rafmacalaba/datause-encoder-data huggingface dataset:
# **B For online publication: Additional Robustness**

## **B.1 Additional robustness of the court event study design**

In this Appendix we perform two additional classes of robustness checks on the event study design presented in Section 5.2.3. First, we analyze changes in volume and composition of the incoming caseload as potential sources of bias. Second, we also discuss bias in the event study estimates coming from selection on trend shock in unobserved case characteristics.

We first investigate the possibility that the timing of the introduction across chambers may be endogenous to chamber characteristics. Given that all estimating equations control for chamber fixed effects and allow for different linear trends across chambers, this would only pose a threat to our identification if either the order of application of the decree was decided based on (expected) differential shocks, or if chambers that were already applying the reform received different treatment compared to those not yet applying it. For instance, differential changes in (expected) chamber caseloads around the reform could have motivated the president of the court to target different chambers for applying the decree at different times, with the expectation that the decree would put these chambers on different trajectories. Alternatively, she could have assigned fewer (or, inversely, more) cases to the chambers that were about to apply the decree.

We first test the hypothesis of a smooth trend in the volume of incoming caseload at the chamber level.<sup>30</sup> We run a structural break diagnostic, akin to our main specifications but at the chamberhearing level. We adapt equation (1) and regress the number of incoming cases in a given chamberhearing on t-since-application dummies and calendar month and chamber fixed effects. In this modified event study specification, the dummies of interest now indicate the number of hearing periods between a given chamber-hearing and the period in which the assigned chamber applied the decree. We find no evidence of a structural break in the trend for the chamber incoming

> 30As noted in Section 2, the size of the incoming caseload varies across chambers. This is attributable to a certain degree of specialization in each chamber. We additionally verify that there were no changes in delays between a case entering court and being distributed into chambers, and between a case’s distribution hearing and first chamber hearing (not reported).

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trade changes by less than 5 percentage points (or less than 30%) under GAFTAEU yet the welfare triples, thus suggesting that the channels through which trade facilitation impacts welfare go beyond increased trade. Decomposition of the welfare gains shows that most of the gains arise from allocative efficiency gains (the terms of trade effects are negative) under GAFTAEU+TF.

Further decomposition of the welfare gains indicates that trade facilitation improvements from lowering indirect trade transactions costs tend to contribute more to the welfare gains than from the reductions in direct trade transactions costs.<sup>8</sup> For instance, some 83% of the additional welfare increase resulting from including trade facilitation improvements to GAFTAEU resulted from reduction in the indirect trade transactions costs. Similar dominance of the impact of lowering indirect trade transactions costs over direct costs is also obtained in OECD (2003). These results point to the importance of addressing issues such as cumbersome customs procedures, port congestion, and poor intermodal transportation services, all of which, inter alia, add to the indirect trade transactions costs.

# **Summary and Policy Recommendations**

This study highlights the importance of RTA’s and trade facilitation improvements to welfare and GDP growth prospects. Both intra-regional integration and integration with the EU are observed to have a favorable impact on trade, growth and welfare in the MENA region. The welfare gains obtained from integrating with the EU are observed to be at least twice as much as that from integrating only with GAFTA, thus pointing to the importance of enhanced trade with the EU. The distribution of the welfare gains are however skewed in favor of MENA countries that already have a high proportion of trade with the EU. This significant increase in welfare resulting from further integration with the EU calls for the need to address constraints that hinder the realization of the full benefits of trade with the EU. These include difficulties in meeting standards and technical regulations, rules of origin, and rigidities in domestic markets. The static gains from intra-MENA trade are also positive, though less than that from integration with the EU. We anticipate that the dynamic gains to be generated from increased investment

> 8The decomposition was done using the sub-totals facility in GTAP.

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# _Indicators, weights and thresholds_

Of the two service coverage domains, prevention is the easier to operationalize given data availability. We use four indicators to capture the prevention domain: four or more antenatal care (ANC) visits; full immunization of a child; breast cancer screening; and cervical cancer screening.<sup>15</sup> In all four cases, there is an objectivelydefined subpopulation in need. We do not attempt any quality adjustment so our indicators are of coverage rather than ‘effective coverage’; in the literature to date, as far as we are aware, quality adjustments have been attempted only in the case of ANC (Lozano et al. 2006; Hodgins and D'Agostino 2014).

