coverage

FCC Finds T-Mobile, Verizon, US Cellular Overstated Rural Coverage

The coverage maps are crucial because they help the FCC determine where the commission allocates government subsidies to fund broadband projects. Now the FCC has to figure out how to ensure the coverage maps will be accurate as it prepares a $9 billion 5G fund.




coverage

Dec. 15 Is the Last Day to Sign up for 2019 Coverage on Delaware’s Health Insurance Marketplace

NEW CASTLE (Dec. 13, 2018) – Delawareans have just days left to sign up for or to change their health coverage for 2019 through the Health Insurance Marketplace. Open enrollment ends on Dec. 15, with coverage for those who enroll by that date and who pay their first premium beginning Jan. 1, 2019. State residents […]




coverage

Enrollment on Delaware’s Health Insurance Marketplace up More Than 6% for Coverage in 2020

Governor John Carney said, “Since the ACA was implemented, we have nearly cut the uninsured population in half in our state. That is another important step in moving us to a stronger and healthier Delaware.”



  • Delaware Health and Social Services
  • Governor John Carney
  • Insurance Commissioner
  • News
  • Office of the Governor
  • ACA
  • Delaware Health Insurance Marketplace
  • Westside Family Healthcare

coverage

Coverage of Kashmir crackdown, Alaska policing, win Pulitzers

The New Yorker took the feature reporting prize for Ben Taub’s piece on a detainee at the U.S. military base at Guantanamo Bay, Cuba, with judges saying the story offered 'a nuanced perspective on America’s wider war on terror.'




coverage

Viewing RTL Code Coverage reports with XCELIUM

Hi,

There was tool available with INCISIV called imc to view the coverage reports.

The question is: How can we view the code coverage reports generated with XCELIUM? I think imc is not available with XCELIUM?

Thanks in advance.




coverage

Specman’s Callback Coverage API

Our customers’ tests have become more complex, longer, and consume more resources than before. This increases the need to optimize the regression while not compromising on coverage. Some advanced...

[[ Click on the title to access the full blog on the Cadence Community site. ]]




coverage

Specman: Analyze Your Coverage with Python

In the former blog about Python and Specman: Specman: Python Is here!, we described the technical information around Specman-Python integration. Since Python provides so many easy to use existing libraries in various fields, it is very tempting to leverage these cool Python apps.

Coverage has always been the center of the verification methodology, however in the last few years it gets even more focus as people develop advanced utilities, usually using Machine Learning aids. Anyhow, any attempt to leverage your coverage usually starts with some analysis of the behavior and trends of some typical tests. Visualizing the data makes it easier to understand, analyze, and communicate. Fortunately, Python has many Visualization libraries.

In this blog, we show an example of how you can use the plotting Python library (matplotlib) to easily display coverage information during a run. In this blog, we use the Specman Coverage API to extract coverage data, and a Python module to display coverage grades interactively during a single run and the way to connect both.

Before we look at the example, if you have read the former blog about Specman and Python and were concerned about the fact that python3 is not supported, we are glad to update that in Specman 19.09, Python3 is now supported (in addition to Python2).

The Testcase
Let’s say I have a stable verification environment and I want to make it more efficient. For example: I want to check whether I can make the tests shorter while hardly harming the coverage. I am not sure exactly how to attack this task, so a good place to start is to visually analyze the behavior of the coverage on some typical test I chose. The first thing we need to do is to extract the coverage information of the interesting entities. This can be done using the old Coverage API. 

Coverage API
Coverage API is a simple interface to extract coverage information at a certain point. It is implemented through a predefined struct type named user_cover_struct. To use it, you need to do the following:

  1. Define a child of user_cover_structusing like inheritance (my_cover_struct below).
  2. Extend its relevant methods (in our example we extend only the end_group() method) and access the relevant members (you can read about the other available methods and members in cdnshelp).
  3. Create an instance of the user_cover_structchild and call the predefined scan_cover() method whenever you want to query the data (even in every cycle). Calling this method will result in calling the methods you extended in step 2.  

