rafmacalaba/datause-encoder-data huggingface dataset:
5 TABLE 1: Goldman Sachs Commodity Index Return Components, 1970-92

|Year|Spot Return(%)|Roll Yield(%)|T-Bill Yield(%)|<br>TotW Return(%)|<br>S&P 500 Renun (%)|
|---|---|---|---|---|---|
|1970|4.80|2.73|6.53|14.06|3.93|
|1971|6.47|8.22|4.44|19.14|13.38|
|1972|27.35|3.88|4.13|35.35|17.38|
|1973|39.64|9.07|7.22|55.94|-15.86|
|1974|18.82|6.41|8.07|33.30|-30.75|
|1975|-36.41|_11.55_|5.96|-18.90|31.63|
|1976|-14.88|- 2.92|5.11|-12.68|21.38|
|1977|0.78|3.73|5.35|9.86|- 7.46|
|1978|19.21|0.92|7.34|27.46|6.35|
|1979|20.84|- 2.09|10.37|29.13|16.92|
|1980|12.21|-13.60|11.90|10.51|28.08|
|1981|-28.70|-11.98|14.53|-26.15|- 5.03|
|1982|- 0.09|0.01|11.02|10.94|19.40|
|1983|7.03|- 0.79|8.82|15.06|20.31|
|1984|-10.02|1.18|9.88|1.04|6.08|
|1985|0.26|1.62|7.66|9.54|27.88|
|1986|-20.78|16.70|6.10|2.02|16.95|
|1987|3.13|12.25|5.95|21.33|5.10|
|1988|11.52|6.30|6.81|24.63|15.54|
|1989|11.68|12.44|8.29|32.41|27.38|
|1990|5.96|11.84|7.73|25.52|- 3.22|
|1991|-21.78|9.92|5.53|- 6.33|26.66|
|1992|2.28|- 1.48|3.53|4.33|7.39|
|Continuously||||||
|Compounded||||||
|AnnualReturns|2.58|3.74|7.49|13.81|10.84|

NOTES: All returns are calculated as changes in logarithms.

Data Sources: GSCI-Goldman Sachs S&P 500-Ibbotson Associates.
**<u>Table 7</u>** **_<u>(continued)</u>_** **<u>. Matching results—direct taxes</u>**

|**Treatment Variable:**<br>**Non-Cooperation**|**1-Nearest**<br>**Neighbor**<br>**Matching**|**2-Nearest**<br>**Neighbor**<br>**Matching**|**3-Nearest**<br>**Neighbor**<br>**Matching**|**Ra**|**dius Match**|**ing**|**Local**<br>**Linear**<br>**Regression**|**Kernel**<br>**Matching**|
|---|---|---|---|---|---|---|---|---|
|||||**r=0.005**|**r=0.01**|**r=0.05**|**Matching**||
||||**Depend**|**ent variabl**|**e: Corpora**|**te Taxes**|||
|[4] Average Treatment on the Treated<br>(ATT)|0.225<br>(0.606)|0.278<br>(0.497)|0.189<br>(0.397)|0.0440<br>(0.375)|0.124<br>(0.362)|0.0260<br>(0.256)|0.0345<br>(0.249)|0.0430<br>(0.265)|
|Number of observations|450|450|450|450|450|450|450|450|
|Number of Treated observations|86|86|86|86|86|86|86|86|
|Number of Control observations|364|364|364|364|364|364|364|364|
|Rosenbaum bounds sensitivity tests|1.2|1.3|1.2|1|1|1|1|1|
|Standardized biases (p-value)|0.85|0.94|0.98|0.99|0.99|0.99|0.85|0.99|
|Pseudo R2|0.01|0.01|0.008|0.004|0.003|0.003|0.01|0.003|
||||**Depen**|**dent varia**|**ble: Payrol**|**l Taxes**|||
|[5] Average Treatment on the Treated|0.339|0.195|0.198|-0.216|-0.210|-0.0921|0.313|-0.178|
|(ATT)|(0.389)|(0.358)|(0.353)|(0.449)|(0.422)|(0.340)|(0.350)|(0.332)|
|Number of observations|<br>132|<br>132|<br>132|<br>132|<br>132|<br>132|<br>132|<br>132|
|Number of Treated observations|23|23|23|23|23|23|23|23|
|Number of Control observations|109|109|109|109|109|109|109|109|
|Rosenbaum bounds sensitivity tests|1|1|1|1.1|1.1|1|1|1|
|<br>Standardized biases (p-value)|0.03|0.54|0.49|0.55|0.55|0.55|0.03|0.81|
|Pseudo R2|0.53|0.22|0.24|0.41|0.41|0.41|0.53|0.21|
||||**Dependen**|**t variable:**|**Social Con**|**tributions**|||
|[6] Average Treatment on the Treated|4.519***|4.151***|4.429***|4.484***|4.424***|4.280***|4.206***|4.346***|
|(ATT)|(1.554)|(1.408)|(1.283)|(1.416)|(1.292)|(1.083)|(1.066)|(1.053)|
|Number of observations|<br>287|<br>287|<br>287|<br>287|<br>287|<br>287|<br>287|<br>287|
|Number of Treated observations|42|42|42|42|42|42|42|42|
|Number of Control observations|245|245|245|245|245|245|245|245|
|Rosenbaum bounds sensitivity tests|2.5|2.5|2.5|2.5||2.5|2.5|2.5|
|Standardized biases (p-value)|0.08|0.47|0.71|0.71|0.81|0.72|0.08|0.71|
|Pseudo R2|0.13|0.07|0.05|0.08|0.05|0.05|0.13|0.05|

