Evidence from the Panama Papers

Political Economy of Oﬀshore Capital Flows:

(Very Preliminary Draft)

Robert Kubinec Assistant Professor, NYU Abu Dhabi Sonal S. Pandya Associate Professor, University of Virginia

November 15, 2019

Abstract

Offshore capital flows are a substantively important dimension of international capital mobility. We use leaked data on the ownership of offshore shell companies to estimate Indian firms’ propensity to move capital offshore. Preliminary findings indicate medium-sized firms are more likely to offshore capital and that state-owned firms increased capital offshoring after the Congress Party’s 2014 national election defeat. These findings highlight new channels through which capital mobility constrains governments. How does international capital mobility constrain governments? This question speaks to what is perhaps

the central normative challenge of global economic integration: whether and how integration exacerbates

wealth inequality. Capital mobility ostensibly constrains governments, rendering them less able to redis-

tribute via tax and macroeconomic policy (Bates and Lien 1985). Current research focuses on ﬁnancial

markets’ response to policy, parsing market response at varying levels of capital asset mobility (Pond and

Zafeiridou 2019) and across diﬀerent types of policy (Mosley 2003).

Absent from current research is a substantively large and analytically important dimension of interna-

tional capital mobility. Oﬀshore capital ﬂows are the transfer to capital ownership to legal entities incorpo-

rated in tax havens. These no/low tax jurisdictions specialize in low-cost incorporation services that conceal

capital owners’ identity. Current estimates place twenty percent of US corporate proﬁts and eight percent of

global household wealth, more than $7 trillion, in tax havens (Zucman 2015). In 2015, tax havens accounted

for 43 percent of global external assets and 47 percent of global foreign direct investment (FDI), both due

in large part to growth in oﬀshore capital ﬂows after the Great Recession (Lane and Milesi-Ferretti 2018).

The political economy drivers of oﬀshore capital ﬂows remain unexplored for the simple but daunting reason

that these flows, by design, cannot be readily measured. Most measurement efforts estimate total offshore

capital holdings using macroeconomic data (Zucman 2014; Damgaard and Elkjaer 2017), which limits the

scope for detailed consideration of political economy motives.

Further analysis of oﬀshore capital ﬂows can contribute important new insights about the capital mobility-

inequality nexus. Offshore capital flows are arguably a more universal conduit for capital flight, used by

corporations and individuals around the world. The current emphasis on ﬁnancial market responses overlooks

channels prevalent in countries with less developed ﬁnancial markets – precisely the countries where the

negative welfare consequences of heightened inequality are most dire. Whereas ﬁnancial markets are treated

as monoliths, detailed analysis of oﬀshore ﬂows can explore more heterogeneity across types of capital owners

and policies. Additionally, oﬀshore capital ﬂight may make asset mobility less important when transferring

ownership of immobile assets to a nominally foreign entity is an eﬀective substitute.

We analyze political economy motives for oﬀshore capital ﬂows using leaked data that reveal the identity

of oﬀshore shell company owners. Shell company is shorthand for the range of legal entities (corporations,

foundations, trusts) used to facilitate offshore capital flows. These data, made public by the non-profit

International Consortium Investigative Journalism (ICIJ), span a series of data leaks during 2013-2017.1

The biggest leak, the 2016 Panama Papers, consists of 11.5 million documents (2.6 TB of data) from the

Panamanian law ﬁrm Mossack Fonseca, which, at the time, was the world’s fourth largest provider of oﬀshore incorporation services. The ICIJ extracted and made publicly available names of shell companies and the

1(Bernstein 2017; Obermaier and Obermayer 2017)

1 names and addresses of the companies’ beneﬁcial owners. Though these data include incorporations as early

as the 1960s, they suggest that global use of shell companies grew in earnest beginning in the 1990s (see

Figure 1).

We use the ICIJ data to evaluate offshore capital flows by Indian firms. India provides an insightful setting

to unpack the political economy drivers of these activities. India’s prevalence of concentrated ownership,

especially by the state and families, mirrors the industrial structure of much of the world (Porta et al. 1999).

By studying the activities of such ﬁrms, we capture a richer set of mechanisms through which industrial

organization facilitates oﬀshore ﬂows. Concentrated ownership blurs the distinction between corporate tax

avoidance/evasion strategies and managerial rent-seeking; the ﬁrm can be a vehicle for both kinds of activities.

Offshore capital flows can also facilitate corruption by concealing payments to elected officials in exchange for

favorable regulation. Indian corporate governance laws are relatively anemic for most of the sample period,

which facilitated ﬁrms’ oﬀshore capital movements (Topalova 2004; Chakrabarti et al. 2008). Finally, India

liberalized capital outﬂows in 2004, which provides an exogenous source of variation in the cost of oﬀshore

capital ﬂows. In investigations after the Panama Papers’ release, India authorities concluded that ninety

percent of shell companies revealed in the data were established under auspices of the 2004 liberalization.3

We match shell company owners revealed in the leaked data to the corporate directors of Indian companies.4

Firms with at least one director revealed in the ICIJ data are treated as engaged in oﬀshore activities.

Multiple robustness checks verify the accuracy of these matches. In most cases, these connections are likely embedded within a more complex, unobserved structure of shell companies that typically spans several countries.

