rafmacalaba/datause-encoder-data huggingface dataset:
might swing highly, and second, multicollinearity reduces the precision of the estimated coefficients, weakening statistical power. These problems are evident from the regression results once the incomplete roads dataset is integrated in the analysis: while coefficients keep the same sign, the coefficient value and statistical power fluctuate highly.

# **Location of mines**

Location of historical roads might be influenced by other factors than geography and military strategies. For instance, roads could have been built in areas with greater economic potential. During the period, which the historical map is depicting, the main non-agricultural sector with economic potential in Armenia was mining. Armenia has a long history of metal mining and is accounting a large part of the economy. According to the UN Comtrade, metals and ore concentrates accounted for more than half of Armenia’s exports in 2017.<sup>18</sup> Copper mining first started in the Alaverdi area of Lori Marz in 1770s. Later, it started in Kapan in Syunik Marz in 1840s and by the mid-20th century the Kajaran copper-molybdenum mine in Syunik Marz started production. Kajaran mine is the largest operating mine in Armenia, accounting for 60% of the total mining turnover in the country (World Bank, 2016). In spite of the historical importance of the mining sector in Armenia, it is unlikely that by the end of the 19th century primary road placement would have been influenced by mine locations. There are two main reasons to argue this. First, most of the historical mines have been operated in Lori (in north Armenia) and Syunik (in south Armenia) regions of the country. In both regions, as 6 shows, large mines are located very far from the historical routes. However, they are connected by railway lines. The only mine which seems to be on the primary historical road route is the Ararat mine. However, the operation of the Ararat mine had been relatively small until 1975. In addition, it is not an active mine but rather a processing plant.<sup>19</sup>

> 18UN Comtrade Database `https://comtrade.un.org/data/` (Reviewed: December 2020).

> 19Source: State Committee of Real Estate Cadastre of Armenia.

28
# **Introduction**

The Government of Rwanda has introduced a series of reforms to the domestic business environment since 2009 in a bid to reduce the administrative and financial burden of business registration. In 2009, the East African country enacted legislation entitled the Company Act, that was intended to strengthen investment protection and create a business registration one‐stop shop. In the same year, it introduced other reforms, including: (i) the adoption of the Secured Transactions Law (increasing the number of collateralizable assets), (ii) the Insolvency Law (easing the process of bankruptcy), and (iii) the Mortgage Law (shortening the property registration process). Combined, these reforms left Rwanda with a substantially more business‐friendly environment. A second package of reforms followed in 2010 with the introduction of online registration, the reduction of registration fees, changes to regulations on obtaining construction permits, and simplified procedures for exports and credits.<sup>1</sup> Cumulatively, these reforms have contributed to improving Rwanda’s business environment. Although it may take time before some of these measures trigger a private sector response, Gathani, Santini, and Stoelinga (2013) argue that the creation of the one‐stop shop increased registrations almost immediately by more than 180 percent.

Utilizing business registration as a proxy for investor and entrepreneur interest in specific sectors, this paper reviews the trends in business registrations across various sectors and subsectors, and identifies drivers of recent growth.

By linking business registrations to tax declarations, it is also possible to verify the level of activity of newly registered companies — measured as interactions with the revenue authority — and quantify additional revenue and employment accruing from newly registered companies by size, sector, and subsector.<sup>2</sup>

> 1 This section relies on Gathani, Santini, & Stoelinga’s Innovative Techniques to Evaluate the Impact of Private Sector Development Reforms: An Application to Rwanda and 11 Other Countries, 2013.

> 2 There are various tax exemptions in Rwanda. The main exemptions are listed in the 2006 investment code, which was under review at the time this paper was written. Further exemptions may arise from negotiations between investors and the Investment Unit of RDB, by virtue of Article 19 of the Investment Code. The number of companies affected by such incentives is limited and most companies are required to report to the Revenue Authority. Although the presence of tax exemptions is unlikely to impact the analysis of registrations, it may impact estimates of tax contributions by newly registered companies (if those companies are exempt from tax reporting). As per article 18 of “Law No 16/2005 OF 18/08/2005 on Direct Taxes on Income,” incomes from agricultural activities below Rwf 12 Milllion are tax exempt. This article, in conjunction with its previous more generous formulation, may render most of the agriculture sector tax exempt. This may explain the little activity recorded from the sector at the tax office.

