rafmacalaba/datause-encoder-data huggingface dataset:
or gas discovery. Here the examples show the differential effects above and below our chose threshold of institutional quality, constraints on the executive measured in 1980. Here we see countries such as Malaysia, Israel and Trinidad & Tobago exceeding their counter-factual synthetic GDP per capita path. In contrast for countries such as Algeria, Colombia and Mozambique, we see the discovery coinciding with countries undershooting their counter-factual growth.

# **4 Threats to**

In this section we examine a range of potential channels and estimate heterogeneous effects of a resource discovery on growth. We seek to determine what may drive our headline result and thus determine the policy implications that may derive from this finding.

There are a range of competing interpretations for our result, including that they may be driven not by growth under-performance, but instead by forecasting error, persistent over-forecasting, or exuberance following discoveries. We address each of these in turn.

**Forecasting error** A number of articles have scrutinized the accuracy of cross-country macroeconomic forecasts. Given its long history, transparency and widespread country coverage, the majority of this research has examined IMF WEO forecasts, which has a coverage that dwarfs other similar datasets (e.g. OECD, Consensus Forecasts).

The accuracy of forecasts is generally evaluated against the performance of a na ̈ıve forecast such as random walk or recursive mean. The ratio of the root-mean squared forecast errors (RMSFE ratio) of actual and na ̈ıve forecast provides a measure of accuracy. Global reviews on the accuracy of the WEO forecasts nd that it generally perform better than na ̈ıve forecasts (Timmermann and Granger (2004) & Genberg and Martinez (2014)). However, country and period selection matters; for a subset of developing countries over certain periods the IMF WEO did not outperform a random walk forecast (Beach et al. (1999) & Arora and Smyth (1990)).

Our results suggest that there is a large and consistent divergence in growth forecasts and measured growth following major resource discoveries. It could be the case that the size and complexity of such an external shock may create forecasting error simply due to the wide range of possible growth responses and difficulties in modelling and predicting how different economies may respond. For example a resource discovery may trigger capital flows which may have differential effects based on the monetary regime or the depth of the financial system.

We can disentangle any potential forecasting error effect from our growth disappointment effect via a test on the **absolute** growth differential as compared to our main variable of interest - the simple growth differential. For details on construction of the variables please refer to Appendix A.

Under conditions of increased but symmetric forecasting error we would expect the estimated coefficient on the discovery effect on absolute growth differentials to be positive. In other words, we would 18
pothesis is also accounted for. To test for each of the two hypotheses while controlling for the other hypothesis’ parameter, we jointly estimate the three key parameters of the model _{μm, μf , σm_<sup>2</sup><sup>_}_us-</sup> ing the following equation: We also estimate a second version of Equation 9, where we also control for GDP per capita. This allows to check whether our main results are not driven by the overall economic development in each country and over time. The data on PPP-adjusted per capita income data are sourced from the Penn World Tables. The drawback of this specification is that it has to be estimated in a smaller sample than our baseline sample, as GDP per capita is not available for all years and all countries included in the baseline sample, particularly for early years. In addition, as per-capita income data is missing for many countries in years prior to the 1960s, we estimate the model parameters in the GDP sample starting from the 1960s rather than the 1950s.

# **6 Results**

## **6.1 Baseline results**

### **6.1.1 Tertiary enrollment**

The model parameter estimates for tertiary enrollment are reported in the upper panel of Table VI. Column 1 and 2 report the parameter estimates of the tail and mean hypotheses, respectively, when they are estimated in separate equations. Both the female mean shift per period _μf_ and the male standard deviation _σm_ parameters are statistically significantly different from their null hypothesis values of 0 and 1, respectively. In addition, the value of the R-squared for the tail and mean dynamics hypothesis specifications reported in column 1 and 2 are very similar. This suggests that 23
<!-- Start of picture text --> c. Average risk-weighted assets over total assets<br><!-- End of picture text --> Source: Archived data from Bureau van Dijk’s Bankscope, BankFocus and staff calculations. Note: Off-shore financial centers are excluded from the calculations. Panel A, B and C present country averages across country groups. Banks are weighted by total assets within a group (top 20% banks) and country in each year, and countries are equally weighted within a country grouping (i.e., a simple average of country-level average ratios for either top 20% banks or the other banks within high-income and developing countries is taken) in each year. A country is included in the calculations in each year if it has data for at least 5 individual banks. Banks are included in the calculations if total assets are larger than USD 10 million.

