rafmacalaba/datause-encoder-data huggingface dataset:
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35
On the one hand, secrecy jurisdictions enable corrupt officials and businesses from countries with unaccountable power structures to hide their illicitly gotten wealth. On the other hand, business actors operating in weak institutional environments might look towards secrecy jurisdictions to _protect_ their assets from potential expropriation by corrupt state officials or more politically powerful business actors. Therefore, for countries with weak property rights, secrecy jurisdictions provide an alternative institutional venue for protecting their property rights from potential domestic expropriation.

Scholars have introduced several new concepts to describe the phenomena related to these issues and differentiate the ‘offshorization’ motives of firms from advanced economies and those operating in less friendly environments. We discuss them below.

# _3.4 Investment Round-Tripping_

The phenomenon of investment round-tripping is an issue closely interlinked with secrecy jurisdictions. It refers to “the process whereby investors resident in a certain country route their investments into their own economy not directly, i.e., through domestic legal entities, but through foreign intermediary companies that are interposed between them and the target of the investment” (Nougayrede 2015, 4). The intermediary companies – frequently referred to neutrally as holding companies, corporate vehicles, special-purpose vehicles (SPVs), or special purpose entities (SPEs) – are also referred to, often, as shell companies or ‘mailbox’ companies.

Investment round-tripping was first described using the case of China in the 2002 study by the World Bank “Global Development Finance 2002,” noting that between 40% and 50% of FDI to China originated from Hong Kong SAR, China. Another study confirmed these numbers, estimating the round-tripping from Hong Kong SAR, China, to mainland China during 1998-2002 to be in the range of 26.3% and 53.4% (with the middle estimate of 39.9%) (Xiao 2004, 21). A big part of this returning capital might have first left China through export mis-invoicing (or other unaccounted ways) that get recorded, in part, in the balance of payments errors and omissions. These accumulated errors reached 12.1% of GDP in 2001 and represented part of the capital flight from China, with the other part being reflected in the current account surplus (Xiao 2004, 14). This pattern of capital leaving (often as capital flight) as an outward FDI to offshore financial centers and coming back – for profit-making – as foreign capital has been labeled ‘round-tripping.’ 10
12 natural logarithm of the corresponding variables, and the sign under each variable indicates the expected direction of its impact on the private capital stock.<sup>14</sup> The available sample comprises annual data from 1960/61 to 1993/9415 . The core of the data are the private and public real fixed capital stocks series, with the latter suitably disaggregated to permit implementation of the model outlined in the preceding section. The Appendix provides more details on their construction.

The empirical analysis below uses a narrow definition of public infrastructure investment, which includes power, water and sewage, and transport and communications. However, empirical experiments using narrower (excluding power and water) and broader (including public agriculture investment, whose major components are irrigation works and rural roads) aggregates led to similar qualitative results.

To find out if a long-run relationship such as ( 11) above exists, and estimate its parameters, the analysis below makes use of some recent developments in the econometrics of cointegrated time series. In this regard, an important methodological issue is the choice of modeling and estimation technique. In principle, the approach best suited for the purpose at hand would be Johansen's (1988) full-information procedure, which is asymptotically efficient. In the case at hand, it would involve the estimation of a fifth-order vector-autoregressive (VAR) error-correction model with _kpr,, kg z, crpr and pk_ as dependent variables. There are practical problems with this approach, however, especially in a smallsample context like the present one. On the one hand, parameter estimation and hypothesis testing in the framework of a five-equation system would be severely hampered by the limited degrees of freedom available (see e.g. Heargraves 1994). On the other, modeling and economic interpretation of the empirical relations in a large system is often problematic, since economic theory may not offer sufficient guidance for a 'complete' structural specification (Urbain, 1995). Further, a VAR is essentially a statistical model of the reduced form; if one is interested not only in the long-run parameters, but also in the short-run dynamics, the reduced-form parameters are of limited structural (and economic) interest.

For these reasons, the strategy adopted below relies on the specification and estimation of a conditional model, along the lines described by Johansen (1992) and Boswijk (1994, 1995). The strategy proceeds in three stages. First, likelihood ratio tests are used to determine the number of cointegrating relations in the full system. Next, a partial structural model is specified and estimated; as argued by

> 14 Notice that (direct) taxation is being ignored in (I l). This is consistent with the fact that most investment is done by the informal private sector, which is not subject to corporate taxation and can be presumed to largely escape most other forms of direct taxation.