We estimate annual firm financial offshoring as a function of various policies and political events. The public ICIJ data do not include shell companies’ detailed financial transactions. Instead we turn to an observable correlate of likely offshore capital flows. Related party transactions (RPTs) are firms’ reported transactions (buying, selling, borrowing, lending) with firms or individuals over which it has some control or otherwise some affiliation.5 RPTs are among the most common vehicles for firms to transfer funds to offshore shell companies (Central Board of Direct Taxes 2017). In the absence of clear external benchmarks for transactions involved specialized goods and services, firms can manipulate transfer prices to reduce taxable income while transferring funds to a separate entity through which the funds can eventually be booked to an offshore entity. RPTs are opaque, difficult to monitor, and, its abuses often cannot be readily

3Economic Times, April 13, 2016 4O’Donovan et al. (2019) use a similar strategy to estimate the leaks’ eﬀect on stock market returns for a global sample of ﬁrms. 5RPTs are not always nefarious. The concentration of ownership in emerging markets such as through family-owned conglomerates, a large and economically important corporate form in India, is ripe for abuse but can also create shareholder value by substituting for weak formal institutions (Khanna and Yafeh 2007; Khanna and Palepu 2000).

2 Figure 1: Growth of Global Shell Company Incorporation Over Time

3 identiﬁed.

We first estimate a zero-inflated Bernoulli model of a firm’s probability of offshoring. This step recognizes that the ICIJ data likely reveals only a subset of Indian firms engaged in financial offshoring. This model provides insight into what kinds of Indian firms select into these activities. We find that the number of reported RPTs is highly predictive of a firm’s exposure in the ICIJ data but the total value of reported RPT is not. This pattern in consistent with smaller, more frequent transactions, which tend to draw less scrutiny.

Our model suggests that the ICIJ data exposes only 18.5 percent of oﬀshoring Indian ﬁrms.

Given our sample selection adjustment, we then estimate a model that predicts offshoring across a large representative sample of Indian firms. We find that those companies most likely to make use of tax havens are those at intermediate levels of capitalization and tax liabilities. We also find that overall 17.6 percent of

firms, or approximately 6,500 companies in our data, engage in some kind of financial offshoring, indicating its prevalence in India well beyond that revealed by the ICIJ data.

Finally, we examine ﬁnancial oﬀshoring after the 2014 national elections in which the long-reigning

Congress Party lost to its rival, the Bharatiya Janata Party (BJP). Such political transitions have been shown to influence the fortunes of politically-connected Indian firms through mechanisms including embezzlement to fund campaigns (Sukhtankar 2012) and favorable regulation (Asher and Novosad 2017). Other mechanisms include preferential access to capital (Leuz and Oberholzer-Gee 2006) and bureaucrats (Szakonyi 2018), and corruption in public procurment (Mironov and Zhuravskaya 2016). We find some evidence suggestive of increased RPT by state-owned firms in the year after the election.

Our research leverages unprecedented new data on oﬀshore capital to estimate the extent of oﬀshoring and its political consequences. Whereas other analyses of the ICIJ data focus on stock market returns

(O’Donovan et al. 2019) and personal income tax evasion (Alstadsæter et al. 2019), we emphasis financial offshoring by a wide range of firm and ownership types more indicative of developing countries. Our analysis of post-election offshoring incorporates more detailed firm characteristics than recent related work using the

ICIJ data (Earle et al. 2019) and cross-border banking data (Andersen et al. 2017).

Our findings engage larger puzzles about when the elite exit rather than exercising voice. In weakly- institutionalized states, where elites have substantial influence over the levers of political power, they could exploit personal and political connections to evade taxes and other government sanctions. Our finding that intermediate-sized firms are more likely to move capital offshore suggest that offshore capital mobility provided a new, lower cost way to shield assets for those who lack sufficient political influence to protect assets from the state. Where as scholars have examined capital mobility’s implications for democratization

(Acemoglu and Robinson 2006; ?), we show its continued salience in established democracies.

Finally, the welfare consequences of these activities are not entirely straightforward. In many countries,

4 oﬀshore ﬂows are routed back as nominally foreign capital, a practice known as roundtripping. For example,

40 percent of FDI into India is from Mauritius. The India-Mauritius tax treaty exempts Mauritius investors from Indian taxation while Mauritius taxes capital gains at three percent. To the extent that roundtripped has positive externalities, such as further development of the stock market, it may yield tangible beneﬁts that can be weighed against the consequences of forgone taxation. Such externalities, however, may have the eﬀect of reinforcing inequality.

1 Background: Oﬀshore Capital Flows

Tax havens specialize in the provision of financial and business services to non-residents. They compete on low taxes and incorporation costs, and the level of privacy offered. For example, some jurisdictions allow the use of nominee shareholders (e.g. non-beneficial owners) and unregistered bearer shares, which confer ownership on whomever possesses a physical document (OECD 2001). Shell companies are easy to establish despite international conventions designed to curb their illicit use (Findley et al. 2014).

Funds are often transferred to the offshore entity through dummy transactions such as payment for services, incorrect invoicing of goods. Once the money is in a shell company it can be used to purchase assets like real estate that are then leased back to the beneficial owner. Banks provide beneficial owners with credit cards that draw on shell company-owned bank accounts. Funds can also be routed back to the home country via tax treaty partners, a practice known as roundtripping.

2 Data

We use detailed balance sheet data for Indian ﬁrms from the Prowess database. Prowess is widely used in academic research (Mullainathan et al. 2002; Alfaro and Chari 2009). It contains over 50,000 public and private ﬁrms that collectively account for 70 percent of industrial output and 75 percent of corporate taxes.

