rafmacalaba/datause-encoder-data huggingface dataset:
The breakdown of employment dynamics in the 2009/2015 and 2013/2015 panels by area of residence as seen in Table 6.6 below demonstrates that panel attrition is concentrated on the unemployed in urban areas. Consistently with what we see in table 6.3 the Calibrated GWSM are more accurate than the UNPS panel weights in estimating employment in rural area in the 2009/2015 sample. Results for the the 2013/15 panel show that the refresher has improved accurancy. In urban area, all methods gave upward estimates and in particular for the 2009/15 panel sample. The findings indicate that individuals leaving the UNPS are mostly unemployed in urban areas, suggesting the need to improve tracking rules to reduce the potential bias of the estimates.

Table 6.6. Employment by area of residence transition matrix estimates by the unweighted, current UNPS, and calibrated GWSM base estimators

||||2009/15|||2013/15||
|---|---|---|---|---|---|---|---|
|||Unweighted|UNPS|Calibrated<br>GWSM<br>base|Unweighted|UNPS|Calibrated<br>GWSM<br>base|
|Unemployed in 2009|Rural|3.8|4.2|3.1|8.9|9.2|7.4|
|(or 2013) and 2015|Urban|9.5|9.7|6.9|20.3|21.3|16.8|
|Unemployed in 2009<br>(or 2013) and<br>employed in 2015|Rural<br>Urban|4.4<br>11.9|4.0<br>11.3|4.3<br>13.3|8.1<br>10.0|7.7<br>9.4|7.1<br>9.7|
|Employed in 2009 (or|Rural|75.4|74.6|77.3|71.8|71.3|75.2|
|2013) and 2015|Urban|62.5|63.1|66.8|59.5|59.9|65.5|
|Employed in 2009 (or|Rural|16.4|17.3|15.3|11.3|11.8|10.4|
|2013) and<br>unemployedin 2015|Urban|16.1|15.8|13.0|10.1|9.3|8.1|

We carried out a similar analysis for the poverty headcount ratio (table 6.7). Estimates from the 2009/2015 panel are all around the official statistics: we have respectively 21.9%, 21.1%, and 21.4% for the unweighted, UNPS and the calibrated GWSM base estimator. The refresh operation (2013/2015 sample) reduced the frequencies to 20.5%, 19.3%, and 20.0%. When we compare these estimates with the official statistics (21.4%), it does not appear that one estimator surpasses the others in terms of quality. Nevertheless, comparing the percent relative variance among the three estimates of the poverty headcount ratio, two from the longitudinal estimates and one from the cross-sectional estimator, we note that the UNPS estimators has 5.2% percent relative variance while the calibrated GWSM estimator has 4.1% of percent relative variance. The unweighted estimator has 4.0% of percent relative variance.

30
12 coefficient of MPV is normally expected to be negative: monetary policies that accommodate high rates of domestic credit growth should lead to lower domestic interest rates and viceversa; however, if such accommodating monetary policies are perceived to eventually lead to higher future inflation or current account deficits (i.e. a lack of policy credibility that expansionary -nonetary policies are only temporary), the sign of the MPV term might be reversed. Finally, the existence of a country risk premium can be tested by the significance, if any, of the coefficient of a country-specific risk variable---in the model, this is approximated by a country-specific dummy variable with value 1 when present, and zero otherwise, in the absence of other indicators (see further analysis of country-risk in the concluding section discussing Indonesia).

# The Data. and Results From a Cross-Country Test

20. The Data. Fifteen countries were chosen in the sample for the cross- country test, comprised of: (a) 6 industrial countries---the US, UK, Japan, Germany, France and Italy; and (b) 9 relatively open developing countries---Singapore, Thailand, Malaysia, Korea, Indonesia, and the Philippines in East Asia, and Chile, Mexico and Colombia in Latin America. The sample of countries is, thus, a broad one.

