rafmacalaba/datause-encoder-data huggingface dataset:
We provide suggestive evidence in support of the latter explanation, namely, that the commitment account helped shield funds from the social network. First of all, the actual amounts saved in the commitment accounts offered were very low (about an order of magnitude lower than the observed increase in inputs), with most savings actually occurring in ordinary accounts. This helps rule out that the impacts of the commitment treatment were due to literally “tying the hands” of treated farmers by mechanically preventing them from spending their profits earlier in the year. In addition, we find that the impact of commitment savings is higher for individuals who are wealthier at baseline, a sub-group of respondents that is likely to face higher pressure to share with others. The impact of the commitment treatment has no large or statistically significant relationship with hyperbolic preferences as expressed in the baseline survey. Thus, commitment savings accounts appear to be most useful to those who are likely to face “other control” problems, rather than those facing self-control problems.

Our results point to a potentially low-cost means for microfinance institutions to raise farm inputs and incomes for current loan customers. There are relatively straightforward opportunities to offer farmers innovative savings facilities in the context of the organized marketing process of many crops, in Malawi and elsewhere in sub-Saharan Africa. It is relatively common for lenders to have direct funds-transfer arrangements with cash crop buyers for loan recovery. When such arrangements exist already, current loan customers can simply be offered direct deposit of crop proceeds into commitment accounts.

Nonetheless, it is important to consider the external validity of these results, as they are likely to be most applicable to cash crop farmers where sale proceeds can be directly deposited into bank accounts. In the central Malawi region we study, however, tobacco farmers have similar poverty and income levels to those of non-tobacco-producing households.<sup>19</sup> As with all empirical research, future studies should test whether these findings hold in other contexts, such as in other countries and with other types of farmers who have differing payment arrangements for their crops.

We do not study the effect of either ordinary or commitment savings accounts in the absence of direct deposit. Direct deposits both reduce transaction costs and operate as a “channel factor” to increase savings. OIBM administrative data reveal that, aside from the direct deposits, other cash deposits into accounts were very low. It is by no means certain that simply setting up

> 19 Based on authors’ calculations from the 2004 Malawi Integrated Household Survey (IHS), individuals in tobacco farming rural households in central Malawi live on PPP$1.48/day on average, while the average for central Malawian rural households overall is PPP$1.51/day.

24
of Ghana: Accra (and neighboring Tema), Kumasi, Takoradi and Cape Coast. The sampling scheme is based on a stratified random sample of urban households from the 2000 census in Ghana. Thus, the data are roughly representative of urban labor markets. The data span a 7 year period from 2004 until 2010, but the 2007 wave is a recall wave in which no indicators of subjective wellbeing were collected and is therefore excluded from the present analysis.

Personal Characteristics and Labor market Indicators: The survey contains the standard measures of age, years of education, gender, whether the respondent is married and is the head of the household, city of residence and ethnicity. We further construct height Z-scores conditional on age and gender as a proxy for the general health of the respondent.

For wage employees the earnings variable measures real monthly wage income, while for the self-employed the real earnings measure is based on enterprises profits. Thus, earnings for self-employed workers reflect both the returns to capital and the returns to labor. In addition to these earnings measures, and hours worked, the survey includes three questions that allow greater precision in the measure of conditional wages: tenure in the present job; years in the formal sector, and whether or not the present position is an apprenticeship.

We are also able to construct a measure of household assets from questions on whether the family owns a bike, telephone, electric stove, motorcycle, car etc. It does not include assets.

Satisfaction Measures: Our subjective wellbeing measures are provided by the answer to the question: “All things considered, how satisfied are you with your current work? (/life?/financial situation?)”. In all three cases, the options given to respondents were: “1.Very Dissatisfied, 2. Dissatisfied, 3. Neither Satisfied Nor Dissatisfied, 4. Satisfied, 5. Very Satisfied”. Information on subjective job satisfaction was collected in all waves (except the recall wave), while information on life- and financial satisfaction was collected in four waves.

Dimensions of Job Satisfaction: The survey also contains variables that help us shed light on 10
subsequently - and randomly - selected from each of the 10 districts. Key informant interviews were then conducted in each of the selected _upazilas_ to rank _unions_ (the lowest administrative tier) within that _upazila_ on the basis of estimated migration rates. Based on this, two _unions_ in each _upazila_ that had the highest migration rates in that _upazila_ were chosen. Once this was done, another set of key informant interviews were undertaken in each _union_ to identify the village within the _union_ that had the highest migrant prevalence. This village was then chosen for the final survey. All in all, twenty villages in ten districts were chosen and Table 1 provides the list of these villages.

