rafmacalaba/datause-encoder-data huggingface dataset:
for the petroleum industry in Equatorial Guinea. GEPETROL also participates in general commercial ventures that enhance economic development of Equatorial Guinea.

Based on Equatorial Guinea’s hydrocarbons law the petroleum sector legal framework is transparent and precise vis-à-vis oil companies operating in Equatorial Guinea. The article 16 provides that the contractor shall at all times maintain at its offices in Equatorial Guinea the original records and books of Petroleum Operations in accordance with all applicable regulations and the accounting procedure. They shall be supported by detailed documents demonstrating the expenses and receipts of the Contractor. Such records and books shall be used to determine the Contractor’s gross revenues, petroleum operations costs and nets profits as well as to establish the Contractor’s income tax and other payment obligations. These record and books should also include accounts showing sales of hydrocarbons.

The production sharing contract element derived from the hydrocarbon law provides that the Ministry, after notifying the Contractor, may have experts of its choice or its own agents examine and audit any records and book relating to petroleum operations. The Ministry has a period of three years from the date the Contractor submits to the Ministry records and books in accordance with Article 16.2, to perform such examinations or audit with respect to the said calendar year and submit its objections to the Contractor for any contradictions or errors found during such examination or audits.

The government has recently stepped up the audit of oil companies’ financial statements and improved other aspect of managing oil-related transactions. Based on the audits that cover selected oil companies’ activities during 1996-2001, the companies have recognized their obligation to pay an additional US$88<sup>34</sup> millions, about 3 percent of GDP to the government.

The government operates under simple fiscal rules. The main rule follow by Equatorial Guinea authorities is that current expenditures are to be funded only from nonoil revenues. However this rule has not been effectively observed in recent years, and current expenditure in the 2005 budget exceeds non-oil revenue by more than 40 percent, and was covered through oil proceeds. This practice follows the Norwegian Petroleum Fund model, where a variable proportion of each year’s net oil-tax revenue is transferred from the government’s Petroleum Fund to the fiscal budget, essentially to cover the nonoil budget deficit.

Equatorial Guinea authorities have created a Fund for Future Generations (FFG) and committed to allocate 0.5 percent of oil revenues into the dedicated fund held at the BEAC. Furthermore the FFG it is clearly stated in the financing component of the budget document and has been adhered to in the recent years.

Equatorial Guinea revenues are collected by the Treasury in its BEAC and commercial bank accounts, through five commercial bank accounts, of which two are

> 34 IMF Country Report No. 03/385, December 2003 – Republic of Equatorial Guinea, 2003 Article IV Consultation, Staff report.

25
# **An Evaluation of Border Management Reforms in a Technical Agency***

Ana M. Fernandes<sup>a</sup> Russell Hillberry Alejandra Mendoza Alcántara The World Bank Purdue University The World Bank

**Keywords:** Impact evaluation, Trade Facilitation, Risk Management, Sanitary and Phytosanitary products. **JEL Classification codes** : F13, F14, F15.

> a Ana Margarida Fernandes (corresponding author). The World Bank. Development Research Group. 1818 H Street NW, Washington DC, 20433. Email: afernandes@worldbank.org.

***** The authors would like to thank the Food and Veterinary Agency and the State Statistical Office of the FYR Macedonia for providing us with data, and Marija Manevska and Zorica Vasileva for valuable clarifications about the data and procedures.  We thank Emine Elcin Koten for research assistance.  Special thanks to Perica Vrboski, Todor Milchevski, and Violane Konar-Leacy for assisting with the data collection and with very helpful discussions and comments on the paper.  We thank Jose-Daniel Reyes, Manon Schuppers and two anonymous reviewers for comments.  We also thank Ma Soukha Ba, Vojdan Jordanov, and Simon Stoiljkovikj for help with data cleaning and Alejandro Forero-Rojas, Ganeshkumar Sathiyamoorthy, and Siddhesh Kaushik for help with WITS monthly data.  Research for this paper has been supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development, the Strategic Research Program on Economic Development, the i2i Fund for Impact Evaluation, and the Investment Climate Impact Program.  The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank.
# _Mode 2: Consumption abroad_

Several ASEAN countries have become significant exporters of “health tourism” services. These are chiefly Malaysia, Singapore, and Thailand. Table 7 presents information on export revenues and the number and origin of foreign patients for these countries. Thailand is the largest exporter in the region, followed by Malaysia and Singapore. Interestingly, in the case of Singapore and Malaysia, the majority of foreign patients come from other ASEAN countries (mainly Indonesia), whereas in the case of Thailand only 7 percent of foreign patients are from the ASEAN region. For Thailand, Japanese nationals account for the largest share of foreign patients.

