rafmacalaba/datause-encoder-data huggingface dataset:
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Challenges**

Blackouts became more frequent during the mid-2000s due to a tight demand-supply balance. Between 2002 and 2006, installed capacity decreased approximately 4.4 percent, while connections rose by 40 percent and production by 10 percent. As a result of the tight demand-supply balance, the incidence of blackouts more than tripled and became longer in duration. According to the Investment Climate Survey (2006), firms in Cape Verde perceive the poor performance of the power sector as the greatest constraint on their operations and growth. Over 60 percent of the firms surveyed see the condition of the power sector as a major or very severe obstacle to their business.

ELECTRA’s operational performance is not all that impressive. It certainly falls well short of the African middle-income-country benchmarks for utility performance. In fact, on aspects such as collection efficiency, distribution losses, and cost recovery, ELECTRA’s performance was very close to the African low-income-country benchmark for 2006 (table 5). While the collection ratio improved gradually during the period 2006–09, system losses deteriorated substantially, reaching 26 percent by 2009 (table 6). These operational inefficiencies led to substantial hidden costs for the sector, amounting to over 1 percent of GDP by 2009 (figure 5).

Cape Verde’s power prices are among the most expensive in Africa (figure 3). Energy production is largely dependent on diesel plants, which in turn rely on expensive fuel imports. A single power price is applied across all the islands despite differences in costs—a geographical cross-subsidization that is justified on the grounds of social equity. Due to the small size of the system, the decision of a single large customer (such as a hotel) to self-supply can substantially increase the costs of power across the system as it reduces scale economies. The authorities are planning to replace diesel-fired generating plants with plants that use fuel oil, which would immediately halve the costs of fuel. The government also plans to invest in renewable energy to improve technical efficiency and reduce oil dependency.

**Figure 3. Comparison of electricity tariffs across Africa**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>US$ cents per kilowatt-hour<br><!-- End of picture text --> _Source:_ Derived from Eberhard and others (2008).

Although power prices are relatively high, they have nonetheless failed to recover the full economic costs of production. Inadequate tariff setting has long been a critical issue, exacerbated by burgeoning oil prices. Given that fuel costs represent 70 percent of power production costs, a lack of adjustment in response to rising oil prices in the mid-2000s resulted in financial deterioration and depletion of the 11
|North|3.3|0.2|4.8|
|---|---|---|---|
|Northeast|-8.7|-2.5|29.0|
|Southeast|5.6|2.4|43.7|
|South|-1.6|-0.2|15.3|
|Center|10.7|0.8|7.0|

_Source:_ Author's own calculations based on PNAD 1999.

_Note:_ Total migrants are all the people with a history of migration, i.e. people who have indicated in the PNAD 1999 that they had migrated prior to 1990 (with unspecified date of migration) or post 1990 (at a specific point in time after 1990). A negative sign indicates a net outflow of migrants.

Migration has substantially increased the population in the SE and Center regions, as net migration over the lifetime is responsible for 5.6 percent and 10.7 percent of the regional population, respectively (Table 2.3). In contrast, the current NE population is almost 9 percent lower than it would have been without migration, reflecting its historical position as a net sender of migrants.

In the following section, we turn to the characteristics of migrants in order to understand how they make their decisions to migrate, and how the decision affects their well-being. This information will provide additional insights into the impacts of migration on regional and household well-being.

# 3. **Characteristics of migrants**

The impacts of migration on the Northeast and Southeast regions and on migrant households are of particular interest to policymakers. To understand these impacts, we construct a profile of inter-regional migrants. In the profile, a person is classified as having out-migrated if he/she lived in the past in the NE and currently lives in the SE; inmigration is classified correspondingly. A household is defined as a migrant household if the household head migrated during the reference period.

This section is organized in two subsections. In the following subsection, we first examine general characteristics of migrant household heads such as their age, gender, educational attainment, and location choice. Second, we analyse differences between migrants and non-migrants in receiving areas and differences between migrants from the NE and SE and other residents of the respective areas. In the second section, we turn to the economic consequences of migration decisions. We analyze first the relationship between migration and household poverty status and differences in incomes between migrant and non-migrant households and second, we examine participation in workforce, sector of employment, and earnings/wages of migrants.

