rafmacalaba/datause-encoder-data huggingface dataset:
Central Statistical Agency [Ethiopia] and ORC Macro (2006), _Ethiopia Demographic and Health_ _Survey 2005_ .Addis Ababa, Ethiopia and Calverton, Maryland, USA: Central Statistical Agency and ORC Macro.

Central Statistical Agency [Ethiopia] and ICF International (2012), _Ethiopia Demographic and Health Survey 2011_ . Addis Ababa, Ethiopia and Calverton, Maryland, USA: Central Statistical Agency and ICF International.

Ferreira, F.H.G. and M.A. Lugo (2012), “Multidimensional Poverty Analysis: Looking for a Middle Ground”, Policy Research Working Paper 5964.

<mark>Federal Ministry of Health (FMOH) (2013), Annual Performance Report-2012/13. Federal Ministry of Health, Addis Ababa, Ethiopia.</mark> Ministry of Finance and Economic Development (MOFED) (2010), _Ethiopia Growth and_ _Transformation Plan 2010/11-2014/15._ Volume I, Main Text. Addis Ababa, Ethiopia.

Ministry of Finance and Economic Development (MOFED) (2012), _Ethiopia’s Progress Towards Eradicating Poverty: An Interim Report on Poverty Analysis Study (2010/11)._ Addis Ababa, Ethiopia.

Ministry of Finance and Economic Development (MOFED) (2008), _Dynamics of Growth and Poverty in Ethiopia 1995/96-2004/05_ . Addis Ababa, Ethiopia.

OPHI (2014), _Global Multidimensional Poverty Index Databank_ . The Oxford Poverty and Human Development Initiative (OPHI), Oxford Department of International Development, University of Oxford.

Plavgo, Ilze, Martha Kibur, Mahider Bitew, Tesfayi Gebreselassie, Matusda Yumi, and Pearson, Roger (2013), _Multidimensional Child Deprivation Trend Analysis in Ethiopia, Further_ 28
would be expected to increase inequality with a turning point Results in the first three columns of Table 3, with no fixed effects, correspond to those usually found in the literature (e.g. Barro (2000), Ravallion (2001), Rama (2002)). Under this specification, trade liberalization raises inequality in poor countries, but reduces it in rich countries (i.e. with income per capita higher than 6,215$ in column 1 and higher than 7,182$ in column 2), in contradiction with HO expectations. The relation is robust to the addition of other controls (column 2).

Adding dummy variables for the source of income inequality data improves considerably the fit, while the results contrary to HO predictions continue to hold (though the turning point is now 4,279$). The signs on the dummies are always as expected with the Gini coefficients based on income being higher than those based on expenditure. Our first finding is that all studies should control for the source of income inequality data (a point already made by Ravallion 2001 and Bensidoun et al. 2005)). Since the coefficients on these dummies are always similar to those in this table, we do not comment on this aspect any further.

Column 4 introduces fixed-effects (FE) into the estimation. Now, the HO predictions hold, though they are still insignificant. However, with a GLS estimator to correct for heteroskedasticity<sup>13</sup> in the coefficient estimates (and in their variance), the coefficients are significant at the 1% level (with a reduction in protection increasing inequality

> 13 The Breusch Pagan test and the White test indicate heteroskedasticity in the error process (σ<sup>2</sup> it<sup>≠ σ2). In case we would have only intra individual</sup> heteroskedasticity we could just perform a White correction on standard error. Since the Wald test indicates the presence of inter individual heteroskedasticity (σ<sup>2</sup> it<sup>≠ σ2</sup> t<sup>), we preferred to use a GLS estimator which</sup> corrects estimates as well as standard errors. This estimator is more efficient in case of inter individual heteroskedasticity.

13
formal financial institutions are available in only 31 percent of the communities, and formal insurance institutions do not exist in any of the communities surveyed. Survey data also revealed that 49 percent of farm entrepreneurs and 60 percent of nonfarm entrepreneurs did not even apply for loans even though they needed access to financial resources. This low application rate for loans suggests limited formal literacy among rural entrepreneurs. The reasons that participating rural entrepreneurs gave for declining to apply for loans varied. The complexity of the procedures required to apply was given as the reason by 15 percent of both farm and nonfarm respondents. Low expectations over the likelihood of their loan applications being approved was also very similar, with 12 percent of farm entrepreneurs and 13 percent of nonfarm entrepreneurs reporting this as their reason for not applying. Doubt as to their ability to repay a loan was given as the reason for not applying by 4.8 percent of farm entrepreneurs and 8 percent of nonfarm entrepreneurs. These findings suggest that in addition to increasing the number of financial institutions and targeting financial literacy through training, credit guarantee schemes introduced by small business promotion agencies to support rural entrepreneurs and improve their ability to negotiate and repay loans could be instrumental in improving the success of their business operations.