15 The observations correspond to the Indian fiscal year, which runs from April to March.
Policy Research Working Paper 8205

# **Abstract**

This paper draws on new individual-level survey data from India to study the costs of opening an account and the efficiency of the account application process. The data show a recent increase in account ownership, especially by women and poor adults. The data also suggest that India’s flagship financial inclusion program, the Jan Dhan Yojana scheme, has made it easier to get an account, through lower costs and greater ease of applying. Yet despite the scheme’s initial successes, people who wish to apply for an account continue to incur a range of costs. The survey results suggest several recommendations that could improve the account application process and increase ownership and usage of accounts.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lklapper@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
appropriate to compare this loss value to the median cost of compliance to get a sense of its proportion. Our specifications imply that the median cost of compliance is $2, which makes the importer’s loss 100 times larger than the median cost of compliance.<sup>59</sup> Equation (12) implies that if 5 in every 1000 shipment were searched, no-one would comply, whereas if 10 in every 1000 shipment were searched, 50 percent of shipments would be compliant. We parameterize the model in a such a way that which ensures that bins with greater ambiguity will be searched more intensively in equilibrium (see equation (10)). The low levels of search activity in Serbian customs means that _sl_ is generally small, and _p_<sup>_′_</sup> ( _s_<sup>_∗_</sup> _l_<sup>)large.Thismeansthat</sup><sup>_α_cannotbetoolargeifinequality</sup> (14) is to hold. We choose _α_ = 0 _._ 13, a parameterization that implies moderate pessimism on the part of border officials.

Our key calibration target is the allocation of search activity across the seven shipment frequency bins. The structural parameters that determine these outcomes are the bin level ambiguity parameters, _δl_ ’s. To calibrate these parameters, we specify a parsimonious functional relationship between _δl_ and _N_<sup> ̄</sup> _l,t−_ 1, the mean lagged shipment frequency for that bin in the 2016 Serbian data. Our functional representation is where _γ_ is a calibration parameter.<sup>60</sup>

# **Equilibrium solution in the benchmark**

The primary targets in our calibration exercise are the inspection rates that were observed for each bin, which we reported in Table 5 and reproduced in the first row of Table 3, Panel A. In our calibration, each bin has the same share of overall shipments as was reported in Table

> 59For the median cost of compliance, we must have Pr ( _Y_ ⩽ _y_ ) = <u>12</u><sup>,thatis,1</sup><sup>_−_</sup> _y_<sup>_<u>b</u>_=</sup><sup><u>1</u></sup> 2<sup>.Plugging</sup><sup>_b_=1</sup> gives us the result.

> 60We experimented with other linear and simple non-linear functional forms, and found that equation (15) performed the best.

39
|**Table 1: Balance Test on Application Data**|F|ull Sampl|e|Semi‐|Final Partic|ipants|
|---|---|---|---|---|---|---|
||Treatment|Control|P‐value|Treatment|Control|P‐value|
|_Variables stratified on_|||||||
|Incorporated/Registered in Croatia|0.25|0.24|0.612|0.25|0.30|0.920|
|Incorporated/Registered in Serbia|0.46|0.46|0.626|0.48|0.48|0.513|
|Baseline Readiness Score|2.95|2.92|0.150|2.99|2.97|0.476|
|Has an outside private investor|0.10|0.09|0.178|0.14|0.06|0.170|
|_Other variables_|||||||
|Market attractiveness score|3.08|3.05|0.851|3.13|3.18|0.579|
|Product technology score|2.47|2.43|0.835|2.56|2.71|0.085|
|Traction score|3.34|3.27|0.507|3.28|3.06|0.382|
|Team score|3.04|3.05|0.878|3.08|3.02|0.207|
|Sector is business and productivity|0.48|0.39|0.107|0.45|0.36|0.436|
|Sector is lifestyle and entertainment|0.18|0.23|0.295|0.20|0.27|0.215|
|Uses Cloud Technology|0.20|0.26|0.231|0.20|0.21|0.984|
|Uses Big Data|0.18|0.21|0.642|0.17|0.20|0.915|
|Place in value chain is developer|0.61|0.55|0.171|0.60|0.57|0.677|
|Place in value chain is service provider|0.59|0.54|0.372|0.60|0.54|0.108|
|Age of firm (years)|2.61|2.66|0.887|2.24|2.29|0.346|
|Early stage firm|0.30|0.33|0.475|0.35|0.37|0.554|
|Revenues in 2014|178073|184760|0.959|37642|144012|0.303|
|Number of employees|6.47|5.88|0.539|4.65|5.32|0.800|
|Age of main founder|38.22|36.81|0.204|38.02|36.67|0.362|
|Main founder has post‐graduate education|0.49|0.48|0.816|0.54|0.55|0.740|
|At least one founder is female|0.16|0.22|0.128|0.16|0.30|0.071|
|Company has a global focus|0.60|0.58|0.576|0.59|0.63|0.569|
|Have accepted outside financing|0.34|0.37|0.656|0.42|0.40|0.836|
|Previouslyin mentoring/accelerator program|0.15|0.16|0.704|0.18|0.22|0.202|
|**Sample Size**|**174**|**172**||**110**|**101**||
|Joint test of orthogonalityof treatmentp‐valu|e||0.621|||0.086|

Notes: Full sample denotes the full experimental sample. Semi‐final participants are the sample that were scored by judges during the semi‐final pitch event. Variables stratified on were the variables used in randomized assignment.

