rafmacalaba/datause-encoder-data huggingface dataset:
44 The share of total expenditure allocated to LPG by user households declined with quintile level except for the top rural quintile (Table A2.12). The coefficient of variation for the prices of LPG was 0.23, indicating that 95 percent of prices were within 50–150 percent of the mean price.

Table A2.12: Shares of total household expenditure on LPG for all households and for purchasing households in Kenya (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|**U**<br>**Rural**|**ser households**<br>**Urban**|<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|0.00|0.00|0.00|0.0|0.0|0.0|
|**2**|0.01|0.00|0.00|5.8|0.0|5.8|
|**3**|0.01|0.02|0.01|5.3|8.7|6.1|
|**4**|0.02|0.06|0.03|3.7|5.4|4.3|
|**5**|0.25|0.53|0.40|4.6|2.6|3.0|
|**All**|0.05|0.34|0.12|4.5|2.7|3.1|

_Source:_ Authors’ calculations.

# **Pakistan (HIES 2004–2005)**

The data are from the 2004–2005 Household Integrated Economic Survey (HIES), administered by the Federal Bureau of Statistics of the Government of Pakistan. The fieldwork for this survey was carried out between July 2004 and June 2005.

Non-purchased items had an average value of 37 percent of cash expenditure, and nonpurchased food accounted for about one third of all non-purchased items. Both ratios were approximately constant across the expenditure distribution. All expenditures on food items were deflated using a Paasche index provided in the survey data set.

The questionnaire asked three separate questions related to the use of LPG. The first two were related to the source of energy for cooking and lighting, respectively, but the categories of possible replies aggregated natural gas (piped gas) and LPG (bottled gas). The third question asked whether the household had consumed any LPG during the previous month and, if so, how much the household had paid and consumed. There was a separate question on the expenditure on natural gas. The availability of natural gas as a household fuel complicates the analysis because natural gas is a superior fuel for cooking and is a clear substitute for LPG. Combining responses from different parts of the survey showed that the number of households who had purchased LPG was considerably larger than the number of LPG-purchasing households that reported that gas (in some form) was their primary cooking fuel. Households may have used LPG as a secondary cooking fuel or for other purposes, but only 0.7 percent used it as the primary lighting source. As expected, few households purchased both natural gas and LPG. Firewood was the most commonly used primary fuel for cooking, while electricity, charcoal, and kerosene were scarcely used as the primary cooking source (Figure A2.3).
Table 3: Event-study estimates – heterogeneous effects by sector and firm size

||Monthly Suspensions|Monthly Closures|Weekly Work Hours|Monthly Income|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**[Panel A] Heterogeneity by sector**|||||
|Manufacturing-Construction|-0.00772|0.0221***|-1.354***|-737.9***|
||-0.0274|-0.0027|-0.173|-39.43|
||[-]|[+74.00%]|[-2.86%]|[-11.87%]|
|Wholesale-Retail|3.499***|0.367***|-3.630***|-947.8***|
||-0.254|-0.0388|-0.199|-84.48|
||[+93.81%]|[+135.93%]|[-7.58%]|[-14.38%]|
|Transportation|0.132***|0.0206***|-6.402***|-1,676***|
||-0.0255|-0.00327|-0.376|-100.3|
||[+23.16%]|[+51.50%]|[-13.32%]|[-21.40%]|
|Hotel-Food-Entertainment|1.119***|0.0977***|-4.634***|-1,125***|
||-0.0547|-0.0107|-0.295|-97.1|
||[+164.56%]|[+108.56%]|[-9.99%]|[-19.51%]|
|**[Panel B] Heterogeneity by frm size**|||||
|Revenue less than 25th percentile|2.195***|0.0551***|-|-|
||-0.0883|-0.00493|-|-|
||[+105.53%]|[+50.10%]|-|-|
|Revenue between 25th and 50th percentile|1.838***|0.0459***|-|-|
||-0.0792|-0.00415|-|-|
||[+112.75%]|[+51.00%]|-|-|
|Revenue between 50th and 75th percentile|1.516***|0.0380***|-|-|
||-0.0844|-0.00369|-|-|
||[+104.55%]|[+47.50%]|-|-|
|Revenue greater than 75th percentile|1.775***|0.409***|-|-|
||-0.211|-0.0438|-|-|
||[+125.00%]|[+204.50%]|-|-|

