rafmacalaba/datause-encoder-data huggingface dataset:
17 Retail kerosene prices are shown in figure 11. Kerosene is an important household fuel for lighting, cooking, or heating in many developing countries. It is the most common source of energy for lighting among those without access to any electricity. Where kerosene is markedly cheaper than gas alternatives (such as LPG or biogas), it is also used for cooking and sometimes heating by those who can afford it.

Figure 11: Retail kerosene prices in January 2012, US$ per liter

|$0.32<br>$0.29<br>$0.29<br>$0.28<br>$0.27<br>$0.23<br>$0.18<br>Nigeria<br>Iraq<br>India<br>Indonesia<br>Angola<br>Yemen, Rep.<br>Egypt, Arab Rep.|
|---|
|$0.39<br>Bolivia|
|$0.54<br>$0.54<br>Gabon<br>Tunisia|
|$0.56<br>Ghana|
|$0.62<br>Sri Lanka|
|$0.69<br>Cameroon|
|$0.73<br>Jordan|
|$0.73<br>Bangladesh|
|$0.81<br>Ethiopia<br>|
|$0.83<br>Madagascar<br>|
|$0.92<br>Honduras<br>|
|$0.96<br>Togo|
|$0.97<br>Nepal<br>|
|$0.97<br>Vietnam<br>|
|$0.98<br>Niger|
|$0.98<br>South Africa<br>|
|$0.99<br> <br>Pakistan|
|$1.00<br> <br>Zambia|
|$1.03<br> <br>Mozambique|
|$1.03<br> <br>Kenya<br>|
|$1.06<br> <br>Costa Rica<br>|
|$1.07<br> <br>Malawi<br>|
|$1.10<br> <br>Argentina<br>|
|$1.13<br>Guatemala|
|$1.15<br>Thailand|
|$1.15<br>Nicaragua|
|$1.16<br>Japan|
|$119<br>Liberia|
|$1.19<br>.<br>Philippines<br>|
|$1.21<br>Côte d'Ivoire|
|$1.21<br>Jamaica|
|$1.22<br>Dominican Republic|
|$1.22<br>Uganda|
|$1.24<br>Chile|
|$1.24<br>Tanzania|
|$1.26<br>Cambodia|
|$1.27<br>Senegal|
|<br>$1.31<br><br>Uruguay<br>|
|$1.63<br>Turkey|
|$0.00<br>$0.20<br>$0.40<br>$0.60<br>$0.80<br>$1.00<br>$1.20<br>$1.40<br>$1.60<br>$1.80|

_Sources:_ Table A2.1 and author’s calculations.
<!-- Start of picture text --> Survey Year<br>0.122<br>2012 +<br>0.021<br>2007 +<br>Region<br>0.027<br>Other urban ——<br>——<br>Rural 0.119<br>Individual characteristics<br>-0.020<br>Higher education<br>-0.064<br>Secondary Education —_—+—<br>0.016<br>Primary Education —+—<br>—<br>Male 0.183<br>-0.100 0.000 0.100 0.200<br>Point estimate and 95% Cl<br><!-- End of picture text -->
# **Table A2: Politician Characteristics across Committees**

