rafmacalaba/datause-encoder-data huggingface dataset:
Given the role of the experience of working itself, we next consider what features of the work may alter one’s psychosocial state. We first examine how working makes one feel: the self-worth imparted by working (through the sense of purpose experienced by the job) vis-`a-vis the family and the community, and the sociability facilitated by the job. We then consider features that impact how the work shapes one’s day: the structure imposed by a work schedule and the occupation of time. Third, we consider how working shapes consumption choices, as what is consumed from wages may differ from what is consumed from a cash transfer. Because we lack experimental variation along most of these margins, we cannot definitively rule in or out singular channels. Our evidence suggests that the role of employment in conferring self-worth, upon an individual within the eyes of their family, may be particularly impactful. Finally, we investigate how different workers gain from working, where we consider their gender, baseline exposure to violence, depression and extroversion.

# **5.7.1 How working makes one feel**

**Self-worth vis-`a-vis the family** We document a significant non-pecuniary impact of employment on self-worth. Our self-worth index asks the following question separately for one’s family and one’s community: “Think of the individual you most respect and who brings greatest value to your family (community). If they are ranked 10, where would you rank yourself?” We find that baseline mental health is highly correlated with how one ranks themselves within their family at baseline (Appendix Table A6). Consistent with this, the non-pecuniary impact of the employment treatment on family-rank is large, at 0.17 SDs ( _q_ = 0 _._ 01) greater than the cash arm (Column 1 of Table 6. Notably, this is a combination of a 0.11 SD increase in ranking among the work group and a 0.06 SD _decrease_ (though imprecise) among the cash group.

The large impact of employment on family status, paired with the potential disempowering effect of cash, hints at these improvements being gendered: because males are the traditional breadwinners in our context, deprivation of that role and receipt of a ‘handout’ may be particularly costly for them.<sup>15</sup> Indeed, we find in Table 7 (Columns 5 and 6) that, while women exhibit improvements in their family ranking under both employment and cash arms, males exhibit a substantial increase in their family ranking from employment and a reduction from cash. The non-pecuniary impact of employment on self-worth appears to be driven almost entirely by men, who exhibit greater [self-perceived] sense of self-worth within their family when employed, but reduced family status from cash alone.

> 15This is despite the fact that we framed the cash transfer as ‘earned’ for the completion of weekly surveys in efforts to limit the potential disempowering effects of cash.

20
a cross-country regression using panel data comprising of 13 countries that have completed accession.

# **3.2 Cross-Country Panel Regression**

The cross-country panel consists of the AC-10<sup>25</sup> as well as Spain, Portugal, and Greece, who joined the EU in the 1980s. This earlier Southern EU expansion is included whilst the incorporation of Austria, Finland, and Sweden in 1995 is omitted, because in the 1995 expansion, there was no significant GDP per capita gap between the accession countries and the EU member states. Furthermore free movement of people was already in place in these countries as they were members of the European Economic Area.

## **3.2.1 Method**

Having ensured, via a Dickey-Fuller unit root test, that the assumption of nonstationary appears valid,<sup>26</sup> I can be confident that OLS results will not suffer from the spurious regression problem. However, initial inspection of a GER time plot suggests that a linear trend may not be appropriate thus, in order to stabilize the variance I use a natural logarithmic transformation of the GER.The empirical approach is therefore to a run panel data regression on 13 countries that have joined the EU of the following form: Where ln _GERct_ represents the natural logarithm of the Gross Enrollment Ratio of country _c_ in time _t_ , _accct_ is a dummy that switches on when country c has completed accession, _δDt_ is the time fixed effect, _γDc_ is the country fixed effect and _ε_ is an error term. Fixed effects at the country level control for the usual array of cross-country differences in history, geography, culture,

> 25Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia.

> 26using the Dickey-Fuller unit root test approach given by Holden and Perman in B Bhaskara Rao Conintegration for the Applied Economist 1994

