rafmacalaba/datause-encoder-data huggingface dataset:
maintenance), financial capital (capital flight), human capital (again through direct impacts— morbidity, health status—or by indirect impacts such as reduced supply of education and health services, outward migration of refugees), and social capital (disruption of communities, involvement of men in conflict, worsening mistrust between fighting factions). Conflict also disrupts trade and markets, leads to theft of business property by armed groups, and imposes “protection costs” on surviving firms. Most of these costs tend to continue for an extended period after the end of the conflict (infrastructure takes time to be rebuilt; improving education and health outcomes, as well as social capital, is a slow process; capital flight tends to continue or even increase after conflict).

Criminal activities can weaken the investment climate. They are often directed against businesses and hence directly inflict losses on them, but more damaging is the harm crime and perceptions of it do to business investments. Foreign investors may be discouraged, domestic investors may not start activities, and financial capital may flee from high-crime areas and countries. In addition, insecurity can generate macroeconomic instability, also weakening the investment climate and further fueling risks of conflict (World Bank, 2003a).

The indirect costs, related to mitigation strategies, also are large. Criminality or risk of conflict increases uncertainty, to which entrepreneurs respond with more risk-adverse strategies.<sup>4</sup> They also can drive business activity into the informal sector, and keep most businesses small (so as to avoid visibility vis-à-vis criminals or corrupt officials). As a mitigation strategy, firms may have to pay for private modes of providing security. For instance, the World Bank-sponsored Investment Climate Surveys quantify the resources paid by firms for security purposes (Figure 1). Obviously, this is a fraction of the true cost of lack of security given the other indirect impacts listed above. As is discussed below, insecurity also weakens public institutions, opening up vulnerabilities to corruption and further weakening the investment climate.

**Figure 1: Payments for Security (% of sales)**

<!-- Start of picture text --> 25<br>Peru<br>20<br>Cambodia<br>15 Afghanistan<br>Lithuania<br>10 Ecuador<br>Macedonia, FYR<br>5<br>0<br><!-- End of picture text --> _Source: World Bank, Investment Climate Survey Database._ These effects on the investment climate have adverse consequences for investment, technology, productivity growth, and more generally the competitiveness of the economy,

> 4 Risk taking can be productive, yielding additional net economic benefits, and therefore can be seen as a production factor like physical or human capital.

6
<!-- Start of picture text --> Total consumption expenditure<br>6 months 12 months 18 months<br>Y<br>8 ‘8 Z 8<br>nN / N / N<br>/ /<br>a a \V ene a<br>/ yo Ne / yrs e-<br>si= 7 S= 3= V J<br>-~-7- sTN “<br>fr i N<br>o / Nog / o 7 ~>s<br>/ / / / \<br>/ / / / \ \<br>/ / \ ‘<br>8 8 gy /<br>Le<br>0 2 4 6 8 1 O 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Food Consumption Score (standardized)<br>6 months 12 months 18 months<br>,<br>* = jo<br>~ ‘ /<br>SY oy oD \ /<br>-” SLL? ~= \ / aeof<br>” \ 7 ” Fa 7 ~K =<br>\ Y --<br>\_/7<br>x “ n<br>“4 L--eo \ \ = ~~ yy _ TM = ~-= ~‘\<br>\ \ 7 / \<br>° \ \ NUZ Z 4 / \<br>\ ° ° “ \<br>a<br>02 4 6 8 1 O 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Reduced Coping Strategies Index (inverted and standardized)<br>6 months 12 months 18 months<br>co / 4 4<br>/<br>/<br>= cect” / /<br>aN a eat / y<br>~<br>-“7 “Ts. S a YY ~S<br>° rey ° —<br>- poe eer NUE<br>NL es<br>_-<br>~\<br>\ \ “é“<br>\Z \<br>2 4 6 8 1 O 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Per capita consumption expenditure<br>6 months 12 months 18 months<br>8 8 8<br>7<br>a“ uc<br>Ig Uc / /<br>° > ed_ —e 7 =<br>ee Uo uo a“<br>7 “ o* ~ ‘<br>A / eo -7 YN .<br>yaa “ \<br>Y \<br>\<br>\<br>g 8 8<br>“0 2 4 6 8 1'°0 2 4 6 8 1 0 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Livelihoods Coping strategy Index (inverted and standardized)<br>6 months 12 months 18 months<br>* ~ ~<br>‘ vy<br>\ Us<br>“U7 =<br>.<br>“4 Nn “0 if x /<br>\ / XN /<br>a — \ / \\ /<br>Sse SN \ / \ ><br>° s, ° \ / ° ~<br>YS<br>-<br>sy sé<br>n, “' aS N -—o a”‘ 4 enw’—7 SN<br>VD7 / \ ‘<br>\<br>\<br>0 2 4 6 8 1 0 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text --> <!-- Start of picture text --> Proportion of children attending school<br>6 months 12 months 18 months<br>nN nN n<br>, 7<br>/ Fa ra 4 vo<br>= a U7 / = 77 L/ = / / y<br>/ / 7 /<br>7 / iar a / / _ a _/ /<br>o =-* / °o / ° —_ —<br>--7 / :<br>yO a /<br>/ / /<br>Fa ‘ U7 / Z /<br>ors v7 os ~ 5 / a<br>/<br>\<br>\re<br>0 2 4 6 8 1 0 2 4 6 8 1 40 2 4 6 8 1<br>Propensity score: Pr(Treat)<br><!-- End of picture text -->
out a large-scale direct-mail field experiment to study the effects of advertising content on real decisions in South Africa, and find that factors such as framing and phrasing, such as including a picture of an attractive female accounts manager, influences loan demand. Similarly Agarwal and Ambrose (2007) study the impact of direct mail advertising on financial decisions and find that advertising campaigns have a persuasive effect on loan contract choices.

