rafmacalaba/datause-encoder-data huggingface dataset:
are often misdiagnosed because pesticide poisonings can mimic other common health problems (FAO, 2001).<sup>2 3</sup> To assess the actual extent of this problem, the World Bank collaborated with the Vietnam Association of Occupational Health to study pesticide poisoning among Mekong Delta farmers in the first quarter of 2004. With 482 farmers participating, the study collected both survey and clinical data. Structured questionnaires were used to collect information on farming systems, pesticide use and practices, applicator precautions, protective measures, and self-reported poisoning symptoms.<sup>4</sup> All participating farmers were examined by the doctors from the Vietnam Association of Occupational Health. Using clinical exam results and self-reported symptoms, the study team selected potentially-poisoned farmers for blood tests that detect acute or chronic contamination by pesticides (organophosphates and carbamates).

The medical survey covered the districts of An Phu and Chau Thanh (An Giang province), Thot Not and Vi Thanh (Can Tho province), Tan Thanh and Thu Thua (Long An province ), Cai Lay and Cho Gao (Tien Giang province), and Tra Cu and Tieu Can (Tra Vinh province) in the Mekong Delta. The 482 surveyed farmers are almost all male (3 females were examined). Their ages range from 15 to 70 years, with an average age of 39. The surveyed farmers average 8 years of education; 74% report that they smoke and 48% report drinking alcoholic beverages. To minimize possible reporting bias, the surveyed farmers were assured of anonymity.

# **4. Health Impairment: Self-Reported and Blood Test Results**

The survey asked farmers to report symptoms experienced after mixing and spraying pesticides. Figure 2 displays the most commonly-reported symptoms: dermal (skin irritation: 63%), neurological (headache: 60%; dizziness: 53%), ocular (eye

> 2 Hospital admission records in Vietnam attribute 11% of all poisonings to pesticide misuse: approximately 840 poisonings in 53 cities and provinces in 1999 (Poison Control Center, Vietnam Ministry of Health, 2000).

> 3 The World Health Organization estimates that there are 50 cases of poisoning for every case reported and registered (WHO, 1990).

> 4 The survey was designed by the World Bank team.

6
PROVISION OF WATER TO THE POOR IN AFRICA

# 3   Analysis of water prices, costs, and regulation

The range for prices of water by service provider goes from 1.3 times the utility price for small piped networks to 10 to 20 times the utility price for mobile distributors (table 3.1). Standpipe prices include “formal”rates by the utility which differ from “retail” prices (referred to here as “informal” prices) faced by the consumer when there is a delegated management model.

### **Table 3.1   Prices by water service provider**

|Country|Largest city|HH<br>connection<br>(US$/m3)*|Small piped<br>network<br>(US$/m3)|Standpipe<br>(US$/m3)|HH reseller<br>(US$/m3)|Water<br>tanker<br>(US$/m3)|Water<br>vendor<br>(US$/m3)|
|---|---|---|---|---|---|---|---|
|Benin|Cotonou|0.41|N/A|1.91|1.91|N/A|N/A|
|Burkina Faso|Ouagadougou|0.90|N/A|0.48|N/A|N/A|1.67|
|Ethiopia|Addis Ababa|0.19|N/A|0.87|1.44|3.85|N/V|
|Mozambique|Maputo|0.96|0.98|0.98|0.98|N/A|N/V|
|Niger|Niamey|0.52|N/A|0.48|N/A|N/A|1.79|
|Nigeria|Kaduna|0.17|N/A|N/V|N/V|3.43|5.71|
|Rwanda|Kigali|0.44|N/A|1.79|1.79|4.48|N/A|
|Senegal|Dakar|0.37|N/A|1.53|N/V|N/A|2.29|
|South Africa|Johannesburg|0.05|N/A|N/A|N/A|N/V|N/V|
|DRC|Kinshasa|0.05|2.11|1.02|1.01|N/A|N/A|
|Ghana|Accra|0.52|N/A|5.51|1.53|5.46|6.89|
|Kenya|Nairobi|0.18|0.60|1.73|N/A|3.74|3.47|
|Lesotho|Maseru|0.40|N/A|2.58|N/V|N/V|N/V|
|Malawi|Blantyre|0.12|N/A|1.16|3.38|N/A|N/A|
|Namibia|Windhoek|1.45|N/A|N/A|N/A|N/A|N/A|
|Sudan|Great Khartoum|0.37|N/A|1.15|N/V|4.32|3.00|
|Zambia|Lusaka|0.56|N/A|1.67|N/V|N/A|3.00|
|Cape Verde|Praia|2.67|N/A|9.44|N/A|9.67|11.38|
|Chad|N'Djamena|0.22|N/V|N/V|N/V|N/A|N/V|
|Cote d'Ivoire|Abidjan|0.04|N/V|0.93|1.82|N/A|3.35|
|Madagascar|Antananarivo|0.11|0.47|1.24|N/V|N/A|2.33|
|Tanzania|Dar es Salaam|0.39|N/V|0.87|0.98|2.40|2.56|
|Uganda|Kampala|0.25|N/A|1.40|1.40|N/V|4.50|
||Average|0.49|1.04|1.93|1.63|4.67|4.00|
||Median|0.37|0.79|1.24|1.49|4.08|3.00|
||Min|0.04|0.47|0.48|0.98|2.40|1.67|
||Max|2.67|2.11|9.44|3.38|9.67|11.38|
||Overprice**||2.14|3.36|4.02|11.03|8.11|

