rafmacalaba/datause-encoder-data huggingface dataset:
Finally, the average household-level crop cutting-based annual cassava yield estimate stands at 8,958 kg/ha across all sample households (Panel C), while the comparable D1, D2, R1 and R2 estimates are 5,208; 6,218; 5,798; and 4,671, respectively (Panel B). The estimation of Equation 3, as reported in Panel C of Table 6, implies that the diary and recall survey treatments, on average, underestimate cassava yield compared to crop cutting, as expected given the reasoning provided in section 3.4. The extent of foreknown underestimation, as a percentage of the crop cutting based yield, stands at 25 percent under D2, 40 percent under D1, 33 percent under R1 and 47 percent under R2. The inter-relationships between the 4 core treatment arms are the same as those reported in Panel B.

Nevertheless, the average crop cutting-based yield estimate, which should be understood as the _upper-bound_ for the cassava yield realized on the farm during the 12 months of interest, is still significantly lower than the FAOSTAT estimates shown in Figure 1, and the MoAIWD APESbased 2015/16 national cassava yield estimate of 17,564 kgs/ha.<sup>34</sup> The latest (2014) FAOSTAT estimate of national cassava yield in fact stands at 24,800 kgs/ha – nearly three times the size of the CVIP crop cutting-based yield estimate, and four times the size of the D2-based counterpart.F<sup>35</sup>

# **6. Conclusions**

In household and farm surveys across the low- and middle-income countries, the prevailing (and most cost-effective) approach to data collection on the production of root, tuber and tree crops is to field a single visit and collect information either specific to an agricultural season or with a 12month recall. In turn, there is worry that the existing survey methods may elicit unreliable and incomplete information on harvests cutting across seasons, taking place over extended periods, and varying by crop variety.

This paper presents the results of a household survey experiment that was implemented in top cassava-producing districts in Malawi and that randomly assigned sampled households to one of four approaches to cassava production measurement over 12 months, namely (1) daily diarykeeping at the household-level for 12-months, with semi-weekly in-person supervision visits (D1) – the traditional gold standard; (2) daily diary-keeping at the household-level for 12 months, with semi-weekly supervisory mobile phone calls (D2); (3) a two-visit recall-based data collection at the plot-level for a 6-month reference period, carried out in two visits 6 months apart during a 12-

> 34 The MoAIWD APES-based cassava statistics are taken at face value most recently by Kanyamuka et al. (2018) who study the cassava value chain in Malawi and provide recommendations for its improvement. However, the concerns regarding the gross overestimation in cassava production and yields reported by the MoAIWD were in fact raised nearly two decades ago by Russel Freeman in a report that was prepared in 2000 for the International Monetary Fund. 35 Similar discrepancies between crop cutting and FAOSTAT-based estimates are noted by Gourlay et al. (2017) in the case of maize in Uganda.

19
Abdel‐Rahman, H. and M. Fujita. 1990. Product variety, Marshallian externalities, and city sizes. Journal of Regional Science 30(2):165–183.

Agarwal, A., G. Giuliano, and C.L. Redfearn. 2012. Strangers in our midst: the usefulness of exploring polycentricity Ann Reg Sci, 48: 433–450 Alonso, William. 1964. Location and Land Use. Cambridge: Harvard University Press.

Anas, A. and Pines, D. 2008. Anti‐sprawl policies in a system of congested cities. Regional Science and Urban Economics, 38: 408‐423.

Anas A. and Rhee, H. 2007. When are urban growth boundaries not second‐best policies to congestion tolls? Journal of Urban Economics, 61: 263–286.

Anas, H. Rhee, 2006. Curbing excess sprawl with congestion tolls and urban boundaries, Regional Science and Urban Economics 36 (2006) 510–541 Anas, A., Xu, R. 1999. Congestion, land use, and job dispersion: a general equilibrium model. Journal of Urban Economics 45: 451–473.

Anas, A., Arnott, R., Small, K.A. 1998. Urban spatial structure. Journal of Economic Literature, 36: 1426– 1464.

Anas, A. and I. Kim. 1996. General equilibrium models of polycentric urban land use with endogenous congestion and job agglomeration. Journal of Urban Economics 40: 232–256.

