rafmacalaba/datause-encoder-data huggingface dataset:
INFRASTRUCTURE AND GROWTH IN AFRICA

# Appendix 1   Econometric methodology<sup>**15**</sup>

The evaluation of the effects of infrastructure development on productivity growth in our panel data set poses some econometric challenges that can be addressed in the context of a dynamic regression equation: where _y_ denotes the real GDP per worker (in logs), _K_ is a set of standard growth or inequality determinants, and _Z_ is a vector of infrastructure-related measures. The terms  _t_ and  i respectively denote an unobserved common factor affecting all countries, and a country effect capturing unobserved ' country characteristics. The second equality follows from defining _Xit =_ ( _K’it, Z’it_ )’ and  (  ,'  )' .

When estimating equation (1) we face the potential problem of endogenous regressors. This affects, in principle, both the standard determinants of productivity growth (variables in matrix _K_ such as education, financial depth, inflation, and so on) and the infrastructure variables in matrix _Z_ given that we can argue that these variables may be jointly determined. Indeed, this may be subject to reverse causality from labor productivity.<sup>16</sup> Furthermore, the lagged dependent variable _yit_ is also endogenous due to the presence of the country-specific effect.

We need suitable instruments to deal with endogeneity issues. Besides the terms-of-trade shocks— which we can assume are strictly exogenous—there are no obviously exogenous variables at hand to construct them, and we may rely primarily on _internal instruments_ within the framework described by Arellano and Bond (1991). These instruments are provided by suitable lags of the variables. Note, however, that the presence of unobserved country characteristics likely means that _E_ [ _X is_  _i_ ]  0 , and hence lagged levels of the regressors are not valid instruments for (1). Therefore, we first eliminate the country-specific effect by taking first differences of equation (1): Assuming that (a) the time-varying disturbance  is not serially correlated, and (b) the explanatory variables _X_ are weakly exogenous (that is, are uncorrelated with future realizations of the time-varying error term), lagged values of the endogenous and exogenous variables provide valid instruments.<sup>17</sup> In other terms, we assume that:

> 15 The present appendix draws heavily from Calderón and Servén (2004a).

> 16 For example, infrastructure accumulation could be driven by productivity growth.

> 17 Note that this still allows current and future values of the explanatory variables to be affected by the error term.

22
Table 2 summarizes the evolution of possible determinants and correlates of fertility between 2005 and 2010. We also include proximate determinants, although they will not be included in the actual analysis for reasons of endogeneity. The first important thing to note in Table 2 is that there is no difference in average age of sampled women between 2005 (28.3 years) and 2010 (28.4 years). This is important because of the life-cycle effect of cumulative fertility: If women sampled in 2010 would have been significantly younger than the women sampled in 2005, then the lower cumulative fertility observed in 2010 could just be due to this age-effect instead of a genuine secular drop in fertility. This is however not the case (if any, women are somewhat older in 2010 than in 2005). The figures in Table 2 show an improvement in female education, one of the main determinants of fertility, between 2005 and 2010: The number of years of education completed increased by 0.8 years, the proportion of women that completed primary school (8 years of education in Rwanda) increased by almost 50 percent, 15.4 percent of women were still in school during the 2010 survey compared to nine percent in 2005 and the proportion of women that never went to school dropped from 23.4 percent in 2005 to 15.5 percent in 2010. Female literacy increased to 68.4 percent in 2010 in line with the improved education outcomes. All changes are statistically significant at the 1 percent level. We also observe an increase in educational attainment of household heads between 2005 and 2010: The average number of years of education completed by household heads increased by 0.4 years.

In the standard Mincer-Becker framework, women’s participation in the labor market influences fertility decisions by increasing the opportunity cost of having children (Mincer, 1963; Becker, 1981). Women’s participation in the labor market increased considerably between 2005 (73.1 percent) and 2010 (83.7 percent). The bulk of working women are engaged in agriculture (83.4 percent in 2005 and 75.9 percent in 2010), but non-agricultural employment is growing fast: the fraction of women with a non-agricultural occupation almost doubled between 2005 (10.6 percent) and 2010 (19 percent), reflecting the more than 500,000 nonfarm jobs that have been created during this period (NISR, 2012).