Note: Standard errors in parentheses computed using 500 bootstrap replications. * _p_ <0.10, ** _p_ <0.05, *** _p_ <0.01.

21
a survey of REDD demonstration and readiness activities, Wertz-Kanounnikoff and KongphanApirak (2009) identify 44 demonstration activities and 65 readiness activities in Asia, Africa, and Latin America. Demonstration activities are focused on potential REDD mechanisms such as promoting sustainable forest management or conservation projects supported by carbon payments. The majority of these activities are in Asia, particularly Indonesia, which has had historically high deforestation rates and accounts for over half of all demonstration projects surveyed. Funding for these projects comes primarily from a mix of bilateral, multilateral, and nongovernmental sources, though private sector funding was an important source for projects in Latin America, particularly in Brazil, where a significant portion of project funds came from American Electric Power, GM, and Bradesco Bank.

Madeira (2009) examined 17 REDD activities underway in Indonesia and found that the majority of site-level interventions followed a concession based model, where prospective landholders gain long-term property rights to an area of forest contracted with the national government, which controls over 98 percent of the forest estate in Indonesia. Unlike REDD projects where an important co-benefit of the project is improved incomes and livelihoods for project participants of limited means, the concession owners of these projects were typically established groups that are well-financed. Only two of the projects focused on mitigation by local actors, which Madeira notes is significant because “...much of the literature hypothesizes that REDD activities will follow the model of PES programs with small-scale resource managers providing the service...” (2009). However, local actors still are an important part of creating and sustaining successful forest conservation projects. All of the concession projects focus on preventing forest conversion because, despite the legal tenure rights of concession holders, local actors often have a significant impact on forestlands. This signifies the need for an integrated 12
- 35 -

<!-- Start of picture text --> Chart XII<br><!-- End of picture text -->

**0**

<!-- Start of picture text --> MONEY WASE<br>18<br>17 - -<br>16<br>15<br>14 -<br>L4~~1<br>10<br>9-<br>1990 1991 1992 1993<br>Year<br>3 Currnt Prices + 1990 Prices<br><!-- End of picture text -->
- World Bank. 2021b. “Female Entrepreneurship Resource Point. Introduction and Module 1: Why Gender Matters.” World Bank, available at

- https://www.worldbank.org/en/topic/gender/publication/female-entrepreneurshipresourcepoint-introduction-and-module-1-why-gender-matters.

