rafmacalaba/datause-encoder-data huggingface dataset:
12 The total share of household expenditures spent on energy averaged across all households (whether or not they used different forms of energy for which data were collected) varied from less than 4 percent in Angola, Gabon, and Sudan to almost 16 percent in The Gambia. In no country did LPG comprise the largest share. The surveys in 10 countries found the largest share had been spent on electricity and in another 10 countries on “others,” which were mostly wood, charcoal, coal, and kerosene. The largest share spent on LPG was 2.1 percent in Lesotho (Figure 4).

**Figure 4  Percentage of total household expenditures spent on different forms of energy**

<!-- Start of picture text --> Electricity<br><!-- End of picture text --> <!-- Start of picture text --> Gasoline and diesel<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : The expenditure shares are averaged across all households, including those reporting zero expenditures. Others = solid biomass and other fuels.

The urban and rural results on the energy expenditure shares can be found in Figure A2.5 and Figure A2.6, respectively, in annex 2. In urban areas, the energy expenditure share varied from less than 4 percent in Angola, Gabon, and Sudan to 16 percent in The Gambia. Electricity comprised the largest share in 11 countries, followed by “others” in nine and gasoline and diesel in three. By contrast, the share in rural areas varied from less than 2 percent in Angola (1.4 percent), Ethiopia (1.0 percent), and Liberia (1.9 percent) to 12 percent in Malawi. “Others” led the largest share in 14 countries, followed by gasoline and diesel in four countries, and finally by kerosene in two countries and electricity also in two countries. The expenditures on “others” should be interpreted with caution because they include imputed costs of freely acquired biomass, which is more readily available in rural areas. Urban Gambia and rural Malawi stand out for households spending more than 10 percent on “others”—comprising largely charcoal and wood in urban Gambia and wood in rural Malawi, with the latter likely to have included a significant proportion of freely acquired wood.

The total energy expenditure share in urban areas exceeded that in rural areas in 15 of 22 countries for which information was available, with the largest difference being 9.3 percentage points in Mauritania. Among the countries in which rural households reported spending more on energy than urban households, Malawi recorded the largest difference, with rural households spending on average 3 percentage points more on energy than urban households. This difference was driven by greater spending on firewood in
|**Table A23: International Research and Exch**<br> <br>|**anges B**<br>|**oard** **M**<br>|**edia S**|**ustain**|**ability**|**Inde**|**x (M**|**SI)**|
|---|---|---|---|---|---|---|---|---|
|**Data Provider**<br>International Research and Exc<br>**Description**<br>International nonprofit organ<br>specializing in education, ind<br>|anges Boa<br>ization h<br>ependent|rd<br>eadqua<br>media,|rtered<br>Interne|in W<br>t deve|ashingto<br>lopment|n, U<br>, and|nited<br>civil|States,<br>society|
|programs.<br> <br>|||||||||
|**Website**<br>www.irex.org<br>**Data Source**<br>Media Sustainability Index|||||||||
|<br>**Type**<br>Expert assessment|||||||||
|<br> <br>**Respondents**<br>Panel of local respondents in ea<br>**Frequency**<br>Annual since 2002<br>**Coverage**<br>Developing country sample<br>**Public Access**<br>Yes|ch country|, subjec|t to cen|tralized|review||||
|**Description**<br>Index rates countries on a varie<br>of media available to citizens,<br>media, and the efficacy of instit|ty of subc<br>profession<br>utions that|ompone<br>al journ<br>support|nts rela<br>alism s<br>indepe|ting to<br>tandard<br>ndent m|freedom<br>s, busin<br>edia.|of sp<br>ess su|eech,<br>staina|plurality<br>bility of|
||2006|2005|2004|2003|2002|2000|199|8<br>1996|
|**Voice and Accountability**<br>Media Sustainability Index|X|X|X|X|X|..|..|..|
|**Political Stability and Lack of Violence**<br>NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**<br>NA|..|..|..|..|..|..|..|..|
|**Regulatory Quality**<br>NA|..|..|..|..|..|..|..|..|
|**Rule of Law**<br>NA|..|..|..|..|..|..|..|..|
|**Control of Corruption**<br>NA|..|..|..|..|..|..|..|..|
|**Country Coverage**<br>**Year of publication**|**38**<br>**2007**|**37**<br>**2005**|**18**<br>**2005**|**18**<br>**2004**|**18**<br>**2002**|**..**<br>**..**|**..**<br>**..**|**..**<br>**..**|

