rafmacalaba/datause-encoder-data huggingface dataset:
# _Annex 5: Costs and benefits of area (or village) targeting in the poorest areas_

To provide an order of magnitude for the ratio of benefits to costs of extending program saturation in the poorest villages to the entire village, we carry out an exercise relating (i) the discounted value of labor market returns to averting stunting at age 36 months to (ii) the discounted costs of the family transfer associated with adding a household, with one 6-12 month old child, to the program roll. Note that the benefits in this exercise are narrowly defined to those associated with lifetime earnings. To estimate the impact of stunting on labor market returns, we use the parameter estimate from Hoddinott et al (2013) who find that hourly earnings among adults who were stunted at age 36 months are 0.58 times the hourly earnings of those who were not stunted, after controlling for a number of contextual factors. Based on the average daily adult wages reported in our sample (US$6.3), we assume that the annual earnings of an adult who was stunted as a child are 0.58 times those of an adult who was not stunted as a child in each year that they work. Since we find that _Pantawid_ increased the prevalence of stunting by 12 percentage points among nonbeneficiary children, we further multiply this value by 0.12 to estimate only the value of the stunting differential attributable to the program. Using these parameters, we estimate that the discounted lifetime benefits of the program’s impact on stunting (manifested in lifetime earnings) equals the discounted program costs when (i) we assume that real wages will grow at a rate of 1.75 percent per year, which is close to the rate observed in 2012 and 2013<sup>37</sup> , and (ii) apply a discount rate of 5 percent. At any _lower_ discount rate (holding projected real wage growth constant) the benefit/cost ratio exceeds one; at any _higher_ projected real wage growth (holding the discount rate constant) the benefit/cost ratio also exceeds one.

To fix ideas, the above estimates assume an annual family transfer of US$132 for when the child is aged 1 to 14 (that is, the basic transfer amount); discounted back to age 0, this amounts to a total per-child value of the transfer (i.e. the cost) of US$1,636. Given the real wage growth estimate of 1.75 percent and a discount rate of 5 percent and attributing 12 percent of the gap in earnings between adults who were stunted and those who were not stunted to the program, this is also equal to lifetime earnings detriment associated with the program.

> 37 1.9 and 1.5 percent real wage growth reported for 2012 and 2013, respectively, in ILO (2014).

84
# **Table 8**

# **Polarization**

|Independent|Brazil<br>|India<br>|United States|
|---|---|---|---|
|Variables||||
|January Polarization|65.7|74.6|...|
||(1.33)|(0.84)||
|April Polarization|-93.7|-112.|7.6|
||(1.92)|(1.10)|(0.52)|
|July Polarization|93.5|-188.|-13.6|
||(2.16)|(0.81)|(0.69)|
|October Polarization|-0.4|-25.9|91.7|
||(0.01)|(0.15)|(4.38)|
|R<sup>2</sup>|.405|.696|.803|

The polarization variables have been added to the regression in column 6 of Table 3 for the United States, column 6 in Table 4 for Brazil, and column 6 in Table 5 for India. The dependent variable is net revenue per hectare for Brazil and India and land value for the United States. Only the added variables are shown here.

