rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Exposure to drought among rural households in India and other countries should, at least in principle, be largely diversifiable. This is because rainfall is exogenous to the household and not likely to be strongly correlated with the systematic risk factors, such as aggregate stock returns, that are relevant for a well-diversified representative investor.

With this principle in mind, the goal of rainfall index insurance is to allow households, groups and governments to reduce their exposure to weather risk by purchasing a contract that pays an indemnity during periods of deficient (or excessive) rainfall. Advocates argue that index insurance is transparent, inexpensive to administer, enables quick payouts, and minimizes moral hazard and adverse selection problems associated with other risk-coping mechanisms and insurance programs (see World Bank 2005; Barnett and Mahul 2007; Giné, Townsend, and Vickery 2007).

This paper uses historical rainfall data to estimate the distribution of payouts on a rainfall index insurance product developed by the general insurer ICICI Lombard and offered to rural Indian households since 2003. Our empirical strategy draws on the observation that rainfall in the region we study is close to a stationary process. Correspondingly we can use historical rainfall data to calculate a putative history of insurance payouts for insurance contracts written against the 2006 monsoon.

We conduct several statistical exercises to better understand the properties of estimated insurance payouts. First, we study the probability distribution of indemnities. Does the insurance contract pay off regularly, providing income during periods of moderately deficient rainfall? Or does it operate more like disaster insurance, 2
Figure 1: Real GDP of Japan <!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>year<br>trillion yen (1990 price)<br>1930 1935 1940 1944 1946 1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> Source: Japanese Prime Minister’s Office.

43
# **Table 5. Production Function for Guatemala: Average Years of Schooling Specification, 1951-2002**

||Dependent variable: Percent change of GDP/worker<br> <br> <sup>/</sup>|
|---|---|
||OLS<br>IV<sup>a</sup>|
|Explanatory variables|(1)<br>(2)|
|Constant|-0.077**<br>(-4.74)<br>-0.077**<br>(-3.76)|
|Percent change of capital/worker|0.871**<br>(30.2)<br>0.774**<br>(5.74)|
|Percent change of capital/worker [-1]|0.120**<br>(3.28)<br>0.169*<br>(2.58)|
|log GDP/worker [-1]<sup>b/</sup>|-0.241**<br>-0.269**|
||(-5.87)<br>(-5.28)|
|log capital/worker [-1]|0.107**<br>(3.76)<br>0.099*<br>(2.29)|
|log average years of schooling [-1]|0.084**<br>(5.00)<br>0.090**<br>(4.54)|
|Step dummy 1977|-0.041**<br>(-4.47)<br>-0.039**<br>(-3.38)|
|Impulse dummy 1963|0.057**<br>(4.69)<br>0.056**<br>(4.15)|
|Impulse dummy 1982|-0.077**<br>-0.087**|
||(-4.88)<br>(-4.09)|
|Long-run elasticity of capital|0.444<br>0.366|
|Long-run elasticity of schooling|0.351<br>0.334|
|Adjusted R<sup>2</sup>|0.964<br>0.956|
|F-statistic|170.5<br>40.67|
|Durbin Watson<sup>c/</sup>|2.003<br>2.112|
|S.E. of regression|0.012<br>0.013|
|N|51<br>50|

a/ Lags of the independent variables are used as instruments. b/ Asymptotic critical values of the t-ratio are from Banerjee et al. (1998). c/ A Breusch-Godfrey test finds no evidence for the presence of first, second and third order correlation in the residuals.

t-statistics in parenthesis. ** Significant at 1%, * significant at 5%.

_Source:_ Author’s calculations.

-32-
income countries and mostly uses information on inequality backed out from distributional national accounts rather than from observed consumption vectors, which is less relevant for our purposes.

In the appendix, we expand the scenario analysis by looking at different combinations of inequality changes and energy and carbon intensity, and we further vary other key modelling parameters: the population growth forecasts until 2050 and passthrough rates from GDP growth to consumption growth. Population scenarios are based on the low, medium, and high variant of the UN’s Population Prospects (United Nation 2022). For the passthrough rates from GDP growth to consumption growth, we use the 10<sup>th</sup> and 90<sup>th</sup> percentile of the distribution from Extended Figure 4, Panel A for all countries. In total, we analyze 243 scenarios, which are outlined in Extended Table 4.

