rafmacalaba/datause-encoder-data huggingface dataset:
Third, the magnitude of discrepancies between different methods varies by crop, with larger difference for continuous crops like cassava and bananas. Moreover, econometric analysis of the difference between different methods to estimate output allows identification of household characteristics that tend to be associated with systematically larger differences. We find that smaller household size, higher levels of productivity (yields), larger cultivated areas, and to some extent higher levels of education and wealth are all associated with lower differences between methods. As these characteristics are also associated with higher levels of drop-out or non-response for diaries, there may be scope to exploit complementarities between different methods, with diaries producing particularly good estimates of output for continuously harvested crops obtained by households with limited amount of formal education at the tail end of the distribution.

The paper is structured as follows: Section two highlights challenges and our approach by reviewing pros and cons of different ways of collecting agricultural production data. This is used to describe why diaries were introduced, how they were implemented, on a trial basis, in Uganda, the challenges they need to respond to.. Section three provides empirical evidence regarding diary coverage and respondent fatigue. Section four compares results obtained by different types of survey both at a descriptive and an econometric level. Section five concludes by pointing towards implications for policy and research.

# **2. Approach and challenges**

To motivate the diary approach, this section briefly reviews pros and cons of different methods used to collect information on agricultural production in situations such as the Ugandan one where large part of crop output is made up of continuously harvested crops. In fact, crops like cassava and banana are generally harvested in small quantities over long periods of time, thus making recall over several months rather difficult. We then describe the methodology used to administer the diary approach in parallel to the 2005/06 National Household survey and the potential biases it was expected to reduce.

# **2.1 The challenge of collecting reliable information on continuously harvested crops**

Data on agricultural production can be obtained through a range of different instruments and the key issue confronting statisticians and researchers is how to do so at an acceptable trade-off between cost and potential measurement errors. Crop cutting, whereby enumerators actually harvest and physically weigh output from sample plots, is generally considered to be the most reliable and precise way of measuring agricultural output, has two disadvantages. First, the need for continued field presence by a skilled staff increases costs to a point where broad application of this method may become unaffordable. Tightening budgetary constraints faced by relevant line agencies do indeed often result in poorly administered crop 3
# **Box 3: Alternative Regulatory approaches**

**Performance-Based Regulations—** specify required outcomes or objectives rather than the means by which they must be achieved.  Thus firms and individuals can choose processes that are more efficient and less costly, which promotes the use of new technology on a broader scale.  Such type of regulation is increasingly used in health, safety, consumer protection, and environmental regulation.  Drawbacks include measurement problems related to desired outcomes, higher administrative and monitoring costs, greater responsibilities for small companies to develop appropriate compliance strategies.  Most countries have resorted to the use of guidelines or “safe harbors” in conjunction with performancebased regulation.  Guidelines provide information on appropriate compliance strategies, while safe harbors allow the benefits of certainty of compliance associated with prescriptive regulation to be attained, while also allowing more innovative firms to take advantage of the benefits of such regulation.

**Process Based Regulations** —require businesses to develop processes that systematically control and minimize production risks. These processes are used in businesses with multiple and complex sources of risk, where ex post testing of the product is either ineffective or expensive.  Process based regulation is predominantly used in health, food safety, and environmental regulation.

**Co-regulation—** businesses take the lead in regulation through endorsement and adherence to codes of practice.  This type of regulation is highly cost effective for the government.  Drawbacks include the possibility for encouraging anti-competitive activities by business or professional organizations.

**Economic Instruments—** taxes, subsidies, tradable permits, vouchers and the like.  Economic instruments allow businesses to achieve regulatory goals in the least costly manner and provide market incentives which reward the use of innovation and technical change.

**Information and Education—** most widely used approach to regulation in OECD member states; empower consumers to adopt actions or make informed choices to change their behavior.  Examples include campaigns aimed at reducing speeding when driving, anti-litter behaviors; reducing the use of drinking water; eco-labeling of products.

**Guidelines** —issued by regulatory authorities, setting out processing or providing interpretations to aid understanding of government objectives by businesses and citizens.  Guidelines may accompany existing regulations, but also are increasingly used as stand-alone documents.  Guidelines, for example, are widely used in the area of consumer protection in Denmark.

