rafmacalaba/datause-encoder-data huggingface dataset:
# **7.4   Treatment Effects on Environmental Contamination**

Medical evidence suggests that the transmission of diarrhea and respiratory infections is mainly through the hands, which constitute disease vectors carrying respiratory microorganisms and fecal material to the domestic environment of the susceptible child. The use of soap reduces germs and bacteria in hands, thus reducing the risk of becoming sick. It is expected that those practicing handwashing with soap at key junctures will have a lower presence of bacteria such as _Escherichia Coli_ or total coliforms on their hands, thereby reducing the risk of contamination when handling, preparing, or serving drinking water at home. Thus, the study collected samples of drinking water<sup>49</sup> from a subsample of 600 households among the school component sample and its control group. These samples were collected during the administration of the questionnaire, and analyzed to detect the presence of _Escherichia Coli_ and total coliforms. Consistent with previous results, the microbiological analysis of drinking water samples summarized in Table 4 shows that the prevalence of _Escherichia Coli_ and of total coliforms was lower in the treated households than in those of the control group, but none of the effects are statistically significant at conventional levels.

# **7.5   Treatment Effects on Child Health**

The last step in the causal chain is to assess whether the positive results found on exposure to the campaign—knowledge of appropriate handwashing practices, access to soap and water, and handwashing with soap behavior—resulted in health improvements. Table 5 summarizes the effects of the treatments in a wide range of indicators of child health and well-being, including symptoms of illness, nutrition, parasite infestations, and anemia. The results show that overall none of the treatments had a significant effect on those variables.

Because the mass media plus DCC provincial-level treatment alone had no effects on previous outcomes, we did not expect to find any impact on child health. If there were significant estimates, they would probably not be causally attributed to the treatment given that it did not reach the target audience (see Table 1). This is the case of a reduction in the prevalence of

> 49 Drinking water is not the water at source, but rather the water that the household drinks directly. Drinking water can be boiled or treated and is usually stored in containers or jars inside the household.

28
approximately 260 km south of the capital city of Antananarivo, and Vatomandry<sup>21</sup> , located on the central east coast of Madagascar. The sampling focused only on two types of fuel use: (i) charcoal and (ii) wood, given that cooking with LPG and electricity is rare.

Since use of improved stoves is also atypical, after a baseline household survey and baseline monitoring of HAP, improved charcoal and improved wood stoves were distributed to a group of households to investigate the effectiveness of improved stoves as potential mitigation measures. While traditional charcoal stoves are made of metal without insulation, which leads to most of the heat escaping, the disseminated improved charcoal burning stove is similar to the Kenya Ceramic Jiko, and consists of an hour-glass shaped metal cladding with an interior ceramic liner that is perforated to permit the ash to fall into a collection box at the base. A single pot is placed on the top of the stove. The disseminated wood-burning stove, provided only in Vatomandry, is called Fatana Pipa and is produced by a company called Bionerr. The stove consists of a metal-covered ceramic bucket with a chimney and supports one pot. The ethanol burning stove disseminated to the households is a stainless steel stove called the CleanCook. The type given to the households in this study has one pot stand. The non-pressurized fuel tanks hold the ethanol in a special adsorptive fiber. The burner flame is adjusted or extinguished by means of a simple regulator.

In selecting households, the criteria used were (i) use charcoal or wood as main fuel during the survey, (ii) purchase at-least half of their fuel, (iii) have enclosed kitchen and (iv) have interest in using an improved stove.<sup>22</sup> Household kitchen concentrations of PM2.5 and CO were measured in every study household three times during February-March 2009 (baseline), April 2010 (round 2), and July-August 2010 (round 3). A number of questions were asked about fuel use, stove type, cooking locations and structural characteristics of kitchen in all three rounds of the survey.