Note: This table shows the event-study estimates of heterogeneous effects across sector and firm’s operating size. Disaggregated sectors (Panel A) include manufacturing & construction; wholesale & retails; transportation; and accommodation, food & entertainment. Firm’s operating size is broken down by revenue quartiles. The coefficients are estimated following Equation 1. Data on household business suspension and closure filings are obtained from the Household Business Tax Census. Data on individual work hours and income are obtained from the Vietnamese Labor Force Surveys. Monthly income is detrended from pre-COVID linear trend and residualized from individual observables including age, gender, educational level, and occupation level. Square brackets show the equivalent of estimated coefficients in percentage changes from pre-COVID means. Standard errors are clustered at the commune level and are presented in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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Table 2: Village-Level Public Health Infrastructure

||Health facility<br>index<br>(1)|Connection<br>index<br>(2)|Public program<br>index<br>(3)|Auxiliary<br>Nurse Midwife<br>(4)|Female<br>doctor<br>(5)|
|---|---|---|---|---|---|
|Fraction of female leaders|1.730*|2.138***|0.973**|0.980***|0.837***|
||(0.919)|(0.528)|(0.455)|(0.246)|(0.263)|
|Observations|7,696|7,700|7,722|7,723|7,722|
|Outcome mean|0.000|0.000|0.000|0.646|0.219|
|State fxed efects|x|x|x|x|x|
|District characteristics|x|x|x|x|x|

_Notes_ : Data comes from the 2012-13 District-level Household and Facility Survey (DLHS) of India. The columns present 2SLS estimates specified in the following equations: where _Yvds_ is an outcome for village _v_ in district _d_ in state _s_ . The other variables in these specifications have the same definitions as in our main specifications described in Section 3. Standard errors are clustered at the district level. The outcomes are as follows: an index for the presence of health facilities in a village (column 1), an index for connection to health facilities with an all-weather road (column 2), an index for public program implementation in a village (column 3), an indicator for the presence of Auxiliary Nurse Midwives in a village (column 4), and an indicator for the presence of a female doctor in a village (column 5). All regressions include district controls, and state fixed effects. District-level controls include the share of female population, the share of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

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# **Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa**

George Azzari1, Shruti Jain2, Graham Jeffries3, Talip Kilic4 and Siobhan Murray5<sup>1</sup> Originally published in the <u>Policy Research Working Paper Series on</u> _April 2021._ This version is updated on _June 2021._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u> JEL Codes: C52, C55, C83, Q12.

Keywords: Agriculture, Maize, Crop Type Mapping, Sentinel-2, Earth Observation, Remote Sensing, Household Surveys, Data Integration, Malawi, Ethiopia.

> 1 The authors are listed alphabetically. 1Chief Technology Officer, Atlas AI P.B.C., george@atlasai.us. 2 Geospatial Data Scientist, Atlas AI P.B.C, <u>shruti@atlasai.us.</u> 3Senior Technical Portfolio Manager, Atlas AI P.B.C, <u>graham@atlasai.us.</u> 4Corresponding author. Senior Economist, Development Data Group, World Bank, <u>tkilic@worldbank.org.</u> 5 Technical Specialist, Development Data Group, World Bank, smurray@worldbank.org. The authors would like to thank Calogero Carletto, Keith Garrett, and the members of the Earth Observation Task Force of the United Nations Global Working Group for Big Data for their comments on the earlier version of the paper. This is a publication of 50x2030 Initiative to Close the Agricultural Data Gap, a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030. For more information on the Initiative, please visit https://www.50x2030.org/.
# **3.2 Market Imperfections**

The basic intuition behind the derivation of markups in equation (4) is that a competitive firm will increase its use of a variable input until its revenue share equals the output elasticity. The first working assumption in the derivation of market imperfections is that if a firm does not increase its variable input use until equality holds but rather increases its output price, such behaviour signals that the firm holds market power in the output market.