21. The domestic interest rate variable used, i, is the 3-month deposit rate, or a similar instrument with comparable maturity and risk and on which interest rates are flexibly determined. This is taken primarily from line 601 of the publication International Financial Statistics (IMF, 1992). The international interest rate variable used, i*, is the 3-month US Dollar LIBOR rate. The domestic inflation rate used, p, is the change in the CPI index, and the variable used for international inflation rate, p*, is the CPI change for the US (corresponding to the use of US Dollar LIBOR). **The** current account balance variable used, CAB, is from line 77 a.d from the IFS (IMF, 1992), expressed as a percentage of GDP. The central bank discount rate variable, CDR, is taken from **line** 60 in the IFS (or line 60b on money market rates, where the discount rate was unavailable in a few cases, expressed in real terms). The domestic credit variable, MPV, is taken from lines 32 (domestic credit) and 99b (nominal GDP) from the IFS, expressed as the difference between domestic credit
TABLE A24: Impacts on social interactions about ECD with other members of the community

||(1)<br># other members|(2)<br>Relatives|(3)<br>Neighbors|(4)<br>Religious leader|
|---|---|---|---|---|
|ITT|0.05|0.03|0.03|-0.01|
||(0.04)|(0.02)|(0.02)|(0.02)|
|ITT Leader|0.04|-0.01|0.02|0.03<sup>_∗_</sup>|
||(0.05)|(0.02)|(0.03)|(0.02)|
|Observations|2073|2073|2073|2073|
|Mean Control|0.95|0.40|0.36|0.19|

_Note:_ The estimation corresponds to the estimator _β_ 2 in equation 3. The dependent variables are the social interactions between the head of the household and social leaders during the previous week. Column 1 is an index accounting for household head interaction with relatives, neighbors and religious leader. The remaining columns are binary: 1 whether the household head had at least one interactions with any respective leader during the referent week. All the regressions include controls for the stratification variables: education of the household, titular of the household is male, whether the leader has access to electricity, targeted child age and sex, enumerator and a dummy variables for a set of households surveyed in 2016 (one year after the intervention). The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 72
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 47 Canada Vancouver, estimates for urban and rural areas

# **A.2.2       Determinants of unit costs of sea dikes**

Unit costs of coastal protection measures are defined as costs per kilometer length per meter height increment of a flood protection measure. Jonkman et al. (2013) identify material and labor costs, design choices related to the alignment of the system, and the types of measures in an urban or rural environment as the main determinants of unit costs of coastal protection on the basis of three case studies. Moreover, land acquisition costs for dike construction will tend to be higher in urban areas than in rural areas (Delcan Corporation 2012), or involve structural solutions without increasing dike width in urban areas (Kok et al. 2008).

In Dronkers et al. (1990) country cost factors ( ) have been applied to derive unit cost ( ) estimates of coastal protection measures at the country level: where C��� are Dutch unit costs of a standard one-meter dike increment of a dike of variable height, which comprises standard dimensions, construction materials and construction methods. The country cost factor of the Netherlands is normalized to one. The country cost factor is a correction for local conditions in material and labor inputs, mobilization of equipment, economies of scale and land acquisition costs. Differences in costs of financial capital are not included (Dronkers et al., 1990). Country cost factors have been informed by expert judgment of the (then) Delft Hydraulics. Hoozemans et al. (1993) improved these unit cost estimates by the development of continuous cost functions for stone protected and clay covered sea dikes, and sand dunes. They also included an allowance for extreme sea levels which influences initial dike heights, and roughly doubled global costs compared to Dronkers et al. (1990) (see Table 1).

To set the stage, we analyzed possible determinants of unit costs of sea dikes with a country-level cross-sectional data set from the DIVA database. The total sample contains 248 observations. We removed 47 observations without low-lying land below 10 meters A.D., as these do not have sea dikes. Furthermore, eight outliers with very high asset densities (e.g. Monaco, Gibraltar, Bahrain) were excluded from the econometric analysis.