**Voluntary Approaches** —initiated by industries, sometimes formally sanctioned or endorsed by government.  They include voluntary initiatives, voluntary codes, voluntary agreements, and selfregulation.  An example of a voluntary arrangement is the chemical industry’s Responsible Care Program, used in 40 countries, which promotes the adoption of rules for sound environmental management practice.

_____________ _Source_ : OECD 2002.

20
# **4.2.    Collective Action Capacity Index**

To apply the model empirically, we first develop an index of collective capacity that captures the ability to provide public goods. Since our ultimate goal is to determine whether collective capacity to provide local public goods has positive impacts on agricultural productivity and welfare, we create an index based on a range of activities tied to public good provision within the community instead of focusing on any one public good in particular (McCarthy et al., 2004; Narayan and Pritchett, 1999). In large part, this is due to the fact that we have information on 17 types of public goods, all of which may affect communal agricultural performance and welfare directly or indirectly, and it is not feasible to take into account the endogeneity associated with 17 different dimensions.

The collective capacity index is based on the raw data collected through the IHS3 Community Questionnaire Module CH: Community Needs, Actions and Achievements. In Module CH, the focus group was probed regarding the community’s need for 17 distinct public goods during the past five years, and was given an option to specify any other public good that was not captured by the list and that the community may have needed. The list of public goods were calibrated through piloting ahead of the actual field work implementation, and included (i) road: construction, (ii) road: maintenance, (iii) bridge: construction, (iv) bridge: maintenance, (v) primary school: construction, (vi) primary school: maintenance/ improvement, (vii) secondary school: construction, (viii) secondary school: maintenance/ improvement, (ix) health center/ clinic/ dispensary: construction, (x) health center/ clinic/ dispensary: maintenance/ improvement, (xi) piped water/ boreholes/ wells: construction, (xii) piped water/ boreholes/ wells: maintenance/ improvement, (xiii) orphanage: construction/ maintenance/ improvement, (xiv) maize mill: construction/ maintenance/ improvement, (xv) public transportation: initiation/ improvement, (xvi) agricultural/ fishery/ livestock extension services, and (xvii) law enforcement: initiation/ improvement. We were able to classify into one of the 17 categories all other public goods that were additionally specified by the focus group.

For each public good, the module included a series of questions to capture the specific actions that the community may have taken to address the need. The actions that Module CH inquired about included (1) mobilizing manpower among community members, (2) mobilizing financial resources among community members, and (3) submitting a proposal to be funded through various external mechanisms, namely MASAF/Local Development Fund, Traditional Authority, District Assembly/Commissioner, Member of Parliament, and nongovernmental/religious/international organization. The focus group was also given an option to specify any other action that the community may have taken. Following these questions, the focus group was asked whether a particular public good need was addressed/being addressed, and _conditional_ on a positive response, provided information on whether the community 16
as the provision of many public and merit goods, as well as of social protection, is shifted to the private sector.

Finally, the data confirm that for **_Group_** _4_ there is little scope for expanding the size of the government. To the extent that the current provision of public goods and services is unsatisfactory, an answer can be found only through speeded-up reform of programs, within the continued strictures of the existing budget constraints.

**Figure** 3:

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Indian height deficit by an increasing association between sanitation and height.

# **2.3 The gap between South Asia and sub-Saharan Africa**

Although people in South Asia are, on average, richer than people in sub-Saharan Africa, children in South Asia are shorter, on average, and open defecation is much more common there. How much of the South Asia-Africa gap can sanitation statistically explain, at the level of country averages?

Table 5 estimates regressions in the form of equation 1, with the sample restricted to countries in South Asia and sub-Saharan Africa and with an indicator variable added for data from South Asia. Of the 140 DHS surveys in figure 1, 11 are from South Asia and 78 are from sub-Saharan Africa. In these data, children in South Asia are, on average, about one-third of a height-for-age standard deviation shorter.<sup>10</sup> How do further controls change the estimate of this South Asia indicator? Merely linearly controlling for open defecation reduces the gap by 30 percent from 0.360 to 0.253. Controlling, instead, for the number of people openly defecating per square kilometer (the product of population density and the open defecation rate, column 4) reduces the coefficient by 83 percent to 0.061. Column 5 verifies that this result is not merely a misleading effect of population density, controlling for which increases the gap.

