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

# **Achievements**

Installed electricity generation capacity in Sudan tripled between 2005 and 2007. Sudan’s installed capacity increased from 801 MW (AICD 2005) in 2005 to around 2,687 MW in 2009 (Platts 2009).<sup>6</sup> Compared to its African peers, Sudan’s installed capacity far exceeds what lowincome countries have put in place but lags behind that of resource-rich countries. In 2005 around 70 percent of the electricity was generated via thermal generation and around 20 percent using hydropower resources. But the composition mix seems to have changed since 2005; with the construction of new hydropower plants, the generation mix has now shifted to 55 percent hydropower and 45 percent thermal.

**Table 12. Relatively high usage of modern fuels for cooking in Sudan**

|Usage of modern fuels||
|---|---|
||All households<br>Richest households|
|**Sudan (2009)**|**36.10**<br>**64.90**|
|Kenya (2003)|3.45<br>16.52|
|Nigeria (2003)|1.19<br>5.51|
|Uganda (2001)|0.87<br>4.34|
|Ethiopia (2005)|0.31<br>1.48|
|South Sudan (2009)|0.30<br>0.80|
|Tanzania (2005)|0.24<br>1.22|
|_Source:_World Bank 2010|b and c DHS various years.|

The National Energy Corporation (NEC) enjoys healthy bill collection. The utility reports that all of the billings are routinely collected by the utility. These rates are higher than all other African peer groups (table 13).

Sudan boasts a relatively high use of modern fuels (gas and electricity) for cooking relative to its African peers. Around 36 percent of Sudan’s population uses modern fuels for cooking compared to 1 percent of resource-rich Nigeria’s population. Over 65 percent of Sudan’s richest quintile use modern fuels for cooking relative to 6 percent in Nigeria and 16 percent in Kenya (see table 12). Even 8 percent of the poorest quintile of the population used modern fuels.

# **Challenges**

Electricity access rates, at around 37 percent of the population, are still low relative to most of Sudan’s resource-rich peers. The figure is more or less comparable to that of African low-income countries, however. Over 52 percent of urban areas have access to electricity compared to 28 percent of rural areas. Rural electrification is broadly comparable with other parts of Africa, but urban electrification lags significantly. There is a difference of over 25 percentage points between access to power in urban areas in Sudan and in other resource-rich countries in Africa.

Inadequate power access retards business activity, with the challenge being more acute in some areas than others. Though firms reporting that power is not widely available varied from 25 to 60 percent, state to state, across all states firms identified power to be a major problem. This challenge is particularly acute in the Nyala region, where around 60 percent of firms say inadequate power constrains business activity (figure 18a). Power outages in Sudan, at 19 days per year, greatly impact productive activity and exceed those found among the country’s resource-rich and low-income peers.

> 6 Based on the estimate that around 97 percent of all parts of Sudan’s power is in Sudan.

> <sup>28</sup>
labor. The consequent large-scale properties were to some degree based on pre-conquest social organizations in which the elites charged taxes. These large-scale structures, legitimated by the Spanish Crown (through the so-called encomiendas), survived even when the main production activities did not display economies of scale. The key aspect was that the rights to operate the tax systems were assigned to a small group of people. Hence, as in the previous group of countries, these economies featured highly concentrated political and economic power that translated into exclusive institutions preserving the power of the elite.

Finally, the third group of countries is composed of the colonies of the North American mainland (Canada and United States). These economies were neither endowed with crops that displayed economies of scale nor with an abundant native population. Therefore, their economies were organized into small units of production in a relatively competitive environment. The existence of abundant land and low capital requirements implied that most adult men operated as independent proprietors creating a relatively egalitarian society in economic and political terms.

Engerman and Sokolo�illustrate with a number of examples and summary statistics that the di�erences in productive processes across New World countries translated into very di�erent patterns of su�rage, public land, schooling policies, �nancial policies, and innovation policies among these countries. Easterly (2001) and Easterly and Levine (2002) provide econometric evidence linking factor endowments to institutional development. Both papers use a group of 11 dummy variables indicating whether a country produced any of a given set of leading commodities (crops and minerals). Easterly (2001) uses cross-country data to relate these measures, jointly the settler mortality variable from Acemoglu et al. (2001), to a variable measuring the \middle-class consensus" (i.e. the share of the three middle quantiles in total income). He shows that factor endowments and settler mortality are correlated with the middle class consensus. The middle class share subsequently a�ects the level of schooling, institutional quality, and openness of countries, and these variables a�ect per-capita income. In a related cross-country study, Easterly and Levine (2002) correlate factor endowments and settler mortality with the development of institutions. They �nd evidence that these variables a�ect income only through institutions.