4
**Table 4: Greenhouse Gas Balances of Biofuels in 2005**

||||**GHG Emissions(% **|**change)**|
|---|---|---|---|---|
|Feedstock|Previous Land Use|No LUC|With Direct LUC|With Indirect LUC|
|**el**<br>Waste Oil|n.a.|-90|n.a.|n.a.|
|**ies**<br>Rapeseed|Cropland|-58|-58|+5 to +69|
|**Biod**<br>Rapeseed|Pasture|-58|-25|+39 to +102|
|<br>Sugar cane(Brazil)|Cropland|-71|-71|-35 to +1|
|**nol**<br>Maize|Cropland|-55|-55|-22 to +11|
|**ha**<br>Maize|Pasture|-55|-37|-5 to +28|
|**Et**<br>Wheat|Cropland|-49|-49|+6 to +63|
|Wheat|Pasture|-49|-20|+36 to +92|

Source: Fritsche & Wiegmann (2008) in Fischer et al. (2009) Note: Waste oil includes waste vegetable and animal oils. LUC stands for land use change. Direct LUC refers to emissions including those arising from the land conversion to cultivate the biofuel, whereas indirect LUC refers to emissions including those arising from the conversion of land elsewhere to replace production displaced by biofuel cultivation.

Using a general equilibrium approach to capture emissions from land use change, Fischer et al. (2009) estimate net GHG savings under various scenarios for the deployment of biofuels (see Table 5). Since the carbon losses from land use change are incurred at the time of land conversion, while greenhouse gas savings from the substitution of biofuels for fossil fuels accrue gradually over time, the net GHG savings resulting from the first-generation biofuels would not be positive in the first 20 years in any of the scenarios, and only in some scenarios in the first 30 years. It would take 50 years to achieve a sizable savings of GHG emissions, even in the aggressive biofuel deployment scenario that includes second generation biofuel (scenario TARV1).

