rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 1: Electricity sector structures in Sub-Saharan Africa**

<!-- Start of picture text --> Group 1: One vertically Group 2: One main vertically Group 3: One main vertically integrated utility Group 4: Partial vertical Group 5: Full vertical<br>integrated state-owned integrated utility with with other operators unbundling, some with unbundling, some with<br>utility generation companies (7 countries) other operators horizontal unbundling<br>(19 countries) (15 countries) (3 countries) (4 countries)<br>GT ••• Benin Burkina Faso Burundi 2a: main utility state owned and a state owned generation company• Republic of Congo DG GSouth Africa DDG G Zambia DDG G Kenya DDG GG Ghana<br>• Congo, Dem. Rep. G G<br>D ••• Ethiopia Malawi Mauritania a DT DT D T DT DT T DT D<br>Not interconnected:<br>• Central African Republic 2b: main utility state owned with some Namibia D (not Inter-Angola Zimbabwe b DDG DDG Nigeria<br>•• Chad Comoros private generation• Botswana G DG G connect-ed) G<br>• Equatorial Guinea DDG G • Mali T T T<br>••• Gambia, The Guinea-Bissau Eritrea DT ••• Rwanda Senegal Swaziland a D DDD D D DT DDD<br>• Liberia • Togo<br>•••• Seychelles Sierra Leone Somalia South Sudan a Not interconnected:••••• Guinea Madagascar Mauritius São Tomé and Principe Cape Verde DDG GDT TanzaniaGTD GDT GTDNiger DGT Lesotho DDDGT Uganda<br>2c: main utility under private concession, Mozambique Key: Sudan<br>with private generation utility or utilitiesDDG G • Côte d'Ivoire DDG G G G = Generation activitiesT = Transmission activitiesD = Distribution activities GG<br>T T T<br>T Not interconnected: Majority state-owned utility<br>• Cameroon D<br>D • Gabon Majority private owned company D<br><!-- End of picture text -->

## _Source_ : World Bank staff illustration.

_Note:_ <u>Countries underlined are excluded in the main analysis presented in this paper due to lack of data.</u> _Utilities of countries in italics_ provide other services such as water and sewerage. Countries are interconnected with one or more other countries unless otherwise indicated. Interconnection refers to HV infrastructure to facilitate power imports and exports, and excludes MV and LV cross-border sales to local communities on the border. The study excludes 75 state-owned generation companies operating a small fleet (less than 15 megawatts) that typically serve isolated rural electrification systems or specific government entities, and distribution companies that account for less than 5 percent of end-user sales, both grid-connected (e.g. Senegal, Uganda, Zambia) and off-grid.

a. Sectors in Ethiopia, Rwanda and Sierra Leone have undergone sector reform subsequent to the reference year, and sector structures have evolved from those illustrated. b. The dotted line represents the parent company that owns the two companies in the sector.
The remainder of the paper is structured as follows. The next section introduces our sample and unique (hand-collected) data. In Section 3, we outline the methodology and describe the estimated impact of two different sets of specialization measures on bank performance. In Section 4, we exploit the multiple country dimension of the database and examine whether the impact of lending specialization on performance is conditional on certain country-specific or bank-specific characteristics. Section 5 concludes the paper with policy implications and avenues for further research.

# **2 Data**

In the analysis, we combine data from several sources. We obtain information on banks’ balance sheets and income statements from Bankscope, which is a database compiled by Fitch/Bureau Van Dijck that contains information on banks around the globe, based on publicly available data-sources. Bankscope contains information for listed, delisted as well as privately held banks. While Bankscope does not contain stock market information on a daily basis, it does contain information on the ticker as well as the ISIN number of (de)listed banks’ equity, which enables matching Bankscope with Datastream. From Datastream, we retrieve information on a bank’s stock price as well as its market capitalization. The combined Bankscope-Datastream sample, cleaned for missing items on variables of interest, yields 12; 689 observations, on 2; 005 banks from 77 countries over the period 2002 � 2011. We include commercial banks, bank holding companies, as well as saving banks and cooperatives.

Our independent variables of interest are several proxies of the degree of sectoral specialization of a bank’s loan portfolio. These data are not directly available from (commercial) databases for a crosscountry sample of banks.<sup>3</sup> Therefore, we take a two-pronged approach. First of all, we rely on return3Authors of studies on lending concentration have either used confidential data gathered by the central bank’s credit register (for single country studies) or relied on syndicated loan exposures (e.g. Cai et al. (2013)). In the latter case, the sample is limited to the subset of very large, internationally active financial instutions (which are mainly located in the US). Moreover, the exposures are 5
category must be aged 18 to 45, meet a minimum level of English language ability, meet health and character requirements, and have an offer of employment in New Zealand. The PAC has broadened the range of opportunities for Tongans to work in New Zealand, but the Tongans migrating through the PAC have had higher than average education levels, with many working in white collar jobs in Tonga prior to migration (Gibson and McKenzie, 2007; McKenzie, Gibson, Stillman 2006). It is therefore of interest to see to what extent participants in the two migration programs overlap, and to what extent those participating in the RSE have not tried to take part in other migration programs.