There was a significant loss of households between the baseline and round 2 due to an unanticipated delay of one year between the baseline and round 2. Scarcity of a safe, effective ethanol stove was the major reason behind the delay. Civil unrest and consequent economic

> 21Vatomandry is located at an altitude of 4m from sea-level and records more rainfall than Ambositra.  It has a hot climate throughout the year, yet distinct wet (December-March) and dry (April-November) seasons. The housing stock is primarily wooden homes with detached kitchens in the back of the main house. Majority of the households use wood and traditional wood stoves for cooking in Vatomandry, but charcoal and traditional stoves are also used. 22 In order to assess health impacts of the mitigation measures, two additional criteria considered were presence of a child under 4 year of age in the household and the mother of the child was the main cook.

6
8 improved access to finance can be construed as one that seeks to help individual enterprises (in the service of the higher level goal of development). Assessment of the impact of such a policy might produce the apparently discouraging conclusion that many SMEs are beyond help: with or without improved access to finance, their chances for long-term survival are low. This would seem to imply a negative assessment of the policy -- if one accepts the critical assumption that the effect on long-term survival is an appropriate performance measure.

The suggestion here is that this assumption is simplistic: a deeper analysis is needed. On the face of it, survival data for enterprises of a particular sort say nothing about the viability of that form of enterprise; newly formed businesses generally replace the failures. Neither do the survival data necessarily reflect adversely on the effectiveness of the form as a way of organizing resources, or even on the social return to the particular investments made by failing firms. Of course, if there were evidence showing that incremental financial resources supplied in the interests of development were systematically dissipated in operating losses or merely funded a temporary increase in consumption, this would indeed imply a negative assessment of the policy. But the ultimate occurrence of a business failure does not, as a matter of logic, imply that the life of that business was "a waste" in either of these significant senses. The strength of the association between business life spans and the extent of the economic contribution
human and physical capital, per effective unit of labour. Further,<sup>_s_</sup> _k_ and<sup>_s_</sup> _h_ are the fractions invested in human and physical capital. Labour and technological progress are assumed to grow exogenously at rates _n_ and _g_ : _L_ ( _t_ ) = _L_ (0) _ent_ and _A_ ( _t_ ) = _A_ (0) _egt_ . Therefore the number of effective units of labour, _L_ ( _t_ ) _A_ ( _t_ ) grows at rate _g+n_ . Capital depreciates at rate _δ_ . The growth rate of technology and depreciation rates are assumed to be constant and both sum 0.05, i.e. 10 _g+δ=0.05_ . _I,_ the Inf Inx, represents the influence of an economy’s infrastructure. Inf Inx is measured by infrastructure indicators, denoted by _ω_ , such as kilometres of paved roads, number of phone lines, percentage of population with access to improved water and sanitation facilities, etc. The index is _I_ = πγ where γ reflects the elasticity of GDP with respect to infrastructure. To a large extent, the exercise conducted in this paper consists in demonstrating that γ is statistically significant as evidence that infrastructure matters to growth. In per capita terms, The evolution of _k_ and _h_ is governed by Equating the right hand side to zero to arrive at steady states, substituting _y_ and solving we get Introducing these steady states in _y_ ( _t_ ) = _Ik_ ( _t_ )<sup>α</sup> _h_ ( _t_ )<sup>β</sup> and taking logs we get

> 10 This assumption is consistent with that adopted in Mankiw, Romer and Weil (1992, pp. 413, Footnote 6) and Islam (1995, pp. 1139) for all countries analyzed (although it is important to point out that this value is only valid for countries comparable to the United States) and is similar to the value 0.03-0.04 used in Romer (1990). Furthermore, the assumption _g+δ=0.05_ has been used in Estache et al (2005) for the SubSaharan Africa dataset. Given that this assumption has been made in the context of diverse datasets, we feel comfortable doing the same for the East Asian sample.

