rafmacalaba/datause-encoder-data huggingface dataset:
# _2) Hydroshed basins_

The unofficial version of drainage basins derived from conditioned DEM in regional subsets downloaded from <u>http://gisdata.usgs.net/Website/HydroSHEDS/viewer.php.</u>

# _3) SRTM coastline_

Vector coastline mask derived by National Geospatial-Intelligence Agency during editing of SRTM version 2.

# _4) DIVA GIS database_

A segmented linear representation of the coastline and a wide range of attributes associated with each segment from the DIVA GIS database -downloaded from http://diva.demis.nl/files/.

# **II.2 Indicators of impacts**

# **(i) Methodology**

Estimates for each indicator were calculated by overlaying the inundation zone with the appropriate exposure surface dataset (land area, GDP, population, urban extent, agriculture extent, and wetland).<sup>10</sup> Exposure surface data were collected from various public sources. Unless otherwise indicated, latitude and longitude are specified in decimal degrees. The horizontal datum used is the World Geodetic System 1984. For area calculation, grids representing cell area in square kilometers were created at different resolutions, using length of a degree of latitude and longitude at cell center.

For the exposure surfaces, two GIS models were built for calculating the exposed value. Since the units for GDP and population are in millions of U.S. dollars and number of people, respectively, the exposure was calculated by multiplying the exposure surface

> 10 The delineated surge zones and coastal zone are at a resolution of 3 arc seconds (approximately 90 m).  The resolution of indicator datasets ranges from 9 arc seconds to 30 arc seconds.  Due to this difference in resolution, a surge zone area may occupy only a portion of a single cell in an indicator dataset.  In this case, the surge zone is allocated only a proportion of the indicator cell value.

8
# **TABLE A21:  Merchant International Group (MIG)**

## **A21: Merchant International Group (MIG)**

http://www.merchantinternational.com Established in 1982, the Merchant International Group Limited (“MIG”) is a strategic research and corporate intelligence company headquartered in London, which provides a range of support services (from identification to evaluation of all manner of risks, weaknesses and threats) to corporates in non-domestic markets.

MIG developed a framework that identifies ten distinctive categories of Grey Area DynamicsTM. Each refers to a range of events, activities and trends that impact upon business. Their impact is of varying severity and may be positive or negative, though typically, Grey Area DynamicsTM take the form of obstacles to progress in nondomestic markets.

In the table below we list the variables included in each of the governance indicators. We use data from 2002 to 2005.

|**Table A21: Grey Area Dynamics(155 d**|**eveloped and devel**|**oping**|**coun**|**tries**|**)**|||
|---|---|---|---|---|---|---|---|
||2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||
|NA|..|..|..|..|..|..|..|
|**Political Stability and Lack of Violence**||||||||
|Extremism|X|X|X|X|..|..|..|
|**Government Effectiveness**||||||||
|Bureaucracy|X|X|X|X|..|..|..|
|**Regulatory Quality**||||||||
|Unfair Trade|X|X|X|X|..|..|..|
|Unfair Competition|X|X|X|X|..|..|..|
|**Rule of Law**||||||||
|Legal Safeguards|X|X|X|X|..|..|..|
|Organized Crime|X|X|X|X|..|..|..|
|**Control of Corruption**||||||||
|Corruption|X|X|X|X|..|..|..|
|_Country coverage_|155|155|155|118|..|..|..|