The treatment domain is harder to operationalize and the data harder to come by. We use three indicators, all relating to young children: whether a baby was delivered by a skilled birth attendant (SBA); whether a child with diarrhea received given oral rehydration salts (ORS) or a home-made solution; and whether a child with acute respiratory infection (ARI) received medical treatment.<sup>16</sup> For the first indicator, the subpopulation in need is objectively defined; for the other two indicators, we must rely on the caregiver’s assessment of the patient’s need.

> 15 These are defined respectively in our analysis as follows: the percentage of mothers aged 15 to 49 who received at least four antenatal care visits from any skilled personnel (as defined in the country’s survey) while pregnant with children born in last two years; the proportion of one year-old children who are fully immunized; the proportion of women aged 40 to 49 who received a mammogram (past 3 years); the proportion of women aged 18 to 49 who received a pap smear during last pelvic examination (past 3 years). In some surveys, the recall period for cancer screening is one year, so the rate is adjusted to a 3- year basis using the formula for the probability of an event over multiple trials (1 (1-x)<sup>3</sup> ), where x is the probably of having a cervical or breast cancer screening in the last year. The same approach is used for surveys where the recall period is 2 years, 5 years and unlimited (in which case mean age for the quintile / population is used to make the adjustment).

> 16 These are defined respectively in our analysis as follows: the proportion of births in the last two years to mothers aged 15-49 that were attended by skilled health personnel; the proportion of children born within 5 years of survey with a cough and rapid breathing in the last 2 weeks for whom medical treatment was sought for acute respiratory infection; and the proportion of children born within 5 years of survey who had diarrhea in the last 2 weeks who were given oral rehydration salts (ORS) or home-made solution.

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Clausing (2001) develops a detailed (at the product-level) analysis of the Canada-United States free trade agreement (CUSTA) of 1988. She also finds that trade creation tends to be the rule, and trade diversion the exception, in most sectors.<sup>2</sup> A somewhat different picture is presented by Chang and Winters (2002), who find evidence that the formation of Mercosur hurt outsiders. However, Mercosur is distinct from CUSTA, NAFTA and all the Asian RTAs, as it is a customs union (CU), rather than a free trade agreement.<sup>3</sup> Now, while structural characteristics of FTA members can make the bloc more or less prone to be trade creating/diverting, perhaps even more critical is to understand the members’ trade policy reactions to the formation of the bloc, in particular their incentives to alter their trade taxes on the imports from outsiders. This follows from two simple observations. First, the country’s external tariffs can be altered unilaterally. Second, the higher the difference between a member’s preferential tariffs and its external tariffs, the greater is the discrimination and the scope for trade diversion. Thus, if the formation of a preferential trading bloc is accompanied by reductions in external tariffs, the arrangement is more likely to enhance aggregate world welfare without harming excluded countries. In contrast, if the trading bloc raises trade barriers against excluded countries (or fails to reduce them), diversion of external trade to bloc members is more likely, harming outsiders and possibly countries in the bloc as well. Therefore, the trade, as well as the welfare consequences of an RTA, depends critically on the member countries’ tariff response. But if the original choice of MFN tariffs resulted from economic and political considerations by the government, those motives would lead to different outcomes when constrained by the presence of preferential rates. Accordingly, we should indeed _expect_ the external tariffs to change after the formation of a trading bloc.

> 2 In a more structural approach, Krishna (2003) estimates trade diversion and trade creation in 24 hypothetical bilateral trade agreements, finding that in 80 percent of the cases trade creation outweighs trade diversion. Furthermore, Baier and Bergstrand (2007) show that, if one takes into account the endogeneity in the formation of trading blocs, the trade impact of RTAs is much larger than conventional estimates suggest.

> 3 In CUs, unlike in FTAs, members are required to align their external tariffs. This can lead to very different tariff-setting behavior, as Estevadeordal et al. (2008) confirm to be the case for Latin America’s trading blocs.

