rafmacalaba/datause-encoder-data huggingface dataset:
|Are you happy and satisfied with your<br>accomplishment at work?|(0.53)|(1.46)|(1.56)||
|---|---|---|---|---|
|Do you feel able to face your problems?|13.29|23.75|10.45|6853|
||(0.65)|(1.36)|(1.51)||
|Do you feel sad and that there is no way out?|23.01|48.31|25.30|6851|
||(0.82)|(1.60)|(1.80)||
|Have you lost your self-confidence?|7.88|22.42|14.54|6853|
||(0.46)|(1.33)|(1.41)||
|Do you see yourself as a useless person?|7.13|16.39|9.26|6852|
||(0.43)|(1.19)|(1.26)||
|Are you able to feel happy notwithstanding|11.11|35.06|23.94|6863|
|the surrounding circumstances?|(0.56)|(1.52)|(1.62)||
|_Average _|15.89|34.00|18.11|6853|

Notes: Responses indicating more severe outcomes are operationalized in this module as responses of “worse than usual”/“much worse than usual” or “more than usual”/“much more than usual”. Estimates are unweighted, and clustered standard errors are reported in parentheses.

|**Statement**|**Depression**<br>**Share of r**<br>**choosing**<br>**that indi**<br>**severe ou**<br>_PPCS_|**espondents**<br>**responses**<br>**cate more**<br>**tcomes (%)**<br>_Facebook_|**Difference**<br>**(Percentage**<br>**point)**|**N**<br>**(combined)**|
|---|---|---|---|---|
|You were happy with a good mood|62.41|66.19|3.78|6855|
||(1.21)|(1.51)|(1.94)||
|You were feeling calm and relaxed|64.84|74.26|9.42|6855|
||(1.12)|(1.40)|(1.79)||
|You were feeling energetic|59.05|65.26|6.21|6849|
||(1.15)|(1.53)|(1.91)||
|You woke up active and relaxed|56.94|67.69|10.74|6857|
||(1.16)|(1.49)|(1.89)||
|Your days were full of things you liked|62.78|72.90|10.12|6854|
||(1.21)|(1.42)|(1.87)||
|_Average _|61.20|69.26|8.06|6854|

Notes: Responses indicating more severe outcomes are operationalized in this module as responses of “never”, “rarely”, and “slightly less than half the time”. Estimates are unweighted, and clustered standard errors are reported in parentheses.

23
|Panel C|||
|---|---|---|
|Received subsidized farm inputs (1/0)|-12.241|(21.464)|
|Received food/cash transfer (1/0)|72.839<sup>**</sup>|(35.939)|
|Subsidized inputs*food/cash transfer (1/0)|-55.317<sup>*</sup>|(31.705)|
|Panel D|||
|Community has irrigation scheme (1/0)|2.341|(23.620)|
|Received food/cash transfer (1/0)|45.362<sup>***</sup>|(16.032)|
|Irrigation scheme*food/cash transfer (1/0)|-40.182|(26.488)|
|Panel E|||
|Received food/cash transfer (1/0)|47.961<sup>***</sup>|(17.949)|
|HH uses an irrigation scheme (1/0)|45.316|(50.633)|
|HH use an Irrigation scheme*food/cash transfer (1/0)|-86.161|(57.079)|

Notes: All models include control variables such as household characteristics and location fixed effects. Obtained coefficients are the same as conditional marginal effects (dy/dx); Standard errors in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01

# **4.2.3 Effects of interventions on coping mechanisms**

Table 8 presents panel Tobit estimates of the effects of social assistance and irrigation investments on food and nutrition security as measured by coping strategy index. The results indicate no significant combined effects from multiple program participation. However, receiving input subsidies alone has significant effect on a household’s ability to cope with food insecurity (Panel A & C). For example, households receiving input subsidies cope between 13-16 percent less with food poverty on average. Conversely, receiving food/cash transfers is linked to significant use of coping mechanisms. On average, beneficiaries of food/cash transfers cope 21-26 percent more with food and nutrition insecurity (p<0.05). A finding also noted in Table A6 where we use quantity of subsidized fertilizers received.

