rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

In recent years, there has been renewed attention on policies to enhance the income generating capacity of the poor. Since both skills and capital can limit productive choices (Lucas, 1978; Banerjee and Newman, 1992), many programs specifically tackle those constraints to facilitate entry and returns to nonagricultural wages, self-employment or entrepreneurship. Programs offering training or relaxing liquidity constraints have proliferated in many developing countries (World Bank, 2012). A growing literature provides experimental evidence on the impact of skills training (Attanasio, Kugler and Meghir, 2011; Card et al, 2011; McKenzie and Woodruff, 2012), micro-finance (Banerjee et al., 2010; Crépon et al, 2011), and grants for small business development (De Mel, McKenzie and Woodruff, 2012a; Fafchamps et al. 2011). Yet, there remain substantial questions about how to design programs for productive employment, starting with the very fundamental question of how to target them. Indeed, compared to the literature on (conditional) cash transfer programs (Alatas et al., 2012; Coady, Grosh and Hoddinot 2004; de Janvry and Sadoulet, 2006), there is little empirical evidence regarding targeting mechanisms for training or small business development interventions.

Despite this, most productive programs have very specific targeting criteria, and these criteria differ immensely across programs. Many interventions are targeted based on demand through self-selection. For example, training courses typically require that individuals self-select to participate (Attanasio, Kugler and Meghir, 2011; Card et al, 2011). Interventions relaxing capital constraints also tend to be offered to participants based on demand. This is particularly common for microfinance (Banerjee et al., 2009; Crépon et al, 2011) but also used for cash grants (Blattman, Fiala and Martinez, 2011). An underlying assumption for selection on-demand is that it maximizes program impacts as those who self-select are also the ones with the highest potential returns from the intervention, as would be predicted by the standard Roy model of selection (Roy, 1951). But this assumption may not hold if certain households have inaccurate perceptions of program returns. For instance, even if returns for the poor might be quite high, many of them may self-select out because of lack of information (Jensen 2010; Baird, McIntosh and Ozler, 2011) or because of limited hope or aspirations (Duflo, 2012). Local peer effects may accentuate such low informational flows and formation of aspirations (Wilson, 1987), and were indeed instrumental in increasing households’ aspirations in the program studied in this paper (Macours and Vakis, 2009). The optimality of targeting on-demand is hence an empirical question.

Other programs target individuals who are already employed in specific activities. Micro-finance services or grants for small business development often target small business owners (De Mel, McKenzie and Woodruff, 2008; Dupas and Robinson, 2013). Many training programs are also targeted to existing 2
households benefiting from new or more work (sim1c versus sim1b and sim2d versus sim 2c).

**Table 8: Variation of poverty indices by groups**<sup>**36**</sup>

|||**Mali**|**UrbanMali**|**RuralMali**|
|---|---|---|---|---|
||FGT0|39,72%|14,54%|42,05%|
|**Référence**|FGT1|17,42%|5,10%|17,03%|
||FGT2|9,96%|2,67%|9,14%|
||∆% FGT0|-0,05%|0,18%|_-0,31%_|
|**Sim 1a**|∆% FGT1|_-0,24%_|_0,54%_|_-0,44%_|
||∆% FGT2|_-0,34%_|_0,39%_|_-0,51%_|
||∆% FGT0|_-0,11%_|0,41%|-0,13%|
|**Sim 1b**|∆% FGT1|_-0,23%_|-0,07%|_-0,28%_|
||∆% FGT2|_-0,25%_|-0,03%|_-0,31%_|
||∆% FGT0|_-0,18%_|-0,63%|-0,20%|
|**Sim 1c**|∆% FGT1|_-0,25%_|-0,20%|_-0,30%_|
||∆% FGT2|_-0,28%_|-0,08%|_-0,31%_|
||∆% FGT0|_-0,44%_|-0,25%|_-1,02%_|
|**Sim 2a**|∆% FGT1|_-0,77%_|_0,13%_|_-1,00%_|
||∆% FGT2|_-0,85%_|_0,13%_|_-1,01%_|
||∆% FGT0|-0,05%|0,18%|-0,26%|
|**Sim 2b**|∆% FGT1|_-0,28%_|_0,42%_|_-0,48%_|
||∆% FGT2|_-0,38%_|_0,30%_|_-0,56%_|
||∆% FGT0|_-0,11%_|-0,16%|-0,13%|
|**Sim 2c**|∆% FGT1|_-0,21%_|-0,05%|_-0,27%_|
||∆% FGT2|_-0,23%_|0,00%|_-0,29%_|
||∆% FGT0|_-0,18%_|-0,39%|-0,20%|
|**Sim 2d**|∆% FGT1|_-0,26%_|-0,18%|_-0,28%_|
||∆% FGT2|_-0,26%_|-0,06%|_-0,29%_|