12
|Cohort|Coefcient|Standard error|Treated|N<br>Control mean|
|---|---|---|---|---|
|**A. Ave**|**rage treatme**|**nt efect on the **|**treated **|**by cohort**|
|2005|-0.068|(0.091)|19|0.437|
|2010|-0.181|(0.113)|42|0.437|
|2017|-0.213|(0.094)|17|0.437|
|2020|-0.309|(0.109)|14|0.437|
|2021|-0.207|(0.087)|4|0.437|
|**B. Aver**|**age treatme**|**nt efect on the **|**treated **|**(all cohorts)**|
|All|-0.153|(0.069)|99|0.437|

**Table S6** : **Average effects of AP management on elephant poaching.** This table displays average treatment effects on the treated (ATTs) derived from the regression described in SI A.1. The dependent variable in this regression is the Proportion of Illegally Killed Elephants (PIKE). Panel A displays all ATTs returned from summarizing the regression object by the calendar year in which protected areas were transferred to AP (“cohort”). The command in R, summary(regression object, agg = “cohort”), returns ATTs for 5 of 6 possible cohorts, perhaps due to insufficient data or variation in the data for the remaining cohort. For comparison, Panel B displays the ATT across all protected areas managed by AP. The Coefficient displays a given ATT and the Standard error column displays the ATT’s standard error. The Treated N column is the number of observations in the given AP cohort (Panel A) or across all protected areas managed by AP (Panel B). The total number of observations in the regression is 578. The Control mean column is the mean PIKE across all control protected areas.

40
loans. Borrowers were significantly more likely to retain wage employment, less likely to experience hunger in their household, and less likely to be impoverished.<sup>9</sup> These findings contradict predictions of behavioral models that suggest consumers may over-borrow because they underestimate borrowing costs, have present-biased preferences, and are overly optimistic (see Laibson, Repetto, and Tobacman, 2005; Ausbel, 1991; Stango and Zinman, 2006).

Randomized experiments and direct measures of access to finance are critical for assessing the impact of finance on welfare. Nonetheless, such studies tend to focus only on the direct use of financial services, not the spill-over effects from better functioning financial systems on the economy. General equilibrium models and natural policy experiments suggest that the indirect effects of financial development are relevant for the poor.

# **3.1.4 Other household studies**

Other studies have used household data to assess the impact of access to finance on a household’s ability to educate, nourish, and care for its children. For example, using household data from Peru, Jacoby (1994) investigates how quickly children with different family backgrounds progress through the primary school system. He finds that children start withdrawing from school earlier in households with lower income and durable goods holdings, but these factors only influence schooling to the extent households are predicted to be borrowing constrained, on the basis of their reported loan activity. The results suggest that lack of access to credit perpetuates poverty because poor households reduce their children’s education. In another paper, Jacoby and Skoufias (1997) show that households from Indian villages without access to credit markets tend to reduce their children’s schooling when they receive transitory shocks more than households with greater access to financial markets. Furthermore, Flug, Spilimbergo, Wachtenheim (1998) use cross-country panel regressions over 1970-1992 to show that lack of access to financial markets reduce average secondary school enrollment rates.

Financial market imperfections also encourage child labor, with adverse implications on human capital accumulation in poor families. Beegle, Dehejia and Gatti (2007) use a household panel survey in Tanzania to show that transitory income shocks (crop shocks) lead to greater

> 9 It is important to note this design picks up the effect of credit for the marginal borrower. Given heterogeneity across borrowers, the effect might be lower or zero for those borrowers who easily got credit, since they may have close substitutes, but much bigger for those who were denied credit by a bigger margin.

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# **VI. Conclusion**

Based on unique youth surveys in Chile and Mexico, we find that more than half of young people in these two countries can be considered at-risk. In Mexico, nearly one-third of people age 12-24 are suffering the consequences of negative youth behaviors – adolescent mothers, early school dropout, not working – while 17 percent of Chilean youth are in this situation. They also come from the poorest families and have the fewest social bonds. Another one-quarter of Mexican and Chilean youth are engaging in negative behaviors and on their way to the worst-off category. A younger group in Mexico (20 percent) and Chile (8 percent) are not engaging in risky behaviors, but they have factor in their lives that suggest that they may be graduating to these more harmful groups before long since they also lack the social supports and mental health that the no-risk group boasts. These findings confirm the U.S. literature that negative factors in a young person’s life is highly correlated with negative behaviors. However, further analysis, preferably with panel data, is needed to understand the transition more clearly.

Some factors repeatedly arise in the best-off or worst-off groups. Positive relationship with the family is a recurring protective factor across the clusters. The clusters show that youth who live(d) with both parents have a lower incidence of all risky behaviors. Those youth who feel connected to a parent, i.e. those who feel that they can relate to a parent, the parent cares for them, they can depend on the parent, etc, also have lower risky behavior and negative outcomes than youth who do not feel connected. Connection with non-family members can partially compensate for absent parental connection, as shown by youth who are connected to non-family members having less risky behavior than those who do not have connections with anyone. Conversely, family abuse, substance abuse and lack of family cohesion cluster with victimization in Chile, while non-positive feedback from parents is correlated with risky behaviors in Mexico.

