rafmacalaba/datause-encoder-data huggingface dataset:
**_Number of Cases of Guinea Worm Prevented in Productive Age Group_**

Based upon epidemiological studies, the estimated total number of cases of Guinea worm in the period 1986-1996 is known. A sharp reduction in the number of cases of Guinea worm infection as a result of GDEC activities has been observed. By 1996, only 329,521 cases (including those in Sudan) were estimated to have occurred." Global 2000 projections indicate that, by 1998, it is likely that dracunculiasis could be eradicated from all countries except for Sudan. The total number of cases of Guinea worm infection prevented as a result of GDEC in any given year is computed as the difference between the projected number of cases of Guinea worm _without_ GDEC and the number of cases _with_ GDEC. We make the following assumptions regarding the number of cases of Guinea worm infection: (a) in the absence of GDEC, the total annual number of cases of Guinea worm would have remained at 1986 levels (around 2.2 million every year in the entire region), (b) two-thirds of all cases reported (or estimated) represent those among the "economically productive" age group of 15-44, and, as mentioned in a previous subsection. Table 3 reports the total number of cases of Guinea worm infection prevented in a given year, based upon the above-mentioned information and assumptions.

**Table 3. Number of Cases of Dracunculiasis (1986-1998).**

|Year|Total Cases of<br>Dracunculiasis<br>(with GDEC)|Total Cases of<br>Dracunculiasis<br>Preventeda|Cases Prevented<br>In Productive<br>Age Groupb|
|---|---|---|---|
|1987|2,178,272|0|0|
|1988|2,118,368|59,904|39,936|
|1989|2,061,543|116,729|77,819|
|1990|1,523,540|654,732|436,488|
|1991|1,202,351|975,921|650,614|
|1992|997,016|1,181,256|787,504|
|1993|1,118,829|1,059,443|706,295|
|1994|521,365|1,656,907|1,104,605|
|1995|408,742|1,769,530|1,179,687|
|1996|329,521|1,848,751|1,232,501|
|1997|315,000|1,863,272|1,242,181|
|1998|300,000|1,878,272|1,252,181|

a. Assuming the annual incidence without GDEC in the productive age group is 1,452,181 out of 2,178,272 for all age groups.

b. Assuming cases in productive age group represent two-thirds of total cases.

As can be seen from Table 3, by 1998, as a result of GDEC, an estimated total of almost 13 and 9 million cases of dracunculiasis would have been prevented in total and in the productive age group, respectively. This implies a unit cost of about $5 and $8 per case prevented in 1997 constant dollars for all age group and in productive group, respectively over the life of the program.

_16_ This is a Global 2000 estimate.

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rural-urban divide to include multiple urban economic sectors that may be differentially affected. We will model the effect of climate change on district urban incomes using insights from these two papers.

# **2.2 Climate change in general**

Like other large world regions, Sub-Saharan Africa has a highly diverse and variable climate. Moisture availability ranges from the hyper-arid Sahara and Kalahari deserts to the humid tropics of Central Africa. In places like the West African Sahel, long droughts have followed extended wet periods. Africa’s climate is shaped by the inter-tropical convergence zone, seasonal monsoons in East and West Africa, and the multi-year El Nino/La Nina Southern Oscillation (ENSO) phenomenon in which changes in Pacific Ocean temperatures indirectly affect African weather (Conway 2009). These processes influence temperatures and precipitation across the continent including extreme events like meteorological droughts, especially in the Sahel, the Horn of Africa and the Southern African drylands, as well as severe floods, most recently in Kenya in 2013. Climate records indicate a warming trend over Africa during the 20<sup>th</sup> Century, continuing at a slightly faster pace in the first decade of the 21<sup>st</sup> Century, independently of ENSO impacts (Collins 2011; Nicholson et al. 2013).

Climate researchers predict future climate change using various emission scenarios as inputs to several different assessment models. The underlying scenarios range from aggressive mitigation of greenhouse gases to a continuation of current trends. While there is fairly broad consensus about global average temperature trends, regional scenarios of temperature and particularly of precipitation patterns remain quite uncertain. Researchers from the Potsdam Institute for Climate Impact Research recently reviewed the predictions of a number of credible climate models for regional climate change in Africa (World Bank 2013). In general, average summer temperature is expected to increase by 1.5°C by 2050 in Africa under an optimistic (2°C) global warming scenario. The area exposed to heat extremes is expected to expand to 45 percent of the region by 2050.<sup>5</sup> Under a more pessimistic (4°C) global scenario, these trends would be exacerbated. Falling precipitation and rising temperatures would likely worsen agricultural growing conditions in large parts of Africa, especially in coastal West African countries and in Southern Africa.