# **5.4 Business Services**

Good business management is critical for rural entrepreneurs who typically have little knowledge and resources of business practices. As Sawada and Harishchandra (2011) identified in rural Ethiopia, rural entrepreneurs can easily improve their productivity and profitability by learning the fundamentals of business management. Markley (2006) states that providing technical assistance, training, and networking are essential to rural economic development strategy. Management knowledge can enable rural entrepreneurs to identify the appropriate levels of financing, technology, skills, processes, quality control, and marketing for a business to be profitable. The following issues related to business services needed to be improved and addressed.

First, the availability and utilization of business services are to be improved for rural entrepreneurs. With appropriate business management knowledge, unprofitable operations can be turned into profitable ones. Profitability is determined by prices and quantity of sales and also by the productivity of business operations. Productivity can be improved through management innovation or improvement, such as product innovation/improvement, process innovation/improvement, setting higher sales prices when selling at a new location, changing the 20
under the areas of institutional reform, infrastructure investment, credibility and public management, and financial intermediation.

**Table 4. Projected per‐capita growth rate if saving rate remains at 20 % of GDP**

|Productivity<br>growth|Per‐capita GDP growth rate ov|er time (%)|
|---|---|---|
||5 years<br>10 years|25 years|
|0%|0.8<br>0.8|0.7|
|1%|2.0<br>2.1|2.3|
|2%|3.2<br>3.5|4.1|

Consider first the privatization of state‐owned firms. In 1991, over 300 state‐owned firms were identified as candidates for privatization in Egypt (Law 203). Evidence suggests that firms privatized under the new law enjoyed a substantial increase in productivity. In effect, this improvement in productivity was observed with great strength during the 1990s, the period when most of the privatization wave took place: privatized firms increased investment expenditures, profitability, and overall efficiency (Omran 1997). Related to this point is the observation that, historically, a unit of investment by the private sector is almost invariably more productive that a unit of public investment (World Bank 2008). Today, many firms identified by Law 203 as candidates for privatization still remain publicly owned—the privatization wave was temporarily stalled in the late 1990s, but partially resumed in mid‐2004. In light of the above evidence—and, more generally, worldwide evidence—it is expected that continuing with the privatization effort is likely to promote significant productivity gains.

Analysis of firm‐level data shows that Egypt has experienced substantial progress in labor and total factor productivity between 2004 and 2008 (World Bank 2009). Moreover, the same study reports substantial progress in improving the overall investment climate during these years. In effect, the country experienced significant improvements in the tax code, in customs and tax administration, and in how costly it is to open a new business—in monetary and non‐monetary terms. Yet, firms still report macroeconomic and regulatory uncertainty as 23
Table 5a. Ghana: Decomposition of percent poverty changes, by Socio-economic Groups

||_A_|_S._|A(|Kj)|_-A_|_P,,j_|_AO_|
|---|---|---|---|---|---|---|---|
||Actual|Weighted|Actual|Weighted|Actual|Weighted|Total|
|_Accra_|||||||_166_|
|Urban Formal|-20.6|_-5.7_|340|_94.2_|13.6|_3.8_||
|Urban Informal*|24.4|_17.6_|46.9|_33.9_|18.7|_13.5_||
|Other Cities|||||||-17|
|Urban Formal|-8.5|_-2.1_|-9.9|_-2.4_|-3.1|_-0.8_||
|Urban Informal|5.6|_3.0_|-18.3|_-9.7_|-12.3|_-6.5_||
|Farming|-0.6|-0.1|19.6|4.4|-14.9|_-3.3_||
|**Rural Areas**|||||||_-19_|
|Formal|-16.5|_-1.4_|9.3|_0.8_|-18.9|_-1.6_||
|Informal|19.7|_3.0_|_-25.6_|_-3.9_|-17.2|_-2.6_||
|Farming|-2.3|-1.7|-3.6|_-2.7_|_-11.5_|_-8.8_||

* There were a few farm households in Accra, which are omitted from the analysis due to the small size of the sample. This is why the change in poverty incidence is slightly different than that in Table 4a.