Notice that the first-order-condition for cost minimization in equation (3) can be rewritten as In a fully competitive environment where firms act as price takers in both input and output markets, the ratio of price to marginal cost would be unity, i.e., _μit_ =<sup>_<u>P</u>_</sup> _λit_<sup>_<u>it</u>_=1.Inthat</sup> case, the first-order-condition would be _θit_<sup>_v_= (</sup><sup>_α_</sup> _it_<sup>_v_).</sup> Secondly, if factor markets were equally competitive, the choice of variable input from the set _V_ = _{L, M }_ should not matter, as the derived markups from materials and labor would coincide: _μ_<sup>_m_</sup> _it_<sup>=</sup><sup>_μl_</sup> _it_<sup>.However, if different conditions exist in the materials and labor</sup> markets, firms are likely to behave differently in the two markets, leading to a scenario where _μ_<sup>_m_</sup> _it_<sup>=</sup><sup>_μl_</sup> _it_<sup>.DobbelaereandMairesse(2013)definedajointparameterofmarket</sup> imperfection _ψ_ as Hence, inequality for the joint parameter of market imperfection ( _ψit_ = 0) is due to imperfections in the labor market settings.

Dobbelaere and Mairesse (2013) based on Hall (1988), showed that the labor market setting (LMS) is categorized into three regimes: perfect competition (PR), efficient bargaining (EB), and monopsony (MO). Although the definitions of Dobbelaere and Mairesse (2013) were based at industry-level, Nesta and Schiavo (2017) were first to show the adaptability of the methodology at firm level. The labor market setting (LMS) is formally characterized by where _κit_ = 1 _−φφitit_<sup>,representstherelativeextentofrentsharing,with</sup><sup>_φ∈_[0</sup><sup>_,_1]being</sup> the absolute extent of rent sharing, that results from the efficient bargaining solution and ( _ε_<sup>_l_</sup> _w_<sup>)</sup><sup>_it_representsthewageelasticityoflaborsupply.10</sup> In an efficient bargaining setting, firms and risk-neutral workers would bargain over wages and employment level. This will lead to an efficient bargaining Nash equilibrium, which is characterized by rent sharing between firms and workers. In this scenario, Dobbe9
# **Survey finding rates**

The survey finding rate, however, varied by treatment status. We were able to find 94 percent of T1 applicants, 69 percent of T2 applicants, and 68 percent of the control group. Given the scant information we had to locate the applicants, the finding rate of the control group (68 percent) is quite high. For reference, Clemens and Tiongson’s (2019) study, which interviews applicants to the Korean EPS migration program in the Philippines based on administrative data, was able to locate and interview only 44 percent of the applicants. The extensive field engagement of the enumerators and, perhaps, the rural setting of Bangladesh, where villagers tend to know about each other, led to the high finding rate in our study.

The almost universal finding rate of the T1 group stems from a few key reasons. First, as the T1 group were required to interact with local officials to prepare for the government intermediation, the local officials were likely to know of them or have updated contact information. Second, winning the lottery made them well-known in the communities and the villagers were also more likely to know them. Third, households with a migrant, particularly international migrants, are more likely to be known in the villages. Given the high desire to migrate internationally in this context, villagers could be more likely to know of international migrants and their families in their village.

This, unfortunately, creates a differential finding rate of 26 percentage points between treatment groups. As explained above, the differential rate reflects the nature of the lottery program and the resulting migration rather than any differences in some underlying economic characteristics. In any case, treating differential finding the same way as differential attrition and estimating bounds (Lee, 2009) resulted in wide confidence intervals for most of the outcomes (except migration and income). Nevertheless, we investigate this issue more deeply in Appendix A.2, and show that under reasonable assumptions the estimates presented in this paper are robust.

# **Comparison of the sample with the population**

Our sample strategy yields a representative sample of the lottery applicants. However, the lottery applicants themselves are a selected sample. For instance, they have a member who is interested in international migration for low-skilled work. They were also expected to finance the migration themselves, albeit at a lower than market cost of migration. This excludes interested applicants who face borrowing constraints to finance migration.

