text:
\section{Introduction}
Are severe health shocks more persistent than milder shocks? If so, what are their effects on individuals' economic outcomes?
To answer these questions, we introduce a complex measure of health and estimate its persistency using a non-parametric model with nonlinear dynamics.
We incorporate this health measure into a life-cycle model of consumption, savings, and labor supply decisions to simulate the effects of various health shocks.

Several articles have studied the effects of health on life-cycle economic outcomes, helping our understanding of the complex interactions between health and economics. However, most of these studies posit a binary or 3-valued health measure
evolving according to a first-order Markov process. Our analysis focuses on assessing the effects of especially severe and persistent health shocks, requiring a more detailed measure of health.%
\footnote{See, for example, \cite{french2005effects},
\cite{de2010elderly}
\cite{capatina2015life}, \cite{low2015disability}, \cite{ameriks2020}, \cite{blundell2021impact}. An exception is \cite{de2017lifetime}, modeling health with a second-order Markov process with age-dependent parameters, showing that health follows complex patterns of duration-dependence. See also the surveys \cite{de2016savings} and \cite{french2017health}.}

We derive a continuous measure of health from several objective health indicators using a latent variable model in the spirit of \cite{Bound1991}.\footnote{See also \cite{dalbianco2022}. \cite{hosseini2021evolution} develop a \textit{frailty index as the cumulative sum of all adverse health indicators} to compare its dynamics with that of a self-reported health indicator, but do not incorporate it into a nonlinear process.}
We assume that this health measure evolves according to a complex, nonlinear dynamic process with a persistent and a transitory component. Drawing from \cite{ABB2017}, we assume the latter component evolves according to a non-parametric process with nonlinear persistence and age dependence.
The persistence patterns resulting from this flexible approach
could not be accounted for by a ``canonical'' first-order Markov process.

We incorporate this health measure into a standard life-cycle model where agents receive utility from consumption, leisure, and bequest of assets; they choose, every period, labor market participation, hours worked, and savings facing uncertainty over health, wages, and life expectancy.
Health affects wages and life expectancy. Following the literature, health affects the time constaint, changing the tradeoff between consumption and leisure. The model also includes a fixed cost of work to fit the evidence on labor market participation and hours worked.

We estimate this model with a Simulated Method of Moments procedure using data from the English Longitudinal Study of the Ageing (ELSA) panel of English households aged 50 and above. Focusing on English data allows us to simplify our analysis by excluding medical expenditure choices in the model (health care in the U.K. is publicly provided). We perform the estimation in two steps. First, we estimate the parameters of the health, wage, and life expectancy processes, which we assume are exogenous to the life-cycle model. Next, we estimate the model parameters (the coefficient of risk aversion, the preferences for bequests, the costs of bad health and of working), using the processes for wages, health, and life expectancy estimated in the first step. To highlight the importance of using a nonlinear process, we also estimate a model specification using a ``canonical'' health process whose persistent component evolves according to an AR(1) model.

The estimates of the nonlinear health process reveal that health indeed follows a complex, nonlinear dynamics. In particular, negative shocks are worse and much more persistent than good shocks when starting in bad health. When individuals start in good health instead, good shocks are only slightly more persistent. These asymmetries cannot be replicated with the canonical process by construction. They are also difficult to ascertain when health is a binary variable, or when it assumes few discrete values because they depend on the arbitrary health cutoffs used implicitly to discretize the health measure.

The moments we match to estimate the model are the age profiles of accumulated assets, of labor force participation by health quantiles, and of hours worked. Our estimates of the life-cycle model match these moments very well both when using the nonlinear model of health dynamics and when using the canonical model.

Using the estimated life-cycle model, we simulate counterfactual health shocks to understand the role and distributional consequences of bad health and its persistence. Specifically, we simulate individuals who start at the age of 51 with different levels of the persistent component of the health process (the 10th, 50th, and 90th percentile of its distribution). We subject the persistent component of health to a good, an intermediate, and a bad shock at the age of 52 (respectively, shocks that bring the persistent component of health to the 10th, 50th, and 90th percentile of its distribution).\footnote{\cite{ABB2017} proposes a similar exercise when studying income dynamics. Health is reported only every two years in ELSA data, therefore we cannot impose the shock at the age of 50, the beginning of the age period we consider in our model.}

We find that the health asymmetries following different shocks in the nonlinear model produce considerable economic differences. Bad health shocks are worse and have more lasting effects when people start poor and in bad health than otherwise.
For example, we find that for people starting at age 51 with \pounds 10,000 in assets and at the 10th percentile of the persistent component of health, a bad shock induces an accumulation for the survivors at age 85 of \pounds 35,000 less than after a median shock. Starting from the median of the persistent component of health, a shock of similar magnitude would induce an accumulation of only \pounds 14,000 less than after a median shock. Starting from \pounds 80,000 in assets, the same difference reduces to no more than \pounds 6,000 and depends little on the initial level of health. We also find that the canonical model does not predict these large differences between shocks for poor people.

Using the estimated value function, we compute that the willingness to pay to avoid a bad shock is about \pounds 34,000 for people starting with \pounds 10,000 in assets at the age of 51, and does not depend much on the level of initial wealth: relative to their wealth, a bad shock is much costlier for the poor.

We find that bad shocks generate higher dispersion in accumulated assets among people that start with poor wealth and health relative to people subject to better shocks. A decomposition exercise where we turn off the impact of health on the separate components of the model affected by health (wages, time endowment, and mortality) reveals that the effects of the three components are not additive (nonlinearities are important) and that the adverse effects of bad health are primarily due to its time cost.

Our results highlight the importance of accounting for a complex health measure, particularly when focusing on outcome inequality. We contribute to the literature first by proposing a continuous measure of health and showing that its evolution follows a complex, nonlinear process. Second, we show that using this measure of health, adverse shocks are costlier, especially for poorer individuals, and generate higher outcome dispersion.

The rest of the paper proceeds as follows. The next section describes our data. Section \ref{sec:health} describes the construction of our health measure and the estimation of the health process. Section \ref{sec:model} describes the life-cycle model and Section \ref{sec:estimation} its estimation. In Section \ref{sec:results} we simulate the effect of different health shocks on the level and the distribution of outcomes.

\section{Data}
We use data from the first seven waves (2002-2012) of the English Longitudinal Study of Ageing (ELSA), a biennial longitudinal survey, representative of the English population aged 50 and above. ELSA contains detailed income and wealth information, and several health-related indicators covering the physical, mental, and cognitive health status of individuals.

We focus on male respondents. The final sample includes about 32,200 observations from 8,200 individuals.
Table \ref{tab:descriptives} reports descriptive statistics conditional on age: labor supply participation decreases sharply with age but a small fraction of respondents is still active after age 70. Hours worked and wages decrease substantially with age, especially after the state pension age (65 for men in the period we consider). Total wealth increases with age as previously reported in the literature (see for example \citet{blundell2016dynamic}).

\begin{table}[!t]
    \centering
    \caption{Descriptive statistics by age}
    \label{tab:descriptives}
 \begin{tabular}{
                  >{\raggedright}m{0.3\linewidth}
                  >{\centering}m{0.1\linewidth}
                  >{\centering}m{0.1\linewidth}
                  >{\centering \arraybackslash}m{0.12\linewidth}
 } \toprule
          & 50-59   & 60-69  & 70-90   \\ \midrule
         \% working  & 79.5 & 40.7 & 6.8 \\
         annual hours worked & 1926 & 1597 & 950 \\
         wages (\pounds) & 16775 & 12057 & 3576 \\
         wealth (1000 \pounds) & 128 & 169 & 193 \\
    \bottomrule
 \end{tabular}

 \caption*{\footnotesize\normalfont \emph{Note:} ELSA data, waves 1-7. Annual hours worked are conditional on  working, and are computed by multiplying average weekly hours by 46 working weeks. Annual earnings are similarly obtained from weekly earnings assuming continuous employment during the year. Wealth refers to total wealth, including housing but excluding pension wealth and is corrected for cohort effects (reference cohort 1946-1955 -- see details in Appendix \ref{sec:app-wealth}).}
\end{table}
\section{Health measurement and dynamics}\label{sec:health}

We construct a continuous index of health using a latent variable model.\footnote{See \cite{Boundetal1999}, \cite{Bound1991}, \cite{Boundetal2010}, \cite{Butleretal2010}}
True health $\psi_{it}$ is a function of a set of objective health indicators $Z_{it}$ and an error term $\xi$:
\begin{align}
\psi_{it} = & Z'_{it}\alpha + \xi_{it} \nonumber
\end{align}
We observe self-reported health, $h^b_{it}$, a binary indicator equal to 1 if the individual declares to be in good, very good or excellent health, and 0 otherwise. We assume that $h^b_{it}$ is determined by the latent variable $h^*_{it}$, equal to the sum of $\psi_{it}$ and a reporting error $\mu_{it}$:
\begin{align}
h^*_{it} & = \psi_{it} + \mu_{it} \nonumber \\
				 & = Z'_{it}\alpha + \left(\xi_{it} + \mu_{it}\right) \nonumber \\
			   & = Z'_{it}\alpha + u_{it} \label{eq:Hhat1}
\end{align}
The individual reports $h^b_{it}=1$ if $h^*_{it}>0$. We also assume that $\psi_{it}$ and $\mu_{it}$ are uncorrelated.
The composite error term $u_{it}$ reflects differences in reporting behavior across individuals. The health index is equal to the predicted value $h_{it}=\hat{h}^*_{it}$, according to (\ref{eq:Hhat1}). This procedure is analogous in spirit to using ``objective'' health information to instrument for the endogenous and error-ridden self-reported health measure (\citet{Boundetal1999}).