## **OLS Estimates**

||**(1) Age of Politician**|**(2) Sex of Politician**<br>**[female=1]**|**(3) Years of**<br>**Education**|**(4) Political**<br>**Competition in**<br>**Constituency**|**(5) Index of Poverty**<br>**in Constituency**|
|---|---|---|---|---|---|
|**Agriculture Committee**|1.27|0.03|-0.45*|0.04|-0.01|
||(1.17)|(0.03)|(0.27)|(0.03)|(0.02)|
|**Education Committee**|2.80**|0.01|0.28|0.00|-0.02|
||(1.18)|(0.03)|(0.27)|(0.03)|(0.03)|
|**Environment Committee**|0.18|-0.03|0.28|0.05**|0.01|
||(1.19)|(0.03)|(0.27)|(0.03)|(0.03)|
|**FCT Committee**|-1.66|-0.01|-0.42|-0.02|0.00|
||(1.19)|(0.03)|(0.27)|(0.03)|(0.03)|
|**Health Committee**|-0.78|0.03|-0.16|-0.02|0.02|
||(1.21)|(0.03)|(0.28)|(0.03)|(0.03)|
|**Housing Committee**|-0.27|-0.03|0.06|0.03|-0.03|
||(1.11)|(0.03)|(0.25)|(0.02)|(0.02)|
|**Water Committee**|1.52|-0.01|-0.07|0.00|0.01|
||(1.09)|(0.03)|(0.25)|(0.02)|(0.02)|
|**Women and Youth Committee**|0.27|0.63***|-0.52|-0.04|-0.04|
||(1.45)|(0.04)|(0.33)|(0.03)|(0.03)|
|**H0: All coefficients equal [p-value]**|0.41|0.00|0.26|0.19|0.78|
|**H0: All coefficients bar Women and**<br>**Youth equal [p-value]**|-|0.63|-|-|-|
|**Observations**|345|345|345|345|345|

**Notes** : *** denotes significance at 1%, ** at 5%, and * at 10% level.  Robust standard errors are in parentheses.  All columns report OLS estimates.  The dependent variable in column 1 is the age of the politician.  In column 2, it is a binary variable reflecting the sex of the politician, which takes the value 1 if the politician is female.  In column 3, the dependent variable is the years of education of the politician.  In column 4, it is the winning vote share in the constituency that the politician represents. In column 5, it is the proportion of poor in the politician's constituency, measured by a national poverty index.  The 'FCT Committee' refers to the Federal Capital Territory Committee that has jurisdiction over the building of infrastructure within the Federal Capital Territory, the municipal area in which the capital, Abuja, sits.  At the foot of the table, I report the p-value on the null that the coefficients in each column are of equal magnitude.  I also report the p-value on the null that the coefficients in column 2, excluding that on the Women Affairs and Youth committee, are of equal magnitude.  Figures are rounded to two decimal places.
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9 Benchmarking utility performance**

||Unit|Low-income fragile<br>countries|**Côte d’Ivoire**|Middle-<br>income<br>countries|
|---|---|---|---|---|
|||Mid 2000s|**2004**<br>**2008**|Mid 2000s|
|Domestic water consumption|liter/capita/day|51.3||171.5|
|Urban water assets in need of rehabilitation|%|36.0|**33.0**|25.0|
|Revenue collection|% sales|96.0|**100.0**<br>**100.0**|100.0|
|Distribution losses|% production|32.7|**21.7**<br>**22.5**|26.8|
|Cost recovery|% total costs|80.0|**73.8**<br>**76.1**|80.0|
|Connections per employee|number|190.7|**352.4**|368.7|
|Total hidden costs as % of revenue|%|350.2|**121.2**<br>**112.2**|167.4|
|U.S. cents per m<sup>3</sup>|**Côt**|**e d’Ivoire**|Scarce water<br>resources<br>Ot|her developing<br>regions|
||**2004**|**2008**|||
|Residential tariff (at 10 m<sup>3</sup>)|**6.4**||60.26|3.0 – 60.0|
|Non-residential tariff (at 100 m<sup>3</sup>)|**107.2**||120.74||

* Based on DHS 1999 and MICS 2006.

Source: Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.**

# **Challenges**

Despite SODECI’s strong historical record, in recent years its financial performance has deteriorated as the real value of the tariff has eroded. The 1987 concession contract provides for tariff revisions every five years. Yet the scheduled revision for 2001 was delayed until 2004 during the recent crisis period,, and as a result the hidden costs due to under-pricing rose substantially (table 10). Currency appreciation amplified the effect of the 2004 tariff hike, and in 2008 the average effective tariff was $0.89 per cubic meter, almost double the 2001 level, but still significantly short of the full costs, which had escalated to $1.17..As a result of these tariff adjustments, the hidden costs associated with under-pricing of SODECI’s services have fallen from around 0.4 percent of GDP in 2001 to less than 0.2 percent of GDP today, but still remain an issue for the sector (figure 8). As of 2001, SODECI’s hidden costs were 94 percent of revenues, comparable to Ghana’s water utility and among the worst in the region. By 2008, however, hidden costs had fallen to 55 percent of revenues and were among the best in the region, comparable to the water utilities of Benin and Cape Verde.