Only 7.5 percent of the RSE applicants say they have a family member in New Zealand who could sponsor them under family-sponsored approval, only 2.6 percent believe they could get in through the skilled/business category, and only 1.6 percent say they are eligible for residence in the USA, Australia, or any other country. Only 11 percent of the RSE applicant sample have previously applied for the Pacific Access Category. This is higher than the 5 percent in the non-applicant sample, but still shows the majority of RSE applicants are individuals who were not trying to participate in the PAC. This may be because they do not meet the requirements of the PAC, such as the requirement to find a job offer at a specified income level in New Zealand, or because the RSE workers do not wish to leave Tonga permanently. When asked, 51 percent of RSE workers say they would prefer to move permanently to New Zealand, whereas the remaining 49 percent would prefer to have a season in New Zealand and the rest of the year in Tonga.

Table 5 compares the characteristics of individuals applying for the RSE to those applying for the PAC, using data on PAC applicants from the Pacific-Island New Zealand

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# **The Entry of Randomized Assignment into the Social Sciences**<sup>*****</sup>

Julian C. Jamison <u>JEL codes: B16, C91, C93, C18, D04</u> <u>Keywords: randomization, RCT, field experiment, lab experiment, selection bias, causality, history of</u> economic thought

* I thank Art Boylston, Jesse Bump, Austin Frakt, Don Green, Judy Gueron, Glenn Harrison, Dean Jamison, Dean Karlan, Chris Lysy, Jack Molyneaux, Andreas Ortmann, Lior Pachter, Charlie Plott, Tasmia Rahman, and Al Roth for helpful discussions and input. My interest was first piqued when I read an article by Druin Burch in _Natural History_ (June 2013) that mentioned van Helmont’s early contribution to the topic. Thanks also go the James Lind Library, which is a wonderful resource for the relevant medical history, including translations of some of the early documents referenced below. The usual caveats apply, including the fact that these are not necessarily the views of the World Bank Group or indeed anyone other than the author.
credit demand and household outcome equations. Differencing equation (3) at two points of time yields the following outcome equation:<sup>15</sup> Consistent estimates of the credit effect δ _f_ and δ _m_ can be obtained from equation (4) by using a household fixed-effects method without recourse to instrumental variable estimation, under the assumption that the error terms of the credit demand and outcome equations are uncorrelated. This is what was followed in Khandker (2005). However, error terms can still be related because the unobserved socio-economic factors, which are assumed to be fixed at the household level, may actually change over time. Under such circumstances, equations (1) and (2) can be rewritten after incorporating the variations in _η_ and _μ_ over time as, which in turn changes the outcome equation as follows: Taking the difference over two periods ( _t_ =1 and 2), we get the following difference equation (4) as: Thus, we see that even household-level panel data may yield inconsistent estimates of program impacts, if the changes in outcome levels depend on changing unobserved household and villagelevel characteristics.

Another source of concern with panel data is the possibility of measurement errors. If credit is measured with error (which is likely), this error may get amplified when differencing is done over

> 15 For panel data consisting more than two time points, this difference is taken between the value of a variable at each time point and the average value of the variable for all time points.

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complementary factors of production that enhance the demand and productivity of higher educated workers.

Table 5: Educational attainment and jobs ladder (All countries)

||(1)<br>Upper tier<br>self-emp.|(2)<br>Formal<br>wage|(3)<br>Upper tier<br>informal<br>wage|(4)<br>Lower tier<br>informal<br>wage|(5)<br>Lower tier<br>informal<br>self-emp.|(6)<br>Unemployed|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Lower secondary<br>completed|0.045|0.194|0.020|-0.070|-0.134|0.053|-0.108|
||(0.047)|(0.131)|(0.024)|(0.106)|(0.117)|(0.044)|(0.141)|
|Upper secondary|-0.042|0.369*|-0.062**|-0.379***|-0.059|-0.010|0.184|
|completed and||||||||
|above||||||||
||(0.049)|(0.208)|(0.029)|(0.118)|(0.135)|(0.048)|(0.177)|
|Total pop. (log)|-0.145**|-<br>0.198**|0.030|0.159|0.123|0.053|-0.022|
||(0.071)|(0.088)|(0.024)|(0.155)|(0.153)|(0.054)|(0.120)|
|GDP p.c. (log)|0.017|0.175|0.023*|0.012|-0.122|0.000|-0.105*|
||(0.025)|(0.116)|(0.012)|(0.045)|(0.094)|(0.019)|(0.061)|
|Observations|429|429|429|429|429|429|429|