Indian firms are required to report related party transactions, buying, selling, lending, and/or borrowing with firms that share ownership, control, or with which key management personal or their families are affiliated. Until recently, Indian corporate governance laws required such transactions be reviewed and approved by the firm’s audit committee but contained loopholes that weakened oversight (OECD 2014).

Given the prevalence of concentrated ownership, manager-owners generally face little oversight. Majority shareholders in public firms, which ostensibly have stricter oversight from minority shareholders, also enjoy great autonomy (Mullainathan et al. 2002). Related party transactions are considered one of the most important vehicles for firms to move money offshore (Central Board of Direct Taxes 2017). The transactions

5 can be easily manipulated but arguably remain legal, making them less risky than outright tax evasion.

2.1 Indian Firms Exposure in ICIJ data

The ICIJ digitized leaked documents such as emails and ﬁnancial records, and used an (unknown) entity recognition machine learning algorithm to produce a network representation of shell companies and owners.

The ICIJ has made these extracted data publicly available; the underlying documents and the details of the

ICIJ’s algorithm remain conﬁdential.

We utilize addresses of shell company owners as extracted from documents by the ICIJ. The addresses connects shell companies to a precise location. While we cannot know the precise format of the financial relationship, it provides a credible link to offshoring in an actual economy. We retrieved all addresses matching the term ”India”, manually removed false matches (such as to municipalities in the United States), which provided us with a list of 1,198 addresses in India proper. A geocoded figure indicating the geographic dispersion of these companies is shown in Figure 2.

We then retrieved from the ICIJ database all natural persons the ICIJ linked to these addresses. This returned a list of 1,325 individuals whose surnames suggested south Asian descent, a partial validation of our measurement strategy. Finally, we matched those individuals to the boards of directors of Indian ﬁrms.

The Indian Ministry of Corporate Aﬀairs (MCA) maintains a corporate registry of all public and private

firms that identifies board members by name and a unique director identification number (DIN). Because our list of names was still of reasonable size, we manually searched the database for each name, using close variants (such as dropping middle names, starting with last name and then scanning for typos, etc.) if a match did not at first appear. We matched 915 individuals revealed in the ICIJ data to Indian firms. They collectively oversaw 4,587 Indian firms. The median director oversaw three companies, with a maximum of

867.

Some ICIJ names matched multiple Indian director IDs. While we had a research assistant perform additional validation to attempt to remove any false positives, we ultimately decided to leave in these potential false matches. Our reasons are three-fold. First, it we are studying individuals who have already shown that they are interested in evading government restrictions on the mobility of capital. As such, standard procedures to remove false matches, such as dropping inactive IDs or seemingly unrelated companies, could disguise actual relationships conducted under different DINs. We do not have confidence that the MCA is able to verify the uniqueness of each record in their database. Second, the vast majority (80%) of our matches were to a single director, and 90% matched at most two directors. As such we are confident that the majority of our data does indeed reflect a true relationship between these individuals, which we documented

6 Figure 2: Geo-coded Locations of Indian Addresses from Oﬀshore Leaks Dataset Overlaid on Indian Parliamentary Districts

7 as well by verifying that many if not all of the public cases brought against Indian individuals based on the

Oﬀshore Leaks data turn up in our matches. Third, and most importantly, we are employing this variable as an outcome, not a covariate. As such, we do not need to assume that the outcome is measured without error. In fact, the type of mis-match present here is almost certainly random measurement error as names are assigned at birth.

To understand how much our estimates could be affected by this residual source of error, we calculated the maximum false positive rate as follows. We first calculated the number of companies for individuals who do not have unique matches (the maximum value is 17). We then dropped the minimum size director in terms of the number companies that each matched director oversaw. To finish, we sum the remaining number of companies for the inexact matched directors, and divide it by the total to give us a figure of

24.4%. What this percentage means is that if we were wrong in every single case in the context of an inexact match,6 at most we would have 24.4% false positives in our final list of offshoring companies, which is still a reasonable amount of error. Given how much data we employ in our models (in the hundreds of thousands of observations), we have confidence that this form of random measurement error is likely to impact our final estimates only marginally.

3 Imputing Firms’ Oﬀshore Financial Activities

After merging the ICIJ and MCA datasets, we obtained a sample of Indian firms with known links to the ICIJ data and a much larger number of Indian firms of which we have no information regarding their likelihood of offshoring. However, we cannot assume that all of these remaining firms in India do not offshore because the ICIJ data cover only a subset of incorporation providers. By carefully modeling sample selection we believe that we can make inferences about the larger Indian population of offshoring firms (insofar as that offshoring is structurally similar to the kind observed in the ICIJ data).

In this section, we explicate our model for inferring capital offshoring across all firms in the Prowess database. We will refer to the population proportion of offshoring firms as P r(δ = 1). Our data is a self- selected sample of firms that engaged in offshoring via law firms connected to the ICIJ data release. We will refer to this observed proportion by the notation P r(O = 1) for observed.

The ICIJ data permit us to make inferences about P r(O = 1), or the reasons that an Indian ﬁrm may either have chosen to use an incorporation service provider whose data were not leaked or have not moved capital oﬀshore. However, what we want to make inferences on is P r(δ = 1), or the total proportion of

6In other words, if we make the assumption that the true match always oversees the minimum number of companies, and that all of the higher number matches are false, we can obtain an exact upper bound on the false positive rate without need for any further assumptions.