57
|**Table 3: Inbound FDI**|**by Sect**<br>**2005**|**or**<br>|**2009**||**20**<br>**12**|**Annualiz**<br>**ed**|**Share**<br>**of**|
|---|---|---|---|---|---|---|---|
||**Total**|**Without**<br>**the three**<br>**main**<br>**round-**<br>**tripping**<br>**FDI**<br>**economie**<br>**s**|**Total**|**Without**<br>**the three**<br>**main**<br>**round-**<br>**tripping**<br>**FDI**<br>**economie**<br>**s**|**Total**|**growth**<br>**rate of**<br>**total FDI**<br>**stock**<br>**2005-**<br>**2012**|**"qualit**<br>**y" FDI**<br>**2009**|
|**All**<br>**sectors/industries**|**49,751**|**33,986**|**109,022**|**68,504**|**136,01**<br>**8**|15.5%|62.8%|
|**Primary**|**13,392**|**12,229**|**26,123**|**21,153**|**40,493**|17.1%|81.0%|
|Agriculture,<br>forestry, and<br>fishing|520|354|1,343|948|1,727|18.7%|70.6%|
|Mining and<br>quarrying|12,872|11,875|24,780|20,207|19,383|6.0%|81.5%|
|Mining of fossil<br>fuels|12,200|11,460|22,567|19,212|17,510|5.3%|85.1%|
|Other|672|415|2,213|995|1,873|15.8%|45.0%|
|**Secondary**|**20,217**|**12,068**|**42,811**|**25,926**|**99,603**|25.6%|60.6%|
|Manufacturing|19,405|11,389|37,095|22,043|44,150|12.5%|59.4%|
|Foodproducts|3,164|2,824|4,782|3,688|8,075|14.3%|77.1%|
|Wood and wood<br>products|959|682|1,905|1,476|2,232|12.8%|77.5%|
|Pulpandpaper|499|401|1,264|1,011|1,970|21.7%|80.0%|
|Refined petroleum<br>and coke|3,589|2,939|4,365|4,331|2,038|-7.8%|99.2%|
|Chemical and<br>pharmaceutical<br>products|607|587|1,847|1,574|3,477|28.3%|85.2%|
|Rubber and<br>plastics|436|391|1,041|836|1,540|19.8%|80.3%|
|Non-metallic<br>mineralproducts|1,222|1,066|3,340|2,422|3,495|16.2%|72.5%|
|Basic metals and<br>metalproducts|6,601|313|12,886|1,464|11,799|8.7%|11.4%|
|Machinery and|378|369|1,493|1,367|2,520|31.1%|91.6%|
|equipment|||||||1|
# Appenmdix Three: Reviewers and Consultants

## John Gibbons

Former Science Advisor to President Clinton, and MSI Panel Chair United States

## Dr. Edward Ayensu

President of the Pan African Union for Science and Technology Ghana

## Dr. Simon Schwartzman

Former Director of the Brazilian Bureau of the Census Brazil

## Dr. R. K. Pachuari

Director of the Tata Energy Research Institute India

## Dr. Enric Banda

Secretary General of the European Science Foundation Spain

## Dr. Peter Tindemans

Director for Science and Technology Policy in the Netherlands Ministry of Education Netherlands

## Dr. Bruce Alberts

President of the National Academy of Sciences Umted States

## Mohamed Hassen

Executive Director, Third World Academy of Sciences Italy

## Dr. Hameed Ahmad Khan

Executive Director, Commnission on Science and Technology for Sustainable Development in the South Pakistan

## Dr. Ibrahim Othman

Director General, Atomic Energy Commission of Syria Syria Dr. Jacques Gaillard Deputy Director, International Foundation for Science Sweden _Mu Shik Jhon_ President, The Association of Academies of Sciences in Asia Korea

## Larry Kohler

Executive Director, International Council for Science

## Dr. Juan Carlos Navarro

Chief _ad interim,_ Education Umt Inter.-American Development Bank United States 38
1964-2013, using 2SLS estimations. The paper finds that the composition of borrowers – whether they are households or firms – matters in terms of how credit affects economic outcomes.

# **Related Literature**

This paper builds on studies that analyze the impact of financial activity on growth and volatility. Studies on finance and growth are plenty. Levine (2005) and Demirguc-Kunt and Levine (2008) discuss the many channels through which finance supports economic activity, including through higher investment, supporting innovation, and enabling consumption smoothing and risk sharing. They discuss both theoretical and empirical papers that demonstrate how finance supports growth. King and Levine (1993), Levine et al (2000), Rousseau and Wachtel (2000), and Beck et al (2000) also discuss the impact of finance on growth. Rajan and Zingales (1998) and DemirgucKunt and Maksimovic (1998) use industry and firm level data to explore the positive impact of finance on growth. More recently, Levine and Warusawitharana (2014) find that external finance supports productivity growth in their sample of European countries.

