rafmacalaba/datause-encoder-data huggingface dataset:
# _Number of farms_

The 2015 Population and Housing Census indicate that some 687,805 households are engaged in rice farming (Table 3). Of this total, 62.3 percent are in engaged in the Upland ecological area and 31.6 percent are in the lowland area. Nearly two-thirds of agricultural households grew upland rice while just under a third cultivated lowland rice, which include inland valley swamp (IVS), boliland, mangrove swamp and riverine ecologies (Population and Housing Census, 2015).

**Table 3: Households Engaged in Rice Farming by Region (2015)**

|**Region**|**Upland**|**Rice**|**Lowlan**|**d Rice**|**Total**|
|---|---|---|---|---|---|
||**No.**|**%**|**No.**|**%**|**No.**|
|**_Sierra Leone_**|_456,470_|_62.3_|_231,335_|_31.6_|_687,805_|
|**Eastern**|158,341|21.6|66,904|9.1|225,149|
|**Northern**|187,997|25.7|128,995|17.6|316,992|
|**Southern**|107,796|14.7|33,842|4.6|141,638|
|**Western**|2,336|0.2|1,594|0.2|3,930|

Source: Sierra Leone Population and Housing Census, 2015.

# _Farm Size_

The 2009 National Sustainable Agriculture Development Plan 2010-2030 gives the average farm size as 1.63 hectares, based on the 1985 census. However, the 2015 Population and Housing Census indicates that 1,694,309 hectares of land is under rice cultivation, comprising 1,133,925 hectares of upland and 560,384 hectares of lowland rice. The total estimate is slightly higher than the estimate from the Ministry of Agriculture (Table 2). The 2015 Census puts the average farm size at 2.46 hectares. This suggests that the average farm size increased by 51 percent over the 30-year period between 1985 and 2015.

# _Labor availability use and earnings_

According to the 2014 Sierra Leone Labor Force Survey, the working-age population is just over 3 million. The overall labor force participation rate is 65 percent, with the male participation rate at 65.7 percent and the female rate slighter lower at 64.5 percent. The overall labor participation rate is much higher in the rural areas (69.4 percent) than urban Freetown (53.9 percent). According to the 2014 Labor Force Survey, 59.2 percent of the Labor force is engaged in self-employment in the agricultural sector, with the percentage slightly larger for men than for youth and women (Table 4).

**Table 4: Sierra Leone-Employment by Sector**

|**Sectors**|**Men**<br>**(%)**|**Women**<br>**(%)**|**Youth**<br>**(%)**|**Overall**<br>**(%)**|**Rural**<br>**(%)**|**Urban**<br>**(%)**|
|---|---|---|---|---|---|---|
|Agricultural, Self-employment|59.7|58.7|58.8|59.2|72.9|0.5|
|Non-agricultural Self-employment|24.8|36.8|31.9|31.3|23.5|59.0|
|Wage employment|15.5|4.5|9.3|9.5|3.6|40.5|
|Unpaid labor|6.6|6.4|7.5|6.5|7.1|2.6|
|Agriculture, Fishing and Forestry|62.2|60.1|61.1|61.1|74.4|0.6|
|Mining and Extractive Industries|3.0|0.2|1.2|1.5|1.1|0.1|
|Manufacturing and Utilities|5.6|0.5|3.1|2.8|2.2|6.5|
|Construction|2.7|0.1|1.3|1.2|0.5|6.1|
|Services|26.6|39.2|33.2|33.4|21.7|86.6|

Source: 2014 Sierra Leone Labor Force Survey 12
fixed effects, respectively, and ���� contains unobserved determinants of household consumption that vary both over time and across households. Finally, ������� indicates the participation of an individual from household � in the scheme. Household characteristics include the variables that affect household production, such as the ownership of land or other productive assets, number of working age adults and the number of young children below age 5 for a given household size, and the average education level of adults (excluding household head). In addition, to capture the characteristics of the household head, variables such as age, education, gender, marital status, and health are included.