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Figure A1 illustrates the structure of the BOP data. In simple terms, capital flows in the BOP are split into three main categories: direct investment, portfolio investment, and other investment.<sup>13</sup> Each of these categories can be split into debt and equity components. For portfolio investment debt and equity and other investment debt, the flows can be further subdivided by domestic sector: banks, corporates, government, and central bank. Other investment debt can also be decomposed by instrument (loans, currency and deposits, trade credit and advances, and other accounts payable/receivable<sup>14</sup> ) and then by sector.

To construct our capital inflows dataset, we start with BOP data by sector, and incorporate data from the BIS and the WB on external bond and loan flows to expand the limited quarterly sectoral coverage available in the BOP.<sup>15</sup> We similarly construct our dataset for outflows, and incorporate data from the BIS to complement coverage for portfolio debt and We undertake a “filling” exercise to complete the missing sectoral data on debt inflows in the BOP. Assuming missing data is zero may or may not be accurate depending on the country under consideration, so we fill missing values with data from other sources.<sup>16</sup> We start by identifying the appropriate variables from the BOP data. This is not as easy as it sounds since, unfortunately, in the public download of the BOP data, the sector breakdown of other investment debt category is shown under other investment equity category.<sup>17</sup>

> 13The remaining categories include reserves (asset flows from the central bank) and financial derivatives (small and sparsely reported, previously a part of portfolio investment).

> 14Another instrument, insurance and pensions schemes, is also detailed, though it is very small and sparsely reported.

> 15The IMF’s Coordinated Investment Portfolio Survey (CPIS) database also reports data on sectoral breakdowns for portfolio equity and portfolio debt flows. However, these breakdowns are available only since 2013 and only at a semiannual frequency; more importantly, the CPIS does not have data on other investment debt

> 16It is difficult to distinguish a missing observation from a true zero in the BOP data.

> 17In the public download of the BOP data, available from the IMF’s website, the variables for other investment debt by sector are mislabeled, and so may be difficult to find. They are labeled as “...Other Investment,

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**Figure 13: Human Opportunity Index for nutritional status indicators**

Not underweight Not wasted No stunting 100 Composite variable No stunting Not wasted for having adequate nutrition Iodized salt Not underweight Iron tablets 90 Iron tablets Iodized salt 80 I ron tablets 70 HOI = 60% Composite indicator of having adequate nutrition 60 HOI = 40% 50 **2000 2008** HOI = 20% 40 10 20 30 40 50 60 70 80 90 100 HOI = 80% Coverage (%) _Source_ : DHS 2000 and 2008; HIECS 2000 and 2008 used for the imputation of household consumption

# **Housing and Access to Basic Infrastructure**

Access to an improved water source, electricity and a non-shared toilet at the household level, as well as children ́s possession of an identity card, all show high human opportunity indexes. These indicators are associated with quite high coverage levels and low dissimilarity indexes (Figure 14). In addition, there have been improvements in the coverage of some of these services (such as improved drinking water at the household level) over the time period analyzed, which had a positive impact on HOI measures. However, disparities still remain across circumstance groups, particularly by location, with rural Upper Egypt and the Frontier Governorates generally showing lower levels of HOI for all categories. Regional variables mostly explain the variation in access to improved water at home and whether the child is registered, while wealth is the main explanatory factor of the differences in access to a non-shared toilet and electricity at home.

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<!-- Start of picture text --> FIG 5: Tourist arrivals from targeted source markets<br>(% US/EU) 2007<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Sierra Leone Ghana Gabon Senegal Gambia<br><!-- End of picture text --> Source: Calculated from UNWTO stat Analyses show that European and American tourists have the highest per capita outbound expenditures (i.e. how much they spend when they travel abroad), and this is the market Sierra Leone has appropriately tried to target. But it has not been very successful. Only about 36 percent of the tourists come to Sierra Leone from its targeted source market, compared to The Gambia which receives 95 percent of its tourists from this market. Gabon and Senegal with similar attractions as Sierra Leone have been more effective in attracting tourists from the higher-spending markets. These may attribute to several causes that are related to each other in a complex way, namely, the absence of an overall incentive regime that stimulates the market forces, lack of hard and soft infrastructure, and compromises on industry sophistication due to firm inefficiency but also a series of government and market failures. Some of these will be discussed further in the following section.