Overall, the existing literature indicates that colonial factors can explain di�erences in economic development across countries. However, they are relatively silent about the e�ects of colonial factors on institutions and development at the sub-national level. In particular, if one takes the papers by Acemoglu et al. literally, colonial factors created 6
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percent of all households switched between being net sellers and net buyers of food.<sup>13</sup> These are very large shifts within a five year period and a period where food output increased significantly. These results for Vietnam raise caution that designating households as food net buyers or net sellers in any given period could be misleading because households are likely to shift between these designations. Thus, one needs to interpret the data presented here and in other studies with some caution and give closer attention to panel data where available.

# **III. A. Share of Households That Are Net Food Sellers and Buyers of Food**

The proportion of households that were net food sellers in rural and urban areas in the nine countries examined is given in Table 1 for the main staples definition of food. Per capita income of the country and the share of households classified as rural are also shown, with the dates in the parentheses indicating the years of the household surveys. The results show that on average only 23 percent of all households and 32 percent of rural households were net food sellers, and as expected, the share of households that are net sellers tends to increase with the share of rural households. Annex table A1.1 shows the same information for the definition that includes all foods and shows that 25 percent of all households and 35 percent of rural households were net food sellers.

**Table 1. Share of Net Food Seller Households**<sup>**a**</sup> **(percent)**

||GDP Per<br>Capita<br>(PPP) 1998|Percent<br>Rural|Share<br>_Urban_|of Househol<br>_Rural_|ds<sup>b</sup><br>_All_|
|---|---|---|---|---|---|
|Ethiopia, 2000|566|50.74|6.32|27.26|23.07|
|Zambia, 1998|678|47.76|2.75|29.56|19.10|
|Cambodia, 1999|1246|59.99|15.13|43.84|39.58|
|Bangladesh, 2000|1407|79.73|3.68|22.94|19.04|
|Vietnam, 1998|1689|71.16|6.55|48.22|38.08|
|Madagascar, 2001|1741|75.75|13.17|37.64|31.71|
|Nicaragua, 2001|1896|43.88|3.77|38.67|17.26|
|Bolivia, 2002|2205|41.98|1.23|24.61|9.97|
|Peru,2003|4180|35.86|2.89|15.47|6.73|
|Unweighted||||||
|Average|1734.2|56.32|6.17|32.02|22.73|

_(a) Food is defined as the main staple crops in each country. See Annex II._ _(b) Share of Urban, Rural, and National Households respectively._ The share of households who are net food buyers is much larger than the net sellers especially in the Latin American countries, Bolivia, and Peru which have low rural 13 She also found that 15 percent of net food buying households (8.7 percent of all households) shifted to being net sellers and 29 percent of net selling households (12.2 percent of all households) shifted to being net buyers. In rural areas, the shifts were even larger. Twenty-one percent of rural net food buyer households in 1993 became net food sellers in 1998 and 29 percent of rural net seller households became net food buying households. Not all the households who switched from net buyers to sellers were marginal buyers. Of the net buyer households whose net purchases were more than 10 percent of their expenditures, 15 percent switched from net buyer to net seller. It was similar in the case of net sellers.

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a flexible microfinance program that includes both production and consumption loans and specifically targets the ultra-poor. Unlike regular microfinance, PRIME has a flexible repayment schedule, a production loan can be used for consumption if needed, the interest rate is not more than 10 percent (compared to 20 percent for regular microfinance), and no fixed savings or weekly meetings are stipulated. In addition to flexible microfinance, PRIME offers year-round services to support income-generating activities, skills-based training, remittances, and primary health care (InM 2009). During the _monga_ season, PRIME also provides emergency loans for consumption smoothing and cash for work related to local infrastructure development. However, PRIME does not offer consumption credit without either subsequent or prior production credit.

