rafmacalaba/datause-encoder-data huggingface dataset:
rule out selection on pre-event trends. We now consider selection on shock and selection on trend shock.

To test for selection on shock, we carry out placebo checks. We remove one chamber from our sample at a time, following the chronology of decree application, and run our main event – study specification displaying all entry cutoffs. Figures A5 A8 display the four sequential sample restrictions; dashed lines indicate placebo cutoffs, which did not apply to the chambers remaining in the sample, while solid lines show actual application cutoffs. As expected, this significantly dampens the sharpness of the jumps after the initial cutoff.

Shifting the application threshold and leaving out chambers by chronological order of decree application does not change the magnitude of the decree impacts. Across all sample cutoffs, we do not observe any significant jump in our main outcomes of interest at the dashed lines, providing reassurance that selection of chamber and cases on shock is unlikely to bias our event study estimates. Judges sharply respond to the decree application by desk-rejecting cases only after their respective chambers become treated. Consistent with our main results, we observe that cases that entered a few hearing periods before the decree was applied in their respective chamber are more likely to exit pre-trial in less than four months, relative to those further away from the cutoff in the pre-period.

These placebo checks also confirm that judges at least partially apply the reform to their ongoing caseload at time of decree application, generating fuzziness at the discontinuity. First, we do not see a larger anticipation window for cases entering chambers in which the decree was applied later. This is consistent with the fact that we do not find any prereform increase in the likelihood that judges desk-rejected cases or imposed strict deadlines on parties requesting an adjournment (Panels C and D, Figures A5 to A8). Second, we see a modest pre-jump in the probability of fast-tracking cases in all placebo tests (Panel E, Figures A5 to A8). Similarly, we find that the number of hearings starts to decline for cases that entered just before the decree was applied (Panel F, Figures A5 to A8). This suggests 18
79 negotiations.

While there is a (correct) tendency to regard these developments as being positive from the viewpoint of all developing countries as a group, some may incur losses. Trade in textiles and clothing has been rigidly controlled over the last three decades through the use of quotas imposed under the Multifiber Arrangement. The phase out of these restrictions over a 10 year period _will subject African exports to aggressive international competition._ The ability of many African countries to maintain a viable textile and clothing export sector depends on their capacity to implement necessary reforms aimed at achieving cost competitiveness. The fact that the MFA liberalization is so heavily backloaded (roughly one-half of the restrictions will be removed at the end of a ten year period) provides ample time for adjustments to be implemented. In any event, losses of market share for textiles and clothing need not be an accurate indication as to the impact on welfare -- particularly if African exports are heavily subsidized. Africa may also face more vigorous competition on products like footwear and ferrous metals, where exports from some other developing countries were formerly restrained by 'voluntary" restraints which were eliminated by the Round.

Any assessment of these likely future development must recognize that policy options are available to combat potential losses. Probably one of the most promising of these involves reforms in the African countries' own trade regimes. Detailed statistics compiled by UNCTAD (1987) on developing countries trade barriers show that African tariffs are generally far higher than average, and that their trade regimes are often NTM-ridden. Extensive studies by the World Bank (see Thomas and Nash, 1991) document the important economic gains that can result from trade policy reform and it appears that such reform could have a major positive impact in Africa. In short, the best response to an erosion of trade preferences in foreign markets would appear to be an aggressive liberalization of African countries own trade barriers.

As far as "unfinished' business is concerned, further initiatives are needed to address NTBs facing energy products, fish and chemicals since these were largely bypassed by the Round. Since little information is now available on their trade impact, efforts should be made to estimate the levels of protection afforded by the postUruguay NTBs (as was done previously in the "tariffication" exercise for agriculture). These NTB nominal equivalents could be helpful in establishing priority sectors for post-Uruguay Round action. Also, there is a need
**Table 5. Sources of data**

|**Data**|**Source**|
|---|---|
|Production, consumption and stocks<br>in each region|<br>U.S. Department of Agriculture’s Production, Supply<br>and Distribution Data base(1)|
|Domestic price of grains, sugar and<br>oilseeds|Food and Agriculture Organization of the United<br>Nations(FAO) (2)|
|World energy price|International Monetary Fund Primary Commodity<br>Prices(3)|
|Biofuelproduction and consumption|Renewable Fuels Association(4)|
|Exchange rates|U.S.Federal ReserveStatistical Database(5)|
|Price and income elasticities of<br>supplyand demand for crops|Food and Agricultural Policy Research Institute<br>ElasticityDatabase and USDA elasticitydatabase(6)|

