rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6: Changes in the Gini Coefficient - Elderly**

<!-- Start of picture text --> 5%<br>Categorical<br>4%<br>Targeted<br>3%<br>2%<br>1%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases The first, striking contrast with respect to transfers to children is that in all countries (with the exception of Nicaragua, and under some circumstances Guatemala), both targeted and categorical transfers to the elderly have a significantly lower impact on poverty than an equivalent program for children. The difference in poverty impact is large: on average, categorical transfers to children are 1.6 times more effective in reducing poverty than categorical transfers of equal budget to the elderly, and targeted transfers twice more effective. The reasons are straightforward: poverty rates among the elderly are, on average, lower than for children; and poorer families have more children, but not more elderly people. The simulations also suggest that the common belief that cash transfers to the elderly can reduce substantially poverty by trickling down to all family members has limited validity: with fewer elderly than children living in poor households, for the trickledown effect to be effective money should be transferred across family members living in different households, a much less likely event.

There is nevertheless considerable variation across countries in the poverty impact of transfers to the elderly. The maximum reduction in poverty rates (close to 20 percent) is achieved, as expected, in countries with high poverty incidence among the elderly, such as Mexico and Colombia. Effectiveness of a targeted program as opposed to a categorical one also varies substantially across countries, being 0.7 times higher in Nicaragua, to 1 in Guatemala, to 2 in Colombia and 3.2 times in Costa Rica, and 2.8 percent on average across countries. Similar conclusions apply for the reduction in poverty gaps (poverty targeted transfers are on average 2.3 more effective in reducing poverty gaps than categorical ones).

13
impacted - such as larger firms who may become prominent targets? These questions are of crucial importance for economic development because the industrial organization of sectors and firms determines the possibilities for entry, exit, and innovation, and thus for market competition and the provision of goods and services (Cefis and Marsili, 2005; Godfrey, 2008).

Studying firms and sectors in conflict-affected countries, however, poses several empirical challenges. In particular, reliable quantitative data is often scarce in insecure contexts, and gathering new data through surveys, for example, may be difficult and even dangerous. As Besley et al. (2011) put it, “We have a paucity of firm-level data during or immediately after [...] conflict.” Past research has either focused on publicly traded firms or on specific settings where high-quality administrative data is available (Guidolin and La Ferrara, 2007; Bruck et al., 2012). Novel methods for measuring the micro-level dynamics of firm activity have the potential to unlock new research avenues into private sector activity in violent settings.

In this paper, we explore the application of commercial mobile phone data as a novel source of information about firm behavior in conflict-affected countries, using Afghanistan as our empirical setting. Our methodology extends work by Blumenstock et al. (2015), who use mobile phone data to analyze the distribution of wealth and poverty in Rwanda.<sup>3</sup> We combine 45 months of comprehensive mobile phone records from over 2,300 firms with administrative data from the Afghan government and an original survey of Afghan firms to create and validate measures of firm location, firm size, and aggregate economic activity.<sup>4</sup> To our knowledge, this is the first concerted effort to measure the activity of private firms in developing countries using passively-collected digital data.<sup>5</sup> Our validation exercises suggest

> 3In recent years, scholars have made great progress integrating new sources of large-scale digital data in empirical social science research (Einav and Levin, 2014; King, 2011). While the vast majority of this work has focused on industrialized economies where internet and social media proliferate, a more recent strand in this literature attempts to adapt these insights to the context of developing and conflicted-affected countries (Blumenstock, 2016). In these environments, fewer sources of ‘big data’ exist, but mobile phone metadata is a prominent exception. Increasingly ubiquitous in even the most remote and dangerous regions, mobile phone networks generate terabytes of information that can provide a novel perspective on growth and development (de Montjoye et al., 2014; Eagle et al., 2010).

> 4The mobile phone Call Detail Records (CDR) data used in this paper cover the period from April 2013 to December 2016.