Outside of the financial realm, Evans (1963) finds that demographic similarities between the client and the salesperson influences retail demand. Similarly, an experiment involving door-todoor charitable fundraising finds that attractive female solicitors secure significantly more donations (Landry et al. 2006). Further evidence on the influence of emotions through media comes from Anschutz et al. (2008), who find that body size of television actors affects personal body dissatisfaction and actual food intake.

# **3. Financial Education in South Africa**

# _3.1. The South African Context_

Household debt has been a large and growing problem for South Africa over the last decade. The ratio of household debt to disposable income was 76.3% in the second quarter of 2012, up from 50% in 2002.<sup>4</sup> In June 2012, the National Credit Regulator (NCR) reported that of the 19.6 million consumer borrowers in its records, 9.22 million (47%) had impaired records.<sup>5</sup> Out of the consumers with impaired records, 19.5% were in arrears for three months or more, 13.3% had adverse listings, and 14.2% had judgments and administration orders issued against them. What is particularly troubling is that the percentage of consumers with impaired records has been above 45% since December 2009.

Financial woes in South Africa are not limited to indebtedness. The savings rate of households is with 1.7 percent in the second quarter of 2012 very low.<sup>6</sup> In fact, according to FinScope (2012), a large percentage of the South African population (67%) does not save at all, even though the majority of adults believe that saving is important. In addition, out of those who save, only 22% save through formal channels, either with banks or other formal financial institutions.

> 4 South African Reserve Bank, Quarterly Bulletin, September 2012, No. 265. 5 National Credit Regulator, Credit Bureau Monitor, June 2012. 6 South African Reserve Bank, Quarterly Bulletin, September 2012, No. 265.

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_Note_ : AEs = advanced economies; EAP = East Asia and Pacific; ECA = Europe and Central Asia; EMDEs = emerging market and developing economies; FCS = fragile and conflict-affected situations; HIC = high-income country; LAC = Latin America and the Caribbean; LIC = low-income country; LMC = lower-middle-income country; MNA = Middle East and North Africa; SAR = South Asia; SSA = Sub-Saharan Africa; UMC = upper-middle-income country. Bars show average correlation between the (Hodrick-Prescott-filtered) cyclical components of real GDP and real government spending within groups. The sample period is 1980–2020.

A.-E. Bars show average correlation between the (Hodrick-Prescott-filtered) cyclical components of real GDP and real government spending within groups. The sample period is 1980-2020.

A.B.E. Whiskers show the 25th and 75th percentiles. Black asterisk indicates a statistically significant difference in means.