When analyzing the impact of the simulations on the two groups, we observe quite intuitive results where the rural households on an aggregate basis, benefit in all simulations for all indices while urban households benefit in some simulations and for some indices. In fact, the positive results observed for urban households are insignificant (standard errors are greater than the variation of the indices). We observe significant negative impact for urban households for simulations 1a, 2a and 2b. In none of the cases do we have a significant result for the headcount index (FGT0) for urban households. For rural households all simulations produce significant results for depth and poverty indices. However, for the headcount index, only sim1a and sim2a produce significant results.

It is interesting to observe that the strongest positive effects overall for the country and rural households is the tax and subsidy option (sim2a). In simulations with an expansion of jatropha production, we have two opposing effects being confronted namely the increase in income from new production but the increase in supply decreases the prices and returns on factors.

> 36 We present complete poverty analysis for each group. However, we indicate significant results (at 0.05%) in italic character. When variations are not in italic, it indicates that the results are not significant.

22
20

# **5. Results**

# _A. VoLL Estimates_

We compute that the average respondent lost approximately 269 hours of service in MONTH 2016, some 49 of which were due to unannounced outages. This translates into just under 1 kWh lost per outage-day (on average 0.80 kWh).

Table 3 presents a summary of the VoLL estimates. The figures in this table are striking. First, even though one-quarter of the sample stated that they would pay nothing at all, it is clear that people _are_ willing to pay for improved service. For comparison, the average price per kWh from the grid paid by the respondents at the time of the survey is 7-8 NR/kWh. Second, there is considerable heterogeneity across respondents in the value they place on grid electricity.

In the first row of Table 3, we take the WTP responses at face value and use the full sample. The median and mean VoLL (5.09 NR and 15.25 NR, or 4.71 and 14.1 US cents, respectively) thus bracket the actual average price per kWh. These results are comparable to two recent studies of lower middle-income countries (Twerefou, 2014; Oseni, 2017), which report mean VoLL estimates of 14-16 US cents. As shown in Figure 6, the distribution of the VOLL is positively skewed.

Rows 2-5 of Table 3 summarize the VOLL among groups of respondents that rely on specific types of equipment during outages. Households with rechargeable batteries and storage appear to place a lower value on grid electricity than those that are forced to use disposable batteries. This is because they announce a zero WTP more often (30.96% of the times v. 23.80% for all others), report lower positive WTP amounts (on average 158.05 NR v. 170.19 for all others) and a lower WTP per outage-day (on average 12.10 NR v. 15.91 for all others), even
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

||**Economy level**|**Sector level**|**Generation**|**Distribution**|**Transmission**|
|---|---|---|---|---|---|
|**2.**<br>**Reform**<br>**measures -**<br>**Privatisation**||-date of privatisation<br>-asset sales revenue split<br>by function|-share of private (domestic,<br>foreign), local, government in G<br>(MW, MWh)<br>-no. of private, local, government<br>firms|-share of private, local, government in<br>delivered energy (MWh)<br>-no. of private, local, government firms|-network ownership<br>(private, public, local)<br>-TSO (public, private,<br>...)|
|**3.**<br>**Reform**<br>**measures -**<br>**Regulation and**<br>**Policy**||-see Item (B)<sup>8</sup><br>*See also survey for the<br>WB:<br>-who sets tariffs<br>-% share of taxes in<br>prices<br>-date of law, regulation<br>and effective dates|-network access (rTPA, nTPA)<br>-entry with authorisation or free|-PCAP, ROR||
|**4.**<br>**Market**<br>**structure**||-system size<br>- ownership structure|-resource mix|||
|**5.**<br>**Institutions,**<br>**Endowments**<br>**and control**<br>**variables**|-Item (A)<sup>9</sup><br>-GDP (US$)<br>-GDP per capita (US$ per capita)<br>-GDP growth rate (%)<br>-country credit rating<br>-debt payment /export<br>ratio<br>-domestic interest rate<br>-inflation rate|-demand growth rate (%<br>p.a.)<br>-total profits after tax<br>(US$)<br>-total assets (US$)<br>-no of employees|-generation assets (US$)<br>-generation resource mix<br>-no of employees<br>-cost of pool or SBM or power<br>exchange.|-distribution assets (US$)<br>-distribution network length (overhead,<br>underground) (km)<br>-no. of customers (residential, non-<br>residential, industrial, total)<br>-energy delivered (MWh) (residential,<br>non-residential, industrial, total)<br>-transformers (capacity (MVA), number)<br>-meters (number)<br>(of which residential and pre-payment?)<br>-maximum demand (MW)<br>-no of employees|-transmission assets<br>(US$)<br>-no of employees<br>-km of transmission<br>wires (split by voltage<br>level if possible)|