**Table 7: Export of health tourism services**

||**Export revenues**|**Number of patients**|**Originof patients**|
|---|---|---|---|
|Malaysia (2003)|RM 150 million<br>($40 million)|More than 100,000|60 percent from Indonesia, 10<br>percent from other ASEAN countries|
|Singapore (2002)|$420 million|210,000|45 percent from Indonesia, 20<br>percent from Malaysia, 3 percent<br>from other ASEAN countries|
|Thailand|Around 20 billion|470,000 (2001) 630,000|42 percent from the Far East (mostly|
||baht in 2003 ($482<br>million)|(2002)|Japan), 7 percent from ASEAN<br>countries|

<u>Sources:</u> Singapore Tourism Board; Abidin, Alavi, and Kamaruddin (2005); Arunanondchai (2005).

The competitiveness of Malaysian, Singaporean, and Thai hospitals primarily stems from two factors. First, they can offer medical services at significantly lower price compared to industrial countries. As illustrated in Table 8, a coronary by-pass graft surgery in the United Kingdom is three times more expensive than in Malaysia. A hospital bed in the United States is almost 25 times more expensive than in Thailand. In view of the labor-intensiveness of hospital services, differences in labor costs are likely to account for much of the observed price differences. Second, hospitals in Malaysia, Singapore and Thailand have established a reputation for high quality services. In Thailand, service quality has been explicitly promoted by an accreditation system administered by a dedicated government agency—the Institute of Hospital Quality Improvement and Accreditation. Foreign patients mainly undergo treatments in the 9 private hospitals having the highest rankings for tertiary care in Thailand. A related aspect is that Malaysian, Singaporean and Thai hospitals can offer specialized services not available in other, especially poorer, ASEAN countries.

**Table 8: Price comparisons (US$, 2001)**

||**Coronary by-pass**<br>**graft surgery**|**Single private**<br>**hospital room,**<br>**per night**|
|---|---|---|
|Malaysia|$6,315|$52|
|Singapore|$10,417|$229|
|Thailand|$7,894|$55|
|UnitedKingdom|$19,700|n/a|

12
**Table 6. The macroeconomic effects of a 5 percent climate-induced shock to capital stock with reconstruction financed by increase in public revenue**

||Year 0|Year 1|Year 2|Year 3|Year 4|Year 5|Year 10|Year 20|Year 30|
|---|---|---|---|---|---|---|---|---|---|
|**Economic Activity**||||||||||
|<br>GDP|0.0|-0.4|-0.4|-0.1|0.0|0.2|-1.2|-0.9|-0.3|
|Private consumption|0.0|-1.8|-1.5|-0.9|-0.4|0.0|-0.3|-0.8|-0.5|
|<br>Government consumption|0.0|-0.2|-0.1|-0.1|-0.0|0.0|-1.6|-1.6|-0.5|
|Total investment|0.0|3.3|3.2|3.4|3.6|3.8|-0.6|0.2|0.2|
|Exports|0.0|-0.8|-1.4|-1.9|-2.4|-2.7|-1.9|-0.6|0.1|
|Imports|0.0|-0.1|0.3|1.1|1.9|2.7|2.0|0.8|-0.1|
|Current account balance<br>(pp of GDP)|0.0|0.5|0.5|0.4|0.4|0.3|-0.3|-0.3|-0.1|
|**Prices**||||||||||
|Producer prices|0.0|1.6|1.8|2.0|2.1|2.2|0.4|-0.1|-0.2|
|Consumer prices|0.0|1.8|1.8|1.7|1.7|1.6|-0.3|-0.2|-0.1|
|Real exchange rate|0.0|2.7|3.5|4.2|4.8|5.3|2.5|0.7|-0.1|
|Import prices|0.0|-0.4|-0.9|-1.4|-1.9|-2.4|-2.3|-0.9|0.0|
|Export prices|0.0|0.3|0.5|0.6|0.7|0.7|0.2|-0.1|-0.1|
|<br>**Fiscal**||||||||||
|Revenues|0.0|17.8|18.0|18.5|18.8|19.1|-0.8|-1.0|-0.4|
|Expenditures|0.0|15.0|15.2|15.5|15.8|16.0|-0.3|-1.0|-0.6|
|<br>Deficit (pp of GDP)|0.0|-0.1|-0.1|-0.1|-0.1|-0.1|-0.1|0.0|0.0|
|<br>Debt (pp of GDP)|0.0|-0.3|-0.2|-0.3|-0.3|-0.3|0.9|0.6|0.0|
|**Supply Side**||||||||||
|<br>Potential GDP|0.0|-3.0|-2.6|-2.1|-1.7|-1.3|-0.6|-0.3|-0.1|
|Capital stock|-5.0|-4.2|-3.5|-2.9|-2.2|-1.6|-1.0|-0.5|-0.2|

