rafmacalaba/datause-encoder-data huggingface dataset:
# **3.1 Access deficit contributions**

According to BT's initial license and successive amendments, interconnection charges had to be calculated around three elements: a) the fully-distributed cost (FDC) of conveyance calculated on a historic-cost basis (HCA), b) the applicable rate of return on the relevant capital employed, finally c) an access deficit contribution (ADC). The ADC intended to take into account the losses which BT suffered for being unable to raise its line rentals. For instance, in the fiscal year 1992/93, BT's profitability by service, using FDC on a HCA basis was as follows:

|Service:|Access|Inland calls|International|Total group|
|---|---|---|---|---|
||||calls||
|ROCE**(%/)**|-9|_+45_|_+53_|+18|

BT had to face regulatory constraints that were onerous for the incumbent: capped prices, low rentals and social obligations had to be funded in some way. The rationale behind ADC was precisely to recover through call charges connection and rental charges that were below their costs.<sup>7</sup> Entrants clearly targeted the most profitable segments. Since interconnection deprived the incumbent from such extra revenues, interconnectors were supposed to compensate the opportunity cost of foregone profits to BT on a per-minute basis. These _contributions were set proportional to BT's profitability of the service provided by the competitor._ It is clear that the fornula used by Oftel was influenced by the debate introduced by the efficient cornponent pricing rule (ECPR): ADC compensates for profits foregone by selling to competing operators instead of selling directly to consumers.<sup>8</sup> Despite the specification of procedures, a fair amount of discretion was left to the regulator, so that ADC did not play a big role in practice. First of all, the DGT could decide which were the relevant overheads over which a "fair" return could be earned (incidentally, note how the process is reminiscent of cost-plus regulation). In second place, the DGT could also issue waivers (full or partial). The waiver criteria, valid until 1997, applied to market shares of at most 10% and as long as BT's share in the market in question did not fall below 85%. Full ADC had to be paid only if an interconnector's market share exceeded 25%. Additional discretion was added since ADC could be lowered if BT did not achieve benchmark efficiency (using US data from RBOC). In practical terms, ADC had to be paid in a few years only for international calls.

> 7 One can in principle distinguish between an _access deficit,_ due to unbalanced tariffs when rental and connection costs exceed the corresponding charges, and an _universal service deficit,_ due to geographically averaged tariffs and uneconomical subscribers. Without a proper accounting system, these two types of deficits are difficult to distinguish. In the initial phase, they were practically pooled together, giving rise to BT's deficit.

> 8 See Annex 3 for practical details. A discussion of ECPR can be found in Valletti and Estache (1998), section 3.2.

8
**Table 6: Panama: Multiplier Effects of Various Sectors**<sup>**a**</sup>

||**National**|**Bocas del Toro**|**Chiriqui**|
|---|---|---|---|
|**Canal**|1.64|1.66|1.63|
|Zone Colon|1.42|1.42|1.42|
|Maize|1.41|1.46|1.37|
|Fruits|2.02|2.07|1.98|
|Shellfish|2.23|2.28|2.19|
|Other Agricultural Exportables|2.18|2.23|2.15|
|Mining|1.55|1.56|1.55|
|Textiles|1.30|1.31|1.29|
|Tourism|2.87|2.90|2.84|

a. Total gain in income from a US$1 exogenous increase in value added from the specified sector.

Households reap about 56 percent of the total gains in incomes from tourism expenditures: the remainder of the gain in income accrues to formal enterprises and government (Table 7). Which households benefit the most, however, depends very much on the region in which the tourism revenues are generated. In the national model simulation, which broadly reflects average tourist expenditures in Panama (so that much of the revenues are spent in the Zone Colon), most of the gains in household incomes (63 percent) go to urban non-poor households. Only 20 percent of the income gains accrue to poor households. In contrast, in the simulation of tourism multipliers in Bocas del Toro, poor households (who account for a larger share of the regional labor force in this region than they do nationally), earn 43 percent of the total increase in household incomes, and the percentage gain in household incomes is nearly the same across household groups. The results for Chiriqui are similar to the national simulation in terms of share of household income gains received by the poor (19 percent), though the share earned by rural households is higher (46 percent in the Chiriqui simulation versus 32 percent in the national simulation).