# **3.1. Education and Demographics**

_Age, Gender, and Race_ 7
systematic assessment of how country-specific initial conditions -- inequality, level of development and poverty line definition -- affect the poverty-reduction effectiveness of growth and income redistribution.

The paper’s approach is based on the use of a lognormal approximation to the size distribution of per capita income. We implement this approach in two stages. First, we perform empirical tests of lognormality of the observed distribution of income, using a large cross-country income and expenditure distribution dataset covering over 100 countries and 40 years. Our testing strategy is based on the comparison of the observed quintile shares of the distribution with their theoretical counterparts under the null hypothesis of lognormality.

The empirical tests, performed on the full data sample as well as a variety of subsamples, are very supportive of the lognormal approximation to the distribution of per capita income, but less so for per capita expenditure. In the former case, the null of lognormality cannot be rejected in any of the samples considered; in the latter case, it is consistently rejected. In both cases, however, the lognormal specification yields a very close approximation to the observed distribution.

Lognormality of the distribution of income allows us to derive, at the second stage of the analysis, some qualitative and quantitative implications for the relative roles of growth and inequality in poverty reduction under alternative initial conditions, using a variety of poverty measures.

Our conclusions can be summarized in four main points. First, inequality hampers poverty reduction, not only because of its negative impact on the growth elasticity of poverty (as stressed in the literature) but, in most scenarios, also because of its negative impact on the inequality elasticity of poverty. Second, for a given poverty line, the impact of growth on poverty is stronger in richer than in poorer countries, and hence the latter will find it harder than the former to achieve fast poverty reduction. Third, the share of the overall variance of poverty changes attributable to growth should be generally lower in richer and more unequal countries. And fourth, for given initial levels of development and inequality, the relative poverty-reduction effectiveness of growth and inequality changes depends on the poverty line -- the higher the poverty line, the bigger the role of growth and the smaller the role of distributional change.

-15-
_<u>Table 3: Number of deaths, injured, homeless and affected people caused by natural disasters per 1,000 people, 1995-2005.</u>_

|Disaster|Type of<br>||199|5|||2|000|||20|05||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|type|Impact|All Count<br>|ries (n=196)<br>|Country<br>rep|with highest<br>orted impact<br>|Al<br>|l Countries<br>(n=196)<br>|Country<br>rep|with highest<br>orted impact<br>|Al<br>|l Countries<br>(n=196)<br>|Country w<br>repo|ith highest<br>rted impact<br>|
|||Total<br>No.|Stdev|No.|Country|Total<br>No.|Stdev|No.|Country|Total<br>No.|Stdev|No.|Country|
|Drought|Death|0|0|0||5.02E-04|3.5E-05|4.82E-04|Moldova|1.12E-03|6.2E-05|7.58E-04|Kenya|
||Injured|0|0|0||0|0|0||0|0|0||
||Affected|258.55|12.56|137.51|Zambia|1,114.99|40.63|486.00|Tajikistan|1,547.98|42.98|385.60|Malawi|
||Homeless|0|0|0||0|0|0||0|0|0||
|Earthquake|Death|0.07|3.31E-03|0.04|Japan|0.01|3.5E-04|3.85E-03|Azerbaijan|0.49|0.03|0.47|Pakistan|
||Injured|0.34|0.02|0.28|Japan|0.11|5.6E-03|0.07|Azerbaijan|0.89|0.06|0.82|Pakistan|
||Affected|10.94|0.33|2.63|Cyprus|5.19|0.16|1.44|China|4.71|0.18|1.69|Chile|
||Homeless|3.99|0.16|2.00|Japan|2.93|0.11|1.11|Nicaragua|32.68|2.39|32.10|Pakistan|
|Flood|Death|0.12|2.74E-03|0.03|Morocco|0.50|2.7E-02|0.36|Bhutan|0.11|3.5E-03|0.05|Guyana|
||Injured|0.07|4.36E-03|0.06|China|0.03|8.4E-04|0.01|Thailand|0.02|6.6E-04|0.01|Colombi<br>a|
||Affected|867.80|21.96|195.55|Azerbaijan|1,195.53|34.27|269.81|Cambodia|562.70|27.95|371.58|Guyana|
||Homeless|72.24|2.21|20.30|Azerbaijan|29.59|1.42|18.51|Botswana|13.57|0.50|5.68|Central<br>Africa|
||||||||||||||Rep|
|Storm|Death<br>Injured<br>Affected<br>Homeless|0.11<br>2.51<br>1,144.72<br>339.63|3.40E-03<br>0.18044<br>71.23<br>17.03|0.03<br>2.43<br>956.35<br>213.11|Antigua &<br>Barbuda<br>Antigua &<br>Barbuda<br>Antigua &<br>Barbuda<br>Lao PDR|0.10<br>2.37<br>1,502.62<br>3.07|4.3E-03<br>0.17<br>59.60<br>0.13|0.06<br>2.28<br>627.20<br>1.57|Belize<br>Belize<br>Moldova<br>Philippines|0.25<br>1.35<br>282.57<br>4.94|9.6E-03<br>0.08<br>10.36<br>0.23|0.12<br>1.06<br>126.83<br>2.91|Guatema<br>la<br>Nicaragu<br>a<br>Albania<br>Mexico|

