rafmacalaba/datause-encoder-data huggingface dataset:
recovery of costs in the system. An accounting model of Standard Bank showed that there were positive net revenues per account holder of R 3 (US$0.67) per month.

Since the interest rate spread is expected to narrow in the future, revenue generation will gradually shift emphasis to transactions fees and higher transactions volumes per account through Auto Bank E outlets. Within three years Standard Bank expects to generate positive operating profits from E Plan. The business plan calls for the number of transactions per account to double to four transactions per month in 2002 through further marketing of the benefits of the savers' lottery, life insurance, further product education, and product expansions. The variable costs of transactions are expected to be flat or rise slightly over the next five years as transactions more than double. This conclusion reflects the belief that E Plan has achieved the scale economies of efficient electronic transaction volume and that the opening of new outlets will take place in an orderly fashion over the planning horizon. Account maintenance costs are expected to fall from R 4.58 to R 2.77 per month between 1997 and 2002, roughly a 40 percent drop. This reflects the migration of transactions out of full service branches into the E Plan outlets, and therefore lower allocation of branch costs to E Plan in the future.

# **Marketing and Innovative Product Design**

Market research has been undertaken on the characteristics and behavior of the low income clients targeted by E Plan to help determine whether E Plan is successful in the market niche it expected to fill . The target market for the E Plan are individuals who earn less than R 3000 (US$667) per month. This puts the E Plan clients in the middle to lower end of the low income consumer market segment.

Some of the characteristics of the population that falls within the low income market, i.e., income of R 0-5000 per month (less than US$1,100), have been identified through market research. Only 27 percent of the low income consumers have a bank account of any sort, so over 70 percent are unbanked. In the consumer category, 81 percent earn less than R 3000 per month, the target market for E Plan. Following South African practice, the population that falls into the R 0-5000 income band has been 16
imports/exports in the supply constrained sectors (Z1) guarantees that prices are fixed for all tradeable goods. Thus, for these models to produce a reasonable approximation of reality, the supply constrained sectors must correspond to tradeable goods with fixed domestic supply at the given fixed price, and the perfectly elastic sectors must correspond to non-tradeable goods. In supply-constrained sectors (Z1), increases in domestic demand merely reduce net exports (E1), which then become endogenous to the system.

Investment in additional productive capacity or the introduction of new technology will trigger expansion in the production of tradeable goods (Z1) such as agricultural cereals, export crops and manufactures. Therefore, the key shocks initiating growth are those that release production constraints in these tradeable sectors. New investment in productive equipment – induced by government policies or incentives – will increase productive capacity of tradeable goods. Public investment in transportation infrastructure or irrigation facilities opens up new regions to external markets. Public investments in agricultural research generate new technology that improves productivity of cereals and other tradeable agricultural products.

The specification of which sectors are considered elastic in supply is crucial to the SIO model results. In this Panama analysis, production of the major agricultural traded commodities (maize, fruits, shellfish, other agricultural exports and processed milk) is fixed exogenously (i.e. treated as completely inelastic in supply). Rice, oil seeds and other domestic agriculture are modeled as elastic in supply, as are livestock of various types (poultry, small livestock, dairy and large livestock), fish, meat and milled grain. Most industrial sectors (mineral, processed milk, other domestic manufacturing, textiles, other export manufacturing and hotels and restaurants) and economic output of the Panama Canal, the Zone Colon are modeled as inelastic in supply; services (electricity/water, construction, trade, transport/communications, private services and public administration) are modeled as elastic in supply.

# **6. Results: Province-level Tourism Spending, Growth Linkages and Poverty**

The aggregate economic impact of the tourism sector on the Panamanian economy is very significant—according to the data from the national accounts, expenditures by foreign tourists reached around 7 percent of the GDP in 2006. Arrivals of foreign tourists in Panama exceeded 2 million visitors in 2006, of which over 1 million were in direct transit; and total expenditures by foreign tourists totaled US$960 million (Table 3).<sup>14</sup>

> 14 Tourists in direct transit are defined as visitors that stay in the airport less than 10 hours, do not leave the airport area and proceed to other destinations.