Anderson, A.N. and W.T. Bogart. 2001. The structure of sprawl—identifying and characterizing employment centers in polycentric metropolitan areas. American Journal of Economics and Sociology, 60: 147–169.

Angel, S. and A Blei. 2015a. Commuting and the spatial structure of American cities. Marron Institute of Urban Management, New York University, Working Paper #20 Angel, S. and A Blei. 2015b. Commuting and the productivity of American cities. Marron Institute of Urban Management, New York University, Working Paper #19 Arnott, R. J. 1979. Unpriced transport congestion. Journal of Economic Theory, 21(2): 294‐316.

Arnott, R., O. Hochman and G. C. Rausserc. 2008. Pollution and land use: Optimum and decentralization. Journal of Urban Economics, 64: 390–407 Arnott, R.J., MacKinnon, J.G. 1977. Measuring the costs of height restrictions with a general equilibrium model. Regional Science and Urban Economics 7: 359‐375.

25
**Table 5: From world to local prices: error-correction model**<sup>_a_</sup>

||Trend|Inmediate<br>adjustment|Adjustme<br>to LR e|nt<br>q.|Spee<br>adjust|d of<br>ment||
|---|---|---|---|---|---|---|---|
||(_γ_)|(_δ_)|(|_θ_)|3 months|1 y|ear|
|Distrito Federal|0.91 <sup>_⋆⋆⋆_</sup>|0.25 <sup>_⋆_</sup>|-0.18|<sup>_⋆⋆_</sup>|0.55 <sup>_⋆⋆⋆_</sup>|0.85|<sup>_⋆⋆⋆_</sup>|
|Goias|1.01 <sup>_⋆⋆⋆_</sup>|0.26 <sup>_⋆_</sup>|-0.19|<sup>_⋆⋆_</sup>|0.61 <sup>_⋆⋆⋆_</sup>|0.95|<sup>_⋆⋆⋆_</sup>|
|Ceara|0.92 <sup>_⋆⋆⋆_</sup>|0.29 <sup>_⋆⋆_</sup>|-0.18|<sup>_⋆⋆_</sup>|0.58 <sup>_⋆⋆⋆_</sup>|0.86|<sup>_⋆⋆⋆_</sup>|
|Pernambuco|0.75 <sup>_⋆⋆⋆_</sup>|0.11|-0.16|<sup>_⋆⋆_</sup>|0.37 <sup>_⋆⋆⋆_</sup>|0.67|<sup>_⋆⋆⋆_</sup>|
|Bahia|0.75 <sup>_⋆⋆⋆_</sup>|0.17|-0.15|<sup>_⋆⋆_</sup>|0.39 <sup>_⋆⋆⋆_</sup>|0.67|<sup>_⋆⋆⋆_</sup>|
|Minas Gerais|1.02 <sup>_⋆⋆⋆_</sup>|0.19|-0.20|<sup>_⋆⋆_</sup>|0.60 <sup>_⋆⋆⋆_</sup>|0.96|<sup>_⋆⋆⋆_</sup>|
|Parana|0.86 <sup>_⋆⋆⋆_</sup>|0.13|-0.19|<sup>_⋆⋆_</sup>|0.47 <sup>_⋆⋆⋆_</sup>|0.80|<sup>_⋆⋆⋆_</sup>|
|S ̃ao Paulo|1.00 <sup>_⋆⋆⋆_</sup>|0.20|-0.17|<sup>_⋆⋆_</sup>|0.54 <sup>_⋆⋆⋆_</sup>|0.91|<sup>_⋆⋆⋆_</sup>|
|Rio de Janeiro|0.94 <sup>_⋆⋆⋆_</sup>|0.18|-0.18|<sup>_⋆⋆_</sup>|0.52 <sup>_⋆⋆⋆_</sup>|0.87|<sup>_⋆⋆⋆_</sup>|
|Rio Grande do Sul|0.77 <sup>_⋆⋆⋆_</sup>|0.13|-0.20|<sup>_⋆⋆_</sup>|0.45 <sup>_⋆⋆⋆_</sup>|0.73|<sup>_⋆⋆⋆_</sup>|
|Para|0.87 <sup>_⋆⋆⋆_</sup>|0.23|-0.16|<sup>_⋆⋆_</sup>|0.49 <sup>_⋆⋆⋆_</sup>|0.79|<sup>_⋆⋆⋆_</sup>|