18
“Fortunes are being made by taking the life savings off gullible people in return for getting them, illegally, into a country like Britain. The sales talk is doubtless about a land flowing with milk and honey, and streets paved with gold.” _The Campaign for Political Ecology_<sup>_1_</sup> “...for our relatives who live in the isle, in their small minds they think that money grow[s] out of trees, and thus expect people overseas to provide them with their need[s]...Tongans returning home for visits make the situation worse by exaggerating their success and wealth and creating unrealistic expectations” _Tongan online discussion group_ (quoted in Lee,2003, p. 36) Does migration make people better off? Revealed preference would suggest yes, as evidenced by the large number of people choosing to pursue life in a different land each year. However, as the above quotes illustrate, some critics contend that migrants may hold unrealistic expectations of the incomes they can earn abroad. Such expectations may be inflated by television and film images of life abroad (Mai, 2004), and by returning migrants presenting an overly positive image of their lives overseas. Typical anecdotes tell of migrants working 14 hours a day and living six to a room returning for a three-day holiday at home, bringing consumer goods and spending a lot to show how successful they have become. As a result, it could be the case that many intending migrants overestimate the incomes they can earn abroad.

Whether or not emigrants are overly optimistic about incomes abroad is a question of key policy importance, given the large and growing migration pressures around the world. For example, surveys of 15 to 24 year olds taken in late 2005 and early 2006 found that 91 percent of Albanians, 88 percent of Romanians, 80 percent of Ethiopians, 78 percent of Bangladeshis and 76 percent of Iraqis said that they would emigrate if they had the legal opportunity (World Bank, 2006). Eagerness to emigrate from Tonga to New Zealand is reflected in the excess applications for an annual immigration quota. If this enormous pressure to emigrate reflects mistaken beliefs about the incomes that can be earned abroad, then migration may lead to disappointment and frustration for the migrant, resulting in social problems in the destination country. But it would also suggest that more accurate information on earnings abroad could help lower immigration pressures.

> 1 http://eco.gn.apc.org/Population/immigration.html

2
To formally introduce double sampling, we make a few assumptions.

**Assumption 7** _The covariates x are observed for all sample households, while y is observed for the first k ≤ K households in all J clusters._<sup>13</sup>

Let _sch_ denote the indicator variable that equals one if the household is in the full sample and zero otherwise. We further denote by _s_<sup>_I_</sup> _ch_<sup>[</sup><sup>_sII_</sup> _ch_<sup>(=</sup><sup>_sch−sI_</sup> _ch_<sup>)]theindicatorvariablethatthe</sup> household is in the sub-sample containing data on both _y_ and _x_ [ _x_ only]. Using this notation, the number of households included in the former and latter sub-samples equals, respectively, _rn_ and (1 _− r_ ) _n_ , where _n_ is the sample size of the full sample and _r_ (= _k/K_ ) is the ratio of sample households with an observation of _y_ .

**Assumption 8** _The distribution of_ ( _xch, ηc, ech_ ) _is independent of_ ( _s_<sup>_I_</sup> _ch_<sup>_, sII_</sup> _ch_<sup>)</sup><sup>_._</sup>

This requires that the selection into either sample carries no information about _xch_ , _ηc_ , or _ech_ . This is a reasonable assumption in our setup because the researcher chooses whether to observe _ych_ .