8 Table 1: Possible States for Indian Firms with Respect to the Data

O = 1 O = 0 (Observed Offshoring) (No record of offshoring) δ = 1 (True Offshoring) Observed Data Missing Data δ = 0 (Never Offshored) False Negative True Negative

Indian firms that may be engaged in offshoring. As such, we have a sample selection problem, where an unknown inclusion probability that we label as P r(θ = 1) determines when an Indian offshoring firm δ = 1 would choose to use a leak-exposed incorporation provider θ = 1 as opposed to another provider θ = 0.

We better understand the sample selection probability P r(θ = 1) by looking at the possible outcomes given

Indian ﬁrms’ decisions about oﬀshoring δ and our observed data O in Table 1.

If we wanted to estimate all of these probabilities, we need to use a model like bivariate probit that would provide independent estimates of each cell. However, we can ignore the False Negative cell, which occurs if we have a positive value in our Panama data but offshoring did not actually occur (O = 1|δ = 0). We can make the assumption that everyone in our ICIJ data is involved in offshoring of some kind. That means we only have to estimate one probability for which we do not have data, the upper-right hand cell where true offshoring occurred but for which we do not have any record in our Panama data P r(O = 0|δ = 1), which as previously mentioned we will label θ.

We can write down the probability of an true zero, or a non-oﬀshoring ﬁrm, in our dataset as follows:

P r(δ = 0) = P r(θ = 1) + (1 − P r(θ = 1))P r(O = 0)

This equal to the combination of two probabilities: θ, or the probability that a firm was actually offshoring but was not in fact in our data, and the probability that a firm was in truth not offshoring multiplied by the probability that we have no record of that firm offshoring in the Panama data.

The probability for a true one (oﬀshorer) in our dataset is then as follows:

P r(δ = 1) = (1 − P r(θ = 1))P r(O = 1)

This probability is simply the multiplication of the probability that a firm not in the Panama data did engage in offshoring with the probability that a firm is in our Panama dataset. We can then interpret this probability as equal to P r(δ = 1), or the true proportion of off-shoring firms in the population. The observed data model P r(O) is deflated by (1 − P r(θ = 1)), or the probability that a firm that engaged in offshoring did actually end up in our Panama data.

9 Fortunately for us, this model is functionally similar to a zero-inflated Poisson, in which we are modeling the distinction between “false” zeroes (firms that are offshoring but reported as zero) versus “true” zeroes

(ﬁrms that are not oﬀshoring and reported as zero). The exception is that we are modeling binary random variables, and so we need to use the Bernoulli distribution with a logit link. We estimate our model within a Bayesian context by assigning weakly informative Normal priors to all the parameters in the model.7. To sample the model, we use the Stan Hamiltonian Markov Chain Monte Carlo (MCMC) engine (Carpenter et al. 2017).

We do have the issue of identiﬁcation conditions for P r(θ = 1), which is not something we can observe.

We simulated data from the model above and estimated the probabilities using the Stan sampler. We showed although the probability itself is not identified with a non-informative prior, it is identified if we include covariates that predict P r(θ = 1). This is the strategy we use in for estimation and it provides global identification of the model.

Given this arrangement, we have two separate logit models for P r(θ = 1) and P r(O = 1). We will refer from now on to the former as the sample selection model and the latter as the observed data model. We will derive our inferences about the effect of covariates from the second model, while the first will provide information about our sample and differences between offshoring firms.

Finally, we run a non-parametric imputation algorithm on our combined dataset (Stekhoven and B¨uhlmann

2012), and produce ﬁve fully imputed datasets. We run a separate Markov chain on each dataset and then combine the results to marginalize over additional imputation uncertainty. All results presented make use of these fully imputed datasets.

3.1 Covariates

We include several controls for firm characteristics that likely influence the propensity of move capital offshore.8 We include all possible types of related party transactions (as reported in Prowess). We also control the amount of different types of taxes paid including direct, indirect, total, and corporate; profit margins, total capital, and total liabilities. Additionally we add over 100 industry fixed effects, firm ownership type (i.e. state-owned enterprise (SOE) or private), and firm incorporation year. Due to dispersion in the continuous variables, we include squared terms in our models for all continuous covariates except firm age. As discussed previously, we are very much interested in the effect of these firm-level covariates on the prevalence of offshoring in the economy.

For the sample-selection model we have relatively less prior information to use. As noted, we have strong

7All covariates receive a N(0, 3) prior 8We use standalone ﬁnancial statements for ﬁrms that belong to larger groups.

10 reason to believe that RPTs are used at times by companies to evade diﬀerent kinds of government controls.

For that reason, we include the full set of related party transaction types in the sample selection model,

along with an indicator for how many reports in total were ﬁled in a given year.

The reason for using these variables is two-fold. First, what distinguishes the ICIJ data is that it shows

relatively complicated international financial transactions. As such, it is quite likely that offshoring firms are

more sophisticated. Second, the ICIJ-exposed ﬁrms show much higher prevalence of RPTs than non-linked

ﬁrms. For both these reasons, we believe that these covariates will help us to identify the sample selection

issues we are concerned about. More importantly, as they are some of the variables we also want to learn

about, including them in the model will give us insight into how these variables aﬀect where ﬁrms decide to

oﬀshore.

As we previously discussed, we do not need to know the signs of the sample selection covariates a priori.

The resulting model is globally identiﬁed so long as there is some predictive power in the covariates. The model produces the same results even with substantial perturbations such as constraining intercepts to the wrong sign, signaling that there is only one global solution for the model.

