rafmacalaba/datause-encoder-data huggingface dataset:
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# **V.** Stock Index Futures

## A. Contract specifications

The underlying instrument. Stock market indexes are time series designed to track the changes in the value of hypothetical portfolios of stocks. Stock indexes differ from one to another with respect to the range of stocks covered, stock weighting, and index computation. Indexes differ in composition because of the need to measure the price movements of the equity markets of different countries and different segments of each of these national equity markets. Though returns on stock indexes of the same country are often highly correlated over time, relative performance can vary sharply over periods such as a month or a quarter.

Index construction. The weight of a stock in an index is the proportion of the portfolio tracked by the index invested in the stock. The stocks in the portfolio can have equal weights or weights that change in some way over time. The most common weighting scheme is market value weighting, used for example in both the S&P 500 and NYSE indexes. Under this scheme the prices of each stock included are weighted by the number of shares outstanding divided by the aggregate number of shares outstanding of all stocks in the index multiplied by its corresponding prices. Finally, the method of averaging also influences the index value. Most market value-weighted indexes represent arithmetic averages. Another averaging technique (used, for example, in the Value Line index) is the use of a geometric mean or the nth root of the product of the individual stock prices, returns or values relatives.

Treatment of dividends. Stock indexes are not usually adjusted for cash dividends. In other words, any cash dividends received on the portfolio are ignore when percentage changes in most indexes are being calculated. This implies that percentage changes in stock indexes do not track total retums on the corresponding portfolio of stocks but, only pnce changes.

Futures contract pgecifications. All futures contracts on stock indexes are settled in cash. Physical delivery of stocks against a futures contract based on an index presents intractable difficulties. First, not every index correspond to a well defined portfolio of stocks (for example, those indexes constructed using geometric means). Moreover, it is not feasible to construct a broad market value weighted portfolio that is both of manageable size to be delivered and contains whole numbers of shares for all companies. To solve these problems stock index futures contracts are settle in cash and the underlying assets are defined to be an amount of cash equal to a fixed multiple of the value of the index.
fuel source).<sup>8</sup> Each data set observes the household’s “primary sampling unit” or PSU, which corresponds to the household’s village, town, or neighborhood as designated in each survey’s sample design. The analysis also merges in data at the district level from the 2011 Indian Census, reported separately for urban and rural areas of each district. These data provide detailed information about a household’s location. For each data set, each household is weighted using the provided household-level sample weights.

# **II Descriptive Statistics and Preliminary Evidence**

To explore the relationship between religion and sanitation, the analysis begins with descriptive statistics that are separated by religion and urban/rural status. Specifically, the data are split by whether the head of household is Hindu or Muslim, as well as by whether the household is in a rural or urban location.<sup>9</sup> Using data from the IHDS, Hindu households are on average 18 percentage points less likely to have a latrine than Muslim households (Table 1). Yet this overall difference masks significant heterogeneity across urban and rural areas. In urban areas, the gap between Hindu and Muslim latrine ownership is 4 percentage points, whereas in rural areas the gap is 18 percentage points.<sup>10</sup> The large overall difference in latrine ownership between Hindu and Muslim households is similar to the rural gap between Hindu and Muslim households, which reflects several features of the data. Latrine ownership is substantially lower in rural areas, and Hindus live disproportionately in rural areas as compared to Muslims.<sup>11</sup> Further, a majority of both

> 8The availability of these variables vary slightly across data sets: the IHDS data do not include information on homeownership, and only the IHDS data include the occupation of the household head and the distance to the closest fuel source.

> 9“Urban” is defined in the 2011 India Census, which is based on total population, population density, and non-agricultural labor activity.

> 10In equilibrium, urban areas and rural areas differ on several dimensions. For example, urban areas have higher wages and higher returns to education than rural areas (Adukia, Asher and Novosad, 2020). Other notable differences between the two group means include the following: family size is slightly smaller among Hindus, and there are fewer Hindus in urban areas that have less than or equal to 5 years of education. The main regression analysis will control for family size and education, along with other characteristics.