10
own children or children from one’s reference group whose conditions may be similar, probably receive greater weights in one’s evaluation of children’s future chances in general, how exactly respondents frame their answers to this survey question is not clear. Still, the results can be cautiously viewed as reinforcing the main results of the analysis, as well as the predictions of theoretical models discussed in section 2, that link inequality in access to key opportunities in life to lower expectations of future mobility; moreover, these lower expectations may be relevant not only in the context of the immediate future, but rather may persist over time, and across generations, thus perpetuating existing inequality traps.

# _7.2. Inequality in access to opportunities and redistributive preferences_

If access to key opportunities in life is perceived to be unfair, on account to whom you know, then resulting inequalities in society are likely to be similarly perceived to be unfair. This is especially likely to be the case for those who do not have connections that may facilitate access to opportunities such as jobs or university education. When inequalities are perceived to be unfair, individuals may be more likely to desire a greater amount of redistribution in society to compensate for such unfair distribution of rewards (see Cojocaru, 2014 for empirical evidence of the link between fairness and inequality aversion based on LiTS data). It is possible to explore with LiTS data whether the “Rawlsian” inequality of opportunity examined in this analysis is associated with stronger redistributive preferences. The latter are elicited in the survey questionnaire by way of the following statement “The gap between the rich and the poor in our country should be reduced”, with which the respondents can either agree or disagree on a 5-step Likert scale that ranges from “strongly disagree” to “strongly agree”. Using responses to this question I construct a binary variable that evaluates to 1 for above neutral responses (either agree or strongly agree) and zero otherwise. The model is re-estimated with the preference for reducing the gap between the rich and the poor as the dependent variable, and the _δ_ vector corresponds, as before, to the intersection of beliefs about the importance of connections and whether one has such connections, as in the baseline model reported in table 4. The results, reported 24
<!-- Start of picture text --> 0 2 4 6 8 10<br>Avg. Loan Size/Income(20th pctile)<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text -->

**Figure 1. Average costs per dollar lent fall as loans get larger** . Source: Cull et al. 2009a. Horizontal axis gives the average loan size as a fraction of the average income of households at the 20<sup>th</sup> percentile of the national income distribution.

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**Figure 2. Interest rates rise with costs** . Source: Cull et al. 2009a. The “premium” is the excess of the microlender’s average interest rate charged to borrowers over the International Monetary Fund’s inter-bank “lending

11
34 Table **5.** Male-Female Differences in School Enrollment and Private Rates of Return to Education

|School Level|Male-Female<br>Difference|<br>Peru|Lima|OUA|Rural|
|---|---|---|---|---|---|
|Primary|School enrollment ratio|<br>0.01|0.01|0.00|0.02|
||Private rate of return|**I**<br>0.00|-0.03|0.02|-0.02|
||Private rate of return|**11**0.00|**-0.02**|0.00|**-0.26**|
|Secondary|School enrollment ratio|<br>0.09|0.01|0.04|0.17|
||Private rate of return|**1**<br>-0.06|**-0.05**|-0.05|-0.04|
||Private rate of return|**11**-0.08|**-0.07**|-0.06|0.19|
|Postsecondary|School enrollment ratio|<br>0.03|0.01|**0.00**|0.07|
||Private rate of return|**I**<br>-0.01|-0.02|-0.02|-0.01|
||Private rate of return|ll -0.04|-0.03|-0.04|0.16|