Beyond the levels of the Tier 1 capital ratio, another aspect of the quality of bank capital is the balance-sheet items allowed in the computation of Tier 1 regulatory capital. As shown in Figure 4, Panels A, B, and C, between BRSS rounds 4 and 5 more countries allowed hybrid debt capital instruments, asset revaluation gains, and subordinated debt to be used in the computation of Tier 1 capital, although with important regional differences. In general, a higher proportion of high-income OECD countries allowed alternative capital instruments to be counted as Tier 1 capital. There was also a notable change between wave 4 and wave 5 in the proportion of countries allowing asset revaluation gains and subordinated debt into Tier 1 capital, predominantly for high-income OECD countries and developing countries, specifically for countries in the developing regions EAP, ECA and SSA. Broadening the definition of Tier 1 capital in this way raises at least three concerns, especially for developing countries. First, a broader definition of bank regulatory capital may increase opacity for private monitors in assessing bank risk, an issue highlighted by the GFC (see, for instance, Haldane, 2011). Second, there may be much less liquidity for debt contracts in developing countries where financial markets are less developed. Third, there is inherent complexity in the valuation of hybrid debt capital instruments, asset revaluation gains, and subordinated 9
- 39 -

# V. BANKING

The commercial banking system is now almost useless for rural people. Gambian farmers perceive banks as remote, intimidating, and not fully trustworthy, and by and large they do not go to them.<sup>39</sup> The senior officers of the banks in The Gambia, on the other hand, imagine smallholding farmers farmers as too poor and financially inactive to be of interest, and not particularly trustworthy as borrowers. There are only two up-river branches: outside Banjul and nearby Serrekunda and Bakao, commercial banks are found only in Farafeni and Basse.40 The numbers of rural bank depositors clearly reflect the distances, social and geographic. A 1975-6 survey of rural Gambian villages found that only two (2Z) of 94 informants had bank savings (Dunsmore et al. 1976: 307). In our 1987 survey, four (3Z) of 138 male and female informants stated they had deposits in commercial banks; four others, again 3Z, said they had money in post office savings. Only one of 69 women interviewed had a savings account in either a bank or post office savings.

Branch bank information confirmed that most farmer depositors hold amounts below about 250 dalasis, that very few of them are women, and that almost no farmers borrow from banks. In May 1987 the Basse branch of the GCDB (one of only two branches of the bank outside the greater Banjul area) reported that 492 of its 858 savings depositors had less than 250 dalasis in their accounts (i.e., less than the value of one sheep). Interviews with bank staff suggested that most of the roughly 100 farmers (people whose primary source of income is farming) saving at the bank were among those with less than 250 dalasis. Only
Figure 6: Imports of goods and services (in percentage points of GDP) <!-- Start of picture text --> 35%<br>Goods Services<br>30%<br>Total Poly. (Total)<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Source: Central Bank of Turkey, IMF World Economic Outlook; author’s estimates<br>Figure 7: Net oil trade and the current account balance (percent of GDP)<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>Net oil exports CAB excluding oil Total CAB<br>Source: Central Bank of Turkey, IMF World Economic Outlook<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

**Turkey’s openness (trade to GDP) grew only slightly post 2001, at a time when its trading partners were rapidly expanding trade.** Turkey’s openness ratio grew from a very low level to around 40 percent by 2001 and 50 percent by 2005, but since then has shown no further sign of increasing. By contrast, the weighted-average openness of Turkey’s trading partners is higher, and the gap has increased, especially after the GFC (Figure 8). Therefore, it is interesting to note that Turkey’s relative reduction in openness has not apparently been accompanied by a reduction in the CAD over the period, and the next section will provide more evidence on the relationship between openness and the CAD, controlling for other variables.

8
of senior women are poor). The same pattern holds for males. Boys under the age of 15 are the poorest and their poverty rate is 8.4 percentage points higher than that of young men ages 15‐24 and over 12 percentage points higher than that of senior men (only 7.3 percent of senior men are poor).

