rafmacalaba/datause-encoder-data huggingface dataset:
Overall, the considerable variation in production and yield estimates by survey treatment underscores the need for adopting improved survey methods to collect the required data for more accurately capturing the contribution of cassava farming to production and welfare outcomes. The findings, together with the cost comparisons, support the use of (i) D2, particularly if deployed as part of a broader mobile-phone based survey effort, or (ii) R1, as a second-best alternative.

The paper is organized as follows. Section 2 covers the country context. Section 3 discusses the data. Section 4 lays out the empirical strategy. Section 5 presents the results. Section 6 concludes.

# **2. Country Context**

Malawi is heavily dependent on agriculture. The agricultural sector makes up 29 percent of the country’s gross domestic product. The household-level incidence of participation in agriculture is 83 percent at the national level, and 93 percent in rural areas (NSO, 2017). Davis et al. (2017) report that the share of household income stemming from agriculture stands at 65 percent. However, much of the agricultural production is for subsistence: the average value of crop sales as a share of the value of overall crop production stands at 18 percent (Carletto et al., 2017).

Cassava is an important staple crop that could provide up to 15 percent of the calories consumed at the household level in Malawi.P5F<sup>6</sup> The labor and non-labor input requirement of cassava cultivation is lower compared to seasonal crops, such as maize, and the crop is known for its adaptability to varying climatic and soil conditions (Kabambe, 2011). While the crop is grown throughout the country, the central and northern region districts along Lake Malawi (i.e. the cassava belt) lead cassava production and gear much of their cassava production towards home consumption. These districts predominantly grow bitter cultivars that constitute an essential component of diets and that can be harvested, depending on the variety, within a 12- to 18-month period. The preference for bitter varieties is anchored in the desire to protect the crop against thieves and animals, and in the tradition to produce _kondowole –_ fermented cassava – from these cultivars (Kambewa and Nyembe, 2008). Elsewhere in Central and Southern Malawi, while the caloric contribution of cassava consumption is not nearly as high, sweeter cassava varieties are grown, over harvest periods of 6 to 12 months, predominantly for commercial purposes (sold either in raw form or as processed snack food) and/or as a last resort food security measure during the lean season or in the face of unforeseen shocks (Moyo et al., 2004). Typically, when non-cassava belt districts in southern Malawi rely on cassava as a staple food, they produce _makaka,_ a nonfermented cassava flour formed from dried cassava chips. The spatial differences in cultivated cassava varieties are also at the heart of spatial differences in production and yield (Kabambe, 2011).

> 6 Based on the authors’ calculations using the Integrated Household Panel Survey (IHPS) 2016 data.

5
(e.g., Nigeria). This level of detail, however, is absent in most other economies’ household surveys and only simple questions regarding ownership status are recorded. We define tenure security as the perceived tenure security as we deem the sheer ownership status of a housing unit too narrow of an estimate for tenure security. In order to measure perceived tenure security, we draw on a recent global perception-based survey of tenure security conducted by Prindex (cf. Section 3.2.3).

On access to electricity (6), we follow the definition of the International Energy Agency (IEA), custodian agency for SDG 7.2 on renewable energy. The IEA defines access to electricity as an HH having initial access to sufficient electricity to power a basic bundle of energy services, at a minimum, several lightbulbs, phone charging, a radio and potentially a fan or television.<sup>5</sup> This level of detail, however, is not available in most HCES. Hence, we apply a simpler measure. We classify HH as adequate if they are connected to an electricity grid, have a renewable stand-alone system, or have a mini-grid connection of enough capacity to deliver the minimum bundle of energy services mentioned above. In contrast, firewood, kerosene, torches, or gas lamps are considered inadequate sources of electricity.

Finally, we follow IEA’s definition on access to clean cooking (7). We consider gas and electricity to be adequate cooking fuels. In contrast, solid fuels like biomass, wood, coal, or kerosene often used in traditional stoves for cooking, are considered inadequate.