We use the households with the observations of _y_ and _x_ to compute the estimator _θ_<sup>ˆ</sup><sup>_I_</sup> of _θ_ , where _θ_<sup>ˆ</sup><sup>_I_</sup> satisfies the following assumption, which is a double-sample analogue of Assumption 4:

**Assumption 9** _The estimator θ_<sup>ˆ</sup><sup>_I_</sup> _of the model parameters θ satisfies the following properties:_

# _where Vθ_<sup>_Iis a symmetric positive-definite κ × κ asymptotic covariance matrix ofθ_ˆ</sup><sup>_I._</sup>

13 Since data on _x_ is already being collected for all households in all clusters of the sample, the marginal cost of collecting data on _y_ from all clusters is identical to the cost of collecting _y_ for the same number of households from half the number of clusters, say. Given that any two households from different clusters carry more information than a pair of households from the same cluster, due to spatial correlation, it is optimal to collect _y_ for a sub-sample of households from all clusters. The added advantage of collecting household expenditure data from all clusters is that it allows the user to construct unit value prices for each cluster which are needed to convert nominal household expenditure data into real terms. Alternatively, the survey could collect its price data by visiting local markets, i.e. by including a community price module. In this study we will abstract away from spatial (and temporal) price adjustments, and assume that this is taken care off. Note however that this is not a trivial matter, see e.g. van Veelen and van der Weide (2008).

11
# **_5.2. Descriptive statistics_**

Table 2 shows statistics comparing IDP and non-IDP women interviewed in the three rounds of DHS. On average, IDP women are younger and less educated than their non-IDP counterparts. They are less likely to be married but more they likely to be widowed, or not be in union. IDP households have one more member than non-IDP households; they have slightly more children and adult members, but fewer elderly. The average time in displacement is 2.5 years.

<u>Table 2. Descriptive statistics</u>

||**Observations**|**Displaced**|**Non-displaced**|**Diff**|
|---|---|---|---|---|
|**Individual characteristics**|||||
|Age|110,992|28.26|29.86|-1.61***|
|||(0.23)|(0.03)|(0.26)|
|Years of education|110,772|7.46|9.02|-1.57***|
|||(0.08)|(0.01)|(0.09)|
|_Marital status_|||||
|Never married|110,977|0.33|0.39|-0.06***|
|||(0.01)|(0.00)|(0.01)|
|Married|110,977|0.14|0.19|-0.05***|
|||(0.01)|(0.00)|(0.01)|
|Cohabiting|110,977|0.36|0.29|0.07***|
|||(0.01)|(0.00)|(0.01)|
|Widowed|110,977|0.03|0.02|0.01***|
|||(0.00)|(0.00)|(0.00)|
|Not in union|110,977|0.14|0.11|0.03***|
|||(0.01)|(0.00)|(0.01)|
|Employed|110,992|0.46|0.51|-0.04***|
|||(0.01)|(0.00)|(0.01)|
|**Household characteristics**|||||
|Size|110,992|5.91|4.88|1.03***|
|||(0.06)|(0.01)|(0.05)|
|Children (0-5)|110,992|0.81|0.54|0.27***|
|||(0.02)|(0.00)|(0.02)|
|Children (6-14)|110,992|1.43|0.96|0.47***|
|||(0.03)|(0.00)|(0.02)|
|Adults (15-64)|110,992|3.54|3.18|0.36***|
|||(0.04)|(0.00)|(0.04)|
|Elderly (65+)|110,992|0.13|0.20|-0.07***|
|||(0.01)|(0.00)|(0.01)|
|Female-headed|110,990|0.41|0.34|0.07***|
|||(0.01)|(0.00)|(0.01)|
|Years in displacement|1,874|2.50<br>(0.04)|||

_Source:_ Author based on DHS 2005/2010/2015. _Note:_ Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