71
**Table 3.10: Change in Real Wages by Group**

||||||year endin|g the second|quarter|
|---|---|---|---|---|---|---|---|
||year end|ingthe s|econdqu|arter of||of||
||2007|2008|2009|2010|2008|2009|2010|
||Hourl|y wage by|sector (p|esos||||
|**BY ECONOMIC ACTIVITY**||201|0)||inter annua|l percentage|changes|
|**Primary**||||||||
|Agriculture|20.5|20.1|19.6|19.3|-1.9%|-2.6%|-1.5%|
|Mining and electricity|55.1|52.9|50.1|50.5|-4.0%|-5.4%|0.9%|
|**Secondary**||||||||
|Manufacturing|31.0|29.3|29.4|27.3|-5.6%|0.5%|-7.1%|
|Construction|34.5|34.1|33.1|32.1|-1.3%|-2.7%|-3.2%|
|**Tertiary**||||||||
|Trade and commerce|30.5|29.6|28.1|27.1|-3.2%|-5.1%|-3.4%|
|Restaurants and hotels|28.8|29.2|26.4|25.5|1.6%|-9.7%|-3.5%|
|Transport and||||||||
|communications|36.4|34.7|33.5|30.2|-4.4%|-3.6%|-9.9%|
|Financial services|46.7|43.5|42.8|40.9|-7.0%|-1.4%|-4.6%|
|Social services|61.9|61.0|61.0|56.8|-1.4%|-0.1%|-6.9%|
|Other services|31.9|32.5|30.8|29.6|1.9%|-5.3%|-3.7%|
|Government services|45.9|45.2|45.3|42.9|-1.5%|0.2%|-5.1%|
|**Not specified**|27.0|28.2|25.3|27.3|4.4%|-10.2%|8.1%|
|**BY FIRM SIZE**||||||||
|Agriculture firms|20.5|20.1|19.6|19.3|-1.9%|-2.6%|-1.5%|
|Micro firms|33.1|32.4|31.4|30.0|-2.2%|-3.3%|-4.4%|
|Small firms|42.9|42.4|40.1|38.4|-1.2%|-5.4%|-4.2%|
|Medium firms|37.8|36.6|36.2|34.2|-3.1%|-1.1%|-5.3%|
|Large firms|45.7|44.0|44.3|41.1|-3.8%|0.7%|-7.1%|
|Government|45.9|45.2|45.3|42.9|-1.5%|0.2%|-5.1%|
|Other|22.6|23.0|22.4|22.4|1.8%|-3.0%|0.0%|
|Not specified|30.2|29.7|29.5|28.1|-1.7%|-0.7%|-4.7%|
|Self employed|33.2|33.1|32.4|31.1|-0.2%|-2.1%|-4.2%|
|Owner|64.1|60.2|55.0|53.9|-6.1%|-8.6%|-2.0%|

**Source: Own calculations using data from ENOE.**

32
<mark>alternative forms of financial services provided by microfinance institutions and savings associations (SACCOs).</mark> This paper carefully documents the key short-term responses from the banking sector to the interest rate caps. The findings are based primarily on quantitative analysis of loan and deposit panel data from commercial banks for the period January 2015 to September 2017. The quantitative analysis is also supported by detailed qualitative evidence from interviews and consultations with key counterparts on the regulatory side as well as financial sector actors themselves.

## **_Data Description_**

The analysis presented in this paper combines insights from qualitative interviews and quantitative data to understand the impacts of interest rate caps on commercial bank loans and deposits in Kenya.

Detailed qualitative interviews were conducted over several weeks in March and April 2017, with the objective of understanding the perspective of affected institutions. Meetings were held with key financial sector regulators including the Central Bank of Kenya (CBK), National Treasury (NT), Capital Markets Authority (CMA), and SACCOS Society Regulatory Authority (SASRA). In addition, we met with financial institutions, including tier 1, 2, and 3 commercial banks, deposit taking microfinance banks, and SACCOs. Finally, we also consulted with development partners including Financial Sector Deepening Kenya (FSD-K) and DFID.<sup>4</sup> These meetings provided valuable observational insight on the impact of the interest rate caps on the financial sector.

The quantitative analysis presented in this paper focuses on the formal banking sector in Kenya, and is based on two panel datasets: (a) a panel of sector-level data compiled by the Central Bank; and (b) a panel of bank-level data provided by the commercial banks in response to our request submitted through the Central Bank. Both datasets span the period January 2015 to September 2017.<sup>5</sup>

# **III. Assessment Challenges**

There are several important challenges associated with assessing the impact of interest rate caps. First and foremost is the fact that only one year has elapsed since the interest rate caps were enacted and any data analysis will at best capture short-term effects, while various economic and financial adjustments are still in progress. The data we analyze primarily cover the period from January 2015 to September 2017. Moreover, there has not been enough time since the passage of the legislation to influence lasting structural changes in the financial sector and for effects to trickle down into the real economy.

> 4 See Appendix B for the full list of meetings.

> 5 See Appendix C for the list of banks included in the panel dataset.