61
**Figure 3 – Change in expenditures due to Tobacco Taxes (direct effect)**

# **Medical expenses**

The study estimated annual medical costs associated with tobacco consumption on the assumption that there is a direct medical impact on health. Although the assumption is unrealistic in the short run, the long-run reduction of tobacco consumption would trend according to this pattern, whereby a decline in tobacco consumption would be strongly associated to a reduction in tobacco-related diseases. Health expenditures are estimated using equation (5) and tables 4 and 7 (table 9; figure 4).

|_Price shock under_|_Decile 1_|<br>_Decile 2_|**Table 9**<br>_Decile 3_|**– Reducti**<br> <br>_Decile 4_|**on in Me**<br>_Decile 5_|**dical Cos**<br>_Decile 6_|**ts (%)**<br>_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Lower-bound elasticity|0.09|0.03|0.03|0.02|0.01|0.01|0.01|0.00|0.00|0.00|
|Medium elasticity|0.14|0.06|0.06|0.04|0.03|0.02|0.02|0.01|0.01|0.01|
|Upper-bound elasticity|0.20|0.10|0.09|0.07|0.04|0.04|0.04|0.03|0.03|0.02|

_Source:_ Based on data from the 2015 Household Budget Survey. _Note:_ The table shows the share of total consumption for each decile.

13
46

# students in rural residential primary and secondary schools.

- Means tested system ueed to target beneficiaries, based on<sup>family income,</sup> mother's education, teacher's opinion, nutritional status, etc.

- 189 davi coverage, and additional summer coverage for<sup>low income</sup> children (Jan/Feb). About 700 calories and 20g protein (33% ofRDA of protein/calories) are provided each day. Daily participation<sup>rates fluctuate</sup> depending on enrollment and socioeconomfic level of region. 57% of students enrolled in eligible rural schools are served, and 28% in urban eligible schools:

1988 1985 Beneficiaries 610,714 616,526 (534,465 exc JUNJI)

- Cost per beneficiary (per year) is around US$19 for breakfast and<sup>US$51 for</sup> lunch (US$70 for a full ration). 85% of budget is allocated<sup>to food costs.</sup> Municipalities usually fund just under 10% of the program.

Sources: Muchnik and Vial (1990), World Bank unpublished<sup>documents, Castaneda</sup> (1990).
Policy Research Working Paper 6561

# **Abstract**

This paper provides new insight into the poverty, malnutrition and vulnerability issues in Mali, using existing household survey data. First, it presents a profile of households that are poor, “food poor,” or have malnourished children. Second, it explores the impact of recent weather and price shocks on household welfare and identifies those affected most by the shocks. Finally, it estimates vulnerability to poverty by modeling both households’ expected consumption and their consumption volatility, and by distinguishing between idiosyncratic and covariate risks. The basic results of the analysis match conventional knowledge about poverty, food poverty, and malnutrition. The prevalence of chronic malnutrition is high in Mali, with 44 percent of Malian households and 66 percent of food poor Malian households having at least one stunted child. A 25 percent increase in cereal prices and a 25 percent decrease in cereal production are estimated to increase the number of food poor by 610,000 people. An estimated US$ 5.4 million of extra aid per year will be needed to lift the newly food poor above the food poverty line. About US$ 182 million is needed to do this for all existing and new food poor. Vulnerability incidence is in general two to three times higher among the poor than the non-poor, except in urban areas and in the region of Sikasso where the vulnerability incidence is five to six times higher among the poor. Overall, vulnerability is mostly driven by poverty induced vulnerability, except in the capital, Bamako, where vulnerability is more driven by risk induced vulnerability.

This paper is a product of the Poverty Reduction and Economic Management Department, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at peozenou@worldbank.org, dmadani@worldbank.org, and rswinkels@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
more innovative than exporters regarding process and organizational innovation (except for Nepal) (Table 7). Further, in all the countries expect Pakistan, two-way traders introduce more marketing innovations than the rest of the groups. The innovation dynamic is different for the other two regions used for benchmarking. While the two-way trader group is the most innovative in ECA, no matter the type of innovation considered, this is true in Africa for process and organizational innovations. Indeed, importers are the most innovative group in terms of inventing new goods in Africa.