> 11Note that the overall difference between Hindus and Muslims is not a weighted average of the difference

6
Table 14: The determinants of Earthquake fatalities and Institutions - ZINB

||14.1|14.2|14.3|14.4|14.5|14.6|14.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDjt-1_)|-0.448***|-0.425***|-0.545**|-0.158|-0.449***|-0.294|-0.288|
|_,_|(0.174)|(0.157)|(0.269)|(0.246)|(0.173)|(0.275)|(0.308)|
|Ln(_AIDj,t-1_)|0.479|0.795***|0.100|0.003|0.344|-0.125|-0.273|
||(0.321)|(0.230)|(0.485)|(0.675)|(0.367)|(0.832)|(0.852)|
|Ln(_GDPj,t-1_)|-1.423***<br>(0.369)|||||||
|_CORR CONTjt_||-1.996***<br>(0.297)||||||
|_GOV EFFjt_|||-1.062<br>(0.665)|||||
|_POL STABjt_||||-1.044***<br>(0.293)||||
|_REG QALjt_|||||-1.305***<br>(0.310)|||
|_RULE LAWjt_||||||-0.284<br>(0.540)||
|_VOICE ACCjt_|||||||-0.014<br>(0.064)|
|_GINIjt_|0.012|-0.003|0.001|-0.012|-0.019|-0.010|-0.010|
||(0.026)|(0.020)|(0.028)|(0.043)|(0.027)|(0.034)|(0.037)|
|Ln(_POP jt)_|<br>0.101|<br>0.384*|<br>-0.193|<br>0.609*|<br>0.230|<br>0.390|<br>0.432|
||(0.297)|(0.207)|(0.513)|(0.348)|(0.271)|(0.341)|(0.345)|
|_DISASTERSjt _|0.127|0.110|0.0966|0.218|0.105|-0.0118|0.0210|
||(0.232)|(0.200)|(0.212)|(0.303)|(0.256)|(0.232)|(0.223)|
|_NATHAZ – RISKj _|<br>0.999|<br>1.075**|<br>0.925|<br>1.921*|<br>1.324**|<br>2.090**|<br>2.263**|
||(0.722)|(0.496)|(1.138)|(0.984)|(0.668)|(0.929)|(0.919)|
|**1**<sup>**st**</sup>**stage Probit**||||||||
|<br>Ln(_AIDj,t-1_)|-0.447|-0.443**|-0.554|-0.459|-0.461**|-0.490|-0.425|
||(0.979)|(0.214)|(0.398)|(0.451)|(0.206)|(0.510)|(0.576)|
|Ln(_GDPj,t-1_)|0.584|0.423|0.925|0.766|0.314|0.851|0.746*|
||(1.811)|(0.397)|(0)|(0)|(0)|(0)|(0.365)|
|Ln(_POP jt)_|<br>-0.150|<br>-0.115|<br>-0.316|<br>-0.199|<br>-0.0789|<br>-0.247|<br>-0.195|
||(1.413)|(0.226)|(0.302)|(0.353)|(0.276)|(0.387)|(0.440)|
|_DISASTERSt_|<br>-10.94|<br>-10.36***|<br>-12.26***|<br>-11.01***|<br>-10.83***|<br>-12.00***|<br>-12.07***|
|_j _|(0)|(0.823)|(1.832)|(1.541)|(1.561)|(1.953)|(1.708)|
|_NATHAZ – RISKj _|1.143|1.146***|1.104**|1.116***|1.319*|1.096***|1.165***|
||(0.955)|(0.407)|(0.437)|(0.398)|(0.674)|(0.364)|(0.392)|
|Obs.|<br>2186|<br>2186|<br>2186|<br>2186|<br>2186|<br>2186|<br>2118|
|Nonzero Obs.|197|197|197|197|197|197|197|
|Log pseudolikelihood|-1194.289|-1186.551|-1198.308|-1195.723|-1194.314|-1200.931|-1201.144|
|Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|VuongTest (z-value)<sup>a</sup>|7.30***|7.33***|7.09***|7.52***|7.28***|7.13***|7.13***|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The 1<sup>st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.<sup>a</sup> The test compares the ZINB with a negative binomial model and selects the one that is closest to the true conditional distribution (Vuong 1989).