**Table 7: Panama: Multiplier Effects of Tourism**

||**National**|**Bocas del Toro**|**Chiriqui**|
|---|---|---|---|
|Output multiplier|1.03|1.04|1.02|
|Value Added multiplier|2.87|2.90|2.84|
|HH Income Gain / Value Added Shock||||
|All Households|1.62|1.63|1.60|
|Income Share of Households|56.4%|56.1%|56.4%|
|% Change Household Income||||
|Urban Poor|—|7.1%|1.3%|
|Urban NonPoor|—|6.3%|1.1%|
|Rural Poor|—|8.2%|1.8%|
|Rural Non-Poor|—|7.9%|1.6%|
|Share of benefits||||
|Urban Poor|5.3%|12.1%|6.2%|
|Urban NonPoor|62.7%|38.7%|47.9%|
|Rural Poor|14.4%|30.8%|13.2%|

15
to the extent that the effects of this health<sup>on economicgrowthare transmitted throughimprovedfinancing</sup> opportunities for SMEs or through increased<sup>overallflowsof bankcredit.Theseregressionsaresubjectto</sup> additional potential endogeneity problems<sup>to the extent that GDP GROWTHdirectly increasesor decreases the</sup> SME employment share or bank lending<sup>relative to GDP.</sup> For our third set of tests for developed<sup>nations, we regress SME EMP and BKLENDGDPon the measures</sup> of the relative health of community banks<sup>andthe control variables,i.e.,usingthe same specificationof the</sup> regressors as in equation (2). We test whether<sup>relative community bank health has the positive effects that would</sup> be predicted by recursive models of<sup>thetwotransmissionmechanisms.Asaboveforthe GDPGROWTH</sup> equation, the SME EMP and BKLENDGDP<sup>equations aresubjectto possible simultaneous-equationsbias if</sup> higher SME employment or greater overall<sup>bank lending affects the relativehealth of community banks in ways</sup> for which we are unable to control. As<sup>examples,the bias may occur if a stronger SMEsector increases demand</sup> for community banking services or if greater<sup>overallbanklendingincreasesGDPgrowththatin tumbenefits</sup> community banks. We again try to mitigate<sup>the potentialproblemby using relativemeasuresof thehealth of</sup> community banks and by measuring<sup>these variables witha one-yearlag.</sup> For the developing nations, we first use<sup>equation(2) and run the same tests as for the developed nations</sup> except for the slightly different definition of<sup>small banks(less than US$100 million in assets, rather than less than</sup> US$I billion), and different regional dummies<sup>(Asia, Africa, and Transition [formerly Socialist Eastem Europe],</sup> with Latin America excluded as the<sup>base case).</sup> For the developing nations, we also<sup>adjust themodeltoallowforthepossibility thatSTATEand</sup> FOREIGN banks may act as community<sup>banks:</sup> GDP GROWTH = a + ,B,*SMALL<sup>SHARE +3</sup> 2<sup>*SMALL EFFRANK</sup> + PI2*SMALL SHARE x EFF RANK + o,*STATE SHARE + 52*STATE EFF<sup>RANK</sup> + 812*STATE SHARE x EFF RANK

- + 01*FOREIGN SHARE + 02*FOREIGN<sup>EFFRANK</sup> + 012*FOREIGN SHARE x EFF RANK

+ y,*MKTCAPGDP + y2*ECON FREEDOM + y3*COMMON LAW + Y4* BANK CR3 17
86% of the IDPs, 91% of the refugees and 88% of the returnees are confident or fully confident that a coalition like this would be capable of providing security.