4 Results

We first present and discuss the sample selection model results followed by the observed data results. Our sample selection model provides inference on an unobservable counterfactual: whether or not an Indian firm pursuing financial offshoring would choose to use an ICIJ-exposed incorporation agent for offshoring, or pursue another route. In this model, positive values indicate that the covariate in question predicts greater willingness to engage in offshoring via the Panama data as opposed to a different type of offshoring. The results of the covariates in Figure 3 show that many of the related party transaction types do not appear to be strongly predictive of this decision. As the value of these covariates includes the amounts of the related party transaction reports, the uncertain results indicate that the level of related party transactions does not

appear strongly predictive of where a ﬁrm will oﬀshore.

However, the total number of related party transaction reports shows a strong and statistically signiﬁcant

relationship between more reports of related party transactions and a higher prevalence of choosing to oﬀshore

with an ICIJ-exposed incorporation agent.9 In other words, while the amount in any one report does not predict ICIJ-exposed offshoring, the total number of reports does. As such, we think that our prior intuition that the ICIJ-linked firms represent more financially-saavy companies is supported by these results, as a greater number of reports would indicate.

9This variable is the last covariate in Figure 3 and is positive and signiﬁcant.

11 Figure 3: Sample Selection Model Covariates

This model is a logit model that predicts whether or not a firm that engaged in financial offshoring in our data selected into an ICIJ-exposed incorporation agent or an non-exposed incorporation agent. The parameters are on the logit scale. The credible intervals are the 5% - 95% high density region of the posterior estimates. Quadratic terms are denoted by the 2 sign. KMP = Key Management Personnel.

12 We next turn to the results of our observed data model, a regression which predicts a binary outcome for whether a company is engaged in financial offshoring. As this is our main model of interest, it has a much larger number of covariates. We plot the full distribution of covariates in Figure 4. As can be seen, we have a large number of both negative and positive predictors of firm off-shoring. Furthermore, it turns out that some of the firm-level covariates, such as Total Capital and Total Taxes, are some of the strongest overall predictors.10. Given the large number of covariates, we proceed by separately examining related groups of covariates in separate plots.

10All of the continuous covariates were standardized and then converted to orthogonal polynomials, rending an exact interpretation of the eﬀect size somewhat diﬃcult

13 Figure 4: Full Distribution of Covariates from Observed Data Model

This figure shows the full set of parameters from the logit model that predicts whether or not a firm in our data is engaged in some form of financial offshoring (either via ICIJ-exposed agents or another route). The parameters are on the logit scale. The credible intervals are the 5% - 95% high density region of the posterior estimates. Quadratic terms are denoted by the 2 sign.

14 First, Figure 5 shows the continuous firm-level covariates we included in the model, including as the total level of taxes paid, total firm capital, direct taxes paid, corporate taxes paid, and the profit ratio.

As these variables were highly skewed, we included squared terms in the model to capture these nonlinear characteristics. As can be seen, the squared terms for for direct taxes, total capital, total liabilities, and total taxes are all highly negative. As the constituent terms are modestly positive, this would suggest that there is an intermediate level of taxes, capital and liabilities at which companies are more likely to oﬀshore.

At very high levels of these variables and at very low levels of these variables, companies are less likely to oﬀshore.

This result is one of the most interesting of our analysis as it suggests that above a certain size, companies either do not need to or do not want to attempt to move assets offshore, while below a certain size they also do not care. While it is easy to imagine that very small firms would have little reason to engage in difficult offshore financial transactions, it is more puzzling why very large firms would be less inclined to do so. This finding suggests that there is more heterogeneity among firms than a straightforward capital mobility hypothesis would suggest.

15 Figure 5: Continuous Firm-Level Covariates

This plot shows continuous firm-level covariates used to predict financial off-shoring in our entire sample (Panama and other destinations). The parameters are on the logit scale. The credible intervals are the 5% - 95% high density region of the posterior estimates. Quadratic terms are denoted by the 2 sign.

16 To better understand the relationship, we calculated the predicted value of offshoring across observed values of total taxes and total capital in the data. Because we included both the constituent and squared terms, the resulting estimates help capture the nonlinear combination of these terms. Because we have two simultaneous quadratic relationships, we plot the result as a heatmap with total capital on the y axis and total taxes on the x axis in Figure 6. The bright bubble in the lower-left of the plot represents the region of highest probability of offshoring. This graphical depiction helps capture the complex nonlinear relationship between these variables and offshoring. At very high levels of capital intensity and tax payments, offshoring is very unlikely. At very low levels of capital and very high levels of taxes, offshoring is similarly unlikely. At very low levels of taxes and very high levels of capital, however, offshoring remains a possibility. Offshoring is most likely at relatively middling levels of firm tax payments and capital levels.

Figure 6: Optimal Level of Oﬀshoring Given Tax and Capital Combinations

This figure shows the predicted level of financial offshoring for all possible combinations of firm capital and tax payments. The range of both variables for which these predictions were calculated came from the observed minimum and maximum values present in the data. The scale of the variables is in standard deviations as both variables were standardized.

Finally, we can look at the industry ﬁxed eﬀects in Figure 7. While these industry categories are relatively

17 ﬁne-grained and thus diﬃcult to easily summarize, there are some patterns worth noting. First, all of the

financial-related industries, including fee-based financial transactions, finance of infrastructure, other financial activities, and auto-related finance, all appear as strong predictors of firm-level offshoring. In addition, some traditionally mobile sectors, such as media, communication, computer software, and courier services, appear strongly associated with offshoring. On the reverse side of the scale we do see more heavy industries, including steel pipes, cement, minerals, oil & natural gas, and aluminum. These industry categories conform broadly to prior intuition about capital mobility: those firms who find it relatively easy to price and sell assets are those that are more likely to make use of offshore destinations for their funds.