$Z$ includes eyesight, hearing, mobility indicators, ADL (limitations with activities of daily living), IADL (limitations with instrumental activities of daily living), depression, heart diseases, other diseases (chronic lung disease, asthma, arthritis, \ldots), eye problems, incontinence, BMI and grip strength (an objective measure of muscular strength).

\begin{figure}[t]
\centering
\caption{Health distribution: data versus model simulations}
\label{fig-healthdistfit}
\includegraphics[width=0.9\linewidth]{images-new/healthdensities.pdf}

\caption*{\footnotesize\normalfont \textit{Note:} Kernel density estimates of the distribution of health index (Data), and simulated data from the nonlinear and canonical models.}
\end{figure}

The solid green lines in Figure \ref{fig-healthdistfit} illustrate that the distribution of the health index $h$ is left-skewed.\footnote{Its shape is consistent with the one obtained by \citet{hosseini2021evolution} using as a health indicator the sum of adverse health indicators.}
Figure \ref{fig-1nonlinear} illustrates the second and higher moments of the shock distribution conditional on previous health deciles and age.\footnote{Our health data is biennial, therefore previous health is 2 years earlier in these computations.} The standard deviation (left panel) decreases as the previous level of health increases (from 0.8 to 0.3) and it increases with age, in particular, if previous health is above the median. Health shocks are negatively skewed (central panel), and the skewness decreases with the health level and is higher for older individuals. Finally, kurtosis (right panel) increases with the level of health and is higher for younger individuals. This evidence suggests that health is a highly nonlinear process with some age dependence and justifies the adoption of a flexible specification of the health process.
\begin{figure}
\centering
\caption{Moments of health shocks by age and previous health deciles}
\includegraphics[width=1\columnwidth]{images-new/healthmoments.pdf}
\label{fig-1nonlinear}
\end{figure}

\begin{figure}[!t]
\centering
\caption{Health's nonlinear persistence}\label{fig:persdata1}%

\begin{subfigure}{\linewidth}
\centering
    \includegraphics[width=.9\columnwidth]{images-new/contour_all.pdf}
    \caption{Average persistence, nonlinear model vs data}
    \label{fig:3dpersistence}
\end{subfigure}
\bigskip

\begin{subfigure}{\linewidth}
\centering
    \includegraphics[width=.66\columnwidth]{images-new/contour_sections.pdf}
    \caption{Average persistence by initial decile of health} \label{fig:slicepct}
\end{subfigure}

\caption*{\footnotesize\normalfont Estimates of the average derivative of the quantile function (\ref{persistencef}), computed directly from data, and from simulations of the nonlinear model estimates (adding the predictions of the persistent and transitory component). Panels \ref{fig:slicepct} illustrate sections of figures in panels \ref{fig:3dpersistence} taken at the 1th and 10th deciles of $\tau_{init}$.}
\end{figure}

Following \cite{ABB2017}, we define the health process as the sum of a persistent and a transitory component:\footnote{Because our health data is biennial, for ease of notation in this section one time period is to be interpreted as two years. See Appendix \ref{sec:app-health} for a description of how we reconcile the biennial health process to the annual time span in the life-cycle model.}
\begin{align}
h_{it}=\eta_{it}+\epsilon_{it},\quad i=1,\ldots,N,\quad t=1,\ldots,T
\label{eq:health}
\end{align}
where $\eta_{it}$ is the persistent component which follows a first-order Markov process, and $\epsilon_{it}$ is a transitory health shock with mean zero, independent of $t$ and $\eta$. Note that from here on $h_{it}$ denotes health residuals. We construct $h_{it}$ as residuals from regressing the health index on a set of demographics, which includes a third order polynomial in age, year of birth, education and an indicator for having a partner. We include these controls to capture individual heterogeneity.
Define the $\tau$-th quantile of $\eta_{it}$ given $\eta_{i,t-1}$ as
\begin{equation} \label{quantilefunc}
\eta_{it}=Q_{t}(\eta_{i,t-1},\tau) \text{ for } \tau \in (0,1).
\end{equation}
The $\eta's$ evolve over time non-parametrically allowing for nonlinear persistence. A special case of (\ref{quantilefunc}) is $\eta_{it}=\rho \eta_{i,t-1} + \nu_{it}$, an AR(1) process.\footnote{see \cite{blundell2021impact}, \cite{hosseini2021evolution} and \cite{dalbianco2022}.}  We will refer to this case as the ``canonical'' model, whereas we call the more general case the ``nonlinear'' model.

A measure of persistence of $\eta$ when hit by a shock
$u_{it}$ of rank $q$ can be defined as:
\begin{equation}
\rho_{t}(\eta_{i,t-1},\tau) \equiv \frac{
    \partial Q_{t}(\eta_{i,t-1},\tau)}
    {\partial\eta}.  \label{persistencef}
\end{equation}
In the canonical AR(1) model, $\rho$ is constant, equal to the auto-regression parameter.

We use the quantile-based panel data method developed by \cite{ABB2017} to estimate the nonlinear process.
Figure \ref{fig:persdata1} reports the average value of $\rho_t$ as a function of current and previous health deciles, comparing values computed directly from the data with simulations from the model estimates. It shows that health persistence is high when both the current health shock and the previous health are low. Health persistence is instead low when the current health shock is high and previous health is low. In particular, in the left panel of Figure \ref{fig:slicepct} we notice that  for individuals with low health, bad shocks are much more persistent than high shocks. Instead, for individuals starting with high health (right panel), persistence is slightly increasing.
The figure also shows that the nonlinearity of the health persistence is well reproduced by the model.

As a term of comparison, we estimate the canonical model.
Figure \ref{fig-healthdistfit} shows that while the nonlinear model reproduces the health index distribution over time quite well, unlike the canonical model.

Figure \ref{fig-healthpercentiles} illustrates the fit of the two models with respect to the health index at different percentiles of the health distribution over the life cycle. The canonical model understates health for younger individuals and overstates it for older individuals. The  nonlinear model performs significantly better over the entire life cycle and at different quantiles of the health distribution.

\begin{figure}[t]
    \centering
    \caption{Health by percentile}
    \label{fig-healthpercentiles}
    \includegraphics[width=.9\linewidth]{images-new/healthByhealth_fit_both.pdf}
    \caption*{\footnotesize \normalfont Note: Health at different quantiles of the health distribution indicated in the legend. The thin solid lines represent values of the data index computed according to (\ref{eq:Hhat1}); the other lines represent averages from 15,000 simulated histories}
\end{figure}%

\section{The life-cycle model}\label{sec:model}

Because of data limitations, we model the behavior of males who enter the model at age 50 ($t=1$), may work until 70 but start receiving pension income at 65 (the state pension age in the UK in the period covered by our data).
In each period $t$, the individual receives utility ($U_t$) from consumption ($c_t$) and leisure ($l_t$). When he dies, he values bequest according to a bequest function, $b(a_t)$, with $a_t$ denoting assets at time $t$.

Subject to the time and budget constraints outlined below, the household head maximizes present discounted utility. Current utility depends on consumption and leisure according to the CRRA specification:
\begin{equation}
U(c_t,l_t)=\frac{1}{1-\nu}(c_t^\gamma l_t^{1-\gamma})^{1-\nu}
\label{eq:utility}
\end{equation}
where $\nu$ is the relative risk aversion parameter and $\gamma$ the consumption weight.

Upon death, the agent also receives utility  from bequests $a$ according to:
\begin{equation}
b(a)=\phi_B \frac{(a + K)^{(1-\nu)\gamma}}{1-\nu}.
\label{eq:bequest}
\end{equation}
Parameter $\phi_B$ measures the intensity of bequest motives, whereas $K$ regulates the curvature of the bequest function and allows for a finite utility of zero bequests.

In each period, the agent chooses savings $a_t$, and, only up to the age of 70, labor supply $s_t$ measured in hours worked. With a time endowment  set to $L$, the agent faces a time cost of bad health which we model as a flexible function $\phi_h(\cdot)$ of health (see Appendix \ref{app:estimates} for details). The time constraint is:
\begin{equation} \label{timeconstr}
l_t=L-s_t -\phi_{h}(h_t)
\end{equation}
Health is included in the model by discretizing the transitory and persistent components, and the transition matrices of the persistent component at each time $t$ into $N$ dimensional grids.\footnote{\cite{de2020nonlinear} use this procedure to incorporate the estimates of a nonlinear model of \textit{earnings} dynamics into a life-cycle model. Additional details are in Appendix \ref{sec:app-health}.}  Health uncertainty affects utility through leisure, the wage offer (see below), and the survival probability.

The earnings process has a deterministic component, $\omega_e(h_t,t)$, which depends on health and age. The composite error term $\psi_t$ is the sum of a persistent ($\vartheta_t$) and a transitory ($\upsilon_t$) component. Persistence in wages is captured by an AR(1) process $\vartheta_t$.
\begin{align}
\log e_t =\enspace & \omega_e(h,age_t)+\psi_t \label{eq:wage} \\
                   &\psi_t=\vartheta_t+\upsilon_t \nonumber \\
                   &\vartheta_t = \rho_e \vartheta_{t-1}+\nu^e_t, \quad \nu^e_t \sim N(0,\sigma^2_{\nu^e}). \nonumber
\end{align}
We assume that at time $t-1$ the individual knows $\vartheta_{t-1}$, but he only knows the distribution of the innovations $\nu^e_t$. We further assume that $\upsilon_t$ captures measurement error only.

The budget constraint is therefore
\begin{equation} \label{budgconstr}
    a_{t+1} =  a_t + s_t \cdot e_t(1-c_p\mathbbm{1}(t<15)) + r\cdot a_t- tax_t + tr_t - c_t - \phi_w(\mathbbm{1}(s_t>0,t))
\end{equation}
Where hours worked $s_t$ are constrained to be zero after the age of 70, $tax$ are taxes, $r$ is the exogenous interest rate on accumulated assets and $tr_t$ are transfers received in such a way that consumption $c_t$ is greater or equal to the consumption floor $\underbar{c}$, plus, starting from age 65, income from pension wealth $p_t$, annuitized at rate $r_p$. Finally, $\phi_w(\cdot)$ denotes a monetary cost of working.\footnote{We model the fixed cost of work as a monetary cost and not as a time cost (see \cite{french2005effects}), to improve the fit of the hours worked age profile.}
Each agent is endowed with initial pension wealth $p$, to which he contributes a fraction $c_p$ of his annual earnings.

The probability of surviving to period $t+1$ conditional on being alive in period $t$ is a function of age and health in period $t$, $\pi^{t+1}=\pi(h_t,t)$. We set the terminal age $T=85$. Time is discounted at factor $\beta$. With these assumptions, preferences can be expressed using a recursive formulation with state-space be $X_t=\left\{a_t, e_t, h_t,p_t\right\}$, which we use to compute the numeric solution to the optimization problem. The expression for the Bellman equation is in Appendix \ref{sec:app-bellman}.