21
Table A6: Effect of Extended Provider Training on the Allocation of Malaria Treatment

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Full S|ample|Has Vali|d Home-Bas|ed RDT|
||Prescribed<br>Anti-<br>malarial|Prescribed<br>Anti-<br>malarial|Prescribed<br>Anti-<br>malarial|Prescribed<br>Anti-<br>malarial|Prescribed<br>Anti-<br>malarial|
|Extended Training|-0.124<sup>_∗_</sup>|-0.141<sup>_∗_</sup>|-0.0911|-0.114|-0.0896|
||(0.0718)|(0.0769)|(0.0713)|(0.0748)|(0.0570)|
|Extended Training _×_ Malaria Risk|0.107<sup>_∗_</sup>|0.293<sup>_∗∗_</sup>|0.0980|0.282<sup>_∗_</sup>|0.189<sup>_∗∗_</sup>|
||(0.0574)|(0.139)|(0.0642)|(0.165)|(0.0747)|
|Malaria Risk Measure|0.150<sup>_∗∗∗_</sup>|0.622<sup>_∗∗∗_</sup>|0.157<sup>_∗∗∗_</sup>|0.635<sup>_∗∗∗_</sup>|0.103<sup>_∗∗_</sup>|
||(0.0407)|(0.104)|(0.0504)|(0.124)|(0.0470)|
|ET + ET _×_ Risk = 0|0.803|0.161|0.918|0.200|0.158|
|Malaria Risk Indicator|Above<br>Median<br>Malaria<br>Risk|Predicted<br>Malaria<br>Risk|Above<br>Median<br>Malaria<br>Risk|Predicted<br>Malaria<br>Risk|RDT<br>Positive|
|Mean (Basic Training, No Pat. Info.)|0.67|0.67|0.71|0.71|0.71|
|N|1971|1971|1093|1093|1093|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. Standard errors for regressions that include a derivative of predicted malaria risk in the set of independent variables are bootstrapped using 500 replications to account for the fact that predicted malaria risk is a generated regressor. All regressions include strata and survey date fixed effects. Additional covariates are selected using double lasso, from the set of all covariates listed in Table 1 and all individual-level characteristics in Table A4 (excluding predicted malaria risk and characteristics only measured at the home survey) as well as the square of patient age and illness duration. We also include pairwise interactions of the aforementioned characteristics. Missing values for all characteristics are dummied out and recoded to the mean prior to forming interactions. Missing dummies are also included in the potential covariate set and set of pairwise interactions. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

A4
14 equation specified well enough to lead to unbiased<sup>estimators of its coeffi-</sup> cients. The specification chosen here draws on results of<sup>a thorough study of</sup> German FDI in developing countries by Agarwal, Gubitz and<sup>Nunnenkamp (1990).</sup> There it is found that German FDI is strongly market oriented<sup>not only in terms</sup> of market size as measured by the GNP of the respective host<sup>country, but also</sup> in terms of market penetration of German exports product<sup>in the host country's</sup> market, while the influence of other factors like real growth<sup>rates, labor costs,</sup> etc. were not unambiguously supported.5 In order to test the significance of the influence of host countries investment policies on German FDI as many flow data to single<sup>countries as</sup> possible should be taken into account in order to reduce<sup>the possibility of</sup> selection bias. The dependent variable should ideally be<sup>split up into real</sup> values and a deflator in order to investigate volume<sup>and price effects</sup> separately. FDI data, however, is always compiled in nominal<sup>terms, in this case</sup> in currency of the source country, i.e. in D-mark. Therefore<sup>one could deflate</sup> the nominal values by a suitable domestic deflator, for<sup>example the German price</sup> index for investment goods. This leads to an unbiased<sup>series for real FDI values</sup> under two assumptions: first, purchasing power parity<sup>(PPP) is fulfilled for the</sup> exchange rate of the host country's currency<sup>vis-a-vis the D-mark, and second</sup> the price index ch.osen is a good proxy for the true unit value of FDI<sup>transac-</sup> tions. Both assumptions are, however, highly questionable.<sup>The real exchange</sup> rates fluctuated widely in the sample period<sup>and deflating a financial flow</sup> variable that is generated by a set of rather heterogenous<sup>transactions would</sup> 5In the above mentioned study it was found that present and past penetration of German export products has a positive impact on German FDI,<sup>but present and</sup> past FDI has no significant impact on German exports to developing countries. Thus neither a substitution effect as implied by the theory of<sup>optimal timing</sup> of a FDI nor a growth effect could be detected.
# **Tables and Figures**

