rafmacalaba/datause-encoder-data huggingface dataset:
# 3. Access to services and resources

# a. Agricultural extension

Agricultural extension systems are meant to link agricultural research and farmers. By providing information on technologies, production methods. types of crops, and cultivation requirements and by motivating farmers and helping them overcome their constraints, agricultural extension is aimed at improving agricultural productivity and raising output.

The present system of agricultural extension in Kenya, under the direction of the Ministry of Agriculture, was initiated in 1983. The reorganization of the system in that year cam about because its impact prior to that time was extremely limited and the program was targeted primarily to male farmers (World Bank, 1989). Extension workers tended to work mainly with male farmers because they were perceived as being the decision-makers and because women's other time commitments precluded their participation in training activities (World Bank, 1983). Some of criticisms of the agricultural extension system stressed that the advice did not meet the needs of farmers and often required higher inputs of labor (Potash, 1985; Ackello-Agutu, 1987). A study of smallholder farmers in Western Kenya found a "persistent and pervasive bias in the delivery of agricultural services [to women farm managers]" (Staudt, 1976 cited in IID, 1986). The present Training and Visit system (T&V) is said to have been "designed and implemented with the deliberate intention of reaching women farmers" (World Bank, 1989).

Under the T&V system, some 3,500 frontline extension agents serve approximately 2.6 million farm families in Kenya's High Potential Districts (World Bank, 1988c). These frontline agents are linked to staff at the Divisional, District and Provincial levels. The frontline agents visit selected contact farmers and women's groups according to a fixed fortnightly schedule. Follower farmers are also supposed to attend these meetings and facilitate dissemination of messages to the community. Subject matter specialists train frontline agents once each fortnight and provide appropriate messages for dissemination.

According to an assessment by the Ministry of Agriculture, women contact farmers (those farmers specifically selected by extension staff to be the main contacts with frontline agents and to serve as a model to so-called "follower farmers") constitute less than 10 percent of all contact farmers. While problems remain with contact farmer selection, it has been found that increasing numbers of women serve as de facto contact farmers because their husbands are not present on the farm on a full-time basis. It is estimated that more than two-thirds of the farmers actually met by a6ents -- contact farmers, wives of contact farmers, women's groups -- are women (Ministry of Agriculture, 1986).

In surveys conducted in Meru and Muranga, it was found that male extension agents could work effectively with women on a one-to-one basis. This is particularly true of younger agents who often prefer to work with women farmers. In Meru, information gathered from the surveys suggests that about 18
costs and the gains from Table A3. We consider the total net gains for the region and do not consider the case when each country would have to pay for the number of kms crossing its territory. Using the low-bound cost estimates, the net gains for the region range from 0.52% when considering only the countries along the corridors to 0.44% when considering all countries. Using the high-bound cost estimates, the net gains for the region range from 0.07% when considering only the countries along the corridors to 0.05% when considering all countries. When only investing in the road corridor, the net gains are almost null, and possibly negative when including maintenance costs.

The net gains increase when including reduction in transit delays along the corridor only. We assume that the costs of such interventions is negligible compared to the estimated cost of the Dakar-Lagos corridor. Using the low-bound cost estimates, the net gains for the region range from 1.28% when considering only the countries along the corridors to 0.83% when considering all countries. Using the high-bound cost estimates, the net gains for the region range from 1.16% when considering only the countries along the corridors to 0.76% when considering all countries. Additional investments in border infrastructure are therefore necessary to get a positive return on investment for the whole corridor. Larger gains are expected with further removals of border frictions.

The Dakar-Lagos corridor can benefit all countries in the region. We consider the net gains from reducing transit delays for all countries as well as reducing by half final border time. The net gains become much larger when significantly reducing border frictions.

17
Governments in many countries around the world are designing reforms in their judicial systems or thinking about how to build consensus or coalitions for reforms. Some interesting policy questions arise –why is the legal and judicial system not relevant for some citizens and in some countries? When and how should one strengthen the roles that judicial systems play in economic development? What does the study of institutional reform in general, and judicial system reform in particular, teach us about the design of systems in various countries?<sup>2</sup> To answer these questions, it is necessary to think not only about the final goal but, the capacity of countries to implement certain reforms and the possible need for a graduated approach.