- World Bank and World Trade Organization. 2020. “Women and Trade: The Role of Trade in Promoting Gender Equality.” Technical Report, World Bank, Washington, DC.

- Zottel, Siegfried. 2021. “Here is a We-Fi innovative approach that helps unlock commercial financing and dreams for women-led businesses in Nigeria.” Blog available at: https://blogs.worldbank.org/africacan/here-we-fi-innovative-approach-helps-unlockcommercial-financing-and-dreams-women-led.

# 10. Appendix

**Table A1. Summary of Evidence on the Effects on Women-Led Businesses**

|**Constraint**|**Intervention**|**Evidence**|**Selected Papers**<br>**Reviewed**|**Conclusion**|
|---|---|---|---|---|
|Human Capital|Traditional<br>business training<br>programs|Evidence suggests<br>that the average<br>effects are<br>positive but<br>modest, but they<br>are typically<br>smaller for<br>women-led<br>businesses|McKenzie and<br>Woodruff 2014<br>(review), McKenzie<br>2021 (meta-<br>analysis) McKenzie<br>et al. 2023 (review),<br>Jayachandran 2021<br>(review), Gine and<br>Mansuri 2021 (RCT),<br>Field et al. 2016<br>(RCT), Lafortune et<br>al. 2018(RCT)|The literature<br>indicates negligible<br>or low effects on<br>average for women.<br>Adapting content to<br>women’s specific<br>constraints and<br>tailoring<br>implementation<br>may improve<br>impact.|
||Variations on<br>business training<br>programs|More causal<br>evidence is<br>needed on cost<br>reduction through<br>virtual training<br>methods,<br>targeting,<br>insourcing and<br>outsourcing, and<br>on the impact of<br>charging for<br>training|McKenzie 2021<br>(review), Maffioli et<br>al. 2023 (RCT),<br>McKenzie and<br>Sansone 2019<br>(heterogeneity<br>analysis), Ellis et al.<br>2022<br>(heterogeneity<br>analysis), Bryan et<br>al. 2022 (RCT),<br>Bardasi et al. 2021<br>(RCT), Davies et al.<br>2024 (RCT),<br>Anderson and<br>McKenzie 2022<br>(RCT)|There is a need for<br>more evidence to<br>demonstrate that<br>training programs<br>can be made cost-<br>effectively<br>optimized to<br>enhance business<br>outcomes for<br>women-led<br>businesses through<br>cost reduction and<br>improved targeting.<br>Alternatively,<br>insourcing and<br>outsourcing<br>services has shown<br>promise in one<br>context.|

30
Figure 10: Effects of Removing FGTS <!-- Start of picture text --> Earnings = 100% Average Earnings=50% Average<br>1 2<br>1<br>0 0<br>-0.08 -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0.00 -0.08 -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0.00<br>-1<br>-1<br>-2<br>Discount rate (+) -3<br>-2 Prob. working in retirement (+)<br>-4<br>-5<br>-3<br>-6<br>High discount rate and high -4 -7<br>cost of working in the<br>formal sector -8<br>-5 -9<br>Change in Contribution Density (% of Time) Change in Contribution Density (% of Time)<br>Change in Retirement Age (# of Years) Change in Retirement Age (#of Years)<br><!-- End of picture text --> _Source:_ Simulation model.

Next we look at the effects of possible reforms that introduce “incentive neutral” benefit formulas in the pension system and make redistribution explicit and targeted to individuals with limited savings capacity. The policy changes that we simulate are summarized in Table 4. The common feature, in all cases, is that the benefit formulas for pensions are unified into one given by: where pR is the pension paid by the system at retirement age R; βw, βe, and βg are the contribution rates paid to the system respectively by the employee, the employer and the government (when there are explicit subsidies); a is the age when the individual joins the system, _irr_ is the rate of return that the system pays on contributions; and GR(irr) is an annuity factor that also depends on _irr_ . In our application, _irr_ is assumed to be equal to the growth rate of the average wage, which as shown in Robalino and Bodor (2008) is a good proxy to the sustainable internal rate of return of a pay-as-you-go system (although in most cases this proxy would be below the sustainable rate).