_Source:_ MFMod, World Bank; Authors’ calculations. _Note:_ Tables show deviations as percent from a baseline scenario.

27
quite small with less than 50 employees, and 43 percent of firms have less than 100 employees. In contrast only 19 percent of firms are very large with employment greater than 1000. Are these figures truly representative of the size distribution of firms? This is hard to know because information on the size distribution of all firms is very hard to obtain in a systematic way across countries. We do have some limited evidence on the distribution of all registered firms for EU countries for 2000.<sup>10</sup> In a sample of 32 EU countries, 24 percent of firms fall in the 10-50 employee size category. In the GCS for these countries, it turns out that exactly the same fraction of firms have employment less than 50. This exaggerates the representativeness of the GCS though because the EU data also report employment for very small firms with less than 10 employees, which account on average for a very long tail some 40 percent of firms. But setting aside these very small firms, the GCS does not appear to be broadly skewed towards large or otherwise "elite" firms.

- Third, the extent to which this critique is valid depends crucially on the extent to which there are fundamental differences between the perceptions of business people and those of other members of society as to what constitutes good governance.  If this is true, then the responses of firms (or commercial risk rating agencies who serve mostly business clients) to questions about governance should not be very correlated with ratings provided by respondents who are more likely to sympathize with the common good, such as individuals, NGOs, or public sector organizations.  In fact, overall there are quite strong correlations among most of our difference types of data sources.  As an example, consider the ingredients of our Government Effectiveness indicator for 2005.  The correlation between two of our major cross-country firm surveys is 0.74, and the correlation of these firms surveys with a survey of households in Africa is very similar at 0.7. More systematically, as we discuss further below, the rankings provided by our aggregate indicators are quite robust to alternative weighting schemes.  This robustness reflects precisely the fact that on average our different types of data sources provide highly correlated assessments.   This in turn suggests to us that it is implausible that the preferences of businesspeople regarding good governance differ so dramatically from those of other types of respondents.

> 10 We are grateful to Leora Klapper for providing this data.

13
13

**Table 3**

**Selected variables: data averages & correlations for the panel**

||___.,_.,._|_||Correlation|of the variables i|n the first column|with:|
|---|---|---|---|---|---|
|Variable|Mean|matl|mat2|mktbtb|dfitb|
|matl|.6556|1.00|.834|.380|.545|
|mat2|.3897|.834|1.00|.195|.589|
|assm|.5683|.459|.596|-.058|.492|
|cfk|.0422|.137|.097|.154|.031|
|lev|.3208|.056|.298|-.241|.192|
|liq|.0817|.156|.117|.316|-.077|
|cr|1.23|.243|.258|.303|-.029|
|banktb|.3744|-.637|-.557|-.383|-.546<br>l|
|dfitb|.2413|.545|.589|-.186|1.00|
|dfiltb|.3681|.153|.262|-.383|.830|
|mktbtb|.2704|.380|.195|1.00|-.186|
|Iprod|11.10|.219|.138|.377|.028|
|Innk|10.65|.440|.445|.313|.289|
|Ishrk|9.14|.375|.360|.358|.225|

Notes: (I) mktbtb = (borrowing against debentures + borrowing from the public)/total borrowing; (ii) Iprod = log of output; (iii) lnnk = log of net capital stock; (iv) Ishrk = log of share capital; (v) matl = LTB/TB; (vi) mat2=LTB/ (TB+Trade dues); (vii) for definitions of other variables see footnotes to Table 2; (viii) "means" are ratios of sums except for means of Iprod, Innk and Ishrk; (ix) the means are weighted means using the denominator of the ratio for weighting.

