rafmacalaba/datause-encoder-data huggingface dataset:
whether climate-induced rural-urban movements lead to pressures on urban labor markets of destination cities, and whether/how that in turn leads to international movements. Apart from the more accurate and detailed information on urban market dynamics available in labor surveys, this integration would also provide adequate temporal data coverage for sequential analysis, which is currently difficult to achieve when only using longitudinal household surveys that often consist of a limited number of waves. To facilitate this integration, efforts should be made to improve the accessibility of data sources, fostering data interoperability by design, establishing total quality frameworks for data integration, and maintaining ethical standards and data confidentiality (United Nations Statistical Commission, 2021). To study the potential outcome of cross-border movements, integration with non-traditional data such as remote sensing, mobile phone traces, social media, and other types of big data is also desirable, given the intrinsic difficulty in capturing international migration in national household surveys. This should be combined with improvements in panel tracking protocols within and across borders for individuals and whole households. This can include using mobile phones to track migrants, relying on CATI/CAWI for data collection to assess ultimate impacts on welfare, and so on.

Based on the above, we identify the following key areas where the return on household surveys for filling climate migration data gaps appears to be higher in the short- and medium-run:

- a. Improving the design of existing survey tools including modifications to questionnaires, updates of tracking protocols, and adoption of mixed data collection modes (e.g., the use of CATI/CAWI in combination with face-to-face interviews).

- b. Exploring and enhancing integration and interoperability with non-conventional data sources (e.g., mobile phone traces, social media, and other types of big data) and other traditional data sources (e.g., census data, administrative records and registries, labor mobility data).

- c. Broadening the perspective for some key research questions to make better use of existing household survey data and provide complementary solutions to the ‘rare event’ problem (e.g., look at climate-induced immobility other than mobility).

In an ideal scenario all these tools and integrations would be operationalized and the longitudinal and multitopic nature of household surveys would: i) allow investigating the prevailing outcomes of slow-onset changes, namely migration _vs._ immobility, and their interactions with contextual factors; ii) shed light on the leading mediating channels at play; iii) facilitate assessing the potential existence of relevant migration chains and the role of climate change as an indirect push factor for cross-border mobility; and iv) gauge final impacts on welfare and how these are distributed across different types of households and individuals.

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on sales and profits. In our qualitative interviews, three out of 10 of the sample we had identified as showing the highest sale growth in the quantitative survey said they believed the program had not increased sales. Moreover, since firms did not increase their use of marketing practices or personal initiative, there could be limited impacts on generating new customers and prolonged sales impacts.

# **4 How might online training be done better?**

Our results demonstrate the feasibility of conducting online training by Zoom, but show the need for improvement in cost-effectiveness and impact. Based on our observations of the training and qualitative interviews with participants, there appear to be several areas where improvement in content and delivery could occur in any future efforts.

In terms of content, we saw that the training did not result in improvements in marketing practices. Nor are firms innovating by digitization or selling new products or services (Table C.1). Hardy and Kagy (2020) have shown how a lack of demand is a key constraint for the growth of many women-owned businesses. Increasing demand through product innovation and better marketing efforts to generate new sales could help the program have greater impact. Revamping the marketing component to provide specific actionable steps suitable for these types of firms could help result in more impact.

In addition to providing less general and strategic content, and more actionable steps, training could also attempt to foster more networking and sharing of knowledge among those participating. There was some interactive participation via audio and chat features in Zoom, and the instructors also set up Whatsapp groups to communicate with the class. But these did not result in much networking or idea sharing between participants (Table C1), instead serving mainly as a means to communicate with the trainer. Incorporating a more structured networking component via the Whatsapp groups, as in Asiedu et al. (2023) could help enhance effectiveness.

Finally, since the main cost is the instructor salary, the main suggestion to improve costeffectiveness would be to increase the size of online classes. Doubling the typical class from 15-20 students to 30-40 students would likely still allow as much, or even more, interaction, while almost halving the cost. Having the most dynamic and effective trainers train larger groups of women at once offers the potential to greatly improve cost-effectiveness and hence scalability.

# **5 Conclusions**

Women running small businesses throughout Mexico and in Guatemala were able to successfully connect to, and attend, business training sessions by Zoom. Widespread usage of mobile phones and social media has now made using technology to offer programs to thousands of microenterprises possible. The logistics of recruiting microenterprises and scheduling resulted in class sizes that were similar to in-person training, so that personnel costs were not lowered much by holding training online, resulting in relatively limited cost 12
# **I.5 Quantitative results for Section 4.3.4**

Table I.5 shows the effects of fiscal decentralization on debt levels with an alternative value of _ε_ .