In terms of contribution rates, we assume that employees pay 8% (equal to the minimum contribution rate today) and that out of the 20 percentage points paid by the employer, 8 percentage points are allocated to finance old-age pensions. Thus the total contribution rate to finance pensions is 16%.

28
# Impact evaluation of conflict prevention and peacebuilding interventions

Marie Gaarder and Jeannie Annan<sup>1</sup> _Keywords_ : Impact evaluation, conflict prevention, peacebuilding, conflict analysis, fragile states, ex-combatants, experimental design, quasi-experimental design, encouragement design _JEL Classification:_ C93, D03, D74, H43, O22 _Sector Board:_ Social Protection 1 Gaarder: Independent Evaluation Group (IEG), World Bank, mgaarder@worldbank.org; Annan, International Rescue Committee, jeannie.annan@rescue.org. We would like to thank a number of people for their support. Ole Winckler Andersen, Denmark’s development cooperation (Danida), Beate Bull, Norwegian Agency for development cooperation (Norad) and Megan Kennedy-Chouane, DAC Network on Development Evaluation, editors of the forthcoming book _Evaluation Methodologies for Aid in Conflict_ , have been a driving force behind this paper (an updated version of which will appear as a book chapter). Christoph Zürcher, Professor at University of Ottawa, Cyrus Samii, Assistant Professor at New York University, Macartan Humphreys, Professor at Columbia University and Julia Leininger, German Development Institute have reviewed and provided invaluable comments to draft versions of the paper. Anette Brown, Deputy Director and Daniela Barba, Research Assistant at the International Initiative for Impact Evaluation (3ie) kindly gave us access to the database and literature survey which formed the basis for the Samii, Brown, and Kulma (2012) survey. Finally, a number of the lead investigators of the impact evaluations discussed in the paper took the time to respond to our survey and we are very grateful for being able to draw on their experiences.
**Table 3: Regression Results on Likelihood of a FDI Surge and Stop – Random Effects Probit Estimates**

|||**Surges**|||**Stops **||
|---|---|---|---|---|---|---|
||**All**|**GF-led**|**M&A-led**|**All**|**GF-led**|**M&A-led**|
|**Global Factors**|||||||
|Global Risk|0.010(.007)|0.006(.007)|0.024(.012)|0.013(.008)|0.007(.008)|0.021(.016)|
|Global Liquidity|0.008(.002)**|0.007(.002)**|0.006(.004)|0.001(.002)|0.001(.002)|0.001(.003)|
|Global Growth|0.086(.032)**|0.038(.033)|0.250(.068)**|-0.094(.030)**|-0.076(.031)*|-0.110(.056)|
|**Regional Contagion**|0.012(.004)**|0.013(.005)*|0.019(.010)*|0.033(.372)|-0.372(.472)|0.014(.011)|
|**Domestic Factors**|||||||
|Stop previousyear|0.140(.103)|0.172(.117)|0.327(.265)|-0.221(.132)|-0.368(.163)*|0.342(.326)|
|Surgepreviousyear|-0.101(.110)|-0.004(.125)|-0.132(.329)|1.108(.131)**|1.003(.102)**|2.261(.199)**|
|GDPper Capita(ln)|-0.048(.021)*|-0.083(.023)**|0.082(.036)*|-0.002(.022)|-0.036(.023)|0.077(.042)|
|Natural Resources|0.001(.002)|0.004(.002)|-0.013(.005)*|0.002(.003)|0.004(.002)|-0.002(.005)|
|Δ Financial Openness|0.181(.132)|0.078(.096)|0.166(.155)|-0.039(.096)|-0.131(.102)|0.316(.161)*|
|Δ Economic and Financial<br>Stability|0.002 (.013)|0.013 (.009)|-0.030 (.015)*|-0.004 (.009)|-0.007 (.009)|0.016 (.016)|
|Δ Political Stability|-0.018(.012)|-0.022(.013)|0.009(.021)|-0.018(.013)|-0.018(.013)|-0.002(.025)|
|Number of Observations|1768|1768|1768|1768|1768|1768|
|Number of Countries|95|95|95|95|95|95|
|Number of Surges or Stops|264|207|57|282|225|57|
|*p<0.05, **p<0.01; cluster-robust<br>M&A dominated surge). The varia<br>windsorized at the 1% level.|standard errors in pare<br>bles Regional Contagio|ntheses. Dependent var<br>n, GDP per capita and|iable is a 0-1 variable ind<br>Natural Resources are la|icating if there is a surg<br>gged (one year). The var|e or stop episode (eithe<br>iables GDP per capita|r GF-led surge or an<br>and natural resources are|