However, a number of recent papers revisit the finance-growth relationship and find more nuanced effects. Gazdar and Cherif (2015), looking at countries in the Middle East and in North Africa, find that finance is more likely to support growth in good institutional contexts. Demetriades and Law (2006) find that financial development has larger effects on GDP per capita when the financial system is imbedded within a sound institutional framework. Barajas et al (2013) find that the beneficial effect of financial deepening on growth varies across countries; lower income countries benefit less because their regulatory and supervisory systems are less developed. Aizenman, Yinjarak and Park (2015) find a nonlinear effect of finance and growth and an uneven effect across sectors. They compare East Asia and Latin America, concluding that both the quantity and quality of finance matter in determining the impact of finance on economic activity. When the financial system channels resources to the most productive sectors of the real economy, then growth in finance and real output is expected to be positively correlated. Arcand et al (2012) find that there is a threshold size for the financial sector beyond which finance does not have a positive impact on growth. Shen and Lee (2005) show that stock market development has a positive effect on growth but banking development does not; they find that the conditioning variables of financial liberalization, high income level and good shareholder protection mitigate the negative effects of banking development on growth. Cecchetti and Kharroubi (2012, 2015) find that the impact of 3
If there were no mobility of labor, each point would lie on the 45° line. What we see is that there is a substantial reallocation of labor. Actually, this reallocation generates a greater concentration of people in the more service-intensive locations. Finally, Figure 7c shows that the equilibrium allocations imply a substantial decline in aggregate prices across all locations, mainly due to the reduction in transportation costs.

Table 6: Investment Allocations

||Fundam|entals and Investment in El|ectricity|
|---|---|---|---|
||_Corr_(log( <sup> ̄</sup>_Aai_), log(_Gi_))|_Corr_(log( <sup> ̄</sup>_Ami_), log(_Gi_))|_Corr_(log( <sup> ̄</sup>_Asi_), log(_Gi_))|
|Data|_−_0.05|0.32|0.20|
|Model (Fully Optimal)|0.20|0.59|0.59|
|Model (Efciency + Coordination)|0.25|0.61|0.63|
|Model (Efciency-Electricity)|0.30|0.60|0.62|
|Model (Efciency-Roads)|_−_0.05|0.32|0.20|
||Funda|mentals and Investment in|Roads|
||_Corr_(log( <sup> ̄</sup>_Aai_), log( <sup> ̄</sup>_Ii_))|_Corr_(log( <sup> ̄</sup>_Ami_), log( <sup> ̄</sup>_Ii_))|_Corr_(log( <sup> ̄</sup>_Asi_), log( <sup> ̄</sup>_Ii_))|
|Data|0.06|0.11|0.05|
|Model (Fully Optimal)|0.09|0.18|0.11|
|Model (Efciency + Coordination)|0.08|0.27|0.21|
|Model (Efciency-Electricity)|0.06|0.11|0.05|
|Model (Efciency-Roads)|0.08|0.27|0.21|

# **5.2 Sources of the Misallocation**

The above findings provide evidence of a substantial spatial misallocation of public capital. In our model, the decision of the government is affected by amenities, the exogenous component of productivities, the geographical barriers, or the complementarities in infrastructure investment. We now consider some of these potential sources in more detail.

## **5.2.1 Differences in Productivity**

We first look at how investment decisions relate to the exogenous component of the productivities. This will allow us to understand better the guiding policies of the government and assess misallocation.

The top panel in Table 6 shows the correlation observed in the data (first row) and the one predicted by the model (second to fourth rows) between the log of sectoral exogenous productivities (log( _A_<sup> ̄</sup> _di_ )) and the log of the electricity infrastructure quality (log( _Gi_ )). In all cases, the allocations predicted by the model have a much larger correlation with the productivities than in the data. The correlations are largest in the Efficiency + Coordination and 28
# **Figure 1: Distribution of unweighted and weighted observable characteristics**

<!-- Start of picture text --> unweighted weighted<br>.25 .25<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br>0 5 10 15 0 5 10 15<br>Education of household head in years Education of household head in years<br>unweighted weighted<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>0 5 10 15 0 5 10 15<br>Education of mother in years Education of mother in years<br>unweighted weighted<br>.3<br>.25<br>.2<br>.2<br>.15<br>.1<br>.1<br>.05<br>0 0<br>0 10 20 30 0 10 20 30<br>Durable asset index (PCA) Durable asset index (PCA)<br>Full sample Phone survey sample<br>Density Density<br>Density Density<br>Density Density<br><!-- End of picture text --> 29
global technology frontier.<sup>4</sup> By contrast, foreign investments in natural resources are often considered a mixed blessing, and even a curse, due to their association with Dutch disease, governance problems, and limited direct job-creating effects.