Agriculture worldwide will feel the effects of climate change more directly than any other sector, but extreme climate conditions on the continent mean that many African farming systems operate in fairly marginal conditions even in the best of times.<sup>6</sup> A significant literature on climate change

> 5 The report defines heat extremes as 3-sigma events with respect to the 1951-1980 local distribution. 6 A number of studies have estimated the impact on the value of crop and livestock production under various scenarios, with a focus on the United States (Mendelsohn, Nordhaus and Shaw 1994, Schlenker, Hanemann and Fisher 2006, Deschênes and Greenstone 2007).

6
28 13. This point is confirmed statistically by the work of Chenery and Syrquin(1975). They regress the share of Gc out of GDP as a function of per capita income, population, net resource inflow and a number of time trends. The data are a pooled time series of developed and developing nations. The government consumption equation is one of twelve relationships considered part of the "resource allocation process." Other such relationships include the share of GDP accounted for by primary production, exports, etc. Of the twelve separate resource allocation regressions, the Gc equation has the lowest R<sup>2</sup> , by far, equaling only .083. (See their Table 5.) 14. The proposition that in a growing economy the scale of government activity tends to expand relative to the national economy is attributed to Adolph Wagner(1890). It is most often interpreted to refer to government consumption and not government transfer payment expenditures. The relationship has been subject to considerable empirical and theoretical debate in the literature. Ram(1987) contains useful references.

15. The problems with comparing the two studies are: (1) they differ in terms of what is included as government expenditure, with the OECD study employing a more comprehensive definition; and, (2) the time periods differ, roughly 1960 to 1975 for developing economies, and 1960 to 1982 for OECD nations.

16. The high elasticities of Gt in Pluta's sample reflect the low levels of transfer payments in developing economies. The median value of the share of Gt out of GDP in the sample is only 2.6% in 1960.

17. Ram (1987), p. 196.

18. On this point, see, again, Ram (1987), especially note 1, for a listing of the relevant theoretical literature.

19. In all fairness to Ram, he does acknowledge this limitation of his data and argues for the robustness of his results, especially in terms of his main purpose which is an empirical test of Wagner's "Law" which is usually viewed as exclusive of transfers. (see Ram(1987), pp. 202-3.) 20. Musgrave (1981) offers an often cited critique of the public choice view of government expansion.

# 21. Saunders and Klau (1985), Section III.

22. Recall, the alternative contemporary formulations of Wagner's law are the elasticity of Gc with respect to GDP versus the elasticity of GC/GDP with respect to GDP per capita. Since the latter formulation is generally seen as capturing Wagner's original hypothesis, the presence of a positive elasticity is the appropriate empirical test.

23. Mueller(1987) argues as follows. "The very definitions of public goods and externalities connote geographic proximity. [For example, t]he smoke from a factory harms more individuals in a densely populated community than when the population is thinly dispersed around the factory."(p.119) Urbanization or population density, thus, can be construed as a "taste parameter" for
This exercise demonstrates the lack of consideration of the agricultural potential in allocating maintenance funds for roads.

# **_The sustainability issue of the current investment strategy_**

Based on the current size of the road network (in our selected districts), it seems that the present allocation only covers routine maintenance needs (for districts roads).<sup>35</sup> In the most favorable district, Tororo, periodic maintenance can ensured around 10 percent of the current network (on top of routine maintenance). In any case, in Bushenyi, the present allocation does not cover routine maintenance for the whole district network, which means that even without further expansion of the _district road_ network,<sup>36</sup> its sustainability may be questionable.

# **Table 14:  Share of Maintenance and Rehabilitation Needs (for districts roads) Covered by the Current Maintenance Allocation (in percentage)**

||**Bushenyi**|**Masindi**|**Tororo**|
|---|---|---|---|
|Routinemaintenance|88%|108%|138%|
|Routine maintenance +<br>periodic maintenance (every<br>sixyears)|29%|36%|46%|
|Rehabilitation|3%|4%|5%|

Source: MOFPED for maintenance allocation per district; needs computed from road unit costs<sup>37</sup> and the size of the network.

Finally, the spending allocation for roads maintenance should be increased in order to make them sustainable. However, this mainly depends on the value added of the

> 35 Our selected districts are not among the lowest in terms of road maintenance allocation.

36 We exclude community roads in our discussion, assuming that it is a second priority order. 37 Following data were used for our computations:

||Road unit costs<br>(inUSDper km)|
|---|---|
|Routinemaintenance|319|
|Routine maintenance + periodic maintenance (every<br>three years)|1,278|
|Periodicmaintenance|3,836|
|Rehabilitation|9,204|
|Low costsealing|17,297|

Source: Ministry of Public Works.