_Source_ : EM-DAT (2008) _Notes_ : Figures greater than 1,000 such as the total number affected by storm in 1995 (  1,145 affected per 1,000 people) are possible due to instances where there are multiple occurrences of a particular disaster in one country in a given year. “Total no.” refers to the sum across the entire sample (196 countries). _Definition_ : Death – Persons confirmed as dead and persons missing and presumed dead; Injured – People suffering from physical injuries, trauma or an illness requiring medical treatment as a direct result of a disaster; Homeless – People needing immediate assistance in the form of shelter; Affected people – People requiring immediate assistance during a period of emergency, i.e., requiring basic survival needs, such as food, water, shelter, sanitation and immediate medical assistance. Appearance of a significant number of cases of an infectious disease introduced in a region or a population that is usually free from that disease.

5
5 and rural households separately, but information is not given at a wealth/income quintile level. Two sets of health-related surveys provide information by wealth quintile. The third round of the Multiple Indicator Cluster Survey (MICS3) of UNICEF, carried out in more than 50 countries, provides information on the main cooking fuel used by households by wealth index quintile and also for urban and rural households separately (UNICEF various years). The surveys do not identify households using LPG as a secondary cooking fuel or for non-cooking purposes. A series of World Bank reports based on the findings of the Demographic and Health Surveys conducted between 1990 and 2005 in 56 countries (World Bank various years) gives data on the use of LPG as a main cooking fuel by quintile group for some countries not covered by MICS3. A further small group of countries not covered by these sources have provided information, derived from national household expenditure surveys, on the use of LPG as a cooking fuel by expenditure quintile. Table A1.1 in appendix 1 provides information on the degree of use of LPG and of solid fuels as the principal cooking fuel in 110 developing countries where this information has been gathered since 2000. The range of LPG use for cooking varies from virtually universal to none, as does the range of solid fuel use. With the exception of Cambodia, Haiti, and the Lao People’s Democratic Republic, all countries with near-universal use of solid fuels for cooking are in Sub-Saharan Africa. The share of households using LPG in most cases is higher in urban than in rural areas. This trend is reversed in seven countries on account of urban household use of natural gas.

Figure 1 plots the distribution of LPG adoption rates across the 110 countries. Of the 44 countries where less than one-tenth of households reported using LPG as the primary cooking fuel, 29 countries were in Sub-Saharan Africa. In about a dozen countries with less than half of the households citing solid fuels as the primary cooking fuel, other forms of clean energy sources were important: kerosene in Djibouti and Indonesia; natural gas in Belarus, the Kyrgyz Republic, the Russian Federation, Ukraine, and Uzbekistan; and electricity in Bhutan, Bosnia and Herzegovina, the former Yugoslav Republic of Macedonia, Montenegro, Serbia, South Africa, and Tajikistan. In 30 percent of the countries, more than half of the households named LPG as their primary cooking fuel.