A. Sample includes 36 advanced economies and 146 EMDEs. *** indicates that the difference between the average correlation for the two country groups is statistically significant at the 10 percent level or better.

B. Sample of EMDEs includes 87 commodity exporters (38 agricultural, 31 energy, and 21 metals) and 59 commodity importers. *** indicates that the difference between the average correlation for the particular country group and that for commodity importers is statistically significant at the 10 percent level or better.

C.D. Panels show the average of the procyclicality measure for the various country groups.

E. For 1980–2006, the sample includes 90 commodity exporters, 91 commodity importers, 22 LICs, and 31 FCS. For 2007-2020, it includes 95 commodity exporters, 95 commodity importers, 21 LICs, and 31 FCS.

F. Share of countries with procyclical policies based on five-year rolling windows. Sample has 184 countries.

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log ( g ) : Fixed θ , m = 7 Number of Models: Fixed θ , m = 7 Model Size: Fixed θ , m = 7 <!-- Start of picture text --> G<br>G<br>G G<br>G G G G G G G G G G G<br>G<br>G<br>1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15<br>12<br>7 200000<br>11<br>6 150000 10<br>5 9<br>100000<br>8<br>4<br>7<br>50000<br>3 6<br>0<br><!-- End of picture text --> log ( g ) : Fixed θ , m = 20.5 Number of Models: Fixed θ , m = 20.5 Model Size: Fixed θ , m = 20.5 <!-- Start of picture text --> G G G G G G G G G G G G G G G<br>G<br>G G G G G<br>G<br>G G G G G G G G G G G G G<br>G<br>G G G G G G G G G G<br>G G G<br>G<br>G<br>1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15<br>7 5e+05<br>6 4e+05 20<br>5 3e+05<br>15<br>4 2e+05<br>3<br>1e+05 10<br><!-- End of picture text --> log ( g ) : Random θ , m = 7 Number of Models: Random θ , m = 7 Model Size: Random θ , m = 7 <!-- Start of picture text --> G G GG G G GGG GGG GGG GGG GGG GGG GGG GGG GGG GGG G GG GG GG GG GG GG GG GG GG GG GG GG GG GG<br>G G GG<br>G G GG GG GG G GG GG GG GG GG GG GG<br>G<br>G G G<br>G G<br>1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15<br>25<br>7<br>6 3e+05 20<br>5 2e+05 15<br>4<br>10<br>1e+05<br>3<br>5<br>0e+00<br><!-- End of picture text --> log ( g ) : Random θ , m = 20.5 Number of Models: Random θ , m = 20.5 Model Size: Random θ , m = 20.5 <!-- Start of picture text --> G G GG G G G G GG GG GG G GG GG GG GG GG GG GG GG GG GG GG GG GG GG<br>G G G G G G G G G G G GG G G<br>G<br>G G G G G G G G G G G G G G<br>1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15<br>7 4e+05 25<br>6 3e+05 20<br>5<br>15<br>2e+05<br>4<br>10<br>1e+05<br>3<br>5<br>0e+00<br><!-- End of picture text -->

**Fig. 6.** FLS data: log posterior medians of _g_ , number of models visited and posterior mean model size The different priors on _g_ are ordered as in Tables 2 and 3. The top two rows correspond to fixed _θ_ (with different _m_ ), while the bottom two rows are for random _θ_ priors on model space

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administration conditions, timing, resources and environment were kept as consistent as possible between schools. Fifth, the inspections and classroom observations were conducted by regular inspectors from the ministry, but they were assigned to a region different from their regular assignment to increase objectivity and reduce the risk of collusion between the teachers and inspector.

The program roll out was accompanied by a strong communication campaign (conducted in December 2012) to teachers and principals. This communication campaign was implemented by a specialized consulting firm using pamphlets, posters, and meetings with communication agents. Understanding of the program was also reinforced during the award ceremonies and disbursement of gifts at the end of the first year (December 2013-January 2014). In the second year, ceremony and reward distribution were initially delayed because of the Ebola crisis (which had led to a ban on people gathering in groups), and were ultimately canceled altogether. See Figure A.1 for the timeline of implementation, data collection, and outbreak of Ebola.