[Insert Table 1] A complete census was implemented in these twenty villages. The census team visited every household in the village and collected information on the demographic profile of households, ownership of farm and non-farm business, and migration status of each household member in the past ten years. In all, 6,282 households were covered by the census survey.

Households that reported having members either currently overseas or having members that had returned from temporary international migration in the last ten years were classified as migrant households. The census thus provides an estimate of the prevalence of international migration using this broader definition. In some parts of the analysis, such as the determinants of migration section, we separate current and previous migrants. The number of households chosen for the detailed household survey was set at 25 per village. Since migrant households were only a small fraction of the total number of households, migrant households were oversampled in order to ascertain that they were adequately represented in the household survey. Hence, in each community, one half of the household survey respondents were drawn randomly from among migrant households and the other half from non-migrant households. The household survey was 5
6 <!-- Start of picture text --> Child mortality (per 1,000 live births) in Percentage of births at health facilities in<br>Burkina Faso (Burkina Faso DHS, 2010) Burkina Faso (Burkina Faso DHS, 2010)<br>200 175 173 100 93.2<br>144 77.1<br>160 131 80 67.3<br>56.0<br>120 97 60 46.1<br>80 40<br>40 20<br>0 0<br>Lowest Second Third Fourth Highest Lowest Second Third Fourth Highest<br>income income income income<br>quintile quintile quintile quintile<br>Figure 2: Income quintiles and health outcomes in Burkina Faso (source: Burkina Faso<br>DHS, 2010)<br>births)<br>facilities<br>Child mortality (per 1,000 live Percentage of deliveries at health<br><!-- End of picture text --> It is also well-understood that improving health and education outcomes among the poor requires greater effort, in part because of the greater investment of time that is required (Ingersoll, 2004; Peters et al., 2008; Wagstaff, Bredenkamp and Buisman, 2014; Loignon et al. 2015; Willems et al. 2005; Street, 1992). In education, Ingersoll (2004) describes these as obstacles to staffing highpoverty schools. The poor also present more complex health conditions. Due to a lack of access, resources, and health-related knowledge, poor individuals tend not to invest in preventative care, yielding additional symptoms that make it harder for health care workers to arrive at the correct diagnosis (Wagstaff, Bredenkamp and Buisman, 2014; Peters et al., 2008; among others). Communication problems can arise because of differences in the use of language, but also in social standing: poor individuals might be shy or overwhelmed by the medical institution (Loignon et al. 2015; Willems et al. 2005; Street, 1992). Heinig (2009) emphasizes the reluctance of low-income patients to confide in medical professionals in maternal contexts like those in the experimental vignettes used in this paper. Loignon et al. (2015) report three main barriers to care for the poor: the complexity of the health care system, low standards of living among the poor (yielding greater complexity in cases), and poor quality of interaction between health workers and the poor, the latter rooted in problems of communication and social distance.

Researchers have shown that these obstacles affect health care. Willems et al. (2005) report the results of a systematic review that found that patients from low socio-economic backgrounds received less information, direction, and emotional support from health workers. Street (1992) analyzed audio recordings of 115 pediatric consultations and found that parents from weaker educational backgrounds received lower levels of support, due to weaker communication. Taken together these papers point to the importance of communication and complexity in treating poor patients. Complexity and communication difficulties require medical professionals to spend greater time in serving the poor. We introduced these characteristics of dealing with poor patients into the video vignettes.
<!-- Start of picture text --> Figure 5: Predicted reduction in trade flows by Distance<br>x<br>aN<br>2<br>©<br>no}<br>Ew<br>£@<br>S<br>5<br>no] ©<br>2<br>Lw<br>ao”<br>ne)<br>2 4 6 8<br>Ln(distance)<br>——— Elasticity=3.9 ——— Elasticity=2.8<br><!-- End of picture text --> Figure 6: Predicted Probability of being isolated by distance <!-- Start of picture text --> ive)<br>©<br>Pa<br>3<br>os<br>gt<br>2<br>oO<br>N<br>fo)<br>0 1000 2000 3000 4000<br>Distance<br>———_ Pre-Pandemic | ——— Post Lock-down<br><!-- End of picture text -->
Table 5: Controlling for Historical Female Literacy Does Not Change Estimates