 The code example below demonstrates these three steps. We chose to extend the end_group() method and we keep the group grade in some local variable. Note that we divide it by 100,000,000 to get a number between 0 to 1 since the grade in this API is an integer from 0 to 100,000,000. 

 struct my_cover_struct like user_cover_struct {
      !cur_group_grade:real;
   
      //Here we extend user_cover_struct methods
      end_group() is also {
      cur_group_grade = group_grade/100000000;        
      }
};
 
extend sys{
      !cover_info : my_cover_struct;
       run() is also {
          start monitor_cover ();
     };
     
     monitor_cover() @any is {
         cover_info = new;
         
         while(TRUE) {
             // wait some delay, for example –
             wait [10000] * cycles;
          
            // scan the packet.packet_cover cover group
            compute cover_info.scan_cover("packet.packet_cover");
          };//while
      };// monitor_cover
};//sys

Pass the Data to a Python Module
After we have extracted the group grade, we need to pass the grade along with the cycle and the coverage group name (assuming there are a few) to a Python module. We will take a look at the Python module itself later. For now, we will first take a look at how to pass the information from the e code to Python. Note that in addition to passing the grade at certain points (addVal method), we need an initialization method (init_plot) with the number of cycles, so that the X axis can be drawn at the beginning, and end_plot() to mark interesting points on the plot at the end. But to begin with, let’s have empty methods on the Python side and make sure we can just call them from the e code.

 # plot_i.py
def init_plot(numCycles):
    print (numCycles)
def addVal(groupName,cycle,grade):
    print (groupName,cycle,grade)
def end_plot():
    print ("end_plot") 

And add the calls from e code:

struct my_cover_struct like user_cover_struct {
     @import_python(module_name="plot_i", python_name="addVal")
     addVal(groupName:string, cycle:int,grade:real) is imported;
  
     !cur_group_grade:real;
  
     //Here we extend user_cover_struct methods
     end_group() is also {
         cur_group_grade = group_grade/100000000;
         
        //Pass the values to the Python module
         addVal(group_name,sys.time, cur_group_grade);      
     }   //end_group
};//user_cover_struct
 
extend sys{
     @import_python(module_name="plot_i", python_name="init_plot"
     init_plot(numCycles:int) is imported;
    
     @import_python(module_name="plot_i", python_name="end_plot")
     end_plot() is imported;
    
     !cover_info : my_cover_struct;
     run() is also {
         start scenario();
    };
    
    scenario() @any is {
         //initialize the plot in python
         init_plot(numCycles);
        
         while(sys.time<numCycles)
        {
             //Here you add your logic     
             
            //get the current coverage information for packet
            cover_info = new;
            var num_items:=  cover_info.scan_cover("packet.packet_cover");
           
            //Here you add your logic       
        
        };//while
        
        //Finish the plot in python
        end_plot();
   
    }//scenario
}//sys
 
  • The green lines define the methods as they are called from the e
  • The blue lines are pre-defined annotations that state that the method in the following line is imported from Python and define the Python module and the name of the method in it.
  • The red lines are the calls to the Python methods.

 Before running this, note that you need to ensure that Specman finds the Python include and lib directories, and Python finds our Python module. To do this, you need to define a few environment variables: SPECMAN_PYTHON_INCLUDE_DIR, SPECMAN_PYTHON_LIB_DIR, and PYTHONPATH. 

 The Python Module to Draw the Plot
After we extracted the coverage information and ensured that we can pass it to a Python module, we need to display this data in the Python module. There are many code examples out there for drawing a graph with Python, especially with matplotlib. You can either accumulate the data and draw a graph at the end of the run or draw a graph interactively during the run itself- which is very useful especially for long runs.

Below is a code that draws the coverage grade of multiple groups interactively during the run and at the end of the run it prints circles around the maximum point and adds some text to it. I am new to Python so there might be better or simpler ways to do so, but it does the work. The cool thing is that there are so many examples to rely on that you can produce this kind of code very fast.