<!-- Start of picture text --> Figure 16: How confident are you that Figure 17: How confident are you that<br>the Government can bring peace to the armed movements can bring peace to<br>North? (%) the North? (%)<br>4<br>12 9 13<br>4 17<br>20 2<br>33 1 7<br>45<br>18<br>75<br>85<br>96 99 98 94 92 24<br>96<br>91 91<br>86 22<br>82 81<br>62<br>43<br>33<br>22<br>14 2<br>3<br>3<br>IDPs Refugees Niger Refugees Returnees IDPs Refugees Niger Refugees Returnees<br>Mauritania Mauritania<br>No confidence Neither nor Confidence No confidence Neither/nor Confidence<br>August October August October August October August October August October August October August October August October<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86%) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75% trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72% of the IDPs and 66% of the returnees) while little to no trust is placed in armed rebel groups (3% of IDPs, 1% of returnees).

The findings suggest that refugees in Mauritania have a very negative view of the army. 65% describe the army as criminal traitors and 16% as incapable and weak. 33% of the refugees in Niger on the other hand, have trust and confidence in the army, 20% describe them as incapable 23
10 <!-- Start of picture text --> Figure 4<br>Brazil: Federal Primary Expenditure Composition<br>100%<br>90%<br>80%<br>R<br>70% I<br>G<br>60% I<br>D<br>50% E<br>X<br>40% P<br>E<br>30% N<br>D<br>I<br>20% T<br>U<br>10% R<br>E<br>0% S<br>1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Intergovernmental Transfers Personnel<br>Source: STN Social Security Transfers Investment and Good and Services<br>(% of Total Primary Expenditure)<br><!-- End of picture text --> As a result of the growing share of mandatory spending, investment and other current expenditures decreased their share from around 51% of primary expenditure to less than 20% in 2001. Clearly, social security transfers are the fastest-increasing type of expenditure, generating a huge deficit that has to be covered by the Treasury. Figure 5 shows the evolution of the social security system imbalances during the period 19902005. In 1990 the deficit was 1.4% of GDP in 2005 it reached 5.7% becoming the most important source of pressure for government accounts.

**Figure 5: Social Security Imbalances, 1990-05**

<!-- Start of picture text --> 7%<br>6% 5.5% 5.8% 5.7% 5.7%<br>5.2%<br>4.9% 5.0%<br>5% 4.5%<br>4% 3.7% 3.7% 3.8%<br>3.3%<br>3% 2.4%<br>2% 1.4% 1.5%<br>1.0%<br>1%<br>0%<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Total PAYG Deficits RGPS RPPS Federal RPPS States RPPS Municip.<br><!-- End of picture text -->
**Figure 2 Costs of Export and Import Procedures in USD**

<!-- Start of picture text --> Export and import costs (Doing Business)<br>2000<br>1800<br>1600<br>DB<br>1400<br>export<br>1200<br>costs<br>1000<br>DB<br>800 import<br>costs<br>600<br>400<br>200<br>0<br>East Asia Europe & Latin Middle South Sub- High<br>& Pacific Central America & East & Asia Saharan income:<br>Asia Caribbean North Africa OECD<br>Africa<br>US$<br><!-- End of picture text --> _Source_ : Doing Business (2008).

# **3. Trade Costs and Their Impact: A Review**

A classification of the different types and sources of trade costs can be performed in several ways. In this review, we group trade costs in four categories, starting with borderrelated costs. These include both tariffs and non-tariff measures. The restrictiveness indices developed by Kee, Nicita, and Olarreaga (2008) provide a summary of both types of measures, allowing comparison across countries. Second, we review the evidence and the literature on transport costs. Next, we focus on trade costs related to behind-theborder issues. These include topics such as governance, transparency, and the business environment. Fourth, we provide a summary discussion of the costs of compliance with rules of origin found in preferential trade agreements. This is an issue central to trade and Africa. In the concluding section, we discuss the contrast between “hard” infrastructure (highways, railroads, ports, etc.) and “soft” infrastructure (standards, administrative procedures, transparency, etc.).