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|observations:|district-years|adult men|adult men|adult men|adult men|
|dependent variable:|IMR or san.|wages|wages|wages|wages|
|Panel A: Infant Mor|tality|||||
|IMR||-0.00174*|-0.00195**|-0.00219**|-0.00235**|
|||(0.00071)|(0.00072)|(0.00066)|(0.00073)|
|female literacy|-0.382<sup>_‡_</sup>|||-0.0165***|-0.0146***|
||(0.258)|||(0.0038)|(0.0040)|
|district FEs|✓|✓|✓|✓|✓|
|year FEs|✓|✓|✓|✓|✓|
|other controls|||||✓|
|_n_|4,770|12,783|11,538|11,538|11,538|
|Panel B: Sanitation||||||
|sanitation||0.00296***|0.00313***|0.00313***|0.00330<sup>_†_</sup>|
|||(0.000509)|(0.00058)|(0.00058)|(0.00184)|
|female literacy|0.432***|||-0.0128*|-0.0125**|
||(0.123)|||(0.0050)|(0.0045)|
|district FEs|✓|✓|✓|✓|✓|
|year FEs|✓|✓|✓|✓|✓|
|other controls|||||✓|
|_n_|4,759|6,134|4,646|4,646|4,646|

Note - _p_ values: _†_ = 0 _._ 1 _, ∗_ = 0 _._ 05 _, ∗∗_ = 0 _._ 01 _, ∗∗∗_ = 0 _._ 001. In the case of _‡_ , the one-sided _p_ -value is 0.069 and the standard two-sided _p_ -value is 0.139. Standard errors are clustered at the district level. In column 1, historical census IMR (Panel A) and sanitation (Panel B) data is the dependent variable. In columns 2-5, log wage is the dependent variable.

42
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36
Table A8: Correlates of Training Attendance

||(1)|(2)|(3)<br>At|(4)<br>tend Traini|(5)<br>ng|(6)|(7)|(8)<br>Total Attendance|
|---|---|---|---|---|---|---|---|---|
|Selected for Kigali|-0.13<sup>_∗∗∗_</sup>|||||-0.14<sup>_∗∗∗_</sup>|-0.07|-1.77<sup>_∗∗_</sup>|
||(0.05)|||||(0.05)|(0.04)|(0.69)|
|Selected for North|-0.07|||||-0.13<sup>_∗_</sup>||-1.65<sup>_∗_</sup>|
||(0.06)|||||(0.07)||(0.90)|
|Female||0.06<br>||||0.02<br>||0.05<br>|
|||(0.04)||||(0.04)||(0.56)|
|Married||-0.06<br>||||0.00<br>||-0.09<br>|
|||(0.08)||||(0.08)||(1.01)|
|Has Children||0.01||||0.00||0.06|
|||(0.08)||||(0.09)||(1.08)|
|Age||0.01<br>||||0.01<br>|0.01<br>|0.10<br>|
|||(0.01)||||(0.01)|(0.01)|(0.09)|
|Province: East||-0.07||||0.07||1.21|
|||(0.08)||||(0.09)||(1.16)|
|Province: Kigali||-0.10<sup>_∗∗_</sup><br>||||-0.03<br>|-0.03<br>|-0.16<br>|
|||(0.05)||||(0.06)|(0.05)|(0.75)|
|Province: North||-0.07||||0.01||0.21|
|||(0.07)||||(0.07)||(0.99)|
|Province West||0.02||||0.10|0.07|1.50<sup>_∗_</sup>|
|||(0.07)||||(0.07)|(0.06)|(0.90)|
|Wealth Index||-0.01<br>||||0.00<br>||0.10<br>|
|||(0.01)||||(0.01)||(0.19)|
|Years since graduation|||-0.03|||-0.03|-0.03|-0.39|
||||(0.02)|||(0.02)|(0.02)|(0.27)|
|Bachelor Graduate|||0.04|||-0.00||-0.20|
||||(0.05)|||(0.05)||(0.66)|
|Public School|||-0.03<br>|||-0.02<br>||-0.38<br>|
||||(0.05)|||(0.05)||(0.60)|
|Good English Language|||-0.10|||-0.06||-0.63|
||||(0.11)|||(0.12)||(1.55)|
|Has ever worked||||-0.06||-0.04|-0.06|-0.65|
|||||(0.05)||(0.05)|(0.05)|(0.70)|
|Works as employee||||-0.21<sup>_∗∗_</sup><br>||-0.20<sup>_∗∗_</sup><br>|-0.17<sup>_∗_</sup><br>|-2.72<sup>_∗∗_</sup><br>|
|||||(0.09)||(0.09)|(0.09)|(1.18)|
|Works as self-employed||||-0.07||-0.06||-0.80|
|||||(0.09)||(0.10)||(1.25)|
|Works as family employee||||0.09||0.09|0.10|1.43<sup>_∗_</sup>|
|||||(0.06)||(0.07)|(0.06)|(0.85)|
|Works as casual employee||||-0.18<sup>_∗∗∗_</sup>||-0.18<sup>_∗∗_</sup>|-0.15<sup>_∗∗_</sup>|-2.21<sup>_∗∗_</sup>|
|||||(0.07)||(0.07)|(0.07)|(0.96)|
|Job accordance to degree||||-0.09||-0.10<sup>_∗_</sup>|-0.11<sup>_∗∗_</sup>|-1.33<sup>_∗_</sup>|
|||||(0.06)||(0.06)|(0.06)|(0.74)|
|Willingness to pay for course (USD)|||||0.00<sup>_∗_</sup>|0.00<sup>_∗_</sup>||0.00|
||||||(0.00)|(0.00)||(0.00)|
|Training: get job faster|||||0.07|0.07|0.07<sup>_∗_</sup>|0.89|
||||||(0.04)|(0.04)|(0.04)|(0.56)|
|Training: get higher pay job|||||-0.01|0.01||0.22|
||||||(0.05)|(0.05)||(0.60)|
|Training: get better job|||||-0.01|0.02||0.24|
||||||(0.05)|(0.04)||(0.57)|
|Observations<br>R-suared|450<br>0014|450<br>0022|450<br>0008|450<br>0080|450<br>0011|450<br>0118|450<br>0104|450<br>0115|
|q<br>Mean dep. var.|.<br>0.74|.<br>0.74|.<br>0.74|.<br>0.74|.<br>0.74|.<br>0.74|.<br>0.74|.<br>9.44|