8
**Table S1.7: Robustness: Days of the week**

||DP<br>(1)|FDC<br>(2)|NRM<br>(3)|UPC<br>(4)|
|---|---|---|---|---|
|Enumerator gender (male)|-0.557***|-0.399**|-0.105***|-0.509***|
||(0.151)|(0.158)|(0.034)|(0.115)|
|Enumerator age|-0.021|-0.005|0.002|-0.033**|
||(0.021)|(0.020)|(0.004)|(0.016)|
|Enumerator education|0.107|0.133|-0.073**|0.064|
||(0.110)|(0.122)|(0.035)|(0.111)|
|Enumerator from urban locality|0.450**|0.570***|0.000|0.429***|
||(0.186)|(0.205)|(0.040)|(0.149)|
|Monday|0.064|-0.017|0.302**|-0.017|
||(0.231)|(0.224)|(0.134)|(0.197)|
|Tuesday|-0.133|-0.166|0.165|0.020|
||(0.179)|(0.215)|(0.110)|(0.143)|
|Wednesday|0.076|0.086|0.318**|0.225|
||(0.301)|(0.228)|(0.144)|(0.264)|
|Thursday|0.024|0.105|0.276|0.307|
||(0.244)|(0.245)|(0.176)|(0.193)|
|Friday|-0.141|-0.130|0.025|-0.077|
||(0.199)|(0.225)|(0.143)|(0.202)|
|Saturday|0.162|-0.087|0.360***|0.172|
||(0.151)|(0.168)|(0.102)|(0.154)|
|Observations|6,240|6,328|6,547|6,315|
|R-squared|0.486|0.459|0.236|0.479|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). Each regression includes community fixed effects and a constant. The excluded category is Sunday. Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

40
Policy Research Working Paper 8192

# **Abstract**

Based on a two-round household panel survey conducted in Eastern Uganda, this study shows that the analysis of the inverse scale-productivity relationship is highly sensitive to how plot-level maize production, hence yield (production divided by GPS-based plot area), is measured. Although farmer-reported production-based plot-level maize yield regressions consistently lend support to the inverse scale-productivity relationship, the comparable regressions estimated with maize yields based on sub-plot crop cutting, full-plot crop cutting, and remote sensing point toward constant returns to scale, at the mean as well as throughout the distributions of objective measures of maize yield. In deriving the much-debated coefficient for GPS-based plot area, the maize yield regressions control for objective measures of soil fertility, maize genetic heterogeneity, and edge effects at the plot level; a rich set of plot, household, and plot manager attributes; as well as time-invariant household- and parcel-level unobserved heterogeneity in select specifications that exploit the panel nature of the data. The core finding is driven by persistent overestimation of farmer-reported maize production and yield vis-à-vis their crop cutting–based counterparts, particularly in the lower half of the plot area distribution. Although the results contribute to a larger, and renewed, body of literature questioning the inverse scale-productivity relationship based on omitted explanatory variables or alternative formulations of the agricultural productivity measure, the paper is among the first documenting how the inverse relationship could be a statistical artifact, driven by errors in farmer-reported survey data on crop production.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sgourlay@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|_Table 3: Determinants of producer maize p_<br>|_rices_|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Model #|||**1**||**2**||**3**|**4**|||**5**|
|||**Without**<br>|**Controls**<br>|**With C**<br>|**ontrols**<br>|**Interactio**<br>|**n Variable**<br>|**Southern**<br>|**Region**<br>|**Norther**<br>|**n Regions**<br>|
|**Variable**||**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|
|Distance to market||-0.802***|0.221577|-0.01***|0.0019228|-0.0045**|0.002063|-0.0118***|0.003157|-0.002887|0.004594|
|Age of household head||||-0.0000164|0.0000964|-0.000015|0.000095|0.0000884|0.00016|0.000135|0.000216|
|Household size||||-0.0013**|0.0005427|-0.0013**|0.000538|-0.003***|0.000863|-0.002338|0.001482|
|Season|Harvest|||0.0911***|0.003766|0.083***|0.003919|0.0935***|0.006689|0.091***|0.008661|
|Gender of household head dummy|Female|||0.001814|0.0036517|0.002374|0.003620|-0.0025958|0.006275|0.002918|0.008279|
|Literacy Dummy|Literate|||-0.0024653|0.0034142|-0.002183|0.003384|-0.0069641|0.005741|0.005459|0.008254|
|Owns radio|Yes|||0.0028993|0.0033103|0.003185|0.003281|0.0104825|0.005516|-0.001781|0.007837|
|Owns phone|Yes|||-0.0668***|0.0215497|-0.063***|0.021369|-0.1009**|0.039242|-0.020726|0.032404|
|Owns bicycle|Yes|||0.0019621|0.0032789|0.001766|0.003250|0.0000057|0.005483|0.000915|0.007617|
|Owns motorcycle|Yes|||-0.0019652|0.0125435|-0.002208|0.012434|-0.0137555|0.020738|0.007946|0.023388|
|Owns car|Yes|||0.0190001|0.0236407|0.017455|0.023436|0.0136565|0.045591|-0.000946|0.041448|
|Price of maize in Dar es Salaam||||0.7349***|0.006605|0.734***|0.006548|0.735***|0.010858|0.753***|0.015299|
|Region||||(|Included but r|esults notpr|esented: signi|ficant at 1%)||||
|Interaction variable: distance to<br>market * season||||||-0.007***|0.001053|-0.0029***|0.001953|-0.011***|0.001987|
|Constant||223.87***|1.098909|1.513***|0.0360621|1.512***|0.035748|1.513***|0.05885|1.417***|0.081869|
|No. of observations||2750||2750||2750||1134||450||
|R - squared||0.0047||0.8348||0.8377||0.8194||0.8664||
|Adjusted R - squared||0.0044||0.833||0.8359||0.8163||0.8618||