1. http://www.fas.usda.gov/psdonline

2. http://faostat.fao.org/ 3. http://www.imf.org/external/np/res/commod/index.asp 4. http://www.ethanolrfa.org/industry/statistics/#E 5. http://www.federalreserve.gov/releases/G5A/ 6. http://www.fapri.iastate.edu/tools/outlook.aspx Finally, to test the robustness of the inventory demand parameters, we simulate a fourth scenario using Carter et al.’s estimates for the inventory demand function as opposed to our own. Note that different from us, Carter et al. [14] estimate the inventory demand based on U.S. data for 2006 through 2008, while we use world data for 2001 through 2008.

# _6.7. Sources of data_

The various data sources are shown in Table 5. Data on production, consumption, beginning and ending stocks, imports, and exports for each region are obtained from the U.S. Department of Agriculture’s Production, Supply and Distribution database. Data on crop prices within each region are obtained from the FAO database. A key set of parameters in simulation models is the elasticities of crop supply and crop demand. Our specification of supply and demand requires information on elasticities of supply and demand with respect to own-price elasticities of supply and demand with respect to energy price and the income elasticity of demand. The range of elasticities contained in FAPRI database and in the literature cited by the USDA database is shown in Table 3.

39
**Table 3. Balance of Payments 1994-2005**

||**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**<sup>**2/**</sup>|**2003**<sup>**2/**</sup>|**2004**<sup>**2/**</sup>|**2005**<sup>**2/**</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||Years of|REER Apprecia|tion||||Years of REER De|preciation|||
|**Levels**_(millions US$)_||||||||||||
|Net aid flow|727.53|698.42|967.74|627.65|795.92|937.31|754.64|667.10|876.29|1140.26|1405.16|
|Non-aid current account balance|-1008.64|-578.20|-784.88|-1230.33|-1194.69|-877.30|-808.26|-818.8|-795.1|-984.7|-1324.30|
|Change in Reserves (- = increase)|57.25|-209.45|-182.14|20.91|-175.41|-197.41|-186.38|-333.44|-348.16|-218.62|-128.49|
|_Potenial REER Impact (-=expected_<br>_depreciation)_|-223.86|-89.23|0.72|-581.77|-574.18|**-137.41**|**-240.00**|**-485.12**|**-267.00**|**-63.10**|**-47.63**|
|Non-aid capital account balance 1/|223.86|89.23|-0.72|581.77|574.18|137.41|240.00|485.12|267.00|63.10|47.63|
|**Percent of GDP**||||||||||||
|Net aid flow|13.84|10.75|12.59|7.49|9.21|10.32|7.99|6.83|8.52|10.08|11.60|
|Non-aid current account balance|-19.19|-8.90|-10.21|-14.68|-13.83|-9.66|-8.56|-8.38|-7.73|-8.71|-10.93|
|Change in Reserves (- = increase)|1.09|-3.22|-2.37|0.25|-2.03|-2.17|-1.97|-3.41|-3.38|-1.93|-1.06|
|_Potenial REER Impact (-=expected_<br>_depreciation)_|-4.26|-1.37|**0.01**|-6.94|-6.65|**-1.51**|**-2.54**|**-4.96**|**-2.59**|**-0.56**|**-0.39**|
|Non-aid capitalaccount balance1/|4.26|1.37|-0.01|6.94|6.65|1.51|2.54|4.96|2.59|0.56|-0.39|
|**Cummulative Changes **_(millions US$)_|||_1995-1999_|||||_2000-2005_||||
|Net aid flow|||3817.26|||||5780.75||||
|Non-aid current account balance|||-4796.74|||||-5608.50||||
|Change in Reserves (- = increase)|||-488.84|||||-1412.50||||
|_Potenial REER Impact (-=expected_<br>_depreciation)_|||-1468.32|||||-1240.25||||
|Non-aid capitalaccount balance1/|||1468.32|||||1240.25||||
|**Changes **_(millions US$)_||||||||||||
|∆Net aid flow||-29.11|269.32|-340.09|168.27|141.38|754.64|-270.21|121.65|473.16|528.87|
|∆ Non-aid current account balance||430.44|-206.68|-445.45|35.64|317.39|-808.26|58.52|13.13|-165.97|-529.17|
|∆ Change in Reserves (- = increase)||-266.70|27.31|203.05|-196.33|-22.00|-186.38|-136.03|-161.78|114.82|219.67|
|_Potenial REER Impact (-=expected_<br>_depreciation)_||134.63|89.95|-582.49|7.59|436.77|-240.00|-347.71|-27.00|422.02|219.37|
|∆ Non-aid capital account balance 1/||-134.63|-89.95|582.49|-7.59|-436.77|240.00|347.71|27.00|-422.02|-219.37|
|<br>**Changes **_(% of GDP)_|||_1995-1999_|||||_2000-200_|_5_|||
|∆Net aid flow||-3.09|1.84|-5.11|1.73|1.11|7.99|-3.50|0.52|3.25|3.09|
|∆ Non-aid current account balance||10.29|-1.31|-4.46|0.85|4.17|-8.56|1.28|0.83|-0.33|-3.21|
|∆ Change in Reserves (- = increase)||-4.31|0.85|2.62|-2.28|-0.14|-1.97|-1.24|-1.41|1.48|2.32|
|_Potenial REER Impact (-=expected_||||||||||||
|_depreciation)_||2.89|1.38|-6.95|0.29|5.13|-2.54|-3.45|-0.05|4.41|2.20|
|∆Non-aid capitalaccount balance1/||-2.89|-1.38|6.95|-0.29|-5.13|2.54|3.45|0.05|-4.41|-2.99|