Pairs of columns 6-7 and 8-9 demonstrate the statistical robustness of the explanatory power of the density of open defecation. After controlling for the log of GDP per capita, adding a further control for open defecators per square kilometer explains 73 percent of the (larger) remaining gap. The density of open defecation reduces by 92 percent the height

> 10 Jayachandran and Pande (2012), using individual-level DHS data from Africa and South Asia, suggest that first-born South Asian children are _taller_ than first-born African children. The country-level data studied here, however, show no similar reversal. If country means are computed using only first-born children, I find that South Asian children are 0.22 standard deviations shorter ( _s.e._ = 0 _._ 05), a reduction but not an elimination of the 0.36 gap in table 5. In this sample of country means of first-borns, the gap falls to 0.15 with a control for open defecation and to 0.08 with a control for open defecation per square kilometer. In the full sample of country-level means of first-borns, analogously to column 1 of table 1, moving from an open defecation rate of 0 to 1 is linearly associated with a decline in height for age of 1.11 standard deviations.

14
Figure 1: Economic Growth and Rate of Return to University Education in Indonesia <!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>-51990 1992 1994 1996 1998 2000 2002<br>-10<br>-15<br>-20<br>-25<br>Economic growth rate Rate of Return to University Education<br><!-- End of picture text --> Source: Computed from Psacharopoulos and Patrinos (2018); Montenegro and Patrinos (2014) Figure 2: Economic Growth and Rate of Return to University Education in Pakistan <!-- Start of picture text --> 20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1991 1996 2001 2006 2011 2016<br>Economic Growth Rate Rate of Return to University Education<br><!-- End of picture text --> Source: Computed from Psacharopoulos and Patrinos (2018); Montenegro and Patrinos (2014); and authors’ calculations.

5
the populations from which they are drawn. The following case involving domestic abuse represents the attitudes visible in some cases: Sri lived in a simple house with her husband on one of the main roads in the urban kecamatan of Pahandut, near the center of Palangkaraya, Central Kalimantan. According to Sri’s sister Eka, when engaging in sexual intercourse, Sri’s husband would be extremely violent, hitting and biting her. Unable to tolerate it any longer, Sri left her husband and told her father what was happening. They reported to problem to the police. After two weeks of inaction, the police suggested that the problem be resolved through the _damang_ , the traditional customary leader.

> Under Dayak _adat_ , if a wife leaves her husband, the assumption is that she is seeking a divorce. So, when the families met before the _damang_ , Sri’s husband requested a divorce. Dayak custom also dictates that on divorce, property and goods must be transferred to the wife, in accordance with a written pre-nuptial agreement. Sri did not want a divorce, just for the violence to stop. However, a divorce agreement was written up, the husband signed and she felt compelled to sign as well. This was partly driven by threats from the husband’s lawyer that she would be fined Rp 100 million for absconding. Sri, ignorant of the law and unable to afford legal counsel, knew no better. ‘It’s hard when people are strong, smart and rich,’ observed her sister, Eka.

> The _damang_ did not deal with the domestic violence aspect, feeling this was being handled by the police. The police, however, had already referred the problem to

> the _damang_ . So, it fell through the cracks. The husband did not honor the agreed division of property. ‘They don’t care,’ said Eka. Although the _damang_ lives literally across the street from the husband, he has taken no action to enforce the agreement. No social sanction has been applied against the husband either – he is still invited to neighborhood and _adat_ events. Indeed, the strong sense was that domestic violence is not treated as a serious problem. When asked about the

> case, the _damang_ ’s secretary laughed and said, ‘It’s just excessive libido.’ (World Bank 2008, p. 46-47)