8 The information requirements for the construction of an Item (B) indicator are specified after the table.

9 The information requirements for the construction of an Item (A) indicator are specified after the table.

65
<!-- Start of picture text --> TABLE I OENAVIU. -eJATIUSUIP<br><!-- End of picture text -->

# FUol Of mm-DS IDENITY

# **Denvd** for Selected **Ffafact Iawssets**

<!-- Start of picture text --> 3<br>Nd,. I C~16.s1j).C1 t ea).C(g*fc).(t+ * @-.(i))1 aa.(r.)).Cl<br>k-1<br><!-- End of picture text --> where:

- as - stock of currency a - stock of deman **d** eposits

- a, - stock of _quasi money_ (time deposits)

- gdpfc - real GDP (at factor cost) growth

- rd - growth rate of real daomstic deposit rate I - domestic inflation k - 1987* 198B, 1989 a - coefficients

<!-- Start of picture text --> Central fInuEAs<br>levi<br>3<br>I Id<br>k-I<br>Td _ _. GDPfc,<br>3<br>L<br>GDPfc ,<br>k-i<br>ujhere:<br>Td - direct taxes<br><!-- End of picture text -->

- TOTNOCapS a goL * _Ni_ * G TOTNonetpa a Lcg t Ldg t Lp .NFA where:

- TOTHonCepS - swietary survey: total capital sources of funds

- TOTNmeapU monetary survey: total capital uses of fiAsi

- Nol - flow of other liabilities (net) "I flow of currency and demand deposits

- ON - flow of qas-mey (tim deposits) Lcg - flo of net credit from the central bank to the govermmnt

- Lds flow of net lendino frm cinwrcula banks to the govrmnt

- Lap - flow of net credit to the private setor

- UFA - flow of net foreign assts

Cntral nnnt Gow_ement Flnce

- lOTGoiCapS - Sg Lce g dg + Notg Lfg + Lsf*

- TOGovCapU - 19 Lgp TOTGovCurS l Td + Othrm-Sihg TOTGoVCurt - Cg + Tp * lNgp * Ilgf * Sg. llsgf

# ibere:

TOTGOvCa46 - Central gwneruent: total sources of capital funds TOTGovCapU - Central goveranent: total uses of capital ftwds
# **ANNEXES**

# **Annex 0.1. French acronyms used in the text**

**ACI** : _Agence de Cession Immobilière_ (Land Development Agency) **APIM** : _Association des promoteurs immobiliers du Mali_ (Property Developers Association of Mali) **CARPOLE** : _Cellule de Cartographie Polyvalente_ (Mapping Center) **CR** : _Concession Rurale_ (Rural Concession) **CRUH** : _Concession Rurale à Usage d’Habitation_ (Rural Concession for Residential Purposes) **CUH** : _Concession Urbaine d’Habitation_ (Urban Concession for Residential Purposes) **DDC-DB** : _Direction des Domaines et du Cadastre du District de Bamako_ (Bamako District Directorate of State Domains and of the Land Register)

**DNDC / DRDC** : _Direction Nationale/Régionale des Domaines et du Cadastre_ (National/Regional Directorate of State Domains and of the Land Register) **FGHM** : _Fonds de Garantie Hypothécaire du Mali_ (Malian Mortgage Guarantee Fund) **LA** : _Lettre d’Attribution_ (Allocation Letter) **OFM** : _Office Malien du Logement_ (Malian Housing Authority) **PAFOC** : _Projet Gestion du Patrimoine Foncier Communal_ (Municipal Land Management Project) **PO** : _Permis d’Occuper_ (Occupancy Permit) **SNQ** : “ _Sauvons notre quartier_ ” (“Save Our Neighborhood” upgrading program” **TF** : _Titre Foncier_ (Property Title)