Indeed, as **Appendix Table 1** shows, the study sample is different from a nationally representative household from the Household Income and Expenditure Survey (HIES) of 2016/2017 (BBS, 2017).

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beneficiaries’ dissatisfaction with their placement. According to a beneficiary assessment conducted on a sample of cycle 2 beneficiaries, some interns expressed dissatisfaction with the placement process and with the fact that their sector‐specific training and internships did not match their preferences and educational backgrounds.

Table 7b shows the number of interns assigned to each sector as a share of the total number of interns in the program. The distribution of interns by sector reflects the capacity of employers in each sector to offer internships and their commitment to supervising interns. Firms operating in the ICT, tourism, and _Jua Kali_ sectors absorbed 27, 25, and 21 percent of interns respectively. These same sectors also experienced the highest dropout rates between the time of being placed with an employer and the completion of the internship, with about 5 percent of interns dropping out in those three sectors compared to only about 2 percent in manufacturing and energy. ICT absorbed the most interns in Nairobi, while tourism absorbed the most in Mombasa.

# **VII. Impact Results**

This section presents our estimates of the impact of the full program on different outcomes and goes on to discuss the Lee bounds estimates (Manski, 2000 and Lee, 2008) that we used to correct for significant differential attrition rates within the treatment group. None of the estimated effects of the life skills training component alone were significant and so are not presented in this paper.

First, in our empirical analysis, we obtained intention‐to‐treat (ITT) estimates by comparing average outcomes for the treatment and control groups in the endline survey data.<sup>13</sup> ITT estimates can be interpreted as the impact of _offering_ the program. Interest by policymakers in this impact may be limited as it does not reflect the actual implementation of the program. To obtain ITT estimates, the following model was estimated using Ordinary Least Squares (OLS):

## �� = α+ ��� +γ �� + sj + ��

where �� was the outcome of interest for individual _i_ at endline, Ti was a binary variable for being randomly assigned to the treatment group, Xi was a set of baseline characteristics to control for any remaining baseline imbalances, sj were the 12 strata fixed effects, and εi was a mean‐zero error term. The model was estimated with and without controlling for baseline characteristics, which included gender, age, education, marital status, employment, salary from wage employment, self‐ employment earnings, hours worked per day, and having a written contract.

To estimate the impact of actually receiving the program (δ), the following second stage equation (of a two stage OLS model) was estimated: The randomized selection procedure was used as an instrument to estimate the predicted compliance (Z) and in turn the average treatment on the treated (ATT) effect δ, that is on those

> 13Endline 1 survey data were used to estimate the program’s impact on participants’ skills and saving behavior, while endline 2 survey data were used to estimate the program’s impact on participants’ labor market activities and earnings.

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# Guiding Question 3: Why are some groups excluded? The _<u>why</u>_ question

The “how question” is relatively easier to answer compared to the “why question”. The former requires analysis of country data to establish the manner and extent to which certain groups face disadvantages in access to markets, services or spaces. The “why question” is more complex since it requires a granular understanding of processes, politics and the nature of vertical and horizontal stratification in societies.

World Bank (2013) highlights the example of tribal women in India and illustrates the nuance in the _how_ and _why_ questions. The National Family Health Survey of India 2004/05 reports that 80 percent of tribal women in India give birth at home, compared to 60 percent of all Indian women. When asked why they opt to deliver their children at home, 72 percent of . responses indicate that women do not consider it necessary to deliver in a health facility<sup>6</sup> This finding could lead health authorities to conclude that the seemingly low demand for institutional births among tribal women is a result of cultural factors and of ignorance. However, low demand may actually reflect complex phenomena that are not immediately discernible. For example, the tribal woman may be unable to deliver at a health center because of the distance to the facility, the absence of female health providers at the facility, the costs involved, or simply because she is not comfortable with the manner in which providers deal with her. She may for instance, feel disrespected because of her culture or ethnicity and way of life. The SCDs were accordingly read with the lens of whether or not they unpacked such processes that perpetuate exclusion of some groups.