37
**Figure 1: Coverage of 2G and 3G Mobile Technology in Ethiopia (2000-2017)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br>Year<br>% 2G Connections % 3G Connections<br>Mobile Technology Coverage (%)<br><!-- End of picture text --> _Source: GSMA, 2020_ The level of development of the ICT sector in Ethiopia is still one of the lowest in the world. Though a significant share of the mobile networks still employs 2G technologies, following the adoption of 3G technologies in 2008/2009, the country has shown significant growth in mobile broadband technology. In 2007, before the launch of 3G in the country, the proportion of individuals using the internet as a percentage of the population was extremely low at less than half of one percent.

After a decade, in 2017, this has risen to more than 18 percent. Comparable figures for SSA were 3.5 percent in 2007 and 25.4 percent in 2017. The introduction of 3G in 2008/2009 has contributed to rapid growth in internet access compared to most countries in SSA, though still at very low levels. In 2007, 100 percent of the country’s mobile connection coverage was 2G, as 3G had yet to be introduced. After a decade, 2G coverage fell to 63 percent, while 3G coverage rose from 0 to 37 percent, narrowing the gap between 2G and 3G coverage (Figure 1).

7
b) Routine cognitive tasks – interpersonal <!-- Start of picture text --> Argentina Uruguay<br>0.5 0.5<br>0.2 0.2<br>-0.1<br>1998 2003 2015 -0.1 1995 2003 2015<br>-0.4<br>-0.4<br>-0.7<br>-0.7<br>after 81 80-76 75-71 after 81 80-76<br>70-66 65-61 60-56 75-71 70-66<br>55-51 65-61 60-56<br><!-- End of picture text --> With respect to the intensity of the non-routine cognitive tasks in the job, both with the analytical and the interpersonal tasks, a clear stable trend can be seen for the cohorts before 1971. For its part, the relative importance of this type of task in occupations chosen by cohorts following this year shows a clearly positive trend. This suggests that older workers have greater difficulty in adopting and performing non-routine cognitive tasks than the young. For its part, the rapid growth in the importance of analytical cognitive tasks between the youngest generations could be attributed to two complementary factors: i) a greater capacity to adapt to change in the combination of tasks that are required, and ii) a composition effect, that is, the entrance of younger generations to the labor market is delayed since they spend more years in education and therefore their weighting in the average increases as they enter.

c) Routine manual tasks <!-- Start of picture text --> Argentina Uruguay<br>0.5<br>0.5<br>0.2<br>0.2<br>-0.1<br>-0.1 1995 2003 2015<br>1998 2003 2015<br>-0.4<br>-0.4<br>-0.7<br>-0.7<br>after 81 80-76 75-71 after 81 80-76<br>70-66 65-61 60-56 75-71 70-66<br>55-51 65-61 60-56<br><!-- End of picture text -->

- 22 -
on the basis of studies on the U.S. economy." The central estimate for economic damage from global warming is set by Cline at I percent of world GDP at benchmark of doubling CO2 concentration. Finally, the function relating damage to warming is assumed to be geometric with an exponent of 1.3. Therefore, non-discounted damages (in percent of world GDP) from global warming in the absence of intervention are given by: where do is the benchmark economic damage for carbon-dioxide-equivalent doubling (set at 1 and calibrated for a climate sensitivity of 2.5 C°), W, the projected temperatre at time t as defined by equation (I1), and y the exponent in the geometrical function (set a 1.3).

Equations (11) and (12) can be used to determine the costs of global warming in the absence of intervention --the accumulation of carbon dioxide leads to an increase in temperature which, in turn, produce damages to the world economy.