Positive institutional factors are also correlated with positive behaviors. School quality matters in Chile, and an individual’s relationship with his/her community (trust, feeling of school quality) is positively correlated with voter registration. Low spirituality/church attendance clusters loosely with the risk type III groups. Surprisingly, social exclusion is rarely correlated with negative behaviors. Instead, the “loner” youth usually showed up as sub-groups in the less at-risk clusters.

Negative behaviors (or outcomes) cluster together. An early age of starting work clusters loosely with an early age at onset of sexual activity for boys and girls. Other risky behaviors by females cluster with risky sexual behavior while the pattern is weaker for males: 22
**Figure 4: Financial System Size Indicators – Median Values Over Time (1980-2007)**

<!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Liquid Liabilities / GDP<br>Currency Outside Banking System / Base Money<br>Financial System Deposits / GDP<br>Private Credit / GDP<br>Stock Market Capitalization / GDP<br>Private Bond Market Capitalization / GDP<br>1980 1985 1990 1995 2000 2005 2007<br><!-- End of picture text -->

**Figure 5: Private Credit to GDP - Median Values By Income Group Over Time (1980-2007)**

<!-- Start of picture text -->
1.20<br>1.00<br>0.80<br>0.60<br>0.40 LOW INCOME<br>LOWER MIDDLE INCOME<br>UPPER MIDDLE INCOME<br>0.20 HIGH INCOME<br>Private Credit / GDP<br>1980 1985 1990 1995 2000 2005 2007<br><!-- End of picture text -->

24
# **Tables and Figures**

**Table 1. Publications Return from Literature Search and Screening Process**

|Selection stage|Scopus|Web of<br>Science|Total|
|---|---|---|---|
|Initial search|1,394|489|1,883|
|Retaining articles only|991|376|1,367|
|Combined set based on ISSN and DOI excluding duplicates|n.a.|n.a.|1,067|
|Full-text articles|n.a.|n.a.|567|
|Abstracts only|n.a.|n.a.|500|

_Note:_ n.a. = not applicable; DOI = Digital Object Identifier; ISSN = International Standard Serial Number.

**Table 2. Distribution of Publications Initially Identified in Scopus and** **<u>Web of Science, by Publication Type</u>**

|Type|Scopus||Web of Science|
|---|---|---|---|
|Article||915|353|
|Book or book chapter||194|25|
|Conference paper||164|57|
|Review||76|31|
|Other||45|23|

15
|EG-MORTGAGE FINANCE|Sahar Ahmed Nasr|
|---|---|
|EG-MORTGAGE FINANCE|Sahar Ahmed Nasr|
|EG-MORTGAGE FINANCE|Sahar Ahmed Nasr|
|EG-MORTGAGE FINANCE|Sahar Ahmed Nasr|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|Orissa Rural Livelihoods Project|Samik Sundar Das|
|POWER TRANSM(DONOR CF)|Arturo S. Rivera|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|AR APL2 Urban Flood Prevention and Drainage|Christophe Prevost|
|STRENGTHENING E-GOVERNMENT PROCUREMENT|Gurcharan Singh|
|STRENGTHENING E-GOVERNMENT PROCUREMENT|Gurcharan Singh|
|STRENGTHENING E-GOVERNMENT PROCUREMENT|Gurcharan Singh|
|STRENGTHENING E-GOVERNMENT PROCUREMENT|Gurcharan Singh|
|TP-Health sector supportprog(TF054511/2)funded byEU|Sarbani Chakraborty|
|TP-Health sector supportprog(TF054511/2)funded byEU|Sarbani Chakraborty|
|TP-Health sector supportprog(TF054511/2)funded byEU|Sarbani Chakraborty|
|TP-Health sector supportprog(TF054511/2)funded byEU|Sarbani Chakraborty|
be 4.8 percent. An important implication of these projections is that the poverty estimates for 2030 based on the 2011 PPP conversion factors and either the $1.82 or $1.92 lines are very similar to the projections reported in World Bank (2014b) based on the $1.25 line at 2005 PPPs. They are all within half a percentage point of each other and suggest that the global poverty goals assessed using 2011 PPPs are not significantly more or less attainable than they were when they were announced in 2013.