The MENA region provides a relevant context to test for sector specificity in the relationship between political instability and FDI. The last decade was characterized, if not defined, by high political instability, epitomized by but not limited to the Arab Spring. Political developments in MENA have been volatile, with many events being intense, but short-lived, resulting in high (intra-year) variability in political instability indicators which might have been attenuated by aggregation to annual averages and motivates our use of quarterly data. Moreover, the region is rich in natural resources, which are an important motivation for FDI, and countries in the MENA region are varied in terms of their institutional set-up and resource-dependence, which aids identification.

Our main results can be summarized as follows. We document a strong negative association between adverse shocks to political stability and aggregate greenfield FDI inflows, which is especially large for inflows into non-oil tradable sectors. By contrast, investments in the natural resource sector and investments in non-tradables are not significantly correlated with political instability. Consistent with these patterns, the significant reduction in greenfield investments in countries with Arab Spring uprisings was starkest in the non-oil manufacturing sector. Thus, political instability affects the composition of FDI portfolios, entrenching resourcedependence by harming the growth of non-resource tradables relative to non-tradables. Conversely, the relationship between political instability and aggregate greenfield FDI inflows is critically contingent on the (initial) sectoral composition of these inflows.

> 4 For evidence on convergence in manufacturing see Rodrik (2013).

5
opportunities and motivation for outward exposure, thus limiting trust in strangers (Ermish and Gambetta 2008). Family ties would thus display a positive relationship with generalized trust in the former case, and negative in the latter case.

The paper by Alesina and Giuliano (2009) shows that the relationship between family ties and generalised trust is negative when the strength of family ties is measured on reported scores for the ‘importance of the family in an individual’s life’, the ‘duties and responsibilities of parents and children’ and the ‘love and respect for ones parents’. This result is based on the WVS and has been confirmed by experimental data by Ermish and Gambetta (2008), who add that the negative causality runs from family ties to generalised trust.

Another aspect of the distinction between bonding and bridging social capital which has been investigated is the relationship between _religiosity and generalised trust_ . Guiso et al. (2003), on the basis of the WVS, find positive and significant correlations between generalised trust and some measures of religiosity, i.e. ‘raised religiously at home’, ‘attending religious services’, and ‘believing in God’. This result is confirmed by Helliwell (2008). However, religious denomination matters significantly (Glaeser et al. 2000; La Porta et al. 1997). For example, Guiso et al. (2010) show that the proportion of Catholics in the population is negatively related with trust in strangers. Regarding economic growth, Barro and McCleary (2003) show that this seems to respond positively to the extent of religious beliefs, but negatively to church attendance.

Generalised trust is clearly included in bridging social capital, but the survey question normally used to measure generalised trust in many studies has raised some problems in this regard. The question, which also appears in the WVS, is “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” The answer could be either “Most people can be trusted” or “Can’t be too careful”. Gleaser et al. (2000) argue, on the basis of a mix of survey and experimental data, that the survey question about trust predicts trustworthiness and not trust. If this result were confirmed by the literature, this question would give ambiguous information about bridging social capital. However, Fehr (2008), who also uses experimental evidence, finds that the trust question captures a component of trust as a social preference, and not just as a belief about others’ trustworthiness and as a risk preference.

As regards the _impact of social capital on individuals’ life satisfaction_ , there are 8
|**Type**|**Our ID**|**Composite**<br>**index**<br>**weight**|**RISE**<br>**index**<br>**weight**|**Type**|**Our ID**|**Composite**<br>**index**<br>**weight**|**RISE**<br>**index**<br>**weight**|
|---|---|---|---|---|---|---|---|
||re_3_3_3|1|0.25|Carbon<br>pricing and<br>monitoring|re_7_2|0.5|0.5|
||re_3_3_4|1|0.25|||||
||re_3_4_1|0.5|0.333|||||
||re_3_4_2|0.5|0.333|||||
||re_3_4_3|1|0.333|||||
||re_4_1_1|1|Not<br>scored|||||
||re_4_1_2_1|1|0.167|||||
||re_4_1_2_2|1|0.167|||||
|Attributes|re_4_1_2_3|1|0.167|||||
|of|re_4_1_2_4|0.333|0.167|||||
|financial|re_4_1_2_5|0.333|0.167|||||
|and|re_4_1_2_6|0.333|*|||||
|regulatory|re_4_2_1|1|0.167|||||
|incentives|re_4_2_2|1|0.167|||||
||re_4_2_3|1|0.167|||||
||re_4_2_4|1|0.167|||||
||re_4_2_5|1|0.167|||||
||re_4_2_6|1|0.167|||||