Table 5b. Ghana: Decomposition of percent poverty changes, by Socio-economic Groups, GLSS2 and GLSS3

|||ASJ|A(|K._)_|_- ,_|_,,j_|_AO_|
|---|---|---|---|---|---|---|---|
||Actual|Weighted|Actual|Weighted|Actual|Weighted|Total|
|**Accra**|||||||_48_|
|Urban Formal|-20.6|-10.3|42.9|21.5|-4.3|-2.1||
|Urban Informal*|24.40|12.2|41.5|20.7|-3.3|-1.6||
|**Other Cities**|||||||_-14_|
|Urban Formal|-8.5|-1.8|16.1|3.4|-7.7|-1.7||
|Urban Informal|5.6|2.9|-9.6|-4.9|-14.6|-7.5||
|Farming|-0.6|-0.2|0.8|0.2|-18.3|-4.9||
|**Rural Areas**|||||||_-19_|
|Formal|-16.5|-1.1|29.1|1.9|-23.1|_-1.5_||
|Nonfarm|19.7|3.0|-12.5|-1.9|-39.7|-6.0||
|Farming|-2.3|-1.8|4.3|3.3|-18.5|-14.4||

16
# **INCENTIVE INDICATORS**

|NOMINAL PROTECTION, 1965-94 ............................................<br>27|
|---|
|_Nominal protection: mill sugar .........................................._<br>_27_|
|_Nominal protection: khandsari .........................................._<br>_34_|
|_Nominal protection: molasses .........................................._<br>_36_|
|_Nominal protection: mill sugarcane ................................_<br>_.......... 39_|
|_Nominal protection. gur and khandsari in UP ...................................._43|
|_Nominal protection of sugar and sugarcane compared ...................... 45_|
|EFFECTIVE PROTECTION FOR SUGARCANE, 1981-90 ............. 46|
|EFFECTIVE SUBSIDY FOR SUGARCANE, 1981-90 ..................... 48|
|PRODUCER SUBSIDY EQUIVALENTS ESTIMATES FOR<br>SUGARCANE, 1981-90 ...........................................<br>51|
|REFERENCES ............................................<br>54|
|STATISTICAL DATA SOURCES ............................................<br>56|

**DATA TABLES Si TO S9**

## **ANNEXES 1 TO** 22

iii
37 Figure 5: Average Expenditure and Residential Burglaries per Capita <!-- Start of picture text --> 0<br>10000 - 0<br>0 10-<br>o 0<br>0 1000 0<br>Cu 0 00 0 0<br>0~~~~~~~~~~~~~~00<br>9- ~~~~0 0<br>a)<br>0.<br>C,) ~~0<br>S~~~~~~~~~~~ o O<br>C 0 m0 0ou 0<br>Both a 21 v a 0 '4 w 0~~~ 0 r 0 0 b<br>00~ 00<br>.0~~~~~~~~~<br>0 0<br>C ~~~~~~~0<br>_ (n ~10 ~~ ~~~0 0<br>T1) 0<br>2- 0 0 0 CImim ocm CO ( D OD 00 0 0<br>125 1000 2000 3000 4000<br>mean household expenditure per capita<br><!-- End of picture text --> Both variables are in logarithms. Observations where<sup>residentialburglaries wereequaltozero havebeen replacedby the smallest valuesin the sample.</sup>
**Table 1.  Description of Survey Questionnaires**