# **Extended Table 4. Parameters in different model scenarios**

|**Parameter**||**Interpretation**|**Value**|
|---|---|---|---|
||High|90<sup>th</sup>percentile of changes in<br>inequality (increase)|+13% in Gini in 2050|
|Inequality|Baseline|No change<br>|0%|
||Low|10<sup>th</sup>percentile of changes in<br>inequality (decline)|-17% in Gini in 2050|
||High|Less growth passes through to<br>consumption|0.44%<br>increase<br>in<br>consumption<br>for<br>1%<br>growth in GDP|
|Passthrough rate of GDP<br>growth to consumption|Baseline<br>Low|Country-specific<br>estimate<br>of<br>passthrough rate<br>More growth passes through to<br>consumption|On<br>average,<br>0.7%<br>increase in consumption<br>for 1% growth in GDP<br>0.98%<br>increase<br>in<br>consumption<br>for<br>1%<br>growth in GDP|
||High|90<sup>th</sup><br>percentile<br>of<br>annual<br>improvements<br>in<br>energy<br>efficiency<br> <br> <br>|Deteriorations in energy<br>efficiency of 2.1% per<br>year<br>|
|Energy consumption per<br>GDP|Baseline<br>Low|Country-specific<br>estimate<br>of<br>annual improvements in energy<br>efficiency<br>10<sup>th</sup><br>percentile<br>of<br>annual<br>improvements<br>in<br>energy<br>efficiency|On average, improvement<br>in energy efficiency of<br>0.9% per year<br>Improvement in energy<br>efficiency of 3.2% per<br>year|
||High|90<sup>th</sup><br>percentile<br>of<br>annual<br>improvement in GHG emissions<br>of<br>energy<br>consumption<br>(increase)|Increase<br>in<br>carbon<br>intensity of energy of<br>2.5% per year|
|GHG emissions of energy<br>consumption|Baseline<br>Low|<br>Country-specific<br>estimate<br>of<br>annual improvement in energy<br>intensity of GDP<br>10<sup>th</sup><br>percentile<br>of<br>annual<br>improvement in energy intensity<br>of GDP (decarbonization)|On average, no change in<br>carbon intensity of energy<br>of 0.1% per year<br>Reduction<br>in<br>carbon<br>intensity of energy of<br>2.2% per year|
||High|High variant of UN’s population<br>projections|Country-specific<br>growth<br>rate|
|Population growth|Baseline<br>Low|Medium<br>variant<br>of<br>UN’s<br>population projections<br>Low variant of UN’s population<br>projections|Country-specific<br>growth<br>rate<br>Country-specific<br>growth<br>rate|

30
# **III. List of Variables and Country Samples with Dummy Variables**

# **Table. List of Variables with Symbols**

|SYMBOL|VARIABLE|
|---|---|
|ADR|actual dependency ratio<br>(RETIREES/EMPLOYED)|
|DDO|old age demographic dependency ratio (i.e. working age population (I 5-64)/old people<br>(65 and 65+))|
|DIRECT|direct taxes in % of GDP (direct taxes = corporate taxes + individual taxes)|
|DIRTAX|direct taxes in % of taxes, fees and contributions (DIRECT/TAXES* 100)|
|DUMMY 1|transition dummy|
|DUMMY 2|Latin-American dummy|
|DUMMY 3|European union & Norway dummy|
|EDUGDP|public expenditure on education in % of GDP|
|EMPLOYED|employees in thousands|
|EXPGDP|total public expenditure in % of GDP|
|EXP$|the 1992 US$ total government expenditure per capita (EXPGDP*GDP$WB/100)|
|EXPPP|the 1992 purchasing power parity dollar total government expenditure per capita<br>(EXP$/$PPP)|
|GDPPPWB|GDP per capita in the 1992 purchasing power parity dollars|
|GDP$WB|GDP per capita in current international $|
|GDPHS|Heston-Summers gross domestic product in purchasing power parities per capita|
|GINVEXP|government investmnent in % of total public spending (GINVGDP/EXPGDP * 100)|
|GINVGDP|government investment in % of GDP|
|GLABOUR<br>|government employment in % of total employment employees in public administration and<br>defence, education services, medical and health services, and other in % of total<br>employment<br>|
|LHE|LTHEXP<br>health expenditure in % of total public spending|
|HEALTHGDP|health expenditure in % of GDP<br>(HEALTHEXP*EXPGDP/1 00)|
|INDIRECT|indirect taxes in % of GDP (indirect taxes = domestic taxes on goods and services +<br>international trade taxes +'other')|
|INEQUAL|inequality (percentage share of income or consumption in the highest 10% of income<br>distribution)|
|INFANTM|infant mortality per 1000 of live births|