Notes. Standard errors are in parentheses and are clustered at the country level. Country and year fixed effects are included in all specifications. The proportion of people with primary education completed as highest attainment or less is the omitted category. Shares refer to all workers with non-missing firm size. * p<0.10 ** p<0.05 *** p<0.01.

To put these coefficients in context, it is helpful to understand the within-country variation in the education variables. The within-country variation for lower secondary education completion is 0.035, while it is 0.03 for post-secondary education. Using the latter, a onestandard-deviation increase in post-secondary education leads to a decrease in informal uppertier wage employment of around 0.2 percentage points. A similar increase in post-secondary education leads to a decrease in informal lower-tier wage employment of around 1.4 percentage points. While these changes seem small, we note that they are relatively large when compared to the means of the five job ladder shares. The mean for informal upper wage employment is 0.015, meaning the one-standard-deviation increase in above secondary employment leads to a 27
interpret values closer to zero (ten) closer to a liberal (conservative) worldview. We create a categorical variable for reported religion, coding the rest of the religions (and lack of religion) as zero. Finally, we proxy life outlook with the question: “ _In the next 12 months do you think that, in general, the economic situation of your country will be much better, a little better, the same, a little worse or much worse than now?”_ When using these questions, we control for the current assessment of the country’s situation to avoid any spurious correlation.<sup>22</sup> We interpret expectations that the country will be better (conditional on the present situation) as a positive life outlook.

Table 4 presents the main results. We control for individual characteristics in all the specifications, and include country and year fixed effects we well.

_Table 4. Logit regressions of unfairness perceptions (very unfair) and individual beliefs_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Gini coefficient|0.486***|0.586***|0.383***|0.305***|
||(0.073)|(0.066)|(0.075)|(0.083)|
|Self-reported Ideology|-0.002***<br>(0.001)|||-0.002***<br>(0.001)|
|Catholic religion||-0.008***||-0.008**|
|||(0.003)||(0.004)|
|Current economic situation of|||-0.114***|-0.113***|
|the country|||(0.002)|(0.003)|
|Positive Outlook|||-0.042***|-0.040***|
||||(0.004)|(0.004)|
|Negative Outlook|||0.076***<br>(0.004)|0.075***<br>(0.004)|
|Observations|113,398|143,246|117,591|90,785|
|% Unfair|26.84|27.66|27.94|27.06|
|Pseudo R-squared|0.0487|0.0472|0.0874|0.0895|

Note: This table presents estimates of the correlation between perception of distribution as very unfair and measures of individual values. Coefficients present the marginal effects at the mean values of the rest of the variables and were estimated through Logit regressions, weighting by individuals’ probability of being interviewed. All regressions control for age, squared age, gender, civil status, maximum educational attainment, labor force participation, and unemployment status, access to basic services and asset holding, as well as country, subnational and yearly fixed effects. ***, ** and * denote significance at 10%, 5% and 1% levels, respectively. Heteroskedasticity-robust standard errors in parentheses.

Our results suggest that, as expected, ideologically conservative people are, on average, less likely to perceive income distribution as very unfair (column 1). Catholics are less likely

> 22 Such assessment comes from the question: _“In general, how would you describe the country’s present economic situation? Would you say it is...? (1) Good, (2) About average and (3) Bad.”_ We recode the variables so larger values correspond to a more positive assessment.

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to show regular "cliffs" across the distribution that are synchronized between both the formal and informal sectors. The next section will test more explicitly for these effects.

# IV. **Econometric Evidence on the Impact of the Minimum Wage from Colombia**

In this section, we follow Neumark (2000) in employing rotating panel data from Colombia to test the impact of a rise in the minimum wage on wages and the probability of becoming unemployed, and for numeraire effects in both. The existence of the panel, as well as the impression from the previous section that the minimum wage is high anid binding made Colombia an obvious case study.