**Table I.5:** Counterfactual Experiment

|||w|ith_ε_=0.1||||
|---|---|---|---|---|---|---|
|||1988–19|96||1996–2|006|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Moments|1988|1996|Counterfactual|1996|2006|Counterfactual|
||||1996 (%)|||2006 (%)|
|Local debt|0.149|0.284|0.398 (184%)|0.284|0.180|0.092 (185%)|
|Central debt|0.482|1.029|0.863 (70%)|1.029|0.593|0.635 (90%)|
|Total government debt|0.631|1.313|1.261 (**92%**)|1.313|0.773|0.727 (**109%**)|

Note: Using _ε_ = 0.1, and all other parameters identical to the baseline. Moments are _not_ matched to data. Total government debt (also known as “general government debt”) is the sum of the local and central government debt. Counterfactual experiments use revenue ( _e_ and _f_ ) and spending responsibility share ( _θ_ ) values from end year, and keep all other parameters at their starting year values. Numbers in **parentheses** show the percent of debt changes that is explained by decentralization. All debt levels are normalized by national GDP.

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**Table** **<u>5: Indicators of Industry Structure</u>**

|**Country**||**Number of**<br>|**insurers**<br>||**% of non-life**<br>**market held**<br>**by the top**|**Share of**<br>**State-**<br>**Controlled**|**Formal**<br>**Bancassurance**|
|---|---|---|---|---|---|---|---|
||**Total**|**Non Life**|**Life**|**Composite**|<br>**three**<br>**insurers**|**Companies**||
|**Algeria**|15|-|1|14|62|72||
|**Bahrain**|36|29|3|4|43|0||
|**Egypt**|28|15|10|3|69|61|Y|
|**Jordan **|28|10|1|17|25|0|Y|
|**KSA **|26|N/A|N/A|N/A|43|22||
|**Kuwait**|29|14|2|13|46|0||
|**Lebanon **|54|18|5|31|31|0|Y|
|**Libya**|9|-|-|9|80|44||
|**Morocco**|18|8|1|9|47|0|Y|
|**Oman **|23|12|2|9|57|0|Y|
|**Qatar **|9|6||3|95|0||
|**Syria**|13|9|9|13|53|47||
|**Tunisia**|17|3|2|12|49|20||
|**UAE**|57|N/A|N/A|N/A|23|0||
|**Yemen**|12|2|-|10|67|14||
|**MENA Av.**|25|11|4|11|52|-|-|
|**OECD Av.**|179|40|106|33|-|-|-|

**Figure 9: Share of Three Top Companies and Share of State Insurers**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>0 10 20 30 40 50 60 70 80<br>Share of State Controlled Insurers (%)<br>Share of 3 Top Insurers (%)<br><!-- End of picture text -->

**Non life distribution in MENA tends to be dominated by direct sales and brokers while life insurance is divided between tied agents for individual business and brokers for group life. However banc-assurance is now also becoming a significant distribution system, particularly in the Francophone countries (Annex Table 6).** Insurers have had relationships with banks for some time, largely to place industrial risks for bank-funded projects, but this relationship is strengthening as banc-assurance becomes more prevalent. However, banc-

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**<u>Appendix table A1: Volume of approved PSG applications under the land program by region and farm size, full sample</u>**

||<br>**Num**<br>|<br>**ber of succ**<br>|<br>**essful applica**<br>|<br>**nts**<br>|<br>**Amount**<br>|<br>|<br> <br>|<br>**Area per far**<br>|<br>**m**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**Total**|**<20 ha**|**20- 50 ha**|**> 50 ha**|**($ mn.)**|**Total**|**<20 ha**|**20- 50 ha**|**> 50 ha**|
|Total|21,050|15,269|3,168|2,613|35.131|19.5|6.5|35.2|77.7|
|**Panel A: By re**|**gion**|||||||||
|Center|9,619|6,250|1,774|1,595|19.506|23.92|6.97|35.03|77.97|
|East|12|11|0|1|0.013|13.18|5.84||93.90|
|North|1,629|1,027|328|274|3.402|24.64|6.65|36.21|78.21|
|South|5,297|4,371|580|346|6.863|15.28|7.41|35.88|80.23|
|West|4,493|3,610|486|397|5.347|14.04|4.44|34.56|76.17|
|**Panel B: By or**|**ganizational**|**form**||||||||
|Individual|14,218|13,076|895|247|9.819|8.15|5.50|30.93|65.68|
|FOP|2,647|1,254|720|673|7.669|34.18|9.79|35.17|78.56|
|Legalentity|4,185|939|1,553|1,693|17.643|49.73|15.65|37.75|79.62|

_Source:_ Own computation based on PSG records.