<u>Table 7. Innovation by Trade Status (% of all firms in each category)</u>

|**Country**|**Category**|**Product**|**Process**|**Prod.**|**Organizational**|**Marketing**|
|---|---|---|---|---|---|---|
|Bangladesh|No Trader|42%|59%|77%|34%|89%|
||Importer|37%|71%|79%|43%|94%|
||Exporter|46%|62%|79%|38%|95%|
||Two-way|54%|74%|79%|48%|97%|
|Pakistan|No Trader|7%|8%|13%|14%|69%|
||Importer|69%|17%|76%|25%|97%|
||Exporter|10%|1%|11%|13%|88%|
||Two-way|7%|31%|32%|20%|71%|
|India|No Trader|53%|59%|83%|55%|82%|
||Importer|52%|65%|75%|65%|76%|
||Exporter|62%|61%|82%|47%|80%|
||Two-way|31%|83%|89%|78%|93%|
|Nepal|No Trader|12%|9%|20%|33%|87%|
||Importer|13%|13%|25%|49%|100%|
||Exporter|9%|24%|34%|40%|81%|
||Two-way|48%|53%|53%|88%|100%|
|ECA|No Trader|16%|10%|22%|52%|-|
||Importer|25%|24%|38%|44%|-|
||Exporter|25%|16%|34%|60%|-|
||Two-way|30%|29%|46%|64%|-|
|Africa|No Trader|25%|30%|44%|41%|-|
||Importer|34%|38%|55%|46%|-|
||Exporter|28%|32%|48%|45%|-|
||Two-way|36%|50%|59%|58%|-|
|Russian Federation|No Trader|18%|14%|25%|62%|-|
||Importer|37%|22%|44%|72%|-|
||Exporter|37%|33%|47%|89%|-|
||Two-way|30%|38%|44%|74%|-|
|Turkey|No Trader|2%|3%|4%|46%|-|
||Importer|36%|24%|36%|7%|-|
||Exporter|10%|10%|12%|67%|-|
||Two-way|9%|23%|27%|92%|-|

Source: Author’s elaboration from Enterprise Survey (2014) <u>Difference across ownership groups: In all the countries of the SAR region except in Pakistan, foreign-</u> owned firms are more innovative both in technological and non-technological terms. However, differences in the innovation rate of domestic and foreign firms are relatively minor in the case of India when we look at product and process innovation. While this pattern is similar to the observed in ECA; it is different to the one displayed in Africa, where domestic firms introduce more products than foreign-owned ones. These findings are statistically different for product and process innovation in Bangladesh, product and process innovation simultaneously in India, marketing in Nepal, and process innovation and marketing in Pakistan.

15
however, considerable variation in these demographic variables despite being drawn from the same population of tobacco farmers in central Malawi.

The summary statistics reveal important features of participants’ wealth and income. When compared to typical households from low-income countries, the households in the sample are poor. In the central Malawi region we study, however, tobacco farmers have similar poverty and income levels to those of non-tobacco-producing households.<sup>22</sup> At the time of the baseline survey, the median participant’s household has zero balances in a formal bank, and the 90th percentile of the bank balance distribution is just 700 Malawi Kwacha (henceforth MK), or approximately US$4.67.<sup>23</sup> The survey also inventoried the household’s non-cash assets including livestock and agricultural and household durables; and participants were asked to estimate how much they could earn by selling those assets. When we include the self-reported value of these assets, we find that the median household held just 4,446 MK of wealth and the 90th percentile held 25,800 MK.

Since the baseline was conducted during the rainy season, several months would elapse before the cash crop or primary staple (maize) would be ready to be harvested in mid-April or early May. The summary statistics bear this out. The median household expects virtually no income between the interview date and April 2010.

# **2.3 Stage One: Choices Under Commitment**

Figure 3 presents the timeline of the experiment. Upon initiating the interview, the household head and his spouse were physically separated. Then, after a few basic questions regarding demographics, each made five independent choices regarding the allocation of 2000MK between tomorrow (“sooner”) and 30 days from tomorrow (“later”).