A major concern is that EGPP participation is based on self-selection and is correlated with the error terms in equation (1). To address this concern, the selection equation is modeled using the following specification; program and 0 otherwise. As extensively discussed in the literature, we address this issue by finding an instrument ��� that satisfies two essential properties. First, it is associated with EGPP participation, and second, it is uncorrelated with other determinants of potential outcomes of interest, ���� . Recognizing that EGPP’s wage payments require access to an individual’s bank account, we use the ease of accessibility (time and distance to a bank) to the banking service as the instrument.

# **4.2. Validity of the instrumental variable strategy**

The survey collected information on whether the particular _Union_ has a bank and other major infrastructure,<sup>17</sup> and if not, how long it takes to get to the closest one via different modes of travel (e.g., rickshaw, on foot, motorcycle, public transportation). Key facilities include bus and train stations; health clinics and private hospitals; _Upazila Sadars_ (administrative center of the _Upazila_ ); post offices; and public schools. Among these facilities, widely available ones (including post

> 17 Major infrastructure includes bus and train stations; health clinics and private hospitals; _Upazila Sadars_ (administrative center of the _Upazila_ ); post offices; and public schools.

19
**<u>Chart 8: Best Practice Liquidity Risk Measurement</u>**

|**Recommended Tools**|**Metrics, Decomposition and Liquidity Stress-**<br>**Testing**|
|---|---|
|**Multi-period cash flow projections**|**Projected Cashflows**:<br>1.<br>Base case projection<br>2.<br>Large & Wholesaler Banks: daily time<br>buckets (first projected week), 3 weekly<br>time buckets, 11 monthly buckets<br>3.<br>Smaller and Retail Banks: monthly buckets<br>**Decomposition:**<br>1. Contractual versus behavior-driven cash<br>in/out flows (both on and off balance sheet).<br>2. Non-discretionary cashflows versus assumed<br>new borrowings, and loans and securities<br>sales (remedial actions).<br>**Stress Levels**: mild (with new loan<br>commitments), serious, and worst-case scenarios.|
|**Quantification of the bank’s liquidity reserve**<br>(that is, pool of assets ready for sale in the event<br>of a liquidity crisis)|**Criteria to Assess Asset Marketability:**<br><br>(Position of asset category held by the<br>bank/volume of asset category traded)<br><br>Turnover ratio for asset category;<br><br>Credit quality of asset;<br><br>Tenor and interest rate of asset;<br><br>Market conditions at point of sale.<br>**Metric**:<br><br>(Net funding Requirement/Liquid Assets)=<br>Survival Horizon.|
|**Key risk indicators and trends (early warning)**|**Differentiate by type of bank:**Wholesaler bank<br>(more vulnerable) and Retail bank (with deposits<br>largely insured).<br>**Metrics for defined time horizon:**<br><br>Forecast of funding requirements (specific<br>bank and banking system);<br><br>Forecast of liquid assets (bank and market);<br><br>Dependence on short-term borrowings<br><br>Changes in credit quality (NPLs and<br>delinquency);<br><br>Operational risk loss;<br><br>Spreads for purchase funds compared to<br>peers;<br>Position of the economy in the business cycle.|