* 4 m3 / month

** Price SSIP/HH connection

Source: AICD WSS database, Other Note: Data from 23 cities. Standpipe price is the “retail” otherwise referred to as informal price paid by the consumer at the tap.

## Standpipes

In almost three quarters of the sampled cities,<sup>16</sup> utilities had set a formal/wholesale standpipe price below the unit price for those with house connections, implying a “social” tariff or tariff reflective of the lower level of service.<sup>17</sup> Only in Sudan, Madagascar, Tanzania and Ghana did utilities have a standpipe rate that was above that of household connection water.

> 16 Analysis based on data from 9 cities.

17 For this analysis it was assumed average household consumption of 10 M3 for house connections (60 l/c/d).

23
Table 1b: Descriptive statistics for unorganized sector

||Lev|els of total acti|vity|Leve|ls of young firm|activity|
|---|---|---|---|---|---|---|
||Plants|Employment|Output|Plants|Employment|Output|
||(1)|(2)|(3)|(4)|(5)|(6)|
||A. Average l|evels of activit|y in 1994 and|2000, combin|ing districts with|in spatial range|
|Total|13,153,144|27,523,611|1.0E+11|1,630,239|3,903,673|1.3E+10|
|Nodal district for GQ|575,139|2,113,751|2.7E+10|157,661|635,383|3.1E+09|
|District 0-10 km from GQ|4,641,334|9,673,250|2.7E+10|477,638|1,098,275|3.4E+09|
|District 10-50 km from GQ|1,425,447|2,851,789|7.8E+09|184,064|381,498|1.1E+09|
|District over 50 km from GQ|6,511,225|12,884,821|3.9E+10|810,875|1,788,517|5.9E+09|
|Urban Total|3,749,445|9,784,626|6.7E+10|685,679|1,969,850|8.5E+09|
|Nodal district for GQ|497,259|1,932,472|2.7E+10|137,517|568,898|2.9E+09|
|District 0-10 km from GQ|1,111,031|2,801,811|1.4E+10|169,981|468,775|1.7E+09|
|District 10-50 km from GQ|376,292|875,257|3.8E+09|58,166|150,983|6.3E+08|
|District over 50 km from GQ|1,764,863|4,175,086|2.2E+10|320,015|781,194|3.3E+09|
|Urban Share|29%|36%|66%|42%|50%|63%|
|Nodal district for GQ|86%|91%|98%|87%|90%|93%|
|District 0-10 km from GQ|24%|29%|51%|36%|43%|51%|
|District 10-50 km from GQ|26%|31%|49%|32%|40%|56%|
|District over 50 km from GQ|27%|32%|57%|39%|44%|56%|
||B. Aver|age Levels of a|ctivity in 200|5 combining d|istricts within s|patial range|
|Total|15,557,212|29,639,174|1.5E+11|1,894,389|3,638,043|1.7E+10|
|Nodal district for GQ|702,054|2,453,966|4.0E+10|162,326|552,483|6.3E+09|
|District 0-10 km from GQ|4,945,968|9,301,020|3.5E+10|639,813|1,056,946|2.5E+09|
|District 10-50 km from GQ|1,619,015|3,136,999|1.3E+10|177,898|343,144|1.5E+09|
|District over 50 km from GQ|8,290,176|14,747,189|5.8E+10|914,352|1,685,471|6.5E+09|
|Urban Total|4,666,313|11,203,922|9.9E+10|678,982|1,678,906|1.1E+10|
|Nodal district for GQ|598,666|2,287,278|3.9E+10|135,523|508,670|6.1E+09|
|District 0-10 km from GQ|1,467,979|3,185,114|2.0E+10|192,290|401,740|1.3E+09|
|District 10-50 km from GQ|429,359|1,121,648|7.7E+09|68,442|172,716|9.5E+08|
|District over 50 km from GQ|2,170,310|4,609,882|3.2E+10|282,726|595,780|2.5E+09|
|Urban Share|30%|38%|68%|36%|46%|65%|
|Nodal district for GQ|85%|93%|97%|83%|92%|98%|
|District 0-10 km from GQ|30%|34%|58%|30%|38%|51%|
|District 10-50 km from GQ|27%|36%|57%|38%|50%|64%|
|District over 50 km from GQ|26%|31%|56%|31%|35%|39%|