Table 8: Effects of interventions on coping with food insecurity

|Dependent var: CSI|Model2||
|---|---|---|
||Coef.|SE|
|Panel A|||
|Received subsidized farm inputs (1/0)|-0.135<sup>*</sup>|(0.074)|
|HH uses an irrigation scheme (1/0)|-0.318|(0.217)|
|Subsidized inputs*use of irrigation scheme (1/0)|0.267|(0.360)|
|Panel B|||
|Received subsidized farm inputs (1/0)|-0.115|(0.077)|
|Community has irrigation scheme (1/0)|-0.001|(0.108)|
|Subsidized inputs*community has irrigation scheme (1/0)|-0.094|(0.203)|
|Panel C|||

17
96 consumption should be integrated with economic studies of household p.oductin, labor supply, and expenditure patterns. The family bargaining framework for evaluating public policies in low income countries should lead to new policy insights. Sinc& not much work has yet **been** undertaken, one can only identify where the payoffs are likely to be 1arge and the direction in which they may move development policy. In thls tentative form, directions **are** offered for research **and** policy.

First, statistical methods have been outlined and the family bargaining framework **has been** mentioned **as a useful** basis for ostimat'ng private and social rates of return to schooling for women and men. Labor force participation and choice of wage employmnt both influence the selection of samples on which market **wage** functions are estimated. Procedures should correct for **these** sources of sample selection bias. Few studies have used comparable identification restrictions. What evidence is available suggests that primary and secondary schooling is a high return activity, and no **less** so for women than for men. It would **seem** inefficient to maintain much lower schooling investments in females than in males, as evident now in South and West Asia and in North and subSaharan Africa. Development agencies have a clear mandate to emphasize this investment gap and encourage cour.trios to raise female schooling rates toward those of males.

How **thls is** to be accomplished at **least** cost has not **yet been addressed** by practical policy **makers** nor **studied extensively** by **researchers.** How do **the** constraints of institutions, household resource endowments, prices and **wages** determine school enrollmnt rates for boys and girls in various cultural regions of the world? The family controls this decision and mUst be motivated to adopt the view that Sirts are as good an investmnt of parent resources as
which reflects energy efficiency rate is very independent of income: it is flat up to 7th income decile and then slightly increases with income. This indicates that the rural households in Bangladesh mostly use traditional technology for cooking and lighting irrespective of income; only for the highest income households modern energy has a higher share. High income households not only have a higher rate of electrification, but they also diversify their electricity use in a wide range of appliances.

<!-- Start of picture text --> .5<br>Energy<br>poverty<br>.4<br>separator<br>.3<br>.2<br>.1<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Income decile<br>Energy cost-Income ratio Electrification rate<br>End use energy-Total energy ratio<br><!-- End of picture text --> Source: BIDS Survey (2004)

**Figure 8: Household electrification, other energy consumption parameters by income decile**

The income-energy cost ratio is an established indicator of household welfare. It declines constantly as income rises. Poor households are very constrained and spend on energy only that much which is absolutely necessary. In addition, the types of fuels that are available to them for basic lighting and cooking are used very inefficiently because of the nature of their cooking and lighting devices.

29
<!-- Start of picture text --> Urban a<br>Larger household +<br>Low education<br>Stopped working<br>F F a ad<br>Stringency index (std.)<br>-.05 0 05 1 15<br>Marginal effect on probability of income loss<br>vs. prepandemic<br>ry N=71,958;If reported C=41 PanelN=21,174;householdsC=19<br><!-- End of picture text --> <!-- Start of picture text --> Urban °<br>LargerLow householdeducation ° |<br>Back working °<br>Did not stop working ,<br>Stringency index (std.) .<br>-06 -.04 -02 0 02 .04 .06<br>Marginal effect on probability of income gain<br>during pandemic<br>o IfN=44,459; reported C=22 IfN=19,119; lost incomeC=17<br><!-- End of picture text -->
Figure 1c: Non-anonymous Growth Incidence Curve - Synthetic vs. Actual Panel Data Peru 2008 and 2009 <!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>0 5 10 15 20<br>Ventiles<br>Truth Lower Bound Upper Bound<br>Growth in Consumption Per Capita (%)<br><!-- End of picture text --> Data source: INEI - Peruvian National Household Survey (ENAHO), 2008-2009. Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Upper bound estimations are based on 50 repetitions.