Before proceeding to a comprehensive econometric analysis of the determinants of maturity, we discuss some interesting bivariate correlations between maturity structure and selected variables (see the last four columns of Table 3). There is a marked positive correlation between the share of long term debt in the loan portfolio of **firms** and the share of fixed assets in the asset portfolio. This matching of the composition of debt and assets supports the prediction of Hart and Moore's (1994) model and is in conformity with the conventional wisdom that term loans should finance longer lived assets.

The association between profitability (as measured by the **ratio** of cash flow to fixed assets -
# **References**

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World. A New Database. World Bank Policy Research Working Paper 2588

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- Caprio, G., Demirguc-Kunt, A., and Kane, E., 2008. The 2007 Meltdown in Structured Securitization: Searching for Lessons, Not Scapegoats. World Bank Policy Research Working Paper Series 4745.

- Calvo, S., and Reinhart, C. M., 1996. Capital Flows to Latin America: Is There Evidence of Contagion Effects? in <u>Private Capital Flows to Emerging Markets after the Mexican Crisis, Calvo, G and Goldstein, M. (eds.), Washington: Institute for International</u> Economics.

- Cecchetti, S. G., 2009. Crisis and Responses: The Federal Reserve in the Early Stages of the Financial Crisis. _Journal of Economic Perspectives_ 23 (1), 51–75.

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2019) and are likely to be especially important in contexts like Vietnam’s, where SOEs continue to play a significant role in the local economy (Dang, Nguyen, & Taghizadeh-Hesary, 2020).

Future research could provide further evidence on the impact of FDI on competition and market power in developing countries as well as analyze heterogeneity. As mentioned earlier, most of the evidence on the relationship between FDI, competition, markups, and market power come from studies of high-income countries. Expanding to further countries beyond Vietnam would thus help to deepen the evidence base for developing countries. In addition, studies across different country settings sometimes draw conflicting conclusions regarding the impact of FDI on markups and market power. Analyzing why the impact of FDI may differ across contexts (e.g., based on policy environment or industry structure) would enrich policymakers’ and researchers’ understanding of the trade-offs of FDI and how to maximize the benefits of FDI. Further research on whether there are differential impacts based on mode of FDI entry (e.g., mergers and acquisitions versus greenfield) would also allow for more tailored policies for maximizing the development impact of FDI. Finally, if additional data were available, analysis of FDI’s impact on competition could be expanded to cover services, especially with respect to digital platforms.

19
� � _μFi_ . Notice that, for those fishermen who adopted before _t_ 0 + 5, _γ_ 1 _Di_ (5) has been added � to _μFi_ . The diagram thus gives the long-run expected gains from technology adoption, which will also be used throughout the rest of this paper. The straight line depicts the 45<sup>_◦_</sup> line. According to these results, three fishermen suffered a loss in sales of more than 1%, 2 experienced virtually no change (less than 1% change), while 20 enjoyed increases in average sales between 3.5 and 158%. The average change equals 40.2% with a standard deviation of 46.8%.

# **5.2 Determinants of the Timing of Adoption**

Since adoption in the context of this study can be interpreted as a one-time transition from one state, catamaran fishing, to another state, fibre boat fishing, the timing of the individual adoption decision is most suitably modelled using methods from the statistical analysis of survival data. In typical applications of these methods, the outcome variable is the time of a ”failure”, e.g. of a light bulb, conditional on certain characteristics of the item considered (e.g. the manufacturer of the bulb). In our application the ”failure” event is the adoption of the new technology.

In cases where the technology is divisible, like the adoption of new seeds in agriculture, the farmer with several plots can choose on how many of them to try the new technology. In contrast, a fishing boat is by nature an indivisible productive asset for a household. Moreover, switching technologies is expensive, while in agriculture, a farmer can return to the old crop variety in subsequent growing cycles without incurring a cost from switching back. To summarize, in the context of adoption of new crop varieties in agriculture, the adoption decision is typically both divisible and reversible, while in the present setup, neither of these two properties hold.

For the estimation, we adopt the common proportional hazard assumption. According to it, the hazard _λ_ , that is the probability that _i_ adopts within the next period given that he has not adopted yet, can be factored into a baseline hazard function, which is the same for all individuals in the population, and a function of individual characteristics, **x** _i_ .