Although the comments of the _damang’s_ secretary may represent the worst concerns of human rights activists and other legal modernizers, it is important to point out that, precisely because local legal institutions are fluid and customs are unwritten (Characteristic 1), they might adapt easily to new social circumstances. This may be particularly true in settings, like Indonesia, with historically syncretic cultural practices. _Forging the Middle Ground_ describes, for instance, the emergence of an effective, indigenous women’s NGO that manages to change 18
# **_Table 15: Descriptive statistics (female)_**

||**Total**|**sample**|**Control**|**M**|**ovie**|**M**|**FB**|**Movie**|**+ MFB**|**Pure**|**control**|**Means b**|**y gender**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**N**|**Mean**|**Mean**|**Mean**|**P-value**|**Mean**|**P-value**|**Mean**|**P-value**|**Mean**|**P-value**|**Male**|**Female**|
|_Personal characteristics_||||||||||||||
|Age of respondent|1642|38.16|38.59|38.38|0.783|38.13|0.547|37.34|0.081*|38.48|0.900|36.79|38.16|
|Married|1674|0.89|0.89|0.88|0.682|0.90|0.676|0.87|0.428|0.90|0.647|0.72|0.89|
|Widowed|1674|0.03|0.02|0.04|0.103|0.01|0.321|0.02|0.929|0.03|0.465|0.00|0.03|
|Single|1674|0.08|0.09|0.07|0.563|0.09|0.998|0.10|0.410|0.07|0.343|0.27|0.08|
|Muslim|1674|0.34|0.34|0.38|0.341|0.32|0.470|0.37|0.484|0.29|0.192|0.40|0.34|
|Christian|1674|0.66|0.66|0.62|0.341|0.68|0.524|0.63|0.436|0.71|0.192|0.59|0.66|
|Can speak English|1667|0.68|0.66|0.64|0.448|0.68|0.561|0.69|0.468|0.72|0.169|0.77|0.68|
|<br>Igbo|1674|0.17|0.15|0.15|0.971|0.19|0.130|0.18|0.271|0.20|**0.050***|0.28|0.17|
|Yoruba|1674|0.78|0.81|0.80|0.746|0.76|**0.086***|0.77|0.178|0.75|**0.058***|0.67|0.78|
|Other ethnicitiy|1674|0.05|0.04|0.05|0.588|0.05|0.512|0.05|0.503|0.05|0.842|0.05|0.05|
|_Education_||||||||||||||
|No completed school education|1673|0.08|0.08|0.08|0.689|0.09|0.415|0.08|0.856|0.07|0.939|0.06|0.08|
|Primary school education|1673|0.23|0.25|0.25|0.995|0.20|**0.091***|0.22|0.341|0.20|0.107|0.20|0.23|
|High school diploma|1673|0.48|0.46|0.45|0.936|0.50|0.253|0.48|0.607|0.51|0.178|0.56|0.48|
|Diploma|1673|0.11|0.12|0.09|0.366|0.11|0.819|0.10|0.625|0.13|0.543|0.09|0.11|
|Graduate school|1673|0.10|0.10|0.11|0.762|0.10|0.956|0.12|0.486|0.08|0.441|0.09|0.10|
|_Household characteristics_||||||||||||||
|Household (HH) size|1665|4.63|4.73|4.71|0.861|4.51|**0.060***|4.53|0.103|4.68|0.703|4.29|4.63|
|<br>Number of children below 12 in HH|1644|1.35|1.38|1.38|0.970|1.33|0.591|1.26|0.193|1.41|0.743|1.27|1.35|
|Number of dependents in HH|1647|2.28|2.32|2.24|0.560|2.25|0.616|2.27|0.716|2.32|0.993|2.82|2.28|
|<br>Number of dependents outside the HH|1572|1.41|1.23|1.38|0.291|1.58|0.024|1.33|0.477|1.58|**0.030****|1.88|1.41|
|_Business characteristics_||||||||||||||
|Months in operation|1632|96.50|95.77|94.33|0.828|99.27|0.616|101.12|0.447|90.33|0.452|99.58|96.50|
|Has a savings account|1668|0.54|0.51|0.55|0.307|0.51|0.913|0.54|0.438|0.64|**0.001*****|0.64|0.54|
|<br>Keeps written financial records|1662|0.37|0.36|0.36|0.902|0.35|0.968|0.38|0.458|0.39|0.363|0.38|0.37|
|Operating inside main market|1648|0.30|0.27|0.32|0.147|0.28|0.679|0.29|0.470|0.33|**0.082***|0.14|0.30|
|Number of employees|1672|1.27|1.38|1.31|0.551|1.29|0.482|1.28|0.435|1.02|**0.004*****|1.86|1.27|
|Business experience inyears|1667|10.49|10.89|10.86|0.956|10.51|0.553|10.08|0.190|10.04|0.219|11.37|10.49|
|*** p<0.01, ** p<0.05, * p<0.1||||||||||||||