28
Policy Research Working Paper 10640

# **Abstract**

Motorbike-based ride-hailing services are widespread in many of the most congested cities in the developing world. These services often predate the construction of modern public mass rapid transit systems. Ride-hailing services may complement such investments by providing important first and last mile connectivity. However, it has also been argued that they undermine the viability of mass rapid transit systems as people prefer to use ride-hailing services given their convenience and low prices. This paper applies an event study research design to proprietary, high-frequency data from one of Indonesia’s largest ride-hailing services, Gojek. The findings show that the opening of stations on Jakarta’s first mass rapid transit line led to large increases in ride-hailing activity in the immediate vicinities of the stations. This was accompanied by a significant decline in the average distance of ride-hailing trips to and from the station locations. These findings are consistent with ride-hailing services complementing public transport by providing first and last mile connectivity to the newly opened mass rapid transit system. Interestingly, this holds for both commuting and non-commuting trips and is strongest for mass rapid transit station locations that were not already served by Jakarta’s bus rapid transit system.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mroberts1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Age|0.199**|0.232**|0.197**|0.195**|0.193**|0.201**|0.208**|0.192**|0.198**|0.203**|0.226**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(2.18)|(2.35)|(2.20)|(2.07)|(2.12)|(2.18)|(2.22)|(2.10)|(2.15)|(2.17)|(2.43)|
|Age_sq|-0.002**|-0.003**|-0.002**|-0.002**|-0.002**|-0.002**|-0.003**|-0.002**|-0.002**|-0.003**|-0.003**|
||(-2.23)|(-2.38)|(-2.26)|(-2.13)|(-2.18)|(-2.25)|(-2.24)|(-2.15)|(-2.19)|(-2.25)|(-2.45)|
|Married|0.677|0.587|0.646|0.614|0.691|0.726|0.534|0.682|0.648|0.707|0.774|
||(1.15)|(0.97)|(1.09)|(1.05)|(1.15)|(1.24)|(0.91)|(1.17)|(1.10)|(1.16)|(1.32)|
|At least secondarysch|1.434***|1.508***|1.432***|1.438***|1.429***|1.458***|1.409***|1.403***|1.415***|1.489***|1.440***|
||(3.09)|(2.99)|(3.00)|(3.03)|(3.11)|(3.13)|(3.05)|(3.05)|(3.02)|(3.03)|(3.00)|
|Gender of head(male=1)|0.497|0.168|0.494|0.495|0.539|0.486|0.397|0.567|0.513|0.613|0.240|
||(0.82)|(0.28)|(0.84)|(0.76)|(0.89)|(0.80)|(0.66)|(0.92)|(0.84)|(1.05)|(0.39)|
|Muslim(base:Christian)|0.833*|0.703|0.831*|0.765|0.810*|0.765|0.769|0.792|0.823*|0.925*|0.813*|
||(1.72)|(1.50)|(1.71)|(1.57)|(1.71)|(1.60)|(1.59)|(1.62)|(1.71)|(1.94)|(1.71)|
|Spousepresent|-1.098|-0.883|-1.079|-1.030|-1.101|-1.178*|-0.810|-1.101*|-1.051|-1.065|-1.131*|
||(-1.64)|(-1.30)|(-1.62)|(-1.56)|(-1.61)|(-1.74)|(-1.22)|(-1.66)|(-1.59)|(-1.56)|(-1.70)|
|Household size|-0.004|-0.035|-0.004|-0.005|-0.003|-0.005|-0.026|-0.000|-0.008|-0.007|-0.022|
||(-0.04)|(-0.35)|(-0.04)|(-0.05)|(-0.03)|(-0.06)|(-0.30)|(-0.00)|(-0.09)|(-0.08)|(-0.23)|
|Poor|0.211|0.208|0.216|0.208|0.210|0.225|0.210|0.229|0.232|0.235|0.143|
||(0.48)|(0.43)|(0.49)|(0.49)|(0.48)|(0.50)|(0.46)|(0.52)|(0.53)|(0.52)|(0.31)|
|Urban|1.044**|1.301***|1.048**|1.066**|1.044**|1.108**|1.100**|1.071**|1.068**|1.066**|1.185**|
||(1.97)|(2.65)|(2.00)|(2.07)|(1.99)|(2.09)|(2.11)|(2.00)|(2.05)|(1.99)|(2.24)|
|Ashanti(base: Northern)|1.099**|1.066*|1.087**|1.008*|1.100**|0.890*|1.030*|1.099**|1.130**|1.173**|1.230**|
||(2.05)|(1.89)|(2.04)|(1.83)|(2.06)|(1.65)|(1.89)|(2.03)|(2.10)|(2.11)|(2.23)|
|Impulsive||-0.046|-0.148|||||||||
|||(-0.15)|(-0.19)|||||||||
|Work centrality||0.286||-0.092||||||||
|||(0.91)||(-0.08)||||||||
|Tenacity||-0.014|||0.606|||||||
|||(-0.05)|||(0.69)|||||||
|Polychronicity||-0.625**||||-0.594||||||
|||(-2.50)||||(-0.85)||||||
|High locus of control||0.385|||||-0.349|||||
|||(1.47)|||||(-0.36)|||||
|Achievement||-0.159||||||0.770||||
|||(-0.56)||||||(0.74)||||
|Power motivation||0.194|||||||0.717|||
|||(0.76)|||||||(1.17)|||