Source: IMF Staff Reports.

1/ Includes errors and omissions.

- 2/ Original data available in fiscal year, converted to calendar year by taking average
distribution committees (Duclos 1995). As such, we now turn to estimates of the factors affecting the participation of households in the VL program. Table 9 presents the determinants of the probability of receiving VL transfers using the 1997 ENNIV survey data.<sup>6</sup> The dependent variable in these probit models is an indicator for participation in the VL among all households in the sample. The explanatory variables include household demographics, characteristics of the household head, per capita consumption as a proxy for wealth, and district fixed-effects dummies. Three models are presented based on different forms in which household per capita consumption enters as an explanatory variable. In the first model, household wealth is controlled for in the form of dummies for the quintile of per capita consumption into which the household falls. In the second and third models, the log of per capita consumption is used, with reported consumption used in the former, and given its endogeneity, instrumented consumption is used in the latter.

> These three models are qualitatively and statistically the same, and confirm that VL transfers are targeted to young children and to poor households. For example, a household with an additional child of age 6 and under is 11 percent more likely to benefit relative to the average household (which has just one such child). Since the questionnaire did not identify women who were pregnant, we cannot precisely identify the remaining Tier I target group.<sup>8</sup> As such, it is not surprising that, controlling for the number of children, the number of women. in the household does not affect the probability of participation. With regard to the Tier II target group, an additional child between that ages of 7 and 15 makes the household three percent more likely to receive VL benefits than the average household.

> 6 Ruggeri Laderchi (2001) estimates simnilar probits for participation in any feeding program, not just Vaso de Leche. Her results are qualitatively similar to those presented here.

> The instruments used in Model 3 are education of the household head, emnployment status of the head, an indicator of ownership of a business or farm, and an indicator for the head holding two jobs. 8Lactating women are not missed since they obviously live in a household with a young child.

_19_
availability of Tanzania’s export and import prices make the real commodity price index our preferred measure of Tanzania’s ToT.<sup>8</sup> Like many other African countries, Tanzania has experienced a secular decline in their terms of trade over the sample period, which broadly corresponds to the long-term trend decline in the REER (Figure 4).