Note: School enrollment ratio is defined as the proportion of the school-aged children enroLled in school. **The** d;fferences in returns to schooling are based on the estimates of ML (panel 1) and househoLd fixedeffect (panel 11) from Table **4.** postsecondary levels, seems consistent with the higher returns recorded by boys. This is possible when unobserved household characteristics are accounted for in the wage estimates. Thus at the postsecondary level, school enrollment of boys is / percent higher than girls', which is consistent with 16 percent higher returns for boys than for girls. When the unobserved household characteristics are controlled, the results from rural areas suggest that there are reasons (unobserved) that influence the parents' decision to send more boys than girls to secondary and postsecondary schools.
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|**Challenges**|**Figure 1: Urbanization Iss**<br>**Instruments**|**ues – New, Resurr**<br>**Analytical and**<br>**Planning Tools**|**ected or Forgotten**<br>**Data and**<br>**Benchmarks**|<br>**Institutions**|**Regions/**<br>**Countries/**<br>**Cities**|
|---|---|---|---|---|---|
|**New**<br>**Issues**<br>Poverty and Inequality|Land tenure/titling|Happiness/life<br>satisfaction analysis|MDG Target 11 on<br>slums|Decentralization|China|
|Gender perspective/<br>Women|Micro‐credit finance|Citizen Report Cards|Socioeconomic and<br>demographic<br>information(SDI)|Ministerial<br>coordination|India|
|ECD/Youth/Old Age|Special approaches/<br>targeted intervention (water,<br>toilets, cement floors, street<br>addressing, etc.)|Urban Poverty<br>Assessment (World<br>Bank)|Poverty definition<br>and measurement|UN‐HABITAT|Africa|
|Crime and insecurity|Private sector<br>engagement/social<br>entrepreneurs|Asset‐based poverty<br>analysis||Cities Alliance|Fragile and<br>conflict‐ridden<br>states|
|HIV/AIDS|Community Driven<br>Development (CDD)|Longitudinal studies||Civil Society<br>Organizations||
|Climate change (CC)<br>mitigation and<br>adaptation|Comprehensive slum upgrading<br>programs|GIS poverty<br>mapping||||
|Compound global crisis<br>(financial, food, energy)|Multi‐stakeholder alliances<br>(incl. public‐private<br>partnerships (PPPs)|Impact evaluation||||
|Cultural heritage<br>preservation|Conditional cash transfers<br>(CCT)|Cost of doing<br>business etc.||||
|Governance (supply and<br>demand driven)|Policy‐based interventions|City quality<br>indicators||||
|Political economy of<br>urban policy|Incentive based instruments<br>(output‐based aid or<br>tournament approaches)|ICT in public<br>administration||||
||Climate change funding|PRSs, National<br>Urban Strategies,<br>City Development<br>Strategies (CDS)||||

5
and distance walked by around 15 percent (Guyatt et al. 1984) and similar impacts of detection bias have been found in other medical RCTs (Noseworthy et al. 1994). The second channel is a simple case of incentive alignment. If data collectors are employed by the organization whose intervention is being evaluated in an RCT, there is a clear conflict of interest that raises concerns about the accuracy of the data collected.

In our assessment, we first look to see whether data collectors are blinded to the treatment status of participants. If the data collectors are not blinded, we then look to see whether the data collectors are contractually related or otherwise linked to the organization administering the treatment in a way which might induce them to bias the data they collect. We also ask whether there is any other reason to suspect data collection might differ between the two arms in a substantive way, such as data collected at different scheduled times or by different individuals for treatment and control groups.

_Reporting bias_ points to the fact that it is exceedingly difficult, in any reading of empirical analysis, to know whether authors are presenting the entirety of the results of the study or only that subset of outcomes which is deemed interesting or sympathetic to the case they are trying to make. Recent meta-analyses have shown evidence of this among studies in economics (Brodeur et al. 2013) and in medicine (Dwan et al. 2008), the latter of which finds that “statistically significant outcomes had a higher odds of being fully reported compared to nonsignificant outcomes (range of odds ratios: 2.2 to 4.7).” A meta-analysis of medical studies on anthelminth therapy and treatment for incontinence found that “more outcomes had been measured than were reported.” This study calculated that with a change in the assumptions about which outcomes the largest study 16
The analysis presented here is based on a database put together as part of the Africa Infrastructure Country Diagnostic (AICD).<sup>2</sup> The database includes the basic institutional characteristics of African power systems as well as standard power sector indicators (performance, capacity, and so on). In addition the database documents the power-tariff regimes of 27 Sub-Saharan African countries in detail.<sup>3</sup> Together, these nations account for over 85 percent of the population and gross domestic product (GDP) of the region. They were carefully selected to represent the economic, geographic, cultural, and political diversity that characterizes Sub-Saharan Africa. They also represent the four Sub-Saharan power pools; include countries with small, medium, and large-scale generation; and constitute a representative mix of predominantly thermal and/or hydro power systems. As such, the sample can be considered a statistically representative basis for inferring tariff-setting trends in Sub-Saharan Africa.