\section{Estimation}\label{sec:estimation}

We estimate the life-cycle model with both the nonlinear health dynamics and, for comparison, the canonical model dynamics.
We adopt a Simulated Method of Moments, matching the age profiles of average assets, hours worked, and participation by quantile of the health distribution (using thresholds at percentiles 20, 30, and 50). The estimated parameters are in Appendix Table \ref{tab:estimates}.

\begin{figure}[t]
    \centering
    \caption{Estimation fit} \label{fig:fit}

                \includegraphics[width=\linewidth]{images-new/employment_by_health_fit_all.pdf}

    \caption*{\footnotesize\normalfont \textit{Note:} Age profiles of average assets, employment rate, and average hours worked conditional on being employed at the estimated parameters. Simulations of 15,000 histories, nonlinear and canonical models. The thin solid lines represent the age profiles in the data.}
\end{figure}

Both the nonlinear and the canonical models match the life-cycle profiles of assets, participation, and hours worked well (see figures \ref{fig:fit}). The fit of the two models is very similar, despite the models reproducing the health patterns differently, as shown in Section \ref{sec:health}. In particular, the model fits participation by health for people in low health very well. Our parsimonious specification, a linear function of the cost of work by age, cannot fully account for the steep decline in participation between ages 60 and 65. In simulations using different parameters, we found that parameters matching a faster decline for people in good health would generate very low employment for people in bad health. A psychic benefit from retiring starting at age 60 (justified by legislation allowing the withdrawal of private pension funds) may help improve matching these moments. Since the model performs fairly well overall, we chose not to add ad-hoc parameters to this purpose.

\section{The effects of health shocks}\label{sec:results}
To assess the economic effect of a health shock and evaluate the impact of the nonlinear health dynamics, we simulate histories of individuals starting with the persistent component of the health process at the 10th, 50th, and 90th percentile of the health distribution at age 51 ($\tau_{init}={.1,.5,.9}$), and we expose them to three different shocks at age 52: a bad, medium, and good shock that bring them, respectively to the 10th 50th, and 90th percentiles of the health distribution conditional on $\tau_{init}$. The resulting health dynamics of the good and bad shock, relative to the medium shock, are shown in Figure \ref{fig:health-count}.

\begin{figure}[t]
    \centering
    \caption{Health after different shocks} \label{fig:health-count}
    \includegraphics[width=\linewidth]{images-new/health_count.pdf}

    \caption*{\footnotesize\normalfont \textit{Note:} The figures illustrate the age profiles of the difference in health between individuals subject to a permanent component of health shock $\tau_{shock}$ ($\tau_{shock}=0.9$ green lines and $\tau_{shock}=0.1$ orange lines) and individuals subject to  $\tau_{shock}=0.5$, starting from different initial levels of the permanent component ($\tau_{init}$). Averages over 5,000 simulated histories.}.
\end{figure}

By construction, the canonical model results in symmetric patterns of health after the good and bad shock, identical in magnitude with respect to $\tau_{init}$. The discrepancies from this theoretical prediction in the right panel of Figure \ref{fig:health-count} (the three lines of each shock level should overlap) are due to approximation errors from the discretization of the health variable.

The patterns resulting from the nonlinear health process, instead, are asymmetric and depend substantially on the initial value of the persistent component of the health process. In particular, the left panel of Figure \ref{fig:health-count} shows that a bad shock is worse and more persistent, relative to a median shock,  when starting in bad health than when starting with medium or high health. Good shocks lead to higher levels of health when starting in bad health (relative to a median shock), but their magnitude is quantitatively smaller, in absolute value, than bad shocks.

\begin{figure}
    \centering
    \caption{Assets after different health shocks}
    \label{fig:asset-shock}
        \includegraphics[width=\linewidth]{images-new/save_count.pdf}

    \caption*{\footnotesize\normalfont \textit{Note:} The figures illustrate the age profiles of the difference in assets between individuals subject to a permanent component of health shock $\tau_{shock}$ ($\tau_{shock}=0.9$ green lines and $\tau_{shock}=0.1$ orange lines) and individuals subject to  $\tau_{shock}=0.5$, starting from different initial levels of the permanent component ($\tau_{init}$). Averages over 5,000 simulated histories of individuals starting at age 51 with \pounds 10,000 in assets. }.
\end{figure}

\subsection{The nonlinear and canonical models produce different economic effects} \label{sec:effects}
These asymmetries have economic effects, as shown in Figure \ref{fig:asset-shock}. When people start relatively poor, with \pounds 10,000 in assets at age 50, a bad shock induces an accumulation at the age of 85 of nearly \pounds 33,000 less than after a median shock.
The difference in accumulation after a bad shock relative to a median shock is 43\% this amount when starting at higher levels of health:  \pounds 14,000 when $\tau_{init}=.5$ and less than \pounds 9,000 when $\tau_{init}=.9$ The gains after a good shock are smaller in magnitude, amounting at about \pounds 14,000, \pounds 3,000, and \pounds 10,000 respectively starting from $\tau_{init}=.1, .5$, and $.9$.

The right panel of Figure \ref{fig:asset-shock} illustrates that the gains and losses from good and bad shocks are smaller and more symmetric in the canonical model. The gains at age 85 range from \pounds 8,000 to \pounds20,000 and the losses from \pounds 15,000 to \pounds 24,000. Even though the effect of shocks on health is theoretically identical across values of initial health, as illustrated in Figure \ref{fig:health-count}, the effects of these shocks on asset accumulation are not the same because of the nonlinearities in the life-cycle model.

\subsection{Bad health shocks are relatively worse for the poor}\label{sec:poor}
Focusing on the nonlinear process, we found that the economic effects of bad shocks are much stronger in absolute terms when people start off poor, as illustrated in Figure \ref{fig:save_cnt}. The left panel reproduces the left panel of Figure \ref{fig:asset-shock}; the other panels report the simulations of individuals subject to the same health shocks, but with assets equal to \pounds80,000 (middle panel), and \pounds200,000 (right panel) at age 50. While the patterns of asset accumulation depend on the value of initial health across wealth levels, they are smaller in magnitude when people start with higher wealth. Patterns for participation and hours worked are illustrated in Appendix figures \ref{fig:work_cnt} and \ref{fig:hour_cnt}.

\begin{figure}[t]
    \centering
    \caption{Assets after health shocks, starting from different wealth levels}
    \label{fig:save_cnt}
    \includegraphics[width=\linewidth]{images-new/save_cnt.pdf}

    \caption*{\footnotesize\normalfont \textit{Note:} The figures illustrate, for different values of initial assets in the canonical model of health dynamics, the age profiles of the difference in assets between individuals subject to a permanent component of health shock $\tau_{shock}$ and individuals subject to  $\tau_{shock}=0.5$, starting from different initial levels of the permanent component ($\tau_{init}$). Averages over 5,000 simulated histories of individuals starting at age 50 with the same level of assets indicated at the top of each panel.}.
\end{figure}

The more damaging effect of a bad health shock suffered by poorer individuals is better measured by the willingness to pay to avoid the health shock.
We computed what wealth transfer would make an individual indifferent between being subject to a bad shock ($\tau_shock=0.1$). For someone starting in relatively bad health ($\tau_{init}=.1$) and \pounds 10,000 in assets, such value at age 50 is \pounds 20,000, and increases slightly for wealthier individuals, reaching \pounds 30,000 for individuals starting with over \pounds 115,000 in assets. The same figure is never above \pounds 8,200 for people starting in good health. Figure \ref{fig:wtp} illustrates these figures as a fraction of initial assets, highlighting the importance of health shocks for poorer individuals, and people in bad health.

\begin{figure}[t]
    \centering
    \caption{Willingness to pay to avoid a bad shock (fraction of initial assets)}
    \label{fig:wtp}
    \includegraphics[width=.6\linewidth]{images-new/wtp_abb.pdf}

    \caption*{\footnotesize\normalfont The figure reports the asset transfer, as a fraction of initial assets, that would make a person indifferent between being subject to $\tau_{shock}=.1$ at age 52, starting from different level of the permanent component of health $\tau_{init}$, and receiving the transfer, or not being subject to the shock.}
\end{figure}

\subsection{The sources of health effects}
In the model, health affects economic outcomes through three channels: mortality, the availability of time for leisure and work, and the wage process. To ascertain the relative importance of these channels we turn them off by assigning to all individuals the mortality rate, the cost of bad health, and wages equal to the values these variables assume at a given quantile of the health distribution, conditional on age.

\newcommand{assigning everyone either the mortality rate, time cost of health, or wage offer computed at the percentile of the age-specific health distribution indicated at the top of each panel}{assigning everyone either the mortality rate, time cost of health, or wage offer computed at the percentile of the age-specific health distribution indicated at the top of each panel}

\begin{table}[!t]
    \centering
    \caption{Decomposition of the effects of health on outcomes}
    \label{tab:decomp-33}
    \input{images-new/tabledecomp2575}

 \caption*{\footnotesize\normalfont \emph{Note:} The table reports average outcomes in the baseline model simulations (first row), and percent changes
from the baseline after assigning everyone either the mortality rate, time cost of health, or wage offer computed at the percentile of the age-specific health distribution indicated at the top of each panel. Assets and (disposable) Income in thousand pounds averaged throughout the life cycle. Employment rate averaged from age 50 to 69. Hours are annual, conditional on employment.
 }
\end{table}

Changing the effects of health for a majority of individuals generates both income effects (higher income and time available), and substitution effects (higher hourly wages), as well as dynamic effects due to changes in mortality and uncertainty about future health.
Obviously, removing the effect of health has very different effects on economic outcomes depending on the reference  health level, as reported in Table \ref{tab:decomp-33}.\footnote{We report additional tables with decompositions of health levels set at the 25th, 50th and 90th percentile in Appendix \ref{sec:other}.}  When the effects of health through all three channels are removed, average assets are 8 percent higher if health were equal for everyone to the 75th percentile of its age-conditional distribution, but 10 percent lower if health were set at the 25th percentile. Employment, hours, and income increase as well in the first scenario, and decrease in the second.

The decomposition of these effects reveals that when people are assigned health at the 75th percentile, Mortality alone accounts for 19\% of the +8\% total effect on assets at the time of death.%
\footnote{\cite{capatina2015life}, finds that shutting off mortality is the main driver of health effects when everyone is assigned to the high level of health of a 3-valued health variable. The main differences between her model to ours are: (1) health takes only three values, that evolve with a Markov process without a transitory component; (2) the model is estimated using data from the U.S., (3) modeling the U.S. market, personal health expenditures are included as a choice variable.}
This effect is driven by the need to finance a longer expected lifetime.
The time cost is the factor contributing the most to all outcomes. When health increases, the reduced time cost shifts the static budget constraint outwards, inducing an increase in leisure, hours worked, and participation, making it less costly to save. The income effect induced by higher wages is different from the one induced by lower time cost because time available does not change. As a consequence hours worked actually slightly decrease (the substitution effect dominates), and higher income and employment generate a more moderate effect on accumulated assets.