28
cussed in (Deaton and Dr`eze 2009). (Duh and Spears 2017) show that infant mortality and other indicators of disease related to nutritional absorption (e.g. latrine ownership, diarrhea, and open defecation) are strongly associated with within-district changes in NSS caloric intake between 1988 and 2005 as well as in the cross-section. They find that this pattern holds even when controlling for household and individual variables that capture much of the variation in TEE explored in this paper, concluding that an improved disease environment can account for a substantial decline in caloric intake in India because households “lose” less calories to disease and therefore choose to consume less. Relative to their work, this article tests a different potential mechanism discussed in (Deaton and Dr`eze 2009), using the most detailed available data on physical activity and physical size to directly quantify TEE. Overall, the finding that TEE is fairly flat over the 1983-2012 period, combined with the evidence on modest gains in nutritional status, suggests that most of the decline in NSS caloric intake is a result of measurement error and lower caloric burden of disease rather than large decreases in population TEE.

The paper is organized as follows: Section 2 describes the data and methodology used for estimating total energy expenditure and discusses some important sources of variation across households, Section 3 presents results for changes in total energy expenditure over time and discusses the broader context of changes in caloric intake measured in the NSS, and Section 4 offers concluding comments and suggestions for future research.

# **2 Data and measurement of components of Total Energy Expenditure (TEE)**

This section briefly outlines the methodology and data used to measure TEE from household datasets. The Appendix contains a more detailed description including robustness to alternative assumptions. TEE measures the amount of energy used by the human body during a given period. When TEE equals caloric intake there is no weight gain or loss. A standard method to calculate TEE is the factorial method described in FAO (2001). (Indian Council of Medical Research 2009) uses this method to estimate prescriptive caloric requirements for Indian households, based on the 95th percentile height and weight observed in rural areas and providing recommended daily caloric in4
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Corporation (BTC), was licensed in 2008. The intensified competition from the third mobile operator sustained growth, pushing penetration over 100 percent in 2010.<sup>7</sup>

**Table 12. Benchmarking ICT indicators**

||||Botswana||Botswan|a<br>Upper-middle-<br>income group|Sub-Saharan<br>African region|
|---|---|---|---|---|---|---|---|
||||2000<br>20|05||as of 2009||
|GSM coverage|% population u|nder signal|63|85||99<br>94|56|
|International bandwidth|bits/person||4|22||220<br>1,281|34|
|Internet|users/100 peop|le|1.5|2.7||6.2<br>34.6|6.5|
|Landline|subscribers/10|0 people|8.2|7.2||7.4<br>22.1|1.5|
|Mobile phone|subscribers/10|0 people|12.9|42.0||96.1<br>100.6|33.3|
|||2005||200|9||2010|
|||Botswana|Botswana|Upper-m<br>income g|iddle-<br>roup|Sub-Saharan<br>African region|Botswana|
|Price of monthly mobile ba|sket|10.2|7.5||9.9|11.8|7.2|
|Price of monthly fixed-line|basket|8.0|15.9||11.7|11.6|15.2|
|Price of monthly fixed broa|dband|—|55.5||26.3|100.1|54.7|
|Price of a call to United Sta|tes per minute|0.88|0.25||—|0.8|0.24|
|Price of an inter-Africa call|per minute|—|0.80||—|1.0|0.76|

_Source:_ Adapted from the BTC, Mascom, AICD, and World Bank ICT At-a-Glance. _Note:_ 2010 tariff data converted to $ using 2009 annual average exchange rates. — = Not available.

