rafmacalaba/datause-encoder-data huggingface dataset:
**_Suggestive corroborative factors_ :** Relative to other Christian women, widows have lower nutritional status. This is not found for widows among Muslim women. These patterns appear to reflect distinct cultural norms in which widows are treated very differently. Furthermore, the results suggest that those who are poor and most vulnerable to the downside consequences of a husband’s loss are on average far better protected under Muslim socio-cultural-religious norms and processes.

The evidence in the DHSs on violence perpetrated by in-laws and reported suffered by widows is consistent with these findings. Table 8 shows that the least common form of violence is the request to carry out rituals as a proof of innocence. This is considerably more often experienced by Christian widows, at 7 and 14 percent in rural and urban areas respectively, compared to 1 and 2 percent for Muslim widows. The most frequent is maltreatment. Again, Muslim widows recount less than half the incidence of such brutality at 11 and 12 percent in rural and urban locations respectively, versus 23 and 27 percent reported by Christians. Remarkably, all forms of violence are more often endured by Christian widows whether in rural or urban areas. A caveat here is that since the majority of widowed Muslims remarry and given that information on violence by in-laws was only collected for current widows, we cannot conclusively say that violence is higher among Christian ever-widowed women.<sup>34</sup> As noted earlier, differences also arise with respect to inheritance practices. Christian widows customarily have few rights to inherit a deceased husband’s property. Often, they can retain the right to live in the matrimonial home only if they have a son. Inheritance rights are more established and protected under Islamic Law.

We explore what this means in practice using data from both DHSs that asked everwidowed women to indicate who received most of their late husband’s property. Table 9 shows the summary statistics separately for remarried and current widows by religious group and location. Christian ever-widows are much more likely to be dispossessed than their Muslim counterparts: 31 (27) percent of Muslim and 79 (42) percent of Christian remarried (current) widows did not receive any of their late husband’s assets or valuables in rural areas. Another marked difference is that while the husband’s family is the most likely recipient among Christian women (54 percent for remarried and 26 percent for currently widowed), the spouse’s children are the most common for Muslim rural women (44 percent for remarried and 40 percent for

> 34 Questions on violence were only asked in 2013 and cover 500 widows.

27
# TABLE 3. Characteristics of Potential Participants<sup>a</sup>

Eligible Ineligible

|Household characteristics|Mean|Std. Dev.|Mean|Std. Dev.|
|---|---|---|---|---|
|Total monthly expenditure (pesos)|2,314|2,404|2,899|3,923|
|Per capita expenditure (pesos)|492|492|<br>777|1,317|
|Family size|5.19|2.20|4.23|1.79|
|Children from 0 to 5 years old|0.97|0.98|0.51|0.71|
|Children from 6 to 11 years old|1.27|1.13|0.68|0.84|
|Children from 12 to 17 years old|0.73|0.97|0.63|0.88|
|Mothers’ characteristics|Mean|Std. Dev.|Mean|Std. Dev.|
|Age|36.7|13.1|38.5|13.7|
|Education (years)|4.03|3.20|4.91|3.40|
|Speaks indigenous language|0.13|0.34|0.06|0.24|

a Observations: 9,944 eligible and 5,755 ineligible households.

_Source:_ ENCELURB.

29
Figure 1 shows the specific types of BE we examine. First, we examine _Basic BE,_ that is, those aspects often viewed as basic and fundamental for development. It includes basic functions of government protection, which include containing corruption and providing basic safety (“Government Protection”). It also includes a good supply of human capital and the availability of infrastructure (e.g. power supply and internet technology), as well as access to finance, which is widely viewed as a key BE element by a large literature (Levine, 1997; Demirguc-Kunt and Maksimovic, 1998; La Porta et al., 2000; Beck et al., 2005; Bloom et al., 2010). Second, we investigate the _Refined BE_ , which includes, _inter alia_ , the barriers to entry and exit (such as access to land) and labor regulations that firms face, as well as the tax environment within which firms operate. The third, and final, dimension that we examine is the _Agglomeration Environment_ . This incorporates whether or not a firm is located in a large city, defined here as a city which is either a national capital or which has in excess of a million residents. It also includes what we refer to as “capacity agglomeration,” which refers to the extent to which a firm is surrounded by “capable” firms.