The table also reveals that the effect of the three components is not additive, a result of the model's nonlinearities. The effect on assets of mortality and time cost combined is less than the sum of the two effects: the increase in assets induced by the reduced time cost is enough to fund the increased lifespan. When mortality does not change, instead, the effect of time cost and wages combined is \emph{more} than the sum of the single effects when health increases, when health is set at the 75th percentile.

\begin{figure}[t]
    \centering
    \caption{Components of willingness to pay to avoid a bad health shock}
    \label{fig:wtp-dec}
    \includegraphics[]{images-new/wtp_baseline.pdf}

    \caption*{\footnotesize\normalfont \textit{Note:} The figure reports the willingness to pay (as a percentage of the baseline willingness to pay) to avoid a bad health shock $\tau_{shock}=0.1$  at age 52, for individuals starting from $\tau_{init}=0.1$ with assets in the horizontal axis, after removing the effect of health on mortality, time available, wages, and their combinations. Health is removed by computing the value of the corresponding variable(s) at the 75th percentile of the health distribution conditional on age}
\end{figure}

We perform the same decomposition on the willingness to pay to avoid a bad health shock. In Figure \ref{fig:wtp-dec} we report the willingness to pay for a bad shock (as a percent of the baseline willingness to pay) after removing the effect of health on each channel, or their combination. To remove the effect of health, we assign individuals the value each variable would take at the 75th percentile of health (conditional on age) as in panel (a) of Table \ref{tab:decomp-33}. We consider a health shock $\tau_{shock}=.1$ at age 52, starting from $\tau_{init}=0.1$.

Interestingly, the results depend on the value of the initial assets of the individual.
The time cost of bad health is again the most important factor for most initial asset levels, by itself accounting for between 50 and 80\% of the baseline willingness to pay. The effect of health on mortality has a small impact only for the poorest individuals; for the highest wealth individuals, removing this effect would decrease the willingness to pay by over 30\%. This is because bequest motives become more important at a higher level of wealth, and are negligible at low wealth. The effects of health on mortality and time endowment combined account for about 80\% of the baseline willingness to pay to avoid the shock throughout most of the initial assets range we considered.
Removing the effect of health on wages increases the willingness to pay to avoid a bad shock, as indicated by the dashed line being above 100\%. This is because in this experiment we are assigning a majority of individuals a higher hourly wage than under the baseline (equal to the hourly wage offered to those at the 75th percentile of health). Following a bad shock participation and hours worked decline, hence compared to the baseline, a bad shock results in a higher income loss. The effect of wages complements the effect of mortality, since adding the effects of mortality and wages generates a willingness to pay closer to the baseline than mortality alone. Instead, a comparison of the dash-dotted brown line with the dotted green line reveals that adding the effect of wages to the effect of the time endowment reduces the willingness to pay, meaning that the substitution effect dominates the income effect when the individual has more hours to allocate and a higher wage offer.

The results from the decompositions performed in this section suggest that policies aimed at reducing the time cost of bad health, such as incentivizing remote work, or providing home-care support, would be especially effective in improving the welfare of those in bad health.

\subsection{The effect of health on inequality}
Our measure of health allows us to compute the effects of health on inequality at a granular level.
The results from the previous section reveal the importance of the model's nonlinearities and interdependency between channels for assessing outcomes; accounting for a continuous measure of health changes results quantitatively and qualitatively.
In this section, we show that different levels of health affect not only outcome levels, but also their variation.

\subsubsection{Removing the effects of health}
We first analyze the same decomposition we used to generate Table \ref{tab:decomp-33}, reporting the results in Table \ref{tab:ineq}. Our baseline model can reproduce the reported measures of inequality from the data fairly well. Removing all effects of health by setting mortality, the time cost of health, and wages at the level they have when health is equal to the 75th percentile by age reduces the 80/20th percentile ratio by 0.42, or 11\%, the 50/20th percentile ratio by 0.14 (6\%), the Gini coefficient of the asset distribution by 0.05 (11\%). The standard deviation of assets increases slightly.

A decomposition reveals that the effect of health on Mortality is the most significant factor contributing to asset inequality in terms of the Gini coefficient and 50/20 percentile ratio, whereas the Time cost of bad health is the most important contributor to inequality according to the 80/20th percentile ratio.

Interestingly, all indices of inequality increase if health is set at the 25th percentile of the distribution, except for the standard deviation which decreases slightly. The reason is that we are imposing a negative health shock on a majority of individuals, which, as highlighted earlier in Figure \ref{fig:save_cnt}, affects the poor more than the rich. These negative shocks are also larger in magnitude than the positive shocks. While setting everyone to the same percentile of health level contributes to reducing variation, the differential effect of the shock by wealth is larger in magnitude.

\begin{table}[t]
\centering
\caption{The effects of health on assets inequality} \label{tab:ineq}
\input{images-new/ineqdecomp7525.tex}

\caption*{\footnotesize\normalfont The table reports measures of assets inequality in the data and the baseline model simulations (first two rows) and percent changes from the baseline (following rows) after assigning everyone either the mortality rate, time cost of health, or wage offer computed at the percentile of the age-specific health distribution indicated at the top of each panel. Standard deviation measured in thousand pounds.  }

\end{table}

\subsubsection{The effects of bad health shocks on inequality}
The interpretation of the previous subsection's results is confirmed if we analyze the effect of the health shocks used to generate Figure \ref{fig:save_cnt} on inequality. Figure \ref{fig:variation_abb} displays
the coefficient of variation of assets, by age, for people starting at age 51 with two initial levels of wealth, following permanent health shocks of three different intensities (marked by lines with different patterns) at the age of 52, and starting from a low or median level of the permanent component of the health shock (marked by lines of a different color).

The dispersion in assets across individuals is higher when starting from a lower initial level of the health shock, and when starting with low wealth (solid green line in the left panel). For poorer individuals, dispersion increases dramatically after a bad shock, peaking above 1.4 before the age of 60, and remaining higher than 1.2. On the other hand, for individuals with the same initial wealth, dispersion is lower after a good shock than after a median shock. This result follows because while the variance of assets is approximately constant after the shock, average assets decrease after a bad shock.  Differences in dispersion are negligible for initially wealthier individuals (right panel), and (not reported in the figure) are even smaller for people starting from an initial level of wealth of \pounds 200,000.

\begin{figure}
    \centering
    \caption{Assets, coefficient of variation following a shock}
    \label{fig:variation_abb}
    \includegraphics[width=.8\linewidth]{images-new/variation_abb.pdf}

    \caption*{\footnotesize\normalfont The Figure reports the age profiles of the coefficients of variations of accumulated assets for different levels of $\tau_{init}$, $\tau_{shock}$, and initial assets. Average of 5,000 histories per simulation. }
\end{figure}

The persistency of bad shocks affects asset accumulation permanently, especially so for individuals starting with a low level of wealth. This evidence helps explain the differential effects of health we uncovered previously when discussing the decomposition results of Table \ref{tab:ineq}. Bad health shocks affect inequality differently by wealth, contributing to increasing variation when health is reduced to the 25th percentile for all. Good health shocks do not affect inequality as differently, hence when all effects of health are computed at the 75th percentile, the equalizing-health effects dominate, and indices of inequality are lower than the baseline.

\subsubsection{The canonical model does not uncover the same differential effects on inequality}
Notice, in the left panel of Figure \ref{fig:variation_abb}, the asymmetric effects of good and bad shocks, with respect to the median shock. Nonlinearities in the health process contribute to emphasizing this asymmetry, as illustrated in Figure \ref{fig:variation_abb-ar1}, which compares the two models focusing on individuals starting with \pounds 10,000 in assets, plotting the coefficients of variation following good and bad shocks, as a fraction of the same measure following a median shock.

The solid green line shows that in the nonlinear model, 4 years after a bad shock the coefficient of variation gets over 35\% larger than after a median shock. In the canonical model, variation increases, but not as dramatically (dotted yellow line). Following a good shock, variation decreases \emph{more} in the canonical model, relatively to following a median shock.

Not shown in the figures, for individuals starting off at age 50 with higher wealth the two models perform similarly. These results show that for poor individuals, the canonical model is not capable of generating the same dispersion of outcomes generated by health shocks.

\begin{figure}[t]
    \centering
    \caption{Assets, coefficient of variation following good and bad shocks, relative to a median shock}
    \label{fig:variation_abb-ar1}
    \includegraphics[width=.6\linewidth]{images-new/variation_abbar1.pdf}

    \caption*{\footnotesize\normalfont The Figure plots the age profiles of the ratio of the coefficients of variations of accumulated assets for levels of $\tau_{shock}=\{0.1, 0.9\}$, and the coefficient of variation of the same variable for $\tau_{shock}=0.5$. Individuals starting with $\tau_{init}
    =0.1$ and initial assets equal to \pounds 10,000. Average of 15,000 histories per simulation. }
\end{figure}

\section{Conclusion}
This paper shows that the health dynamics is highly nonlinear and that the quantile-based methodology developed by \cite{ABB2017} can fit the health process' main features. Incorporating the estimated health process into a life-cycle model reveals substantial heterogeneity in the response to severe health shocks by initial health and wealth.
A canonical model of health dynamics that fits the life-cycle profiles of assets, labor force participation, and hours worked equally well generates different economic effects of health shocks, especially for poor individuals subject to a bad shock. Our research reveals the importance of accounting for a complex measure of health, especially when investigating the distributional effects of health shocks on poor individuals.

Our simulations highlight the time cost imposed by bad health as the most significant factor affecting sick individuals' welfare. This result suggests of the importance of policies aimed at relieving individual time pressure, such as facilitating work from home, or providing home care.

redpajama_set_name: RedPajamaArXiv


text:
Guyanese cartoonist highlights oil boom, corruption
By Edom Kassaye • 23 February 2018
When Paul Harris entered a newspaper cartooning competition in Guyana in 1983, he did it not because he wanted to but to make his father happy. [caption id="attachment_9104" align="alignleft" width="173"] Guyanese cartoonist Paul Harris (courtesy)[/caption]…
Tiny Guyana threatened by 'oil curse'
By Jason McLure • 22 February 2018
Most people probably know little about the tiny South American nation of Guyana. In the U.S., it made headlines back in the late 1970s after an American cult leader named Jim Jones took his followers…
Venezuela's descent
By Jason McLure • 15 September 2016
When you think of humanitarian crises, they're usually caused by war or some natural disaster like a drought or earthquake. But in Venezuela today millions of people face shortages of food and medicines for a…
Oil-cursed Equatorial Guinea
By Jason McLure • 24 December 2015
At first glance, the small West African nation of Equatorial Guinea is doing well. Sandwiched between Cameroon and Gabon on the Gulf of Guinea, the oil-rich nation of 820,000 has a per capita GDP equivalent…

redpajama_set_name: RedPajamaCommonCrawl


text:
El Serrat dels Moros és una muntanya de 790,8 metres d'altitud del terme municipal de Castellcir, a la comarca del Moianès.