# plot_i.py
import matplotlib
import matplotlib.pyplot as plt
plt.style.use('bmh')
#set interactive mode
plt.ion()
fig = plt.figure(1)
ax = fig.add_subplot(111)
# Holds a specific cover group
class CGroup:
    def __init__(self, name, cycle,grade ):
        self.name = name
        self.XCycles=[]
        self.XCycles.append(cycle)
        self.YGrades=[]
        self.YGrades.append(grade)  
        self.line_Object= ax.plot(self.XCycles, self.YGrades,label=name)[-1]             
        self.firstMaxCycle=cycle
        self.firstMaxGrade=grade
    def add(self,cycle,grade):
        self.XCycles.append(cycle)
        self.YGrades.append(grade)
        if grade>self.firstMaxGrade:
            self.firstMaxGrade=grade
            self.firstMaxCycle=cycle          
        self.line_Object.set_xdata(self.XCycles)
        self.line_Object.set_ydata(self.YGrades)
        plt.legend(shadow=True)
        fig.canvas.draw()
     
#Holds all the data of all cover groups   
class CData:
    groupsList=[]
    def add (self,groupName,cycle,grade):
        found=0
        for group in self.groupsList:
            if groupName in group.name:
                group.add(cycle,grade)
                found=1
                break
        if found==0:
            obj=CGroup(groupName,cycle,grade)
            self.groupsList.append(obj)
     
    def drawFirstMaxGrade(self):
        for group in self.groupsList:
            left, right = plt.xlim()
            x=group.firstMaxCycle
            y=group.firstMaxGrade
           
            #draw arrow
            #ax.annotate("first maximum grade", xy=(x,y),
            #xytext=(right-50, 0.4),arrowprops=dict(facecolor='blue', shrink=0.05),)
           
            #mark the points on the plot
            plt.scatter(group.firstMaxCycle, group.firstMaxGrade,color=group.line_Object.get_color())
          
            #Add text next to the point   
            text='cycle:'+str(x)+' grade:'+str(y)   
            plt.text(x+3, y-0.1, text, fontsize=9,  bbox=dict(boxstyle='round4',color=group.line_Object.get_color()))                                                                      
       
#Global data
myData=CData()
 
#Initialize the plot, should be called once
def init_plot(numCycles):
    plt.xlabel('cycles')
    plt.ylabel('grade')   
    plt.title('Grade over time')  
    plt.ylim(0,1)
    plt.xlim(0,numCycles)
 
#Add values to the plot
def addVal(groupName,cycle,grade):
    myData.add(groupName,cycle,grade)
#Mark interesting points on the plot and keep it shown
def end_plot():
    plt.ioff();
    myData.drawFirstMaxGrade(); 
   
    #Make sure the plot is being shown
    plt.show();
#uncomment the following lines to run this script with simple example to make sure #it runs properly regardless of the Specman interaction
#init_plot(300)
#addVal("xx",1,0)
#addVal("yy",1,0)
#addVal("xx",50,0.3)
#addVal("yy",60,0.4)
#addVal("xx",100,0.8)
#addVal("xx",120,0.8)
#addVal("xx",180,0.8)
#addVal("yy",200,0.9)
#addVal("yy",210,0.9)
#addVal("yy",290,0.9)
#end_plot()
 

 In the example we used, we had two interesting entities: packet and state_machine, thus we had two equivalent coverage groups. When running our example connecting to the Python module, we get the following graph which is displayed interactively during the run.

 

    

 

When analyzing this specific example, we can see two things. First, packet gets to a high coverage quite fast and significant part of the run does not contribute to its coverage. On the other hand, something interesting happens relating to state_machine around cycle 700 which suddenly boosts its coverage. The next step would be to try to dump graphic information relating to other entities and see if something noticeable happens around cycle 700.

To run a complete example, you can download the files from: https://github.com/okirsh/Specman-Python/

Do you feel like analyzing the coverage behavior in your environment? We will be happy to hear about your outcomes and other usages of the Python interface.

Orit Kirshenberg
Specman team




coverage

Specman’s Callback Coverage API

Our customers’ tests have become more complex, longer, and consume more resources than before. This increases the need to optimize the regression while not compromising on coverage.

Some advanced customers of Specman use Machine Learning based solutions to optimize the regressions while some use simpler solutions. Based on a request of an advanced customer, we added a new Coverage API in Specman 19.09 called Coverage Callback. In 20.03, we have further enhanced this API by adding more options. Now there are two Coverage APIs that provide coverage information during the run (the old scan_cover API and this new Callback API). This blog presents these two APIs and compares between them while focusing on the newer one.