22
<!-- Start of picture text --> Figure 4.5: Share of Income Per Decile; Bahia<br>1995 and 2001<br>0.6<br>0.5<br>Bahia 1995<br>0.4<br>Bahia 2001<br>0.3<br>0.2<br>0.1<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Source: Author’s calculations base on PNAD 1995 and 2001.<br><!-- End of picture text -->

# **QUALITY OF LIFE**

This section presents an analysis of the quality of life in Bahia. The quality of life index ( _indice de qualidade de vida-_ IQV) is a composite index containing 21 indicators of socio-economic well being related to outcomes in sectors such as education, health, and water supply.

**<u>Figure 4.6: The Quality of Life Index, 1991-1999</u>**

<!-- Start of picture text --> 0.85<br>0.80<br>0.75<br>0.70<br>0.65<br>0.60<br>0.55<br>0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>Source: IPLANCE data.<br>DF SP SC RJ RS PR MG ES RR GO MS MT AP AM PA RO TO SE RN BA AC PE CE PB PI MA AL Northeast Brazil<br><!-- End of picture text --> The length of the bar in Figure 4.6 indicates the advance made in a state during 1991-1999. Hence, the bottom of the bar shows the IQV in 1991 and the top the IQV in 1999. Great advances have occurred throughout the period and the poorest states have made much greater advances than rich states in Brazil.

During the 1990s, Figure 4.7 reveals that the northeastern states have greatly improved. Bahia experienced an increase of 29 percent in IQV close to the average of the Northeast (30 percent), which is double the improvement in the quality of life 18
2.4% for consumption. Furthermore, much of the variation in aluminum production may be accounted for by reference to changes in expected consumption. We may see this by considering the three variable two lag VAR obtained by regressing the log of aluminum production and consumption respectively on two lags of production, consumption and the deflated, exchange rate adjusted aluminum price (see paragraph 95). An F-test for the exclusion of the lagged consumption variables from the production VAR gave the highly significant value of F2 ,22 = 5.88 while that for the exclusion of the lagged production variables from the consumption VAR gave an insignificant value of F2,2 2 = 2.23. This test confirms that at a least a certain proportion of the variation in aluminum production may be explained by lagged consumption levels, and this is most easily rationalized in terms of changes in expected consumption.

95. The most satisfactory equation for the aluminum price over the sample 1960-87 is (68) ln(ALPtEXRt/USPPt) = -0.043 - 0.010t + 0.512(inCONSt - Et_llnCONSt) (0.071) (0.003) (0.216)

- 1.5451n(PRODt-l/CONSt-1)