The firms within the WBES data that we use are randomly selected and are representative of the non-agricultural private sector in each economy. Unlike purely cross-country studies, the size of our sample is such that we are able to more confidently disentangle the role of the different elements of the BE in driving firm growth. Unlike the Doing Business (DB) project in which the indicators are _de jure_ measures based on the answers of a few mid-sized firms (with the same firm size across the world), the BE measures in our data set are _de facto_ , measuring actual experiences of firms. Since enforcement is a big issue in developing countries, _de facto_ BE measures are likely to be more relevant for understanding the relationship between the BE and firm performance. Because the sample is random, there are plenty of small and young firms, to which we shall pay particular attention.

There are further advantages of using our micro data to answer questions of how the broadly-defined BE affects development. First, a key endogeneity issue that faces cross-country studies is that of reverse causality from growth and other development outcomes to the factors, such as institutions, being investigated. Using firm-level data mitigates these concerns because it is less likely that an individual firm’s growth influences the overall BE. Second, by exploiting the extensive city-industry-level variation in the BE measures that exists in our data, we are able to vastly alleviate the multicollinearity concerns that would have existed had we instead chosen to 4
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48
of a patient’s underlying malaria status, test results are not available for those who did not participate in the home survey or did not consent to take the RDT. To address this, we follow Cohen et al. (2015) and Lopez et al. (2022) and use the home-tested sample to predict malaria positivity using patient symptoms and demographics. Specifically, we use probit regression to estimate the following: where _posict_ is a dummy variable identifying those who test RDT positive and **xict** includes dummy variables for symptoms listed in Appendix Table A4: days since onset of illness, patient age, a dummy equal to one if the patient is under age 5, the interaction between age and the under 5 dummy, patient gender, and patient pregnancy status.<sup>17</sup> Appendix Table A5 reports results.<sup>18</sup> When analyzing the allocation of malaria treatment for the full sample of patients, we use the predicted value _pos_ ˆ _ict_ as a measure of each patient’s malaria risk, including those who were not tested at home.

# **3.5 Randomization**

Table 1 verifies that clinic characteristics and provider knowledge are balanced across the extended and basic training groups. Column 1 of the table reports the average value in the control group, and column 2 reports the difference between the two groups, conditional on randomization strata (clinic pair) fixed effects. Throughout the paper we cluster standard errors at the clinic-pair level (de Chaisemartin and Ramirez-Cuellar, 2024).

Overall, the extended training randomization was balanced, with no treatment-control differences significant at conventional levels. Point estimates do, however, suggest that providers in the extended training arm had greater faith in RDTs at pre-test than their peers in the basic training arm. Extended-training clinics were also 11 percentage points less likely to have a lab capable of performing microscopy tests. These differences could bias us – toward finding more use of RDTs in the extended training arm to address this risk while tying our hands in terms of covariate selection, we use the post-double-lasso procedure by Belloni et al. (2014) to select covariates throughout our main analysis.<sup>19</sup>

> 17We also control for demographic characteristics that may correlate with malaria risk, including the survey respondent’s ethnicity, ability to speak French, literacy in French, education, and a dummy variable indicating cases where the patient and respondent are different people. Our results are similar if we omit these demographic characteristics from the specification.

> 18This regression specification matches that in Lopez et al. (2022), but for this paper, we only include observations from the 58 clinics included in our analysis sample.

> 19We include clinic averages of all covariates listed in Table 1 and, for patient-level regressions, characteristics considered for double lasso selection in Lopez et al. (2022): all individual-level characteristics in Table A4 (excluding predicted malaria risk and characteristics only measured in the home survey) as well as

12
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_Table A 7_ : Regressions of self-reported harvest realizations on expected harvest and a range of controls.

# **Ethiopia - Production**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||E<br>|SS15<br>|||ESS<br>|18<br>||
||SR maize|roduction|SR sorghum|Production|SR maize|Production|SR so|rghum|
|VARIABLES|(k|g)|(|kg)|(k|g)|Produc|tion(kg)|
|Expected maize production (kg)|0.285**|0.176**|||0.395***|0.179**|||
||(0.115)|(0.0817)|||(0.0800)|(0.0755)|||
|Expected sorghum production (kg)|||0.253***|0.113*|||0.478***|0.279***|
||||(0.0924)|(0.0602)|||(0.137)|(0.104)|
|Planted area harvested (%)||0.618*||0.328||0.882***||0.508|
|||(0.361)||(0.225)||(0.337)||(0.353)|
|Damage on crop (%)||-1.072**||-1.914***||-0.581*||-0.401|
|||(0.478)||(0.332)||(0.339)||(0.423)|
|Constant|144.1***|177.3|126.0***|196.3|81.48***|-183.6**|129.8***|374.6*|
||(28.29)|(119.2)|(19.27)|(124.0)|(18.71)|(86.99)|(48.08)|(208.9)|
|Observations|2,928|2,841|2,397|2,336|1,685|1,629|1,302|1,240|
|Adjusted R-squared|0.181|0.359|0.109|0.379|0.303|0.544|0.053|0.528|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|