_Figure 2. Poverty rates and geographic distribution of the poor by sex_ _2.a. Poverty rate geographic area and sex 2.b. Geographic distribution of the poor by sex_ <!-- Start of picture text --> 20 18.7 17.9 100<br>20.2 19.8<br>80<br>15<br>60<br>10<br>5.7 5.4 40 79.8 80.2<br>5<br>20<br>0 0<br>Urban Rural Urban Rural Female Male<br>Female Male Rural Urban<br>Percentage Percentage<br><!-- End of picture text --> Note: Total sample 89 countries Source: WB Staff’s calculations based on GMD.

Children are the poorest across all regions, but the patterns vary by region (Appendix Figure A1). For example, in Sub‐Saharan Africa, 49.3 percent of girls and 49.5 percent of boys live in poor households and boys represent a slightly larger share (51 percent) of poor children than girls do. Differences with other age groups are even starker than the global ones: boys and girls under 15 years of age are 10 percentage points poorer than their young adult (15‐24) counterparts, and girls are 17.2 percentage points poorer than females above 60. In contrast, in South Asia, girls are poorer than boys (22.2 and 20.1 percent, respectively) and more numerous than boys among the poor (50.5 percent), but differences in poverty rates between children and older adults —although sizable—are smaller than in other regions.

11
Table F.8. Heterogeneous Effects on Social Capital by PEP Benefits Knowledge

||Positive<br>Reciprocity<br>Index|Negative<br>Reciprocity<br>Index|Altruism|Trust|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_β_1=I(Treat)_×_I(PEP Benefts Knowledge)|0.095|-0.087|0.032|0.160|
||(0.141)|(0.145)|(0.140)|(0.140)|
|_β_2=I(Treatment)|-0.077|0.034|-0.054|-0.154|
||(0.121)|(0.124)|(0.120)|(0.120)|
|Knowledge PEP Diff. Effect =_β_1+_β_2|0.018|-0.054|-0.022|0.006|
||(0.073)|(0.075)|(0.073)|(0.073)|
|R-squared|0.032|0.003|0.026|0.015|
|Observations|1,002|1,002|1,002|1,002|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with a variable =1 if the respondent know about the PEP Benefits. The variable in column (i) is an index constructed using the methodology of Kling et al. (2007) and the answer on a 1 to 5 scale of the approval of the statement: _when someone does me a favor, I am willing to return it_ , and the answer regarding the hypothetical money the respondent may give to a stranger as a thank-you for helping him on the street; (ii) is an index constructed using the methodology of Kling et al. (2007) and the reported answer on a 1 to 5 scale of the approval of the following statements: _How willing you are to punish someone who treats you unfairly, even when there are risks to you of personal consequences_ ; _How willing are you to punish someone who treats others unfairly, even when there are risks to you of personal consequences_ ; and _If I am treated very unfairly, I will take revenge on the first occasion, even if I have to pay a cost for it_ ; (iii) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _How willing are you to donate to charitable causes without expecting anything in return_ ; (iv) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _I always assume that people have only the best intentions_ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

95
Table 6 False Experiments Average Impact of Anti-Dumping on Income non-Mekong South Provinces

||Total<br>Income<br>|Per capita<br>Income<br>|Net<br>Income<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|Low-Exposure|0_._009|0_._010|0_._034|
||(0_._026)|(0_._026)|(0_._024)|
|Mean|0_._032|0_._035|0_._117|
||(0_._086)|(0_._084)|(0_._081)|
|High-exposure|0_._071|0_._077|0_._233|
||(0_._157)|(0_._154)|(0_._157)|
|Observations<br>|384|384|384|
|R<sup>2 </sup>(within)|0.331|0.306|0.315|

Note: Estimates of a growth equation for total household income (column 1), per capita household income (column 2), and net income (column 3). Results from the quadratic model at three different levels of exposure measured by the pre-shock catfish shares: the median (low exposure), the mean (average exposure), and the median share for farmers with shares above the mean (high exposure). The false experiments are run on the sample of fishing households in non-Mekong South Vietnam. Robust standard errors within parenthesis.