# _Aid flows_

Tanzania has received large and sustained aid flows for much of the last thirty years. Even when aid inflows decreased in the early 1980s, Tanzania received higher aid inlfows than most SSA countries (Ndulu and Mwase 2005). Following the reformist government’s move to market-base economy in 1985, aid flows surged to over 25 percent of GDP by 1992. When commitment to further economic reform weakened in the early to mid-1990s, aid flows again declined, but remained higher than most other SSA countries at almost 10 percent of GDP (Muganda 2004). Since 2000, the government’s commitment to macroeconomic and structural reform emphasizing the importance of restricting the role of the state in the economy has coincided with another surge in aid flows, with the aid-to-GDP ratio over 12 percent in recent years (IMF 2005).

Aid inflows that are spent in the local economy will contribute to an increase in the demand for both tradables and nontradables.<sup>9</sup> When **Figure 5. Aid Inflow and Real Effective Exchange Rate** tradable goods’ prices are fixed by international prices it is 300 35 nontradables prices that would 250 30 tend to increase. The pressure 25 of expanded liquidity on 200 20 domestic demand and prices of 150 nontraded goods would lead to 100 15 a real exchange rate 10 appreciation. Figure 5 indicates 50 5 that no such relationship 0 0 appears to be present in 1970 1975 1980 1985 1990 1995 2000 2005 Tanzania. In fact, the series are Aid (% of GDP) WPI-based REER highly negatively correlated, Source: OECD DAC and authors’ calculations i.e. a depreciation (appreciation) of the REER is associated with an increase (decline) in aid flows.

The ‘equilibrium’ relationship described could be muted or nonexistent for a number of reasons. There may be no change in the REER if aid is spent entirely on imports, implying no impact on money supply or aggregate demand in the local

> 8 The correlation coefficient between the real commodity price index and the ratio of Tanzania’s export and import prices taken from the IMF’s World Economic Outlook (WEO) database is 0.84 over the period 1980 to 2004.

> 9 To make local purchases, foreign exchange would need to be converted into local currency that would expand the money base, fuelling the increase in demand.

9
without meeting with a public official in the period from July 2011 to June 2012. The new data could be particularly valuable for development practitioners because they capture not only transparency efforts made through online solutions, but also other, less capital intensive ways of making information available, for instance, by means of brochures and billboards. This approach allows us to include many developing economies in the sample, which do not necessarily have the resources to invest in capital- and resource-intensive E-Government solutions.

The paper is structured as follows: first, we present descriptive statistics on how the practice of making fee schedules available varies across different government agencies, regions and income groups. We find that meeting an official is still the most common means of acquiring regulatory information in Sub-Saharan Africa and the Middle East and North Africa. And, while richer countries generally tend to make more information available through websites and other information material, there are also some poor countries that achieve transparency levels comparable to those of richer countries in the sample. Of the four different regulatory agencies considered in this paper, company registries are by far the most transparent, a result that may be related to the significant number of reforms that have taken place in this particular area of business regulation in the past ten years.<sup>2</sup> Second, we examine commonalities among those economies that are more transparent in their business regulatory practices other than that they tend to be richer. Like other authors before us, we find that greater access to information is associated with greater control of corruption and better overall governance. We also find that countries that make it easy to access information tend to be more democratic.

Third, using various data sets on the quality and efficiency of regulatory systems, we find that easier access to information is associated with greater regulatory quality and efficiency.

The next section of the paper describes the data methodology. Section III presents descriptive statistics of the data by region and income groups. Section IV includes the main analysis and Section V concludes the findings.