18 Figure 7: Industry-Level Intercepts from Observed Data Model

This figure shows industry fixed effects from the logit model predicting firm offshoring (whether to Panama or elsewhere). The parameters are on the logit scale. The credible intervals are the 5% - 95% high density region of the posterior estimates.

19 We also calculated the average predicted values for both the sample selection model and the observed data model. For the sample selection model, we found that 18.4% (HPD 17.5%, 19.4%) of offshoring firms in the Prowess dataset chose to an ICIJ-exposed agent as opposed to a non-exposed agent. This figure largely confirms our prior intuitions that the ICIJ data, while amazingly detailed, represent only a fraction of total offshoring occurring in an economy like India.

Of course, given that our sample selection model estimated that the true number of offshoring firms was far larger than our linked Panama dataset would suggest, the predicted number of offshoring firms in the

Prowess dataset was likewise far higher. Our analysis produced a posterior median value of offshoring for the whole dataset of 17.6% (HPD 16.6%, 18.5%). Given the 36,252 that we had in our final analysis, this figure suggests that between 6,017 and 6,706 of these firms were engaging in an form of offshoring that was simply or comparable the Panama dataset. We would note that this value is far higher than the total number of matches we obtained from the initial data, which amount to only 943 companies.

Of course, the Prowess dataset does not represent the universe of Indian companies. However, they have very strong coverage of companies that are either publicly listed or file accounting reports with the Indian authorities. As such, this figure is quite meaningful for these kind of substantial firms that have the potential resources to engage in these kinds of financial transactions. Our analysis suggests that offshoring is prevalent and that the ICIJ data, at best, represent only a tip of the iceberg of the total levels of offshoring, likely representing those firms that are already engaging in complicated forms of related party transactions.

Finally, we can look at the companies not originally present in the ICIJ-linked data that the model estimated had the highest probabilities of offshoring. The top 15 companies that meet these criteria are shown in Table 2. Interestingly, the company with the highest probability of offshoring is a state-owned enterprise: the Airports Authority of India (AAI). AAI has been riddled with corruption scandals for decades, so it is not surprising to see it may also have some kind of offshore financial operations. What is also important to note is that the other top companies are also involved in airport operation, though the rest of the companies are privately operated. The Reliance Group, a conglomerate, apparently manages several of these companies.

While we cannot know the exact relationship between these companies in the same industry, the fact that the AAI is known to struggle with corrupt practices and that these firms were identified as likely financial offshorers suggests a great deal. We do not have evidence of collusion among these companies, exactly, but rather a strong signal that they are all engaged in similar kinds of business practices. Further research might help untangle the corporate relationships in this industry to better understand who is offshoring and to whom. From our data alone we cannot identify where they may be sending assets or through what channels, only that it is likely that they are engaged in that activity to some degree.

20 Table 2: Top 10 Most Likely Oﬀshoring Companies Nprobabiot Originally Identiﬁed in Panama Data

Pr(Oﬀshoring) Ultimate Owner Industry Company Name

0.952 Central Govt. - Statutory Bodies Air transport infrastructure AIRPORTS AUTHORITY OF INDIA

0.943 GVK Reddy (Novopan) Group Air transport infrastructure MUMBAI INTERNATIONAL AIRPORT

LTD.

0.941 Sahara India Group Air transport infrastructure AAMBY VALLEY AIRPORT PROJECT

LTD.

0.941 G M R Group Air transport infrastructure DELHI AVIATION SERVICES PVT.

LTD.

0.941 Reliance Group [Anil Ambani] Air transport infrastructure NANDED AIRPORT LTD.

0.941 Adani Group Air transport infrastructure MUNDRA INTERNATIONAL AIRPORT

PVT. LTD.

0.940 Reliance Group [Anil Ambani] Air transport infrastructure LATUR AIRPORT LTD.

0.940 Reliance Group [Anil Ambani] Air transport infrastructure OSMANABAD AIRPORT LTD.

0.929 Reliance Group [Anil Ambani] Exhibition of ﬁlms ADLABS MULTIPLEX LTD. [MERGED]

0.929 Inox Group Exhibition of ﬁlms FAME INDIA LTD. [MERGED]

0.928 Kanakia Group Exhibition of ﬁlms VISTA ENTERTAINMENT LTD.

[MERGED]

0.927 Inox Group Exhibition of ﬁlms INOX LEISURE LTD.

0.924 Pentafour Group Exhibition of ﬁlms MAYAJAAL ENTERTAINMENT LTD.

0.922 Government Local Bodies Exhibition of ﬁlms DURGAPUR CINEPLEX LTD.

0.922 Reliance Group [Anil Ambani] Exhibition of ﬁlms RAVE ENTERTAINMENT PVT. LTD.

[MERGED]

0.922 Inox Group Exhibition of ﬁlms SATYAM CINEPLEXES LTD.

[MERGED]

0.921 Kanakia Group Exhibition of ﬁlms ODEON SHRINE MULTIPLEX LTD.

[MERGED]

0.921 Reliance Group [Anil Ambani] Exhibition of ﬁlms ADLABS MULTIPLEXES & THEATRES

LTD. [MERGED]

0.921 Wadhawan Group (Rakesh Kumar) Exhibition of ﬁlms CARNIVAL FILMS ENTERTAINMENT

PVT. LTD.