These seven criteria are the most-often cited criteria of adequacy with clear links to HH welfare, and socio-economic and health outcomes within economic literature. Due to data limitations in HH surveys and to ensure cross-country comparison of the AHI, we abstain from including other variables that are often referred to as a prerequisite for adequate housing such as location, affordability, or cultural adequacy.

Regarding location, few data sources we use include information on where the household is located; or the variable is coded so diversely across countries that meaningful cross-country comparisons are impossible. Newer surveys in the LAC region, for instance, Mexico (ENIGHS 2018 survey) or Brazil (PNADC 2018 survey), include a variable on whether HHs live in a hazardous location.<sup>6</sup> Other regions, and older surveys, do not include such question (yet) and rather capture location through the distance to closest markets, schools, health facilities, or other services. Yet again, there are not enough comparable data points available across surveys. Surveys in

> 5 <u>https://www.iea.org/articles/defining-energy-access-2019-methodology</u>

> 6 In Brazil, for instance, of 151,864 HHs, 320 HHs are reported to live in a hazardous location – 0.02 percent, and in Mexico, 565 HHs of 73,513 HHs, 1 percent.
achieving student tends to interact more closely with other low achieving students while a high achieving student tends to interact more closely with other high achieving students; this complicates the connection between peer effects and ability tracking: exposure to high achieving students may benefit a low achiever, but he or she would be hindered by exposure to his or her low achieving peers.

The proceeding analysis also provides a Korea-specific contribution by examining the 2003 Korea PISA sample. This paper examines the association between mathematics achievement and within-school tracking where schools assign students into groups depending on their ability. Particularly, this paper focuses on how this association differs between males and females.

In the following sections, estimates of linear regression models present the association between learning achievement and tracking and reveal a statistically significant association for females, no statistically significant difference for males, and a statistically significant difference in association between genders. Using PISA’s categorical measure of achievement, its mathematics _proficiency levels_ , multinomial logit estimates show, among females, a statistically significant decline in the association between tracking and the odds of being in one proficiency level relative to the previous for higher levels of proficiency. Finally, comparisons of the standard deviation of achievement between males and females in schools with tracking and not with tracking, as well as estimates of conditional quantile regressions, reveal no evidence that tracking associates with inequities in achievement.

However, none of the proceeding results should be interpreted as causal. The crosssectional nature of the PISA dataset, in conjunction with unobserved factors, prevents establishing a counterfactual: we can not estimate the achievement of a student in a school with tracking had he or she been in a school without tracking. Furthermore, grade 10 students compose almost the entire sample, and the PISA examination occurred in June, 2003; since grade 10 is the first year of high school, students have only been in the observed school for three months; consequently, students have been exposed to tracking or non-tracking for a maximum of three months although likely less since midterm exams which are used to determine a student’s ability are often held in late April. While we find statistically significant associations, they likely reflect other factors that are correlated with tracking but not observable. However, these associations are certainly interesting and expose a need for more in-depth, empirical research on tracking in Korea.

3
Ihe final expression to be tested empirically, incorporating the possibility of government spending at a rate different from the private sector is therefore: (VII.10) Log(RERM) = Co + w.Log(Pm/Px) + z.ts + e.Ag where the sign of E is the same as the sign of the difference between the government and the private sector propensities to consume non-traded goods. If the government has a higher demand for N-T goods than the private sector, an increase in governrnent spending for a given level of total demand implies a shiFt in demand towards N-T goods and therefore a fall in the ir,ort real exchange rate.

Unfortunately, we do not have available quarterly or monthly series of government spending so we were restricted to the use of annual data for the period 1964-87. The results below show the estimation of the structural relation for the import real exchange rate. This variable is constructed as the ratio of the imported component of the Wholesale price index to the Consumer Price Index: LRERM= Log(Price of Imports from WPI/Consumer Price Index).

The explanatory variables are: (1) TSGDP: Ratio of the Trade Account Balance to GDP (since the Trade Account is denominated in dollars, the nominal GDP was converted into dollars using the official exchange rate for commercial transactions from the FIEL data bank). The expected sign of the effect of this variable on RERM is Dositive.