> 5This approach also relates to recent work using satellite imagery to measure productivity and wealth (Henderson et al., 2012; Jean et al., 2016), but such approaches do not typically differentiate private sector

3
Annex 1:

**Table Al. Classification table for management of childhood fever**

|_Signs_|_Classifyas_|_Treatment_|
|---|---|---|
|_Highmalariariska_|_rea_||
|-Anygeneral<br>danger sign<br>-Stiffneck|Verysevere<br>febriledisease|-Givequinineforseveremalaria(firstdose)<br>-Givefistdoseofanappropriate antibiotic<br>-Treatthechildtopreventlowbloodsugar<br>-Giveonedoseofparacetamolinclinicforhighfever<br>-referurgentlytohospital|
|-Fever(byhistory<br>orfeelshot or|Malaria|<br>-Ifnocoughwithfastbreathingtreatwithoralantimalarial<br>orifcoughwithfastbreathing, treatwithcotrimoxazolefor5days|
|temperature2<br>||-Advisemotherwhentoreturnimmediately|
|<br>37.5<sup>0</sup>C)||-Follow-upin2daysiffeverpersists<br>-Iffeverispresent everydayfor morethan7days,referforreassessment|
|_Lowmalariariskar_|_ea_||
|-Anygeneral<br>dangersign|Verysevere<br>febrile disease|-Givequinine forseveremalaria(firstdose) unlessnomalariarisk<br>-Givefistdoseofanappropriate antibiotic|
|<br>-Stiffneck||<br>-Treatthechildtopreventlowbloodsugar<br>-Giveonedoseofparacetamolinclinicforhighfever<br>-referurgently tohospital|
|-Norunnynose<br>and nomeasles<br>and noother|Malaria|<br>-Ifno coughwithfastbreathingtreat withoralantimalarial<br>orifcoughwithfastbreathing,treatwithcotrimoxazole for5days<br>-Advisemotherwhentoreturnimmediately|
|causeoffever||-Follow-upin2daysiffeverpersists<br>-Iffeverispresenteverydayfor morethan7days,refer for reassessment|
|- Runny nose<br>presentor|Fever- malaria<br>unlikely|<br>-Giveonedoseofparacetamolinclinicforhighfever(38.5°Corabove)<br>-Advisemotherwhentoreturnimmediately|
|<br>measlespresent||-Follow-upin2daysinfeverpersists|
|<br>or othercauseof||<br>-Iffeverispresenteverydayfor morethan7days,referforreassessment|
|feverpresent|||
|_Source:_Adaptedfro|mGove (1997).||

**Figure Al. Climate suitability for stable malaria**

<!-- Start of picture text --> ,mdorlob(. o oeF. ..<br>I 0<br><!-- End of picture text --> _Source:_ Adapted from MARA (1998).

_35_
For example, in the Upazilas with high local rainfall variability, members of the households with access to credit or safety net are more likely to be self employed in agriculture as compared with households in Upazilas with high local rainfall variability and no access to credit or safety net (Table 3, columns 16 and 17). However, the coefficients of the interaction between local rainfall variability and access to credit and safety net are small as compared with the coefficients of local rainfall variability by itself. Thus, access to credit and safety net is unable to completely negate the need diversify from self employed agriculture to other economic activities when households are faced with higher local rainfall variability.

As with the flood risk, access to market may completely obliterate the households’ need to diversify from self employment in agriculture if they live in Upazilas with high local rainfall variability (Table 3, column 18). Thus we find that access to market may prevent households to be pushed to occupational and employment diversification while access to credit or safety net does not completely prevent such push from flood and local rainfall variability.

These results are not robust to the choices of different occupational focus indicators used in the left hand side of the equations. In some of these cases the interaction coefficients have the unexpected negative sign. In others we do not get the differentiated results between access to credit or safety net in one hand and access to market in the other. These results suggest access to credit or safety net may have similar effect on sectoral or employment focus of households as access to market. These results are presented in Table 3, columns 4 through 15.