Overall, teachers’ awareness of the program was high, but knowledge about the specific aspects of program design was weaker (Table A.3). Almost all teachers, whether in the incentive treatment or in the control group, reported being part of the program. We suspect that this is due to their (incorrect) interpretation of the regular visit by inspector and teams from ministry for the student test and interviews. It is thus a possibility that we are underestimating effect sizes if teachers in the control schools also changed their behavior due to incorrect beliefs about their participation in the program. In general, knowledge of key aspects of the program were high, but not perfect. For example, at midline about 75 percent of teachers in the treatment groups—but not all—believed that the program targeted grade 3 and 4 teachers. At midline 91 percent of teachers in the in-kind arm believed that the award had an in-kind component, and 62 percent of teachers in the recognition arm believed that the program included a certificate. Teachers were also relatively well-informed about the inputs to the award function.

# **2.2 Sample and Experimental Design**

The evaluation sample consists of 420 randomly selected primary schools, stratifying by regions and zones (rural-urban), from the population of public French (non-Arabic) speaking schools found in the Education Management Information System (EMIS) database and using the number of teachers in targeted grades to define the selection probability of a given school.<sup>9</sup>

> 9The sampling frame was limited to public schools because they are the schools under direct authority of the MEPU-EC.

6
targeting macroeconomic aggregates that are available.<sup>14</sup> Regional accounts were available for the GDP components of government consumption, investment, exports, imports, as well as aggregated value added and disaggregated value added figures for agriculture, manufacturing, and services. The GTAP SAM for SSA in 2011 is a preliminary version and the deviations from the regional accounts targets are higher than those in the other two years.<sup>15</sup> Even so, they still provide valuable observations to use, albeit more noisy, in the Bayesian entropy approach.

# Model definitions

Our Bayesian method estimates all the parameters in the CGE model simultaneously relative to their initial or prior values, subject to the specifications of the CGE model and the information from all available SAMs. In table 3 we state four model specifications or cases that define the behavior of the CES-CET functions in the standard CGE (figure 1) and illustrate technical aspects of the procedure. Given convergence issues and to make comparable the results among the country/regional applications, models 1, 2 and 3 are applied for Korea, while models 1, 2 and 4 are designed for SSA. Whereas the model specifications are a debate in theoretical econometrics, DSGE, and CGE literature, in our specifications we merely look for flexible features of the method and their link to entropy (information) gains. In the next subsections we explicitly state the behavior of the CES functions.

# Elasticities

Elasticities are critical unobserved behavioral parameters in the standard CGE model, which are usually defined by using the CES functions (Arrow, K. J., et al. 1961). For example, the trade elasticities express the substitution possibilities between domestic and foreign goods in the CET (constant elasticity of transformation) and CES (constant elasticity of substitution) functions. Suppressing the sector and time subscripts, the two functions can be written symmetrically, using the same form, 1 (20)<sup>�</sup> ∙( λ )<sup>ρρ</sup> ] . ii ii ii ρρ XXIn the two factor case, = SS [∑δδ ∙xx is the CES or CET composite of factor 1 and 2, the shift parameter, the CES share or distribution parameter and 1 2 = 1, λ a factor augmenting or biased productivity XX xx xx SS̅ parameter for ii , and ρ the exponent: 1 2 1 2 ii λ 1, λ 2). The CES substitution elasticity δδ δδ + δδ σ and CET transformation elasticity Ω are given by ⁄ in the CES case and ii xx XX = FF(xx , xx ; δδ , δδ , ρρ, SS,<sup>�</sup> Ω = 1⁄ in the CET case. σσ= 1 (1 −ρρ) ; −∞< ρρ< 1 (ρρ−1); 1 < ρρ< ∞

> 14 The regional macroeconomic aggregates are available from World Bank World Development Indicators.

> 15 Given bigger divergences between the 2011 GTAP SAM information and the World Bank World Development Indicators (WDI) macro data and to achieve an optimal convergence in the 2011 rebalanced SSA SAM procedure, we had to allow bigger macro target standard deviations with size of 0.05 the macro aggregate prior mean values, i.e., the symmetric macro aggregate error support covers a range of ±0.15 the macro aggregate prior mean value.