7
# **References**

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de Freitas, L. C., & Kaneko, S. (2011). Decomposing the decoupling of CO2 emissions and economic growth in Brazil. _Ecological Economics_ , _70_ (8), 1459–1469. https://doi.org/10.1016/J.ECOLECON.2011.02.011

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- FY Fan, Y. L. (2016). Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing. _Transp Res D_ , _42_ , 135–145. https://doi.org/10.1016/j.trd.2015.11.001

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17
Table 2: Labor Market Participation by Gender, Age and Year in Rural Areas

||6-15||16-2|4|25-6|4|
|---|---|---|---|---|---|---|
||1993|1998|1993|1998|1993|1998|
|Women|||||||
|Participation in Last Seven Days (%)|||||||
|Wage Work|2.2|1.4|14.1|14.2|8.7|8.6|
|Agriculture|1.5|0.9|6.7|5.7|4.4|3.0|
|Non-Agricultural activities|0.7|0.5|7.4|8.4|4.4|5.6|
|Within Household Work|69.0|64.3|90.2|90.3|96.1|97.4|
|Agriculture|30.8|24.1|61.8|61.2|69.7|76.8|
|Household Enterprise|4.9|2.8|16.1|15.0|17.4|20.1|
|Household Production|62.6|62.7|86.1|87.5|93.8|95.4|
|Overall (any work)|69.4|64.6|93.3|94.4|96.7|98.0|
|_Market Work_|35.8|27.0|80.8|79.9|83.0|89.5|
|Hours Worked per Week|||||||
|Total hours|21.5|16.7|42.1|38.4|45.6|49.6|
|Wage Work|15.0|17.3|18.4|20.9|18.1|20.4|
|Agriculture|14.1|14.5|14.7|13.8|15.6|14.3|
|Nonagricultural activities|18.0|24.7|23.1|27.6|21.5|26.4|
|Within Household Work|20.9|16.3|39.0|34.4|43.2|46.8|
|Agriculture|17.5|16.2|25.1|24.7|25.6|32.0|
|Household Enterprise|17.6|18.5|20.5|25.6|24.0|26.5|
|Household Production|12.1|9.0|14.9|10.7|17.4|13.5|
|_Market Work_|19.1|17.9|29.9|31.4|30.8|38.3|
|**Men**|||||||
|Participation in Last Seven Days|||||||
|Wage Work|1.9|1.1|20.2|21.0|20.7|21.9|
|Agriculture|1.4|0.7|9.3|7.5|8.9|6.3|
|Non-Agricultural activities|0.6|0.5|10.9|13.6|11.9|15.7|
|Within Household Work|60.8|56.6|79.1|76.9|90.7|90.2|
|Agriculture|29.3|23.6|60.6|53.6|67.4|69.2|
|Household Enterprise|3.5|2.8|13.2|11.5|18.6|20.8|
|Household Production|47.7|50.6|54.8|60.6|73.8|73.8|
|Overall (any work)|61.9|57.2|90.3|87.6|96.1|96.6|
|_Market Work_|33.4|26.1|84.0|76.3|88.6|91.9|
|Hours Worked per Week|||||||
|Total hours|18.4|14.9|36.7|33.0|41.6|43.6|
|Wage Work|18.0|17.0|20.5|23.0|18.8|22.3|
|Agriculture|17.7|15.8|18.7|16.9|17.8|19.2|
|Nonagricultural activities|19.1|17.7|21.8|27.0|19.5|23.6|
|<br>Within Household Work|17.9|14.5|32.3|26.8|36.5|37.3|
|Agriculture|17.6|14.9|26.1|22.5|26.3|28.4|
|Household Enterprise|13.8|12.4|18.7|23.8|20.5|24.7|
|<br>Household Production|9.9|7.7|10.2|7.0|12.3|8.6|
|_Market Work_|18.7|15.8|31.7|30.6|33.2|37.8|

All means are weighted to reflect sample design. Market work includes work for wages, work in agriculture within the household, and work in household enterprise. Because people can paricipate in more than one activity the subcotegories of various participation variables do not necessarily add up to participation in wage work, within household work, and overall(any) work. Hours worked in each category are conditional on participation in that particular category. As a result, they do not add up to total hours worked (and subcategories of wage work and within household work do not add up to hours worked in wage work and within household work).
**<u>Table 8: Estimated precipitation trend (mm/decade) for seven stations in Chile</u>**

|**Station**|**Trend**|**t-value**|**P-value**|**# of obs.**|
|---|---|---|---|---|
|Arica|-0.02|-0.28|0.782|632|
|Antofagasta|0.02|0.75|0.451|585|
|La Serena|0.05|0.10|0.923|618|
|Isla Juan Fernandez|2.26|1.75|0.080|537|
|Valdivia|-5.01|-2.24|0.026|621|
|Puerto Montt|-5.26|-3.18|0.002|601|
|PuntaArenas|0.31|0.58|0.565|595|

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

The regions that have seen significant reductions in precipitation over the last 60 years, are the ones that initially (and still) received the largest amounts of precipitation, and the significant reductions are limited to the season with the heaviest rains (May to August). Valdivia, for example, used to receive about 280 mm of rain per month during the rainy season, but this amount has been going down by about 5 mm per decade over the last half century, or about 10 percent over 50 years.