39
28 Woolard, The Contribution of Income Components to Income Inequality in South Africa: A Decomposable Gini Analysis, Living Standards Measurement Study Working Paper No. 125 (Washington, DC: World Bank, 1996), p. 4.

° This analysis ignores feedback effects, that is, the effects that a change in any source income share might have on distribution within any source income. Of course, such an assumption might be quite unrealistic for large changes in any source income share.

1 1 This household survey was conducted by the International Food Policy Research Institute, working in collaboration with the Egyptian Ministry of Agriculture and the Egyptian Ministry of Trade and Supply. 12 For more details on this 1997 Egypt Integrated Household Survey, see Gaurav Datt, Dean Joliffe and Manohar Sharma, "A Profile of Poverty in Egypt, 1997," draft report (Washington, DC: International Food Policy Research Institute, March 1998).

> 13According to the data, 90 percent of rural households in the survey own their own house. 14 While in Jordan agriculture accounts for 6 percent of GDP and employs about 10 percent of the labor force, in Egypt it accounts for 17 percent of GDP and employs 38 percent of the labor force.

**_15_** As noted in the text, no livestock income was collected in Jordan.

**16** Only the simple correlation between transfer income and total income (0.204) is lower than that for livestock income. The simple correlations for the other source incomes are: nonfalm income, 0.362; and rental income, 0.442.

17 It should be noted that the definition of nonfarm income used here is narrower than those used in other studies. For example, Reardon includes migration (remittances) income in nonfarm income. See Thomas Reardon, "Using Evidence of Household Income Diversification to Inform Study of the Rural Nonfarm Labor Market in Africa," World Development 25 (May 1997): 735-747.

1<sup>8</sup> Joachim von Braun and Rajul Pandya-Lorch (eds), Income Sources of Malnourished People in Rural Areas: Microlevel Information and Policy Implications, Working Paper on Commercialization of Agriculture No. 5 (Washington, DC: International Food Policy Research Institute, 1991, Table 13). For other estimates of the share of nonfarm income in total rural income, on Africa see Reardon, ibid.; and on Latin America, see Peter Hazell and Stephen Haggblade, "Farm-Nonfarm Growth Linkages and the Welfare of the Poor," in Including the Poor. eds. M. Lipton and J. van der Gaag (Washington, DC: World Bank, 1993).
factors that affect these results, the impacts of road improvement on income gains, forest clearing and biodiversity loss seem likely to vary significantly across countries, and across regions within countries. In this paper, we focus on the forest clearing and biodiversity components of the problem.

# **4.  Data**

# **4.1  Road Networks**

Table 1 and Figure 1 summarize 2015 road network information for Lao PDR in digital maps (shapefiles) provided by Delorme, Inc. As Table 1 shows, the maps identify 2,055 road network links. Network links are graded by status: primary roads (purple in Figure 1), secondary roads (red), and tertiary roads (orange).

**Table 1:  Delorme road links by network status:  Lao PDR**

|Road Type|Links|
|---|---|
|Primary|370|
|Secondary|309|
|Tertiary|1,376|
|Total|2,055|

10
# **6.1 MNREGA had little impact on production**

I find that MNREGA had little impact on the area planted (in 000s of HA) or total tonnage produced of crops most associated with fire production (wheat, rice, and sugarcane) (Jain et al., 2014). When I use the full, national sample I find no effect on total area planted or total tonnage of fire associated crops or in total area planted and tonnage in non-fire associated crops. In the appendix I show event studies for each of these crop outcomes. In all cases the trend after the implementation of MNREGA is flat or declining and in no case is it different from zero.

Using the same difference-in-differences approach as described by equation 1, replacing agricultural fires ( _Fimy_ ) with the area in each crop ( _Aimy_ ) and tonnage produced ( _Timy_ ) in each crop as the outcome, I confirm the lack of impact suggested by the event studies (see Table 5). In all crops I examine (total crops, other non-fire associated crops, wheat, and sugarcane), except rice, I estimate an effect of MNREGA that is not statistically different from zero and is, in most cases, estimated to be close to zero with relatively high precision. The only crop for which I estimate a statistically significant effect at standard levels is rice, which sees a small (approximately 3%-4%) increase in area under production and total tons produced. This increase is small relative to the estimated change in fires and relatively imprecisely estimated. Based on these estimates I can reject an increase in fires of more than 0.40 % due to increasing area in wheat production at 95% confidence. Similarly I can reject changes greater than 1.60 % and 0.27 % for changes in the area in rice and sugarcane production.<sup>26</sup> MNREGA may have led to small increases in the area planted and total tons produced of rice. I find no evidence that it led to meaningful increases in area planted or tonnage of other crops. The estimated increase in fires due to the changes in area produced is small both in absolute terms and relative to the estimated overall impact of MNREGA on the frequency of fires. No more than 30% of the smallest estimated increase in fires is estimated to be due to changes in area under production and the estimated changes in production would account for no more than approximately 10% of