**Table 3: Interaction between flood or local rainfall variability and policy action variables.**

||I<br>|nteraction with Floo<br>|dprone Upazilas<br>||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Dependent variables|Bo|th in the same occu|pation||Both the same s|ector|
|Policyinteraction term:|Credit|SafetyNet|Market|Credit|SafetyNet|Market|
|Flood prone Upazilas (b4)|**-0.353*****|**-0.372*****|**-0.623*****|**-0.318****|**-0.322*****|<br>**-0.450****|
||(-2.702)|(-2.906)|(-3.419)|(-2.461)|(-2.581)|(-2.406)|
|Policy X Flood (b6)|0.0505|0.105|**0.729***|0.129|0.151|0.472|
||(0.280)|(0.562)|(1.717)|(0.768)|(0.828)|(1.047)|
|Test b4+b6= 0|**-0.303****|**-0.267***|0.107|-0.189|-0.170|0.0223|
|χ<sup>2</sup>(1)|3.879|2.746|0.127|1.745|1.158|0.00494|
|χ2 > Prob|0.0489|0.0975|0.721|0.187|0.282|0.944|
|Observations|3709|3709|3709|3709|3709|3709|
||Local ra<br>|infall variabilityin n<br>|on-floodprone Up<br>|azilas<br>|||
||(7)|(8)|(9)|(10)|(11)|(12)|
|Dependent variables|Bot|h in th same sector||Bot|h self employed||
|Policyinteraction term:|Credit|SafetyNet|Market|Credit|SafetyNet|Market|
|CV Rain CRU (b4)|**-5.404***|**-5.537****|-3.787|**-11.90*****|**-16.57*****|-7.575|
||(-1.898)|(-2.058)|(-0.794)|(-2.712)|(-3.703)|(-0.981)|
|Policy X CV Rain CRU (b6)|2.286|1.709|-2.062|-0.938|**12.83****|-16.26|

15
# Research Componrnts of world Sank Education Projects. since 1982.

|Area|CountryandLoan/Credit[<br>Number|Approved|Research Components<br> <br>I|J<br>Current Status of Studies,<br>including<br> Completed<br>Documents/Data|
|---|---|---|---|---|
||||1989)<br>(para.<br>2.06p12).<br>(ii) DivE to monitor promotion and repetition<br>rates in schools;<br>(iii)perforwance of staff in inservice<br>programa<br>wndturnover<br>rates<br>of staff<br>in schools<br>(iv) provide final evaLuation report.<br>(v) Cost recovery survey (2.15 15).|tii)<br>no monitoring or promotion of repetition<br>rates<br>since 1989/90<sup>is first year of operation;</sup><br>(iii)<br>nostudV of staff perforzmnces in inservice<br>programs<br>due to staff<br>shortages/constraints;<br>(iv) evaluation report due 1992;<br>(v) cost recovery survey delayed, but under contract.|
|ENEA|Turkey LN 2776-TU|1967|Non-formal Vocational Training Project.<br>Evaluation<br>study<br>to include<br>fotlou<br>up of<br>graduate employment experience (due June 1990)<br>[para. 2.12 p.14 Anmex 15).|Project suffered 2-year delay. No grsduates before<br>1991.|
|**ENEMA**|TuwkeyLM 2922-TU|1988|Second Industrial<br>Traininc Proiect. No research<br>component.|Confirmed.|
|ENEMA|<br>Yeme<br>AR CR 1773-YAR|1987|Teacher<br>Training<br>Project.<br>Pre-investment<br>study<br>at the Sans University's Faculty of<br>toassess effectivenress of current training<br>program (due July 1989)(S0.6m).<br>(ii)<br>Study of building<br>maintenance<br>procedures<br>(SO.06m).<br>Oiii)<br>Strengthening<br>of Directorate<br>of Teacher<br>Training inctuding<br>establishment<br>of<br>information systemto aid<br>teacher<br>recruitment<br>and deployment (SO.12m).<br>(iv)<br>Conduct<br>review<br>to consider<br>fading<br>out<br>teacher<br>incentives<br>(due Dec 1990)(SO.06m).<br>(v) Evaluation to be carried out by July 1991.|Studies<br>not<br>yet<br>initiated.|