42
computes the cumulative probability or nonexceedance probability of the Gamma distribution<sup>8</sup> given shape and scale parameters of the distribution computed from the _L-moments_ parameters.<sup>9</sup> In addition, the ―lmomco‖ package is used to create 0.90 confidence intervals for BWI derived from a Monte Carlo simulation. This PDF gives estimates for both low- and high-flow.

# **Risks of being below treaty supply parameters**

Applying the probability density function, a theoretical estimation of runoff can provide a probability of low flow, and, in the case with specific water requirements, this probability can be extended to estimate the probability of treaty non-compliance.

# **ASSESSING THE PERFORMANCE OF THE MODEL**

# **Basin selection**

One of the leading databases on international river basins, Transboundary Freshwater Dispute Database (TFDD), reports on 276 international river basins. The foundation of a defensible model has several necessary requirements that apply to the selection of gauge and satellite data (Table 1 includes a summary of these criteria and references) in order to select the basins. Natural (unimpeded) flow is a fundamental assumption of the model. Therefore regulated rivers are identified through GIS survey of TFDD international river basin. ESRI ArcGIS defined watersheds from 3535 gauging stations (GRDC) within international river basins derived from the HydroSHEDS database.<sup>10</sup> The universe of unregulated multilateral basins with available

> 8 There is much evidence that the distributions of hydrologic variables are heavy tailed (Katz et al. 2002). This is even more pronounced in arid and semi-arid environments (Anderson et al. 1998; and Farquharson, Meigh, and Sutcliffe 1992).

where F(x) is the nonexceedance probability for the quantile x. The parameters have the following interpretation in the R syntax; α is a shape parameter and β is a scale parameter.

> 10 A small correction factor of up to approximately 2 Km is used to snap the location of the gauging station to the HydroSHEDS 15 second flow accumulation grid based on the highest flow accumulation value.  A correction factor of 1 Km yielded several inconsistencies between the reported station catchment area from GRDC and the area

12
|**Table A4: Multinomial Logit Selection**<br>**Progr**<br>**Constru**|**Model**<br>**am**<br>**ctor**|**Prog**<br>**Contr**|**ram**<br>**actor**|**Non-P**<br>**Contr**|**rogam**<br>**actor**|
|---|---|---|---|---|---|
|**Coeff.**|**S.E.**|**Coeff.**|**S.E.**|**Coeff.**|**S.E.**|
|Age<br>-0.01|0.02|0.04|0.03|-0.02|0.02|
|Primary Complete<br>0.78|0.49|-0.12|0.81|0.43|0.74|
|Secondary Complete<br>1.10**|0.50|0.66|0.80|0.29|0.74|
|College<br>0.57|1.33|0.80|1.55|1.24|1.30|
|TVET complete<br>0.70**|0.28|-0.21|0.48|1.34***|0.41|
|Sex<br>0.23|0.27|0.01|0.51|-0.25|0.38|
|Apprentice in the past<br>0.72|0.21|0.62|0.39|0.46|0.32|
|**_Employment History_**||||||
|-ever unemployed<br>0.91|0.23|-0.35|0.39|0.55|0.34|
|-government employee<br>0.58|0.36|1.04**|0.56|0.79|0.52|
|-employee<br>-0.23|0.28|0.63|0.49|0.07|0.41|
|-domestic<br>0.67|0.41|0.91|0.62|1.55**|0.55|
|-self employed<br>-0.84|0.57|-1.82|0.98|0.18|0.86|
|-working in a coop<br>1.67|0.49|0.25|0.92|0.92|0.68|
|-casual worker<br>-0.66*|0.35|-0.35|0.65|-1.71**|0.79|
|**_Activity Prior to Current Job_**||||||
|-unemployed<br>0.48|0.52|0.90|1.18|2.03|1.21|
|-employee<br>0.54|0.54|0.39|1.18|2.05|1.21|
|-self employed<br>1.24|0.76|1.63*|1.45|0.46|1.70|
|-casual worker<br>1.00*|0.54|-0.53|1.29|2.20|1.22|
|-student<br>0.34|0.53|1.03|1.17|1.46|1.25|
|-govt employee<br>0.32|0.74|-1.46**|1.61|1.92|1.38|
|N<br>661||||||
|LR Chi2 (90)<br>912.41||||||
|PseudoR2<br>0.2067||||||

Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-26-
AGE and three non-AGE schools in five communities in the Mexican state of Campeche.<sup>8</sup> In addition, we carried out a larger qualitative survey of school directors’ perceptions in 115 randomly selected AGE schools in the states of Campeche, Guerrero, Michoacán, Sinaloa and Tamaulipas.