14
mobile internet users were urban.<sup>13</sup> Given a 1.324 Billion population of India in 2016, and a 31% urbanization rate from the 2011 Census, a naive calculation implies that 52% of urban residents, including residents of smaller cities, and children, have smartphones. In setting up their phones, users may choose to opt out of sending information to Google. However, the opt-out rate, which Google does not publish, would have to be extremely high to affect our results. Crucially, to estimate slowed traffic on a block, Google only needs one vehicle with a phone, and by definition, time-varying congestion implies many vehicles. Put together, this suggests that all cities have enough phones to generate high-quality speed estimates. We discuss further evidence regarding the reliability of Google Maps information below.

# **_2.3 City-level data_**

Several pieces of information were derived from administrative data. Daily labor earnings by district and gender are from the Employment and Unemployment Survey of the National Sample Survey (nss-eue) 2011–12. Population, and share of population with access to a car or motorcycle by “town” (fourth administrative level) are from the 2011 Census. We assign city populations as follows. The population of those towns falling completely within a city light are fully included. Towns falling partially within a city light contribute a share of their population defined by the share of the town’s land area falling in the light. The other census variables (earnings, share of households with access to a car, motorcycle) are analogously aggregated using the resulting town population shares.

Weather data are from Weather Underground.<sup>14</sup> Data were available for 112 of 154 cities, for from one to 144 periods per day, with a median of eight. Population growth from 1990 to 2015 is from United Nations (2015). We also use variables that characterize ‘urban shape’ computed by Harari (2016). Data on characteristics of the road network within a (lightsbased) city are from OpenStreetMap via GeoFabrik, and processed through OSMnx.<sup>15</sup>

> 13Source: `http://indianexpress.com/article/technology/tech-news-technology/ mobile-internet-users-in-india-to-reach-371-mn-by-june-2016/` . While this is not just smartphones, presumably smartphone users are substantially more likely than other mobile phone users to be mobile internet users.

> 14 `https://www.wunderground.com/`

> 15 `http://download.geofabrik.de/asia/india.html` Accessed 2016/9/23.

6
The coefficients of the instrumental variables are _b_ 1, _b_ 2 and _b_ 3, while the other variables will not be excluded from regression (14).

In terms of validity of these instruments, I imagine that households will make their migration decision based on their information about the possibilities they might have at destination; thus, it is reasonable to believe that the experience of previous migrants best represents the information set available to potential migrants. The implicit assumption is that migrants’ wages are not determined by the individual characteristics of the migrant but are somehow exogenously set so that nonmigrants would be faced with the same wages if they migrated. This assumption is supported by the data reported by the Indonesian Statistical Office (Badan Pusat Statistik 2007) about jobs of international migrants. These show that there is strikingly little variation in the types of jobs Indonesian migrants get at destination: 53% of migrants who were abroad in 2007 worked as domestic helpers, while 42% as either construction, factory, or plantation workers. Although this information is not available by gender, one can see a clear difference between typical female jobs and typical male jobs: domestic workers are very likely to be the female migrants (whose total percentage is in fact around 55% of total international migrants in the IFLS), while the construction, factory, and plantation workers are likely to be the men. As these are all very low skilled jobs, it becomes reasonable to assume that wages are fixed and exogenous. Moreover, at least for women, there is vast anecdotal evidence that they are hired to go work abroad as domestic workers under standard contracts that specify the same wage and duration of employment for all (Lan 2006; Parrenas 2001; Suradji 2004).

# IV. RESULTS

The outcomes of interest are the shares of total household income spent for a given type of commodity. To compute the denominator, I follow Dai et al. (2011) who have estimated the distribution of household income using the same data from the fourth wave of the IFLS. Income is thus obtained summing up five components: labor income; income from agricultural business; income from nonagricultural business; household nonlabor income (scholarships, pensions, other transfers); household assets income. Moreover, to correct for attrition, a two step Heckman procedure is applied, in which the probability of response is predicted by dummy variable for whether the respondent is the head of the household. Levels of (log) income and predicted income are reported in table 8 suggesting, as in table 4 that migrant households are on average poorer than nonmigrant households and households with migrant mothers poorer than households with migrant fathers.

Table 8 further shows descriptive statistics for the shares of income allocated to the various types of commodities, distinguishing, in particular, between common goods, that is, food, education, health, and durables and exclusively adult goods, which include 18