and OLS (3 – 4) regressions, with legal-status, location, industry,
time, and country dummies (not reported). Columns (1), (3), and (5) are OLS regressions on the
unmatched sample, (2), (4), and (6) are on the matched sample. Exact matching is performed
using a conditional propensity score.
*** p < 0.01
** p < 0.05
* p < 0.10.

43. 41
Table 7: Political influence and firm innovation
Notes: Coefficients and standard errors on “influence” are reported. All regressions include, in
addition to influence, the following variables: age of firm, exporter dummy, domestic dummy,
workers (linear and quadratic), time trend, legal-status, location, industry, time, and country
dummies. Equations (1) – (8) also include firm-specific systemic bias. Figures in italics are for
matched data using propensity-score matching based on a logit model. Estimation is by logit
regression.
*** p < 0.01
** p < 0.05
* p < 0.10.
Outcome Eq. Coeff. S.E. N Pseudo R2
,
R2
p > χ2
Opened new product line (1) -0.052*** 0.021 7,891 0.125 0.000
(2) -0.078*** 0.025 6,355 0.111 0.000
Opened new plant or facility (3) -0.141*** 0.029 7,884 0.117 0.000
(4) -0.185*** 0.035 6,348 0.140 0.000
Closed obsolete product line (5) -0.084*** 0.023 7,885 0.122 0.000
(6) -0.151*** 0.029 6,349 0.131 0.000
Closed old plant or facility (7) -0.067*** 0.033 7,879 0.085 0.000
(8) -0.064*** 0.038 6,343 0.099 0.000
Conducted R&D activities (9) -0.073*** 0.034 2,353 0.122 0.000
(10) -0.134*** 0.073 788 0.173 0.000

44. 42
Table 8: Political influence, productivity, and investment
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Real Sales Growth
(log)
Investment
(log)
Investment horizon
(months)
Output
(log)
TFP
Influence -0.015*
(0.009)
-0.029*
(0.017)
-0.012
(0.040)
-0.167*
(0.089)
-0.058***
(0.005)
-0.066***
(0.008)
-0.030**
(0.012)
-0.029
(0.022)
-0.029**
(0.012)
-0.035*
(0.021)
Age -0.004***
(0.001)
-0.002
(0.001)
-0.006*
(0.003)
-0.000
(0.007)
0.001*
(0.000)
0.000
(0.001)
0.001
(0.001)
-0.004**
(0.002)
-0.001
(0.001)
-0.006***
(0.002)
Exporter -0.035
(0.031)
-0.070
(0.048)
0.209
(0.138)
0.103
(0.262)
0.077***
(0.017)
0.075***
(0.025)
0.182***
(0.044)
0.026
(0.070)
0.136***
(0.043)
-0.017
(0.068)
Domestic -0.007
(0.042)
0.005
(0.058)
-0.308*
(0.181)
-0.393
(0.279)
-0.127***
(0.020)
-0.051**
(0.025)
-0.211***
(0.062)
-0.198**
(0.086)
-0.097
(0.060)
-0.020
(0.084)
State-owned -0.060
(0.136)
-0.127
(0.164)
-2.066***
(0.639)
-2.231***
(0.817)
-0.187**
(0.087)
-0.214**
(0.092)
-1.493***
(0.340)
-1.431***
(0.378)
-1.569***
(0.329)
-1.593***
(0.366)
Capital (log K) 0.376***
(0.012)
0.297***
(0.019)
Workers (log L) 0.049
(0.043)
0.018
(0.059)
0.852***
(0.192)
0.693***
(0.265)
0.118***
(0.021)
0.129***
(0.024)
0.388***
(0.019)
0.293***
(0.029)
-0.809***
(0.060)
-0.996***
(0.080)
Workers2
0.000
(0.005)
0.001
(0.007)
0.014
(0.021)
0.021
(0.029)
0.005**
(0.002)
0.001
(0.003)
0.090***
(0.007)
0.094***
(0.009)
Bias -0.002
(0.003)
0.002
(0.004)
N 2,609 1,186 1,242 447 2,677 1,306 2,600 1,140 2,600 1,140
Adj. R2
0.072 0.092 0.718 0.821 — — 0.874 0.907 0.156 0.248
p > χ2
, F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Notes: Results for equations (1) – (8) from OLS regressions, results from (9) and (10) from Poisson regressions. All equations include legal-status,
location, industry, time, and country dummies (not reported). Columns (1), (3), (5), (7) and (9) are estimated on the unmatched sample, (2), (4),
(6), (8), and (10) are on the matched sample. Exact matching is performed using a conditional propensity score based on a logit model.
*** p < 0.01
** p < 0.05
* p < 0.10.