This paper presents some general conclusions regarding institutional development (see World Bank, 2001), which are also relevant for the judiciary and some of the evidence which indicates how critical a role the formal legal system can play to support the development of private business.<sup>3</sup> It discusses why it is important for governments and citizens to take an active and practical approach to judicial system development, and the value of a comparative and cross-country, empirical approach for reformers, illustrating this point by drawing upon a recent project undertaken by the World Bank and others.<sup>4</sup> One of the main conclusions that has emerged from the study of institutional design and institutional change is that despite the prevalence of international norms and the popular notion of “best” practice, a great part of effective institution building relies on

> 2 Recent thinking underscores how looking across different types of institutions rather than confining oneself to the study of one particular institution yields useful lessons.

> 3 Institutions are the informal and formal rules that guide behaviour. Sometimes “institutions” is also used to refer to organisations and organisational structure.

> 4 Parts of this paper are based on the WDR 2002, and on a project conducted jointly by Harvard University, Lex Mundi and the World Bank.  This project is described in Djankov et al, 2003.

3
interviewed in both surveys, this reduces the probability of a discrepancy but only for the yes‐no combination. More surprisingly, the introduction of explanatory cards does not affect the probability of observing a discrepancy. This suggests that overall most of the discrepancies are not likely to be the result of lack of understanding of innovation concepts, and other factors may be at play.

One of these factors is the recall period. The longer it takes for the firm to be re‐interviewed, the more likely are discrepancies to arise. Another important factor to increase the likelihood of discrepancy, at least for the combination yes‐no, is the number of interviews for which the same individual supervises over as a proxy for the quality of the enumerator interview. Interestingly, the perception of the enumerator about the quality of the first interview is not a good predictor of observing discrepancies.

Regarding the context of the firm and potential survey fatigue, those firms that have payed bribes are less likely to have a no‐yes discrepancy across surveys. As we saw in table 1 in majority of cases the change from no to yes was confirmed as innovation. The “no” answer provided in the short questionnaire may have been then originated by the desire to protect the innovation or by a reluctance to share correct information if considered as sensitive. In this case one potential explanation is that firms that are candid about paying bribes are more open also to disclose innovation and therefore less inclined to reply no in the short questionnaire. On the other hand, firms experiencing positive growth in their performance represented by employment growth, appear more likely to a no‐yes discrepancy. The average number of meetings with officials as a proxy for survey fatigue is not statistically significant explaining these discrepancies. Finally, large firms and foreign firms, (in one specification) are more likely to have one type of discrepancy (no>yes).

# **5.2 Explaining the quality of the responses in the short questionnaire**

One important element to consider when interpreting these results is that in a significant number of cases that answered yes in the second questionnaire are then reclassified after the verification process as non‐ innovators. Therefore, looking at the discrepancy between surveys is not enough to assess the accuracy of the answers provided in the short questionnaire. For this, we need to look at the differences between the answers in the short questionnaire and the cleaned responses in the long questionnaire.

Table 7 and 8 show the estimates of the probit and multinomial logit model for the determinants of providing an inaccurate response in the short questionnaire. Since we are focusing on the ability of the short questionnaire to measure accurately innovation we focus on the potential determinants of errors in the first interview. As such, the time elapsed between interviews or whether the same person 21
the causal effect of Sanskritization, however, is quite difficult since there are many possible mechanisms linking changes in survival rates to caste status.