Figure 1: Country statistics for LPG as primary cooking fuel <!-- Start of picture text --> 50 50<br>40<br>40 40<br>30 30<br>18<br>20 14 20<br>10 11<br>7<br>10 10<br> 44 11 15 12 8 20<br>0 0<br>0–10% 10–20% 20–40% 40–60% 60–80% 80–100%<br>Percentage of households using LPG as primary cooking fuel<br>% of countries<br>Number of countries<br><!-- End of picture text --> _Source:_ Authors’ calculations.

Figure 2 provides evidence on the use of solid fuels, which are usually the main competitor to LPG in countries where neither natural gas nor electricity is widely used. At one
15 Tabe 3. Evaluadon of the Rdative Imporace of ach UNIDO Noncore Product in Developing Countries Exports

|srrc|Desedption|1987 value<br>of dovdoping<br>country expor<br>(Smill)|U.S. Capital<br>intensty<br>ratio|% of devdopin<br>accounts for more<br>250/o<br>to 49%|g countris whe<br>than 25% of no<br>50ff to 74Y|the product<br>n-ce exports<br>o 75Y**o**r aboe|_% of_tota<br>developing<br>country exports<br>of te non-me<br>products|
|---|---|---|---|---|---|---|---|
|01|Meat and prparations|2,972|100.0|5.6|1.4|0.0|6.1|
|02|Dairy products|322|138.0|0.0|0.0|0.0|0.7|
|032|Preserved fish|1,875|101.9|2.8|1.4|0.0|3.8|
|042.2|<br>Rice ghzed or polshed|386|156.5|0.0|0.0|0.0|0.8|
|046|Meat and flour of wheat|10|167.3|0.0|0.0|0.0||
|047|Meat and flour of non weat creals|17|167.3|0.0|0.0|0|-|
|048|Ceeal preparations|230|388.1|0.0|0.0|0.0|0.5|
|052|Dried fruit|214|134.5|1.4|0.0|0.0|0.4|
|053|Preseved fruit|2,716|129.9|5.6|0.0|0.0|5.5|
|O55|Preserved vgeables|1.232|129.9|1.4|0.0|0.0|2.5|
|06|Sugar and preparations|1,979|150.4|5.6|6.9|4.2|4.0|
|071.3|<br>Cotton|353|Da|0.0|0.0|0.0|0.2|
|072|Cocro powder|90|na|0.0|0.0|0.0|0.2|
|072.3|<br>Cocoa butter|783|na|4.2|0.0|0.0|1.6|
|073|Chocolate pepatons|181|a|0.0|0.0|0.0|0.4|
|074|Tea and mat6|1,128|en|1.4|1.4|1.4|2.3|
|081|Feeding stuff for animals|3.547|134.7|5.6|2.8|0.0|7.3|
|09|Misc.foodpreparations<br>|542|160.6|0.0|0.0|0.0|1.1|
|11|Bevraand spirits|744|198.4|2.8|0.0|0.0|1.5|
|122|Tobacco manufactues|1O|357.4|0.0|0.0|_0.0_||
|221.9|<br>Flourandmeal ofollseeds|5|166.2|0.0|0.0|0.0|-|
|231'|Natural and syntetic rubber|123|92.5|0.0|0.0|0.0|0.3|
|243|Shaped or worked wool|2,754|161.8|9.7|2.8|0.0|5.7|
|251|Paper pulp|724|180.6|0.0|0.0|0.0|1.5|
|262.6|<br>Wool shoddy|I|ua|0.0|0.0|0.0|-|
|262.7|Carded or combed wool|61|na|0.0|0.0|0.0|0.1|
|262.8|Wooltops|230|na|1.4|0.0|0.0|0.5|
|262.9|Wate of wool,ns|24|oa|0.0|0.0|0.0||
|263|Cotton, carded, combed or raw|2,457|a|4.2|4.2|6.9|5.0|
|266|Syntheci fibers|287|190.2|0.0|0.0|0.0|0.6|
|267|Texl waste material|17|na|0.0|0.0|Q0|-|
|332|Refined petoleum product|22.,46|337.3|13.9|9.7|15.3|469|

I The capital intensity inde is based on 1982 U.S. Cesus of Manufactr data and is defined as, C. = ( _-N.)- j*tNt) x_ 10 where V. and V represet value added ia indury j and alg U.S. manufcturing respectively, whb N and N represen **t** he number of woers in the industry and all Lanu 8ring activity. A value of 100 indicates the ector has the same capital intensity as le avere of all U.S. manufacturing acivity with values above 100 indictng progevdy higher capital intity

> 2 Calculated from the share of non oem products in the exports of uh of the 72 countries listed in Table 1.