More precisely, each participant was presented with a small bowl containing 20 beans (tokens) and two empty dishes, dish _A_ and dish _B_ . One token allocated to dish _A_ corresponded to 100MK tomorrow. One token allocated to dish _B_ corresponded to100 _MK_  1(  _r_ ) 30 days from tomorrow, where _r_ is the rate of return for waiting 30 days. The rate of return took on five different values: 0.10, 0.25, 0.50, 0.75, and 1.00. The rates of return rose, in order, with each of

> 22 Based on authors’ calculations from the 2004 Malawi Integrated Household Survey (IHS), individuals in tobacco farming rural households in central Malawi live on PPP$1.48/day on average, while the average for central Malawian rural households overall is PPP$1.51/day.

> 23 At the time of the study, the relevant exchange rate was roughly 150 MK per US dollar.

10
opposition party are right, left or center in their orientation or whether, on the contrary, their orientation is either not discernible in the sources employed or unrelated to economic policy. Our key variable is the share of these parties that is identified as having a right, left or center orientation ( _rlc_avg_ ). Across all the loans in our sample, about half of all parties do not exhibit such an orientation. We use the value for _rlc_avg_ that corresponds to the first year of the loan, which is unlikely to be influenced by the successful or failed implementation of the loan and makes it more difficult to attribute our findings to a pattern of causality running from bureaucratic quality to programmatic parties. Results are robust to other timing assumptions.<sup>9</sup> The coding of the DPI is based on descriptions of the party in yearly political almanacs. When these descriptions indicate that the party pursues a more liberal, right-wing economic agenda, enumerators classify it as “right”. When they indicate that the party pursues a more left-wing economic agenda – is more redistributionist, oriented towards greater regulation of the economy, or favors legislation that privileges workers over firm owners – enumerators classify it as “left”. They code the party as centrist when sources indicate that the party’s mobilization strategy relies on elements of both a left- and rightwing economic agenda. When, however, parties are not organized around an economic program, and instead around the promotion of an individual leader, the party is coded as having no economic program.

The formal coding rules aim at detecting whether parties are programmatic or not, reasonably classifying as programmatic the African National Party in South Africa, the main

> 9 Such as averaging _rlc_avg_ over the project period or using 3 and 5 year backward averages.

21
2

# **1. Context**

After averaging US$18 a barrel throughout the 1990s and rising to an average of US$27 in the first four years of the last decade, the price of crude oil began to soar in 2004. The prices of oil products followed similar trends, reaching historic highs in mid-2008, collapsing, then rising again. The prices of liquefied petroleum gas (LPG) reached a new high in 2012 before halving in the following months. Figure 1 shows the history of monthly average free-on-board (FOB) prices of gasoline, diesel, and LPG since 2004.

Figure 1: International Prices of Gasoline, Diesel, and LPG since 2004 <!-- Start of picture text --> $1.40<br>Regular gasoline, $/liter Diesel, $/liter LPG, $/kg<br>$1.20<br>$1.00<br>$0.80<br>$0.60<br>$0.40<br>$0.20<br>$0.00<br>Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13<br>$/liter or kg<br><!-- End of picture text --> _Sources_ : Industry sources.

_Note_ : Gasoline and diesel prices are from Singapore. LPG prices are average Saudi Aramco contract prices for propane and butane. Kerosene prices are not shown because they track diesel prices closely.

Earlier in history, the annual average spot price of three benchmark crudes (Brent, West Texas Intermediate, and Dubai Fatah), expressed in 2012 U.S. dollars, reached a historic high of US$89 a barrel in 1980. This record price was surpassed in 2008 when the annual average price soared to US$103 a barrel. The prices in 2011 and 2012 were slightly higher, averaging US$105 (Figure 2).

Figure 2: Annual Average Spot Prices of Three Benchmark Crudes in 2012 U.S. Dollars <!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>$/barrel<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> _Sources_ : World Bank 2013 for the prices of Brent, West Texas Intermediate, and Dubai Fatah; Federal Reserve Bank of St. Louis for the implicit price deflator for U.S. gross domestic product.
the conflict event counts are collapsed into binary indicators. These are more blunt indicators of conflict intensity, but they are also less prone to measurement error.