Botswana has taken most of the steps necessary to create a sustainable ICT sector. Prior to the introduction of mobile competition, the Botswana Telecommunications Authority (BTA) was created as a sector regulator in 1996. In 2006 wide-ranging reforms were introduced including the legalization of Voice over Internet Protocol (VoIP) and a technology-neutral licensing scheme whereby existing operators could provide any telecommunications service. Mascom and Orange launched their own international voice gateways and Internet services while the BTC entered the mobile market. One reform that remained stalled for some time was the privatization of the BTC, but plans are underway for an IPO to sell 49 per cent of its shares of the Botswana Stock Exchange.

# **Challenges**

In contrast to the performance of the mobile sector Botswana’s fixed-line sector is stagnant, and the number of subscriptions has fallen since 2008. Internet penetration is also below average considering the nation’s status as an upper-middle-income economy. Botswana’s fixed-line penetration was some three times less than the upper-middle-income average in 2008, the peak year. Growth has been stagnant, due the popularity of mobile phones as well as tariff rebalancing (raising local call charges and reducing international call tariffs). Although fixed-line competition is legal, in practice there is no strong

> 7 - See International Telecommunication Union (ITU) Mobile Cellular Statistics, available at: http://www.itu.int/ITU <u>D/ict/statistics/.</u>

28
finished primary to percent of male voters who have finished primary from Tribunal Superior Eleitoral. Given that voting is obligatory in Brazil for people aged 18 to 70, this is a good proxy for education gap. Administrative records from the Ministry of Social Development provide the fraction of women in a municipality receiving Bolsa Familia Brazil’s conditional cash transfer program given to impoverished women for keeping their children in school, and the municipal per capita transfer amount.

To capture municipal wealth we include GDP per capita and GDP per capita from agriculture and industry from IBGE. FINBRA, a data base from the national secretary of the treasury, provides municipal spending information. We also include overall per capita municipal spending and per capita municipal spending on civil defense, intelligence, public security. The MUNIC survey also asks about other public safety organizations besides the women’s police stations, so we dummies for if the municipality has a local militia and a Public Safety Council, as all these are likely to affect crime overall. Expenditure on hospital and health spending as well as having a municipal health council can prevent attempted homicides from becoming deaths. In an effort to take attitudes into account we also consider if the municipality has a human rights council and per capita spending on human rights and public communications.. Summary statistics are in Table 1 and we also include simple correlations of the right hand side variables with the outcome variables. We are considering the male homicide rate in addition to the female homicide rate as a placebo test. As men are much less likely to be victims of intra-partner violence, any effects on domestic violence instead of on the general crime rate should be restricted to women.

We use these data on treatment, outcomes and controls to form a municipal level panel spanning 2004 through 2009. The panel begins in 2004, after a law was passed in 2003 which required health professionals to report abuse to the police; this law could affect trends in domestic violence. Additionally, this is also the first year for which some of our key control variables become available. The panel ends in 2009, the last year for MUNIC data. Although DEAMs were only in 8% of municipalities in our sample in 2004, these covered more than 50% of women, growing to cover 62% of the female population by 2009.

In addition to quantitative data used to assess effectiveness of DEAMs in reducing domestic violence, our work also relies on qualitative data to understand the mechanisms through which DEAMs work and possible impediments to their smoother functioning. Qualitative data were 12
# **Kenyan Imports of Agricultural and Other CRTS Products in 2007**