Guiding Question 4: Does social inclusion feature as a priority and are there recommended actions? The _<u>what next</u>_ question The final question that guided the review was whether social inclusion was identified as a priority by the SCD and if so, whether the SCD recommended specific actions. This is important because an SCD is intended to provide the analytical foundation for the CPF and to inform discussions with governments and other stakeholders on how best the WBG can support the country’s efforts to address its development challenges. Accordingly, the interim guidelines for conducting the SCD call for using the analyses in the SCD to “identify a select set of priorities for a country, in order to maximize its progress toward the goals of poverty reduction and welfare improvements among the less well off”.<sup>7</sup> It is not necessary for the SCD to spell out actions that can help the country move toward the identified priorities. Equally, the CPF is not bound to adopt these actions or reflect on them, guided as it may be by the nature of demand from the country. However, and as suggested earlier, in the event that the CPF chooses to focus on areas that are not among the priorities identified by the SCD, the former has to document the rationale for such a choice. In short, the priorities that are set in the SCDs are important for the CPF.

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cocoa has relatively fewer very small scale producers and is characterized by less-pronounced dualism.

**<u>Table IV.7   Cocoa Land Area Distributed by Size of Producers, 1998</u>**

|**In Hectares**|**# of**<br>**Producers**|**%**<br>**Producers**|**Area in**<br>**Hectares**|**%**<br>**Land**|
|---|---|---|---|---|
|<1.6|1227|3.2|904|0.6|
|1.6-3.1|16255|40.1|22382|14.6|
|3.2-5.0|9957|24.5|31029|20.3|
|5.1-10|7555|18.6|36988|24.2|
|10.1-20|3785|9.3|30853|20.2|
|>20|1761|4.3|30836|20.2|
|**TOTAL**|40,590|100%|152,992|100%|

Source: Secretaria de Estado de Agricultura, Departamento de Cacao, “Anuario de los Países Productores de Cacao” 1998.

# _Cultivation Practices and Technology_

Traditional cocoa plantings consist of mixes of traditional Amelonado and Trinitarito varieties. Many plantations have a high proportion of trees more than 35 years year old, the age at which production begins to decline. Fewer than 40% of cocoa trees in Dominican farms are estimated to be of a prime growing age.<sup>59</sup> Traditional plantations are characterized by a low tree populations and excessive shade trees (usually Erythrina). In some cases there are voids in the cocoa plantations, and the lack of appropriate shading can cause unbalanced plant nutrition and weed proliferation. Many traditional producers do not practice appropriate pruning or fertilization, and experience lower yields as a result.

More progressive cocoa producers have adopted improved cultivation techniques using hybrid varieties and higher density plantings of cocoa trees, improved shade tree management, improved plant nutrition using organic or chemical fertilizers, preventative disease and pest management, and appropriate pruning. Adoption of these improved technologies requires additional labor inputs, finance and higher intensity of management. To achieve sustainable increases in productivity, the Cocoa Department acknowledges the need to introduce better varieties of cacao and improve production techniques. The destruction caused by Hurricane Georges has helped transform the cocoa industry; post-Georges rehabilitation focused on replanting and rehabilitation with an improved hybrid variety. Current estimates are that 60% of existing plantations are in traditional varieties, while 40% are newer hybrids. In comparison, in Cote d’Ivoire and Ghana about 70% of cocoa area is planted to hybrids, and in Ecuador about 35%.<sup>60</sup> The Cocoa Department in SEA classifies cocoa producers into three rough typologies. The first type uses no purchased inputs, minimum maintenance, and produces for own-consumption with surplus going to the market. Yields among such producers are about 250-300 kg/ha. The second type of producer has conducted renovation with improved varieties, uses good management and

> 59 Secretaria de Estado de Agricultura, Departamento de Cacao, “Anuario de los Países Productores de cacao” 1998.

> 60 Secretaria de Estado de Agricultura, Departamento de Cacao “Informe Anual de las Estadísticas del Cacao 2001/2” 2002.

59