The parental focus groups revealed that parents believed that the AGE had indeed improved interaction and communication with school directors and teachers resulting in better educational outcomes. Parents expressed the view that the AGE helped generate and facilitate dialogue between parents, teachers and school directors. Parents in AGE beneficiary schools were pleased with the fact that they were better able to meet with their child’s teacher and to follow their child’s progress more closely. They reported that teacher instructed them on how to improve their child’s performance. They believed that this fostered parental involvement in school and with their children’s education. Parents also perceived that AGE had a positive impact on teacher effort. When asked what impacts they had noticed, parents commented on the fact that teachers stayed longer hours in schools to help students who were falling behind academically. The focus groups established that AGE helped parents be vocal representatives of the school clients as they articulate demands, generate expectations and promote participation. These findings are in line with previous qualitative evidence in the state of Tabasco, which revealed that AGE increased parental participation in school activities, improved parentteacher relations, and reduced teacher absences (World Bank 2000).

The survey of school principal confirmed the perception that the AGE led to an increase in parental participation in school matters. In fact, _all_ school principals believed that this was the case. When asked about the most important change induced by parental

> 8 See Patrinos (2006) for full details.

8
|stock<br>(7)<br>(8)|(-3.43∗∗∗)<br>(-4.41∗∗∗)|-(0.40)∗∗∗<br>-(0.39)∗∗∗|(-0.24∗∗∗)<br>(-1.20∗∗∗)<br>-(0.08)∗∗∗<br>-(0.07)∗∗∗<br>(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-Yes∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|-12364∗∗∗<br>-12364∗∗∗<br>-0.28∗∗∗<br>-0.16∗∗∗<br>report an occupation with direct links<br>old/Household-head controls include<br>levels: ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.|
|---|---|---|---|---|---|---|
|Live<br>(5)<br>(6)|(-4.47∗∗∗)<br>(-4.40∗∗∗)|-(0.35)∗∗∗<br>-(0.39)∗∗∗|(-1.15∗∗∗)<br>(-1.20∗∗∗)<br>-(0.07)∗∗∗<br>-(0.07)∗∗∗<br>(-No∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-12364∗∗∗<br>-12364∗∗∗<br>-0.03∗∗∗<br>-0.16∗∗∗<br>e excludes households who<br>are in parentheses. Househ<br>e of dwelling. Signifcance|
|(4)|(-0.42∗∗∗)|-(0.48)∗∗∗|(-0.83∗∗∗)<br>-(0.07)∗∗∗<br>(-Yes∗∗∗)|(-No∗∗∗)|(-Yes∗∗∗)|-12364∗∗∗<br>-0.10∗∗∗<br>0. The sampl<br>tandard errors<br>ehold, and typ|
|Gold & Silver<br>(2)<br>(3)|(-0.43∗∗∗)<br>(-0.34∗∗∗)|-(0.48)∗∗∗<br>-(0.48)∗∗∗|(-0.83∗∗∗)<br>(-0.95∗∗∗)<br>-(0.07)∗∗∗<br>-(0.08)∗∗∗<br>(-Yes∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-12364∗∗∗<br>-12364∗∗∗<br>-0.10∗∗∗<br>-0.17∗∗∗<br>n (2) after settingβ2 = β3 =<br>ources of income. Robust s<br>, number of males per hous|
|(1)|(-0.48∗∗∗)|-(0.53)∗∗∗|(-1.15∗∗∗)<br>-(0.06)∗∗∗<br>(-No∗∗∗)|(-No∗∗∗)|(-No∗∗∗)|-12364∗∗∗<br>-0.05∗∗∗<br>nts of equatio<br>ir three main s<br>household size|
|Variables|Variance of Income (log)||Income (log)<br>Household Controls|Province Fixed Efects|Confict|Observations<br>R-squared<br>The table shows the estimated coefcie<br>to some livestock activity as one of the<br>gender, age, marital status, education,|

32