Source: Matz, Leonard, ‘Liquidity Risk- New Lessons and Old Lessons’, Sungard, Bancwire Risk Monthly, September 2007.1.3 Internal Warning Triggers
|Table2Short-runeffects of Banglade<br>Dependent: Change in proportion of d|sh flood onschoolattendance<br>ays attended from round 1 to 3|||
|---|---|---|---|
|Flood variable: Depth|Days Repair cost Outhome|Depth Days|Repair cost Outhome|
|Flood -0.0125<br>|-0.0004 -0.00002 -0.0006<br>|-0.0170 -0.0011<br>|-0.00006 -0.0009<br>|
|(1.500)|(0.720) (2.480) (0.880)|(1.490) (1.080)|(3.930) (0.870)|
|Flood * land||0.00009 2.29-E-06|1.55E-08 4.06E-07|
|||(2.700) (2.900)|(0.940) (0.050)|
|Flood * max educ||-0.0001 0.00004|4.94E-06 0.00004|
|||(0.110) (0.420)|(3.270) (0.490)|
|Flood * female 0.0163|0.0002 0.00002 0.0013|0.0183 0.0002|0.00002 0.0013|
|(1.620)|(0.490) (1.730) (1.530)|(2.040) (0.410)|(1.610) (1.430)|
|Union FE yes|yes yes yes|yes yes|yes yes|
|Number of observations 630|630 630 630|584 584|584 584|
|Number of unions 21<br>|21 21 21<br>|21 21<br>|21 21<br>|
|R squared (within) 0.0226|0.0191 0.0431 0.0209|0.0370 0.0292|0.0713 0.0226|

Numbers in parentheses are absolute t values using robust standard errors with union clusters. Age and female dummies are included in all specifications.

24
# **A. External Validity**

One of the major problems of randomized evaluations is that, because they often focus on specific group of individuals, it remains unclear whether the results can be extrapolated to other populations. In our case, this is a particular concern given that students self-select into the registration for the program. However, through the SISBEN surveys, we have access to information on all eligible students living in the two localities, and we can directly compare students whose families registered them for the program to those that did not.

This comparison is presented in Table 2. Each row contains estimates for the indicated demographic variable. Columns 1 and 3 provide the average value for all registered children, and columns 2 and 4 provide the simple difference between registrants and non-registrants using Equation 1. While the size of the sample (515,885 children) is sufficiently large that most differences are statistically significant, they are all very small in magnitude except for those concerning school participation. Families have similar numbers of assets, similar household characteristics, and similar scores on the poverty indexes. Figure 1 shows the entire distribution for our income estimate and similar to the mean, the entire distributions of registrants and nonregistrants are comparable.

The main difference is school participation. On average, those registered for the program were more likely to have been attending school when the study was administered (19 and 20 percentage points). There are two reasons for this. First, this particular program targeted students who were already attending school. Second, a primary means of disseminating information about the program was through school principals. This is also born out in Figure 2 where we compare the families using our proxy attendance estimate. Registrants are significantly less likely to be children with similar characteristics to low attending children and much more likely to be similar to those with attendance rates close to 80 percent. The primary implication is that these results are most applicable to the students for which the interventions were targeted through the eligibility requirements: students who are currently enrolled in school and who have completed at least the fifth grade.

Finally, because students are eligible for the tertiary treatment only in Suba, we need to make sure that the students in Suba are similar to those in San Cristobal in order to compare properly the magnitudes of the treatment effects. This is done in columns 5 and 6. Column 5

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Table A7: Balance check. Soft Skills (Indexes 1-5)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Cont|rol|Offe|red||
||Mean|S.D.|Mean|S.D.|p-value|
|**Panel A. Likert Scales**||||||
|Emotional Awareness|3.91|0.48|3.93|0.49|0.65|
|Self Awareness|4.13|0.49|4.11|0.48|0.63|
|Emotional Regulation|4.01|0.47|3.99|0.45|0.54|
|Self Control|3.53|0.73|3.56|0.68|0.55|
|Perseverance|4.19|0.52|4.17|0.51|0.55|
|Personal Initiative|4.29|0.43|4.29|0.46|0.90|
|Problem Solving|4.14|0.48|4.18|0.44|0.19|
|Listening|4.09|0.50|4.10|0.48|0.80|
|Empathy|4.17|0.43|4.19|0.46|0.56|
|Expressiveness|4.02|0.54|4.01|0.53|0.73|
|Relatedness|4.23|0.46|4.22|0.47|0.61|
|Infuence|4.00|0.53|4.01|0.48|0.99|
|Negotiation|4.06|0.50|4.08|0.51|0.63|
|Collaboration|4.30|0.43|4.33|0.45|0.36|
|**Panel B. Joint test for panels A**||||||
|p-value (joint F-test)|||||0.42|
|Observation|447||448||895|