(0.362) R2 = 0.569 DW = 1.81 s.e. = 0.105 LM test for serial correlation: F3,21 = 2.16 where ALP is the LME settlement price from 1978 and prior to that the _Metal Bulletin_ 'certain other transactions' indicator price, both in c/lb, USPP is the US producer prices index (all items), EXR is the GDP-weighted exchange rate index referred to in paragraph 91, PROD is non-socialist world production of primary aluminum and CONS is 56
|Federal HighwayRehabilitation and Decentralization Project|Aymeric-Albin Meyer|
|---|---|
|Federal HighwayRehabilitation and Decentralization Project|Aymeric-Albin Meyer|
|Federal HighwayRehabilitation and Decentralization Project|Aymeric-Albin Meyer|
|Federal HighwayRehabilitation and Decentralization Project|Aymeric-Albin Meyer|
|Federal HighwayRehabilitation and Decentralization Project|Aymeric-Albin Meyer|
|Federal HighwayRehabilitation and Decentralization Project|Aymeric-Albin Meyer|
|Federal HighwayRehabilitation and Decentralization Project|Aymeric-Albin Meyer|
|Federal HighwayRehabilitation and Decentralization Project|Aymeric-Albin Meyer|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|Minas Gerais WaterQualityand Pollution Control|Yoko Katakura|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|WATERQUALITY AND POLLUTION CONTROL PROJECT|Carlos E. Velez|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|AIDS and STD Control Project|Theresa Jones|
|AIDS and STD Control Project|Theresa Jones|
|AIDS and STD Control Project|Theresa Jones|
|AIDS and STD Control Project|Theresa Jones|
|AIDS and STD Control Project|Theresa Jones|
|RIO DE JANEIRO METROPOLITAN TRANSPORT DECENTRALIZATIO|N PJorge M. Rebelo|
|RIO DE JANEIRO METROPOLITAN TRANSPORT DECENTRALIZATIO|N PJorge M. Rebelo|
|RIO DE JANEIRO METROPOLITAN TRANSPORT DECENTRALIZATIO|N PJorge M. Rebelo|
|RIO DE JANEIRO METROPOLITAN TRANSPORT DECENTRALIZATIO|N PJorge M. Rebelo|
|RIO DE JANEIRO METROPOLITAN TRANSPORT DECENTRALIZATIO|N PJorge M. Rebelo|
# included. Region fixed effects and age and male dummies control for trends.

# Table 6 to be inserted

First, Column 1 shows that the 2001 drought has a negative effect on school progression, but it is not statistically significant. Column 2 includes its interaction with total asset value. Interestingly, the drought has a significant negative effect on grade progression (about 0.37 year reduction), but asset holdings significantly mitigate the impact of the drought.

In Column 3, similar to Bangladesh, we use disaggregated measures of the household assets. None of them are shown to be significant. In the Ethiopian survey, we have information on distance to the nearest town, with which we can test the market and institutional effects on the effectiveness of the ex-ante actions.<sup>16</sup> In Column 4, interestingly, the effects of maximum education and household size (both capturing human capital) become significantly positive when the village is distant from towns. Though it is not statistically significant, this is also observed for livestock. This result implies that human capital may play a more important role in mitigating disaster impacts in distant villages.

# Figure 3 to be inserted

Figure 3 shows simulation results based on the above estimates in Column 4. Similar to the Bangladesh case (Figure 2), a large family has more advantage than education in Ethiopia. This could be due to the fact that schooling level is generally very low in the rural areas. Risk diversification by having a large family may be more effective in mitigating the effect of droughts.

# **5.1.3. Malawi**

For Malawi, we also use the 2001 drought to investigate the disaster impact on school progression. Since the drought was followed by a flood in 2001-2002, we also analyze the confounding effect. In all specifications, age and male dummies are included. Region fixed effects and age and male dummies control for trends.

# Table 7 to be inserted

Table 7 reports the results on school progression in Malawi. Column 1 only includes the 2001 drought indicator and its interaction with the male indicator, both of which are found insignificant. In Column 2, the total asset value was interacted with the drought indicator, which shows that household asset accumulated prior to the 2001 drought mitigated the negative impact, though the direct effect of the drought is not statistically significant (with a negative coefficient).

Column 3 includes the interactions with maximum education in the household, land size, household size and livestock value. First, we find that the 2001 drought significantly decreases school progression (by nearly a year, which is more than twice

> 15Due to a problem in matching children in the two rounds in peasant association no.7, we did not include this peasant association in the analysis. Therefore, the total number of peasant associations is 14.

> 16Note, however, that we have only 14 peasant associations in the analysis.

14
# **Figure B12: Silver**

<!-- Start of picture text --> Log scale<br>5<br>Log price Permanent component<br>4<br>3<br>2<br>1<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.5<br>Medium-term cycle Transitory component<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 53 —_