Està situat a l'extrem nord-oest del terme, al mig dels Plans de la Tuta, a llevant i nord-est de la Quintana de la Tuta. És a la dreta del Sot del Sastre poc abans que aquest torrent es converteixi en el torrent de la Mare de Déu. És a ponent de la Font de l'Arrel i del capdamunt de la Vall de Llàgrimes, a davant i a l'oest de l'extrem meridional del Serrat de la Barraca.

Referències

Enllaços externs
 El Serrat dels Moros en el seu entorn, a l'Institut Cartogràfic de Catalunya; el topònim no surt en el mapa

Moros
Moros

redpajama_set_name: RedPajamaWikipedia


text:
Paedophile given life sentence after convincing authorities he was no longer a threat – Michael Collins prosecutes
4th January 2022 Park Square Barristers
The defendant, Kevin Thistlewood, was found guilty of the rape and 12 other serious sex offences of a child.
He has appeared in court three times in total for child sex offences. Despite being regularly assessed; he was able to commit his latest offence by convincing authorities he was no longer a threat to children.
Thistlewood has been given a minimum 16-year sentence following the trial at Leeds Crown Court.
Full media coverage here: https://www.yorkshireeveningpost.co.uk/news/leeds-paedophile-given-life-sentence-for-raping-girl-after-convincing-authorities-that-he-was-no-longer-a-danger-to-children-3506166
For more information about Kitty and Michael, please contact their clerks:
Andrew Thornton on 0113 213 5202
Gina Hawkins on 0113 213 5205
Rebecca Wilson on 0113 213 5203
Hannah Dempsey on 0113 213 5211
Zena Charlton on 0113 245 9763
Robyn Nichol on 0113 213 5253


Make the Dry Mix as above.
Make the Wet Mix as above.
Make sure that it has a runny consistency as in the video. The idea is to coat it in the Wet Mix, roll it in the Dry Mix and then fry.
After cooking the chicken set aside in a strainer to remove any excess oil.
Add a little oil to a pan and add the two Ramekins from earlier.
Let it cook until the garlic is slightly turning too brown.
Then add your Chicken and roll it around in the oil and spices to soak up the flavor.
Serve with Rice and Chips.
Optional: Drizzle with some sweet chili finish it off.


text:
Q: Prevent repeating special character and by specific count using RegEx For Email How can I use RegEx to test for the following pattern:

*

*String length doesn't matter.

*The special character sign (-) should not be repeated consecutively.

*The special character sign (-) should not occur more than twice in the entire string and (.) sign not to be present more than two times before mail's domain

Sample Cases:
Match:
Jessica-Parker@gmail.com
Jessica-Parker@gmail.com
Jessica-Parker-Smith@gmail.com
Jessica.Parker-Smith@gmail.com
Jessica.Parker.Smith@gmail.com

Unmatch:
Jessica--Parker@gmail.com
Jeff--smith-Parker@gmail.com
Jessica-Parker--@gmail.com
Jessica-Parker-@gmail.com
Jessica.P.Jane.Smith@gmail.com
Jessica..P.Jane.Smith@gmail.com

Struggling with third part,I figured out the First two part as:
^(?!.*(-)\1{2})(\w[\w\.-]*@[a-zA-Z]+\.[a-zA-Z]+)\b

A: You may use
^(?![^@]*([-.])\1)(?![^@]*([-.])(?:[^@]*\2){2})\w(?:[\w.-]*\w)?@(?:[a-zA-Z]+\.)+[a-zA-Z]+$

See the regex demo.
Details

*

*^ - start of string

*(?![^@]*([-.])\1) -  no double hyphen/dot anywhere after 0 or more chars other than @ are allowed

*(?![^@]*([-.])(?:[^@]*\2){2}) - no 3 or more -/. chars after any 0 or more chars other than @ are allowed

*\w - a word char

*(?:[\w.-]*\w)? - an optional sequence of 0 or more word, . or - chars followed with a word char

*@ -  a @ char

*(?:[a-zA-Z]+\.)+ - 1 or more occurrences of 1+ letters followed with a dot

*[a-zA-Z]+ - 1+ letters

*$ - end of string.

redpajama_set_name: RedPajamaStackExchange


How to Win Your Home in a Busy Real Estate Market!
So you can determine how much home you qualify for, to know your top offer price…Search in a lower price point to give you room to compete. Seller's are more comfortable with buyers who have a mortgage approval letter from their lender. This is a step above pre-qualification. Ask your Realtor to explain this!
You have the right to inspections, however in a competitive market, inspections may already have been provided by the seller and when done by accredited inspectors, should be sufficient for you to judge the homes condition. With your loan pre-approval, as long the home appraises for the offer price, you should have few concerns and be able to make your offer non-contingent, raising you to the top of the multiple offer pile!
It's a nice idea to go in under the asking price, however if you anticipate multiple offers, be more realistic. Make a strong, non-emotional and competitive offer after reviewing area comps.
Make the best down payment you can, even exceeding the required 10-20%. It gives the seller the impression that you will have no issues qualifying for your loan.
Give yourself room to consider homes other than the perfect home, one that may need cosmetic upgrades. Look at it as an investment. Be sure to consider all costs and find the home that matches your all-in budget.
Partner with your real estate agent to make an aggressive, informed strategy for winning your home in any market.


text:
JENNY & TYLER APPEAR ON GOOD DAY DALLAS
(l-r) Good Day Dallas' Dan Godwin; Tyler & Jenny Somers;
and Good Day Dallas' Lauren Przybyl.
NEWS SOURCE: Turning Point PR
DALLAS- Folk/pop singer/songwriters Jenny & Tyler recently appeared on FOX 4's Good Day Dallas to perform "Little Balloon," the lead track from their critically-acclaimed recording, Open Your Doors. Pictured on the set are (l-r) Dan Godwin, Good Day Dallas; Tyler & Jenny Somers; and Good Day Dallas' Lauren Przybyl.
Produced by Mitch Dane (Jars of Clay, Bebo Norman), Open Your Doors released earlier this spring and features 10 selections written or co-penned by Jenny & Tyler, along with two instrumental offerings. In an exclusive iTunes promotion, the album is currently available for $3.99 through Wednesday, July 18.
The couple met as students at the University of Delaware where they began leading worship and writing songs together. Since their marriage in 2007, Jenny & Tyler have performed around the country in colleges, churches and homes, and have toured with such acclaimed artists as Sara Groves and Audrey Assad, among others. Open Your Doors is the duo's fourth full-length recording. Their previous projects include Faint Not; Love Came Down: Christmas EP; This Isn't a Dream; and A Prelude. Jenny & Tyler support the work of International Justice Mission (IJM) to fight human trafficking.
To view Jenny & Tyler's performance of "Little Balloon" on Good Day Dallas: http://www.myfoxdfw.com/video?autoStart=true&topVideoCatNo=default&clipId=7495358.
For further information, visit www.jennyandtylermusic.com


text:
package com.opengamma.strata.product.index.type;

import static com.opengamma.strata.basics.date.DateSequences.MONTHLY_IMM;
import static com.opengamma.strata.basics.date.DateSequences.QUARTERLY_IMM;
import static com.opengamma.strata.basics.index.IborIndices.EUR_EURIBOR_3M;
import static com.opengamma.strata.basics.index.IborIndices.GBP_LIBOR_3M;
import static com.opengamma.strata.basics.index.IborIndices.USD_LIBOR_3M;

/**
 * Market standard Fixed-Ibor swap conventions.
 *

 * https://quant.opengamma.io/Interest-Rate-Instruments-and-Market-Conventions.pdf
 *
 * @deprecated Use {@link StandardIborFutureContractSpecs}
 */
@Deprecated
final class StandardIborFutureConventions {

  /**
   * The 'GBP-LIBOR-3M-Quarterly-IMM' convention.
   *

   * The 'GBP-LIBOR-3M' index based on quarterly IMM dates.
   */
  public static final IborFutureConvention GBP_LIBOR_3M_QUARTERLY_IMM =
      ImmutableIborFutureConvention.of(GBP_LIBOR_3M, QUARTERLY_IMM);

  /**
   * The 'GBP-LIBOR-3M-Monthly-IMM' convention.
   *

   * The 'GBP-LIBOR-3M' index based on monthly IMM dates.
   */
  public static final IborFutureConvention GBP_LIBOR_3M_MONTHLY_IMM =
      ImmutableIborFutureConvention.of(GBP_LIBOR_3M, MONTHLY_IMM);

  //-------------------------------------------------------------------------
  /**
   * The 'EUR-EURIBOR-3M-Quarterly-IMM' convention.
   *

   * The 'EUR-EURIBOR-3M' index based on quarterly IMM dates.
   */
  public static final IborFutureConvention EUR_EURIBOR_3M_QUARTERLY_IMM =
      ImmutableIborFutureConvention.of(EUR_EURIBOR_3M, QUARTERLY_IMM);

  /**
   * The 'EUR-EURIBOR-3M-Monthly-IMM' convention.
   *

   * The 'EUR-EURIBOR-3M' index based on monthly IMM dates.
   */
  public static final IborFutureConvention EUR_EURIBOR_3M_MONTHLY_IMM =
      ImmutableIborFutureConvention.of(EUR_EURIBOR_3M, MONTHLY_IMM);

  //-------------------------------------------------------------------------
  /**
   * The 'USD-LIBOR-3M-Quarterly-IMM' convention.
   *

   * The 'USD-LIBOR-3M' index based on quarterly IMM dates.
   */
  public static final IborFutureConvention USD_LIBOR_3M_QUARTERLY_IMM =
      ImmutableIborFutureConvention.of(USD_LIBOR_3M, QUARTERLY_IMM);

  /**
   * The 'USD-LIBOR-3M-Monthly-IMM' convention.
   *

   * The 'USD-LIBOR-3M' index based on monthly IMM dates.
   */
  public static final IborFutureConvention USD_LIBOR_3M_MONTHLY_IMM =
      ImmutableIborFutureConvention.of(USD_LIBOR_3M, MONTHLY_IMM);

  //-------------------------------------------------------------------------
  /**
   * Restricted constructor.
   */
  private StandardIborFutureConventions() {
  }

}

redpajama_set_name: RedPajamaGithub


I'll be brief-- the Jaguar XE SV Project 8 is jawdropping. The second vehicle built by the company's Special Vehicle Operations (SVO) arm (and first at its new Technical Center in Warwickshire, England), the hand-built XE SV Project 8 is powered by a 5.0L supercharged V8 that produces 592 horsepower, 516 lb-ft. torque, goes from 0-100 km/h in 3.7 seconds and has a top speed of 322 km/h. The car also has an adjustable ride height, active aero, carbon ceramic brakes and F1-style silicon nitride ceramic wheel bearings. Only 300 will be built (all left-hand drive), but unlike the rest of the world, North America will not be getting the two-seat, Track Pack version.
The XE SV Project 8 is making its worldwide debut this weekend at the Goodwood Festival of Speed in West Sussex, England which runs from June 30-July 2.
Pricing and on-sale date have not been announced. See below for Jaguar's official release.
MISSISSAUGA, ON – Jaguar Land Rover Special Vehicle Operations (SVO) has revealed the most powerful, agile and extreme performance Jaguar road car ever produced – the 200 mph, 592HP Jaguar XE SV Project 8 sedan.
"SVO's raison d'être is to produce halo vehicles that push the boundaries in terms of luxury, performance and all-terrain capability. Project 8 is a great example of what happens when enthusiastic designers, engineers and manufacturing specialists are given the opportunity to create an extreme performance sports car without compromise," said John Edwards, Jaguar Land Rover Special Operations Managing Director. "Project 8 will be assembled by hand in the new SVO Technical Center, and we're confident that our enthusiastic and demanding clients will love driving Project 8 as much we do!"


text:
Georges Renaud (* 8. Januar 1893 in Nancy; † 28. Juli 1975 in Peille) war ein französischer Schachspieler.