The tariff data set includes one published tariff regime for each country in the sample. Results presented here are based on the latest published tariff regime available for each country during the AICD data collection period (2003–08). For most countries that year was 2006 (see annex 1).

Using the AICD database, we seek to characterize African power tariffs by (i) describing prevalent tariff structures, both residential and nonresidential; (ii) analyzing their ability to recover costs; (iii) assessing their economic efficiency against long-run marginal costs; and (iv) exploring their equitability and affordability vis-à-vis country-specific purchasing power.

# **What power tariff structures are prevalent in Sub-Saharan Africa?**

Most electricity tariffs—and Africa is no exception—are based on block tariff-pricing schemes; that is, the price of power is linked to the level of consumption.

Power tariffs are commonly structured around blocks. A block is a pre-determined range of power consumption; with the unit price of each kWh being fixed within the block. The relation between blocks and prices defines three types of tariff structures:

- Increasing block tariffs (IBTs) is a regime in which the unit price per kWh follows an increasing step-function linked to sequentially defined blocks

- Decreasing block tariffs (DBTs) is a regime in which the unit price per kWh follows a decreasing step-function linked to sequentially defined blocks

- Linear tariffs (LTs) are a regime in which all units of power consumed are charged at exactly the same rate.

> 2 http://www.infrastructureafrica.org .

> 3 Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Chad, Congo, the Democratic Republic of Congo, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe.

4
migration and destination. But their emergence often also depends on distance in the first place. Some distance-related aspects, such as transport costs and information, may be more amenable to policy, and can reduce friction even over short distance, as illustrated by the effect of cash transfers on seasonal migration in Bangladesh (Bryan, Chowdhury and Mobarak, 2014). Others may take more time (such as the development of social safety nets). Together it would suggest that the effect of distance on destination choice is likely to continue for some time.

Finally, two potential spill-overs following the interplay between distance, towns and migration may further accelerate the structural transformation and deserve further attention. First, many new towns are arising in Tanzania, as they are across much of the African continent. OECD/SWAC (2020) combines demographic data with satellite and aerial imagery to document a total of 7,617 urban agglomerations of at least 10,000 people in Africa in 2015. In 1950 that number stood at just 624. Growth in the number of urban agglomerations has been especially strong in recent years. From 2000 to 2015, 2,475 new urban areas came into existence through in-situ urbanization. With every new small town (a subset of) rural dwellers will see the distance between their home village and the nearest urban agglomeration shrink. Given the primacy of distance as a determinant of destination choice documented here, we would expect frictions to rural-urban migration to come down through the rise of small towns. Such rural-urban migration is an important component of structural transformation, so the rise of small towns is not only a consequence of this process, but could actually speed it up. Relatedly, there are good reasons to believe other frictions, for example related to the flow of goods, services and information between urban and rural areas will also come down with the emergence of small towns. While important studies like Dorosh and Thurlow (2013, 2014) and Gibson et al. (2017) have studied how urban growth in large versus small towns affects rural poverty reduction, we still know very little about the exact mechanisms through which this happens.