Notes: the p-value reported in Column 5 is obtained from a regression of each variable on an assigned to treatment dummy with standard errors robust to heteroskedasticity, controlling for lottery dummies. **p-value (joint F-test)** : corresponds to the orthogonality test in a regression of assigned to treatment dummy on covariates, the regression also controls for lottery dummies.

32
47 (1996). “Regulatory Incentives and the Thrift Crisis: Dividends, Mutual-to-Stock Conversions, and Financial Distress.” TheJournal ofFinance, September. Lindgren, C., G. Garcia and M. Saal (1996). Bank Soundness and Macroeconomic Policy. Washington, DC: International Monetary Fund. Lupia, Arthur and Mathew D. McCubbins (1994). “Learning from Oversight: Fire Alarms and Police Patrols Reconstructed.” Journal ofLaw, Economics and Organization 10:1 86 - 125, Mehrez, Gil and Daniel Kaufmann (1999). “Transparency, Liberalization and Banking Crises.” Mimieo, The World Bank Institute, Washington. North, Douglass C. and Barry R. Weingast (1989). “Constitutions and Commitment: The Evolution of Institutions Governing Public Choice in Seventeenth-Century England.” Journal ofEconomic History 49:4, 803-32 (December). Poterba, James M. (1994). “State responses to fiscal crises: The effects of budgetary institutions and politics.” The Journal of Political Economy 102: 4, 799-821 (August). Rasmusen, Eric and J. Mark Ramseyer (1994), “Cheap bribes and the corruption ban: A coordination game among rational legislators.” Public Choice 78: 305-327. Rogoff, Kenneth (1990). “Equilibrium Political Budget Cycles.” The American Economic Review 80: 1, 21-36 (March). Romer, Thomas and Barry Weingast (1991). “The Political Foundations of the Thrift Debacle.” In Alberto Alesina and Geoffrey Carliner, eds., Politics and Economics in the Eighties. Chicago: University of Chicago Press. Rosenbluth, Frances McCall (1989). Financial Politics in Contemporary Japan. Ithaca: Cornell University Press, Schwartz, Thomas (1994). “Representation as agency and the pork barrel paradox.” Public Choice 78:3-21 (January). Segal, Jeffrey A., Charles M. Cameron and Albert D. Cover (1992). “A spatial model of roll call voting: senators, constituents, presidents, and interest groups in Supreme Court Confirmations.” American Journal ofPolitical Science 36: 1 (February): 96-121. Sullivan, Michael J. (1991). Measuring Global Values. New York: Greenwood. Taylor, Charles and David Jodice (1983). World Handbook ofPolitical and Social Indicators, 3rd ed. New Havén: Yale University Press. Tsebelis, George (1999). “Veto players and law production in parliamentary democracies: an empirical analysis.” American Political Science Review 93:3 September): 591-608. United States Central Intelligence Agency (1999). The World Factbook. Washington, DC.
farms are chosen more often in desert, lowland dry savannah, and lowland semi-arid AEZs. However, with warming, farmers increase their use of this farm type across all the AEZs. If precipitation increases, farmers reduce this farm type across all the AEZs. Livestock-only farms are chosen more often in the desert and mid elevation semi-arid AEZs. An increase in rainfall reduces the chance this farm type is chosen across all the AEZs. Cattle and sheep which are major commercial livestock are raised in drier places (Seo and Mendelsohn 2008). Warming increases the chance of livestock-only farms to be chosen in high elevation and mid elevation AEZs, but not in lowland AEZs. Farmers choose to have livestock when climate turns too hot for crops (Seo and Mendelsohn 2008).