(2) Internal terms of trade (in logarithm:LPXM:Log(PX/PM), equal to the ratio of the agricultural component of the WPI to the imported component of the same price index. This variable incorporates the substitution effects due to the external terms of trade and of taxes and subsidies to foreign trade. The expected sign of the effect of this variable on RERM = PM/PH is negative (a raise in PX raises PH by a smaller proportion and thus reduces PM/PH).

(3) Government spending, captured by the ratio of nominal government spending to nominal GDP. The expected signr of this variable is negative under the reasonable assumption that the government has a larger propensity to consume non-traded goods than the private sector.

60
component and an imported component. A CET function is used to allocate total domestic production or domestic output of a good or service to domestic consumption and exports. The standard small-country assumption implying no good or service produced in Nepal can influence the international price of that good or service.

**Households and government:** The Stone-Geary linear expenditure function<sup>8</sup> is used to model household consumption. Hicksian equivalent variation (Hicks, 1943) is estimated to measure household welfare effects of counterfactual simulation, here load shedding scenario. Household savings are derived based on exogenously specified marginal propensity to consume. Government collects taxes, makes public expenditure and saves, receives and provides transfers.

**Macro closure:** The model we developed for this analysis is a static model, where investment is assumed to be at its steady state rate. Total investment is financed through domestic savings (household savings, government savings, firm’s savings) and foreign savings (or borrowing from foreign countries or institutions). The supplies of primary factors (i.e., labor, capital and land) are fixed for a given year. For labor, in fact, the working-age population for a given year is exogenous and fixed. However, the model allows labor to move across the sectors. The agricultural capital is mobile (can be moved from one crop to another based on their profitability) while industrial capital is sector specific. The model closes keeping foreign saving (current account balance) as exogenous. The exchange rate is fixed and the real exchange rate acts as the price numeraire.

# **3. Data**

We used the social accounting matrix (SAM) of year 2007. The social accounting matrixes of later years (e.g., 2012) are also constructed by some studies.<sup>9</sup> We used the 2007 SAM by design,

> 8 The Stone-Geary function is used to model household demand when households are assumed to have a subsistence level of consumption irrespective of the prices of goods for subsistence consumption and household income. Households then make a choice for further consumption. It uses the linear expenditure system (LES) for estimating consumer demand. The LES functional form was first introduced by British economist Sir John Richard Nicholas Stone (Stone, 1954) and explained by Ragner Frisch (Frisch, 1954), the Norwegian economist and the co-recipient of the first Nobel Memorial Prize in Economic Sciences in 1969.

> 9 Macroeconomics & Fiscal Management Global Practice, South Asia Region, Economic Impacts of Hydropower Investments, World Bank, April 2017, unpublished document.

7
The difficulty is that the SFD has, for some of its programs, targeted poorer areas, where many of the outcomes of interest (literacy rates, mortality rates, poverty rates, expenditure levels) are inherently worse. This also shows up in Table 3, which shows expenditure levels and poverty rates for villages with and without the program interventions of interest. Thus we see that household expenditure per capita levels are lower in localities with an SFD program; and the poverty rate is 30% in villages where the SFD had a program to provide potable water, compared with a poverty rate of 18% elsewhere in Egypt.

**<u>Table 3.  Poverty Rates and Expenditure Levels for Areas With, and Without, SFD Programs</u>**

||Without any SFD<br>program|With at least one<br>SFDprogram|All|
|---|---|---|---|
|Per capita annual expenditure|2,680|2,292|2,556|
|Per capita annual food expenditure|1,165|1,076|1,127|
|Headcount poverty rate (%):||||
|Overall|18.6|21.6|19.6|
|If there was an SFD intervention in||||
|Education|19.8|15.0|19.6|
|Health|19.5|22.1|19.6|
|Microcredit|18.8|23.1|19.6|
|Paving roads|19.5|21.8|19.6|
|Potable water|18.2|30.2|19.6|
|Waste water|19.6|12.8|19.6|
||Ranked|by expenditure per capita<br>||
||Poorest 40%|Middle 40%|Top20%|
|% having any SFD intervention||||
|Urban|10.8|10.4|7.6|
|Rural|9.9|9.9|10.5|

_Source:_ Authors’ calculations based on HIECS and community survey 2004/2005.