Renaud, der als Journalist für L'Eclaireur de Nice arbeitete, gewann 1923 die erste französische Einzelmeisterschaft und 1944/45 die zehnte französische Fernschachmeisterschaft. International vertrat er Frankreich bei der inoffiziellen Schacholympiade 1924 in Paris und bei der Schacholympiade 1927 (der ersten offiziellen Schacholympiade) in London.

Zusammen mit Alan Campbell White veröffentlichte er 1924 Le Problème d'Echecs, zusammen mit Victor Kahn schrieb er die Werke de Les Echecs (1945), L'Art de Faire Mat (1947), Les Six Candidats au Championnat du Monde (1948), La Partie Espagnole (1949) und Les Echecs dans le Monde 1951 (1952).

Weblinks
 Biographie auf heritageechecsfra.free.fr (französisch)

Einzelnachweise

Schachspieler (Frankreich)
Schachspieler
Nationaler Einzelmeister (Schach)
Fernschachspieler
Geboren 1893
Gestorben 1975
Franzose
Mann


text:
package org.pentaho.di.core.exception;

/**
 * This exception is thrown in case there is an error in the Kettle plugin loader
 *
 * @author matt
 *
 */
public class KettlePluginException extends KettleException {

  private static final long serialVersionUID = -7251001771637436705L;

  public KettlePluginException() {
    super();
  }

  /**
   * @param message
   * @param cause
   */
  public KettlePluginException( String message, Throwable cause ) {
    super( message, cause );
  }

  /**
   * @param message
   */
  public KettlePluginException( String message ) {
    super( message );
  }

  /**
   * @param cause
   */
  public KettlePluginException( Throwable cause ) {
    super( cause );
  }

}


text:
<?php
// Warning: This is PHP template written as RAW.
?>
<?php // Do not delete these lines
	if ( ! defined('HABARI_PATH' ) ) { die( _t('Please do not load this page directly. Thanks!') ); }
?>
<?php
    if(!empty($tweets[0])):
        $tweet = $tweets[0];
?>

    <a href="http://twitter.com/<?php echo urlencode( Options::get( 'twitter__username' )); ?>" title="Follow me at twitter">twitter

		<a href="http://twitter.com/<?php echo urlencode( Options::get( 'twitter__username' )); ?>" title="Follow me at twitter">
            <img id="twitter-picture" src="<?php echo htmlspecialchars( $tweet->image_url ); ?>" width="48" height="48" alt="<?php echo urlencode( Options::get( 'twitter__username' )); ?>">

			<?php echo $tweet->text; ?>

<?php endif; ?>


I am trying to avoid wiring the house with cat6. My only aprehension is that I thought that was for mobiles only. Find More Posts by grkg. Since it is practically hidden when attached to a USB port, accidentally damaging your wireless adapter is now also practically impossible. Many ISP's link that connection to the 1st wired client they were connected to.
Buying Format see all. Never been to Heaven WiFi adapter finds no wireless networks. Find More Posts by windkws. There seems to be a problem serving the request at this time. Show only see all.
Device shows as install and working fine, defo something on the desktop, hopeing with baited breath all I have to do is go to mobility centre and turn on wireless Wineows internal chip antenna. I am trying to avoid wiring the house with cat6. Originally Posted by grkg Dont think this is going to work as laptops have no issue with windiws router, as my desktop will not see any wireless networks that are seen by my laptop in the same position. Similar help and support threads.
Whoops I misunderstood, thought you meant nothing could connect since move! My only aprehension is that I thought that was for mobiles only.
Please provide a valid price range. More refinements More refinements Got one to sell? I have moved and the desktop is too far away to hard wire.
I have an edimax n router and it works flawlessly with my laptops one with 7 64bit, the other with 7 32bit I have until now hard wired my desktop to the Ethernet ports. Dont think this is going to work as laptops have no issue with the router, as my desktop will not see any wireless networks that are seen by my laptop in the same position. What shows up in device manger for this new wireless NIC?
Too Many Wireless Networks I'm thinking it's just me being like Monk, but is there a way to remove some or all of the various other wireless networks that my laptop can see. It works great from my acer laptop and from my android phone.
Sometimes it needs it to be dicoverable. Originally Posted by grkg Hi guys, Here's the issue. I place my laptop next to the desktop the laptop picks up my home network as well as numerous other neighbours computers. My problem is that it works as an access point so both my phone and my laptop need to connect as clients. Originally Posted by grkg.
The adapter is easy to set up using the included EZMax setup wizard, which is available in 16 languages. Hi guys, Here's the issue.
I can see the wireless network icon in lower left of the screen and it has a red cross through it. It is brand new laptop and original windows.
We also provide an extensive Windows 7 tutorial section that covers a wide range of tips and tricks. All Auction Buy It Now.
Local pick up is not allowed. The time now is Within 2 miles 5 miles 10 miles 15 miles 20 miles 50 miles 75 miles miles miles miles miles miles miles miles miles of.
Originally Posted by Darryl Licht.


TV and media execs echoed this sentiment throughout the 2017 Consumer Electronics Show in Las Vegas. But we first heard this quoted by Mark Greenberg, President and CEO of Epix, a premium entertainment network. Greenberg sat on a panel with other entertainment executives to discuss how content developers must compete with the maddening level of choices faced by consumers today.
What the group concluded was that it all comes down to mastering "discoverability." And it turns out that what we learned is something every marketer can benefit from.
But while there is no shortage of binge-watching going on, an increase in options has made it more difficult for consumers to discover new shows they'll like. Similarly, marketers now have a much harder time figuring out how to spend their budgets knowing that people are consuming TV in any of 25+ different ways.
The secret lies in knowing your audience and serving them content you know will get them hooked. And that, my friends, can only happen with data.
Viewers who streamed Fincher's work tended to also stream shows and movies with Kevin Spacey casted as the lead.
The original, BBC-version of "House of Cards" had a strong audience on Netflix, despite being over a decade old.
Bundle up these insights and we can understand why Netflix felt confident in making a $100 million investment for just 26 episodes.
What makes this story so great is not that we all got hooked on "House of Cards," but that it showcases the real power of data. When utilized correctly, data empowers brands and marketers to personalize content to targeted individuals. It's turning Big Data into Smart Data, as the saying goes.
With consumer attention at a premium these days, it's data and personalization that will save the day for marketers looking to make a real impact among an audience.
It's not too late to catch up on more marketer takeaways from last month's CES 2017! Check out our live recap blogs from Day 1 and Day 2.


Vadodara, Apr 3 (UNI) Vadodara, the cultural capital of Gujarat, is perhaps the only city in the country where people have developed a new concept of donating blood and pledging their body parts during ''Besna'', the prayer meeting that is held after one's death.
According to Su-Raktam Blood Bank, a private city-based organisation, which recently launched the novel campaign for collection of blood during Besna, the response has been quite encouraging.
The Blood Bank today organised its second donation camp and got over 100 volunteers, including many eminent personalities of the city. They had assembled at the Vitthalnagar hall to pay homage to noted social worker Madhavlal Vaidya (85), who died of cancer here on March 28.
Blood Bank trustee Dr R B Veshania said for popularising the blood and body donation movement, they have decided to approach the bereaved families in and outside the city seeking their support for the noble cause. The move, in fact, had been appreciated by all sections of people, he said.
Talking to this correspondent, Vaidya's eldest NRI son Dr Krishna Chandra Vaidya, who has been staying in South Africa for the last 25 years said he readily accepted the proposal of the Blood Bank to hold the camp during today's ritual as his father believed that service to humanity is service to God.
''I am confident that this novel idea will be popular very soon as people have realised the importance of blood and life,'' he added.