Since 1997, the National Statistical Agency (DANE) has created a rotating panel by reinterviewing 25% of households interviewed in the previous round of the Encuesta Nacional de Hogares (ENH-National Household Survey) yielding a set of twio consecutive quarterly observations on the same households. Individuals were identified by household and then on the basis of gender, age, mnarital status, relation with the head of household, schooling level and years of education, variables which do not change between quarters. The Euclidian distance from each individual with respect to the rest of the inhabitants of a house, is calculated and the match accepted if the distance is below a predetermined threshold. Roughly 15% of the individuals in a survey can be linked to the past one in 11 rotating surveys. As table 2 shows, when we restrict to that used in the kernel density plots- men working 30-50 hours per week, we have a total of 10,633 observations who are employed in the first period. Slightly over 19% of these will become unemployed in the second period. Of these, 66% report being salaried workers and 34% report being self-employed. Although the year chosen to estimate the kernel 11
Table 1: Balance at baseline: Individual characteristics

||Co|ntrol (C)||Own||BAF|C - Own|C - BAF|Own - BAF|
|---|---|---|---|---|---|---|---|---|---|
||N<br>(1)|Mean/SD<br>(2)|N<br>(3)|Mean/SD<br>(4)|N<br>(5)|Mean/SD<br>(6)|Dif.<br>(7)|Dif.<br>(8)|Dif.<br>(9)|
|Age|350|25.617<br>(2.653)|156|25.590<br>(2.745)|165|25.867<br>(2.560)|0.027|-0.250|-0.277|
|Years of schooling|350|9.420<br>(4.536)|156|9.737<br>(4.090)|165|9.564<br>(4.675)|-0.317|-0.144|0.174|
|Hindu|350|0.949<br>(0.221)|156|0.910<br>(0.287)|165|0.927<br>(0.260)|0.038|0.021|-0.017|
|Scheduled Caste or Tribe|350|0.437<br>(0.497)|156|0.449<br>(0.499)|165|0.448<br>(0.499)|-0.012|-0.011|0.000|
|Other Backward Class|350|0.460<br>(0.499)|156|0.449<br>(0.499)|165|0.382<br>(0.487)|0.011|0.078*|0.067|
|Wears ghunghat|350|0.871<br>(0.335)|156|0.891<br>(0.313)|165|0.903<br>(0.297)|-0.020|-0.032|-0.012|
|Worked last year|346|0.173<br>(0.379)|155|0.097<br>(0.297)|165|0.115<br>(0.320)|0.077**|0.058*|-0.018|
|Co-residence with MIL|350|0.683<br>(0.466)|156|0.699<br>(0.460)|165|0.648<br>(0.479)|-0.016|0.034|0.050|
|Mobility score|350|1.003<br>(1.729)|156|0.737<br>(1.507)|165|0.824<br>(1.577)|0.266*|0.179|-0.087|
|Asset score|350|-0.098<br>(1.588)|156|0.046<br>(1.534)|165|0.164<br>(1.797)|-0.144|-0.262|-0.118|
|Number of general peers|350|1.643<br>(1.033)|156|1.654<br>(1.039)|165|1.655<br>(1.004)|-0.011|-0.012|-0.001|
|Number of close peers inside village|350|0.549<br>(0.607)|156|0.500<br>(0.585)|165|0.612<br>(0.686)|0.049|-0.064|-0.112|
|Number of children|350|1.943<br>(0.910)|156|1.987<br>(0.930)|165|1.945<br>(0.945)|-0.044|-0.003|0.042|
|Wants another child|346|0.500<br>(0.501)|156|0.494<br>(0.502)|165|0.497<br>(0.502)|0.006|0.003|-0.003|
|Using any FP method|349|0.481<br>(0.500)|156|0.468<br>(0.501)|165|0.473<br>(0.501)|0.013|0.009|-0.005|
|Using modern FP method|349|0.175<br>(0.380)|156|0.205<br>(0.405)|165|0.164<br>(0.371)|-0.030|0.011|0.041|
|Ever visited a clinic for FP|350|0.360<br>(0.481)|156|0.333<br>(0.473)|165|0.352<br>(0.479)|0.027|0.008|-0.018|
|MIL opposed to FP|349|0.172<br>(0.378)|154|0.130<br>(0.337)|164|0.140<br>(0.348)|0.042|0.032|-0.010|
|F-test of joint signifcance: p-value<br>F-test: Observations|||||||0.383<br>493|0.324<br>504|0.895<br>317|