There are a number of possible solutions to the problem of endogenous program placement. In most of what follows we use propensity score matching, but we check the robustness of our results using a common impact model. Some further explanation of these approaches is in order.

# _Propensity Score Matching_

Even if the placement of SFD programs (“treatments”) has not been done randomly, it may be possible to measure the impact of the program by using matched comparisons. The idea is to match each participant, in this case a village, with an otherwise identical non-participant (the comparator) – based on observed pre-treatment or exogenous characteristics – and then to measure the average difference in the outcome variable between them.

_Impact Evaluation: Egyptian Social Fund for Development_ _Version of June 29, 2009_ _Page 7 of 36_
- (vii) _Municipio area:_ While fire spot density controls for the intensity of fire or smoke, the addition of area to the model allows the absolute quantity of fire or smoke to affect

municipal perceptions.

# **_Trivariate probit model_**

The modeling challenge lies in the endogeneity of environmental institutions. A correlation between the presence of environmental institutions and of problem perceptions might be attributed to some common but unobserved factors. That is, the presence of environmental institutions may simply be an indicator of the locality’s interest in environment, and not a causal influence on problem perception.

To control for this possibility, we use _municipio_ population as an instrument for the presence of environmental institutions. Based on the supply-side, economies-of-scale argument, we propose that this variable affects the presence of institutions but, after controlling for education, income, rurality, and biome, has no influence on problem perception. Further, we allow for correlation among unobserved determinants of institutional presence and of problem perception. This allows for the possibility that more environmentally sensitive places are more likely both to perceive problems and to establish environmental institutions.

This is done via a trivariate probit model. The specification of the three-equation model is: secretary* _= β1 X +γ1 ln population + ε1_ with secretary = 1 if secretary* > 0 | secretary = 0 otherwise

council* _= β2 X + γ2 ln population + ε2_ with councili = 1 if council* > 0 | council = 0 otherwise

10
The present paper is focused on one of the most important and rapidly-spreading scientific methods of knowledge acquisition in economics, that of randomized trials.

The method of randomization has long been a part of the instruments of investigation in epidemiology, and it is often treated as the gold standard for making causal inferences. Randomization and probabilistic inference as a method for acquiring scientific knowledge goes back to, at least, the nineteenth century. An interesting early example of probabilistic inference occurred in studying telepathy and parapsychology. In an experiment in 1884, Charles Richet wanted to see if one person’s drawing and looking at a playing card and the information thus acquired by the person gets transmitted to others. His study showed that of the 2,927 guesses about the card, 789 were correct whereas the expected number of correct guesses if they were completely random would have been 732.

This allowed him to conclude that there is a small and error-ridden but, nevertheless, positive transmission of knowledge that occurs between people’s minds. These early parapsychology studies and debates drew in some prominent thinkers including philosophers and economists, like Henry Sidgwick and Francis Ysidro Edgeworth. For a fascinating account of this early history of the subject, including some proper randomization studies, see Hacking (1988)<sup>1</sup> .

The arrival of randomization as a systematic method of investigation in economics and, in particular, development economics, is, however, relatively new. But it has arrived here with such gusto that this has become a major story of our time, giving rise to widespread use around the world by development economists, and, in equal measure, debate and discussion about its value and validity.<sup>2</sup>

> 1 As must be obvious from this paragraph, what constitutes a proper randomization experiment is not beyond dispute. Even in modern development economics many variants, around the broad idea of randomization, have been tried and critically evaluated (see Bruhn and McKenzie, 2009).

> 2 See, for instance, Banerjee, 2005; Banerjee and Duflo, 2009, 2012; Bardhan, 2013; Barrett and Carter, 2010; Basu, 2005, 2011; Cartwright, 2010; Cartwright and Hardie, 2012; Deaton, 2009, 2010; Duflo, Glennerster and Kremer, 2008; Elbers and Gunning, 2013; Heckman and Smith, 1995; Mookherjee, 2005; Ravallion 2009; and Rodrik, 2008.

3
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