# **2.2 Perspectives from Field Studies**

In recent years a large literature by anthropologists, sociologists and economists has analyzed qualitiative and quantitative data on the relationship between caste and gender. A series of ethnographies have largely confirmed that in most parts of rural India upper-caste women are more likely to practice _purdah_ (female seclusion), are more likely to use the veil, and face significant restrictions on their mobility and labor force participation opportunities (Srinivas 1977, 1979; Chen, 1995, Kapadia, 1995; Jeffrey and Jeffrey 1996; Seymour 1999; and many others). These studies have also noticed that increased caste status is associated with a greater subordination of women (Hutton 1946; Srinivas 1977).<sup>10</sup> Field studies have also provided insights into the rigid sexual division of labor by – caste and class. Boserup (1970: 69 70), for example, uses data on female labor force participation rates from several states of India to highlight the division of labor in agriculture; ploughing is a male task but post-agricultural processing and preservation of grain are female tasks, and almost all female labor is drawn from the poorest castes/classes. In her comparison of India and Bangladesh, Chen (1995) reports that in India, villagers display high levels of preoccupation with ensuring status-appropriate work for women. They rank castes by “...whether they allow women to work in the following activities: only within their courtyards/homesteads, only on their own farms, only within the courtyards or homesteads of others, only at the farms of others, in other activities within the village, or in other activities outside the village. Using this ranking scale, the more secluded the woman, the higher her household’s status or prestige” (Chen 1995: 46). Mencher (1988) conducts detailed analyses of female labor force participation in paddy cultivation in Tamil Nadu and highlights the reluctance of upper-caste women to enter the labor force, even when facing conditions of severe poverty that place them in a ranking of income below the SC and ST women in the village.

The sexual division of labor is also seen in non-agricultural occupations. In artisan jatis such as potters and weavers, men specialize in specific skills, while female household members are active participants in every other non-male stage of production (David and Kramer 2001). Some studies have also found evidence that greater opportunities among the lower-caste women can translate into empowerment. Kapadia (1995), for example, finds that among the Pallar of Tamil Nadu—a highly impoverished, landless SC who largely perform agricultural labor—women form a major share of the local labor force. They enjoy significant independence in the domestic domain as well as in negotiations with

> 10 A review of this literature is found in Liddle and Joshi (1986) and Mohanty (2004).

8
|Average for all 3 years|68.36|42.21|57.79|
|---|---|---|---|
|**Manufacturing and processing**||||
|2000|69.17|38.45|61.55|
|2005|69.55|21.57|78.43|
|2010|44.44|41.79|58.21|
|Aggregate|58.98|34.49|65.51|
|**Trade**||||
|2000|55.61|63.74|36.26|
|2005|55.33|35.33|64.67|
|2010|30.94|38.49|61.51|
|Average for all 3 years|47.06|47.58|52.42|
|**Transport**||||
|2000|56.86|10.77|89.23|
|2005|64.34|13.7|86.3|
|2010|29.97|23.22|76.78|
|Average for all 3 years|45.21|15.58|84.42|
|**Misc. small activities**||||
|2000|54.25|25.96|74.04|
|2005|82.10|40.37|59.63|
|2010|49.29|42.30|57.70|
|Average for all 3 years|57.25|35.06|64.94|
|**All activities**||||
|2000|66.92|40.46|59.54|
|2005|75.81|33.47|66.53|
|2010|50.14|44.06|55.94|
|Average for all 3 years|63.48|39.01|60.99|

Source: HIES 2000, 2005, 2005 46
Table 4: Marginal probability effects for working-age women based on usual status