The target beneficiaries of PRIME are the ultra-poor, identified based on a village census prior to the program intervention using the following criteria: (i) households have strictly less than 50 decimals of land; (ii) household per capita monthly income does not exceed Tk1,500 (equivalent to US$25) and (iii) one household member is a daily wage worker. The pre-program intervention data collected by the PKSF in 2006 reveals that, in listing the households for PRIME intervention, eligibility conditions are strictly enforced. PRIME is worthy of a rigorous impact evaluation, which must determine whether the intervention is effective in reaching the hard-core poor, as well as mitigating both seasonal and hard-core poverty.

Important to the identification of appropriate policies is an understanding of the extent of the seasonal nature of poverty vis-à-vis chronic poverty. While transient or seasonal poverty may be addressed by offering credit and bolstering other safety nets that stabilize income and consumption, the roots of chronic poverty are deeper and must be addressed through long-term investments in human and physical capital (Jalan and Ravallion 10
depends upon the ability to mobilize resources from outside the affected group, and where these ties are absent groups stand particularly vulnerable.

# **<u>Section 2.0 Toward Better Environmental Governance</u>**

Research thus far suggests an important role is played by institutions in determining the readiness of countries to withstand the shocks associated with climate change. In this section, we broaden our enquiry to examine the institutional preconditions of ‘environmental governance’ a concept than encompasses not merely the capacity to adapt to climate change induced shocks, but also the management of natural resource endowments, past and present pollution levels, environmental management efforts, contributions to protection of the global commons, and a society's capacity to improve its environmental performance over time (YCELP 2005).

Why do some countries have higher levels of environmental sustainability than others? In answering this question, this section of the background paper draws from the findings of Dulal, Foa and Knowles (2008), in which the authors test for significant associations between recent measures of environmental governance and the quality of social and political institutions.

# **Previous Literature on Social Institutions and Environmental Governance**

Previous studies have proven inclusive regarding its institutional preconditions of environmental governance. Grafton and Knowles (2004) analyze the relationship between social capital and national environmental performance for a sample of 35 countries, with their data sample being made up largely of high-income countries. Their social capital data are taken from the third wave of the World Values Survey (Inglehart et al. 2000). They employ three different proxies of social capital: _WVSTRUST_ , _WVSCIVIC_ and _WVSASSOC_ . WVS _TRUST_ measures the proportion of the population who answer “most people can be trusted” to the question ‘generally speaking do you think that most people can be trusted, or that you can’t be too careful in dealing with people?’ _WVSCIVIC_ is an index measuring the extent to which people think certain behaviors (such as cheating on your taxes, or avoiding a fare on public transport, if you had the chance) can be justified. _WVSASSOC_ measures the extent of membership of different voluntary groups (such as church or religious groups and sports clubs). These social capital proxies were first used in cross-country empirical work by Knack and Keefer (1997), in the context of explaining cross-country differences in the rate of economic growth.

Grafton and Knowles find a significant _negative_ correlation between both _WVSTRUST_ and _WVSASSOC_ and the ESI, which is counter to expectations, but a significant positive correlation between _WVSCIVIC_ and the ESI. Grafton and Knowles also analyze the relationship between what they term ‘public social capital’ and the environment. Public social capital is proxied by a measure of democratic accountability and a measure of the extent of corruption, though both proxies are generally insignificant. Of the other control 14
To create a balanced data set, we drop any firm that does not report in all ten years. Missing years often occur when the size of a firm shrinks below the large and medium enterprise size threshold, or a firm experiences a change in ownership due to industry reform, mergers, or changes in location. Many firms are missing at least one year between 1995 and 2004, and in order to maintain the continuity of data (and to be able to create technology development stocks), we were forced to drop these firms from the study. Our final balanced data set consists of 2,000 firms per year from 1995–2004, or 20,000 observations in total.<sup>2</sup> We then combine the economic and S&T data sets with the energy data set, which includes measures of approximately 20 individual energy types in both quantity and value terms. Merging with the energy data set reduces the number of observations significantly, largely for two reasons: (1) the energy data set focuses only on the most energy-intensive enterprises and therefore is not as comprehensive as the economic and S&T data sets; and (2) it only covers the period 1999– 2004 while the economic and S&T data sets cover a longer period, 1995-2004. Although the number of observations is significantly reduced when the energy data set is included, total energy consumption in our balanced dataset comprises a significant portion of total energy consumption in each industry. For example, total energy consumption of the enterprises in our merged dataset accounts for 40% of total industrial energy consumption in 1999.<sup>3</sup> Most variables used in the analysis are included in the original data set. However, a few variables had to be constructed, namely the technology development stock variable:

> 2 We examine the implications of using a balanced versus unbalanced data set in Section VII (“Robustness tests”)

> 3 National Bureau of Statistics of China, 2000.