# **3.1. Border-Related Costs**

# **_Trade Policy and Border Barriers_**

As goods enter a country, they are subject to a variety of trade policy barriers that increase the costs of trading. Traditional trade policy barriers include tariffs (ad-valorem and specific), quotas, and a combination of both (tariff-rate quotas, TRQ). Other less “traditional” trade policy instruments include anti-dumping duties, countervailing duties, 6
Table 9. Who Suffers the Casualties?

||Post-Election Period|Pre-Elect|ion Period|
|---|---|---|---|
||Casualties sufered by:|Casualties|sufered by:|
||AFP<br>Rebel|AFP|Rebel|
||(1)<br>(2)|(3)|(4)|
|Incumbent victory|0.61**<br>0.10|–0.012|0.14|
||(0.30)<br>(0.071)|(0.33)|(0.11)|
|Margin|–9.37*<br>–1.89|5.53|–2.25|
||(4.98)<br>(1.33)|(9.46)|(3.24)|
|Margin _×_ incumbent victory|–2.98<br>0.33|–24.5|–1.92|
||(12.9)<br>(1.36)|(15.6)|(4.39)|
|Mean of dep. var.|0.38<br>0.08|0.50|0.10|
|No. of municipalities|153<br>153|153|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC). _Note:_ The running variable of the RD design is the incumbent margin of victory. All regressions are weighted by a triangular kernel with a bandwidth of 0.05 and include the same control variables as in Table 3. Reported values are marginal effects, calculated at the sample means. The dependent variable in columns marked “AFP” is the number of casualties suffered by the Armed Forces of the Philippines. The dependent variable in columns marked “Rebel” is the number of casualties suffered by insurgent groups. Standard errors are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels.

33
- 21 -

Table 3: Determinants **of** "Eauilibrium" Price Level in Final Round

|ENTERPRISE CHARACTERISTICS<br>11|LEVELS|LOGS|
|---|---|---|
|Equallon|31.|32.|
|Corstanl t|0.06|-1-.07|
||(t4.75)|(10.32)|
|Bookvaue|0.00<br>(0.62)|1.14<br>(3.41)|
|Employmdnt In 1991|0.00<br>(5.38)|0.61<br>(225)|
|Equity/Empioymrn-|**0oo**<br>(1.48)|0.67<br>(4.15)<br>-|
|Equny/Oulput|0.00<br>(3.42)*_||
|Prolit/Outputl|-102<br>(3.10)<br>*|-0.12<br>(1.39)|
|Profit/EquIty|.0.01<br>(3.52)|0.03<br>(0.30)|
|Employment/OutAput|035<br>(1.02)|0.46<br>(5.80)|
|LUabllity/Eqully|0.00<br>(0.14)|0.14<br>(5.78)|
|Share d equity for selilng<br>to foreign owner|O.W<br>(3.25)|-0.35<br>(6.74)|
|Share d equity for seiling<br>to domestic owner|0.00<br>(3.03)|.026<br>;<br>(6.90<br>**|
|RepubUlc 2/|0.03<br>(6.45<br>*-|0.61<br>(7.17)<br>*|
|Book vaue squared|0.00<br>(0.09)|4.05<br>(4.62)|
|Employment squared|0.00<br>_(.37)<br>*-|-0.01<br>74)|
|R-quared|0.10|0.30|
|Adjusted fsquared|0.10|0.29|
|<br>S.E. of regssion|0.08|1.00|
|Log likelIhood|1542.89|-2083.33|
|Durbln-Waeson<br>stat|1.92|1.64|
|F-stalstiLc|13.05|50.85|
|Number of obsernatlons|1468|1468|

# Note:

- 1/ The let hand side wadable Is deflned as shares per pots Implng thal cheaper shares have highervalues. a posulve sign en a right hand side vwable should be Intrpted as a _ngalve_ effect n te share prce whNle a negaIe sign Indicates a poslle elect on share pric.