Before we get into the specifics of each API, let’s discuss what is common between these APIs and why we need them. Typically, people observe the coverage model after the test ends, and get to know the overall contribution of the test to the coverage. With these two APIs, you can observe the coverage model during the test, and hence, get more insight into the test progress.

Are you wondering about what you can do with this information? Let’s look at some examples.

  1. Recognize cases when the test continues to run long after it already reached its coverage goal.
  2. View the performance of the coverage curve. If a test is “stuck” at the same grade for a long time, this might indicate that the test is not very good and is just a waste of resource.

These analyses can be performed in the test itself, and then a test can decide to either stop the run, or change something in it configuration, or – post run. You can also present them visually for some analysis, as shown in the blog: Analyze Your Coverage with Python.

scan_cover API (or “Scanning the Coverage Model”)

With this API you can get the current status for any cover group or item you are interested in at any point in time during the test (by calling scan_cover()). It is very simple to use; however it has performance penalty. For getting the coverage grade of any cover group during the test, you should
1. Trigger the scan_cover at any time when you want the coverage model to be scanned.
2. Implement the scan_cover related methods, such as start_item() and end_bucket(). In these methods, you can query the current grade of group/item/bucket.
The blog mentioned earlier: Analyze Your Coverage with Python describes the details and provides an example.

Callback API

The Callback API enables you to get a callback for a desired cover group(s), whenever it is sampled. This API also provides various query methods for getting coverage related information such as what the current sampled value is. So, in essence, it is similar to scan_cover API, but as the phrase says: “same same but different”:

  1. Callback API has almost no performance penalty while scan_cover API does.
  2. Callback API contains a richer set of query methods that provide a lot of information about the current sampled value (vs just the grade with scan_cover).
  3. Using scan_cover API, you decide when you want to query the coverage information (you call scan_cover), while with the Callback API you query the coverage information when the coverage is sampled (from do_callback). So, scan_cover gives you more flexibility, but you do need to find the right timing for this call.

There is no absolute advantage of either of these APIs, this only depends on what you want to do.  

Callback API details

The Callback API is based on a predefined struct called: cover_sampling_callback. In order to use this API, you need to:

  1. Define a struct inheriting cover_sampling_callback (cover_cb_save_data below)
    1. Extend the predefined do_callback() method. This method is a hook being called whenever any of the cover groups that are registered to the cover_sampling_callback instance is being sampled.
    2. From do_callback() you can access coverage data by using queries such as: is_currently_per_type(), get_current_group_grade() and get_current_cover_group() (as in the example below) and many more such as: get_relevant_group_layers() and get_simple_cross_sampled_bucket_name().
  2. Register the desired cover group(s) to this struct instance using the register() method.

Take a look at the following code:

// Define a coverage callback.
// Its behavior – print to screen the current grade.
struct cover_cb_save_data like cover_sampling_callback {
    do_callback() is only {
       // In this example, we care only about the per_type grade, and not per_instance
       if is_currently_per_type() {           
            var cur_grade : real = get_current_group_grade();
            sys.save_data (get_current_cover_group().get_name(), cur_grade);
        };//if
    };//do_callback()
};// cover_cb_send_data


extend sys {
    !cb : cover_cb_save_data;

   // Instantiate the coverage callback, and register to it two of my coverage groups
    run() is also {
       cb  = new  with {
        var gr1:=rf_manager.get_struct_by_name("packet").get_cover_group("packet_cover");
        .register(gr1);
        var gr2:=rf_manager.get_struct_by_name("sys").get_cover_group("mem_cover");
       .register(gr2);
       };//new  
    };//run()

  save_data(group_name : string, group_grade : real) is {
        //here you either print the values to the screen, update a graph you show or save to a db 
  };// save_data
};//sys

In the blog Analyze Your Coverage with Python mentioned above, we show an example of how you can use the scan_cover API to extract coverage information during the run, and then use the Specman-Python API to display the coverage interactively during the run (using plotting Python library - matplotlib). If you find this usage interesting and you want to use the same example, by the Callback API instead of the scan_cover API, you can download the full example from GIT from here: https://github.com/efratcdn/cover_callback.

Specman Team

 

 




coverage

IMC: toggle coverage for package array

Hello!

I have input signal like this  ->  input  wire [ADM_NUM-1:0][1:0] m_axi_ddr_rresp.