0.919 Reliance Group [Mukesh Ambani] Exhibition of ﬁlms RELIANCE REVIEW CINEMA LTD.

[MERGED]

0.919 P V R Group Exhibition of ﬁlms CINEMAX INDIA LTD. [MERGED]

0.918 SRS Group Exhibition of ﬁlms S R S ENTERTAINMENT LTD.

0.914 State and Private sector Computer software ELNET TECHNOLOGIES LTD. 21 5 Governments Drive Capital Oﬀshore?

In this section we turn to our analysis of some of the potential political predictors of firm offshoring. As mentioned previously, we focus in this section on firm ownership as a critical intermediating variable between the political effects of elections and the need to move firm assets offshore. We re-estimate our main model with the our indicator for firm ownership and year fixed effects instead of industry fixed effects. To test whether political variables affect offshoring, we interact the year fixed effect for 2015 with the firm ownership categories. We use 2015 because it is the year after the most momentous transition in political power in recent decades in India as the long-reigning Congress Party was upstaged by the the BJP. Given the previous literature, we expect that this should lead to higher levels of firm offshoring as firm owners with political connections attempt to move assets to offshore tax havens less they lose their assets to the new political owners. We choose the year 2015 because that is the year when this offshoring would be reported as annual reports are filed in the spring.

The results of this analysis are shown in Figure 8. Each firm category is shown with two estimates, a treatment and a control estimate. The treatment estimate represents the year 2015, and the control represents offshoring for these firm categories in all other years in the sample. While this analysis has some similarity to difference-in-differences, we would note that as we have a multi-period model, we cannot make that straightforward interpretation.

It is important to note as well that the baseline category are conglomerates, or firms that are owned by other firms. As such, conglomerates only has a single estimate for treatment, while the zero line is the control value for conglomerates and is used as a baseline for the other firm categories. As can be seen, the treatment has little if any effect on conglomerates, though on average, conglomerates tend to offshore more than other firm categories.

22 Figure 8: Eﬀect of Treatment (2014 Election) on Firm Categories

This figure shows the treated value for different firm ownership categories versus a control value. The treated value represents those firms’ propensity to offshore in 2015 following the change in power via Indian elections. The control value represents the average of all other years in the sample. All parameters are on 23 the logit scale. Unfortunately, the estimates reveal that we are somewhat hampered by low power in this test. We have relatively few SOEs and co-operatives in the sample, which makes it hard to detect differences between treatment and control. However, we would note that except for conglomerates and state-level corporations, all of the firm categories show a pronounced increasing in propensity to offshore during the treatment period. Unfortunately, we can only identify the difference with high power for domestic and foreign single- owner companies, likely because of the high number of these firms in our sample. For these companies, their offshoring increased to the average level for conglomerates during the post-election year.

To provide a more powered test of our hypothesis, we obtain a Bayesian p-value by calculating the difference between treatment and control for co-operatives and federal and state SOEs. For each posterior draw, we record whether these combined categories are greater than 0. We find this is the case in more than 93% of posterior draws, which gives us a Bayesian p-value of 0.0665. While this p-value cannot be interpreted in the conventional sense as the probability of obtaining the coefficient conditional on the null hypothesis, it does have a more straightforward interpretation. In the vast majority of posterior samples, the treatment effect of these three categories of firms combined was strictly positive, which we find as strong evidence that there was a overall association between the post-election year and increased offshoring among government-owned corporations.

6 Discussion

Our research probes much further into the fine-grained nature of financial offshoring than previous work, especially in developing countries. Through this analysis we raise a number of new hypotheses along with new conclusions. First, it would seem that our results suggest a potential revision to the idea that firms engage in financial offshoring to escape predatory taxes or other forms of government confiscation. The companies with the most assets and highest tax payments in our sample were those that were less likely rather than more to engage in financial offshoring.

We surmise that this quadratic relationship is a facet of India’s economic structure and problems with political connections. First, the empirical association agrees with the reality that many Indian family-owned business groups are horizontally rather than vertically integrated. By dispersing risk across sectors, the size of any one individual firm is limited. Given that we also know that conglomerates or family-owned business groups are more likely to engage in financial offshoring, it follows that there may be a relationship between the size of the firm’s operations (and tax payments) and it’s probability of off-shoring.

Another potential explanation is that the largest companies are those with such high levels of access to politicians that they do not need to fear government predation. Very loose campaign ﬁnance laws in India

24 permit businesses to provide staggering levels of funding to candidates. It stands to reason that the largest

ﬁnancial donors may become true patrons able to obtain favors across the political aisle, and as such have little to fear from changing political fortunes.

7 Conclusion

Our research explores very finely grained analysis of a previously difficult-to-study topic: the decision by companies and wealthy individuals to move assets into financial safe havens. We merge the ICIJ data release on international tax havens with a comprehensive dataset of Indian firm performance to document the rise of financial offshoring in India. In addition, by modeling the way in which firms choose to pursue financial offshoring, we are able to make inferences about the probability of offshoring among the full set of Indian

ﬁrms.

Our results show that while capital mobility is strongly linked to companies that pay taxes and own high amounts of capital, it is also most prevalent among medium-sized companies as opposed to very large ﬁrms.

Furthermore, we show that political events such as the 2014 transition in power in India are also associated with increased offshoring, suggesting that financial offshoring may be a way for firms to protect their assets from changing political fortunes. Finally, we show that firm ownership is strongly associated with offshoring, as conglomerates and family-run businesses are much more likely to engage in this kind of activity.

This research stimulates additional hypotheses about the causes and consequences of ﬁnancial oﬀshoring.