Offered Training is the coefficient from an OLS regression controlling for the randomization stratum fixed effects. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 33
Table 2 presents the following above the line' items:

- A. Current Revenue

- B. Current Expenditure

- C. Capital Revenue

- D. Capital Expenditure

E. Total Revenue (including<sup>remanent of prev. Fiscal Years</sup> and contributions) F. Total Exuenditure (incl.<sup>contributions)</sup> G. Deficit or Financing Needs (F-E) TABLE 2. Consolidated<sup>Nonfinancial Public Sector "Above the Line"</sup> Data (%GDP)

||Item|A.|B.|C.|D.|E.|F.|G.|
|---|---|---|---|---|---|---|---|---|
|Year|||||||||
||1961<br>|23.17<br>|17.41<br>|0.55<br>|10.68<br>|28.67<br>|33.03<br>|4.36<br>|
||62|18.63<br>|17.84<br>|0.76<br>|9.32<br>|23.85<br>|31.61<br>|7.76<br>|
||63|18.47<br>|17.93<br>|0.68<br>|8.22<br>|23.37<br>|30.37<br>|7<br>|
||64|13.00|18.11|0.37|6.89<br>|22.81<br>|29.43<br>|6.62<br>|
||65|18.86|16.88|0.35<br>|6.41<br>|22.31<br>|26.39<br>|4.08<br>|
||66|20.13|18.98<br>|0.36<br>|6.45<br>|24.52<br>|29.45<br>|4.93<br>|
||67|23.35<br>|18.26<br>|0.48<br>|7.65<br>|27.78<br>|29.86<br>|2.08<br>|
||68|22.8|17.24|0.E4|1.33<br>|27.58<br>|29.81<br>|2.23<br>|
||89|22.5.3|16.53|0.45<br>|8.21<br>|26.67<br>|28.42<br>|1.75<br>|
||70|22.843|16.65<br>|0.43<br>|8.48<br>|26.68<br>|28.55<br>|1.87<br>|
||71|20.51|17.04|0.34|8.2<br>|24.72<br>|29.12<br>|4.4<br>|
||_72_|18.35|15.66<br>|0.32<br>|8.81<br>|23<br>|28.&<br>|5.8<br>|
||73<br>|19.01|19.11<br>|0.11<br>|1.5<br>|25.07<br>|32.55<br>|7.48<br>|
||74|22.12<br>|21.8'<br>|0.49<br>|8.81<br>|30.09<br>|38.17<br>|8.08<br>|
||75|15.87|22.16|0.15<br>|8.94<br>|27.1<br>|42.18<br>|15.08<br>|
||76|18.14|18.92<br>|0.15<br>|13.96<br>|27.78<br>|39.47<br>|11.69<br>|
||77|22.93<br>|14.78<br>|0.4<br>|13.26<br>|28.85<br>|33.56<br>|4.71<br>|
||'8|25.44|19.75|0.35|12.54<br>|31.29<br>|37.79<br>|6.5<br>|
||79|24.44|21.1|0.25<br>|10.51<br>|31.84<br>|38.35<br>|6.51<br>|
||80|26.89<br>|25.75<br>|0.31<br>|9.51<br>|33.75<br>|41.22<br>|;.47<br>|
||81|24.39|29.11|0.25|9.65<br>|32.95<br>|A6.21<br>|13.26<br>|
||82|22.53|29.9|0.49<br>|8.56<br>|30.51<br>|45.63<br>|15.12<br>|
||83|23.08|29.03<br>|0.23<br>|9.68<br>|40.24<br>|55.39<br>|15.15<br>|
||84|22.49|27.03|0.24|7.82<br>|33.65<br>|45.57<br>|11.92<br>|
||85|26.96|26.9|0.25<br>|7.06<br>|40.92<br>|46.94<br>|6.02<br>|
||86|26.17<br>|24.54<br>|0.21<br>|7.48<br>|40.1<br>|44.8?<br>|4.73<br>|
||87|23.33|23.23|0.25|6.13|34.59|40.13|5.54<br>|
||88|na|na|na|na|na|na|7.39*|