# **5. Climate Change Predictions**

Using the empirical model of farm type choice in the previous section, we predict how future climate scenarios may affect the future distribution of each farm types across each AEZ. We explore two distinctly different future climate predictions for 2100: the CCC (Canadian Climate Centre) scenario (Boer et al. 2000) and the PCM (Parallel Climate Model) scenario (Washington et al. 2000). These two scenarios reflect the range of outcomes predicted in the most recent IPCC (Intergovernmental Panel on Climate Change) report (IPCC 2007). In each of these scenarios, climate changes at the grid cell level were summed with population weights to predict climate changes by country. We then examined the consequences of these country level climate change scenarios for 2100.

To obtain district level climate predictions for each scenario, we added the predicted change in temperature from the climate model to the baseline temperature for each season in each district. For precipitation, we multiplied the predicted percentage change in precipitation from the climate models by the baseline precipitation for each season in each district. Table 4 presents the African mean temperature and rainfall predicted by the two models for each season for the year 2100. Across all African countries in 2100, PCM predicts an average increase of 2°C increase while CCC predicts an average increase of 6.5°C increase in annual mean temperature. PCM predicts an average 10% increase in annual mean rainfall in Africa but CCC predicts an average 15% decrease. So the PCM scenario is mild and wet whereas the CCC scenario is hot and dry. Even though the 10
To investigate this further, we compute separate distribution functions and transition matrices for areas located near a road or not. Buildup forecasts based on fitted transition matrices predict a strong increase in fully built-up areas – i.e., towns and cities – but no similar increase in the proportion of cells that are partially built-up. Apart from a small proportion of lowly built cells that seem to correspond to scattered farms, partially built-up areas seem to serve a transitory role as urban areas expand over time. This analysis also confirms that proximity to roads is associated with a much larger increase in future buildup.

We also investigate whether new construction locates near newly built roads. For this purpose, we use the 1975 and 1990 data and compare the new construction in cells that are close to a road in 1986 but not in 1976 to cells that are already close to a road in 1976, and also to cells that not close to a road in either years. We find that proximity to either type of road predicts a larger increase in future buildup – but proximity to a road in 1986 is the more robust predictor. Of course without proper identification we cannot tell whether new construction near a new road is a net addition or whether it displaces construction that would have taken place either near pre-existing roads or far from a road. More research is needed, using quasi-natural experiments if they can be found, such as those used by Banerjee et al., 2012 and by Faber, 2014, among others.

The last part of the analysis examines the evidence regarding agglomeration effects by examining spatial correlograms. Because cells located near a road are so different from those that are not, we conduct the analysis separately. We find in both cases that buidup in a cell predicts more buildup in cells located up to 3 to 5 km. For cells located near roads, there is still some evidence of more buildup up to 40-50 km away. For cells not near a road, the effect fades away faster. We find no evidence that densely built areas tend to be separated by a specific distance, as would be captured by a spike in the spatial correlogram. Over time, the covariogram increases, capturing the general increase in buildup.

This paper contributes to the literature on urbanization and regional development. Data on built-up area at a highly disaggregated level has recently become available (Pesaresi et al. 2016). This has encouraged a newly emerged literature that seeks to make use of this information to explore important economic realities. Bellefon et al. (2018), for instance, use buildup proportion as a way of identifying the edges of cities, arguing that partial buildup identifies sub-urban areas. We adopt a similar philosophy in the sense that we regard building concentration as an indication 3
- 12 -

# Figure **1.** Money Market Rates<sup>and Three-Month</sup> Euro-Dollar Rates

|12.:|If<br>iS.0~~~~~~~~~~~~~~~~~~.<br>Ia<br>.'<br>;NZ (z~<br>M<br>m<br>rte|II|:|
|---|---|---|---|
||ER = Eurodollc9<br>1r80 1981 1982 198a i<br>MR"<br>Monev muarket<br>race<br>ER Eurodolla**r**ate|18e||
strategies employed by households in urban and rural areas, may translate into reduced income and reduced food availability at the household level.