34
|**Sample**|**Variable**|**PBF**|**Standard**<br>**Error**|**N**|
|---|---|---|---|---|
||Khatlon region|0.04|(0.28)|177|
||Health houses|-0.21|(0.53)|177|
||Anyhealth house|-0.01|(0.12)|177|
||Catchment size medium|0.10|(0.13)|177|
||Catchment size big|-0.13|(0.15)|177|
||dist_hospital_10km|0.09|(0.15)|177|
||Reception area|0.04|(0.09)|177|
||Heating|0.09|(0.15)|177|
||Waterpiped intoplot|0.01|(0.09)|177|
||Improved source of water|0.02|(0.10)|177|
||Incinerator|-0.08|(0.05)|177|
||Sterilizationprocedure|0.06|(0.07)|177|
||decontaminationprocedure|0.02|(0.11)|177|
|Rural Health|Biowaste disposalprocedure|-0.02|(0.16)|177|
|Centers|Container for sharps and needles|0.38**|(0.18)|122|
||Share ofgeneral equipment|-0.00|(0.09)|177|
||Share of essential drugs|0.09|(0.14)|177|
||Share of family planning products|0.09|(0.12)|177|
||Share of vaccines|0.06|(0.11)|177|
||Share of diagnostic kits|0.03|(0.08)|177|
||Share ofprotocols|0.02|(0.09)|177|
||External evaluations of facility|1.17|(0.80)|177|
||External evaluations of staff|-0.11|(0.91)|177|
||Internal evaluations of staff|-2.71|(2.46)|176|
||Staff meetings inpast 3 months|-0.44|(1.35)|177|
||Solicitpatient opinion|-0.28*|(0.16)|177|
||Staff left inpast 12 months|0.25|(0.26)|177|
||Anyleft inpast 12 months|0.05|(0.14)|177|
||Laboratoryavailable|0.04|(0.10)|177|
||age|0.62|(1.55)|646|
||Male|-0.04|(0.07)|646|
||Physician|0.02|(0.11)|646|
||Midwife|0.01|(0.01)|646|
||Nurse|0.04|(0.05)|646|
|Health|20years of experience|-0.01|(0.06)|646|
|providers|Born in district|-0.06|(0.06)|646|
||Monthlyincome|-24.62|(41.12)|642|
||Days absent inpast month|-0.04|(0.32)|646|
||Hours workedpast week|-7.72|(4.74)|646|
||Patient seen inpast day|-4.63***|(1.32)|645|
||Satisfaction score|-0.01|(0.06)|646|
||Age|0.15|(0.27)|2,466|
||Primaryeducation|-0.01|(0.04)|2,466|
||Secondaryeducation|-0.00|(0.03)|2,466|
||2<sup>nd</sup>wealthquintile|0.02|(0.06)|2,466|

41
34 is the first time that such<sup>information is available at the household level</sup> for Indonesia. Although the<sup>rich are widely believed to self select away from</sup> public facilities, the numbers<sup>in Table 17 provide little evidence for this in</sup> rural areas. Indeed, the absence<sup>of any pattern across consumption deciles in</sup> the rural distribution of total<sup>public hospital outpatient or inpatient visit</sup> percentages is striking. This<sup>is less the case in urban Indonesia where there</sup> is some evidence of a negative<sup>correlation between the share of visits which</sup> are public and consumption<sup>levels. The rural numbers no doubt reflect lower</sup> rural densities and consequent<sup>lack of a feasible public/private choice for</sup> many in the sector.

It is also important to allow<sup>for the fact that hospital visits</sup> include both inpatient and<sup>outpatient care and that different subsidy</sup> magnitudes are associated with<sup>each. In addition, as a proportion of total</sup> hospital visits, inpatient visits<sup>tend to increase with consumption.</sup> In order to determine the level of each<sup>subsidy, I have solved for xl in the identity</sup> H = _xoNo_ + **x1Nj,** where H denotes<sup>the hospital budget net of user fees, xo is the</sup> average subsidy to a hospital<sup>outpatient visit, xl is the average subsidy to</sup> one inpatient day, No stands<sup>for the number of public outpatient visits and NJ</sup> for the number of public inpatient<sup>visits. N, and No are known from the</sup> SUSENAS and H is also known<sup>as discussed earlier. An estimate of the ratio of</sup> the outpatient to inpatient<sup>rate of subsidy (xo/x,) must be made. Several</sup> studies have evaluated unit<sup>costs for individual health facilities in</sup> Indonesia. In these studies,<sup>unit costs are derived by adding up the</sup> individual cost components<sup>for a specific service output.> Such studies for</sup> Indonesia consistently find<sup>unit costs to vary enormously from one facility to</sup> another. The present study<sup>is unable to take this into consideration and must</sup> therefore average over various<sup>estimates. One study of a sample of 40</sup> hospitals covering the entire<sup>gamut of hospital categories located in</sup> 2 This method represents a very different<sup>approach to the one pursued in</sup> this paper. But, it shares some of the<sup>same difficulties, including those</sup> encountered in collecting the<sup>data.</sup>
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