_Source: RISE dataset and authors’ elaboration based on methods described in this paper. Notes: *Not scored; **Do not contain the year, cannot be used in a panel format._ 54
|p-value: Treat 1 = Treat|0.912|0.840|0.620|0.918|0.771|0.579|0.738|
|---|---|---|---|---|---|---|---|
|2 for females||||||||
|p-value: Treat 1 = Treat|0.299|0.416|0.302|0.961|0.755|0.319|0.459|
|3 for females||||||||
|p-value: Treat 2 = Treat|0.405|0.728|0.202|0.888|0.930|0.165|0.356|
|3 for females||||||||
|p-value test of equality|0.003|0.004|0.004|0.010|0.015|0.041|0.010|
|for males||||||||
|p-value test of equality<br>for females|0.094|0.079|0.164|0.012|0.041|0.187|0.091|

<mark>Notes: Data pooled for all follow-up surveys. Sales and profits are converted from local currency to US dollars. Variables are winsorized at</mark> the 99th percentile. Specifications include strata dummies, a variable representing the initial outcome at baseline, a variable indicating missing data at baseline, and dummies for survey round. Panel B includes a dummy for “female”. Z score index constructed following Kling, Liebman, and Katz (2007). Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p-values and q-values are reported below standard errors (q-values in italics). Clustered standard errors by firms in parantheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

a Z-score includes different measures of sales and profits including weekly outcomes, non-winzorized outcomes, and inverse hyperbolic sine. Variables are detailed in b and c.

b Z-score on sales includes five variables. Those are: last month revenues in U$S, last week revenues in U$S, last month revenues in U$S winzorized at 99th percentile, last week revenues in U$S winzorized at 99th percentile, and the inverse hyperbolic sine of the last month revenues in U$S.

c Z-score on profits includes five variables. Those are: last month profit in U$S, last week profit in U$S, last month profit in U$S winzorized at 99th percentile, last week profit in U$S winzorized at 99th percentile, and the inverse hyperbolic sine of the last month profit in U$S.

38
# **Table 1: Sample characteristics by sample weights**

|Household characteristics|(1)<br>Unweighted<br>full sample|(2)<br>Unweighted<br>phone<br>survey|(3)<br>Weighted<br>phone<br>survey|
|---|---|---|---|
|Household head is male|0.89|0.91|0.89|
|Household head age in years|38.64|38.13|38.51|
|Education of household head (in years)|2.88|3.59|2.90|
|Highest level of education (in years) in household|5.61|6.34|5.58|
|Mother's education (in years)|2.37|2.95|2.37|
|Mother has no education|0.56|0.48|0.56|
|Household size|5.70|5.70|5.67|
|Dependency ratio|0.02|0.02|0.02|
|Food gap in months over the last 6 months|1.26|1.08|1.26|
|Number of tropical livestock units (TLU) owned|3.33|3.31|3.17|
|Own corrugated iron roof|0.49|0.57|0.49|
|Household has access to electricity|0.36|0.42|0.36|
|Land area (in hectares) operated|0.94|0.92|0.94|
|Durable asset index (Principal Components Analysis (PCA)|0.00|0.50|0.04|
|Household owns a mobile phone|0.49|0.72|0.49|
|Household member was engaged in business activity|0.14|0.17|0.15|
|Household member was employed in wage earning|0.22|0.22|0.22|
|Household is active in farming|0.90|0.89|0.90|
|Household participated in PSNP Public Works (PW)|0.35|0.32|0.33|
|Household received Temporary Direct Support (DS)|0.08|0.06|0.07|
|household received Permanent Direct Support (PDS)|0.06|0.05|0.06|
|Household benefitted from PSNP (PW, TDS or PDS)|0.42|0.38|0.40|
|Head or spouse was born in this location|0.95|0.94|0.95|
|Head's parent an important person in the community|0.36|0.37|0.36|
|Tigray|0.25|0.25|0.25|
|Amhara|0.26|0.25|0.26|
|Oromia|0.24|0.25|0.25|
|SNNP|0.25|0.24|0.24|
|Numberofobservations|2,535|1,497|1,497|

22