56
# **Table 5. IV Regression (Instruments for T: Catholic, Muslim Share in Population and Latitude)**

|Dependent<br>Var|Private|Credit|NPL/tot|al loan|
|---|---|---|---|---|
||With<br>pmindex|With<br>apmindex|With<br>pmindex|With<br>apmindex|
|T_245|28.6***|31.8***|-7.8***|-11.6***|
||(3.82)|(3.2)|(-3.0)|(-2.99)|
|legor_uk|-23.3|-7.27|2.24|.37|
||(-1.03)|(-.35)|(.97)|(.14)|
|legor_fr|-41.5|-31.7|5.07|2.93|
||(-1.93)|(-1.57)|(1.98)|(1.19)|
|legor_ge|-16.2|9.5|2.47|1.99|
||(-.86)|(.48)|(2.64)|1.15()|
|legor_so|-76.9|-68.3|5.02|8.65***|
||(-4.06)|(-3.7)|(.02)|(3.17)|
|pmindex|1.95||.35||
||(.74)||(.43)||
|apmindex||-.66||1.96|
|||(-.23)||(2.08)|
|Constant|-69.4|-77.8|44.5|52.3|
||(-1.52)|(-1.8)|(2.87)|(3.23)|
|# obs|87|113|61|77|
|R<sup>2</sup>|.77|.76|.56|.47|

Note: _1) Legor_uk are those countries with English legal system origin, legor_fr – French legal system origin; legor_ge – German legal system origin; legor_so – socialist legal system origin. (see the appendix 2 for source and variable description)_ _(2): numbers in parentheses are t statistics._ _(3): Coefficients with * denote significance at the .10 level, ** at the .05 level and *** for the .01 level. (4): The instruments passed the over-identification test, with Hansen J statistic quite small (p-value for the χ_<sup>_2_</sup> _larger than .95). I also looked at the first step regression in the 2SLS, to make sure the instruments are correlated with the endogenous explanatory variable with the p-value for the F-test smaller than .05._ It is possible that in a country with overall better governance, economic transparency would not have an additional impact on financial development. I run the base regressions again controlling for measures of governance such as law and order and corruption. When supervisory indices and measures of governance are used as controls in the OLS specifications the coefficient on transparency remains strongly significant.

Table 6 shows different IV specifications that include measures of governance. In all the specifications for the npl variable, T continues to be significant, indicating that better 22
effect of which is transitory and confined to the output of the current period. The use ef ordinary leastsquares may be justified by the fact that the c2pital stock of the previous year, the labor torce and land are either pre-determined or exogenous and that whatever country-specific effect there may be have been taken into account. In principle, however, the rate of utilization is endogenous, and one may wish to use the method of instrumental variables. Finding a set of country-specific instrumental variables that work for the entire sample is no **easy task. Limited experimentaion suggests that the resul rf the instrumental** variables estimation will not be qualitatively different from those of oruinary least-squares **as long as the chosen set of instrumental variables** can explain the rate of utilization adequately.

# 3. **The Data**

The principal source of data used in this study is the Bank Economic and Social Database (BESD) of the World Bank, which consists of a compilation of various databases from different sources (including the International Labour Office (ILO), the International Monetary Fund (IMF), the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the World Bank itself).

The sample analyzed in this study consists of a set of 58 developing countries from five regions of the world. 26 corintries are located in Sub-Saharan Africa, 7 in East Asia, 15 in Latin America, 6 in the Middle East-North Africa region (MENA) and 4 in South Asia. Other developing countries have been excluded from the sample solely on the basis of data unavailability, with the exception of four European countries (Hungary, Malta, Portugal and Yugoslavia) which have been excluded because their historical development experiences, both economic and educational, were not directly comparable with those of the more typical developing countries. The complete list of c-rntries in the sample is presented in Appendix 1.

The aggregate real output of each country is measured by its real GDP in constant 1980 U.S. dollars, data on which are taken directly from BESD. The aggregate level of labor input is measured by 8
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 61

# **Appendix 4. The Dynamic Interactive Vulnerability Assessment (DIVA) Model**

The Dynamic Interactive Vulnerability Assessment (DIVA) model (Hinkel et al., 2009) currently offers, to our knowledge, both the most detailed global scale representation of the coastal zone and the implications of sea-level rise and the most comprehensive and advanced representation of relevant processes at the global scale, including adaptation. It includes a global database which represents the world’s coasts (excluding Antarctica), using more than 12,000 linear segments with more than 100 parameters per segment. The DIVA model focuses on various aspects including coastal erosion impacts and adaptation (Hinkel et al., 2013), coastal wetland change (Spencer et al., 2016) and coastal flood impacts and adaptation (Hinkel et al., 2014). Here we will focus on flooding and use the same model as Hinkel et al (2014). The flood module can assess the impacts of increased coastal flooding on the population and coastal assets by comparing results obtained using various available data sources and adaptation strategies under a comprehensive sample of state-of-the art socioeconomic and sea-level rise scenarios. Flood risk is considered in terms of expected annual damage to assets, expected annual number of people flooded, and adaptation costs in terms of the dike and other defense investments and the additional maintenance costs to maintain the new defenses. The maintenance costs can be significant and are important to consider as it is important that such maintenance is included in long-term plans.