3 Calculead from the share of non com products in the £i exot of the 72 countries 1sed in Table 1.
# **I. Introduction**

In 1980, China and India, the two most populous countries on earth, were both very poor. India had a nontrivial lead in per capita GDP; per capita GDP was $2.38 per calendar day, nearly one dollar more than in China.<sup>2</sup> In 1980, China was already more than two years into its gradual economic reforms, whereas India did not embark on economic liberalization until 1991. Economic liberalization in both countries was followed by accelerated economic development. By 2000, daily GDP per capita had risen to $7.30 in China and to $4.71 in India, giving China a lead of $2.59. By 2008, China’s per capita GDP (measured using the PPP exchange rate and 2005 international dollar) stood at $5,515 per year, or $15.11 per calendar day, and India’s per capita GDP at $2,721 per year or $7.45 per day. While both China and India are success stories in economic development, the performance gap between China and India deserves attention. With only two countries to compare, macro data are of little use since time-series variations alone may not offer sufficient degrees of freedom for statistical inference. Using firm-level data, however, we can transcend the constraint of macro data by examining cross sectional, as well as temporal, variations. We can link firm productivity—presumably the most important factor contributing to income per capita (Islam 1995; Hall and Jones 1999)—to city-level business environment, such as skills, infrastructure, labor regulation, regulation burden, and access to finance. In particular, we recognize that constraints to economic development tend to be location-specific. Following the diagnostic approach to growth (Hausman et al. 2005), we employ firm level data to identify key constraints to development (Lin and Monga 2010). Our presumption is that, even within a country, there are sufficient variations in the city-level aggregates that we can use to gauge the effects of the policy and business environment on firm-level productivity.

Our firm-level data are from comparable samples of manufacturing businesses in the two countries, namely, the World Bank–sponsored Investment Climate Surveys conducted in 2003. The Indian survey covers 1,860 manufacturing establishments sampled from the country’s top 40 industrial cities and major exporting industries. The Chinese survey covers 2,400 enterprises sampled from 18 cities covering 15 provinces and 5 geographic regions. In addition to collecting annual data on each sample firm’s characteristics, financial accounts and operations between 2000 and 2002, the survey also collected data on the local business and policy environment that each sample firm faced

> 2 We calculate daily income based on GDP per capita, measured using purchasing-power-parity (PPP) exchange rate and 2005 constant international dollar, as reported in the 2010 World Development Indicators published by the World Bank. Per capita GDP in China was $1.43 per calendar day in 1980.

2
**Table 6. Absolute convergence in location premiums after sorting at different administrative levels by country and time period (contd.)**