# **9. A Porter Diamond Analysis**

A comparative assessment of the cluster’s factor and demand conditions against its industry strategies revealed a very weak current competitiveness for the Sierra Leonean tourism industry. Few Sierra Leonean tourism firms are linked directly with the targeted source markets of the EU and the US, not to mention the higher end clientele within those markets. Majority of customers in the hotels and resorts surveyed were either the residential expatriates from within West Africa or business travelers who visited Sierra Leone because of their businesses necessities. Quite a few hotels have been established after the 1997 conflict, but they were established largely to cater to the demand coming from humanitarian workers who came to Sierra Leone on the peacebuilding and reconstruction projects led by the United Nations. Most of the development agencies are clustered where hotels were present but there are very few tourist enterprises in 13
Whether estimating the _θ_ parameter through both forms of variation is appropriate depends upon model assumptions, beliefs about unmeasured factors, and measurement error. It is helpful to illustrate by considering the exports of Germany in automobiles. The study examines trade over the 1980–1999 period. Throughout this period, Germany held strong technological advantages and labor productivity for manufacturing automobiles relative to the rest of the world. Over the course of the period, this productivity also changed in relative terms. If one can feasibly isolate these productivity variables, then having both forms of variation is an advantage. A second and related issue is that first differencing the data exacerbates the downward bias that measurement error causes for estimates of the _θ_ parameter. There are plenty of reasons to suspect non-trivial measurement error in industry-level labor productivity estimates developed from the UNIDO database.

On the other hand, removing time-invariant differences to identify the θ parameter can be an advantage. The basic identification constraint for the econometric analysis is that technology levels of exporters cannot be distinguished from other unobservable factors that also vary by exporter-industry or exporter-industry-year for the long-term technology levels and their longitudinal changes, respectively. The first is particularly worrisome given its general nature. First differencing is not foolproof against omitted factors, but it does require that the changes in these factors correlate with the changes in the focal productivity level in the exporters of<sup>_z_</sup> _itk_<sup>. This latter approach of panel</sup> estimation, while very common in microeconomic analyses, has yet to be extended to the Ricardian literature.<sup>5</sup> Beyond this discussion, a few other notes about the estimation of (3) are warranted. The dependent variable is bilateral manufacturing exports by exporter-importer-industryyear. The lack of trade for a large number of bilateral routes at the industry level creates

> 5. Estimations of the Costinot et al. (2012) model rely on fixed effects to handle delivery costs and other aspects of trade that are not due to the productivity of exporters. Thus, a cross-sectional estimation (1) requires unmodeled delivery costs be only comprised of a bilateral component and an importer-industry component ( _dijk_ = _dij_ · _d kj_ ). A panel estimation (3) allows this proportionate structure to be extended to _dijtk_ = _dijt_ · _d kjt_ · _dijk_ , where the third term represents the long-term delivery costs for the exporter to the importer by industry.
<!-- Start of picture text --> Users of the DMS Type of System Functions of the DMS<br>Minister, DMOmanagement team A *Tailored reports<br>Front and Middle Office * Debt* Cost portfolioand risk analyses<br>* Planning and forecasting new commitments<br><!-- End of picture text --> <!-- Start of picture text --> ‘cena —<br>* Recording debt data<br>* Maintaining debt data<br>* Debt related payments<br>Front & Back Office Transaction Processing System * Debt issuances<br>* Repo/reverse repo<br>* Securities lending facility<br>* Liability management,<br>__ derivatives<br><!-- End of picture text -->
# **Tables & Figures**

|**Tables & Figures**|**_Page_**|
|---|---|
|Table 2-1: Spectrum of entry policies: examples ......................................................|15|
|Table 4-1: Enforcement problems ......................................................|60|
|Table 5-1: Economic Regulation can Deter Entry .....................................................|.<br>69|
|Table 7-1: Summary of recommendations ......................................................|83|
|Figure 2-1: Policy spectrum ......................................................|13|
|**Figure 3-1: Benefits of free entry as administrative capacity varies .....................**|**..................**27|
|Figure 4-1 : Enforcement requirements under restricted entry and free entry .............|.. ................<br>57|

**_4_**
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