> 26To determine these bounds, I take the estimated change in area planted due to MNREGA from Table 5 and convert that estimated change in area into standard deviation units based on the distribution of area planted in each crop across all of India. This makes the units comparable to the units in Table 1 where I estimate the correlation between area planted in wheat, rice, and sugarcane and the frequency of fires. Based on the correlations in Table 1, I estimate the predicted change in fires for each crop based on the predicted change in area planted from Table 5. I use the delta method to calculate standard errors for these estimates. I report the upper end of the 95% confidence interval for the estimated change in fires.

20
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close to the regional level (51 vs. 52.6 percent in LAC) and significantly above those of other countries with similar overall participation rates.

**Figure 3 – Labor Force Participation (%) in LAC**

<!-- Start of picture text --> Female labor force participation<br>Labor force participation<br>90<br>Male labor force participation<br>85<br>79.06<br>80<br>75 73<br>70 65.3<br>65 61.4<br>60<br>55 52.62<br>50.9<br>50<br>45<br>40<br><!-- End of picture text --> Source: ECVMAS 2012 and LAC Equity Lab.

# **Box 2 : Jobs and Labor Market Definition**

As highlighted by the World Development Report on Jobs (World Bank, 2013), the definition of a “job” can vary greatly across countries and regions. Globally, almost half of the workforce does not work for a salary, and this share is even higher in developing countries. A job, therefore, should be understood as a broad concept of “activities that generate actual or imputed income, monetary or in kind, formal or informal” (World Bank, 2013). This box defines some of the labor market concepts discussed in this paper (for a detailed version see Annex 2). _Working-age population_ : Population 15 years of age or older. _Employed or occupied_ : People in the working-age population who worked for at least an hour in the week before the survey or who did not work that week but have a job that will resume in less than a month. _Unemployed_ : <u>Definition of the International Labour Organization</u> (ILO): People in the working-age population who do not have a job but are looking for one and are immediately available to work if they find one; Extended: Contains all unemployed people under the ILO definition, plus those individuals who have stopped looking for a job due to discouragement but are willing to work if offered one. _Informal sector_ : Unincorporated enterprises (household businesses) that are not registered or do not keep formal accounts and are not in the primary sector (agriculture). _Informal employment_ : All contributing family workers, all independent workers in the informal sector, and all employees without written contracts and not benefiting from social protection. These jobs are not in the primary sector (agriculture).

8
Report concerning the effects of labor market reforms on female labor supply and the time siblings spend on domestic work, market work and schooling.

The EMNV98, by including a time use survey as a module of an LSMS, has a further advantage over the stand alone SSA TUS2000. Time use data collected for one day (or even two) have a high “noise to signal ratio” and can include a high proportion of zero observations because the respondent did not undertake the activity on the designated day.<sup>54</sup> By running a survey as a module of an LSMS or Household Income/Expenditure survey, time use data for one or two days can be supplemented with information on several key variables that are reported in the latter surveys for a longer time period. For example, the Nicaraguan LSMS collected information on hours worked in the last week, month and year, and also for previous years of employment, for each respondent. These data, together with information on earnings over a corresponding period, allow hourly earnings to be computed as a “less noisy” measure of the gross wage for each respondent. This can be important for computing measures of household living standards that attempt to incorporate domestic output when there is strong heterogeneity in female labor supply and data on output are missing.

With noisy time use data and missing information on wages, the SSA TUS2000 does not permit the construction of a welfare ranking other than on household income based measures. In contrast, the EMNV98 provides a rich data source for deriving a range of measures that adjust for market consumption and the market/domestic time allocations of household members. In addition to detailed consumption data, the file contains a derived _annual consumption aggregate_ that incorporates the value of food consumed within and outside the home, housing and the use of durable goods, consumer durables and services. The aggregate is adjusted by indexes that take account of sampling design and geographical price differences. _Per capita consumption_ (pcc) is obtained by dividing the aggregate by household size, and is included in the file as a measure that can be used to make living standard comparisons.

> 54 These issues, and alternative procedures for handling them, are examined by Klevmarken (2002).

54