41
|0-2 years old|4.2|41.1|0.9|40.4|3.3|41.3|1.7|41.4|3.3|41.4|1.7|41.3|<br>3.0|41.2|1.3|41.4|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Malnutrition (stunting)|0.4|21.4|0.0|9.7|0.3|24.5|0.2|27.9|0.3|24.0|0.2|30.1|<br>0.3|24.4|0.2|29.4|
|Mortality||9.1||6.4||10.2||12.1||10.2||12.0||10.8||12.1|
|5-15 years old|21.1|26.6|4.3|19.1|16.8|29.6|8.6|31.3|16.7|29.4|8.4|30.6|15.3|29.9|6.0|30.4|
|Attend school|16.0|83.4|3.6|93.4|12.3|80.9|5.6|71.7|12.2|80.8|5.5|72.6|11.1|79.9|3.7|69.3|
|Do not attend school|5.2|16.6|0.7|6.6|4.5|19.1|3.0|28.3|4.4|19.2|2.9|27.4|<br>4.2|20.1|2.3|30.7|
|5-11 years old|12.8|60.7|2.5|58.8|10.3|61.2|5.4|63.5|10.2|61.3|5.3|63.4|<br>9.4|61.4|3.8|63.1|
|Attend school|8.7|79.8|2.0|94.4|6.7|76.3|3.0|65.8|6.6|76.4|3.0|66.7|<br>6.1|75.3|2.0|63.3|
|Do not attend school|4.2|20.2|0.5|5.6|3.6|23.7|2.4|34.2|3.6|23.6|2.3|33.3|<br>3.4|24.7|1.8|36.7|
|15-25 years old|16.2|20.5|5.4|24.1|10.8|19.1|4.4|16.1|11.0|19.3|4.6|16.7|<br>9.7|18.8|3.1|15.5|
|25-50 years old|20.3|25.6|7.5|33.2|12.8|22.5|5.8|21.0|12.8|22.5|5.8|21.0|11.3|22.0|4.2|21.1|
|Female head|3.7|34.3|1.8|45.1|1.9|28.2|0.9|28.1|2.0|28.5|0.9|29.2|<br>1.7|28.3|0.7|30.6|
|over 50 years old|11.3|14.3|3.1|13.5|8.3|14.6|4.4|16.2|8.3|14.7|4.5|16.4|<br>7.7|15.1|3.5|17.6|
|over 60 years old|6.3|55.4|1.6|53.6|4.6|56.0|2.5|57.2|4.6|55.6|2.6|57.6|<br>4.3|55.9|2.0|58.2|
|Monoparental|1.1|6.8|0.5|7.5|0.6|6.3|0.4|7.6|0.7|6.3|0.4|7.6|0.6|6.4|0.3|8.4|
|Female|1.0|84.1|0.5|89.5|0.5|79.8|0.3|80.8|0.5|79.8|0.3|78.7|<br>0.5|79.7|0.3|78.6|
|Lactating and pregnant women|3.9|3.9|34.6|0.8|23.3|<br>3.1|39.9|<br>1.7|47.3|<br>3.1|39.7|<br>1.7|45.9|<br>2.8|40.4|<br>1.2|
|_Community characteristics (from DHS 2000)*_|||||||||||||||||
|Primary school|||||||||||||||||
|With|71.1|89.7|19.4|96.2|51.7|87.4|20.1|80.1|50.7|87.2|18.7|81.3|44.4|86.1|12.1|80.3|
|Without|8.2|10.3|0.8|3.8|7.4|12.6|5.0|19.9|7.4|12.8|4.3|18.7|<br>7.2|13.9|3.0|19.7|
|First cycle secondary school|||||||||||||||||
|With|31.0|39.1|14.0|69.1|17.0|28.8|3.4|13.4|16.5|28.4|3.4|14.9|13.1|25.4|2.0|13.5|
|Without|48.3|60.9|6.2|30.9|42.1|71.2|21.7|86.6|41.6|71.6|19.6|85.1|38.4|74.6|13.1|86.5|
|Second cycle secondary school|||||||||||||||||
|With|24.6|31.0|12.6|62.6|12.0|20.3|1.2|4.7|11.6|19.9|1.3|5.8|8.6|16.6|0.7|4.5|
|Without|54.7|69.0|7.6|37.4|47.1|79.7|23.9|95.3|46.6|80.1|21.7|94.2|43.0|83.4|14.4|95.5|
|Market|||||||||||||||||
|With|32.3|40.8|15.0|74.1|17.4|29.4|3.2|12.7|16.8|28.9|3.4|14.8|13.1|25.4|2.0|13.2|
|Without|46.9|59.2|5.2|25.9|41.7|70.6|21.9|87.3|41.4|71.1|19.6|85.2|38.4|74.6|13.1|86.8|
|Hospital|||||||||||||||||
|<br>With|11.7|14.8|5.8|28.7|6.0|10.1|0.5|1.9|5.7|9.9|0.6|2.7|4.5|8.7|0.3|1.9|
|Without|67.6|85.2|14.4|71.3|53.1|89.9|24.6|98.1|52.4|90.1|22.4|97.3|47.0|91.3|14.8|98.1|
|HealthCenter|||||||||||||||||

21
**Table 8: Vegetation indices and sunflower crop rotation in Ukraine, 2016-18 period**