16
Figure 4: Extreme Weather Losses, 1970-1999 Historical and Best Practice

# **Killed by Floods**

<!-- Start of picture text --> Historical<br>Best Practice<br><!-- End of picture text --> <!-- Start of picture text --> 30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>1970 1980 1990 2000<br>Affected by Floods (‘000)<br>300,000<br>250,000<br>200,000<br>150,000<br>100,000<br>50,000<br>0<br>1970 1980 1990 2000<br><!-- End of picture text -->

# **Affected by Droughts (‘000)**

<!-- Start of picture text --> 300,000<br>250,000<br>200,000<br>150,000<br>100,000<br>50,000<br>0<br>1970 1980 1990 2000<br>41<br><!-- End of picture text --> \
non-statistically significant effects of the introduction of the NHIS on the expenditures in outpatient care. Despite obtaining a large point estimate of the effect of the NHIS on drug expenditures, the effect is not statistically significant.

An issue with interpreting the effects from the NHIS on medical expenditures on the full sample comes from the fact that the GLSS only records medical expenditures of individuals reporting an illness or injury, and thus the effect is estimated from comparing the responses between these households reporting an illness or injury, and households that did not. Since households reporting an illness or injury are possibly different before and after NHIS implementation (see Table 1), we split the sample of households reporting some illness or injury by the previously defined measures of health shock.

Columns (2) and (3) limit the sample by days of sickness of adults in the household, and Columns (4) and (5) by days in which ADL was limited due to the sickness of adults in the household. The results yield an estimated effect of the NHIS on OOP payments of -35 percent among households with more sickness intensity. In contrast, we do not find statistically significant effects among households with less sickness intensity. This drop was driven by large savings in medicine purchases, which we estimate to be 66% among households with more sickness.

We reach similar conclusions when splitting the sample by days of ADL limitations. Households with less ADL limitations experienced lower savings in their OOP payments than households with more incapacitating sickness. Again, savings in OOP were driven by a drop in drugs expenditures, of 63%. Because of the closeness in the estimates for both health shock measures, we will present in what follows the results only by the measure of intensity of sickness. Results by ADL limitation are available upon request.

# **5.2 Risk Coping Strategies**

**Consumption.** The first row in Table 5 shows the RD coefficients on the logarithm of frequent consumption. We find a small statistically significant increase of 3% in frequent consumption from the NHIS implementation for the full sample. This effect was driven by households experiencing more sickness, for whom we estimate an 8% increase in consumption.