When i want to analyze coverage from IMC  this signal not covered!

Can i collect coverage for this signal?

 




coverage

Can't collect AXI4 burst_started coverage

I have a problem connected with my AXI4 coverage.

I enable coverage collection in AXI4 

      set_config_int("axi4_active_slave_agent_0.monitor.coverModel", "burst_started_enable", 1);
      set_config_int("axi4_active_slave_agent_0.monitor.coverModel", "coverageEnable", 1);

but i don't have a result.

I think the problem in Callback, but i try to connect all callback and i don't have positive result.

Can you help me?




coverage

Coverage error

Hi  all,

          I am getting this warning in while generating the coverage report, can you help me to clear this warning?

ncsim: *W,COVOPM: Coverage configuration file command "set_covergroup -optimize_model" can be specified to improve the performance and scalability of coverage model containing SV covergroups. It may be noted that subsequent merging of a coverage database saved with this command and a coverage database saved without this command is not allowed.




coverage

Is it possible to get a diff between two coverage databases in IMC?

I'm in the process of weeding a regression test list. I have a coverage database from the full regression list and would like to diff it with the coverage database from the new reduced regression test list. If possible I would than like to trace back any buckets covered with the full list, but not with the partial list, into the original tests that covered them.

Is that possible using IMC? if not, is it possible to do from Specman itself?

(Note that we're not using vManager)

Thanks,

Avidan




coverage

Creating transition coverage bins using a queue or dynamically

I want to write a transition coverage on an enumeration. One of the parts of that transition is a queue of the enum. I construct this queue in my constructor. Considering the example below, how would one go about it.

In my coverage bin I can create a range like this A => [queue1Enum[0]:queue1Enum[$]] => [queue2Enum[0]:queue2Enum[$]]. But I only get first and last element then.

typedef enum { red, d_green, d_blue, e_yellow, e_white, e_black } Colors;
 Colors dColors[$];
 Colors eColors[$];
 Lcolors = Colors.first();
 do begin
  if (Lcolors[0].name=='d') begin
   dColors.push_back(Lcolors);
  end
  if (Lcolors[0].name=='e') begin
   eColors.push_back(Lcolors);
  end
 end while(Lcolors != Lcolors.first())

 covergroup cgTest with function sample(Colors c);
   cpTran : coverpoint c{
      bins t[] = (red => dColors =>eColors);   
   }
 endgroup

bins t[] should come out like this(red=>d_blue,d_green=>e_yellow,e_white)

 




coverage

Cloudflare's global coverage

US web infrastructure and cyber security company Cloudflare wants to improve people’s internet experiences through affordable, reliable and accessible interconnection points, especially in less privileged parts of the world. 




coverage

Coronavirus Coverage – Policy Issues to Ponder

The global impact of Coronavirus is now undeniable, as one might expect from a highly contagious disease originating from the world’s second largest economy and most populous nation.  Insurance claims are bound to follow and whilst market...




coverage

The time has come for universal health coverage -- by Patrick L. Osewe

Investments in universal health coverage are investments in economic growth. They play a critical role in leveraging opportunities, anticipating challenges, and delivering the knowledge, expertise, and financing countries need to achieve universal health coverage.




coverage

News Coverage of Teen Suicides May Have Ripple Effect

Title: News Coverage of Teen Suicides May Have Ripple Effect
Category: Health News
Created: 5/2/2014 7:35:00 AM
Last Editorial Review: 5/2/2014 12:00:00 AM




coverage

ACA Medicaid Expansion and Insurance Coverage Among New Mothers Living in Poverty

BACKGROUND:

Medicaid plays a critical role during the perinatal period, but pregnancy-related Medicaid eligibility only extends for 60 days post partum. In 2014, the Affordable Care Act’s (ACA’s) Medicaid expansions increased adult Medicaid eligibility to 138% of the federal poverty level in participating states, allowing eligible new mothers to remain covered after pregnancy-related coverage expires. We investigate the impact of ACA Medicaid expansions on insurance coverage among new mothers living in poverty.