A least squares regression analysis was performed to investigate how experts appear to have evaluated the importance of local differences in determinants of unit costs, as revealed
_Figure 6: Electricity generation capacity (percent) by Fuel Type 2005 & 2017_ <!-- Start of picture text --> wind 2005 solar wind solar<br>1.0% 0.0% 11.5% 2.1% 2017<br>hydro coal<br>33.1% coal<br>32.8%<br>35.3% hydro<br>20.1%<br>gas<br>7.3% oil<br>oil<br>gas 10.1%<br>23.4%<br>23.4%<br><!-- End of picture text --> _Source: Authors based on data from ONEE_ Thirdly, ONEE’s share of generation has declined greatly over the years. From 92 percent in 1991 and a peak of over 98 percent in 1996, ONEE’s total share of generation is at 30 percent in 2017 (Figure 7). The share of private power generation, through IPPs from 1994 has grown rapidly to 18,906 GWh (50.8 percent) in 2017.<sup>35</sup> However, electricity imports have also grown consistently during the same period, reaching 5,955GWh (16 percent) in 2017.

_Figure 7: Power generation by status of producer (1991 – 2017)_ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>IPPs Imports Tiers ONEE 13-09 Law<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source: Authors based on data from ONEE_ 35 (ONEE, 2017, p. 5) 17
# **Appendix B:  Estimates of the Ad Valorem Equivalents (AVEs) for Non-Tariff Measures (NTMs) for the Regions of our Model**

Our estimates of the AVEs of NTMs are based on the estimates of Kee _et al_ ., (2008; 2009). Kee et al. estimate the AVEs of NTMs for 105 countries at the 6 digit level. These estimates, as well as aggregated estimates for manufacturing and agriculture for the 93 countries, are available on the World Bank website.<sup>28</sup> At the six digit level, the estimates are sometimes subject to a substantial margin of error that may lead to misleading results in a CGE model policy analysis. Consequently, we have chosen to use the aggregated estimates of Kee et al. at the sector level, i.e., for each country, we have one AVE of the NTMs in manufacturing and one AVE of the NTMs in agriculture. For seven of the ten regions of our model (Kenya, Tanzania, Uganda, Rwanda, the United States, China and the European Union) estimates are available in the data set of Kee et al. For COMESA, SADC and our Rest of the World, we further aggregate these values for 105 countries to the regions of our model.

Specifically, we use the values for the Overall Trade Restrictiveness Index (OTRI) and for the Tariff-only OTRI (OTRI_T) at the aggregated sector level. The OTRI measures the uniform tariff equivalent of the country’s tariff and NTMs that would generate the same level of import value for the country in a given year. The OTRI_T focuses only on tariffs of each country. We use the values based on applied tariffs which take into account the bilateral trade preferences. Both indices are available for 105 countries<sup>29</sup> and for two different types of aggregated products: agricultural goods and manufacturing goods. Calculating the difference between OTRI and OTRI_T gives us an AVE for NTMs only for each product and county.

Aggregation of resulting AVEs to the regions of our model includes two steps:

1. Aggregation of 105 countries to the GTAP 8.1 countries and regions.

2. Aggregation of GTAP countries to the specific regions of our model.

> 28 The data set is available at

> ~ <u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22574446 pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html</u>

> 29 There are more countries available in the data set, but the values are missing for some countries due to nonavailability of a tariff schedule for the reported year in the source database (TRAINS).