Notes: Descriptive totals taken from the National Sample Statistics.

27
annually. This is in stark contrast with the population growth in rural areas that fell from 4.3 percent in the late 1970s to 1.4 percent in the 1990s. Hence, this rapid expansion of the urban population has caused increased pressures on public services in urban areas.

**Table 2.5:     Population Growth Rate per Residence Area in Pará, 1980/91/00**

|**Urban**|**Rural**|
|---|---|
|**1980**<br>49.0|51.01|
|**1991**<br>53.0|47.6|
|**2000**<br>66.5|33.5|
|_Souce_: SEOF/DIEED/BDE based on IBGE.||

Pará is home to more males than females. The gender distribution in the North of Brazil, including Pará, shows that 51 percent of the resident populations are males compared to 49 percent in Brazil as a whole. This leads to a gender ratio of 1.02 percent that is slightly higher than in total Brazil (0.97 percent) and the ration increases to 116.5 in rural areas Metropolitan Belém is the only region that is home to more females than males in Pará (see Table 2.6). The share of female-headed households in total households is increasing rapidly in Pará as elsewhere in Brazil. In 2000, 22.6 percent of all households were female headed, up from 12.6 percent in 1980.

**Table 2.6:          Population by Gender, Residence Area, and Literacy –** **<u>By Meso Regions Pará, 2000</u>**

|**Meso regions**|**Residen**|**t Popul**<br>**Reside**|**ation, G**<br>**nce Are**|**ender a**<br>**a**|**nd**<br>**Re**|**sident Populat**|**ion Aged 10 or Older**|
|---|---|---|---|---|---|---|---|
||**Total**|**Male**|**Femal****~~e~~**|**~~U~~rban**|**~~R~~ura****~~l~~**|**Total**|**Literacy**|
|Pará|6 192 307|<br>50.6|49.4|66.5|33.5|75.1|83.7|
|Baixo Amazonas|10.3|51.2|48.8|56.3|43.7|72.8|84.8|
|Marajó|6.1|52.1|47.9|38.8|61.2|68.4|68.0|
|Metropolitan Belém|33.7|48.2|51.8|93.8|6.2|79.6|93.6|
|Northeast Pará|23.8|51.7|48.3|47.4|52.6|72.4|76.7|
|Southeast Pará|19.3|51.8|48.2|63.7|36.3|74.3|79.3|
|Southwest Pará|6.9|53.0|47.0|47.3|52.7|74.4|78.4|

_Source_ : SEOF/DIEEST/BDE (IBGE, _Censo Demográfico_ 2000).

The share of children in the total population is falling. In 1980, children aged 14 and under accounted for 46 percent of the total population in Pará (Table 2.7). In 1991, the share of children aged 14 and under was down to 42 percent and in 2000 to 37 percent, slightly higher than in Brazil as whole (30 percent). This contrasts the development in the share of youth in the total population, which is increasing and in 2000, 22 percent of Pará’s population were between 15 and 24 years of age. The number of elderly dependents has not caught up with the reduction in children’s share in the population. In 2000, only 3.9 percent of the population was 65 or older (Table 2.7). This will have a significant effect on the state’s efforts to reduce poverty. For the next few decades, the 13
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# **Table 7: Baseline Estimates**

**Dependent variable = 1 if purchased insurance, = 0 if did not purchase.** Weighted probit model . Robust z-statistics in parentheses. Coefficients normalized to display marginal effects. Regression also includes village dummy variables (results omitted).