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_Brazilian spatial labor dynamics Page 26_

**Table 10: Non-Metropolitan Brazil excluding North Region**

|Dependent Variable:<br>Delta ln Wage|2SLS Est.|2SLS SE|Spatial GMM<br>Est.<br>(Cutoff = 0.5)|Spatial GMM<br>SE<br>(Cutoff = 0.5)|
|---|---|---|---|---|
|Intercept|-.15950009|.10277873|-.12074952|.17974661|
|ln Teacher Qualification in 1991|.0052535|.00254925|.00236128|.00410509|
|Years of Schooling in 1991|.06246437|.00536942|.06088094|.0082013|
|Total Rainfall|.00007979|.00001297|.00009786|.00002296|
|Government Accountability|-.00584819|.01443974|-.00617702|.02409457|
|Delta ln Employment|-.53408098|.07787127|-.63237196|.14710456|
|ln Transport Cost SP in 1995|-.0404624|.00821161|-.04469861|.0127028|
|ln Transport Cost Capital in 1995|-.04374225|.00881383|-.0422301|.01622356|
|Delta ln Transferences|.22328359|.04042839|.18064082|.07351591|
|Delta ln Market Potential|.58404297|.11290579|.62033021|.18144919|
|crit. fn. test of overid. restrictions|----|----|44.336521|44.336521|

|Dependent Variable:<br>Delta ln Wage|Spatial GMM<br>Est.<br>(Cutoff = 1)|Spatial GMM<br>SE<br>(Cutoff = 1|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|---|---|---|---|---|
|Intercept|-.04462965|.19892652|.00716942|.22615147|
|ln Teacher Qualification in 1991|.00093057|.00426831|.00001553|.00453575|
|Years of Schooling in 1991|.06330124|.00894142|.06656591|.01025243|
|Total Rainfall|.00010275|.00002402|.00010328|.00002395|
|Government Accountability|-.01646986|.02421216|-.02936971|.02324613|
|Delta ln Employment|-.68066132|.16307582|-.76383001|.18872485|
|ln Transport Cost SP in 1995|-.04688983|.01363969|-.042707|.01531044|
|ln Transport Cost Capital in 1995|-.04913757|.01864702|-.05431722|.02134752|
|Delta ln Transferences|.17948617|.0803842|.15744109|.09175749|
|Delta ln Market Potential|.58104956|.19482417|.52911681|.22650729|
|crit. fn. test of overid. restrictions|33.939201|33.939201|21.138334|21.138334|
developed by Galster et al. (2001) was adopted.<sup>59</sup> This Centrality Index (CI), measures the average distance of the population from the city center, normalized by the total area of the city. This measurement is slightly different than the density gradient, in that it is not influenced by the city’s size, and therefore better suited to compare cities of different sizes. <mark>Figure X</mark> shows Nairobi and Addis having a higher level of centrality than Kigali, suggesting that Kigali is more compact and has a larger proportion of its population living close to the city center. Nairobi and Addis, on the other hand, have a slightly more polycentric population distribution. Decentralization of urban areas is often cited as a cause for longer travel distances or times and inefficiencies of land use. CI helps to understand the extent to which the urban area is characterized by a monocentric form as opposed to a polycentric form.

<!-- Start of picture text --> Figure A1: Centrality Index<br>Centrality Index<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>Addis Ababa Nairobi Dar es Salaam Kigali<br>Note: Less sprawling cities have lower CI values.<br>Data Source: Landscan 2012 and GHSL 2014. Calculations made by Author<br><!-- End of picture text -->

# **Clustering**

Building upon the notion of compactness, the study turns to measure the spatial clustering of land cover. Interested in understanding how disperse or clustered each land cover type was, a global Moran’s I statistic was generated for Industrial/Commercial, Residential Regular, and Residential Irregular. Moran’s I measures the spatial autocorrelation of features based on their location, in other words, the degree to which spatial features are similar to their neighbors. Cities where features are highly spatially correlated would have a Moran’s I statistic closer to 1. A value closer to zero indicates little or no clustering.