For adaptation, Hinkel et al. (2014) follow earlier studies, such as Hoozemans et al. (1983) and Nicholls (2004), and consider a common protection approach using dikes contrasting various adaptation strategies. One initial DIVA approach to adaptation was based on a demand for safety function (Yohe and Tol, 2002) which attempts to model human behavior based on empirical observation. However, the DIVA framework is flexible, and a wide range of alternative adaptation strategies can be formulated and explored (e.g. Sadoff et al., 2015). In each case, as sea levels rise, the capital and maintenance costs, as well as residual damages can be estimated.

DIVA has been used to investigate the implications of sea-level rise in earlier assessments by the World Bank and the Asian Development Bank, including the global analysis of Nicholls et al. (2010), the East Asia analysis of Nicholls et al. (2013) and the national analysis of Kebede et al. (2010).

## **A4.1 References**

Hinkel J. and Klein R.J.T. (2009) Integrating knowledge to assess coastal vulnerability to sea-level rise: The development of the DIVA tool. Global Environmental Change 19(3), 384– 395.
chosen for simplicity. It implies a constant marginal change in utility as a result of a change in effort.<sup>8</sup> The dynamics of the model thus depend on the vector of parameters  ={ 1,  2,  ,  0,  1,  , δ’=δ*  p } that needs to be estimated; four exogenous parameters/sequences ( δu , u , { _wt_ } and { _rt_ }); and the rules of the Brazilian social insurance system. We set _wt_ =ξ.W0*(1+g)<sup>t</sup> , where W0 represents economy wide average earnings in the base year and ξ captures the level of income of the representative individuals in the cohort. Then across simulations we set g=3% and r=4%. In addition, using the labor force survey of workers in metropolitan areas (PME) we estimate that δu =0.83 and  u = 0.7.

For a given  and ξ we solve the model using a dynamic programming algorithm and generate a “behaviors vector” **M** d(a,e,k,v,R|  **,** ξ) that gives the optimal rule for decision **d** ={q*,c*,R*} as a function of the age a of the individual, his/her state e, the level of assets he/she holds, the vesting period v (that is the number of years the individual has contributed to the social security), and the retirement age R (if retired). The vesting period is important because of benefit formulas in the pension system.

In the dynamic programming algorithm the vector **M** d has the following dimensions: 80 ages, 4 activity states, 250 levels of capital, 45 vesting periods, and up to 20 retirement ages. The optimal level of the control variables **d** is computed recursively at every point in this space taking as given the dynamics of wages, the interest rate, the benefits provided by the social insurance system, the probabilities of being alive, and the probabilities of loosing/finding a formal sector job _given_ the level of effort. The four states for e are: (1) out of the social security without unemployment benefits; (2) out of the social security receiving benefits; (3) contributing to the social security; and (4) retired. We track separately being out of the social security with or without unemployment benefits to control for the fact that individuals cannot receive benefits in two consecutive periods. As for the capital “grid,” 250 points give a reasonable resolution for a maximum capital equivalent to 25 times initial average earnings, so that each grid point is equivalent to 10% of average earnings. Still, the numerical approximation results in somewhat jittery optimal savings and levels of effort as a function of capital. Thus, we also use a fourth degree polynomial to smooth the optimal values in **M** d.

The vector **M** d is then used to simulate the behaviors at age _a_ of the representative individual across _m_ future states of the world. Thus, we generate a new vector **C** b(a,m|Md(.|  ,ξ), **Е** )

> 8 In a previous version of the model we used _U_ ( _c_ , _l_ , _q_ )  <mark></mark> _c_  1 _l_ 1  1 <mark></mark> 1  /  1   <mark></mark>  1  2 _q_  <mark>,</mark> but this complicates the solution of the optimization program without bringing additional insights.

12