87
**Table 11. Oil Prices and Fiscal Balances of Fuel Exporters**

|Dependent variable:|Fiscal balance/GDP<br>(%)|Total revenues/GDP<br>(%)|Total<br>expenditures/GDP<br>(%)|
|---|---|---|---|
|Constant|-9.17<br>(-1.19)|-10.70<br>(-1.51)|-9.70<br>(-1.63)|
|Lagged dependent<br>variable|0.699***<br>(10.64)|0.886***<br>(24.36)|0.814***<br>(16.81)|
|Ln (real $ oil price)|14.8***<br>(8.08)|11.45***<br>(6.96)|-2.90<br>(-1.63)|
|Lagged ln (real $ oil|-12.9***|-8.47***|6.56***|
|price)|(-7.14)|(-5.37)|(3.97)|
|Hard peg dummy|0.258<br>(0.22)|-0.0968<br>(-0.76)|-1.55<br>(-1.55)|
|Political stability|0.0369|0.0662***|0.0661**|
|(0 to 100)|(1.44)|(2.64)|(2.27)|
|Time trend|-0.257|-0.377|-0.392*|
|(=0 in 2000)|(-1.00)|(-1.63)|(-1.93)|
|n|297|305|297|
|R-squared|0.564|0.811|0.749|
|RMSE|4.65|4.36|4.08|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel > 25 percent of total exports. RMSE = root mean square error, or sample standard deviation.

20
9. Because the policy of paying not to consume gasoline would elicit false representations about the level of gasoline that the consumer would purchase in the absence of the payment, this policy is inferior to the more direct policy of taxing gasoline consumption on practical grounds.

10. To achieve least cost pollution abatement it is also necessary that gasoline consumption by each consumer type impose the same social cost of pollution on the economy.

11. These figures are based on Table 3 in Poterba (1991).

12. Shah and Larsen (1991) have shown that a carbon tax could appreciably reduce emissions of local and regional pollutants such as nitrous oxides, carbon monoxide, particulates and sulphur dioxide.

13. Strictly speaking, the comparison between expected marginal benefits and costs of crime is appropriate only if the individual is risk-neutral. For a discussion of the optimal frequency and magnitude of fines under conditions of risk-aversion, see Polinsky and Shavell (1979).

14. Harrington (1988) and Russell (1990) both use a Markov game model of enforcement which emphasizes the importance of past firm behavior. Enforcement costs associated with a target level of compliance are found to be lessened by placing firms into categories with different monitoring probabilities based on past performance records.

15. While tradeable permit programs have been somewhat successful in the United States, and the new Clean Air Act encourages their future use as an integral part of the regulatory structure of the U.S., Hahn (1989) and Hahn and Hester (1987) have also noted their failure to deliver on their theoretical efficiency properties in practice.

16. Enforcement practices in the United States and other developed countries are far from optimal. Magat, Krupnick, and Harrington (1986), Russell, Harrington, and Vaughn (1986), and Russell (1990) suggest that increasing enforcement budgets, restructuring enforcement procedures, and stiffening fines for noncompliance would be desirable. 17. Baron (1985) has an interesting model of noncooperative regulatory behavior in which an environmental protection agency must take as given the pricing policies of a utility regulator. Not surprisingly, welfare is lower in the noncooperative equilibrium than in a cooperative one in which pricing and environmental policies are coordinated.

70
# **Figure 3**

# **Individual distributions of the financial stability indicators**

The distribution of each financial stability indicator along with the country code for the minimum, maximum and median value. Data averaged over the period 2007– 2014 for Global Financial Development Database (GFDD), FAS and Laeven and Valencia (2013).