For the purposes of the simulations in the following section, we will assume a 10 percent reduction in precipitation over the last 50 years for all municipalities located between 35oS and 45oS.

# **5. Simulating the impact of recent climate change**

Since life expectancy was not found to depend significantly on temperature, and since precipitation changes are limited to a small region in the middle of the country with already abundant precipitation, we will limit the simulation analysis to the impact of climate change on average incomes.

To find the impacts of recent climate change we will compare the following two scenarios: 1) Climate Change, which is the factual scenario, and 2) No Climate Change, which is the counterfactual scenario. The Climate Change temperatures are the actual temperatures in each municipality, whereas the No Climate Change temperatures are the actual temperatures minus the temperature changes experienced over the last 50 years, according to the analysis in the previous section. Similarly, Climate Change precipitation are the actual precipitation levels, while No Climate change precipitation is 10% higher in all municipalities located between 35oS and 45oS, as indicated by the analysis in the previous section.

The ratio of Climate Change Income to No Climate Change Income can be written as: After estimating this ratio for each municipality, it is easy to calculate the percentage change in income levels that can be attributed to climate change.

19
## B.1.4.3 Welfare

We set the social discount rate to 5 percent, which has been previously used in the economic analysis of land use in agriculture and forestry (Plantinga 1996, Stavins 1999). We set the scrap value function to zero. We have tried other functional forms and found it has a small eect on allocation of global land use over the rst hundred years, which are the focus of this study.

# C.1 Solution Algorithm

This section presents the algorithm of parametric dynamic programming (DP) with value function iteration for nite horizon problems employed to solve the dynamic stochastic model. Detailed discussion of numerical DP can be found in Cai (2010), Judd (1998) and Rust (2008). In dynamic programming problems, when the value function is continuous, it has to be approximated. We use a nitely parametrized collection of functions to approximate a value function, _V_ ( _x, θ_ ) _≈ V_<sup>ˆ</sup> ( _x, θ_ ; **b** ) , where _x_ is the continuous state vector (in this study, it is the ( _v_ max + 4) dimensional vector, � _L_<sup>_N_</sup> _, L_<sup>_A_</sup> _, L_<sup>_R_</sup> _, L_<sup>_C_</sup> _, X_<sup>_F_�</sup> , and _θ_ = _θ_<sup>_c_</sup> is the discrete state) and **b** is a vector of parameters. The functional form _V_<sup>ˆ</sup> may be a linear combination of polynomials, or it may represent a rational function or neural network representation, or it may be some other parametrization especially designed for the problem. After the functional form is xed, we focus on nding the vector of parameters, **b** , such that _V_<sup>ˆ</sup> ( _x, θ_ ; **b** ) approximately satises the Bellman equation, which can be written in a general form: where _Vt_ ( _x, θ_ ) is the value function at time _t ≤ T_ (the terminal value function _VT_ ( _x, θ_ ) is given), _a_ is the action variable vector (in this study, it includes ∆<sup>_N,A_</sup> _,_ ∆<sup>_N,R_</sup> _,_ ∆<sup>_C,A_</sup> _,_ ∆<sup>_C,H_</sup> _,_ ∆<sup>_C,C_</sup> _,_ ∆<sup>_F,E_</sup> _t ,_ ∆<sup>_F,n_</sup> _t ,_<sup>_−→_</sup> _y_ , etc.), _x_<sup>+</sup> is the next-stage state vector � _L_<sup>_N_</sup> _t_ +1<sup>_, L_</sup> _t_<sup>_A_</sup> +1<sup>_, L_</sup> _t_<sup>_R_</sup> +1<sup>_, L_</sup> _t_<sup>_C_</sup> +1<sup>_, X_</sup> _t_<sup>_F_</sup> +1� , _D_ ( _x, t_ ) is a feasible set of _a_ , _ω_ is a random variable, _δ_ is a discount factor and _Ut_ ( _x, a_ ) is the payo function at time _t_ .

20
involvement in ensuring diary completion, including oral questioning and enumerator assistance, can blur the line between a diary and recall survey, but allow the method to act as a benchmark of reliable consumption (Beegle et al., 2012). Given these challenges and the importance of a highly accurate benchmark, we undertook strict protocols for implementation of the FAFH diary.