First, we note that it is still an empirical anomaly that companies in India, where corruption within the government is considered widespread, would nonetheless feel the need to move assets to oﬀshore havens.

Second, the long-term consequences of this type of financial offshoring on Indian institutions and government are not fully known. If as many as 18% of Indian firms are moving assets overseas, then Indian tax collection and regulatory authority is likely to be severely undermined.

25 References

Acemoglu, D. and Robinson, J. A. (2006). Economic origins of dictatorship and democracy. Cambridge University Press. Alfaro, L. and Chari, A. (2009). India Transformed? Insights from the Firm Level 1988-2005. Working Paper 15448, National Bureau of Economic Research. Alstadsæter, A., Johannesen, N., and Zucman, G. (2019). Tax Evasion and Inequality. American Economic Review, 109(6):2073–2103. Andersen, J. J., Johannesen, N., Lassen, D. D., and Paltseva, E. (2017). Petro Rents, Political Institu- tions, and Hidden Wealth: Evidence from Oﬀshore Bank Accounts. Journal of the European Economic Association, 15(4):818–860.

Asher, S. and Novosad, P. (2017). Politics and Local Economic Growth: Evidence from India. American Economic Journal: Applied Economics, 9(1):229–273. Bates, R. H. and Lien, D.-H. D. (1985). A Note on Taxation, Development, and Representative Government:. Politics & Society. Bernstein, J. (2017). Secrecy World: Inside the Panama Papers Investigation of Illicit Money Networks and the Global Elite. Henry Holt and Company. Carpenter, B., Gelman, A., Hoﬀman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A. (2017). Stan: A probabilistic programming language. 76. Central Board of Direct Taxes, M. o. F. (2017). White Paper on Black Money. Technical report.

Chakrabarti, R., Megginson, W., and Yadav, P. K. (2008). Corporate Governance in India. Journal of Applied Corporate Finance, 20(1):59–72. Damgaard, J. and Elkjaer, T. (2017). The Global FDI Network: Searching for Ultimate Investors. Technical Report 17/258, International Monetary Fund.

Earle, J. S., Gehlbach, S., Shirikov, A., and Shpak, S. (2019). Obfuscating ownership. Working Paper. Findley, M. G., Nielson, D. L., and Sharman, J. C. (2014). Global shell games: Experiments in transnational relations, crime, and terrorism. Number 128. Cambridge University Press. Khanna, T. and Palepu, K. (2000). Is Group Affiliation Profitable in Emerging Markets? An Analysis of Diversified Indian Business Groups. The Journal of Finance, 55(2).

Khanna, T. and Yafeh, Y. (2007). Business Groups in Emerging Markets: Paragons or Parasites? Journal of Economic Literature, 45(2):331–372. Lane, P. R. and Milesi-Ferretti, G. M. (2018). The External Wealth of Nations Revisited: International Financial Integration in the Aftermath of the Global Financial Crisis. IMF Economic Review, 66(1):189– 222. Leuz, C. and Oberholzer-Gee, F. (2006). Political relationships, global ﬁnancing, and corporate transparency: Evidence from Indonesia. Journal of Financial Economics, 81(2):411–439. Mironov, M. and Zhuravskaya, E. (2016). Corruption in Procurement and the Political Cycle in Tunneling: Evidence from Financial Transactions Data. American Economic Journal: Economic Policy, 8(2):287–321.

Mosley, L. (2003). Global Capital and National Governments. Cambridge University Press. Mullainathan, S., Mehta, P., and Bertrand, M. (2002). Ferreting Out Tunneling: An Application to Indian Business Groups. Quarterly Journal of Economics, 117(1):121–148.

26 Obermaier, F. and Obermayer, B. (2017). The Panama Papers: Breaking the Story of How the Rich and Powerful Hide Their Money. Oneworld Publications, London, revised edition edition. OECD (2001). Behind the Corporate Veil Using Corporate Entities for Illicit Purposes: Using Corporate Entities for Illicit Purposes. OECD Publishing.

OECD (2014). Improving corporate governance in India: related party transactions and minority shareholder protection. Corporate governance. OECD, Paris. O’Donovan, J., Wagner, H. F., and Zeume, S. (2019). The Value of Offshore Secrets: Evidence from the Panama Papers. The Review of Financial Studies, 32(11):4117–4155. Pond, A. and Zafeiridou, C. (2019). The Political Importance of Financial Performance. American Journal of Political Science, 0(forthcoming). Porta, R. L., Lopez-De-Silanes, F., and Shleifer, A. (1999). Corporate Ownership Around the World. The Journal of Finance, 54(2):471–517. Stekhoven, D. J. and Bühlmann,P. (2012). Missforest-non-parametric missing value imputation for mixed- type data. 28(1):112–118. Sukhtankar, S. (2012). Sweetening the deal? political connections and sugar mills in india. American Economic Journal: Applied Economics, 4(3):43–63. Szakonyi, D. (2018). Businesspeople in Elected Office: Identifying Private Benefits from Firm-Level Returns. American Political Science Review, 112(2):322–338.

Topalova, P. (2004). Overview of the Indian Corporate Sector; 1989-2002. Technical Report 04/64, Interna- tional Monetary Fund. Zucman, G. (2014). Taxing across Borders: Tracking Personal Wealth and Corporate Proﬁts. Journal of Economic Perspectives, 28(4):121–148.

Zucman, G. (2015). The hidden wealth of nations: The scourge of tax havens. University of Chicago Press.