<!-- Start of picture text --> Bank Z Score Bank NPLs to gross loans (%) Bank capital to total assets (%) Bank credit to bank deposits (%)<br>High Inc High Inc High Inc High Inc<br>GRC KWT CZE LUX BEL LTU NLD GRC ARE JPN RUS DNK<br>Upp Mid Upp Mid Upp Mid Upp Mid<br>ECU MKD JOR TKM ZAF KAZ TKM BGR SRB IRQ MKD CHN<br>CIV<br>Low Mid Low Mid Low Mid Low Mid<br>NGA PHL SEN COG KEN YEM CMR HND KGZ YEM VNM<br>MOZ<br>Low Inc Low Inc Low Inc Low Inc<br>MOZ CAF HTI MOZ TZA SLE MOZ BDI SLE GIN NER<br>0 20 40 60 0 5 10 15 20 0 5 10 15 20 0 200 400 600 800<br>Min Median Max Min Median Max Min Median Max Min Median Max<br>Liquid assets to deposits & ST funding(%) Provisions to NPLs (%) Standard deviation of the bank lending rate growth Standard deviation of the bank deposit rate growth<br>High Inc High Inc High Inc High Inc<br>KOR HUN HKG MLT DEN VEN SGP MLT GBR SVK HRV HKG<br>Upp Mid Upp Mid Upp Mid Upp Mid<br>GRD PAN MEX AZE MKD MEX VCT JAM SRB VCT JOR PRY<br>Low Mid Low Mid Low Mid Low Mid<br>IND ARM BGD KEN MAR BOL BTN LAO KGZ MAR BGD DJI<br>UGA SLE<br>Low Inc Low Inc Low Inc Low Inc<br>BFA ZWE GIN SLE BDI ETH AFG COD ETH HTI<br>0 50 100 150 0 50 100 150 200 250 0 .2 .4 .6 .8 0 .5 1 1.5<br>Min Median Max Min Median Max Min Median Max Min Median Max<br>Standard deviation of the outstanding loans Standard deviation of the outstanding deposits<br>CZE MLT<br>High Inc High Inc<br>CHE EST JPN EST<br>JAM IRN<br>Upp Mid Upp Mid<br>MYS CHN JAM MNE<br>LKA VNM<br>Low Mid Low Mid<br>IND NGA GTM TJK<br>MWI HTI<br>Low Inc Low Inc<br>BEN ZWE BFA TZA<br>0 1 2 3 4 0 .2 .4 .6 .8 1<br>Min Median Max Min Median Max<br><!-- End of picture text --> 31
under U.S. law. At the same time, Table 7 shows that, before the crisis (i.e. when jurisdiction was insignificant), CACs did indeed affect spreads. This is important because it places our research in the context of the existing literature: even though it is true that CACs increased spreads, their effect disappeared after the crisis.

**Table 7: Regression results with collective-action clause variable** Dependent <u>variable: Log of spread-to-benchmark at launch</u>

||(1)<br>|(2)<br>|
|---|---|---|
||Pre-crisis|Post-crisis|
|IG dummy|-0.318***|-0.193*|
||(-2.77)|(-1.81)|
|S&P Rating|0.128***|0.060***|
||(6.08)|(3.10)|
|Log(size)|0.024|0.006|
||(0.64)|(0.11)|
|Years to Maturity|0.008**|-0.004|
||(2.50)|(-1.14)|
|U.K. Law|0.114|0.293***|
||(1.00)|(4.29)|
|VIX|0.019***|0.025***|
||(2.74)|(4.65)|
|GDP growth (_t-1_)|-0.004|-0.023**|
||(-0.40)|(-2.58)|
|Collective-action|0.154**|0.018|
|clause dummy|(2.37)|(0.21)|
|EAP|0.047|-0.242**|
||(0.39)|(-2.12)|
|ECA|-0.008|0.035|
||(-0.08)|(0.35)|
|LAC|0.101|-0.163|
||(0.99)|(-1.44)|
|SAS|0.148|0.269***|
||(0.95)|(3.04)|
|SSA|0.194*|0.153|
||(1.71)|(1.56)|
|Observations|218|147|
|R-squared|0.691|0.710|

Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies and a constant term were included in all specifications, but not reported. The default region is Middle East & North Africa (MNA); the regional dummies represent East Asia & Pacific (EAP), Europe & Central Asia (ECA), Latin America & Caribbean (LAC), South Asia (SAS) and Sub-Saharan Africa (SSA).