> _a_ An error-correction model is run for each State to capture differences in price transmission across regions. A SUR technique is used to correct for common shocks across States. A _⋆⋆⋆_ implies that the null hypothesis that the coefficient is equal to zero is rejected at the 1 percent significance level; _⋆⋆_ for rejection at the 5 percent level and _⋆_ for rejection at the 10 percent level. World price is Contract number 5 of the London Daily Price for refined sugar, f.o.b. Europe, spot. Local prices are consumer prices.

35
while not statistically different from zero, are also not statistically different from 0.2 standard deviation. In other words, moderate differences between the treatment and control groups cannot be ruled out. In the sample of students in non-attrition schools, the differences between treatment and control groups tend to be lower. None of the reading domain differences for these students are statistically different from zero, but several, including dictation and oral reading fluency, are not statistically different from 0.3 standard deviation as well.

Table 5. Baseline differences in reading scores between treatment and control groups at baseline (standard deviations)

||All baseline schools|Non‐attrition|
|---|---|---|
|||schools|
|Reading domains targeted by|the intervention||
|Initial sounds|0.091|0.046|
||(0.074)|(0.078)|
|Familiar words|0.246*|0.169|
||(0.14)|(0.161)|
|Other reading domains|||
|Letter names|0.122|0.041|
||(0.094)|(0.099)|
|Unfamiliar words|0.132|0.058|
||(0.099)|(0.112)|
|Reading comprehension|0.112|0.06|
||(0.106)|(0.134)|
|Oral comprehension|0.036|0.014|
||(0.104)|(0.123)|
|Dictation|0.288**|0.159|
||(0.14)|(0.154)|
|Oral reading fluency|0.251*|0.179|
||(0.143)|(0.17)|

Table presents estimates of the difference in reading score between treatment and control groups. Positive differences imply that the treatment group has a higher score than the control group. Standard errors presented in parentheses. Statistical significance at the 10, 5 and 1 percent levels denoted by *, **, and ***, respectively.

Differences in the available background variables between treatment and control groups are estimated in Table 6. Statistically significant differences exist for the proportion of females, availability of printed materials at home, whether someone reads to the child at home, class size, and whether a majority of the tests at the school are in _Tok Pisin_ . The differences between treatment and control groups are roughly the same whether comparing students in the baseline 10
Figure 15. Debit Card Ownership Equity Adjust Coverage, (Household-level, LITS) <!-- Start of picture text --> 80<br>70.1<br>70 64.8 63.8<br>60.7<br>55.7 57.5<br>60 53.0 51.6 50.5<br>47.0<br>50<br>40<br>30<br>20 11.4 12.1<br>7.3 6.5 8.9<br>10<br>0<br>Latvia Czech Republic Turkey France Croatia<br>Coverage Dissimilarity EACR<br>Equity Adjust Coverage, (Individual level, FINDEX)<br>2011 2014<br>90 90<br>77.0<br>80 80<br>7060 50.3 55.4 56.6 58.0 52.0 60.7 52.1 7060 69.6 67.1 58.4 65.7 56.5<br>50 39.9 45.2 42.9 50 39.9 43.3<br>40 40 33.3 33.9<br>3020 20.8 18.5 24.3 10.4 14.1 3020 16.5 9.6 21.6 12.9 14.1<br>10 10<br>0 0<br>Belarus United Arab Turkey Rep. of Korea Mongolia Belarus United Arab Turkey Rep. of Korea Mongolia<br>Emirates Emirates<br>Coverage Dissimilarity EACR<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base and LITS is household level. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS The role of gender in having a debit card is reduced substantially at the hosuehold level from 57.4 percent to 16.8 percent. (Figure 16) The sources of this decline are likely to be parallel to the reduction in the contribution of gender to the dissimilarity index in the usage of bank accounts. Income and education are the main sources of dissimilarity as they collectviely account for almost 75 percent of the d-index. Comapred to benchmark countries, this value is relatively higher as age or the urban/rural divide play a more important role in countries with similar coverage rates at the household level.