METHODS:

We define new mothers living in poverty as women ages 19 to 44 with incomes below the federal poverty level who report giving birth in the past 12 months. We use 2010–2017 American Community Survey data and a difference-in-differences approach using parental Medicaid-eligibility thresholds to estimate the effect of ACA Medicaid expansions on insurance coverage among poor new mothers.

RESULTS:

A 100-percentage-point increase in parental Medicaid-eligibility is associated with an 8.8-percentage-point decrease (P < .001) in uninsurance, a 13.2-percentage-point increase (P < .001) in Medicaid coverage, and a 4.4-percentage-point decrease in private or other coverage (P = .001) among poor new mothers. The average increase in Medicaid eligibility is associated with a 28% decrease in uninsurance, a 13% increase in Medicaid coverage, and an 18% decline in private or other insurance among poor new mothers in expansion states. However, in 2017, there were ~142 000 remaining uninsured, poor new mothers.

CONCLUSIONS:

ACA Medicaid expansions are associated with increased Medicaid coverage and reduced uninsurance among poor new mothers. Opportunities remain for expansion and nonexpansion states to increase insurance coverage among new mothers living in poverty.




coverage

National Surgical, Obstetric, and Anesthesia Plans Supporting the Vision of Universal Health Coverage




coverage

Aged care royal commission has had a third of the coverage of banks' bad behaviour. It doesn't add up

The theory in media circles is that the banking royal commission was big news because "everyone has a bank account". There seems to be no corresponding extrapolation for aged care, writes Anne Connolly.




coverage

ESPN2 will air 12 hours of esports coverage on April 5th

TV networks are already trying to fill the void in sports coverage left by the COVID-19 pandemic, but ESPN is going all-out. The broadcaster's ESPN2 channel is airing a 12-hour ESPN Esports Day on April 5th starting at 12PM Eastern, and there will be some high-profile live events in the mix. The highlight may be the second F1 Esports Virtual Grand Prix at 3PM, when racers (including some current Formula 1 drivers) will compete online in a digital version of Australia's Albert Park. The marathon will also represent Rocket League's debut on ESPN television (it was only streamed before) with the Season 8 World Championship's Grand Finals broadcasting live at 4:30PM.




coverage

Funeral insurer who sold coverage to babies barred from taking on customers

Funeral insurer Youpla, slammed by the banking royal commission in a previous brand iteration for misleading Indigenous customers, has now been denied a licence to provide financial services and prevented from signing up new customers.




coverage

What's on TV Thursday: 'Tommy' on CBS; coronavirus TV coverage

What's on TV Thursday, May 7: 'Tommy' on CBS; coronavirus TV coverage; movies on TV; TV talk shows and more




coverage

U.S. Reaches Agreements with Kings Mountain, N.C., and Sandy Springs, Ga., to Terminate Coverage from Preclearance of the Voting Rights Act

The Justice Department announced that it reached agreements this week with the city of Kings Mountain, N.C., and the city of Sandy Springs, Ga., in the form of consent decrees filed in the U.S. District Court for the District of Columbia.



  • OPA Press Releases

coverage

Promoting Universal Health Coverage: Sharing a Prosperous and Healthy Future

Leaders from around the world gathered at the United Nations General Assembly in New York on 23 September for the first-ever United Nations High-Level Meeting (UNHLM) on Universal Health Coverage (UHC): Moving Together to Build a Healthier World.




coverage

New Bipartisan ChiPACC Act Provides Better Medicaid Coverage to Children in Need

WASHINGTON, D.C. – Five lawmakers introduced a bipartisan bill giving a full range of medical services to families with children who have life-limiting illnesses and who qualify for Medicaid, which currently has gaps in such coverage.

The Children’s Program of All-Inclusive Coordinated Care (ChiPACC) Act (H.R. 6560) would let states create comprehensive care programs for these children. Its authors are the Co-Chairs of the Congressional Childhood Cancer Caucus: Representatives Michael McCaul (R-TX), Jackie Speier (D-CA), G.K. Butterfield (D-NC), and Mike Kelly (R-PA), together with Representative Diana DeGette (D-CO), a senior member of the House Energy and Commerce Committee.

Families with children facing life-limiting illnesses need all the support they can get, and they should be empowered to seek out that support,” the bill’s sponsors said in a joint statement. “We owe it to these kids and their loved ones to help ensure more compassionate care in their most trying times.