Note: Regression of realized production on expected produdction, planted area and plant productivity loss, and a number of plot-, manager-, and household controls as well as inputs and zone fixed effects. Controls: Inputs - Plot area, total labor, any hired labor dummy, seed quantity, improved variety dummy, inorganic fertilizer quantity, any organic fertilizer dummy; Household - dependency ratio, any HH member has primary education dummy, agricultural asset index; Manager - female dummy, age, primary education dummy; Plot - intercropped dummy, precipitation of wettest month, soil fertility index; Zone fixed effects. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 _Table A 8_ : Ethiopia - Shock prevalence across different proxies.

||||**Pl**<br>|**anted area loss**<br>||||
|---|---|---|---|---|---|---|---|
||Round|Any|Drought|Irregular rains|Insects|Animals|Other|
||ESS13|0.13|0.04|0.04|0.01|0.03|0.03|
|Maize|ESS15|0.19|0.11|0.11|0.01|0.03|0.02|
||ESS18|0.17|0.02|0.05|0.03|0.03|0.04|
||ESS13|0.21|0.03|0.13|0.08|0.04|0.02|
|Sorghum|ESS15|0.34|0.21|0.20|0.01|0.03|0.01|
||ESS18|0.25|0.02|0.16|0.01|0.04|0.02|
||||**Plan**<br>|**t productivity loss**<br>|<br>|||
||Round|Any|Drought|Too much rain|Insects||Other|
|Maize|ESS13|0.41|0.13|0.03|0.05||0.19|

57
47 employment programs, as well as to evaluation studies available for comparable programs In other industrialized nations, would be useful for supplying guidance to pollcymakers responsible for structuring basic education programs for displaced workers.

A third Issue draws on the brief discussion In Section D of enterprise training In Japan. The main reason the discussion was brief Is that government wage and training cost subsidies of the type supplied by the Japanese government are outside the scope of this essay. Nevertheless, it would clearly be helpful In designing institutional training systems to have available more Information on how employers In Japan and in other Industrialized nations supply job skills to employees.

Last but certainly not least, considerably more attention needs to be paid to the transferability of the experience of industrialized nations to the design and implementation of retraining programs In low-income developing and middle-income countries. Resource constraints will be substantially more severe for these countries than the case for the OECD nations described, putting limits on both access to relatively expensive skill training and basic education programs and avallabilifty of Income-maintenance allowances provided while workers are being retrained. At the same time, as noted In Section F, low-Income developing and middle-income nations face the prospect of very high open unemployment as they attempt to integrate themselves Into the world economy.
**Table 7: Country** **<u>groups - basic statistics</u>**

|**Country-**<br>**group**|**Countries**|**# of countries**|**Avera**|**ge GDP per**|**capita**|**A**|**verage grow**|**th**|**SD of**<br>**growth**|**Coef. of**<br>**variation**<br>**of growth**|**Share of Af**<br>**(%**|**rica's GDP**<br>**)**|**Share of**<br>**populat**|**Africa's**<br>**ion (%)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||**1975**|**2005**|**1975-05**|**1976-90**|**1991-05**|**1976-05**|**1976-05**|**1976-05**|**1975-80**|**2000-05**|**1975**|**2005**|
|**AA**|Angola, Botswana, Cameroon, Cape Verde,<br>Comoros, Cote d’Ivoire, Gabon, Gambia,<br>Ghana, Guinea, Lesotho, Mauritania,<br>Mauritius, Namibia, Senegal, Seychelles,<br>South Africa, Swaziland, Togo, and<br>Zimbabwe|<br>20|3,424|4,241|3,648|1.07|1.02|1.05|1.44|1.37|64.3|62.6|31.7|31.1|
|**BB**|Benin, Burkina Faso, Burundi, Chad,<br>Congo, Eritrea, Ethiopia, Guinea-Bissau,<br>Kenya, Madagascar, Malawi, Mali,<br>Mozambique, Niger, Nigeria, Rwanda,<br>Sierra Leone, Tanzania, Uganda, and<br>Zambia.|20|931|933|880|-0.07|0.81|0.37|1.73|4.67|28.3|32.1|56.2|56.7|
|**BA**|Equatorial Guinea and Sudan|2|1,161|1,853|1,787|-0.71|8.84|4.06|9.16|2.25|3.1|4.0|4.8|4.5|
|**AB**|Central African Republic and Democratic<br>Republic of Congo|2|1,930|862|1,301|-2.18|-3.04|-2.61|3.46|-1.32|4.3|1.4|7.3|7.6|

Notes: t-test does not reject the hypothesis of equality of means of growth rates (1976-2005) between AA and BB, but reject the equality of means of GDP per capita.