text:
Tell Your World on wasabeat
hightrancesea Uncategorized January 25, 2012
After hitting number one on the Japan iTunes Store singles charts and doing appreciably well on the US iTunes Store's Electronic chart, livetune's Tell Your World is now available on wasabeat, a dance music download store from Japan that caters to a world-wide audience for those who can't or don't want to buy from the iTunes Store. The song is available as a 320kbps MP3 file for 200 JPY or uncompressed audio file for 250 JPY, payable through either credit card or PayPal. The audio files can be downloaded directly from the website after payment, and they can also be played directly using the site's built-in music player.
About: hightrancesea
3 thoughts on "Tell Your World on wasabeat"
Dasd says:
Nice, bought. Already preordered the album, but wanted a better quality version to listen to in while I wait.
AlphaCananogram says:
FYI,, for me, it only showed the MP3 and price, but if you click the ¥200 button and then the Check Out button, then you can click WAV in there for ¥250
YAY WAV's~
Bought, too bad these guys are not in relation with KarenT, I would have loved to see more album on their site, iTunes is a closed platform and Amazon really offer a barebone service, and HearJapan seems to always miss most of the releases.
And as said by AlphaCano, lossless format is a nice bonus (though I have to admit that I am unable to hear the difference between an OGG/MP3 at 320kbps and a FLAC.
Leave a Reply to Dasd Cancel reply
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Summary Cardio, or aerobic exercise, can help promote fat burning and fat loss to help slim down your face. Forgiving Face Extra weight stretches the skin no matter where it is on the body. Multiple studies have found that cardio can help promote fat burning and increase fat loss 45. Conversely, studies show that sleep deprivation can increase food intake, cause weight gain and lower metabolism 1920 Limit Alcohol Consumption While enjoying the occasional glass of wine with dinner is fine, going overboard with your alcohol intake can be one of the biggest contributors to increased facial fat and bloating.
When older women diet, it shows first in their face, then their body. More epistane fat burning are needed to evaluate how these exercises may impact facial fat in humans. Switching up your diet, adding exercise to your routine and adjusting some of your daily habits are all effective ways to boost fat loss and slim down your face.
Based on the actions of fillers, this day cream plumps skin with ingredients such as hyaluronic acid. Forgiving Face Extra weight stretches the skin no matter where it is on the body. When lips appear too large for personal taste, self-confidence can decrease. When the body is dehydrated, it holds onto water, and that can cause areas of the body to retain the water in a visible way.
So it could new weigh weight loss lenoir city that the key to younger skin are the diet-friendly foods in your fridge. Features Because you tend to know your own face and the faces of our friends, family and even favorite celebrities pretty well, even the slightest changes are noticeable. Although different people are predisposed to lose fat in different body areas first, eventually even the fat stored in your feet will get burned for energy.
Alcohol diet plan for smokers weight loss smaller lips in calories but low in nutrients and may be associated with an increased risk of weight gain Stay motivated' A woman said she was happier and healthier than ever before after losing 70lbs five stone and her glowing skin certainly proved her point A man who shed 9st 4lbs in two years said he'd experience 'face gains' as a result After shedding lbs more than 14 stone this woman revealed her incredible bone structure This man's newfound confidence was shining through after shedding lbs more than nine stone She shed lbs more than 21 stone in just 18 months which weight loss smaller lips out her eyes and got rid of double chins Since swapping her food addiction for e-z weight loss pills love of exercise and slimming down by 95lbs 6st 11lbs this woman has discovered her bone structure A weight loss of lbs 10 stone revealed a handsome face that was hiding beneath excess padding In the first picture s4l lipo cell fat burner plus woman wasn't exactly radiating happiness.
Studies have shown that high cortisol levels can increase appetite and alter metabolism, resulting in high fat loss diet fat storage 16 Without plenty of water, your body tends to hold onto whatever moisture it can get, which leads to bloating.
Solutions To help lose weight loss smaller lips in the face naturally, adjust your diet to include nine 8 oz. Studies suggest that water can keep you feeling full and enhance weight loss.
Summary Reducing your intake of sodium may help reduce fluid retention and decrease bloating and puffiness in your face. Conversely, studies show that sleep deprivation can increase food intake, cause weight gain and lower metabolism 1920 Because they contain very little fiber, they are digested rapidly, leading to spikes and crashes in blood sugar levels and a higher risk of overeating Some of the most popular exercises involve puffing out your cheeks and pushing the air from side to side, puckering your lips on alternating sides and holding a smile while clenching your teeth for several seconds at a time.
In addition, skin damaged by excessive sun exposure may not be as resilient after weight loss. Share on Pinterest Facial exercises can be used to improve facial appearance, combat aging and improve muscle strength 1.
This can make the lips look larger. Cutting back calories per day and burning an additional calories per day can help you lose a pound a week. The worst thing you can do to your face at this age is to yo-yo diet. But Tew also points out that the skin of the face is different and usually doesn't weight loss smaller lips up looking saggy and stretched after major weight loss.
Ideally, aim for at least eight hours of sleep per night to aid weight control and facial fat loss. Much of that perception is based on the comparison of your current look to that of weight loss smaller lips heavier appearance to which weight loss belt friends and family had grown accustomed. Richard Tew notes, someone who undergoes profound weight loss, of say, pounds may have excess or sagging skin as a result of that stretching.
Processed foods, such as canned soups, are often packed with sodium, so look for low-sodium products and avoid adding salt to foods you prepare. Another study showed that high fat loss diet facial muscle exercises twice a day for weight loss smaller lips weeks increased muscle thickness and improved facial rejuvenation 3.
Switch up Weight loss smaller lips Sleep Schedule Catching up on sleep is an l carnitine weight lose general weight-loss strategy. Facial skin, partly because it doesn't fill in with as much extra weight as other parts of the body, is more resilient and can return to a more normal appearance as you lose weight.
Summary Sleep deprivation can alter metabolism and increase food intake, weight gain and cortisol levels. But some changes definitely catch people by surprise.
This will give the effect of smaller lips with a minimum of extra effort. The Bottom Line There are plenty of methods available to help you lose extra fat in your face. James Roland What essential oils do i use for weight loss Roland started writing professionally in However, the more weight you gain, the more you're going to see it in your face, regardless of whether the rest of you is an apple or pear shape.
Summary Excessive alcohol intake may contribute to weight gain, including fat gain in the face. Try to get — minutes of moderate to vigorous exercise each week, which translates to approximately 20—40 minutes of cardio per day 7. Multiple studies have found that cardio can help promote fat burning and increase fat loss 45. Losing weight can increase fat loss and help slim down both your body and weight loss smaller lips.
Not only high fat loss diet losing weight loss smaller lips help you with feet issues including painbut it can also make your feet smaller. This includes weight gain, weight loss, new wrinkles and even minor plastic surgery or treatments such as Botox injections.
Instead, use a lipstick that is a similar color to your skin tone.
Created by the make-up artist to Gwyneth Paltrow and Kate Moss, this maximum weight loss 12 days transforms tired-looking eyes. Slow weight loss 5 2 diet to US Dietary Guidelines, moderate drinking is defined as up to two drinks per day for men and up to one drink per day for women As Rodale Wellness pointed out, your body just responds better to sexual situations after a weight loss: One study found that better sleep quality was associated with an increased likelihood of weight loss success Wrinkle Return One "advantage" of weight gain in the face is that it tends to fill out wrinkles and creases that come with age.
And some of us snore after significant weight gain especially if that weight is stored around the neck. Like when I wash my face, my nose is wider. My eyes are crinklier than ever, my skin duller and more lined.
Limit Alcohol Consumption While enjoying the occasional glass of wine with dinner is fine, going overboard with your alcohol intake can be one of the biggest contributors to increased facial fat and bloating. Consider reducing your sodium intake to make your face appear slimmer. Lipstick does not plump the lips, and using a color that is similar to your skin tone can make lips less noticeable.
Instead, use a lipstick that is a similar color to your skin tone. Some common examples of cardio exercise include running, walking, biking and swimming. Summary By toning your facial muscles, face exercises could make your face appear slimmer. Enunciating tongue twisters is a good exercise for the lips.
Likewise, alcohol in any form can add to the bloated weight loss smaller lips in the face, as can too much sodium. If you lose a lot of weight over a fairly short time and are thinner than you have been in a long time, people may think you look gaunt and unhealthy.
It may also reduce fluid retention to prevent bloating and swelling in your face. Sleep deprivation can cause an increase in levels of cortisola stress hormone that comes with a long list of potential side effects, including weight gain Exercise will get rid of excess fat on the face and lips, which will ultimately make your lips a little smaller.
Share After losing lbs more than eight stone naturally this woman's face was much more how can i lose weight in three months Over the course of seven years, this man went through a slow transformation what essential oils do i use for weight loss eight stone and now boasts clear skin, chiselled features and sparkling eyes Shedding lbs more than 14 stone turned this woman into a glamorous siren who takes much more pride in her appearance After shedding lbs - more than half her body weight - this woman found she had a high fat loss diet more streamlined jaw and defined features This man captioned his transformation: Keep in mind that research is lacking on the effectiveness of facial exercises for fat loss specifically.
Comments that contain profane or derogatory language, video links or exceed words will require approval by a moderator before appearing in the comment section. So, like a lot of women, I find myself approaching 40 wanting to lose weight and look younger.
The human face tends to resemble a heart or valentine when we're young, but through weight gain and age, that shape can get inverted as skin begins to sag a little and you put on weight around the jowls. Your clothes get baggy, you need to resize your rings, and you may need high fat loss diet pick up a smaller bra.
Some foods, however, help to slow this decline, such as dark green vegetables and colourful fruits and vegetables. These carbs have been heavily processed, stripping them of their beneficial nutrients and fiber and leaving little behind besides sugar and calories. Summary Drinking water can decrease calorie intake and temporarily increase metabolism. Hence, waists get wider and diets get harder.
Weight loss smaller lips Am I So Cold? If you want 13 stone need lose weight lose weight, aim for around 2lbs a week.
A smile will pull your lips and make them weight loss smaller lips. Fortunately, plenty of strategies can increase fat burning and help slim down your face. Contains retinol to boost collagen production and even out skin tone. Small changes can naturally make lips appear smaller and give back the confidence that self-consciousness can cause.
It may also help you lose facial fat. She has studied English and psychology and hopes to get a Ph. But in I moved to the suburbs where I drive everywherehad another baby and ended up with an extra stone that I want to lose but never get round to dealing with.
Scientists from McMaster University in Ontario recently found that all types of regular moderate exercise slow down skin ageing.
Cardio, or aerobic diets that lose belly fat, is any type of physical activity that increases your heart rate. But since losing weight she's glowing with confidence. Your skin stretches and then it goes back and then it stretches.


Of the 7.4 billion people who inhabit this earth, no two people are exactly alike. My goal in photographing people is to communicate their uniqueness.
Breaking down personal barriers to create one-of-a-kind images that say something about you.
Creating images that tell a story about you during a coming of age time in your life.
Creating individual looks for business, theatrical and personal Head Shots.
Shot in Southern Illinois featuring clothing individually designed and produced by Teresa Stanton of Half Street Studio.
One of the most interesting aspects of the human race is their faces. Of the 7.4 billion humans populating this earth, no two faces are exactly alike. Faces are a primary means of conveying social information, a form of nonverbal communication. Micro expressions are very brief facial expressions. They occur when a person either deliberately or unconsciously conceals or exposes a feeling. See what you can determine from each person's expression in this gallery.