_Notes:_ Standard deviations (SD) are in parentheses. BAF denotes Bring-a-Friend, FP denotes family planning, and MIL denotes mother-in-law. Variable definitions are in Online Appendix.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 34
Table 6: Intervention impact on toilet quality

||(1)<br>(2)|(3)|
|---|---|---|
||Underground<br>Overground 1|Overground 2|
||_Panel A: Overall_||
|SL|0.0140<br>0.0624|0.0537|
||(0.0220)<br>(0.0339)|(0.0272)|
|Cluster-robust p-value|[0.5251]<br>[0.0660]|[0.0483]|
|Romano-Wolf p-value|[0.5215]<br>[0.1309]|[0.1249]|
|Strata FE|Yes<br>Yes|Yes|
|Interviewer FE|Yes<br>Yes|Yes|
|Household covariates|Yes<br>Yes|Yes|
|Ratio sample clients/GP size|Yes<br>Yes|Yes|
|Control Mean|1.379<br>2.429|0.370|
|N|1289<br>1289|1289|
|_Panel B: _|_By toilet ownership at baseline_||
|SL - toilet at BL|0.0022<br>0.0468|0.0523|
||(0.0289)<br>(0.0459)|(0.0306)|
|Cluster-robust p-value|[0.838]<br>[0.341]|[0.078]|
|Romano-Wolf p-value|[0.838]<br>[0.663]|[0.296]|
|SL - no toilet at BL|0.0289<br>0.0820|0.0555|
||(0.0292)<br>(0.0469)|(0.0350)|
|Cluster-robust p-value|[0.415]<br>[0.057]|[0.110]|
|Romano-Wolf p-value|[0.663]<br>[0.296]|[0.303]|
|HH owns a toilet at BL|0.00282<br>0.0664|0.0150|
||(0.0273)<br>(0.0442)|(0.0273)|
|Strata FE|Yes<br>Yes|Yes|
|Interviewer FE|Yes<br>Yes|Yes|
|Household covariates|Yes<br>Yes|Yes|
|Ratio sample clients/GP size|Yes<br>Yes|Yes|
|F-test|0.487<br>0.581|0.930|
|Control Mean (no toilet BL)|1.361<br>2.424|0.406|
|Control Mean (toilet BL)|1.391<br>2.432|0.345|
|N|1289<br>1289|1289|

_Notes_ : Sample of households owning a toilet at endline observed by interviewers: 1,289 households. SL equals sanitation loan arm. Standard errors clustered at the village level are shown in round parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level, referring to Romano-Wolf p-values. These are obtained from a step-down procedure to adjust for multiple hypothesis testing performing 1000 bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of clients to village size. Column 1 shows intervention impact on quality of the underground chamber, Column 2-3 refer to quality of the overground structure (component 1 and 2). Data source: household survey.

are being used. We analyse this in Table 7 by looking at intervention impacts on self-reported open defecation practices by study households. Column (1) studies impacts on whether everybody in the household engages in OD, and column (2) considers a broader definition of whether _anyone_ in the households still goes for open defecation. Our estimates indicate a reduction of 10-11 percentage points in open defecation along both measures. The magnitudes of these coefficients are closely in line with the impacts on toilet uptake, suggesting that households who construct a toilet also generally use it. Given that very few loans were used to repair or upgrade existing toilets, we can conduct a loan-to-toilet conversion analysis, where we regress toilet ownership on sanitation loan uptake, instrumenting the loan 29
# **Box 1  Southwest Regional Model**

The Southwest Regional Model (SWRM) covers an area of 37,300km<sup>2</sup> in Bangladesh. The area is surrounded by the Ganges River to the north, the Bay of Bengal to the south, Meghna Estuary to the east, and the international border with India to the west. The total length of the rivers and channels in this region is approximately 5,600km. Major nontidal rivers, the Gorai, Arial Khan, Jayanti, and upper Meghna, are located in the northern part of the area. The southern rivers mostly comprise the tidal estuary systems—the major rivers being the Baleswar, Jamuna, Lower Meghna, Malancha, Pussur-Sibsa, and Tnetulia. A dense network of tidal channels interconnects these major rivers; the channel network located in the Sunderban Forest is particularly complex.

The cross-sections of most of the river branches were updated with data surveyed in 2009–2012. Among the upstream boundaries, Gorai Railway Bridge on the Gorai River, Baruria on the Padma River, and Bhairabbazar on the Upper Meghna River are the major entry points for freshwater flow from the Ganges-Brahmaputra-Meghna basins. It is ideal to use discharge data from the upstream boundaries for the hydrodynamic model. Rating curves (for the statistical relationship between water level and discharge) were updated and used to generate discharge data. Data from Gorai Railway Bridge were used for the Gorai boundary and data from Baruria were used for the Padma boundary. No reliable discharge estimates were available for Bhairabbazar. Scattered data and tidal influence during the dry season prevented generation of satisfactory rating curves at Bhairabbazar. Time-series water level data were used for the upstream boundary of the Upper Meghna River. Observed data and results from the Bay of Bengal model were used to generate the water level and salinity at the downstream boundaries.

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