24
Figure 2: The Price of ICT in the US <!-- Start of picture text --> (A) US Price of ICT and NICT (B) US Price of Harw., Softw., and R&D<br> Non-ICT Capital Price (Chain Index, 1983=1) Non-ICT Capital Price (Chain Index, 1983=1)<br> ICT Capital Price (Chain Index, 1983=1) ICT: Hardware Capital Price (Chain Index, 1983=1)<br> ICT: Software Capital Price (Chain Index, 1983=1)<br> ICT: R&D Capital Price (Chain Index, 1983=1)<br>1940 1960 1980 2000 2020 1940 1960 1980 2000 2020<br>Year Year<br>2.5 2.5<br>2 2<br>1.5 1.5<br>1 1<br>Price Index (1983=1) Price Index (1983=1)<br>.5 .5<br>0 0<br><!-- End of picture text --> _Notes:_ Panel A illustrates the price if ICT and NICT based on Eden and Gaggl (2014). Panel B disaggregates ICT into software, hardware, and R&D based on the same data and procedures as in Eden and Gaggl (2014).

(ICP)—2005 and 2011.<sup>11</sup> Unfortunately, these data are only available for a small number of countries in 2005 and even in 2011 there are many missing observations for prices at the item level. Nevertheless, we are able to construct a fairly comprehensive country specific measure for the price of ICT relative to that of NICT, defined as where _pi,j_ denotes the price of item _i_ in country _j_ and _j_ = _us_ indicates the US. This measure uses US prices as a benchmark, and compares prices of ICT and non-ICT items relative to the US. We use the US as a benchmark because, due to limited data availability, we cannot construct ICT and non-ICT investment bundles that are comparable across countries. The role of the comparison with the US is to remove item fixed effects. To see the importance of this, consider a hypothetical scenario in which there are two countries. In country 1, we have data on the price of computers (an ICT item) and a sewing machine (a non-ICT item). In country 2, we have data on the price

> 11We manually classify items as ICT and non-ICT investment goods. Our classification is in line with the usual definitions of ICT and details are available upon request.

13
overall migrant experiences, including living and working conditions, financial inclusion, and sexual health, also discussed in Part II.

Similar to their initial motivations, domestic workers’ intentions differ from those of textile workers. Domestic workers are somewhat less likely to see themselves living away from home in five years (57 percent) and only half of those expect to still be domestic workers. That domestic workers are less likely to intend to stay away from home evidences their different reasons for moving: they are less likely to be supporting children back home, and more likely to be young and unattached. As one domestic worker’s sending household said, the migrant is “still young and has her whole life ahead of her”.

Regarding the decision of _where_ to move, the most important factor is where the jobs are. Unlike textile workers, who move to a specific location where they know someone and then look for a job, domestic workers tend to move for a specific job, which is lined up in advance through personal networks. Almost 85 percent of surveyed domestic workers reported learning about their job through family or friends. They also learn about potential employers through neighbors, pastors, or other community members, as well as over the radio, although this is less common. The majority has a job lined up before moving for work ‐ 60 percent of respondents – and many of their employers provide financial assistance for transport to the urban area, with some even picking the domestic worker up in their village. Importantly, few domestic workers sign a contract (less than 3 percent of domestic survey respondents did). Instead, some discuss terms of employment with their employer, although the majority simply accepts work conditions as non‐ negotiable expectations of the household head.

Surveyed domestic workers’ work patterns and trajectories differ from textile workers: they have lived and worked away from home for an average of 5.8 years, have worked in the domestic sector for 5, and with their current employer for 3, all less than the corresponding averages for textile workers. Similar to textile workers, about 7 percent of domestic workers have worked in South Africa previously. Surprisingly, almost a third of survey domestic workers had previously worked in the textile district in Lesotho prior to their current job, indicating a higher than expected degree of bi‐directional movement between these two industries.

30