39
51 Table 4 Correlation Matrix of Residuals of the Intrinsic Bubble Model 1995-2001

||PRINTRINBUB<br>ARG|RINTRINBUB_B|RA<br>RINTRINBUB<br>CHL|RINTRINBUB<br>COL|RINTPRNBUB_ME|XRINTRNBUB<br>US|
|---|---|---|---|---|---|---|
|PTRWINBIUBARG|||||||
||1.00|-0.25|<br>0.61|-0.31|<br>0.52|40.01|
|RINTRINBUB_BR|A<br>-0.25|100.1|6|-0.10|40.25|-0.27|
|RlNTRtNBUB CHL|0.61|-0.16|1.00|-0.02|0.51|40.16|
|RtNTRtNBUB<br>COL|-0.31|-0.10|-0.02|1.00|-0.04|0.33|
|RINTNINBUB<br>MEX|0.52|.0.25|0.51|40.04|<br>1.00|0.40|
|RlNTRINBUB<br>US|-0.01|**-0.27**|**-0.16**|0.33|0.40|1.00|
From this di ff erence between (2.7) and (2.8), it follows that comparing Ckm for total consumption with Ckm<sup>hforspecificconsumptionsubsetsprovidesinformationabouttheisolationcost</sup> associated with public goods.<sup>7</sup> For rationed goods cr we have: Suppose that cr ≈ cd<sup>−λr</sup> . It follows that V<sup>r</sup> = log c −λ r log d. We can thus estimate a regression of the form:<sup>8</sup> This suggests a way of identifying rationed goods: for them, total expenditures Xu do not enter the regression.

Since expenditures do not enter the equation for W<sup>r</sup> , we cannot estimate λ r using (2.5) because we do not have α<sup>r</sup> 1<sup>.Wemay,however,obtainanorderofmagnitudeforλrifweare</sup> willing to make some strong assumptions regarding the way subjective adequacy questions were answered. Suppose we are willing to assume that gu(.) ≈ gr(.). This equivalent to saying that individuals answer adequacy questions about one subset in a way that is commensurate to the contribution of the subset to total utility. If this were the case, α<sup>u</sup> 1<sup>wouldbethesameforall</sup> unrationed goods and we could also use it to normalize the welfare cost of isolation for rationed goods: If we have di ff erent unrationed goods u, we will have di ff erent estimates of λ r, one for each

> 7 It is imporant to recognize that this comparison holds strictly only for Cobb-Douglas preferences. For more general preference functions, the consumption of amenities cr may a ff ect satisfaction derived from unrationed goods because of complementarities between subsets. We revisit this issue below.

> 8 It is conceivable that consumption is rationed for certain consumers but not others. we will be estimating a model that is a mixture of the rationed and unrationed case. Although we do not discuss this case explicitly here, it is intuitively clear that, as a result of attenuation bias, the coe ffi cient of total expenditures will be smaller.

10
# **Kenya’s Mobile Revolution and the Promise of Mobile Savings**

Gabriel Demombynes Aaron Thegeya

## JEL Codes: G21, O12

Keywords: Kenya, mobile phone, mobile money, savings ___________________________________ The authors are part of the Poverty Reduction staff of the Poverty Reduction and Economic Management (PREM) unit of the World Bank‘s Kenya office. They thank Andrew Dabalen, Wolfgang Fengler, Louise Fox, Jane Kiringai, Humberto Lopez, John Randa, Waly Wane, and Johannes Zutt for helpful comments on an earlier draft. Part of this paper draws on work carried out with Frederick Owegi for the World Bank‘s December 2010 _Kenya Economic Update._ The views expressed in this paper are the authors‘ alone, and in no way reflect those of the World Bank, its Executive Directors, or the countries they represent. Corresponding email: gdemombynes@worldbank.org