# **3. Experimental design and context**

To test different food expenditure data collection methods, we implemented a randomized survey experiment in Vietnam across approximately 2,400 households in urban Hanoi. In collaboration with the General Statistics Office (GSO) of Vietnam, we took the biennial VHLSS as a starting point for design of the experiment and implemented four additional treatments varying the recall period and FAFH collection method. The survey, fielded between August and October 2016, was conducted entirely using computer assisted personal interview (CAPI) methods utilizing the World Bank’s CAPI software Survey Solutions.

## 3.1 Experimental design

Households across 40 enumeration areas (EAs) in urban Hanoi were randomly assigned to one of five treatment groups. The randomization was performed within the EA so that in each EA there were 12 households assigned to each treatment group. The five treatment arms had several variations in terms of the recall period, the respondent answering the FAFH section, the number of times the household is visited, as well as the methods used in inducing accurate recall (see Figure 1). For the purpose of this paper – which focuses on variations in data collection methods to capture FAFH consumption rather than on the recall period - we omit analysis of the data collected in the “status quo” arm, T0, which was unrelated to FAFH.

The first arm, T1, followed the standard VHLSS methodology and asked for an aggregate of all FAFH for only three categories: (1) total FAFH of present household members, (2) total FAFH of temporarily absent household members, and (3) other FAFH. We expect that these household-level aggregated categories will lead to mismeasurement of FAFH and this arm to perform least well compared to the benchmark. Although FAFH was technically collected for three different categories or lines, we shall still refer to T1 as the “one-line” treatment arm since the majority of FAFH consumption is reported on the first line (FAFH of present members).

The next treatment arm, T2, collected FAFH using a daily individual-level diary, and is the benchmark against which all other arms will be compared. Finally, the next two arms, T3 and T4, tested possible alternatives which are less intensive than a diary (T2), but more intensive than collecting from three highly aggregated categories (T1). T3 was collected at the individual-level directly from all adult members of the household. By contrast, T4 (like T1) was collected from a single individual informant for the entire 7
-5-

# Figure 1: _2ANEAND7 SW zA X7qU7Ct 7 A 21NIN &4.7AZ_

<!-- Start of picture text --> 250<br>mitow CAI.^ -f<br>cam, an -<br>lAg suga<br>200 -<br>150 /<br>100<br>1979/80 1980/81 1981/82 1982/83 1983/84 1984/85 1985/86 1986/87 1987/88 1988/89<br><!-- End of picture text -->

- Source: World Bank, Brazil<sup>Agriculture Operations Division,</sup> Latin America and the Caribbean<sup>Region.</sup>
more risk-averse people are more likely to use informal financial services and are less likely to be excluded.<sup>19</sup>

**Table 14.  Who Use Financial Services in Kenya?**

||Formal|Formal, other|Informal|Excluded|
|---|---|---|---|---|
|Own mobile phone|0.166|0.422|0.084|-0.270|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|Female|-0.018|-0.025|0.191|-0.097|
||(0.115)|(0.132)|(0.000)***|(0.000)***|
|Log(age)|0.180|0.144|0.088|-0.159|
||(0.000)***|(0.000)***|(0.001)***|(0.000)***|
|Employed|0.140|0.160|0.118|-0.144|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|Self employed|0.126|0.100|0.210|-0.164|
||(0.000)***|(0.001)***|(0.000)***|(0.000)***|
|Agriculture|0.079|0.122|0.164|-0.156|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|Risk aversion|0.015|0.027|0.057|-0.047|
||(1.26)|(0.092)*|(0.004)***|(0.009)***|
|Rural|-0.081|-0.112|-0.015|0.062|
||(0.000)***|(0.000)***|(0.624)|(0.023)**|
|Log of total expenditures|0.078|0.064|0.074|-0.090|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|Primary education|0.085|0.105|0.118|-0.123|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|Secondary education|0.214|0.278|0.077|-0.141|
||(0.000)***|(0.000)***|(0.034)**|(0.000)***|
|Tertiary education|0.422|0.422|0.017|-0.202|
||(0.000)***|(0.000)***|(0.696)|(0.000)***|
|Numeracy|0.023|0.015|-0.006|-0.006|
||(0.000)***|(0.117)|(0.460)|(0.489)|
|Observations|6326|6323|6328|6328|

Source: Beck (2009). The dependent variable is whether a respondent uses (i) bank, (ii) other formal, (iii) informal services or (iv) is excluded. Marginal effects are reported. Regressions are weighted and stratified.

19 Risk aversion is a dummy variable that takes on value one if the individual responds yes to the following question: You avoid taking risks with your money or resources.

29