<mark>Figure X</mark> shows the Moran’s I value for each built‐up land cover class in Nairobi, Dar es Salaam, Kigali, and Addis Ababa. Generally speaking from a land planning perspective, a high Moran’s I value is seen as advantageous, as it suggests there are significant concentrations of land cover. A low Moran’s I, in contrast, is often observed in decentralized, sprawling urban areas, as they experience strip and more discontinuous development.<sup>60</sup>

> 59 Galster, George, Hanson, Royce, Ratcliffe, Michael, Wolman, Harold, Coleman, Stephen, and Freihage, Jason. 2001. Wrestling Sprawl to the Ground: Defining and measuring an elusive concept. Housing Policy Debate, 12 (4), 681‐717.

> 60 Tsai, Yu‐Hsin. 2005. Quantifying Urban Form: Compactness versus ‘Sprawl’. Urban Studies, 42 (1): 141‐161.

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sector and gender; and the determinants of sector participation using a bivariate probit model of participation. Recent studies by Appleton (1999), Deininger and Okidi (1999), and Abdulai and Delgado (1999) explore different aspects of these same issues for Ghana and Uganda, but none focuses specifically on the gender, non-farm, poverty nexus.

A large body of recent research has demonstrated the importance of the non-farm sector.' The non-farm sector provides employment, household income diversification and security, market linkages for agriculture, and thus, the potential for reducing poverty and inequality.2 Most authors have concluded that non-farm activities can be seen as a route out of poverty, and that the impacts of non-farm growth on inequality depend on the type of non-farm activity, lancl tenure patterns, and physical and human capital requirements in question. The role of non-farm work has been examined from a number of perspectives, but one area in the non-farm literature that has not been examined in much detail is that of the different impacts of non-farm development by gender.

Analysis of the gendered impacts of non-farm activity has been included in general overviews of the non-farm sector. But from the little that has been written, the policy implications can be contradictory. Most scholars conclude that growth in non-farm activities is beneficial to women since women are said to participate more in these activities.<sup>3</sup> But the opposite was described in a recent paper by Abdulai and Delgado about non-farm participation in Northern Ghana. They write that men are more active than women in non-farm work. Others assert that women's traditional role of processing

> ' See Reardon (1997) and Ranis and Stewart (1993) as examnples.

> 2 See Lanjouw and Lanjouw (1995) :for a recent survey of the literature. Also see Lanjouw (1997), van de Walle (1993), Hazell and Haggblade (1993), Adams (1994 1999), Reardon and Taylor (1996), and Leones and Feldman (1998).

3
5 through 8 replicate 1 through 4 but include fixed effects for the month of the year in which the survey took place.

In all eight OLS specifications reported in Table 2, the post-election coefficient is negative and significant at at least the 99 percent confidence level, suggesting that experiencing the crisis lowered the number of risky choices a respondent made by between 0.561 and 0.798 choices. In the pre-crisis period, the average number of risky choices was only 2.566 out of five, so the change observed after the crisis represents a dramatic drop in the willingness to bear profitable risk. Adding controls for gender, education level, age, and marital status has almost no impact on the estimated coefficient. In Table 3, we estimate analogous ordered logit specifications which account for the discrete but ordered nature of the outcome, the number of risky choices. Again we find that the indicator for being surveyed after the post-election violence is negative and significant (at at least the 99 percent confidence level) in all specifications. In Table 4, we take an alternative approach to controlling for time effects by including separate linear time trends in the pre- and post- crisis period. Columns 1 through 4 report OLS specifications, and Columns 5 through 8 report ordered logit results. Again, the indicator for being surveyed in the post-crisis period is negative and significant in all specifications.