||**E**|**VI**|**L**|**AI**|**FA**|**PAR**|**LS**|**WI**|
|---|---|---|---|---|---|---|---|---|
||**2017**|**2018**|**2017**|**2018**|**2017**|**2018**|**2017**|**2018**|
|After maize|-0.652***|-0.402***|-16.42***|-9.266***|-3.250***|-2.309***|-0.434***|-0.159*|
||(0.0661)|(0.0652)|(1.563)|(1.595)|(0.321)|(0.313)|(0.0919)|(0.0872)|
|After cereals|-1.119***|-1.454***|-29.86***|-38.77***|-5.344***|-6.658***|-0.605***|-1.247***|
||(0.0588)|(0.0598)|(1.389)|(1.462)|(0.285)|(0.287)|(0.0817)|(0.0799)|
|After other|-0.187**|0.149**|-13.67***|-5.705***|-0.107|1.469***|0.789***|0.971***|
||(0.0771)|(0.0749)|(1.818)|(1.833)|(0.373)|(0.360)|(0.107)|(0.100)|
|Self twice|-2.530***||-67.10***||-12.90***||-1.916***||
||(0.0740)||(1.745)||(0.358)||(0.103)||
|Self twice only||-1.986***||-51.89***||-9.586***||-1.171***|
|||(0.102)||(2.496)||(0.490)||(0.136)|
|Self thrice||-2.651***||-81.42***||-15.16***||-2.552***|
|||(0.138)||(3.363)||(0.660)||(0.184)|
|No. of VCs|5060|5211|5049|5200|5049|5200|5049|5200|
|R<sup>2</sup>|0.280|0.409|0.323|0.452|0.305|0.413|0.140|0.302|
|Mean dep.|3.979|4.190|58.61|61.41|21.31|21.89|-0.445|0.0819|
|SD dep.|0.640|0.695|15.50|17.57|3.138|3.333|0.808|0.852|
|Elast. twice|-0.0700||-0.126||-0.0667||-0.475||
|Elast. twice||-0.0351||-0.0625||-0.0324||-1.058|
|Elast. thrice||-0.0164||-0.0345||-0.0180||-0.809|
|F-test|||||||||
|Self 2x=self 3x||13.08***||43.26***||40.00***||31.60***|

_Note:_ Results are from regressions at village council level and the soybean-sunflower rotation is the omitted category. A constant is included throughout but omitted. Standard errors in parentheses: * p<0.10, ** p<0.05, *** p<0.010.

22
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mountainous areas of Nepal. When aggregated by an administrative unit (municipality or ward), this metric allows planners to visualize where accessibility‐enhancing investments would be most impactful.

Following these operations, we divided the cumulative hours of travel for an administrative unit by its population to calculate the average hours of travel per person to the service in question. These averages provide useful headline indicators of municipal accessibility and another data point for rapid comparison between administrative units.

## **_Equation 6: Cumulative travel times_**

## **_Equation 7: Average travel times_**

## **_Where,_**

_a_ = administrative unit _PDi_ = Grid square population density _s_ = service, season and modality _Popa_ = administrative unit population i = grid square _TTsi =Travel time from grid i to service location s Ia_ = total grid squares in administrative area _a_ A _s_ a = Average travel time to service _s_ in _Csa_ = Cumulative person-hours of travel to service _s_ administrative unit _a_ in administrative unit _a_

# Isochrone analysis

We analyzed isochrones, a.k.a. travel time categories, to detect variations in accessibility _within_ administrative units and ensure remote populations were not obscured by administrative aggregates. Isochrones are areas enclosed by an isoline representing an equal travel time from a given point, in this case a service facility. We classified isochrones for each facility or facility category layer according to the following rubric:

**_Table 9: Travel time classifications_**

|**Class**|**Single day**<br>**Time**_(in hours)_|**Class**|**Multi‐day**<br>**Time**_(in hours)_|
|---|---|---|---|
|1|0 – 0.5|5|4 ‐ 8|
|2|0.5 ‐ 1|6|8 ‐ 16|
|3|1 – 2|7|16 – 32|
|4|2 – 4|8|32+|

A further analysis routine followed. Each facility‐specific cost time raster file was converted to a polygonal vector data set with each travel class forming one polygon. We then imposed provincial and local government unit boundaries onto these polygons. Finally, we aggregated the population per travel class per administrative area based on the WorldPop 2015 UN Adjusted population density data set. To show the percentage population breakdown for each travel class in each area we divided each polygon’s population by the total population for its administrative or physio‐geographic area. Figure 10 illustrates this process in detail. Simple
**o -** **<u>Table 3: Average monthly temperature ( C) for 1960 1990, by station</u>**