34
|**Table A1: Distrib**<br>|**utional cha**<br>|**nges in gl**<br>|**obal pove**<br>|**rty profiles**<br>|**with root**<br>|**N allocatio**<br>|**n rule**<br>|
|---|---|---|---|---|---|---|---|
|Region|Per<br>capita<br>poverty<br>rate (%)<br>at**$3.20**|Root N<br>poverty<br>rate<br>(%) at<br>**$7.36**|Change<br>in<br>poverty<br>(pp)|Millions<br>of per<br>capita<br>poor|Millions<br>of root<br>N poor|Change<br>in<br>millions<br>of poor|Absolute<br>deviations<br>in<br>millions<br>of poor|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Middle East &<br>North Africa|20.3|18.5|-1.8|67|61|-6.0|9|
|Sub-Saharan Africa|67.3|65.6|-1.8|704|686|-18.3|32|
|South Asia|43.4|43.2|-0.2|763|759|-3.6|54|
|**World**|**29.6**|**29.6**|**0.0**|**1738**|**1738**|**0.0**|**130**|
|Europe and Central<br>Asia|4.6|4.7|0.1|23|23|0.4|4|
|Other High Income|0.9|1.1|0.3|9|12|2.7|3|
|Latin America &<br>Caribbean|9.3|10.6|1.2|54|62|7.1|8|
|East Asia & Pacific|18.4|21.2|2.8|118|136|17.6|20|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table reports poverty estimates for the 2017 reference year. For countries with no surveys conducted</u> exactly in 2017, the poverty estimates are extrapolated from the latest survey if conducted before 2017, otherwise extrapolated or interpolated from the closest surveys before and after 2017. The extrapolation and interpolation rules applied are the same ones the World Bank applies when “lining-up” global poverty estimates for every year (see the - Poverty and Inequality Platform (PIP) Methodological Handbook via <u>https://worldbank.github.io/PIP Methodology/lineupestimates.html). The table is arranged in ascending order of change in poverty (column 4). These</u> results are based on the full sample of 162 countries covered in the paper.

44
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The presence of multiple operators in the mobile market has contributed to rapid expansion and an impressive drop in prices. Between 2000 and 2009 the number of connections increased 100-fold—from 39,000 connections to almost 4 million. The price of a mobile basket dropped from $15.5 in 2005 to $11.8 in 2009. Similarly, the price of calls dropped more than 75 percent between 2000 and 2009 (table 14).

Benin’s Internet market has benefited greatly from being connected to the SAT-3 cable and fiber optic lines. Benin Telecom has four transmission arteries operating over optical fibers: Cotonou-ParakouMallanville (Niger border), Parakou-Porga (Burkina Faso border), Cotonou-Hilacondji (Togo border), and Cotonou-Igolo (Nigeria border). Internet users increased from 0.2 per 100 people in 2000 to 6.5 in 2009. Internet connectivity increased from 3 to 19 Mbps between 2000 and 2008 (figure 19a). Benin’s Internet penetration is above the average for the ECOWAS region (figure 19b). A growing number of users are accessing the Internet via their mobile phones. According the ATRPT, there were 119,477 mobile Internet subscribers in mid-2009, more than eight times the number of fixed Internet subscriptions.<sup>7</sup> Given the popularity of mobile, the launch of third-generation mobile networks is due. 3G mobile networks would enhance high-speed connectivity options and create greater competition in the broadband market.

**Figure 19. Benin’s Internet market in the ECOWAS context**

<!-- Start of picture text --> a. Internet service trends, Benin b. Internet service, ECOWAS, 2008<br>7 25 14 350<br>6 12 300<br>20<br>10 250<br>5<br>8 200<br>15<br>4<br>6 150<br>3<br>10 4 100<br>2<br>2 50<br>5<br>1 0 0<br>0 0<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>Internet users<br>International internet bandwidth<br>International internet bandwidth<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 Sierra Leone Liberia Niger Guinea-Bissau Mali Burkina Faso Mauritania Gambia Senegal Benin Ghana Cote d'Ivoire Cape Verde Togo Nigeria<br><!-- End of picture text --> _Source_ : World Bank, including AICD analysis.

Another factor driving the expansion of the internet market is a long-standing cybercafe culture, dating back more than a decade (Lohento 2000). According to the national statistical office there are around 200 cybercafes in the country charging on average 344 CFA per hour ($0.72) (INSAE 2010). Most have an ADSL broadband connection, making it cheaper for a user to go to a cybercafe for broadband access than to obtain their own home connection. Dial-up accounts for only 6 percent of fixed Internet subscriptions; most connections are by fixed wireless CDMA or WiMAX (ATRPT 2009.

> 7http://www.atrpt.bj/Observatoire/telecom2009/internet/TABLEAU%20DE%20BORD%20INTERNET%20JUIN% 202009.pdf

33
auspices of the Reconstruction and Development Program (RDP) and its successor, Breaking New Ground (BNG), the State constructed over a million fully serviced houses across South Africa. Following a standard format with a 40m<sup>2</sup> dwelling on a serviced residential land parcel averaging 160m<sup>2</sup> in size, the roll-out of so-called “RDP houses” greatly expanded the opportunity for backyarding throughout urban areas.