13
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1 006 Preparing Multiyear Railway<br>Investment Plans: A Market-Oriente<br>Approach|Jorge M. Rebelo<br>d|October 1992|A. Turner<br>30933|
|WPS1007 Global Estimates and Projections<br>of Mortality by Cause, 1970-2015|Rodolfo A. Bulatao<br>Patience W. Stephens|October 1992|0. Nadora<br>31091|
|WPS1 008 Do the Poor Insure? A Synthesis of<br>the Literature on Risk and<br>Consumption in Developing Countrie|Harold Alderman<br>Christina H. Paxson<br>s|October 1992|C. Spooner<br>32116|
|WPS1009 Labor and Women's Nutrition:<br>A Study of Energy Expenditure,<br>Fertility, and Nutritional Status in Gha|Paul A. Higgins<br>Harold Alderman<br>na|October 1992|C. Spooner<br>32116|
|WPS1010 Competition and Efficiency in<br>Hungarian Banking|Dimitri Vittas<br>Craig Neal|October 1992|W. Pitayatonakarn<br>37664|
|WPS1 0.11 How Tax Incentives Affect Decisions<br>to Invest in Developing Countries|Robin Boadway<br>Anwar Shah|November 1992|C. Jones<br>37754|
|WPS1012 The Brady Plan, the 1989 Mexican<br>Debt Reduction Agreement, and Ban<br>Stock Returns in the United States<br>and Japan|Haluk Unal<br>k Asii DemirgOq-Kunt<br>Kwok-Wai Leung|November 1992|W. Patrawimolpon<br>37664|
|WPS1013 The Impact of Mexico's Retraining<br>Program on Employment and Wages|Ana Revenga<br>Michelle Riboud<br>Hong Tan|November 1992|D. Young<br>30932|
|WPs1014 Ethnicity, Education, and Earnings<br>in Bolivia and Guatemala|George Psacharopoulos|November 1992|L. Longo<br>39244|
|WPS1015 Benefit Incidence Analysis in<br>Developing Countries|Thomas M. Selden<br>Michael J. Wasylenko|November 1992|C. Jones<br>37754|
|WPS1 016 Europe and Central Asia Region,<br>Middle East and North Africa Region<br>Population Projections, 1992-93<br>Edition|My T. Vu<br>Eduard Bos<br>Ann Levin|November 1992|0. Nadora<br>31091|
|WPS1017 Rural Poverty, Migration, and the<br>Environment in Developing Countries<br>Three Case Studies|Richard E. Bilsborrow<br>:|November 1992|WDR<br>31091|
|WPS1018 Tarfff and Tax Reform: Do World<br>Bank Recommendations Integrate<br>Revenue and Protection Objectives?|Anand Rajaram<br>|November 1992|P. Pender<br>37851|
Figure 2 shows the domestic increase in food CPI for 76 developing countries between January 2005 and December 2007 and compares it with the increase in the international food CPI.<sup>5</sup> In all but three countries, the domestic food price index increased _less_ than the international food prices (74 percent). Differences between the domestic and international food price indices could be explained by differences in the consumption basket with domestic food baskets containing non-traded food items. International and domestic food CPIs can also differ due to: (i) a weak price transmission in internationally traded food commodities (Baffes and Gardner, 2003), (ii) imperfect domestic markets characterized by lack of competition (Levinsohn, 1996) and poor infrastructure, and (iii) government intervention in the form of subsidies and price controls, and other market distortions.

The food CPIs in Figure 2 are expressed in local currency units (LCU) and are therefore influenced by local inflation rates. To account for local inflation rates, Figure 3 reports the change in domestic food CPI _relative_ to the change in non-food CPI between January 2005 and December 2007 and compares these indices with the change in international food CPI relative to the manufacturing unit value (MUV) index.<sup>6</sup> In 18 of the 76 developing countries included in our sample the non-food price index increased at a _faster_ rate than the change in food prices, in other words, non-food items became relatively more expensive. This is not surprising given the large price increases observed in an important non-food item such as fuels. For the great majority of the developing countries analyzed (58 out of 76) food items became more expensive in terms of nonfood items. On average, relative food prices increased 5.6 percent far below the 31 percent increase registered by the international food CPI relative to the MUV.