|**Table 1: Summary Stati**|**stics**<br>|||
|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|
||Mean|S.D.|N|
|**All Children**||||
|Age (years)|9.97|2.93|4331|
|Female (fraction)|0.47|0.50|4331|
|Enrolled (fraction)|0.76|0.43|4331|
|Educational Activity (minutes/day)|117.30|88.98|4331|
|Paid Tutorials (minutes/day)|14.36|39.11|4331|
|Study (minutes/day)|57.83|60.22|4331|
|School Preparation (minutes/day)|45.11|34.97|4331|
|**Enrolled Children**||||
|Public Schools (fraction)|0.71|0.45|3305|
|Mother Responsible for the Choice of School (fraction)|0.07|0.25|3270|
|Mother Responsible for the Enrollment Decision (fraction)|0.10|0.31|3270|
|Does anybody help the child or read to him/her? (Y/N)|0.38|0.49|3149|
|Time anybody in the family spent helping child or reading to<br>him/her (hours/week)|3.00|4.81|3149|
|School Expenditures (Rs./Month)||||
|Paid Tutorials|9.40|34.72|3124|
|School Fees|31.12|55.93|3124|
|Supplies|35.90|26.07|3124|
|Uniforms and Shoes|41.40|26.34|3124|
|**Mothers**||||
|Education (years)|1.34|2.75|1437|
|Educated (Y/N) (fraction)|0.24|0.43|1437|
|Age|37.87|7.56|1437|
|Girls School Present in Birth Village by Age 7|0.55|0.50|1437|
|Time Spent on Children’s General Needs (Minutes/Day)|94.47|141.29|1437|
|Time Spent Helping Child With Schoolwork|5.05|22.77|1437|
|Was Any Time Spent Helping Child With Schoolwork?<br>(Y/N) (fraction)|0.06|0.23|1437|
|Paid Work (Minutes/Day)|40.67|131.71|1437|
|Any Paid Work (Y/N) (fraction)|0.11|0.31|1437|
|Time Outside the House|99.33|157.58|1437|
|Time Inside the House|501.88|225.67|1437|

_Notes_ : Data from the LEAPS survey of households in 2004. “Paid Tutorials” are expenditures on private tuitions outside school. All expenditures are reported in Pakistani Rupees (Rs.) (at the time of the survey, $1 was approximately Rs.65).

30
to $54 billion in 2000 and now make up 14 percent of Japan's total imports, surpassing the share of imports from the EU (12 percent) and coming close to that of ASEAN (16 percent). On the other hand, Japan's exports to China, although having increased somewhat since 1990, are still only 6 percent of total exports, the same as that of South Korea.

**CHINA** -Between 1996 and 2000, China's imports grew 70 percent to $236 billion<sup>and</sup> exports doubled to $311 billion. Although still supplying the largest share of China's imports, the share from Japan has dropped from 27 percent in 1980 to 14 percent in 1990 but rose 2 percentage points by 2000. (See Figure 4.3.) The shares of imports from the EU and U.S. also declined between 1980 and 2000 to 12 and 8 percent, respectively.<sup>The</sup> shares of imports from ASEAN and South Korea have risen over the last 10 to 20<sup>years</sup> to reach the same level as that of the U.S. in 2000.

9
- 35 -

among agents. If quotas are adopted, they should involve positive incentives rather than punitive measures for program managers.

3.48 Proactive, voluntary approaches are preferable to quotas. All agents should be properly trained in analyzing farm households to recognize the resources, constraints, incentives, --..d obligations of household members, including women. They should also be taught to organize and communicate with women farmers. They should then monitor a certain nuaber of women farmers, the methods used to reach women, and women's adoptioti rates and responsiveness to extension messages.

# 7. Women Farmers' Access to Tra%ing

3.49 Women's access to extension offered at residential farm training centers will be limited unless transportation to the centers is arranged and separate residences for women are made available at the centers. Time constraints and child care obligations further limit women's participation in courses away from home.

3.50 Mobile training courses for farmers can be particularly helpful for wemen farmers. They are much more accessible to women who cannot (or are not allowed to) stay away from their homes for the required training time. In Zambia, for example, the Agricultural Sector Support Programme used mobile farmers' training courses which a recent evaluation found most effective for women farmers. In the Eastern Province, for example, approximately one-third of the participants in mobile courses were women. The Government, recognizing the effectiveness of these mobile courses, has decided to shift from Farmer's Training Centers to mobile training.

**Case** 5: ZAMIA: **UIEI FARNERS** ACCESS TO **TRAIN11G A study** in Northern, Central, and Southern Provinces reports that onty **5X** of the women in those provinces had attended farmer training courses (Gaobepe and Mwenda 1980 cited in Safi lios-Rothschlld 1985, p.20). In Eastern Province, only 15X of those attending were women. The aim was to raise women's participation to Z5X; the main constraint was transportation. (IROP Annual Report 1982 cited In SafiliosRothschild 1985). In the Samfya District, where 41% of the farm households are headed bv women. no women attended the courses offered at the residenti'al Samfva Farmers Training Centre throughout 1983. One of the main reasons was that women farmers wiho were heads of households were unable to take severat weeks off to attend ctasses away from their village.

3.51 Reaching rural audiences through mass media: Radio and television are promising as adjuncts to -- not as substitutes for -- face-toface extension. Attention should be paid to timing messages to coincide with agricultural operations and to scheduling transmissions at times suitable for women.