||Ad<br>|Annual<br>ministrative lev<br>|locationpremiu<br>el 1<br>|mgrowth(after<br>Ad<br>|sorting)<br>ministrative lev<br>|el 2<br>|
|---|---|---|---|---|---|---|
|**Honduras**|2004/06-<br>2017/19|2004/06-<br>2009/11|2009/11-<br>2017/19|2004/06-<br>2017/19|2004/06-<br>2009/11|2009/11-<br>2017/19|
||-0.0253**|-0.0326*|-0.0307*|-0.0413***|-0.0669***|-0.0694***|
||(0.00944)|(0.0181)|(0.0165)|(0.00364)|(0.00656)|(0.00760)|
|Obs.|16|16|16|243|243|243|
|R-squared|0.338|0.188|0.199|0.349|0.301|0.257|
|**Costa Rica**||2001/03-<br>2008/09|||2001/03-<br>2008/09||
|||-0.00782|||-0.0307***||
|||(0.0215)|||(0.00753)||
|Obs.||<br>7|||<br>79||
|R-squared||0.026|||0.177||
||2003/05-|2003/05-|2009/11-||||
|**Argentina**|2017/19|2009/11|2017/19||||
||-0.0125**|-0.00396|-0.0379***||||
||(0.00598)|(0.00994)|(0.0135)||||
|Obs.|<br>29|<br>29|<br>29||||
|R-squared|0.139|0.006|0.226||||
||2000/02-|2000/02-|2008/10-||||
|**Uruguay**|2017/19|2008/10|2017/19||||
||-0.0206***|-0.0416***|-0.00549||||
||(0.00658)|(0.0110)|(0.0138)||||
|Obs.|<br>19|<br>19|<br>19||||
|R-squared|0.364|0.457|0.009||||
||2001/02-|2001/02-|2011/13-||||
|**Bolivia**|2017/19|2011/13|2017/19||||
||-0.0327***|-0.0280**|-0.0704***||||
||(0.00423)|(0.0103)|(0.0142)||||
|Obs.|<br>9|<br>9|<br>9||||
|R-squared|0.895|0.516|0.779||||
||2001/03-|2001/03-|2009/11-||||
|**Panama**|2017/19|2009/11|2017/19||||
||-0.0190|0.00568|-0.0515*||||
||(0.0133)|(0.0123)|(0.0265)||||
|Obs.<br><br>|<br>12<br>|<br>12<br>|<br>12<br>||||
|R-squared<br><br>|0.168<br>|0.021<br>|0.274<br>||||
|Note:γ0<br>abrefers|to the initial loc|ation premium|after sorting, estim|ated with regressi|on model (2). Fo|r Mexico the analysis|
|is based on cens|us data for all p|eriods while in|Brazil this is the c|ase for period 20|00-2010. For the|rest of the countries|
|the analysis is ba|sed on SEDLA|C harmonized h|ousehold surveys.||||

24
Table A6. Regional and sectoral output (mln RUB, 2006)

||**Federal District**|**s of the Russian Feder**|**ation**|||||
|---|---|---|---|---|---|---|---|
||**FD1-Central**|**FD2-Northwest**|**FD3-South**|**FD4-Volga**|**FD5-Urals**|**FD6-Siberian**|**FD7-FarEast**|
|A|368 289,00|96 458,00|367 871,00|436 079,00|131 192,00|249 916,00|61 477,00|
|B|640,00|44 306,63|5 026,23|342,42|839,74|366,88|81 536,60|
|CA_col|106 709,18|127 135,77|38 986,57|14 094,12|70 582,45|267 696,51|80 650,42|
|CA_gas||5 053,99|13 390,99|145 773,25|1 327 768,23|836,14|1 306,07|
|CA_oil||25 511,08|8 165,33|476 401,42|580 550,49|1 542,35|1 797,04|
|CB|78 199,59|56 836,94|7 024,68|32 771,35|48 643,37|58 861,64|146 917,68|
|DA|654 896,22|298 465,22|207 019,12|266 014,91|80 567,66|169 907,16|51 000,78|
|DB|72 004,30|11 781,52|11 829,66|23 244,02|4 648,13|5 480,88|1 152,56|
|DC|13 715,10|1 309,06|2 218,06|2 582,67|1 549,38|1 370,22|288,14|
|DD|44 574,09|49 744,20|4 436,12|23 244,02|13 944,40|26 034,16|4 610,24|
|DE|168 010,02|108 651,81|14 787,08|51 653,38|9 296,27|34 255,48|3 601,75|
|DF|651 467,44|53 671,38|122 732,76|550 108,50|342 412,54|206 903,07|12 101,88|
|DG|216 012,89|81 161,60|48 797,36|309 920,28|26 339,43|79 472,70|2 881,40|
|DH|96 005,73|15 708,70|14 787,08|90 393,42|13 944,40|23 293,72|2 305,12|
|DI|229 727,99|64 143,84|56 190,90|92 976,08|71 271,39|45 217,23|11 669,67|
|DJ|404 595,57|301 083,34|121 993,41|286 676,26|718 911,39|565 900,45|17 288,40|

98
<!-- Start of picture text --> Panel a: Model-based prediction<br><!-- End of picture text --> <!-- Start of picture text --> Panel b: After priority reassignment for small range species<br><!-- End of picture text --> Biodiversity Index Value 1st dec. 3rd dec. 5th dec. [| 7th dec. || 9th dec. 2nd dec. ath dec. J) 6th dec. J sth dec. I t0th dec.