**Figure 3: Distribution of Cumulative Increase in Relative Food Prices (LCU, Jan 2005 – Dec 2007)**

<!-- Start of picture text --> Cumulative Increase in<br>International Food Prices = 31 %<br>-20 0 20 40<br>Percentage Change in Relative Price<br>20<br>15<br>10<br>5<br>Percentage of Developing Countries<br>0<br><!-- End of picture text --> As we mentioned before, there are several reasons why domestic and international prices can differ; nevertheless, this section shows that focusing on the international food CPI to

> 5 The domestic food CPIs are collected by ILO (http://laborsta.ilo.org/) directly from the national statistical offices (or central banks). The international food CPI is constructed by the research department at the World Bank (http://go.worldbank.org/MD63QUPAF1).

> 6 The MUV index comes from the World Bank (http://go.worldbank.org/VDQ5AA3VP0)

6
33

**Table 21: Types of funds used**<sup>**for voucher privatization in the Czech and Slovak Republics**</sup>

|Criteria|Joint stock company<br>|Closed-end unit trust<br>|Open-end unit trust<br>|
|---|---|---|---|
|Legal set-up|Fund has its own legal entity with<br>own governance boards|Fund has no legal entity|Fund has no legal entity|
|Type of security sold|share|fund certificate|fund certificate|
|Security is held by|shareholder|investor|investor|
|Voting right|full voting right 1/|no voting right|no voting right|
|Capital|capital is limited to number of<br>points invested in the fund (plus<br>some cash); shareholders can<br>decide on capital increase|capital is limited to number of<br>points invested in the fund|capital is variable|
|Fungibility|shares can only be sold on<br>secondary market|certificates cannot be exchanged<br>at the fund; they can be sold only<br>on the secondary market|fund has to exchange certificates<br>against cash|
|Administrative cost|relatively high cost for organizing<br>shareholder meetings for small<br>shareholders|lower cost than shareholder<br>investment fund, possibility for<br>higher dividends|lower cost than shareholder<br>investment fund, possibility for<br>higher dividends|

1/ in case of voting shares; shares<sup>can also be issued as non-voting shares, but they still remain shares, in contrast to fund certificates.</sup> Related Laws in the Czech and Slovak<sup>Republics: Law No. 248 of May 29, 1992 about investment companies and investment funds; Law No. 591</sup> of November 20, 1992 on securities<sup>(bonds and shares), Law No. 513 of January 1, 1992 (Commercial Code). See Trade Links (1994).</sup>
17 reforms, since not doing this lowers our estimates of pre-reform performance. One can argue that that the performance of the ISI period should be judged considering both its years of stable growth and those of economic crisis that come as a result of the distortions it created. However, we prefer to apply a most stringent test on the benefits of reform and present much of our analysis removing the years of turmoil in which the ISI model derailed<sup>17</sup> .

**Figure 1: GDP Per Capita Growth Before and After Reforms**

<!-- Start of picture text --> 3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9<br>Years Since Reform<br>GDP Per Capita Growth<br><!-- End of picture text --> Liberalization in our sample countries was followed in average by growth increase of more than 1 percent vis-à-vis the period of stable growth under ISI<sup>18</sup> . Actually if we do not remove crisis years from our comparison the increase in GDP per capita growth is from 0.38 in years [-12, -1] to 2.03 percent in years [0,9]; or an increase of 1.6 percent. Appendix 8 confirms that there is an acceleration in all country subgroups classified according to geographic region, population size and income per capita.

Appendix 10 in turn shows that growth increased in the large majority of our sample countries (31 out of 39, or 80 percent of them).

> 17 Since our sample includes those countries for which we have GDP growth data for all years within the [-12,9] period, Figure 1 and other tables in this section are not distorted by a time-varying country sample. 18 Year -12 is preceded by two years of growth below 1 percent and Year 9 is followed by an average growth around 2 percent.