In Cline's approach, the benefits from an aggressive abatement policy are fixed at 80 percent of the costs of global wanning in the absence of intervention during the entire period. This fraction, equivalent to the unavoidable dLmages in the long term, overestirnates the benefits from intervention in the beginning of the period since the level of greenhouse gases is higher in 1990 (6.7 GtC) than the ceiling proposed by Cline (4 GtC). A more precise approach is here followed since the benefits are It. The general approach has been to analyze the different economic sectors affected by global warming. Some problems arise for agriculture, with the controversial so-called ferdlization effect, according to which CO% concentration may, up to a certain level, improve photosynthesis. Other major problems concem the monetary value of non-maket damages, such as helth effects, changing amenities, species extinction and social costs of migrtions due to sea-level rise. The indirect damages, due to greenhouse gases and conventional air pollutants should also be included in the estimates (Ayres and Walter, 1991).

12
# **Table A3:  Family Management Practices and Firm Characteristics**

|**Model**|||**OLS with Co**|**untry FE (Cr**|**oss-section)**|||
|---|---|---|---|---|---|---|---|
|**Outcome Variable**|||**Overall**|**Management**|**Score**|||
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|All Family Management x Share of same<br>family ownership (%)|-0.021<br>(0.072)|||||||
|All Family Management x Size (in logs)||-0.0004||||||
|||(0.010)||||||
|All Family Management x Exporter|||0.005|||||
||||(0.021)|||||
|All Family Management x Foreign<br>ownership||||0.024||||
|||||(0.038)||||
|All Family Management x Age (in logs)|||||-0.025*|||
||||||(0.015)|||
|All Family Management x Manufacturing||||||0.016||
|||||||(0.019)||
|All Family Management x Female<br>Manager|||||||0.010|
||||||||(0.023)|
|All management is family Y/N|-0.026|-0.043|-0.046***|-0.046***|0.028|-0.051***|-0.047***|
||(0.071)|(0.044)|(0.015)|(0.013)|(0.046)|(0.016)|(0.014)|
|Share of same family ownership (0 to 1)|0.015|0.014|0.014|0.014|0.013|0.014|0.014|
||(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|Owner, Manager or Board Political<br>Connection Y/N|-0.010|-0.010|-0.010|-0.010|-0.009|-0.010|-0.010|
||(0.020)|(0.020)|(0.020)|(0.020)|(0.020)|(0.020)|(0.020)|
|Female top manager Y/N|0.011|0.012|0.011|0.011|0.011|0.012|0.008|
||(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|(0.013)|
|Firm Formally Registered when Started<br>Operations|0.047**|0.047**|0.047**|0.048**|0.046**|0.046**|0.047**|
||(0.022)|(0.022)|(0.022)|(0.022)|(0.022)|(0.022)|(0.022)|
|Top manager experience in sector (years)|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log of age of firm|-0.014*|-0.014*|-0.014*|-0.014*|-0.006|-0.014*|-0.014*|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Log of size|0.035***|0.035***|0.035***|0.035***|0.035***|0.035***|0.035***|
||(0.004)|(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Direct exports 10% or more of sales Y/N|0.031***|0.031***|0.030**|0.031***|0.031***|0.032***|0.031***|
||(0.010)|(0.010)|(0.012)|(0.010)|(0.010)|(0.009)|(0.010)|
|Foreign ownership Y/N|0.025**|0.025**|0.025**|0.021*|0.026**|0.026**|0.025**|
||(0.011)|(0.011)|(0.011)|(0.012)|(0.011)|(0.011)|(0.011)|

29
Surveys (DHS). Health and nutrition indicators have been measured for different strata of households defined in terms of a proxy indicator of “wealth” constructed by principal components analysis from the DHS (following a methodology introduced by Filmer and Pritchett, 1999).<sup>6</sup> Again the indicators do not include the crude death rate since this cannot be measured reliably with the sample sizes of a typical DHS. The differences in death rates for children indicate that on average the poorest quintile in developing countries (in terms of the proxy wealth indicator) has an infant mortality rate that is 17% higher than average (implying _DRD_ =1.2), though this rises to 35% ( _DDR_ =1.4) if one focuses on low and middle income countries only.