# 2.3. Landlord and tenant incentives

The improvement in living conditions enjoyed by the RDP household was rarely accompanied by an improvement in its economic prospects. In fact, the peripheral location and dormitory nature of the sprawling RDP housing settlements often resulted in poor job accessibility. In response, housing recipients became landlords by erecting and then renting out informal dwellings in their backyards. In doing so, they successfully exploited one of the few resources at their disposal: space (Govender, Barnes, and Pieper 2011).

Why do poor people live in backyard dwellings, dependent on landlords and liable for rent, rather than moving to an informal settlement and experiencing an independent and rent-free lifestyle? The principal reasons appear to include better access to services, better locations, a reduced threat of eviction, and greater personal safety (Tshangana, 2014, Lemanski, 2009). For poor households dependent on irregular and informal employment, backyard dwellings offer a degree of locational flexibility in response to economic opportunities not available with static residence in a peripheral, dormitory RDP settlement (Lemanski, 2009). Such locational flexibility is particularly attractive to newly urbanized residents seeking job opportunities (Lemanski, 2009). Perhaps counter-intuitively, both official statistics and case studies confirm that the economic and educational profiles of backyard dwellers are superior to those of their landlords (Govender et al., 2011). In addition, 2011 Census data analysed by Rabe (2017) show that backyard dwellings in Cape Town are more likely to contain a single person, and less likely to contain more than four persons, than the average dwelling in the city, consistent with the view that backyard households are more mobile than the general population. Moreover, a study of backyarding in Greater Soweto (Johannesburg) indicated that backyard tenants are significantly younger than their landlords (36 vs. 56 years for household heads) and more likely to be foreign immigrants (Beall et al., 2003).

6
# **7 References**

Acemoglu, D., Hassan, T.A., and Tahoun, A. (2014), “The Power of the Street: Evidence from Egypt’s Arab Spring”, _NBER Working Paper 20665_ .

Alm, J. (1999), “Tax Compliance and Administration” In Handbook on Taxation, ed. W. Bartley Hildreth and J. A. Richardson, 741– 68. New York: Mercel Dekker Andreoni, J., Erard B., and Feinstein J. (1998), “Tax Compliance”, _Journal of Economic Literature_ , 36(2):818–60.

Ayadi, L., Benjamin, N., Bensassi, S. and Raballand, G. (2013), “Estimating Informal Trade across Tunisia's Land Borders”, World Bank Policy Research Working Paper 6731 _._ Association Tunisienne des Contrôleurs Publics (ATCP) (2015) “La Petite Corruption: Le Danger Banalisé: Etude exploratoire sur la perception de la petite corruption en Tunisie” Banque Mondiale (2014), _Pour une Meilleure Gouvernance des Entreprises Publiques en Tunisie,_ Rapport 78675-TN, Washington DC : World Bank.

Bhagwati, J. (1964), “On the Underinvoicing of Imports”, _Oxford Bulletin of Economics and Statistics_ 26(4), 389-97.

33
# **Table 2: Total Factor Productivity Growth Regressions (OLS)**

Dependent Variable: TFPG Method: Least Squares Sample(adjusted): 1971 1997 Included observations: 27 after adjusting endpoints

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Variable|Coefficient|t-Statistic|Coefficient|t-Statistic|Coefficient|t-Statistic|
|C|0.021|4.394|0.014|2.535|0.012|1.955|
|DI (1974-75)|-0.055|-4.486|-0.055|-4.482|-0.057|-4.245|
|D2 (1971-73, 76-84)|0.027|2.974|0.027|2.963|0.018|1.806|
|D3 (1985-86)|-0.013|-0.940|-0.008|-0.537|-0.026|-1.774|
|LOANG|-0.161|-4.071|-0.146|-3.614|-0.112|-2.381|
|FDIG|0.039|4.531|||||
|MKG|||0.089|4.438|||
|OPENG|||||0.166|3.650|
|LOGBB|0.295|3.719|0.270|3.388|0.174|1.958|
|R-squared|0.851||0.847||0.818||
|Adjusted R-squared|0.806||0.802||0.764||
|S.E. of regression|0.014||0.014||0.016||
|Sum squared resid|0.004||0.004||0.005||
|<br>Log likelihood|80.380||80.101||77.740||
|Durbin-Watson stat|2.082||1.932||1.856||
|Mean dependent var|0.000||0.000||0.000||
|<br>S.D. dependent var|0.032||0.032||0.032||
|<br>Akaike info criterion|-5.436||-5.415||-5.240||
|Schwarz criterion|-5.100||-5.079||-4.904||
|F-statistic|18.975||18.518||15.012||
|Prob(F-statistic)|0.000||0.000||0.000||