|||||||Mo|nth|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Station**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|**9**|**10**|**11**|**12**|**Year**|
|Camiri|26.2|25.6|24.5|22.4|20.2|17.6|18.4|20.7|23.4|25.5|26.2|26.6|23.1|
|Charaña|10.4|10.2|9.7|8.7|6.4|4.4|3.9|5.5|7.8|9.5|11.0|11.1|8.3|
|Cobija|26.0|26.1|26.0|25.7|25.2|24.0|24.3|25.6|26.6|27.0|26.7|26.4|25.8|
|Cochabamba|19.5|19.1|19.1|18.6|16.7|14.7|15.0|16.9|19.1|21.0|21.4|20.2|18.5|
|Concepción|25.7|25.6|25.5|24.6|23.0|21.4|21.8|23.9|25.7|26.5|26.2|26.0|24.7|
|La Paz / El Alto|9.1|9.0|8.9|8.8|8.2|7.3|6.9|8.1|8.7|10.1|10.5|9.7|8.8|
|Oruro|12.9|12.5|12.6|11.5|8.5|6.3|6.2|8.3|10.6|12.7|14.0|13.5|10.8|
|Puerto Suarez|28.6|28.2|27.5|26.8|24.4|22.5|22.7|24.7|26.6|28.7|28.8|28.7|26.6|
|Riberalta|27.2|26.9|27.2|27.1|26.5|25.6|26.1|27.4|28.1|28.3|27.8|27.3|27.1|
|Roboré|28.3|27.9|27.2|25.8|23.6|22.2|22.8|24.9|26.6|28.2|28.5|28.6|26.3|
|Rurrenabaque|27.1|26.9|26.9|26.5|24.9|23.3|23.2|24.9|26.5|27.6|27.8|27.5|26.1|
|San Ignacio de Velasco|26.5|26.3|25.9|24.8|23.2|21.3|21.8|23.8|26.1|27.4|27.0|26.8|25.2|
|San Joaquín|27.3|27.0|26.9|26.9|25.9|24.9|25.3|26.7|28.0|27.9|28.0|27.0|26.8|
|Santa Cruz Trompillo|26.7|26.6|26.2|24.7|22.6|20.4|21.0|23.0|24.9|26.4|27.1|27.1|24.8|
|Sucre|16.4|15.8|15.9|15.4|15.2|13.8|13.9|14.8|16.1|17.1|17.4|16.7|15.7|
|Tarija|21.7|21.4|20.8|19.2|17.1|14.8|15.1|16.3|18.6|20.9|21.8|21.8|19.1|
|Trinidad|27.2|27.3|27.3|26.7|25.5|23.8|24.3|25.6|27.1|28.0|27.9|27.6|26.6|
|Yacuiba|26.7|26.0|24.2|21.3|19.0|16.0|16.8|19.4|22.6|25.5|26.5|27.0|22.6|

_Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

Using the “normal” values for each station and each month, we calculate monthly anomalies for each station for the whole period (actual temperature minus normal temperature for that month). Anomalies are easier to analyze than the raw temperature and precipitation data, since the seasonal variation is eliminated through the subtraction of normal monthly temperatures. Figure A1 in the Appendix plots the temperature anomalies for each station.

The precipitation data applied in the analyses have been subjected to the same procedure, and all precipitation anomalies are plotted in Figure A2 of the Appendix.

# **3. Modeling climate and human development**

Bolivia is a very heterogeneous country both with respect to climate and with respect to development. Some regions have extremely harsh climates with sub-zero temperatures most of the year and very little rain. Other regions are hot and constantly humid. Some people live in remote areas without road access in simple one-room dwellings without electricity, piped water, bathroom, or any other basic conveniences. Other people live in mansions with home cinema, swimming pool, fitness room, and servants.

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<u>Table 2. Distribution of spells withpoverty rates higher than 2 percent across region and time period</u>

||Num<br>before<br>2000|ber of sp<br>2000-<br>2010|ells<br>2011<br>and<br>above|Sha<br>u<br>before<br>2000|re of spe<br>nweighte<br>2000-<br>2010|lls,<br>d<br>2011<br>and<br>above|Sha<br> <br>before<br>2000|re of spe<br>weighted<br>2000-<br>2010|lls,<br> <br>2011<br>and<br>above|
|---|---|---|---|---|---|---|---|---|---|
|East Asia and Pacific|26|23|20|38|33|29|48|31|21|
|Europe and Central Asia|7|59|43|6|54|39|8|57|35|
|Latin America and Caribbean|82|117|79|29|42|28|38|40|22|
|Middle East and North Africa|7|3|1|64|27|9|53|27|20|
|South Asia|6|11|11|21|39|39|15|53|32|
|Sub-Saharan Africa|12|28|40|15|35|50|7|31|62|
|Total||575||24|42|34|21|39|39|

<u>Notes: Table shows the number of spells by region and time period. Only 575 spells with both start- and end-year poverty rates of</u> at least 2 percent are included.

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