It is not clear how well the DHS “wealth index” reflects actual wealth or the most widely used indicator of living standards, namely aggregate expenditure on consumption (including imputed values for consumption in kind). So it is of interest to also see what can be learnt from surveys for which more familiar consumption or income measures are available. Using nine such surveys for developing countries, Wagstaff (2000) provides estimates of the infant and under-5 mortality rates grouped into quintiles according to household income or expenditure per person. Using the poorest quintile, the implied values of _DRD_ are in the range 1.1 to 1.8.<sup>7</sup> What about adult deaths? While there is currently nothing like the NLMS available for developing countries, there are some useful clues to likely differentials in overall (child + adult) death rates.<sup>8</sup> The demographic surveillance system implemented in Matlab thana in Bangladesh by the International Center for Diarrhoeal Disease Research has been an important source of data

> 6 Details on the methodology can be found at <u>http://devdata.worldbank.org/hnpstats/pvd.asp.  The</u> estimates for various demographic and health variables are also available at that site, and (for a broader set of variables) at <u>http://www1.worldbank.org/prem/poverty/health/</u> .

> 7 Wagstaff (2003) also estimates child mortality and nutrition indicators at the $1 a day line for 32 developing countries.  However, his methodology does not allow one to calculate the _DRD_ .

> 8 I know of one large sample survey (in the field at the time of writing) that did collect recall data on mortality for both children and adults, namely the Malawi Integrated Household Survey.

10
synthetic panels is mostly descriptive, with little explicit attention to underlying causal mechanisms. More research is thus needed on the application of these synthetic panels in such contexts. Put differently, it is useful to know to which extent synthetic panels can substitute for actual panel survey data.

# **VII. Further Reflections on Related Issues**

We are more often than not faced with contexts where we either have no consumption data, or the available consumption data have quality. Indeed, Serajuddin _et al._ (2015) find that over the period 2002- 2011, almost one-fifth (i.e., 28) of the 155 countries have only one poverty data point in the WDI database, and as many as 29 countries do not have any poverty data point in the same period. Another recent survey by Beegle _et al._ (2016) indicates that slightly more than half (i.e., 27) of the 48 countries in Sub-Saharan Africa had two or more comparable household surveys for the period between 1990 and 2012. Clearly, poverty imputation is useful, and may likely be the only choice in these cases. But what about other contexts where we have a choice over poverty imputation and implementing a full-scale household consumption survey?

Dang _et al._ (2017a) suggest that even in these contexts, there are still a couple of advantages to poverty imputation methods, particularly in the following scenarios

- i) In the immediate term (when micro survey data are not fully available for all countries) ii) Survey costs and/or survey implementation pose a challenge

- iii) Back-casting consumption from a more recent survey for better comparison with older surveys.

It can be useful to offer some additional commentary on these cases. Cases (i) and (ii) are closely related and are perhaps the main driving factor behind poverty imputation, since very few, if any, developing countries can afford the financial and logistical challenges of fielding a household 23
on participation in village assemblies but negatively impacts the likelihood of joining collective action activities, such as construction and maintenance of community infrastructure.

Our propensity score matching estimeates indicate that bridging trust ( _i.e.,_ trust toward strangers) increased as a result of the project. In project areas, individuals have structured opportunities to interact with outsiders ( _i.e.,_ the local project team) that might change their perceptions of others and thus lead them to be more trusting. Similarly, as meetings are open to everyone and tend to be well-attended, individuals can interact with other members of their community who they did not know previously. This is consistent with findings from social psychologisits. For example, Glanville and Paxton (2003) argue that ”encounters with persons who do not share one’s sociodemographic characteristics could be particularly important in gauging how much to trust people in general.”

# **6 Conclusion**

In this paper, we build on and expand existing research on the impacts of community driven development approaches. First, using a unique household panel dataset collected before the project started and after one cycle of subproject implementation in both treatment and matched control communities, we assess whether we can attribute to the project changes in social capital indicators. Second, we refine previous research by having data on a large number of social capital measures. As a result, we are able to provide a more accurate picture of how CDD projects operate within Philippines villages.

Overall, our results indicate the CDD operation led to changes in village-level social and institutional dynamics. It increased participation in village assemblies, the frequency with which local officials meet with residents and bridging ( _i.e._ , generalized) trust. Furthermore, 22