- 2/ The Republc dummy used was 0.1. for Slovk Rep and Czech Rep In levell tems: while It was1.2. for Czech Rep, and Slovak Rep. In **log tems.**

# Inkticates signficarnt at 5% level d signIilcance.

xNdicates slgntkcant at 1% leve of signMcance. The r stass we In parenesis.
medical professionals. Insurers could then widen their networks to include these internationally accredited providers overseas.

One concern pertains to the effect of trade in health care on the local health system of the destination country. Might the inflow of industrial country consumers crowd out poorer local patients? This concern may be exaggerated for several reasons. First of all, a large number of developing country doctors and nurses emigrate every year to industrial countries, as we saw in Section 4. Inflows of rich country consumers could lead to higher incomes at home and a reduced incentive to emigrate. Furthermore, in these circumstances, it would be possible to cross-subsidize care for the poor by taxing the increased export revenues or by requiring providers also to extend care to the poor. Finally, health care capacity in destination countries will not remain fixed, but is likely to expand as increased foreign demand leads to greater domestic and foreign investment.

# **Bibliography**

Aetna (2004), “Aetna Enhances Its Aexcel Specialist Network”, Aetna Press Release, June 29, 2004.

Albright, C.D., S.G. Schuman, and P.A. Netland (2002), “Usage and Cost of Laser Trabeculoplasty in the US”, Ophthalmic Surg. Lasers 33(4): 334-336, July-Aug 2002.

Albro, Katherine & K. Norton (1997), “Cross-border Collaboration in Medical Practice”, Chapter 6 in David Warner (ed) NAFTA and Trade in Medical Services Between the U.S. and Mexico, US-Mexican Policy Studies Program Policy Report No. 7, LBJ School of Public Affairs, University of Texas.

Alliance for Health Reform (2004), “Covering Health Issues, 2004: A Sourcebook for Journalists”, available at http://www.allhealth.org/sourcebook2004/toc.asp.

American Medical Association (2004), “Physician Characteristics and Distribution in the US: 2004-2005”, AMA Press, Chicago.

Birkmeyer et al. (2002), “Hospital Volume and Surgical Mortality in the US”, New England Journal of Medicine 346(15): 1128-37.

Brush, Barbara L., Julie Sochalski, and Anne M. Berger (2004) “Imported Care: Recruiting Foreign Nurses to U.S. Health care Facilities”, Health Affairs 23(3): 78-87.

Business Standard (2005), “Apollo Wooing Foreign Insurers: Says it’s the prescription for health tourism growth”, article in the Business Standard, Feb. 16, 2005.

Census Bureau (2004), “Health Insurance Coverage in the US: 2003”, available at <u>http://www.census.gov/prod/2004pubs/p60-226.pdf.</u> 26
**Figure 12 Recursive plots, ECM for manufacturing wages**

<!-- Start of picture text --> LP___= _ DLCFI= LGDPcso =<br>I4<br>± 2 Z... __ 3 DLCPI - ± 2..... _ .S<br>_ _ ; ~~~~ ~~ : -6 t 2 ...... .. .'' '<br>-2 -9e<br>LT985 9 198 1995 1985 1995<br>+ ".4.,_ _LE.4 Mke- 6- .. ..........=<br>_3 .4 <S.--,-------- 3 _<br>.2 . ...... _ - v _:<br>± 2-*S.E =- -: . ..<br>. . . . ........... ,| 6<br>1985 1995 1985 1995 1985 1995<br>DLIo t_u=_ I 2§SZ.E=_ _<br>:4 ~~~~~~~~~~~-4. ......<br>-3_<br>1985 1995<br><!-- End of picture text -->

Nominal wages in the manufacturing sector seem unaffected in the long term by increased unemployment (see Table 6). This indicates that unemployment as a measure of the strength of demand in the labor market is not very important for manufacturing wages in the long run. Therefore, there exists no long-run trade-off between unemployment and wages. On the other hand, the short-term dynamics of the labor market affect wages through changes or growth in unemployment. The higher volatility or growth in the unemployment rate or informal sector activity the lower nominal wages, _ceteris paribus._ We now consider the other demand pressure measure, namely GDP. The estimates reveal a positive effect on wages from increased demand. GDP affects wages both in the short and long term and an increase in economic activity impacts manufacturing sector wages positively.