|||Import|value($'00|0)||Im|port shares||||
|---|---|---|---|---|---|---|---|---|---|---|
|Product|COMESA15|EAC5|EU27|ROW|WLD|COMESA15|EAC5|EU27|ROW|WLD|
|**AGRICULTURE**|||||||||||
|Maize|625|14,194|0|1,445|16,265|0.038|0.873|0.000|0.089|1.000|
|Wheat|62|2|3,618|140,505|144,187|0.000|0.000|0.025|0.974|1.000|
|Rice|8,919|2,563|12|58,559|70,054|0.127|0.037|0.000|0.836|1.000|
|Barley|0|0|1|0|1|0.000|0.000|1.000|0.000|1.000|
|Other cereals|0|9,083|3|53|9,139|0.000|0.994|0.000|0.006|1.000|
|Cotton|214|4,322|0|119|4,655|0.046|0.929|0.000|0.026|1.000|
|Sugar|72,342|1,914|4,939|35,055|114,249|0.633|0.017|0.043|0.307|1.000|
|Coffee|41|635|78|1,347|2,101|0.020|0.302|0.037|0.641|1.000|
|Tea|0|86|22|8,088|8,196|0.000|0.011|0.003|0.987|1.000|
|Roots and tubers|0|29|662|205|896|0.000|0.032|0.739|0.228|1.000|
|Oil seeds and pulses|803|16,126|164|5,296|22,388|0.036|0.720|0.007|0.237|1.000|
|Fruits|1,492|2,848|2,444|7,358|14,141|0.105|0.201|0.173|0.520|1.000|
|Vegetables|1,589|19,450|5,546|22,592|49,177|0.032|0.396|0.113|0.459|1.000|
|Cut flowers|0|1,844|7|161|2,012|0.000|0.917|0.003|0.080|1.000|
|Other crops|55|9,461|2,337|4,599|16,452|0.003|0.575|0.142|0.280|1.000|
|Beef|0|0|0|0|0|0.000|0.000|1.000|0.000|1.000|
|Dairy products|693|458|779|3,437|5,367|0.129|0.085|0.145|0.640|1.000|
|Poultry|0|0|0|0|0|0.000|0.000|1.000|0.000|1.000|
|Meats of sheep and goats|0|0|0|8|8|0.000|0.000|0.000|1.000|1.000|
|Other livestock|67|36|246|1,787|2,136|0.031|0.017|0.115|0.836|1.000|
|**OTHER CRTS GOODS**|||||||||||
|Fishing|3,155|640|194|4,326|8,315|0.379|0.077|0.023|0.520|1.000|
|Forestry|1,084|16,979|4,388|9,851|32,301|0.034|0.526|0.136|0.305|1.000|
|Mining|518|1,272|1,774|33,094|36,658|0.014|0.035|0.048|0.903|1.000|
|Meats and dairy|781|458|868|5,143|7,249|0.108|0.063|0.120|0.709|1.000|
|Grain milling|10,092|1,341|4,728|19,656|35,817|0.282|0.037|0.132|0.549|1.000|
|Sugar & bakery confectionary|3,151|1,400|6,280|20,475|31,307|0.101|0.045|0.201|0.654|1.000|
|Textiles and clothing|4,815|18,592|10,903|279,109|313,418|0.015|0.059|0.035|0.891|1.000|
|Leather and footwear|170|117|551|20,191|21,029|0.008|0.006|0.026|0.960|1.000|
|Wood and papers|30,504|7,720|79,746|115,781|233,751|0.130|0.033|0.341|0.495|1.000|

Source: Based on UN COMTRADE Statistics.

85
Figure 6: MEXICO <!-- Start of picture text --> (a)<br>fli t<br>0.45<br>6 0.4<br>5<br>4<br>23 0.25 -<br>2<br>0.15-<br>0<br>0.05-<br>first pxincipal componnt<br>- -aheda pofprincipal compo _L Jnts<br>(c)<br>r r t X<br>30- 0.25<br>20- 0<br>10~~~~~~~~~~~~~~~~~~~~~~.<br>0 Y<br>-0 _. _ . _._._ . .. _. . . ._ . . . . .<br>-40-..--.-*.*- ..... . - ..-.-.<br><!-- End of picture text --> <!-- Start of picture text --> (b)<br>Money& Credit<br>0.45<br>0.4<br>~~~~~~~~~~~~~~~~~~0.35<br>~~~~~~~~~~~~~~~~~0.3-<br>0.25 -<br>~~~~~~~~~~~~~~~~~~~~~~0.2<br>0.15-<br>~~~~~~~~~~~~~~~~~0.1-<br>0.05-<br>(M2/GNDl)tJ - - (priatecrerdiGNiNDi)t<br>(d)<br>(Sry) t<br>0.25<br>0 525<br>...........<br>-<br>adjusted unadjusted<br><!-- End of picture text --> 57
27