# **II. Methodology**

The new indicators presented here have been collected in the context of the annual data update of the Doing Business indicators published jointly by the World Bank Group (WBG) and the International Finance Corporation (IFC).<sup>3</sup> The Doing Business indicators of the WBG/IFC have been tracking business regulatory requirements and practices since 2003. In its current format, the Doing Business data set comprises 11 indicators that measure the regulations affecting domestic small and medium-size enterprises in the largest business city of 185 economies. The

> 2 In its 10-year addition of the Doing Business Report (Doing Business 2013) the authors of the report show that a total of 368 reforms were undertaken in 149 economies in the area of business startup since 2005. 3 www.doingbusiness.org

3
# STRATEGIC INTERDEPENDENCE IN

EAST-WEST GAS TRADE A Hierarchical Stackelberg Game Approach

## Wafik Grais

and

## Kangbin Zheng

Europe and Central Asia Regio The World Bank Washington D.C. 20433 (202) 473-6974

* The authors axe grateful to Dale Gay, Ricardo Martin, F. Desmond McCarthy, and James Falk for helpful discussions and suggestions. We ourselves are fMlly responsible for the opinions expressed herein, which do not necessarily reflect the -views of the World Bank. A GAMS program for numerical solutions of the hierarchical Stackelberg game can be obtained from the second author.
51 in WDP is not for outsiders (for example, the State Government, the funding agency, -.omen's studies, scholars, etc.) but is meant primarily for WDP workers and for program improvement.

6.9 Output monitoring is almost completely lacking in WDP. The program does indeed have financial monitoring and accounts are maintained systematically and are furnished periodically to UNICEF and the Department of W C and N, and the spread of the program is carefully monitored. But there is practically no quantitative monitoring of the work done by Prachetas and Sathins. No proformae have been prescribed, nor targets laid down. The result is that neither the State Government, nor IDS, nor UNICEF have precise quantified data, with reference to a particular period of time, to indicate specific activities undertaken in the field. What PDs, State IDARA, IDS and the State Government do have are interesting illustrations of the kind of work which Prachetas and Sathins have done, the kind of processes they have set in motion -- episodes which illustrate the manner in which bondage of women can be loosened and they can face in unison suppression within family, the neglect by the developmental agencies, and the oppression of the police and the criminal elements.

6.10 Recently the Directorate of WC and N has circulated proformae on the basis of which types of group actions and the numbers should be recorded on a quarterly basis. Also, the
In a second step, the study examines if the correlation between MNEs and the disclosure rate varies by type of development category under study. It differentiates between the average disclosure rate on economic, labor, social, human rights, environmental (energy‐related, water‐related, and other) and governance indicators, but does not find a significant relationship for any of these categories. In order to address the heterogeneity of indicators, this study applies a Probit model in a third step to assess how MNE status is related to the likelihood of disclosure at the detailed GRI indicator level. Overall, the study finds that MNE status is significantly correlated with the probability of reporting for twelve indicators. Company size, however, has a much larger explanatory power.

This paper is structured as follows. Section 2 introduces the econometric specification, explains the GRI data set, and presents first summary statistics. Section 3 shows patterns of disclosure and reports the regression results focusing on the correlation between MNE status and the average disclosure rate overall and also by development category. Section 4 examines the correlation between MNE status and the likelihood of reporting economic, labor and social, environmental, and governance indicators at the detailed indicator level. Finally, section 5 summarizes the results and concludes.

# **<mark>2. ECONOMETRIC MODEL AND DATA</mark>**

## **2.1 ECONOMETRIC SPECIFICATION**

A company _i_ ’s propensity of disclosing information on a certain sustainability indicator, _disclosure_ , can be defined as: �� �������������� �1���� ���ß�������) where α designates the constant and _firm_ firm‐level characteristics, while subscript _c_ denotes countries and _s_ sectors. _disclosure_ takes the value of 1 if a company discloses information (entirely or in part), and 0 if not.

We focus on the following estimation equation: ������������� ���ß������� � ��� � ���� where _Dcs_ denotes country‐sector fixed effects and _εisc_ the idiosyncratic error term.

The equation is specified as follows: ������������� ���ß� ������ �ß� �������� � ��� � ���� _(1)_ where _MNE_ is a dummy which equals 1 if the company is multinational, and 0 otherwise; and _large_ a dummy which equals 1 if the company has at least 250 employees, and 0 otherwise. The company‐level dummies are computed based on the 2015 GRI data set which is described in more detail in section 2.2.

Since the propensity of disclosure is only available at the detailed indicator‐level, we use the average disclosure rate, _DR_ , in some specifications which is the average across all 54 indicators and can range from [0,1]. The specification in equation (1) changes to: 3