17
# **1. INTRODUCTION**

# **1.1 Why Focus on Small-scale Private Service Providers?**

In communities where national, regional or municipal utilities are not providing sufficient or reliable electricity or water services, households depend instead on a wide variety of local providers. These providers finance, develop and manage systems, ranging in sophistication from water tankers and diesel generator dealers to stand-alone networks and power grids. Organizationally, they encompass community groups, village administrations and private entrepreneurs. And while a portion of them receive some form of public funds from governments, donors and NGOs, many are privately financed by business people, financial intermediaries and users.

This report focuses on those small-scale _private_ service providers (SPSPs) that have contributed to the extension of water supply and electricity services at their own initiative and with resources mobilized largely from private sources. SPSPs often bear regulatory and political risk, in complex and remote locations that may also present commercial risk.

SPSPs have traditionally played three basic roles: “gap filler” in countries with high coverage levels but low service quality (measured by the number of days or hours within which services are available); “pioneer”--developing and operating systems in areas where there is no public service but there is customer demand; and “subconcessionaire”--buying water or electricity in bulk from the utility and selling it on to customers. In addition, SPSPs are increasingly assuming the function of “manager” of small public systems in need of improved efficiency.

An analysis of the scope, scale, constraints and roles of SPSPs is important because:

- **_They are significant service providers in many countries, particularly in periurban, rural and remote regions, and may be the only viable operators for the foreseeable future_ .** SPSPs are estimated to reach as much as half the population in some countries, particularly in post-conflict situations and other cases of weak or failed states. Overall, it is estimated that up to a quarter of the urban population in Latin America and nearly half of urban dwellers in Africa rely on SPSPs for at least a portion of their water supply. In low-coverage countries, or poor regions in higher coverage countries, SPSPs can play an important role in service provision, compensating for - or supplementing - the limited financial and human resources of the public sector.

- **_The local private sector currently accounts for over 85 percent of all private sector investment in water security_**<sup>**_5_**</sup> **_and the potential for private financing of small-scale waster supply is significant._** The local private sector has demonstrated its ability and interest in the development and management of water supplies even in remote or difficult locations that are unattractive to formal providers. Despite unclear legal or operational status and sub-optimal financing

> 5 _Towards Water Security:  A Framework for Action_ , Global Water Partnership,  2000.

6
# **Public Perceptions of the Seriousness of the Climate Change Problem**

19. How serious a problem do citizens of Tajikistan perceive climate change to be? The answer to this question depends upon how the issue is posed to them. Respondents in the P-LITS 2 were asked a series of questions regarding the extent to which they think climate change is a very serious problem confronting the world. First, they were presented a list of various issues, and were asked which of these they considered to be “very serious problems” facing the world.

**Table 2: Respondent Opinions about Major Problems Currently Facing the World**

|**Issue:**|**Percentage of**<br>**consider the iss**<br>|**respondents in the**<br>**ue to be a very ser**<br>|**country that**<br>**iousproblem**<br>|
|---|---|---|---|
||**EU27 countries**|**Kazakhstan**|**Tajikistan**|
|Poverty, lack of food and drinking water|70|66|76|
|The spread of an infectious disease|24|70|84|
|International terrorism|55|50|47|
|**Climate change**|**64**|**36**|**41**|
|A major global economic downturn|25|47|38|
|Proliferation of nuclear weapons|24|33|29|
|Armed conflicts|39|53|28|
|The increasingworldpopulation|20|12|17|

Source: Authors calculations based on P-LITS 2 data Special EU Barometer 300/wave 69.2 20. Three main findings are noteworthy (Table 2): (i) “poverty, lack of food and drinking water” and “international terrorism” emerged among the top 3 serious problems (ranked by votes received) in all three countries; (ii) however, a majority of respondents in Kazakhstan and Tajikistan noted “spread of infectious diseases” to be a serious problem (70 and 84 percent respectively); (iii) climate change was selected by a sizeable share of respondents in Kazakhstan (36 percent) and Tajikistan (41 percent), though not as high as in EU27 countries (64 percent).