|**Survey**|**Sample**|**Unit of analysis**|**Structure**|**Interviewee**|**Scope – which assets?**|**Definition of**<br>**ownership**|**Distinguishes**<br>**joint and**<br>**individual **|**Value**|
|---|---|---|---|---|---|---|---|---|
|Gender Asset<br>Gap Project|Representative<br>at national level<br>for Ghana and<br>Ecuador, and<br>state level for<br>Karnataka, India|Asset, with<br>individual ID<br>codes so can<br>assign<br>individual<br>ownership to<br>household<br>members|Household asset<br>roster and<br>individual<br>questionnaire|Primary man and<br>woman in each<br>household|Dwelling;<br>Agricultural Land;<br>Livestock;<br>Agricultural Equipment;<br>Other Real Estate; Non-<br>Farm Enterprises;<br>Financial Assets;<br>Consumer Durables;|Reported,<br>Documented, (as<br>applicable)<br>Additional questions<br>on rights over assets<br>in Ghana and<br>Karnataka..|Yes|Yes|
|MEXA|National sample<br>of 2,027<br>households,<br>distributed<br>across 140<br>enumeration<br>areas|Asset, with<br>individual ID<br>codes so can<br>assign<br>individual<br>ownership to<br>household<br>members|Arm 1-Arm 3:<br>One asset roster<br>created in each<br>asset domain<br>Arms 4-5:<br>Independent<br>asset rosters<br>created by each<br>interviewee in<br>each asset<br>domain|Arm 1: Self-Identified<br>Most Knowledgeable<br>Household Member<br>Arm 2: Randomly<br>Selected Member of the<br>Principal Couple<br>Arm 2: Members of the<br>Principal Couple<br>(Together)<br>Arms 4-5: Up to 4<br>adult household<br>members (alone,<br>simultaneous)|Dwelling;<br>Agricultural Land;<br>Livestock;<br>Agricultural Equipment;<br>Non-Agricultural Land<br>and Real Estate; Non-<br>Farm Enterprises;<br>Financial Accounts;<br>Consumer Durables;<br>Valuables.|Reported,<br>Documented (as<br>applicable),<br>Economic|Yes|Yes|
|Gender,<br>Agriculture,<br>and Assets<br>Project|Varied across<br>projects|Usually<br>individual|Varies||||||
|WEAI|Representative<br>of USAID Feed<br>the Future<br>Zones of<br>Influence in 19<br>countries|Individuals|Module of larger<br>survey;<br>questions asked<br>to individuals|Principle man and<br>woman in the<br>household|Productive assets|Reported|Asks who<br>owns most of<br>each asset<br>type; codes<br>include<br>individual<br>and joint<br>options|No|

33
The following chart indicates the tax space and the tax gap in the ECA region, and if we take into account the shadow economy that escapes taxation, the tax gap and tax space are larger. Interestingly, the tax gap and tax space displayed an uneven pattern across three time periods (2002, 2007, 2010). However, if the shadow economy is taken into account, there is a clear trend - the tax gap and tax space increased across all groups following the global recession in 2008 (except for Turkey). While there is a negative tax space and tax gap in several groups, if we take into account the shadow economies in these countries, the negative tax space and tax gap considerably decreases, and in some years actually shows a positive space, indicating that there could still be room to increase tax revenues if the shadow economy is brought into the tax net. The ECA/EU(6), the oil exporting countries and Turkey had the highest tax gap and tax space (with shadow) indicating that there is greater scope to increase revenues in these countries. Annex D gives a graphic presentation of the tax space, tax gap and tax effort for all ECA countries over the period 2002-2010.

_Chart 1._ T **ax Space and Tax Gap in the ECA Region** <!-- Start of picture text --> 4 6<br>2002 2007 2010 2002 2007 2010 4.4<br>2 1.5 4<br>0 2.0<br>2<br>-0.3 1.1 0.6<br>-2 -1.4 -1.7 0.6 0.2 0.0<br>-2.7 0<br>-2.8<br>-4-6-8 -6.8 -5.4 -6.5 -6.5 -5.5 -3.5 -3.6 -3.0 -4.3 -6.0 -4.4 -4.5 -3.0 -2-4 -3.7 -2.3 -3.4 -2.8 -2.9 -0.8 -0.4 -2.0 -3.1 -1.3 -1.7 -0.2<br>Tax Space as Percent of GDP -4.6<br>-10 -10.1 -8.5 -6 Tax Space with Shadow as Percent of GDP<br>-7.0<br>-12 -8<br>6 2002 2007 2010 4.5 8 2002 2007 2010 7.0 6.4<br>4 3.5 6 5.1 5.4<br>3.1<br>2.4 4.1 4.3<br>4.0 4.0<br>2 0.4 1.7 1.1 1.3 1.5 4 2.2 2.8 3.0 2.6 2.5 2.6<br>0 2 1.0 1.5 1.4 0.9<br>-0.1<br>-2 -0.9 -1.0 -1.6 -1.0 -0.9 -0.7 0 -0.4<br>-2.0<br>-4 -3.4 -3.0 -2<br>-1.7 Tax Gap with Shadow as Percent of GDP<br>Tax Gap as Percent of GDP<br>-4.9 -4<br>-6 -3.9<br>-7.0 -6<br>-8<br>Percent of GDP<br>Balkans CIS-South Caucasus ECA/EU(6) Oil Exporters Turkey AVERAGE ECA (23) ECA Graduated Balkans CIS-South Caucasus ECA/EU(6) Oil Exporters Turkey AVERAGE ECA (23) ECA Graduated<br>Percent of GDP<br>Balkans Turkey Balkans ECA/EU(6) Turkey<br>ECA/EU(6) Oil Exporters<br>Oil Exporters ECA Graduated<br>ECA Graduated<br>CIS-South Caucasus AVERAGE ECA (23) CIS-South Caucasus AVERAGE ECA (23)<br><!-- End of picture text --> 14
## **Figure 7 Cost overruns of selected nuclear new build projects (as of June 2010)**