|||Marginal effects scaled<br>b oulation takeu|
|---|---|---|
||Marginal effects|y pp p<br>rate|
|_Utility function_|||
|Risk aversion|-0.011|-0.24|
||(1.84)*|(1.84)*|
|Ambiguity aversion|-0.000<br>(0.07)|0.00<br>(0.07)|
|Patience|0.009<br>|0.20<br>|
|_Beliefs about return on insurance_|(0.95)|(0.95)|
|Pessimism|0.004|0.09|
||(1.19)|(1.19)|
|_Basis risk_|||
|Use acc. rainfall to decide to sow|-0.000|0.00|
||(0.05)|(0.05)|
|% cultivated land used for groundut|0.027|0.59|
||(3.40)***|(3.40)***|
|% cultivated land used for castor|0.016|0.35|
||(2.84)***|(2.84)***|
|_Wealth and credit constraints_|||
|log(wealth in Rs, start of Kharif)|0.004|0.09|
||(1.19)|(1.19)|
|log(landholdings, start of Kharif)|0.002|0.04|
||(0.70)|(0.70)|
|**F-test: wealth and land [p-value]**|0.02**|0.02**|
|% of cultivated land that is irrigated|0.003|0.07|
||(1.12)|(1.12)|
|Household is constrained (1=yes)|-0.014|-0.30|
||(3.29)***|(3.29)***|
|_Familiarity with insurance and BASIX_|||
|BUA member (1=yes)|0.368|8.00|
||(4.70)***|(4.70)***|
|Credit from BASIX (1=yes)|0.066<br>(4.83)***|1.43<br>(4.83)***|
|Has other insurance (1=yes)|0.003|0.07|
||(1.35)|(1.35)|
|_Technology diffusion / networks_|||
|Progressive household|0.007|0.15|
||(2.12)**|(2.12)**|
|Member Gran Panchayat|0.081|1.76|
||(2.87)***|(2.87)***|
|No. other groups hh is member of|0.007|0.15|
||(3.51)***|(3.51)***|
|No. of well known households|0.000|0.00|
||(0.36)|(0.36)|
|No. well known hhs who bought insurance|0.006|0.13|
||(5.06)***|(5.06)***|
|_Other covariates_|||
|Education of household head (years)|0.001|0.02|
||(1.09)|(1.09)|
|log(age of household head)|-0.015|-0.33|
||(2.59)***|(2.59)***|
|Head spent whole life in village (1=yes)|-0.030|-0.65|
||(1.79)*|(1.79)*|
|Gender of household head (1=male)|-0.008|-0.17|
||(1.37)|(1.37)|
|log(household size)|0.002|0.04|
||(0.41)|(0.41)|
|Village dummies|yes|yes|
|Number of observations|752|752|
|Pseudo R<sup>2</sup>|0.44|0.44|

Robust z-statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 21
_7_ _ally, national soverelgnty over governmenc policy co a much larger degree of international cooperation in government policy. This could be the system shock of the 1990s and could get us out of the present problems, in an orderly fashion._ _If the transition is disorderly, I.e., inflationary, as Paul Armington suggests, or is achieved via a much more profound recession than presently on the cards, then I would have thought that the chances for growth in the 1990s are that much dimmer, that much worse, capacity or no capacity, because the policy background would be so much less favorable._ _Therefore, to my mind the issues are, on the one hand, this broad idea of whether the capacity is actually there, and growth can go beyond the 2-1/2 percent rate; secondly, what is the nature of the transition process. Is it liKely to be favorable, orderly, smooth or, as I fear, is it instead going to generate inflation or recession, or conceivably both? And thirdly, if that were the case, what other system shocks, if one believes in the system shock idea, would be necessary or desirable to get us back to a faster growth path at a later stage, towards che end of the 1990s?_ _MR. BANETH: Perhaps we can, in fact, take as not yet fully resolved the short-term cransition problem, and start the discussion on the nature of the short-run problem and how we could get out of it._ _I have a great deal of sympathy with Boltho's analysis and his findings, including the conclusion. It is that conclusion which generates profound pessimism. It is difficult to see the leadership or thb. policies or the social attitudes, which In fact will allow us to return to faster growth. There are a couple of issues that I would like us to discuss here. One is the social consequences in the industrial countries if we do not return to faster growth. Fifteen years ago there was a general expectation that if unemployment reaches, say, 5 percent in Europe, there is going to be a catastrophe. And to use a French expression, some people have referred to the Divine Surprise, that you could go above 10 percent and not get revolution._ _But I personally am not convinced that these things happen immediately and without lags, and the fact that we had only limited harmful political and social consequences in the short-run does not necessarily mean that this can continue._ _Obviously, for us of greatest interest is the impact of all this on developing countries. There was a lot of talk of decoupling growth. Is that likely to happen? What is the interaction both ways? How do the developing countries contribute to this process, negatively through debt crisis and other negative system shocks, or_ WPlPROC/SF:jsq
**Table 10. Determinants of Exit from the Nonfarm Enterprise Sector: Probit Results**