Planning a trip to the Canadian capital? Start your holiday in Ottawa with cheap flights to Macdonald-Cartier International Airport (YOW), the city's main airport. Air Canada has direct flights from London Heathrow to Ottawa. US Airways, British Airways, United Airlines, among others fly with stopovers.
Ottawa is frigidly cold in winter with average temperatures around -14°C. Summer is by far the most popular time of the year to visit Ottawa so be sure to book your flight tickets to Ottawa at least six months before you plan to depart.
Peak season: Summertime, especially the first week of July.
Low season: Winters, except New Year's Eve.
Direct flights to Ottawa take 7 hours 40 minutes from London.
Queen Victoria chose Ottawa to be the capital of Canada in the 1850s and today it's here that you'll find a fine selection of national museums, the houses of parliament and all things culturally Canadian, especially in winter when the canal freezes and the locals take out their ice skates.
Parliament Hill: This is the city's number one attraction, overlooking the Ottawa River. The houses of parliament are a fine example of the Gothic revival style. Tours will take you around, including into the restored offices of John A Macdonald and the Privy Council Office.
Canadian Museum of Civilisation: This is the most visited museum in Canada and is a must for any first-time visitor. The museum really illuminates the history of Canada through different exhibitions and studies.
Rideau Canal: In winter the city's Rideau Canal turns into the world's largest outdoor ice rink. You can rent skates and buy cups of hot chocolate right out on the ice. Some locals even take advantage of it and commute via ice skates.
Find more information on Ottawa Tourism.
Ottawa has plenty of hotels including several backpackers hostels for budget travellers. Most hotels are located in the city centre and many cater to business travellers and politicians which means that you can often grab a room deal on the weekends.


text:
GUNFIRE TO GLORY
Roberto Firmino had to run home from training to dodge gunfire as a kid & would be dead if he had taken the wrong path
Grant Rollings
16:35 ET, Jul 3 2020
Updated: 3:45 ET, Jul 4 2020
TOP-FLIGHT footballers are pampered and earn huge salaries – but for Liverpool star Roberto Firmino, there was nothing comfortable about his route to success.
The Brazilian international had to dodge gunfire during training in the troubled neighbourhood where he grew up, playing in borrowed boots.
Roberto Firmino and his model wife Larissa PereiraCredit: Mirrorpix
His parents were so fearful the youngster would get caught up with gangs that they would not let him out to play.
And The Sun can reveal that five of his team-mates in Maceio, on the east coast of Brazil, had been murdered in turf wars before they turned 19.
Roberto, though, dreamed of winning the Premier League. And thanks to his singular determination to get away from the shanty towns — known as favelas — his wish came true with Liverpool last week.
When the Reds are presented with the trophy, after a 30-year drought, no smile will be bigger or brighter than that of the team's No9.
A young Roberto in his old school photo
He is known as having the whitest teeth in the side, so much so that manager Jurgen Klopp visited Roberto's dentist after being ribbed for his off-colour gnashers.
The 28-year-old striker, who fans affectionately call Bobby, will also surely share the immense joy of him being rewarded for his perseverance.
Back in Maceio, the coach who first spotted Roberto's talents told The Sun of the risks the youngster took to go to training.
The star joined amateur youth side Flamenguinho when he was nine and helped them win 30 trophies.
Roberto aged 14 in local side Clube de Regatas do Flamengo
'Boys don't have anything to keep them from crime'
Andre Lima Teixeira, former coach of Flamenguinho, the first amateur team Roberto played for as a child, said: "He might well have taken the wrong path and ended up dead too if his life hadn't taken the turn it did.
"It was especially dangerous round here when Roberto was growing up.
"Often, when the time came for them to come for training, Roberto and the other boys had to stay at home, locked in, because the gunfire had started up.
"At other times, when they were on their way for training or a meeting, the shooting would start and they would have to run and hide underneath their beds."
Roberto, second from left, celebrates winning the Premier League with his Liverpool teammatesCredit: Instagram @mosalah
Roberto's dad Jose Cordeiro was a street vendor selling bottled water outside football grounds.
Sometimes Roberto would help his dad by passing on change to the customers.
His mother, Mariana, stayed home to look after the children.
Their home was in Trapiche da Barra, an impoverished neighbourhood next to a dangerous favela and a polluted lake.
Roberto and wife Larissa kiss on holiday in IbizaCredit: The Mega Agency
Both parents wanted a better life for young Roberto and did everything they could to bring him up properly.
When friends invited the youngster for a street kickabout he had to sneak out by leaping over the wall.
Once he fell to the ground and injured his knee, and he still has the scar today.
Jose Cicero da Silva, 29, known as Toto, was a childhood friend.
The couple in the same outfit as their daughtersCredit: Instagram
Now working as a plasterer and still living in Trapiche, the dad of a two-month-old daughter said: "I was his friend from 13 to 17 years old.
"I was about 12 when I started playing for Flamenguinho.
"I lived four to five streets up from Roberto. We went to training together.
"His mother wouldn't let him play.
"So we would go and knock on his door and call him to come, otherwise he wouldn't go. It was a good time.
"We were on the young team, aged 12 to 13.
"We were poor so each one borrowed their boots from another one.
As a lad, circled, with his Flamenguinho team
"I think God knows what he does.
"Roberto is where he is because of his hard work.
"I follow his career and see the Liverpool games.
"Flamenguinho was really important in his career.
"Andre made us run on the sand.
"Even without many resources he still managed to train us really well.
"I remember how many of the boys on the team ended up dead because they got involved in drugs gangs.
With mum Mariana, who stayed home to look after the childrenCredit: Instagram
"It's worse now because boys don't have anything to keep them away from crime."
Andre recalled: "Roberto's mother and father didn't used to let him play football with us.
"The other boys didn't like going there to call for him because they always said no.
"So I would pick him up and drop him off at the doorstep.
"It was the only way his parents would allow him to play."
Roberto holding the Champions League trophy last year Credit: Getty Images - Getty
Everyone from Roberto's old neighbourhood has described him as a shy and humble boy.
One coach even worried he was mute, because he said so little.
At the age of 19, Roberto was on his way to Europe, having been signed by German side Hoffenheim, and five years ago he joined Liverpool for £29million.
By that time five of his friends from his youth team who stayed at home had been brutally killed.
Andre said: "They died after getting involved in the drugs gangs.
"They were all 17 or 18, murdered by members of rival factions."
Roberto, who hasn't forgotten the people of Brazil, playing for his national sideCredit: EPA
Roberto now earns £180,000 a week on Merseyside, and last week he arrived for training in a £165,000 yellow Lamborghini Urus.
He is also known for his eye-catching designer clothes and an often-changing haircut.
But he has never forgotten the people of Brazil.
Roberto has paid for two brothers with spinal muscular atrophy to receive vital treatment, distributed food parcels in his old neighbourhood and helped other friends.
He also paid for a plush new home for his beloved parents.
All this he has done secretly.
Roberto's wife Larissa, a model, said: "The most beautiful thing is that he does it without wanting anything in return. It's from the heart."
The ace with his wife and daughters, Valentina, six, and Bella, fourCredit: Instagram
The couple met in 2014 and married three years ago in a lavish ceremony in his home town.
They have two daughters, Valentina, six, and Bella, four, who often appear on his Instagram feed.
'Winning it was his biggest dream of all'
The girls are always pictured dressed in matching clothes and standing next to their beaming father.
When lockdown started in the UK on March 23, Roberto posted a video of himself singing at the piano with the girls.
In another image he did some home schooling with his daughters, sporting spectacles.
Family is very important to the South American.
Valentina's name is tattooed across his chest and he has a picture of his mum's face inked on his right bicep.
Showing off some of his inkings in selfie with wife
Other body art includes a picture of the Champions League trophy being held aloft last summer, when Liverpool beat Spurs in the final, and the word "Blessed" on his torso.
There is just about enough room left on his body to add a tattoo of the Premier League trophy.
Undoubtedly, Roberto's creative flair and his goals have been central to Liverpool's success under manager Klopp.
His cheeky flicks, deft feints and neat passes offer goal-scoring opportunities to his fellow two frontmen Mo Salah and Sadio Mane.
The couple in white clobber and bling
Liverpool legend John Barnes even hailed Roberto as the team's "most important player".
Having been at the club longer than all of the regular starting 11, apart from captain Jordan Henderson, winning the league will be all the sweeter for the long-serving Brazilian.
And it certainly makes a long-held, seemingly impossible, dream come true.
His old coach Andre said: "For a lot of Brazilian boys, their dream is to play for Barcelona.
"But Roberto knew the Premier League is the best in the world and winning it was his biggest dream of all.
'ONE OF THE GREATS'
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"I'm so proud to see how far he has come, and how he has managed to make all of his dreams come true.
"It all happened through training and willpower.
"He was a boy who never gave up and did everything with so much effort. That's why he achieved his objectives."
'Agent' Firmino invites Coutinho to party in Brazil amid Liverpool transfer speculation
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Monterey Fire Department serves the City of Monterey, City of Pacific Grove, City of Carmel-by-the-Sea and Sand City as the well as the Presidio of Monterey, Naval Postgraduate School, La Mesa Village and the Monterey Regional Airport. The department operates out of six stations strategically located to achieve the shortest response time to emergency calls. The Monterey Fire Department prides itself as being a customer service organization by providing excellent emergency services as well as additional public safety support through Fire Prevention, Public Education and Special Operations (Urban Search and Rescue, Marine Safety, and Aircraft Rescue and Fire Fighting).
The Fire Prevention Plans Examiner/Inspector is a non-sworn position in the Fire Department. The position will play a key role in the enforcement and interpretation of fire prevention laws, codes, ordinances and regulations. The successful candidate must possess strong organizational and customer service skills. The position may be required to respond to calls on evenings, weekends, and holidays.
Candidates who successfully complete all components of this recruitment process will be placed on an eligibility list. The eligibility list is active for 12-months and may be used to fill additional vacancies including lower-level positions determined to have similar duties and requirements.
•	The successful completion of a certificate program in Fire Technology or Fire Science from an accredited college or university. And the successful completion of California State Board of Fire Services accredited courses Fire Inspector 1A, 1B, 1C and 1D, Fire Investigation 1A and 1B, and Plans Examiner 1A, 1B, and 1C or equivalent.
•	Completion of an Associate's Degree from an accredited college or university in Fire Technology, Fire Science or a closely related field and California State Board of Fire Services accredited Fire Inspector 2A, 2B, 2C and 2D and Public Education 1, or equivalent, are highly desirable.
Experience: Three (3) years of experience in organized fire prevention including fire inspection, plan review and public education/relations.


text:
La gara dei tuffi dal trampolino 3 metri femminile dei Giochi della XXXII Olimpiade di Tokyo è stata disputata tra il 30 luglio e il 1º agosto 2021 presso il Tokyo Aquatics Centre. Vi hanno partecipato 27 atlete provenienti da 17 nazioni. La gara si è svolta in tre turni, in ognuno dei quali l'atleta ha eseguito una serie di cinque tuffi.

La competizione è stata vinta dalla tuffatrice cinese Shi Tingmao, mentre l'argento e il bronzo sono andati rispettivamente all'altra cinese in gara Wang Han e alla statunitense Krysta Palmer.

Programma

Risultati

Preliminari e semifinale

Finale

Note

Collegamenti esterni

Tuffi ai Giochi della XXXII Olimpiade