3.52 Radio has a long history as a communication tool, especially in agriculture. Its low cost and wide reach make it a relatively simple, effective technology for development. Television is less widely used because
# **Table 23: Cross-country covariates associated with the geographic penetration of branches**

This table summarizes the results of OLS regressions of the (logarithm of) number of branches per squared km in commercial banks on different country characteristics. Each column in each panel represents the result of one regression. The definition of each variable is shown in Appendix 1. Robust standard errors are in parentheses. ***= significant at the 1% level; ** = significant at the 5% level; * = significant at the 10% level.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|0.58***<br>(0.03)|0.67***<br>(0.08)|0.57***<br>(0.04)|0.31***<br>(0.09)|0.24***<br>(0.07)|0.54***<br>(0.06)|0.55***<br>(0.04)|0.56***<br>(0.05)|0.56***<br>(0.03)|0.56***<br>(0.04)|
|Population Density (log)|1.03***<br>(0.05)|1.06***<br>(0.07)|1.02***<br>(0.05)|1.03***<br>(0.06)|1.00***<br>(0.05)|0.95***<br>(0.08)|1.03***<br>(0.05)|1.03***<br>(0.05)|1.02***<br>(0.05)|1.02***<br>(0.05)|
|Gini||-0.02**<br>(0.01)|||||||||
|Inflation|||-0.01<br>(0.01)||||||||
|Electricity consumption||||0.28***<br>(0.10)|||||||
|Phone lines|||||0.41***<br>(0.07)||||||
|Road density||||||0.16<br>(0.10)|||||
|Contract enforcement|||||||-0.00<br>(0.00)||||
|Absence of violence||||||||0.04<br>(0.10)|||
|Concentration|||||||||-0.23<br>(0.40)||
|Branch approval||||||||||-0.03<br>(0.17)|
|Constant|-7.6***|-7.0***|-7.4***|-7.4***|-5.6***|-7.5***|-7.2***|-7.5***|-7.2***|-7.4***|
||(0.44)|(1.10)|(0.48)|(0.52)|(0.55)|(0.60)|(0.50)|(0.52)|(0.59)|(0.49)|
|N<br>|119|49|115|102|118|84|119|118|115|116|
|R<sup>2</sup>|0.86|0.77|0.86|0.86|0.89|0.87|0.86|0.87|0.86|0.86|

59
Figure 4: Distribution of the eg Index (naics 6-digit) in 2004 (top panel), 2009 (middle panel), and 2014 (bottom panel). All industries (left panel) and manufacturing (right panel).

<!-- Start of picture text --> EG index of all industries (6−digits) in 2004 EG index of manufacturing industries (6−digits) in 2004<br>−1 −.5 0 .5 1 1.5 −1 −.5 0 .5 1 1.5<br>EG index EG index<br>EG index of all industries (6−digits) in 2009 EG index of manufacturing industries (6−digits) in 2009<br>−1 −.5 0 .5 1 1.5 −1 −.5 0 .5 1 1.5<br>EG index EG index<br>EG index of all industries (6−digits) in 2014 EG index of manufacturing industries (6−digits) in 2014<br>−1 −.5 0 .5 1 1.5 −1 −.5 0 .5 1 1.5<br>EG index EG index<br>200 60<br>150<br>40<br>100<br>Number of industries Number of industries 20<br>50<br>0 0<br>200 60<br>150<br>40<br>100<br>Number of industries Number of industries 20<br>50<br>0 0<br>200 60<br>150<br>40<br>100<br>Number of industries Number of industries 20<br>50<br>0 0<br><!-- End of picture text --> 14
Groh, Matthew, David McKenzie, Nour Shammout, and Tara Vishwanath (2015) “Testing the importance of search frictions and matching through a randomized experiment in Jordan”, _IZA Journal of Labor Economics_ , 4:7 Independent Evaluation Group (IEG) (2012) _Youth Employment Programs: An Evaluation of World Bank and International Finance Corporation Support_ . World Bank, Washington D.C.

Levinsohn, James, Neil Rankin, Gareth Roberts. and V. Schöer (2013) “Wage subsidies to address youth unemployment in South Africa: Evidence from a randomised control trial”, Mimeo.

McKenzie, David (2012) “Beyond Baseline and Follow-up: The Case for More T in Experiments”, _Journal of Development Economics_ 99(2): 210-21.

World Bank (2014) “Predictions, Perceptions, and Economic Reality”, _MENA Quarterly Economic Brief,_ issue 3. <u>http://wwwwds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2014/08/06/000470435_2014 0806105353/Rendered/PDF/898440REVISED00ue030JULY020140FINAL.pdf</u> [accessed August 14, 2015].