15
Berhane, G., Ragasa, C., Abate, G. T., & Assefa, T. W. (2018). “The State of Agricultural Extension Services in Ethiopia and their Contribution to Agricultural Productivity.” Addis Abeba and Washington D. C.: International Food Policy Research Institute (IFPRI).

- Bernard, Tanguy, Simrin Makhija, Kate Orkin, Alemayehu Seyoum Taffesse, and David J. Spielman. 2016. “Video-Based Agricultural Extension - Analysis of a Pilot Project in Ethiopia.” Project Note. Addis Abeba and Washington D. C.: International Food Policy Research Institute (IFPRI).

- Cameron, A. Colin, and Pravin K. Trivedi. 2005. _Microeconometrics: Methods and Applications_ . Cambridge: Cambridge University Press.

- Cole, Shawn A., and A. Nilesh Fernando. 2020. “'Mobile'izing Agricultural Advice: Technology Adoption, Diffusion and Sustainability.” Harvard Business School/University of Notre Dame.

- Davis, K., E. Nkonya, E. Kato, D.A. Mekonnen, M. Odendo, R. Miiro, and J. Nkuba. 2012. “Impact of Farmer Field Schools on Agricultural Productivity and Poverty in East Africa.” _World Development_ 40 (2): 402–13. https://doi.org/10.1016/j.worlddev.2011.05.019.

- De Brauw, Alan, Patrick Eozenou, Daniel O Gilligan, Christine Hotz, Neha Kumar, and J V Meenakshi. 2018. “Biofortification, Crop Adoption and Health Information: Impact Pathways in Mozambique and Uganda.” _American Journal of Agricultural Economics_ 100 (3): 906–30. https://doi.org/10.1093/ajae/aay005.

- Feder, Gershon, Rinku Murgai, and Jaime B. Quizon. 2004. “The Acquisition and Diffusion of Knowledge: The Case of Pest Management Training in Farmer Field Schools, Indonesia.” _Journal of Agricultural Economics_ 55 (2): 221–243.

- Fisher, Monica, Stein T. Holden, Christian Thierfelder, and Samson P. Katengeza. 2018. “Awareness and Adoption of Conservation Agriculture in Malawi: What Difference Can

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# **Table 12: Unorganized Sector log (TFP)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Average TFP of Horizontal-linked|_Other-_|_sector Produ_|_ctivity Spill_|_overs_|
|Organized Plants|0.0161|0.0125|0.0115|0.0107|
||(0.0184)|(0.0182)|(0.0180)|(0.0179)|
|Average TFP of Forward-linked|||||
|Organized Plants|0.0325|-0.0007|-0.0793**|-0.2853***|
||(0.0324)|(0.0326)|(0.0336)|(0.0895)|
|Average TFP of Backward-linked|||||
|Organized Plants|0.0840***|0.0712**|0.0791***|0.1377*|
||(0.0295)|(0.0303)|(0.0300)|(0.0745)|
||_Other-_|_sector Emplo_|_yment Spill_|_overs_|
|Horizontal Employment Spillovers from|||||
|Organized Sector||0.0121**|0.0099*|0.0035|
|||(0.0052)|(0.0052)|(0.0056)|
|Forward Employment Spillovers from|||||
|Organized Sector||0.1528***|0.0449|0.1352|
|||(0.0545)|(0.0548)|(0.1985)|
|Backward Employment Spillovers from|||||
|Organized Sector||0.0872***|0.0303|0.1462**|
|||(0.0307)|(0.0303)|(0.0700)|
||_Own-_|_sector Produ_|_ctivity Spillo_|_vers_|
|Average TFP of Forward-linked|||||
|Unorganized Plants|||0.1751***|0.2492++|
||||(0.0398)|(0.1838)|
|Average TFP of Backward-linked|||||
|Unorganized Plants|||0.1725***|0.2182**|
||||(0.0302)|(0.0980)|
|Constant|0.0093|-2.4737***|-1.4736**|-3.4996**|
||(0.1360)|(0.6940)|(0.6771)|(1.4269)|
|Observations|5888|5888|5888|5888|
|R-squared|0.0189|0.0243|0.0433|0.1360|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Column 4 contains state-year and industry-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 41
world through aid appears limited both in absolute terms and in comparison with what may be observed in single high-income and even middle-income countries.