Second, given their proximity, towns can be particularly important in facilitating the first move, which is often the hardest, thereby potentially instigating a virtuous cycle of physical and economic mobility. The results from the Kagera life histories highlight the difficultly and importance of the first move, an oft ignored phenomenon. It is difficult because it is often a step in the dark that prospective migrants need to undertake with little preparation 18
|**Intervention Category**<br>**(down) vs. Outcomes**<br>**(across)**|**Increasing Educational Attainment**|**Delaying Marriage**|**Delaying Fertility**|
|---|---|---|---|
|**Removing bans on**<br>**pregnant girls**<br>**attending school**|**Unknown.**One study of policy changes in<br>nine African countries. Observe more<br>pregnant girls in school after removal of<br>bans. Suggests removal of bans can<br>improve human capital for pregnant girls.|**Unknown.**|**Unknown.**From the one study, there is<br>no evidence that removing bans led to<br>increased pregnancies.|
|**Raising Age of**<br>**Marriage Laws**|**No Effect**in Mexico and mixed results in<br>Ethiopia.|**Mixed.**Positive effects in delaying<br>marriage in Ethiopia. Decrease in formal<br>marriages in Mexico (although<br>accompanied by increase in informal<br>unions).Laws were not effective in<br>curbing early marriage in Benin,<br>Mauritania, Kazakhstan, and Bhutan.<br>Limited effectiveness in Tajikistan and<br>Nepal. Increase in short-run marriage<br>among adolescent girls in Bangladesh<br>when informed about upcoming law<br>changes.|**Mixed.**No effect found in Mexico while<br>positive results in Ethiopia.|
|**Job Opportunities**<br>**and information on**<br>**job opportunities **|**Promising.**Creating job opportunities for<br>women effective in Bangladesh. In areas<br>where job opportunities exist, providing<br>information on job opportunities appears<br>highly effective in India.|**Promising.** _See ‘Increasing Educational_<br>_Attainment’_|**Promising.** _See ‘Increasing Educational_<br>_Attainment’_|
|**Vocational Training**|<br>**No effect.**Training offered to older<br>adolescents or out-of-school young adults,<br>so we do not expect improved schooling<br>outcomes. Evidence suggests while<br>programs often improve labor market<br>outcomes, many are not cost effective.|**Unknown.**Studies tend to focus on labor<br>market outcomes (and occasionally<br>fertility) but not marriage.|**Mixed.**Weak evidence to suggest<br>training may delay pregnancy while in<br>program.|
|**Life skills training,**<br>**mentoring, and**<br>**empowerment**<br>**programs via Girls**<br>**Clubs **|**Mixed**. Typically, no effect on educational<br>attainment - considered a success when<br>clubs do not interfere with schooling.<br>However, evidence that girls’ clubs may be<br>protective of schooling during crises (e.g.,<br>Ebola in Sierra Leone), and cause modest<br>gains in educational attainment in<br>Bangladesh.|**Mixed**. Beneficial effect in Uganda and<br>Bangladesh; no effect elsewhere.|**Mixed**. Some beneficial effect in Uganda,<br>no effect elsewhere. Caveat: can be<br>protective of pregnancies out of wedlock<br>during crises, e.g., Ebola in Sierra Leone.|

9
TABLE 8 Regression results for labor force participation rates, robustness test: 10-year moving average

||**15-19 y**|**ears old**|**20-24 y**|**ears old**|**25-49 ye**|**ars old**|**50-64 y**|**ears old**|**65+ ye**|**ars old**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|
|Fertility|0.706***||0.076**||0.004**||||||
||(0.000)||(0.009)||(0.002)||||||
|Secondary enrollment|0.202***|0.149***|||||||||
||(0.000)|(0.000)|||||||||
|Tertiary enrollment|||-0.112***|-0.171***|||||||
||||(0.000)|(0.000)|||||||
|Completion of secondary|||0.022|0.030|0.252***|0.149***|0.341***|-0.014|||
|education|||(0.540)|(0.296)|(0.000)|(0.000)|(0.000)|(0.786)|||
|Completion of tertiary<br>|||0.167**|-0.070|0.354***|0.335***|0.570***|0.145|||
|education|||(0.002)|(0.166)|(0.000)|(0.000)|(0.000)|(0.265)|||
|Life expectancy|||||||0.621***|1.127***|0.101***|0.227***|
||||||||(0.000)|(0.000)|(0.000)|(0.000)|
|Cycle|26.37***|34.46***|5.54***|19.59***|0.336|0.663|-2.63*|-0.789|1.74|51.62|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.832)|(0.077)|(0.042)|(0.566)|(0.826)|(0.127)|
|Cycle * life expectancy|||||||||-0.023|-0.594|
||||||||||(0.574)|(0.193)|
|Fertility * EMDE|-0.664***||-0.067**||||||||
||(0.000)||(0.005)||||||||
|Secondary enrollment *<br>EMDE|-0.332***<br>(0.000)||||||||||
|Completion of secondary|||-0.023|-0.057|||||||
|education * EMDE|||(0.565)|(0.080)|||||||
|Completion of tertiary|||-0.127|0.153*|||||||
|education * EMDE|||(0.056)|(0.000)|||||||
|Life expectancy * EMDE|||||||||-0.143***|-0.608***|
||||||53||||(0.000)|(0.000)|