31
|**Country**<br>**Intervention**|**Method**|**Test**|**Effect**<br>**Sizes**|**Reference**|
|---|---|---|---|---|
|||**Kiswahili reading**<br>**comprehension**|**0.23**||
|||**Kiswahili reading fluency**|**0.56**||
|||Kiswahili listening<br>comprehension|0.9||
|||Kiswahili ability to decode<br>words and read aloud to<br>listener|0.17||
|||**Kiswahili high reading fluency**|**0.14**||
|||**Kiswahili basic reading**<br>**fluency**|**0.57**||
|||Letter naming fluency|0.7||
|||Phonemic awareness|0.59||
|||Familiar word fluency|0.8||
|Liberia<br>EGRA Plus|RCT|Unfamiliar word fluency|0.87|Piper and Korda, 2011|
|||Oral reading fluency|0.81||
|||**Reading comprehension**|**0.81**||
|||Listening comprehension|0.49||
|Mali|RCT|Orientation to print|0.21|Spratt, King and Bulat, 2013|

30
5

**Table 2: World Merchandise Imports and Shares by Income Group and Region**

||19|80|199|0|200|0|20|10|
|---|---|---|---|---|---|---|---|---|
||Imports|Share|Imports|Share|Imports|Share|Imports|Share|
|CountryGroup|<br>($bill)|<br>in %|<br>($bill)|<br>in %|<br>($bill)|<br>in %|<br>($bill)|<br>in %|
|**BY INCOME LEVEL**|||||||||
|Developed economies|1,465|72.9|2,775|78.2|4,792|72.0|9,508|62.0|
|Developing economies|485|24.2|784|22.1|1,863|28.0|5,764|37.6|
|High income developing economies|153|7.6|327|9.2|749|11.3|1,726|11.3|
|Upper middle income economies|202|10.1|298|8.4|808|12.1|2,859|18.7|
|Lower middle income economies|112|5.6|136|3.8|269|4.0|1,051|6.9|
|Low income economies|18|0.9|23|0.6|37|0.6|129|0.8|
|Of which: Least developed countries|25|1.2|26|0.7|43|0.6|170|1.1|
|**Memo:**|||||||||
|Developing econ, excl. China & India|451|22.4|707|19.9|1,586|23.8|4,019|26.2|
|Upper middle income, excl. China|182|9.1|245|6.9|582|8.7|1,464|9.6|
|Lower middle income,excl. India|97|4.8|113|3.2|217|3.3|700|4.6|
|**BY REGION**|||||||||
|World|2,009|100.0|3,550|100.0|6,659|100.0|15,326|100.0|
|Developing economies|485|24.2|784|22.1|1,863|28.0|5,764|37.6|
|Asia|181|9.0|476|13.4|1,216|18.3|3,873|25.3|
|Latin America & Caribbean|116|5.8|123|3.5|384|5.8|891|5.8|
|Europe, Middle East & North Africa|122|6.1|127|3.6|181|2.7|693|4.5|
|Sub-Saharan Africa|66|3.3|58|1.6|82|1.2|307|2.0|
|**Memo:**|||||||||
|BRICS 5 countries|79|3.9|168|4.7|410|6.2|2,280|14.9|
|OPEC 12 members|122|6.1|100|2.8|148|2.2|595|3.9|

Source: Based on UN COMTRADE database.
represents the end-use for a majority of fuelwood consumed in the country (Practical Action Ethiopia; Bizzarri, 2010).

Second, using a satisfaction survey our study examines whether people are likely to actually want to use the _Mirt_ ICS and assesses the attributes users prefer. This CCT evidence on per-meal fuelwood savings and inferences regarding satisfaction allow us to shed significant light on the potential for ICS adoption to serve as an input-based REDD+ contract element. Finally, we analyze the determinants of fuelwood savings within this field experiment framework with a special emphasis on 6 randomized monetary incentive, price and networking behavioral treatments.

Concurrently with well-warranted concerns about the heavy reliance on biomass fuels in developing countries, climate change has emerged as an important environmental threat. Evidence published in March 2013 suggests that the earth is now on average hotter than about 3⁄4 of the last 11,000 years (Marcott et al., 2013) and IPCC (2014) assessed with medium confidence that the period 1983-2012 was hotter than the last 1400 years. Based on ice core evidence, IPCC (2014) also estimates that the concentration of greenhouse gases in the atmosphere is now higher than at least the last 800,000 years (known for sure) and the rate of increase in the last century is unprecedented in the last 22,000 years (high confidence). As Martin Weitzman noted in a non-technical review, “An unprecedented and uncontrolled experiment is being performed by subjecting planet Earth to the shock of a geologically instantaneous injection of massive amounts of greenhouse gases.” (Weitzman, 2011) A critical part of the international response to the climate change threat is the UN Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation (REDD+), which is seeks to mobilize finance from the developed countries for developing countries like Ethiopia to fund measurable reductions in deforestation and forest degradation. These reductions represent potentially important climate change contributions, because deforestation and forest degradation account for between 12% and 20% of annual CO2e emissions. In the 1990s, largely from the developing world, forests released about 5.8 Gt per year, which was more than all forms of transport combined (Saatchi et al., 2011; van der Werf et al., 2009). Bailis et al. (2015) estimate that worldwide emissions from fuelwood are approximately 1 Gt CO2e per year.

3 | P a g e
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