*:For 1988 the data<sup>available does not include the deficit of</sup> Provinces and the city of<sup>Bs.As..For the restof</sup> the CPS the deficit is 6.93% of<sup>GDP. We estimate a deficit of Provinces and</sup> Bs.As. equal to the one in<sup>1987 that was 0.46% of GOP.</sup>
with reliability. As with SAIFI, the most commonly employed measure to record interruption data is the System Average Interruption Duration Index (SAIDI)--box 2.<sup>35</sup> An analysis of Eskom’s distribution performance during 1997-1999 indicates that the number and duration of outages (SAIFI and SAIDI) were very high by international standards. In 1999, for example, the average frequency of sustained interruptions in South Africa was 12.97 as compared to .40 in the Netherlands, 1.15 in Ireland, 1.22 in France, 1.38 in Sweden, 3.09 in Hungary, 3.32 in Finland, and 3.81 in Italy (figure 10). During the same year, the average time of customer interruption was 1796.4 minutes in South Africa as contrasted to 26 minutes in the Netherlands, 156.4 minutes in Spain, 165.8 minutes in Sweden, 191.8 minutes in Italy, 198 minutes in Finland, 273.6 minutes in Ireland, 411.0 minutes in Hungary, and 459.0 minutes in France (figure 11).<sup>36</sup>

**Figure 10 System Average Interruption Frequency Index (SAIFI)**

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>97 98 99 00 01 02 03 04<br>year<br>Finland France Hungary<br>Italy Ireland Netherlands<br>Spain Sweden South Africa<br>SAIFI - interruptions per customer per year<br><!-- End of picture text --> The deterioration in Eskom’s reliability metrics from 1997 to 1999 (SAIDI increased from 19.50 hours in 1977 to 29.94 hours in 1999, and SAIFI from 8.30 interruptions in 1977 to 12.97 interruptions in 1999) was attributed by its management to the: (i) increase in the number of customers, which were typically prepaid customers at the end of weak networks, resulting in poorer performance of these networks; (ii) ageing of plant; and (iii) unexpectedly bad weather (heavy rain and floods) during 1998 and

> 35 A major drawback to reliability metrics is the difficulty of comparing them from one geographic area to another and exactly how to apply the input data in making the necessary calculations.  There are also concerns about how to normalize the indices for adverse weather.

> 36 Council of European Energy Regulators (CEER). 2005. _Third Benchmarking Report on Quality of Electricity Supply_ .  Brussels, Belgium. Ref: C05-QOS-01-03.

23