21. However, when asked to select the single-most serious problem confronting the world, only 5-7 percent of respondents in Tajikistan and Kazakhstan chose climate change (ref. 31 percent in EU27 countries); instead, respondents in these two countries ranked “spread of infectious diseases” and “poverty, lack of food and drinking water” as the two most important problems (Figure 6).

**Figure 6: Tajikistan: Most Serious Problem Currently Facing the World**

<!-- Start of picture text --> 100%<br>Other<br>80%<br>A major global economic downturn<br>60% Climate change<br>International terrorism<br>40%<br>The spread of an infectious disease<br>20%<br>Poverty, lack of food & drinking water<br>0%<br>EU27 countries Kazakhstan Tajikistan<br><!-- End of picture text -->

**Source:** Authors calculations based on P-LITS 2 data; Special EU Barometer 300/wave 69.2
_6_ in 1960.3 In the past twenty-seven years, the share of production has increased by about _50_ percent in the secondary sector and fallen slightly in the primary and tertiary sectors.

Essentially, the traditional part of agriculture still produces miJlet and cattle for domestic consumption, and the exportoriented part produces groundnuts. Domestic agricultural production does not cover consumption--nearly one-half of Senegal's basic food st. ffs are imported.' As we show later, the modern sector produces primarily light industrial goods and is highly concentrated regionally and in terms of ownership.

TABLE 2.1 Sencpl Structure of Production

|Seocor|1960'|Share of<br>(in curr<br>1970'|GDP (%)<br>ent prices)<br>1980|1<sup>986b</sup>|1987b|
|---|---|---|---|---|---|
|Primnty|243|24.1|19.1|21.8|21.6|
|Seconday|18.6|21.5|24.9|24.8|25.7|
|T<br>ary|57.1<br>i||56.0|53.4|52.7|

-urce: iWorld Bank, Senegal: Countuy Economic Memorandum 1984, _pA._ Oiltematorad Monetary Fund (IMF) Senegal - Statistical Anna. 1988, p. iv. (Figures for 1987 are preliminary).

The limited change in the structure of production reflects the general lack of dynamism in the economy. As can be seen in Table 2.2, real GDP growth declined from an average of 2.5 percent a year throughout the 1960s to an average of 1.9 percent 3. The primary sector contains agriculture, livestock, fishing and forestry. The secondary sector contains industry and mining, construction and public works, and enerr. The tertiary sector is composed of transportation, commerce, and **govenument** and other private serices.

4. For a recent account of issues related to Senegal's agriculture, see Gersovitz and Waterbury (1987).
**_Table 6: Compliance Table_**

|Treatment Assignment|Did not<br>attend|Placebo<br>Atte|Movie<br>nded the f|MFB<br>ollowing s|Movie+MFB<br>creening|
|---|---|---|---|---|---|
|Pure Control|99.0%|0.0%|0.2%|0.4%|0.4%|
|Control/Placebo|41.0%|57.9%|1.0%|0.2%|0.0%|
|Movie|38.0%|0.2%|61.5%|0.3%|0.0%|
|MFB|42.6%|0.3%|0.5%|56.6%|0.0%|
|MFB+Movie|41.1%|0.0%|0.2%|0.5%|58.3%|