As an additional set of robustness checks, we consider alternative formulations of the outcome variable in Table 5. In the first column, the outcome variable is an indicator for always choosing the highest variance and/or highest expected value lottery option (i.e. behaving in a manner consistent with risk neutral or risk-loving preferences); in the second column, the outcome is an indicator for always choosing the lowest variance alternative (i.e. behaving in a manner consistent with extreme risk aversion). We find that the post-election violence led to a 5.7 percentage point (26 percent) decrease in the likelihood of making risk neutral or risk loving choices, and an 11 percentage point (82 percent) increase in the probability of always choosing the lowest variance (and lowest expected return) lottery option. Next, we consider each decision problem in isolation. Importantly, this allows us to test whether the effect that we find is likely to be driven by a change in the preference for certainty, a finding that would be consistent with the results reported in Callen, Isaqzadeh, Long, and Sprenger (2014).<sup>17</sup> We find that the post-election violence led to a significant decrease

> 17Callen, Isaqzadeh, Long, and Sprenger (2014) find that Afghan respondents primed to recollect fearful episodes

16
The increase in the poverty gap is prevalent across most NUTS2 regions in Turkey. Between 2006 and 2013, only one out of the 26 regions, TR61, experienced a fall in its poverty gap (Figure 5). Amongst the 25 regions in which the poverty gap rises, the four least successful performers are located in Eastern Turkey (TRA1, TRA2, TRB1, and TRB2). Just as before, breaking down the time period into crisis and post-crisis components produces noteworthy results for changes in the poverty gap. During the crisis period, the poverty gap and the headcount ratio were mostly parallel. Five of the 6 NUTS2 regions that reduced poverty were also successful in diminishing the poverty gap. These regions were both able to move people out of poverty and decrease the depth of poverty at the same time. Conversely, after 2009, the poverty gap diminished in only two of the 24 regions that experienced a reduction in moderate poverty. In this respect, even though Turkey was successful in removing people out of moderate poverty after the crisis, a similar achievement could not be accomplished in lessening the depth of moderate poverty.

Figure 5. Change in the Poverty Head count and the Poverty Gap, NUTS2 2006-2013 <!-- Start of picture text --> TR61<br><!-- End of picture text --> <!-- Start of picture text --> TRB2 TRC1<br><!-- End of picture text --> _Notes_ : OLS imputed income poverty, spatially deflated. _Source_ : LFS, SILC 2006-2013; authors’ calculations.

# **<u>Is There Regional Poverty Convergence?</u>**

In principle, poverty convergence implies that regions with higher initial poverty rates should also experience higher rates of growth in consumption or income and subsequently higher rates of poverty reduction. The implication will be poverty rates gradually becoming similar across countries or regions. Cross-country studies do not find evidence of this effect (Ravallion, 2012<sup>13</sup> ). This is because other effects cancel out mean convergence. Two factors dampen the effects of poverty convergence; the initial level of poverty slows the impact of poverty reduction, and there is also a lower poverty-to-growth elasticity when initial poverty levels are high.

> 13 See Ravallion (2012) on methodological notes on estimating poverty convergence.

11
blood tests. We therefore use only the screening tasks to construct three measures of performance: whether the worker performed any of the screening actions (60 percent of participants), whether she performed all five (22 percent), and the total number of actions she performed (two of five, on average).

The PFP trial started in July 2014, when the two financing interventions were rolled out. The evaluation endline survey that contained our experiment was conducted between August and October 2017. The impact evaluation found that districts with PFP or DFF performed better than those in the control group and the impacts of PFP and DFF were comparable, with few exceptions (Kandpal et al., 2019). Both PFP and DFF had a practically and statistically significant impact on the quantity of key maternal and child health services. For example, both significantly increased fully immunized child coverage and modern contraceptive prevalence. However, directly observed clinical quality of care, which may be most directly related to provider effort, showed limited gains, especially in the DFF arm.

For context, 75 percent of all health workers in our sample reported working seven days a week, for an average of six hours a day. The median monthly gross salary is 43,000 Nigerian Naira or about USD 113. Wages are stagnant and often paid with a delay: only a third of health workers reported receiving a salary increase in the last two years, and a quarter said they had not received their full pay for the previous month. Indeed, 63 percent reported not having received their entire salary for the past year.

# **4.2 Outcomes, moderators, and possible confounders**

Our measure of overall performance is the proportion of clinical actions that participants correctly identified as medically appropriate for each partograph case. Since actions can be clinically indicated or not, we score as correct those actions that are indicated and named by respondents as well as actions that are unnecessary and not named.<sup>5</sup> In other words, a fully correct set of actions would be one in which the participant identifies all indicated actions and none that are unnecessary. We

> 5In four instances, actions can be ambiguous based on the partograph Table A.1. Because performing ambiguous actions can be unnecessary or harmful to patients, we consider these actions to be not indicated.

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