**Table 1. Long-run Correlations of Aid, Real Exchange Rates (RER), and Terms of Trade (TOT) in Post-Conflict Countries**

|**Country**|**Corr. (RER, Aid)**|**Corr. (RER, TOT)**|**Corr. (TOT, Aid)**|
|---|---|---|---|
|Argentina|-0.28|**0.74**|-0.30|
|Bangladesh|0.11|**0.90**|0.22|
|Burundi|**-0.80**|**0.33**|**-0.50**|
|Central African Rep.<br>|0.17<br>|0.47<br>|**0.78**<br>|
|Chad|-0.19|0.57|-0.61|
|Congo, Dem. Rep. Of|**-0.42**|0.50|**0.50**|
|Congo, Rep. Of|0.34|0.62|-0.41|
|Egypt|0.18|-0.38|0.15|
|El Salvador|**-0.96**|0.03|-0.23|
|Ethiopia|-0.10|-0.07|-0.23|
|Guatemala|**-0.61**|-0.42|0.21|
|Guinea-Bissau|0.12|-0.59|0.14|
|Indonesia|**0.99**|0.73|-0.08|
|Iran<br>|-0.52|**0.87**|**-0.62**|
|Israel|0.37|0.00|-0.26|
|Jordan|**0.72**|**0.63**|**0.57**|
|Kenya|**-0.59**|**0.61**|-0.34|
|Mali|0.26|0.26|0.36|
|Morocco|**-0.86**|**0.63**|-0.34|
|Mozambique<br>|**-0.55**|0.23|-0.07|
|Namibia|-0.41|-0.20|**-0.71**|
|Nicaragua|-0.26|**0.79**|**-0.53**|
|Nigeria|**-0.66**|**0.74**|**-0.66**|
|Pakistan|0.38|**0.50**|**0.54**|
|Peru|0.01|0.36|**-0.73**|
|Rwanda|**0.50**|**0.71**|0.25|
|Senegal|0.15|-0.19|0.25|
|Sierra Leone|**0.91**|0.35|-0.01|
|South Africa|**-0.82**|**0.69**|**-0.70**|
|Sri Lanka|**-0.60**|**-0.64**|0.10|
|Sudan|**0.67**|**0.79**|**0.64**|
|Syria|0.51|-0.04|0.10|
|Thailand|**0.66**|0.28|0.00|
|Turkey|-0.04|0.14|**-0.72**|
|Uganda|-0.28|0.24|**-0.71**|
|Zimbabwe|**-0.74**|**-0.46**|0.21|
|Average correlation|-0.07|0.30|-0.10|
|Standard deviation|0.54|0.44|0.44|

_Note:_ _Values in bold indicate that the correlation is significant at the 10 percent level._ The table is based on all the conflict countries that have RER misalignment data. See Appendix A for details.
**Table la. Regional Breakdown of Economic Performance**

|||Regional Unwe|ighed Averages||
|---|---|---|---|---|
|Indicator|||||
||OECD<br>1992|Central|Europe|Asia &<br>Latin America<br>1992|
|||1988/89<br>without Slovenia|1992||
|GDP per capita in current<br>$|19489|n.a.|3379|2356 (without<br>Singapore)|
|Government<br>Expenditure in % of<br>GDP|44.33|54.2|50.8|19.91|
|Government<br>Revenue<br>in % of GDP|42.6|50.7|47.6|19.77|
|Inflation Rate|8.14|n.a.|27.73|14.29 (without<br>Argentina)|
|Unemployment Rate|8.50|0.0|11.90|4.67|
|Direct Taxes in % of<br>Expenditure|33.65|24.78|20.52|30.56|
|Indirect Taxes in % of<br>Expenditure|29.07|29.89|31.10|55.49|
|Borrowing and others in<br>% of Expenditure|14.29|20.85|14.91|5.32|

Source: Fakin & de Crombrugghe (1996), p.<sup>6</sup> .

5