TFPG: Total Factor Productivity Growth LOANG: Growth in (loans/GDP) ratio. FDIG: Growth in (FDI/GDP) ratio MKG: Growth in (Imported capital goods/GDP) ratio BB: Budget balance/GDP OPENG: Growth in (exports+imports)/GDP _15_
Table 1: AGGREGATE INDUSTRIAL STRUCTLFRE, 1967 AND 1979

||1967<br>(1)|1979<br>(2)|Ratio<br>(2)1(1)|
|---|---|---|---|
|Market Conditionsa||||
|Effective Protection|120Z|15%|.13|
|Export Share|42|132|3.25|
|Import Penetration Rate|20Z|29T|1.45|
|Intra-Industry<br>Trade Index|.14|.28|2.00|
|Manufacturing Unemp. Rate|5Z|132|2.60|
|Plant Size Distributionb||||
|Average Gross Output per Plant|37,241|<br>37,425|1.01|
|Number of Plants|7,060|<br>6,771|<br>.96|
|10th output percentile|1,430|<br>1,051|<br>.74|
|25th output percentile|3,006|<br>2,358|<br>.78|
|median output|.,203|<br>5,723|<br>.79|
|75th output percentile|20,972|<br>16,126|<br>.77|
|9Gth output percentile|75,536|<br>60,442|<br>.80|
|99th output percentile|507,189|595,724|1.17|
|Factor Use and Productivityb||||
|Value Added per Plant|21,403|<br>15,978|<br>.75|
|Workers per Plant|39|34|<br>.88|
|Efficiency Labor per Plantc|188|185|<br>.98|
|Capital per Plantd|30,760|<br>59,853|1.95|

- a All figures refer to manufacturing. Trade flow data are taken from de Melo and Urata (1986). Let X, M and Q represent exports, imports and domestic production, respectively. Then the export share is X/Q; the import penetration rate is M/(Q+M-X), and the index of intra-industry trade is 1.0 - IX-MI/(X+M). Effective protection figures are taken from Behrman (1976) and Aedo and Lagos (1979). We omit the glass industry in calculating mean effective protection rates because its 1967 value of 3500X was an extreme outlier. Unemployment figures come from Banco Central de Chile (1986).

- b These figures refer to the census data after cleaning. Variables valued in 1967 prices were converted to 1979 prices.

- c wage bill divided by the minimum wage, which is held constant in real terms across census years.

- d figures are based on plants reporting complete capital stock data. Such plants constituted 38 percent of the sample in 1967 and 35 percent in 1979.
Table 2. Probit regression of whether Mirt stove was in place in 2016

|Incentive treatment|0.050|0.043|
|---|---|---|
||(0.302)|(0.443)|
|Pay treatment|0.202|0.634|
||(0.312)|(0.409)|
|Network treatment (1 if yes, 0 otherwise)|-0.302|-0.242|
||(0.245)|(0.394)|
|Male headed household (1 if yes, 0 otherwise)||0.101<br>(0.228)|
|Stove is in main house (1 if yes, 0 otherwise)||-0.388<br>(0.215)*|
|Participated in CCT (1 if yes, 0 otherwise)||0.267<br>(0.134)**|
|Corrugated roof (1 if yes, 0 otherwise)||0.422<br>(0.282)|
|Distance to all weather road in hours (two-ways)||-0.143<br>(0.115)|
|Injera baking freq. since receiving stove (1 if once a month or<br>more, 0 otherwise)||0.807<br>(0.306)***|
|Frequency of stove use per week in Round 4||-0.015<br>(0.033)|
|Constant|0.349|0.255|
||(0.292)|(0.433)|
|N||327|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01.  Dependent variable is stove use, which is 1 if stove is still in use and 0 otherwise.

# _4.2. Stove use in the longer-run_

Our analysis of stove use in the very long run is presented in this sub-section. We look both at the intensive margin, which in this case is households that have not abandoned their Mirt stoves, and at the extensive margin using selection models to adjust for abandonment, mainly due to breakage. Our analysis focuses attention on period 5, with the objective to analyze very long-run effects. Controls are shown in Table 3. All variables are from our baseline survey conducted in 2013 and include a mix of socioeconomic and cooking metrics.

16