**_Table 7: Self‐reported exposure to interventions_**

|Exposure variables|Reme<br>receiv<br>invit|mbered<br>ing an<br>ation|Attended|the event|Remem<br>seeing<br>called Th<br>Go|bered<br>a movie<br>e Story of<br>ld|Remember<br>an event i<br>communit<br>Accion|ed attending<br>n one of the<br>y halls where<br>presented|Correctly<br>the mess<br>mo|identified<br>age of the<br>vie|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Movie only|0.01|‐0.00|0.04|‐0.01|0.23***|0.30***|0.02|0.02|0.26***|0.36***|
||(0.014)|(0.011)|(0.029)|(0.016)|(0.031)|(0.031)|(0.019)|(0.027)|(0.030)|(0.035)|
|MFB|‐0.00|‐0.01|0.01|‐0.00|‐0.01|‐0.01|0.04**|0.06**|‐0.02|‐0.04|
||(0.014)|(0.011)|(0.030)|(0.016)|(0.031)|(0.033)|(0.019)|(0.028)|(0.030)|(0.036)|
|MFB + Movie|‐0.00|‐0.00|0.00|0.01|0.21***|0.33***|0.04**|0.07**|0.26***|0.41***|
||(0.014)|(0.011)|(0.029)|(0.016)|(0.031)|(0.032)|(0.019)|(0.028)|(0.030)|(0.036)|
|_Observations_|_1,976_|_1,259_|_1,975_|_1,259_|_1,974_|_1,258_|_1,974_|_1,259_|_1,979_|_1,261_|
|_R‐squared_|_0.00_|_0.00_|_0.00_|_0.00_|_0.05_|_0.14_|_0.00_|_0.01_|_0.08_|_0.18_|
|_Controls_|_NO_|_NO_|_NO_|_NO_|_NO_|_NO_|_NO_|_NO_|_NO_|_NO_|
|_Restricted Sample_|_NO_|_YES_|_NO_|_YES_|_NO_|_YES_|_NO_|_YES_|_NO_|_YES_|
|_Control Mean:_|_0.948_|_0.984_|_0.673_|_0.958_|_0.404_|_0.593_|_0.0734_|_0.102_|_0.286_|_0.419_|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 31
- 51 -

# Table **6:** Alternative Estimators of the 1984 Lorenz Curve

|p|Actual|Kakwani|Kakwani<br>Podder|Elliptical|
|---|---|---|---|---|
|10|3.40|3.42|3.54|3.52|
|20|8.14|8.12|7.87|8.15|
|30|13.82|13.77|13.20|13.77|
|40|20.42|20.35|19.75|20.36|
|50|27.97|27.93|27.72|27.95|
|60|36.62|36.64|37.40|36.64|
|70|46.57|46.70|49.08|46.65|
|80|58.40|58.55|63.12|58.43|
|90|73.31|73.21|79.93|73.18|

# Table 7: Alternative Estimation Methods for the Density Function at the Poverty Line

|Poverty<br>line||Econome<br>meth|tric<br>od|Non-par<br>meth|ametric<br>od|
|---|---|---|---|---|---|
||Sector|f(z)<br>xlOOOO|lo|f(z)<br>xlOOOO|no|
|11000|Urban|0.362|-3.27|0.348|-3.17|
||Rural|0.718|-2.00|0.735|-2.05|
||Total|0.618|-2.05|0.639|-2.13|
|15000|Urban|0.413|-2.19|0.410|-2.19|
||Rural|0.504|-1.18|0.533|-1.24|
||Total|0.472|-1.27|0.501|-1.34|
<mark>Although we use the official maize unit-condition-state specific conversion factors that are further augmented with MAPS-based measurements that provide additional nuance to expression of unshelled maize in shelled equivalent terms, the quality of conversion factors that are used for computation of farmer-reported maize production in kg-equivalent terms may mediate the accuracy of the self-reported maize production. However, there are open empirical questions regarding the extent that conversion factors should be specific in spatial and temporal terms, and whether an improved set of conversion factors, including through the introduction of non-standard measurement units that recognize the variation in sizes for specific units, would be enough to overcome other challenges that may plague self-reported production information, as reviewed in Section 2.</mark> <mark>Finally, given the absence of the IR based on the objective productivity and plot area measures in our sample, the results provide further support for promoting a policy environment that reduces the scope for further subdivision of land and that prioritizes investments in land titling and land market development. It is important to note, however, that our findings do not suggest a broad development policy focus shift away from the needs of smallholder farming households since there are social and economic reasons for why one inherently cares about the improvement of living standards across a large segment of the population relying on farming. And to the extent that cultivating small plot(s) is positively correlated with poverty and that the smallest plots are associated with the most upward bias, the actual yields attained by the poorest may be less than previously estimated.</mark> 26