To better illustrate the impact of the jurisdiction difference, one can use the fitted model to predict the value of the dependent variable. Consider a typical sovereign bond issuance of, say, $100 million with a seven-year maturity, rated in the speculative or highly speculative range (BB+ to B-). Assuming postcrisis values for the VIX index at 20 and for the issuing country’s lagged GDP growth at 3.25 percent, such a bond, if issued by an African emerging-market economy, would command a spread at launch of 377 basis points if it were rated BB+ and under U.S. law. The U.K. Law coefficient from the CAC (post-crisis) regression would result in an about 34 percent spread increase, to 506 basis points. The absolute impact is 15
**Table 3. External Business Environment Constraints, Kazakhstan, the Kyrgyz Republic, Tajikistan, and Europe and Central Asia**

|**BEEP**|**S indicators, various years**|**Europe &**<br>**Central Asia,**<br>**2009**|**Kazakhstan,**<br>**2009**|**Kyrgyz**<br>**Republic,**<br>**2009**|**Tajikistan,**<br>**2008**|
|---|---|---|---|---|---|
||Number of days to obtain an<br>operating license|26|32|18|23|
|Permits and|Number of days to obtain a<br>construction-related permit|78|68|65|63|
|Licensing|Number of days to obtain an<br>import license|16|23|10|14|
||Percentage of firms identifying<br>business licensing and permits as<br>a major constraint|16|25|16|17|
||Percentage of senior<br>management time spent on<br>dealing<br>with requirements of<br>government regulations|11|5|5|12|
|Regulation<br>and tax<br>issues|Average number of visits or<br>required meetings with tax<br>officials in a typical month|2|3|2|2|
||Percentage of firms identifying<br>tax rates as a major constraint|40|46|48|56|
||Percentage of firms identifying<br>tax administration as major<br>constraint|21|24|32|17|
||Percentage of firms expected to<br>pay informal payments<br>to public officials (to get things<br>done)|17|23|38|41|
||Percentage of firms expected to<br>give gifts to get an operating<br>license|13|30|26|39|
|Corruption|Percentage of firms expected to<br>give gifts at meetings with tax<br>officials|13|25|39|33|
||Percentage of firms expected to<br>give gifts to secure a government<br>contract|26|55|56|31|
||Percentage of firms identifying<br>corruption as a major constraint|34|44|59|38|

_Source:_ BEEPS data, various years.

11
dissatisfied with his life than a person with low expectations. Thus, the life satisfaction variable captures indirectly the effect of a gap between expected and real welfare.

**Table 1: Life Satisfaction in Developing MENA Countries in the Common Sample, (200910)**

|**Variable**|**Observations**|**Mean**|**SD**|**Min.**|**Max.**|
|---|---|---|---|---|---|
|Algeria|3,588|5.58|1.65|0|10|
|Egypt, Arab. Rep.|1,628|4.88|2.14|0|10|
|Jordan|691|6.23|1.81|0|10|
|Iraq|2,432|5.07|1.72|0|10|
|Lebanon|3,382|5.29|2.29|0|10|
|Morocco|3,144|4.97|1.67|0|10|
|Palestine|2,942|4.83|2.14|0|10|
|Syrian Arab Republic|2,169|4.86|2.12|0|10|
|Tunisia|2,048|5.17|1.69|0|10|
|Yemen, Rep.|3,184|4.66|2.21|0|10|

_Source:_ Gallup World Poll 2013.

Our main variables of interest relate to the domain-specific characteristics thought to have a most profound influence on life satisfaction in the wake of the Arab Spring as discussed in section 2. The Gallup World Poll does not have a question on the degree to which people are satisfied with the political system in the MENA countries. Since in autocracies people’s ability to make choices is restricted, we instead turn to the question: “ _Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”_ We recognize, however, that this question also reflects how satisfied people are with their freedom to make individual choices about education, marriage, children, and employment. The answer to this question is zero for those who are satisfied and one for those who are dissatisfied with their freedom to make choices.<sup>12</sup> We control for objective measures of standards of living by including individual income (given in international dollars). We also include subjective evaluations of living standards based on the answers to the following question: “ _Are you satisfied or dissatisfied with your standard of living, all the things you can buy and do?”_ The answers to this question reflect how people value monetary and

> 12 People answering “don’t know” or who refused to answer this and other questions were omitted from the sample.

12