# **Annex 0.2. French/English glossary of technical terms**

_Acte de vente_ : Deed of sale _Acte de donation_ : Deed of Gift _Acte notarié_ : Notarial deed _Adjoint au maire_ : Deputy Mayor _Allocataire (de terrain_ ): Allottee (for a land plot) _Antenne de la DRDC_ : DRDC’s branch _Arrêté_ : Administrative order _Arrêté municipal_ : Mayoral order _Attestation de recasement / Certificat de recasement_ : Certificate of resettlement (Administrative occupancy permit issued by a mayor following a _lotissement_ , i.e. land readjustment and/or formal land subdivision) _Attestation de vente_ : Certificate of sale _Attestation de vente authentifiée_ : Certificate of sale authenticated by local authorities _Autorisation de paiement_ : Payment authorization _Autorisation de transfert_ : Transfer authorization _Bail_ : Lease _Bail emphythéotique_ : Long-term lease _Bornage_ : Land plotting _Bulletin_ : Bulletin _Bureau specialisé de l’Antenne de la DRDC_ : Specialized section of DRDC’s branch _Cahier des charges_ : Regulatory act of the commune administration, which lists obligations and requirements for use and development of the land that each landholder is to fulfill _Cercle_ : Administrative jurisdiction which includes several communes _Chef de Quartier / Délégué_ : Neighborhood headman _Chef de village_ : Village headman _Chef de terre_ : Land chief / customary chief _Code domanial et foncier_ : Land code _Collectivités territoriales_ : Local authorities _Commune_ : Administrative juridiction/municipality _Conseiller municipal_ : Municipal councilor _Convention de vente (de terrain_ ): (Land) Sale agreement 40
countries. As opposed to the Figure 6 where the colors indicated the changes in the levels of GDP per capita, Figure 7 displays the changes in the growth rates of GDP per capita. Therefore, different shades of colors denote the _acceleration_ or _deceleration_ of the series. Note that the first four countries are plotted using the same scales for Output, Consumption and Investment growth despite the differences in vertical axis boundaries displayed here. Therefore, different shades and colors are comparable across these countries. Zimbabwe exhibits the largest variability and is plotted using its own scale. There are several short and medium term decelerations in the growth of per capita GDP in Zimbabwe in the beginning of the 1990s preceding the big decline. The lower middle income group representative in our sample, Fiji, also exhibits some variation in long term growth performance. One may observe a persistent decrease in the Output growth that started in mid 1970s. It was followed by a short-term recovery which did not last, the Output growth change slowed down in the 1980s, which resulted in a significant Output decline through the 1980s (see panel (b) of Figure 6).

Similar economic decline indicators can be observed in the changes in Output growth series of Brazil in 1970s, which resulted in a debt crisis in the 1980s. Based on the wavelet scalogram, there was a recovery phase, however did not translate into a longterm trend. Again, in the years before the South American crisis of 1990s, medium and long term scales indicate a reduction in the Output growth rate, signaling not a one-off deterioration, but a more persistent problem.

Turkey, on the other hand, went through a series of short- and mid-term changes in its growth rate. The most prominent short term reductions occurred in the early 1990s and 2000s, whereas the medium and long term reductions happened in the late 1990s and 2000s. In comparison, the changes in the growth rate in the United States are quite mild at all scales.

Note that, a green color in the scalogram of the first difference series could correspond to either a positive or negative growth rate. It shows whether the growth or decline rate is increasing. Wavelet volatility analysis used together with the growth analysis described in previous section can provide more details on the nature of the growth, i.e.

The growth literature generally uses the term “growth acceleration” for a positive change in the growth rate of output. We use the terminology in a similarly flexible way. However, technically speaking, acceleration denotes a second derivative of the original series, therefore a positive change in the growth rate is an “acceleration of output”.

16
- Bennett, C. and C. Hatzimasoura (2011). Poverty measurement with ordinal data. IIEPWP-2011-14, George Washington University.

- Berger, R. (1997). Likelihood ratio tests and intersection-union tests. In S. Panchapakesan and N. Balakrishnan (Eds.), _Advances in Statistical Decision Theory and Applications_ , pp. 225–237. Boston: Birkhauser.

- Bosmans, K., L. Lauwers, and E. Ooghe (2017). Prioritarian poverty comparisons with cardinal and ordinal attributes. _Scandinavian Journal of Economics 120_ (3), 925–942. DOI: 10.1111/sjoe.12238.

- Bossert, W., S. Chakravarty, and C. D’Ambrosio (2013). Multidimensional poverty and material deprivation with discrete data. _Review of Income and Wealth 59_ (1), 29–43.

- Chakravarty, S. R. (1983). Ethically flexible measures of poverty. _Canadian Journal of Economics 16_ (1), 74–85.