In addition, this study uses two indicators of Maoist control over a given district by 2003 based on classifications reported by Hattlebak (2007). I consider, in turn, two alternative definitions of Maoist control. I first categorize as under Maoist control any district that is categorized as such by both Maoists and the government (Definition 1). I then apply what Hattlebak (2007) considers a more reliable classification, the government classification (Definition 2).

# IV. **<<A>>** Empirical Strategy

I exploit the fact that surveyed individuals to have been exposed to varying degrees of conflict intensity according to their district of residence, year of birth, and whether they were surveyed in 2001 or in 2006.

The baseline estimation strategy is similar to that in much of the literature estimating the impact of civil conflict on individual outcomes reviewed in section I. The strategy exploits differences in exposure to conflict by birth year cohort and district of residence for individuals surveyed at the end of the conflict in 2006.

To check the robustness of the baseline results, I use a second identification strategy in which the source of identification is the change in the intensity of conflict within the district between 2001 and 2006, just before and just after the escalation of the conflict. By 2006, individuals born in 1991 and 1996, for example, would have been exposed to the same total amount of conflict before or during their schooling careers (albeit at different times in their lives). Hence, a comparison of the schooling outcomes of individuals born in 1991 and 1996 in the 2006 DHS would not be particularly informative. However, individuals born in 1991 who were observed at age 10 years in the 2001 survey experienced much less conflict by the time their education data were collected in 2001 than individuals born in 1996 and observed at age 10 years in the 2006 survey. Therefore, comparing their education outcomes at age 10 years is informative.<sup>6</sup> This second approach allows 10
endogenous **variables are evaluated** at their predicted rather than actual values). The simulated values of endogenous variables and their actual historical values are plotted together over the respective estimation periods in the Appendix. In general, simulated values track historical values quite well. Based on these results and the estimation statistics reported above, the USAGMKTS model appears to provide a sufficient basis for simulating U.S. agricultural policy effects on corn, sorghum, and soybean markets.

# **satimated Policy Sensitivitv**

The remainder of this report turns to examination of the estimated sensitivity of agricultural prices and trade to agricultural policy instruments. Using the model discussed above, several policy alternatives are simulated to determine the effects of major changes in farm commodity programs on the farm level prices af corn, sorghum, soybeans, beef, pork, and broilers. The policy alternatives considered are as follows:

1. A reduction of 10 percent in price supports for feed grains (with corresponding changes in price controls for the farmer owned reserve).

2. An increase of 10 percent in price supports for feed grains.

3. A reduction of 10 percent in both price supports and target prices for feed grains.

4. An increase of 10 percent n both price supports and target prices for feed grains.

5. A reduction of the diversion requirement by 10 percent.

These various alternatives are investigated by simulating the changes for two years beginning first with the 1981 crop year and then with the 1984 crop year. A period of two years was chosen because the policy changes do not affect production until late in the first year. Thus, the first year effects are largely an indication of how markets are affected holding production fixed 26
- 31 -

Table 2.7s Share of Coarse Grains, Rice, and Wheat in Total Cereal Consumption, 1960 and 1980

|||Share in|Total Cer<br>(percent)|eal Consum|ptiona||
|---|---|---|---|---|---|---|
|Region<br>||1960|||1984||
|Country|Coarse<br>Grains|Rice|Wheat|Coarse<br>Grains|Rice|Wheat|
|East Asia|17.8|74.7|7.5|28.1|60.5|11.4|
|China|30.2|47.6|22.2|29.6|39.8|30.6|
|Japan|21.9|57.8|20.3|56.1|27.0|16.9|
|Southeast Asia<br>Indonesia|18.0|80.8|1.2|16.7|78.9|4.4|
|Thailand|0.2|99.1|0.7|14.9|83.2|1.9|
|South Asia|11.8|72.2|16.0|7.3|64.7|28.0|
|India|32.2|48.2|19.6|23.7|44.0|32.3|
|Pakistan|14.7|13.0|72.3|10.1|14.6|75.3|

aTotal cereal consumption includes coarse grains, wheat, and rice. Coarse grains includes amounts consumed indirectly from livestock products.

Source: United States Department of Agriculture, Foreign Agriculture Service, Washington, D.C.