When disaggregating the measure of frequent consumption by its components, we find non-statistically significant effects of the NHIS on food consumption for the full sample and a statistically significant increase of 0.6% for the sample of households with more sickness intensity. In comparison, non-food consumption increased by 1.2 percent for the full sample after NHIS implementation. The point estimate of this effect was again larger for households with more sickness intensity (2.5 percent). These results suggest that non-food expenditures 21
Table 2. Estimation of the probability of being unemployed. Year 2015

|Variables|Argentina|Uruguay|
|---|---|---|
|Non-routine cognitive|-0.0514244*|-0.0172726*|
|| ́(0.029028)|(0.0010277)|
|Routine cognitive|-0.1406224***<br>(0.018459)|-0.0769139***<br>(0.009799)|
|Routine manual|0.3346739***|0.0505115***|
||(0.041706)|(0.013147)|
|Non-routine manual|-0.1563471***|-0.0074673|
||(0.038226)|(0.015701)|
|Man|-0.1227095***|-0.2669335***|
||(0.034698)|(0.020472)|
|Years of education|-0.0014153|-0.045073***|
||(0.005044)|(0.003176)|
|Age|-0.0233769***|-0.0244753***|
||(0.001292)|(0.000695)|
|Constant|-0.791312***|-0.0257861|
||(0.104482)|(0.044926)|
|No of observations|24299|53740|
|LR Chi2|902.52|1989.95|
|Prob> Chi2|0.0000|0.0000|
|Pseudo R2|0.1024|0.0787|
|Loglikelihood|-3954.6|-11648.1|

Source: Our own estimation based on household surveys and O*NET Note: Standard error is shown in parenthesis. *** significant to 1%, ** to 5% and * to 10%.

The results found confirm the trends observed and partially confirm the hypothesis suggested for developed countries.

In this sense, the variable “Routine manual” has the positive sign expected and is significant to 1%. That suggests that the possibility of becoming unemployed increases as the relative importance of the routine manual tasks performed in that occupation grows. In other words, those workers in occupations that require a higher intensity of routine manual tasks face a greater risk of finding themselves unemployed, with this effect being greater in Argentina than in Uruguay. These results allow us to suggest that technological change, and therefore, the possibility of replacing tasks with automation, increment the probability of unemployment for those workers who spend the majority of their working hours performing routine manual tasks.

However, and in line with that discussed previously, no positive relationship is found between the probability of being unemployed and the intensity of routine cognitive tasks in the previous

- 26 -
In a hypothetical scenario fashioned after the current pandemic period – where firms have no revenues due to a lockdown or collapsed demand – the median firm has retained earnings and other sources of financing to last 8 (in retail) to 19 weeks (in other manufacturing), while the average firm does so within 12 (in construction) to 38 weeks (in manufacturing of chemical, plastic and mineral products).

Across countries, the median Ukrainian firm is the most liquidity constrained, while the median Peruvian firm has the most breathing space. The former has 7 weeks buffer in retained earnings and other sources of financing, the latter 16 weeks. The same is true when we turn to the analysis of means, the average Ukrainian firm has a 10-week buffer in retained earnings and other sources of financing, while the average Peruvian firm has 27 weeks.

Our analysis does not support the Schumpeterian view that economic crises cleanse the private sector from inefficient firms. The evidence suggests that firms suffer untimely illiquidity regardless of age, size and productivity levels. In particular, survival times are not associated with higher firm productivity, with the exceptions of firms in Greece, Kenya and Peru. Larger firms have longer survival times only in Kazakhstan. Older firms are more resilient only in Jordan and Morocco.

The results here complement recent work by Bachas and Brockmeyer (2020), who use corporate tax records to show that even small revenue shocks push the majority of firms in developing economies into loss-making territory.

Section II describes the data, section III outlines the methodology, section IV illustrates the base results, section V delineates the hypotheses used, section VI presents regression results and Section VII details initial policy measures. Section VIII concludes.

# **II. Data**

The calculations use data for 11,759 businesses from the World Bank’s Enterprise Surveys conducted in a dozen economies across Africa, Central Asia, Europe, Latin America, and the

> and low-income economies where the informal sector constitutes a large share of economic activity. In future research we intend to advance the methodology to account for informality.

3