|Dependent Variable: Labor Force|Participation|||||
|---|---|---|---|---|---|
||Model A|Model B|Model C|Model D|Model D1|
|Age|0.045***|0.046***|0.046***|0.052***|0.052***|
|age_sq|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|
|schooling|-0.034***|-0.033***|-0.033***|-0.038***|-0.031***|
|schooling_sq|0.002***|0.002***|0.002***|0.003***|0.002***|
|marital_dummy|-0.055***|-0.058***|-0.058***|-0.060***|-0.064***|
|log_land|0.040***|0.034***|0.033***|0.007***|0.003*|
|log_land_sq|0.003***|0.004***|0.004***|-0.001**|-0.001***|
|log_hhsize|-0.056***|-0.052***|-0.053***|-0.011|-0.005|
|children_under_6|-0.066***|-0.080***|-0.080***|-0.085***|-0.099***|
|children_above_6|0.069***|0.060***|0.060***|0.117***|0.099***|
|female_adult|0.033|0.031|0.033|0.040|0.079***|
|female_dependent|0.057***|0.056***|0.056***|0.046**|0.098***|
|male_dependent|0.135***|0.140***|0.141***|0.140***|0.185***|
|female_hh_dummy|0.133***|0.133***|0.134***|0.141***|0.160***|
|max_schooling|-0.012***|-0.009***|-0.010***|-0.012***|-0.008***|
|st_dummy|0.220***|0.202***|0.200***|0.135***|0.101***|
|sc_dummy|0.093***|0.077***|0.077***|0.077***|0.072***|
|obc_dummy|0.072***|0.065***|0.066***|0.050***|0.047***|
|hindu_dummy|-0.057***|-0.056***|-0.056***|-0.023***|-0.049***|
|muslim_dummy|-0.173***|-0.165***|-0.164***|-0.095***|-0.118***|
|survey|-0.110***|-0.116***|-0.115***|-0.003|0.005|
|rank==2||-0.022***|-0.020***|0.012**|0.008*|
|rank==3||-0.044***|-0.032***|0.024***|0.030***|
|rank==4||-0.117***|-0.114***|0.084***|0.082***|
|rank==5||-0.167***|-0.165***|0.030***|0.022**|
|rank==6||-0.154***|-0.157***|0.033*|0.018|
|gap|||-0.052***|-0.051|0.086***|
|all_farmers_share||||1.617***|1.834***|
|all_non_farm_self_share||||1.645***|1.865***|
|all_casual_share||||1.525***|2.066***|
|all_non_farm_regular_share||||1.559***|1.765***|
|District Fixed Effects|No|No|No|Yes|No|
|Number of Observations|317046|317046|317046|316978|317046|
|Wald Chi Sq|13869|15716|15831|33169|26706|
|Pseudo R2|0.13|0.14|0.14|0.28|0.26|
|note: ***p<0.01,**p<0.05,*p|<0.1|||||

In the estimation, the fixed location-specific effects intended to capture the tightness of the labor market ( Vj −Uj ) are defined at the district level. This choice is guided by computational limitations and will be revised in subsequent versions of the paper. Introducing a large number of fixed effects in a Probit regression can bias the estimates. To account for this risk, model D was 22
There is likely to be heterogeneity in impacts within one area at a given time, which can also fuel debate. Consider the different views of Akram-Lodhi (2004, 2005) and the Center for Rural Progress (2004). Both draw (in part) on field work in the Mekong Delta, and at about the same time; yet they come to very different conclusions, one favoring PILE and the other not. This may partly reflect horizontal inequalities in economic and social change, whereby similar people _ex ante_ fare differently; two researchers can then come back from field work at the same time in the same area with very different stories, depending on who they talk to. This speaks to the need for representative survey data when attempting to form generalizations.

Later we will see what light survey data can cast on these issues. However, the debate will not be resolved by data alone. There are also differences in the value judgments made. Some observers see rising land inequality as a bad thing _per se_ , even if it comes with falling poverty. Here we will assume that, while inequalities in various dimensions may be instrumentally important to absolute levels of living, the latter are the overriding consideration.

Differences in the attribution of empirical observations to causes can also play a role. Suppose that a household suffers a serious health crisis, and has to sell its land. The household becomes both poor and landless. This would not have happened if there had been a ban on selling land. But the existence of a land market did not cause the poverty; indeed, the absence of a market could entail even greater poverty, by effectively de-valuing the household’s main asset.

# **3. Land markets, occupational choice and welfare**

The issues in this debate can be captured in a transparent way using a relatively simple model of how the occupational choices of poor farm-households are affected by a policy reform that removes restrictions on land markets. The model essentially pits the gains from expanded choice and specialization against the reform’s “second-round” effects on other income sources, 8
**Table 2: PSM estimates of the impact of Global Forum membership on tax revenue**