<!-- Start of picture text --> 4000 3,961<br>957 3,313<br>3000 +32%<br>1250 2,500<br>2,396<br>438<br>2000 +61% +21%<br>1636<br>3004<br>1000 2063 2063<br>+216%<br>759<br>0<br>South Texas 3&4 Olkiluoto 3 Flamanville 3 U.S.A. average<br>(U.S.) 1) 2) (Finland) 1) (France) 1) 3) (1966-78) 2)<br><!-- End of picture text --> 1) Estimates, project in progress (South Texas construction not started yet) 2) In EUR (conversion date May 20, 2010) 3) Initial overnight estimate 2005; cost update 2008 _Source_ : Arthur D Little (2010).

The experience of the Tokyo Electric Power Company (TEPCO), in the mid-1990s appears to support the renewed optimism about the industry‘s learning becoming reflected in new reactor designs. According to the 2003 MIT study, verifiable data indicate that TEPCO constructed two advanced boiling-water reactors at costs and schedules close to manufacturers‘ estimates. Unfortunately, the more recent experiences with Olkiluoto 3, Flamanville 3, and South Texas 3&4 have once again highlighted the tremendous financial risks facing the nuclear industry and shattered the prospects of a nuclear renaissance driven by a cost-competitive industry (figure 7). It should be noted, however, that while multiple delays and budget overruns have been observed recently in the construction of nuclear power plants with large reactors, the experience with smaller units has been more encouraging—e.g., the deployments of the 220 and 540 MWe Indian pressurized heavy water reactors and the 640MWe Canadian CANDU6 have been on schedule and within budget (Kuznetsov and Barkatulla, 2009).

# **New Generation of Advanced Nuclear Reactors**

Nuclear power is an inherently hazardous and costly technology. By the early 1980s it became clear that, due to heightened concerns about safety and the cost performance problems 20
# **Table 6(b): Subjective welfare regressions for Guatemala**