Numerous studies have examined the impacts of natural disasters and extreme weatherrelated shocks on different dimensions of welfare (see Baez and Mason 2008, and World Bank 2010b for a thorough review of this literature). In general, they all show that agricultural incomes and, thus, food, basic non-food consumption and investments in human capital, health, nutrition and productive physical assets are likely to be negatively affected by extreme weather events. Many of these studies however, tend to rely on the perceptions of respondents about the incidence of different types of shocks or use data on rainfall and temperature as a tool (e.g. as instrumental variables) to analyze the effect of shocks to income on other outcomes, such as consumption or investments in human capital.<sup>24</sup> Hardly any studies use actual weather data to analyze the general relationship between weather and the level of welfare. A recent study by Skoufias, Vinha, and Conroy (2011) examines whether climatic variability, namely deviations of rainfall and temperature from their long run means, have significant impacts on the average wellbeing of rural households in Mexico. They report that the timing of the rainfall or temperature shock results in a substantial difference in its estimated impact on welfare. For example, per capita expenditures are 14 percent higher if the prior agricultural year (October to September of next calendar year) was at least one standard deviation drier than the average of the last 35 years (1951-1985). However, if the rainfall shock were to be defined within the time frame of the wet season (April to September) then neither positive nor negative rainfall shocks during that wet season appear to have any significant effect on household per capita expenditures.

In a study building on these insights, Skoufias, Essama-Nssah, and Katayama (2011) use data from rural Indonesia and consider two rainfall-related shocks: (i) a delay in the onset of monsoon and (ii) a significant shortfall in the amount of rain in the 90-day post-monsoon period. Focusing on households with family farm businesses, they find that rice farm households located in areas experiencing low rainfall following the onset of the monsoon are negatively affected. A shortfall in the amount of rainfall in the post onset period is associated with a 14 percent reduction in the per capita expenditures of rice farmers. Rice farm households

> 24  There is a large literature on the extent to which short-term weather shocks in poor rural areas can have long-term effects on education, health, and nutrition, especially of children. For a recent review of these studies see Baez and Mason (2008).

18
**Table 1: Loan Applications and use of loans (for recipients only) by source and zone**

||**Total**|**Semi-formal**<br>**Gojjam**|**Wollo**|**Total**|**Informal**<br>**Gojjam**|**Wollo**|
|---|---|---|---|---|---|---|
|**Loan application/participation**|||||||
|Applied for loan|0.23|0.27|0.20|0.07|0.10|0.04|
|If yes, fully approved|0.79|0.72|0.88|0.87|0.86|0.89|
|If yes, partially approved|0.17|0.24|0.10|0.09|0.10|0.08|
|If yes, rejected|0.03|0.04|0.02|0.04|0.04|0.03|
|Number of households applied for loan|364|197|167|109|71|38|
|Total number of households|1587|741|846|1587|741|846|
|**Reasons for loan application (recipients**|**only)**||||||
|Buy ag. tools, implements and inputs|0.47|0.81|0.07|0.29|0.40|0.08|
|Buy livestock|0.35|0.17|0.56|0.08|0.10|0.05|
|Start off-farm business|0.04|0.01|0.10|0.08|0.08|0.05|
|Buy food and other consumption items|0.08|0.01|0.16|0.17|0.18|0.16|
|Buy construction materials|0.03|0.01|0.06|0.07|0.02|0.14|
|Pay for health and education expenses|0.01|0.01|0.01|0.16|0.09|0.30|
|Other: travel,wedding,funeral,etc.|0.03|0.01|0.04|0.17|0.15|0.22|
|Number of households|352|189|163|105|68|37|

**Source:** Own computation from AAU/Gothenburg/WB survey 2006/07. **Note:** None of the sampled households applied for a loan in the formal sector during the time to the survey.

16