||**Marginal effect**||**Standard error**|
|---|---|---|---|
|**_Household Size_**|-0.003||0.004|
|Household size<br>||||
|**_Characteristics of the Manager_**<br>||||
|Managers’ age<br>|0.001||0.003|
|Managers’ age Squared/1000|-0.009||0.040|
|Manager’s Gender|-0.013||0.021|
|Managers’ Schooling|-0.017||0.011<br>|
|Managers’ Schooling Squared/1000|1.329||1.050|
|**_Location_**||||
|Rural town|-0.188***||0.018|
|**_Distances_**||||
|Distance to all weather road|-0.035***||0.008|
|Distance to the food market<br>|0.032***||0.012|
|**_Firm Size_**<br>||||
|Workers(log)<br>|-0.042<br>||0.036<br>|
|Days per year (log)|-0.069***||0.009|
|**_Activity_**||||
|-Hotels and restaurants|-0.065*||0.035|
|-Retail trade via stalls and markets|-0.015||0.032|
|-Services (services, manufac.|-0.089***||0.030|
|<br>-Whole sale trade|-0.074**||0.033|
|-Transport services|-0.177***||0.032|
|<br>-Manufacturing (excl: grain milling, food|-0.067***||0.025|
|-Grain milling|-0.087||0.060|
|-Retail (not stalls/mkts)|-0.008||0.030|
|**_Firm age_**||||
|Firm age (log)|0.129***||0.040|
|Firm age Squared<br>|-0.041***||0.011|
|**_Contribution to hh income_**||||
|% enterprise sales of total household<br>|-0.001**||0.000|
|**_Region_**<br>||||
|Tigray|-0.069***||0.009|
|Amhara|0.051||0.033|
|SNNP|0.059**||0.024|
|**_Location_**||||
|- in this community|-0.057**||0.023|
|**_Base_**||||
|-home, inside residence|0.071*||0.039|
|-traditional market|0.072||0.046|
|-mobile|0.122**||0.057|
|-roadside|-0.062||0.055|
|-shop in commercial area|0.030||0.071|
|<br>-other|0.195***||0.075|
|Number of observations||2,644||
|Pseudo R2||0.106||

_Note:_ Standard errors are clustered by enumeration area. Significance at the 10%, 5%, and 1% level is indicated by *,** and ***, respectively. The omitted activity is the manufacturing of food and beverages. The omitted region is Oromia. The omitted base category is “home, outside the residence”

-30-
TABLE **2** Comparison **of** the Ratio **of Fems.le** to **Male Age-specific**<sup>Mortality Rates</sup>

|.49.~~<br>BANGIADESH<br>93<br>_*._<br>.<br>12-.<br>SYRIA.0.9<br>_.._|
|---|
|ALGERIA-<br>0.91<br>_.02_|
|- MALAYSIA.<br>0.83<br>1.00|
|MALI<br>0.81<br>0.94|
|CoLUMBrA<br>0.80<br>0.91-|
|PHILLIPINES<br>0.74<br>0.90|
|BOLIVIA.-<br>0. 86<br>0.90|
|MALAW<br>.<br>0.85<br>0.89|
|JORDAN<br>0.87<br>0.83|
|SINGAPORE<br>0.74<br>0.80|
|MAURITIUS<br>O.66<br>0.58|

Source: United Nations Demographic<sup>Yearbook, 1988</sup>
to the prevailing nominal rates.<sup>23</sup> This exercise is reported in Table 3. VAT productivity<sup>24</sup> is comparable in Latin America to that observed in other world regions, including rich countries, although the regional average conceals significant cross-country variation.<sup>25</sup> For income taxes, however, Latin America places far behind industrial countries, even below Sub-Saharan Africa. This suggests that the problem behind Latin America’s low income tax receipts is primarily one of narrow tax bases rather than tax rates.