Expectations and macro economic stability proxied by prices do only marginally affect wages in the long run and in the short run. Hence, nominal wages are not adjusted to price increases and, therefore, wages are eroded both in the short and long run.

29
**Table 3: Testing the results along additional dimensions – FHS results only**

**_Panel A: The relationship between labor force outcomes and lagged WBL reform_**

||**Y = Fem**<br>**par**|**ale labor force**<br>**ticipation**|**Y = Gend**|**er wage gap**|**Y = Occupat**|**ional segregation**|||
|---|---|---|---|---|---|---|---|---|
||**WBL in t‐5**|**WBL in t‐10**|**WBL in t‐5**|**WBL in t‐10**|**WBL in t‐5**|**WBL in t‐10**|||
|WBL index|0.059***|0.070***|0.061|0.016|‐0.006|0.019|||
||(0.009)|(0.010)|(0.199)|(0.084)|(0.110)|(0.362)|||
|Regional non‐ag VA<br>*|‐0.001|‐0.004*|‐0.112|‐0.086|‐0.012|‐0.047|||
|Oil rents|(0.002)|(0.002)|(0.177)|(0.076)|(0.047)|(0.223)|||
|_N|4,910|4,859|984|959|1,370|1,370|||
|**_Panel B: The relation_**<br>|**_ship between i_**<br>**Going**<br>**Places**|**_ndividual indicator_**<br>**Starting a Job**|**_s and female labo_**<br>**Getting Paid**|**_r force participati_**<br>**Getting**<br>**Married**|**_on_**<br>**Having**<br>**Children**|**Running a**<br>**Business**|**Managing**<br>**Assets**|**Getting a**<br>**Pension**|
|WBL index t‐1|0.048|0.010*|0.014|0.032**|0.011|0.014**|0.011|0.022|
||(0.097)|(0.005)|(0.013)|(0.013)|(0.015)|(0.006)|(0.013)|(0.066)|
|Regional non‐ag VA<br>*|0.016|‐0.006|0.017|0.007|‐0.018|‐0.002|0.004|0.045|
|Oil rents|(0.108)|(0.007)|(0.021)|(0.006)|(0.018)|(0.006)|(0.011)|(0.106)|
|_N<br>**_Panel C: The relation_**|4,927<br>**_ship between i_**|4,927<br>**_ndividual indicator_**|4,927<br>**_s and occupation_**|4,927<br>**_al segregation_**|4,927|4,927|4,927|4,927|
||**Going**<br>**Places**|**Starting a Job**|**Getting Paid**|**Getting**<br>**Married**|**Having**<br>**Children**|**Running a**<br>**Business**|**Managing**<br>**Assets**|**Getting a**<br>**Pension**|
|WBL index t‐1|‐0.453|‐0.006|0.014*|‐0.048***|0.060***|‐0.107|‐0.088|‐0.037**|
||(0.933)|(0.019)|(0.008)|(0.017)|(0.021)|(0.105)|(0.147)|(0.016)|
|Regional non‐ag VA<br>*|0.085|‐0.027|0.009|0.015|0.041|0.067|‐0.181|0.010|
|Oil rents|(0.544)|(0.040)|(0.040)|(0.012)|(0.036)|(0.096)|(0.214)|(0.025)|
|_N|1,370|1,370|1,370|1,370|1,370|1,370|1,370|1,370|

**Notes: Standard errors are presented in parentheses; .01 ‐ ***; .05 ‐ **; .1 ‐ *. All regressions include country and year fixed effects**

23