18
11 family’s nonmedical consumption, _C_ - _M_ , to below _Z_ times the poverty line. We can classify families with 0< _M_ /( _C-NM*_ )<1 into groups based on the values of two ratios: _R_ 1≡ _M_ /( _C-NM*_ ) and a second ratio _R_ 2≡ _M_ /( _C-Z∙NM*_ ). This interpretation is illustrated in Figure 1, Figure 2 and Table 1. Figure 3 brings the two approaches to catastrophic payments together, and nests them in the broader unified approach. Each of the dots in Figure 3 is a real household from a country participating in the World Health Survey – the dataset we use in the empirical illustrations in section 4; the poverty line has been set at $1.25 per capita per day (one of the lines used in the empirical illustrations) and _Z_ has been set at 120 percent.

Consider first the non-catastrophic payment subcase (3b). In this case, we have 0< _M_ /( _C-NM*_ )<1 and 0< _M_ /( _C-Z∙NM*_ )<1. This is box 5 in Figure 3. Here _C-Z∙NM*_ >0 and therefore the household’s total consumption would have been more than _Z_ times the poverty line in the absence of out-of-pocket payments. And because _C-M>Z∙NM*_ , even with out-of-pocket payments the household’s nonmedical consumption is more than _Z_ times the poverty line. Payments in this case are not catastrophic in that they do not push the family too close to the poverty line. Consider next one of the catastrophic payment subcases (3a) where 0< _M_ /( _C-NM*_ )<1 and _M_ /( _C-Z∙NM*_ )>1. This is box 2 in Figure 3. In this case too, _C-Z∙NM*_ >0 so that in the absence of out-of-pocket payments, the household’s total consumption would have been more than _Z_ times the poverty line; given we also have _C-M<Z∙NM*_ and _M_ /( _C-NM*_ )<1, we can conclude that after out-of-pocket payments the household’s nonmedical consumption is somewhere between the poverty line and _Z_ times the
cannot control for this variable in our probit models. We address this problem in two ways. First, we estimate a linear probability model including parents‟ information about parent class representatives. Second, we restrict the sample to parents who are informed about the existence of parent class representatives.

# **Table 6**

# **Cooperativeness and the short route to accountability**

# **Controlling for information**

||**Dependent variable:**<br>Dummy equal to 1 if the parent voted in the elections<br>of classparent representatives,0 otherwise<br> <br>|
|---|---|
||Linear probability model<br>Probit|
||(1)<br>(2)|
|**Invested in group account in the game**|0.04*<br>0.07*|
||(0.065)<br>(0.059)|
|Age|0.00<br>0.00<br> <br>|
||(0.395)<br>(0.390)|
|Female|0.03<br>0.03|
||(0.136)<br>(0.280)|
|Wealth index|0.01<br>0.02|
||(0.281)<br>(0.312)|
|Years of schooling|0.02***<br>0.02***|
||(0.000)<br>(0.000)|
|Rural location|-0.02<br>-0.02|
||(0.415)<br>(0.485)|
|Distance from school (Km)|-0.00<br>-0.00|
||(0.591)<br>(0.662)|
|Member of a social organization|0.06<br>0.09|
||(0.275)<br>(0.252)|
|Participated in community activities|0.06**<br>0.07*|
||(0.033)<br>(0.061)|
|Thinks village members would work together|0.05**<br>0.08**<br> <br>|
||(0.033)<br>(0.026)|
|Generalized trust in others|-0.02<br>-0.04|
||(0.493)<br>(0.428)|
|% of relatives and friends among other parents|0.05<br>0.07<br><br>|
||(0.482)<br>(0.435)|
|Help pupil with homework|-0.00<br>0.00|
||(0.994)<br>(0.972)|
|Number of meetings with head teacher|0.01**<br>0.01*|
||(0.045)<br>(0.071)|
|Informed about parent class representatives|0.63***<br>(0.000)|
|Constant|-0.20*|
||(0.057)|
|District fixed effects|YES<br>YES|
|_Joint significance of district f.e.(p-value)_|_0.000_<br>_0.000_|
|Geographical areas dummies|YES<br>YES|
|_Joint significance of geographical areas(p-value)_|_0.942_<br>_0.577_|
|Observations|1,800<br>1,302|

Note: Robust standard errors have been clustered at the school level. P values in parentheses: *** p<0.01, ** p<0.05, * p<0.1. In Column 2, we report marginal effects of continuous variables and the effect of a change from 0 to 1 for dichotomous variables. In all regressions we control for the four geographical areas dummy variables; Tirana is the excluded geographical area.

20