Gaps in Medicaid coverage of hospice and palliative services have deprived many beneficiaries of the care they need because the program does not cover some of children’s unique medical needs.

Under this bill, the family of every child who qualifies for Medicaid will receive a specialized care plan covering a range of services – palliative, counseling, respite, expressive therapy and bereavement – providing them and their families greater comfort and peace of mind.

###

 




coverage

Did Media Coverage Enhance or Threaten the Viability of the G-20 Summit?

Editor’s Note: The National Perspectives on Global Leadership (NPGL) project reports on public perceptions of national leaders’ performance at important international events. This fifth installation of the NPGL Soundings provides insight on the issues facing leaders at the Seoul G-20 Summit and the coverage they received in their respective national media. Read the other commentary »

The week before the Seoul G-20 Summit was one in which the main newspapers read in Washington (The New York Times, The Washington Post and Financial Times) all focused their primary attention on the “currency war,” global imbalances, the debate on quantitative easing (QE 2), the struggle over whether there would be numerate current account targets or only words, and the US-China relationship. As early as Wednesday, November 10, The Washington Post front-page headline read: “Fed move at home trails U.S. to Seoul; Backlash from Europe; Obstacles emerge for key goals at G-20 economic summit.” By Thursday, November 11, things had gotten worse. “Deep fractures hit hopes of breakthrough; governments are unlikely to agree on a strategy to tackle economic imbalances” read the Financial Times headline on Alan Beattie’s article from Seoul. Friday, November 12, The New York Times front-page headline declared: “Obama’s Economic View is Rejected on World Stage; China, Britain and Germany Challenge U.S.; Trade Talks with Seoul Fail, Too.” By Saturday, the Financial Times concluded in its lead editorial: “G-20 show how not to run the world.”

From these reports, headlines and editorials it is clear that conflicts over policy once again dwarfed the progress on other issues and the geopolitical jockeying over the currency and imbalances issues took centre stage, weakening G-20 summits rather than strengthening them. Obama was painted as losing ground, supposedly reflecting lessening U.S. influence and failing to deliver concrete results. China, Germany and Brazil were seen to beat back the U.S. initiative to quantify targets on external imbalances. Given the effort that Korean leaders had put into achieving positive results and “consolidating” G-20 summits, it was, from this optical vantage point, disappointing, to say the least.

How was the Rebalancing Issue Dealt With?

At lower levels of visibility and intensity, however, things looked a bit different and more positive. Howard Schneider and Scott Wilson in Saturday’s edition of The Washington Post (November 13) gave a more balanced view of the outcomes. Their headline read: “G-20 nations agree to agree; Pledge to heed common rules; but economic standards have yet to be set.” They discerned progress toward new terrain that went beyond the agreement among G-20 finance ministers in October at Gyeongju, which other writers missed.

“By agreeing to set economic standards, the G-20 leaders moved into uncharted waters,” they wrote. “The deal rests on the premise that countries will take steps, possibly against their own short-term interests, if their economic policies are at odds with the wider well-being of the world economy. And leaders are committing to take such steps even before there’s an agreement on what criteria would be used to evaluate their policies.”

They continued: “In most general of terms, the statement adopted by the G-20 countries says that if the eventual guidelines identify a problem, this would ‘warrant an assessment of their nature and the root causes’ and should push countries to ‘preventive and corrective actions.’”

The Schneider-Wilson rendering went beyond the words of the communiqué to an understanding of what was going on in official channels over time to push this agenda forward in real policy, rather than declarative terms. As the Saturday, November 13, Financial Times’ editorial put it, “below the headline issues, however, the G-20 grouping is not completely impotent,” listing a number of other issues on which progress was made including International Monetary Fund (IMF) reform which the Financial Times thought might actually feed back into a stronger capacity to deal with “managing the global macroeconomy.”

The Role of President Barack Obama

Without doubt, the easy, simple, big-picture message coming out of Seoul was that Obama and the United States took a drubbing. And this did not help the G-20 either. The seeming inability of the U.S. to lead the other G-20 leaders toward an agreement in Seoul on global imbalances, the criticism of U.S. monetary easing and then, on top of it all, the inability to consummate a US-Korea trade deal, made it seem as if Obama went down swinging.