Tax evasion is rampant across the region, although precise figures are obviously hard to calculate. Some recent estimates for Argentina, Brazil and Chile place evasion rates at 50 percent or higher in the case of the personal income tax, and around 40 percent for the corporate income tax. Even for the VAT the estimates are quite large. Some rough calculations suggest that a 30 percent reduction in evasion would increase tax revenues by 17 percent in Argentina, 14 percent in Brazil and 12 percent in Chile (Pessino and Fenochietto, 2006).

**Table 3. Tax Collection and Tax productivity**

|Region|Collection(%|of revenue)|Tax Pro|ductivity|
|---|---|---|---|---|
||Income|VAT|Income|VAT|
|East Asia Pacific|34.38|25.45|0.15|0.36|
|Latin America and the Caribbean|18.27|36.83|0.08|0.33|
|Middle East & North Africa|16.93|29.05|0.07|0.32|
|OECD|25.87|25.96|0.15|0.34|
|South Asia|23.02|29.61|0.05|0.18|
|Sub Saharan Africa|23.00|33.10|0.11|0.33|

Source: Author’s calculations based on Eichhorn (2006), WDI 2006, OECD tax Database, Doing Business Database.

The practice of tax evasion in Latin America is encouraged by weak tax administrations across the region. Indeed, opinion polls show that large majorities of individuals perceive tax collection as largely arbitrary and unfair -- only 23 percent of those surveyed by Latinobarómetro in 2003 thought tax collection was ‘impartial’. The culture of evasion is likely further promoted by the overwhelmingly negative views about the state’s capacity to spend wisely – in the same survey, just 15 percent of respondents believed that tax revenues would be put to good use.

A key factor behind poor tax compliance (and hence tax collection) is informality, which is pervasive in Latin America. The size of the ‘shadow economy’ relative to the formal one is estimated to average around 40 percent, the highest figure across world

> 23 In the case of the income tax, our productivity measure is based on the top marginal rate.

> 24 The calculations in the table use GDP to approximate the VAT base. Using aggregate consumption instead yields similar qualitative results.

> 25 Across the region, VAT productivity ranges from less than 25 percent in Mexico to over 50 percent in Honduras (Martner and Aldunate 2006).

12
Page 46

# BOX: 11

Agricultural Extension for Women Farmers In Kenya. Nigeria, and Yemen A. R.

Most women in Kenya are farmers. Agriculture is the core of Kenya's economy, smallholders are the core of agriculture, and women are the core of the smallholders. The national agricultural extension system Kenya is instituting is a model of a program that reaches both women and men. Based on the "training and visit" system, it features regular visits to farmers in their fields and strong links between the farmers, extension agents, and researchers who develop extension advico. In areas where extension reaches farmers regularly, yields of maize (the main food crop) have risen 20-40 percent (with wide variation). The effort is still recent, but a government study reports that about half of Kenya's extension agents work primarily with female farmers. Male agents in Kenya often prefer to work with women becuase they actually do the farming, make many day-to-day decisions, and willingly adopt advice. Having female agents helps, but male agents can usually work with women farmers if care is taken to respect cultural traditions. Some male agents who are accustomed to working with male farmers must be encouraged to work with women.

Kenya's experience demonstrates that groups of 15 to 20 women will meet regularly with extension agents, but that such large groups of men are reluctant to do so. The government believes that by working with more women's groups, extension could probably double its reach and reduce its costs through savings in agent travel and increased time on task. Results so far suggest that by reaching female farmers, the extension program has in^reased both women's income and food security by increasing agricultural output.

# Source: World Bank, 1989a

Many women in Nigeria are also farmers, and the Government of Nigeria is also instituting measures to bring regular extension services to both women and men through a "training and visit" approach. Because of the wide variations in culture in Nigeria, the approaches taken differ considerably from place to place. In Imo and Kaduna states, for example, women who formerly worked in the extension service as specialists in home economics are being given agricultural training so they can work as regular extension agents with a broad knowledge of women's concerns. Female farmers can work more easily with male extension agents in Imo than in Kadurna, which will have to rely more heavily on female agents.

# Source: PHRWD UNDP Project

In Yemen A. R., many women have begun to farm more actively since their husbands have emigrated to work in the oil fields of nearby countries. These women cannot work with male extension agents, so the Government is developing a cadre of female agents. Cultural restrictions make it difficult to post the female agents in rural areas so approaches are being developed to provide appropriate shelter and transport mechanisms for the female agents. It is much more difficult to bring extension to women in such circumstances.

ISource:_ World Bank Yemen SURDP Project