Of course, the preceding conclusion in no way means that aid is ineffective. If the assumption of an equal distribution of aid benefits within the population is accepted, then an 8 to 15 percent increase in the real income of the poorest is not negligible. Figure 2 also reveals that, under the assumption that aid income is equally shared within countries aid is very effectively targeted towards the poorest of the poor. Approximately 41 percent of all the aid made available by donors (measured in PPP dollars, and including technical cooperation and debt relief) goes to the poorest decile, and 25 percent is allocated to the second decile.<sup>18</sup>

**Figure 2: Per capita PPP GNI, Percent Impact of Aid, By Decile**

<!-- Start of picture text --> 16.00%<br>14.00%<br>"Maximum" Scenario<br>12.00%<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>"Preferred" Scenario<br>2.00%<br>0.00%<br>D1 D2 D3 D4 D5 D6 D7 D8 D9 D10a D10b<br>-2.00%<br><!-- End of picture text --> Source: Authors’ calculations based on data from World Bank (2004b) and aid flow data from DAC (2004). See text for description of the scenarios.

> 18 We conducted a similar analysis for aid in 1985.  The major difference in the results was that less aid was distributed to the bottom deciles and more aid to the middle deciles, suggesting that the targeting of aid to the poorest countries has improved since the end of the Cold War.

14
# **Table 1 Real GVC income in EU27 countries, all manufactures**

||Real GVC in<br>mil cons|come (in<br>tant$)|Shares in E<br>inco|U27 GVC<br>me|Real GVC<br>due to f<br>final dem|income<br>oreign<br>and(%)|
|---|---|---|---|---|---|---|
||1995|2008|1995|2008|1995|2008|
|Germany|617,836|665,164|29.8|26.4|46.3|69.9|
|France|292,330|330,216|14.1|13.1|53.1|60.0|
|United Kingdom|253,548|260,443|12.2|10.3|52.6|68.5|
|Italy|289,055|354,158|13.9|14.0|45.2|52.8|
|Spain|127,696|171,836|6.2|6.8|39.1|53.3|
|Netherlands|94,133|118,973|4.5|4.7|79.3|87.8|
|Belgium|66,357|69,783|3.2|2.8|84.1|89.2|
|Sweden|55,536|70,548|2.7|2.8|70.3|77.5|
|Austria|50,081|62,674|2.4|2.5|53.9|78.5|
|Greece|20,468|30,564|1.0|1.2|17.5|31.3|
|Denmark|36,484|41,700|1.8|1.7|79.8|87.5|
|Finland|28,868|36,952|1.4|1.5|64.5|74.3|
|Ireland|21,583|40,480|1.0|1.6|88.3|88.1|
|Portugal|23,730|27,228|1.1|1.1|48.5|59.6|
|Luxembourg|3,784|6,743|0.2|0.3|94.1|97.5|
|All EU15|1,981,489|2,287,462|95.6|90.7|||
|Poland|33,439|85,700|1.6|3.4|42.7|63.0|
|Czech Republic|14,477|41,450|0.7|1.6|57.3|79.0|
|Romania|11,896|32,585|0.6|1.3|32.4|44.4|
|Hungary|11,120|27,140|0.5|1.1|44.9|73.1|
|Slovak Republic|5,060|17,624|0.2|0.7|64.4|79.8|
|Other EU12|14,694|31,019|0.7|1.2|||
|All EU12|90,686|235,518|4.4|9.3|||
|All EU 27|2,072,175|2,522,981|100.0|100.0|||

Note: Real GVC income for all manufactures and in constant 1995 prices using US CPI as deflator. Decomposed into part due to domestic final demand and part due to foreign final demand.

_Source:_ Author’s calculations based on World Input-Output Database, April 2012.

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