||**1st **<br>**nearest**|**3rd **<br>**nearest**|**Rad**|**ious mat**|**ching**|**Kernel**<br>**matching**|<br>**llr**<br>**matching**|
|---|---|---|---|---|---|---|---|
||**D**<br>|**ependent**<br>|<br>**r=0.045**<br>**variable:**<br>|<br>**r=0.09**<br>**Tax reven**<br>|<br>**r=0.18**<br>**ue in pro**<br>|<br>**portion of**<br> <br>|<br> <br>**GDP**<br>|
|ATT|0.094|0.102|0.086*|0.070|0.089**|<br>0.083|0.083*|
||(0.080)|(0.066)|(0.052)|(0.046)|(0.043)|(0.051)|(0.050)|
||||**Qual**|**ity of the**|**matching**|||
|Pseudo R2|0.030|0.013|0.007|0.009|0.015|0.008|0.030|
|Standardized bias (p-value)|0.425|0.882|0.976|0.954|0.820|0.967|0.425|
|Rosenbaum upper bound sensitivity||||||||
|test|1.1|1.1|1.1|1.1|1.1|1.1|1.1|
|||<br>**Se**|<br>**nsitivity a**|<br>**nalysis of**|<br>**the main**|<br>**results**||
|ATT||||||||
|[1] Controlling for corruption|0.139|0.112|0.136*|0.129**|0.112**|<br>0.131*|0.135*|
||(0.093)|(0.082)|(0.071)|(0.060)|(0.050)|(0.071)|(0.074)|
|[2] Controlling for tax compliance|0.082|0.054|0.113**|<br>0.068|0.089*|0.098*|0.073|
||(0.078)|(0.067)|(0.056)|(0.052)|(0.049)|(0.057)|(0.067)|
|[3] Controlling for human capital|0.172*|0.118|0.111|0.124*|0.115**|<br>0.120*|0.161**|
||(0.091)|(0.082)|(0.069)|(0.067)|(0.058)|(0.068)|(0.068)|
|[4] Controlling for first exchange of<br>information|0.125|0.119*|0.083|0.070|0.089**|<br>0.079|0.084|
||(0.076)|(0.068)|(0.055)|(0.049)|(0.044)|(0.051)|(0.054)|
|[5] Controllin for the commitment to||||||||
|g<br>the AEOI|0.071|0.086|0.079|0.066|0.089**|<br>0.078|0.083|
||(0.081)|(0.061)|(0.054)|(0.049)|(0.044)|(0.055)|(0.053)|
|[6] Controlling for remittances|0.090|0.059|0.060|0.085*|0.101**|<br>0.070|0.075|
||(0.080)|(0.064)|(0.053)|(0.048)|(0.045)|(0.052)|(0.051)|

Note: robust standard errors in brackets; *p<0.10, **p<0.05, and ***p<0.01 Source: authors’ elaboration

# **5. Conclusion and policy implications**

The analysis conducted in this paper provides empirical evidence on the impact of cross-border exchange of information for tax purposes on tax revenue for African economies. The results confirm that EOI levels the playing field in terms of information asymmetry and boosts tax collection. More specifically, joining the Global Forum’s endeavors for tax transparency can pay off, with extra revenues potentially increasing from 5% to 19% worth of GDP. Over and above the monetary gains for administrations, such effects signal increased fiscal fairness and have the possibility to impact tax morale – an effect not included in the estimate.

13
to equilibrate the credit market-with foreign borrowing taken as exogenous.

# **3.9** Public Sector

The public sector in our framework consists of the government and the central bank. We consider them in turn and relate changes in official reserves to the balance of payments.

## **3.9.1 Central Bank and the Balance of Payments**

From the central bank's balance sheet, its net worth, _NWCB,_ is given by where _DCG_ denotes domestic credit to the government, _FF_ the stock of foreign reserves, and _MB_ the monetary base. Assuming that capital gains and losses are not monetized, changes in the monetary base reflect changes in credit to the government, as well as changes in official reserves: Assuming no operating costs, net profits of the central bank, _PROFCB,_ are given by the sum of interest payments on loans to the government, and interest receipts on holdings of foreign assets: where _IFG_ is the interest rate on foreign loans to the government. We assume that net profits of the central bank are transferred to the government.

Whereas domestic credit to the government, _DCG,_ is treated as an exogenous policy variable, the accumulation of foreign reserves depends on the balance of payments (see Figure 5), as any current account surplus (or deficit) must be compensated by a net flow of foreign capital: 45
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