14
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE services. The gap in universal access to telephony can be closed relatively easily for Sudan on a commercial basis. A small subsidy could aid in reaching the uncovered 2 percent of the population.

For broadband infrastructure, simulations present an optimistic picture. Around 96 percent of all parts of Sudan’s population could gain access to limited performance WIMAX<sup>8</sup> broadband infrastructure on a commercially viable basis. A gap of 3 to 4 percent exists in the northwest region of all parts of Sudan— primarily a desert—and in the south (figure 27)

# **Figure 26. A small subsidy could have closed the mobile telephony coverage gap in all parts of Sudan**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient Market Gap Coverage gap<br>Source: Mayer and others 2009.<br>Percent of population<br>South Africa Nigeria Ghana Benin Côte d’Ivoire Senegal Sudan Malawi Burkina Faso Cameroon Ethiopia Tanzania Lesotho Mali Chad Niger Madagascar Congo-DRC<br><!-- End of picture text --> The advent of undersea fiber-optic cables has enabled Sudan to develop a strong ICT infrastructure backbone (figure 26). Three undersea cable systems land in Port Sudan: SAS-1 (providing a direct link to Saudi Arabia), Flag Falcon, and, most recently, the Eastern Africa Submarine Cable System (EASSy). In addition, there are terrestrial links to Egypt and Ethiopia and a 10,000 km domestic fiber backbone (Sudatel 2009). The EASSy has established a landing station in Sudan. Significant private investment has facilitated Sudan’s fiber-optic connectivity. Sudan needs to establish overland connections to the cable to complete the national ICT backbone The emergence of competition in gateways as a result of connectivity to the undersea cable has facilitated very attractive prices for ICT services in Sudan—among the most attractive in Sub-Saharan Africa. Sudan pays a third of what resource-rich countries pay and a fourth of what low-income countries pay for the mobile basket. Landline telephony, which has not experienced any growth, has also seen its prices slashed. But prices for fixed broadband are the most striking. In 2005 all parts of Sudan paid 10 percent of what resource-rich countries paid for their ICT services and 8 percent of what low-income countries paid (table 15).

> 8 Worldwide interoperability for microwave access.

> <sup>36</sup>
Table 6 shows average welfare levels **for** Lima, other urban areas, and rural areas. The figures show considerable differences in welfare between adults and children, and between urban and rural areas. The large differences between corresponding medians and means indicate extremely skewed distributions, which are confirmed by the estimates of the A-coefficient In table 7.

Table 7 shows only insignificant differences In inequality across per capita household consumption among households and persons. This Is in line with the results reported by Berry (1988). More surprising Is the finding that the inequality in per-caplta household consumption dfffers little from Inequality in the corresponding distribution of total household consumption (compare tables 3 and 7). This result is due to an extremely unequal distribution of consumption (Income) In Peru In 1985-86. Glewwe (1987) reports that this was also the case in 1966, when the Ginicoefficient **for** per capita income inequality among persons was 0.666. We estimate the Gini-coefficient of the distribution of per capita household consumption among persons in 1985-86 to be 0.789 (see Appendix 1, table G3).

Table 6. Mean and Median Per Capita Consumption Among Persons by Sex, Age, and Region

|Popula-<br>|Pe<br>|ru<br>|Li<br>|ma<br>|Oth<br>urban<br>|er<br>areas<br>|Rura<br>|l areas<br>|
|---|---|---|---|---|---|---|---|---|
|tion|Mean|Median|Mean|Median|Mean|Median|Mean|Median|
|All**...........**|**....**<br>11,692<br>(24,|3,332<br>126)|10,668<br>(6,|5,983<br>541)|19,139<br>(6,9|4,190<br>52)|7,454<br>(10,|1,404<br>633)|
|Females ...........|13,282<br>(7,|3,508<br>376)|10,406<br>(2,|6,036<br>256)|25,154<br>(2,1|4,143<br>85)|6,654<br>(2,|1,332<br>935)|
|Malee .............|12,207<br>(7,|3,820<br>004)|11,529<br>(2,|6,418<br>090)|20,013<br>(2,0|4,423<br>54)|7,097<br>(2.|1,516<br>860)|
|Children4'**..........**|10,118<br>(9,|2,965<br>746)|10,1V8<br>(2,|<br>5,404<br>195)|13,630<br>(2,7|3,945<br>13)|8,150<br>(4,|1,380<br>838)|

Note: Intis figures at June 1985 prices. Number of observations in parenthesis.

41 Less than 15 years old.