||(1)|Pre-v|(2)<br>ignette||(3)|Post-vi|(4)<br>gnette||
|---|---|---|---|---|---|---|---|---|
||Ordered p|robit|CHOP|IT|Ordered pr|obit|CHOP|IT|
||Coeff.|s.e.|Coeff.|s.e.|Coeff.|s.e.|Coeff.|s.e.|
|_Household Characteristics_|||||||||
|log annual per capita<br>consumption|0.449***|0.080|0.351***|0.066|0.507***|0.076|0.402***|0.064|
|log household size|0.223**|0.098|0.173*|0.093|0.267**|0.105|0.309***|0.090|
|female headed household|0.464**|0.228|0.561**|0.228|0.467**|0.229|0.489**|0.221|
|_Respondent Characteristics_|||||||||
|respondent is head of household|-0.417*|0.222|-0.515**|0.223|-0.423*|0.223|-0.399*|0.216|
|respondent is female|-0.275|0.252|-0.440*|0.241|-0.375|0.255|-0.435*|0.234|
|age of respondent|0.002|0.003|0.001|0.003|0.002|0.003|0.001|0.003|
|year of education of respondent|0.073***|0.020|0.091***|0.019|0.073***|0.019|0.093***|0.018|
|respondent is employed|0.195|0.136|0.147|0.124|-0.068|0.127|-0.0322|0.122|
|_Respondent Marital Status_<br>_(reference: married)_|||||||||
|single / never married|-0.555**|0.227|-1.063***|0.244|-0.531**|0.241|-0.951***|0.233|
|union|-0.175**|0.083|-0.171**|0.086|-0.175**|0.079|-0.170**|0.084|
|separated/divorced|-0.930**|0.411|-0.474|0.306|-1.035***|0.318|-0.808***|0.310|
|widowed|-0.550***|0.174|-0.481***|0.171|-0.418**|0.182|-0.395**|0.163|
|Municipality (reference: Cuilco)|||||||||
|San Gaspar Ixchil|0.067|0.106|0.653***|0.109|-0.060|0.108|0.477***|0.106|
|Santa Ana Huista|0.484***|0.108|0.131|0.105|0.236**|0.094|0.061|0.101|
|Jacaltenango|0.110|0.157|0.599***|0.147|-0.005|0.157|0.550***|0.143|
|_Migration characteristics_|||||||||
|any migrant LA (excluding<br>respondent)|-0.163|0.140|-0.156|0.118|-0.013|0.130|-0.155|0.114|
|any migrant US (excluding<br>respondent)|0.500***|0.131|0.595***|0.132|0.313**|0.145|0.556***|0.128|

29
grants for both the low group and middle group (excluding Nigeria), which becomes even stronger during 1989-93. For the low group this complementarity turned out to be $0.09 of new lending for each additional $1 in grants during 1984-88, and increased to $0.19 per $1 during 1989-93 (both results statistically different from zero and one). For the middle group this complementarity was negative (although not statistically different from zero) during 1984-88 and $0.09 per $1 during 1989-93 (significant at 6.5 percent in a one-tail test).

Table 7. Impact of incremental total grants on other financial transfers

|_(A)_<br>**_Total period_**|**_(D)_**<br>**_Period_**<br>|(CU)<br>**_Period_**<br>|
|---|---|---|
||**_(1984-88)_**|**_(1989-93)_**|
|Low group (16 countries)<br>0.234|0.090|0.190|
|(3.652) **(1.5|40)<br>*|(3.512)|
|Middle group (8 countries)<br>0.143|0.028|0.094|
|(2.516)<br>**_*_**|(-0.309)|(1.520)|
|High group (7 countries)<br>0.029|0.160|0.058|
|(0.779)|(1.068)|(1.350)|
|N igeria<br>-1.384|||
|(-I1.733) *|..||
|R2<br>0.42|0.48||
|Adjusted R2<br>0.35|0.41||
|F-test<br>5.86<br>|7.03|***|
|Low group (16 countries)<br>_-0.151_|_-0.051_|.-0.120|
|(-2.560)<br>**_*_**|(-0.428)|(-1.754)<br>**_**_**|
|Middle group (8 countries)<br>-0.379|-0.410|-0.388|
|(-2.736)**_**_**|(-1.858) *|(-2.450)<br>*|
|High group (7 countries)<br>-0.040|-0.003|-0.032|
|(-0.833)|(-0.026)|(-0.588)|
|Nigeria<br>-5.000|||
|(-2.121)<br>**_**_**|||
|R2<br>0.62|0.47|<br>-|
|Adjusted R2<br>0.58|.<br>0.41||
|F-test<br>13.34 *<br>|6.83|***|
|Low group (16 countries)<br>0.056|0.112|0.073|
|(0.688)|(0.803)|(0.796)|
|Middle group (8 countries)<br>0.029|0.161|0.067|
|(0.324)|(0,888)|(0.587)|
|High group (7 countries)<br>0.042|0.174|0.071|
|(0.867)<br>|(1.239)|(1.321)|
|Nigeria<br>-1.910|||
|(-0.357)|..|..|
|R<br>0.58|0.52||
|Adjusted R2<br>0.530|0.46||
|F-test<br>11.32<br>**_***_**|8.25|***|

Note: t-statistics are in parentheses.

*: Significant at 10 percent on one-tail test

**: Significant at 5 percent on one-tail test

***: Marginal significance level of less than I percent

16