- Clark, S., R. Hemming, and D. Ulph (1981). On indices for the measurement of poverty. _The Economic Journal 91_ (362), 515–526.

- Cowell, F., M. Kobus, and R. Kurek (2017). Welfare and inequality comparisons for uniand multi-dimensional distributions of ordinal data. Mimeograph.

- Dhongde, S., Y. Li, P. Pattanaik, and Y. Xu (2016). Binary data, hierarchy of attributes, and multidimensional deprivation. _Journal of Economic Inequality 14_ , 363–378.

- Ebert, U. (2007). Ethical inequality measures and the redistribution of income when needs differ. _Journal of Economic Inequality 5_ , 263–278.

36
# **These Methods Can Be Readily Applied, Though They Pose Some Challenges**

In this study, we used the same economic models and data that analysts used in the original analysis; we just used them differently. Rather than using the data and models to assess performance in a single best estimate of the future, we used them to stress test our options in hundreds of possible futures. This approach was more forgiving of data gaps: where a quantity was unknown, we could use a wide range of plausible values, rather than tenuously choosing a single value. This also enabled us to include uncertainties that may not have been feasible in the original analysis.

Nevertheless, methods like RDM may have a steep learning curve – from understanding how to structure a robustness analysis, to learning software that aids in scenario discovery, to interpreting the results of scenario discovery, to communicating the findings to stakeholders. However, prior applications of RDM with other organizations and agencies suggest that these challenges can be readily overcome with time and training.

# **Managing Uncertainty Requires Rethinking “Good” Decision Making**

Analysts and decision makers routinely face pressures to demonstrate that a decision is risk-free. Political and cultural expediency press them to ignore rather than acknowledge uncertainty and present their decision as advantageous and certain. Such thinking keeps us in the dark about the real threats to our decision, and may lead us to brittle decisions that fail when the future surprises us.

To manage uncertainty, we may need to revisit our beliefs about what makes a decision “good.” Instead of ignoring uncertainty, we should seek more fully to understand the threats it may pose to our choices. This will enable us to make decisions that are robust to an unpredictable future.

Such a change requires a cultural shift as much as it requires an analytical shift. Yet methodological innovations like RDM can help. By motivating and equipping analysts to manage uncertainty, they can shape how we think, discuss, and ultimately make decisions.

18
# **Appendix 1. The complete model for the estimation of eq.** (5)

Table A. I reports the model used for the estimation of eq. (5) in section 3. The version of eq. (5) used in this paper is different from the one used in Padoan (1996) which does not take into account regional effects of technology diffusion. As discussed in detail in Padoan (1996) the model was estimated simultaneously (FIML) where the parameters entering equations (1)-(4) and (7)-(8) where constrained to take on the values obtained in a preliminary stage of estimation. Eq. (11) was not used in estimation. Eqs. (8a) and (8b) were added to allow for the regional specification of high tech imports. The continuos time model is estimated by taking its approximate discrete analogue as described in Gandolfo (1981). Stock and flow variables are treated differently according to Gandolfo (1981). See also Appendix 2.1. TRANSF and RESIMUL programs, developed by Cliff Wymer have been used. Table A. 1 Model Equations Export Share. Traditional Goods _DlogSA=a2(logSA*-_ log _SA) (I) logSA *=_ logrl -Pilog _P_ Export Share. Scale Intensive Goods _DlogSB=a3(logSB*-logSB)_ (2) - _logSB*=_ logy2 _P2logP +,B3logT/Tw_ Export Share. Specialized Suppliers _DlogSC=a4(1ogSC*-logSC)_ (3) _logSc*= log y3-1,41og P +/P5logT/Tw_ Export Share. Science Based Goods _DlogSD = cs_ (log _SDo*-log SD) (4)_ logSD *=logr4+ f,61ogT/ _Tw_

# Knowledge Accumulation

Dlog _T=ca6(1og T_ *-log _T)_ + a'ilog _TFi_ + **a2log** _TF2_ **_+a 3_** log **_SM HI_** _+_ **_a4logSMH2_** _(5)_ logT*=logYs+a5logF _+ a6IogSD_

# Aggregate Export Share

_DlogSx=DlogSA_ **_(SA_** _/SX)(WAI W)+A A(SAI Sx)(WAIW)+DlogSB(SBI Sx)(WBIW) +AB(SB/Sx)(WBIW)+DlogSc (ScISx)(WcIW)+ Ac (Sc ISx) (WcIW) +DlogSD(SDISX)(WDIW) +AD(SD/Sx)(WD/W)-DlogW_ (6) 30