But again, below the surface of the simple, one got a different picture. Obama himself did not seem shaken or isolated at the Seoul summit by the swirl of forces around him. At his press conference, he spoke clearly and convincingly of the complexity of the task of policy coordination and the time it would take to work out the policies and the politics of adjustment.

“Naturally there’s an instinct to focus on the disagreements, otherwise these summits might not be very exciting,” he said. “In each of these successive summits we’ve made real progress,” he concluded. Tom Gjeltin, from NPR news, on the Gwen Ifyl Weekly News Roundup commented Saturday evening that the G-20 summits are different and that there is a “new pattern of leadership” emerging that is not quite there yet. Obama seems more aware of that and the time it takes for new leadership and new patterns of mutual adjustment to emerge. He may have taken a short-run hit, but he seems to have the vision it takes to connect this moment to the long-run trajectory.

Reflections on the Role of South Korea

From a U.S. vantage point, Seoul was one more stop in Asia as the president moved from India to Indonesia to Korea to Japan. It stood out, perhaps, in higher profile more as the locus of the most downbeat moments in the Asia tour, because of the combination of the apparent lack of decisive progress at the G-20 along with the needless circumstance of two presidents failing to find a path forward on something they both wanted.

From a Korean vantage point, the summit itself was an event of immense importance for Korea’s emergence on the world stage as an industrial democracy that had engineered a massive social and economic transformation in the last 50 years, culminating in being the first non-G8 country to chair the G-20 summit. No one can fault Korea’s efforts to reach significant results. However, the fact is that the Seoul Summit’s achievements, which even in the rebalancing arena were more significant than they appeared to most (see Schneider and Wilson), but included substantial progress on financial regulatory reform, international institutional reform (specifically on the IMF), on development and on global financial safety nets, were seen to be less than hoped for. This was not the legacy the Koreans were looking for, unfortunately.

Conflicts among the major players on what came to be seen as the major issue all but wiped out the serious workmanlike progress in policy channels. The leaders level interactions at G-20 summits has yet to catch up to the highly significant degree of systemic institutionalization of the policy process of the G-20 among ministers of finance, presidents of central banks, G-20 deputies and Sherpas, where the policy work really goes on. On its watch, Korea moved the agenda in the policy track forward in a myriad of significant ways. It will be left to the French and French President Nicolas Sarkozy to see if they can bring the leaders into the positive-sum game arrangements that are going on in the policy channels and raise the game level of leaders to that of G-20 senior officials.

Publication: NPGL Soundings, November 2010
     
 
 




coverage

Social Security coverage for state and local government workers: A reconsideration


Since it was created in 1935, Social Security has grown from covering about half of the work force to covering nearly all workers.  The largest remaining exempted group is a subset of state and local government workers (SLGWs).  As of 2008, Social Security did not cover about 27 percent of the 23.8 million SLGWs (Congressional Research Service 2011).  Non-coverage of SLGWs is concentrated in certain states scattered around the country and includes workers in a diverse set of jobs, ranging from administrators to custodial staff.  Some police and fire department employees are not covered.  About 40 percent of public school teachers are not covered by Social Security (Kan and Alderman 2014).    

Under current law, state and local governments that do not offer their own retirement plan must enroll their employees in Social Security.  But if it does offer a retirement plan, the state or local government can choose whether to enroll its workers in Social Security.  

This paper reviews and extends discussion on whether state and local government workers should face mandatory coverage in Social Security.[1]  Relative to earlier work, we focus on links between this issue and recent developments in state and local pensions.  Although some of the issues apply equally to both existing and newly hired SLGWs, it is most natural to focus on whether newly hired employees should be brought into Social Security.[2]  

The first thing to note about this topic is that it is purely a transitional issue.  If all SLGWs were already currently enrolled in Social Security, there would not be a serious discussion about whether they should be removed.  For example, there is no discussion of whether the existing three quarters of all SLGWs that are enrolled in Social Security should be removed from coverage.

Bringing state and local government workers into the system would allow Social Security to reach the goal of providing retirement security for all workers.  The effects on Social Security finances are mixed.  Bringing SLGWs into the system would also help shore up Social Security finances over the next few decades and, under common scoring methods, push the date of trust fund insolvency back by one year, but after that, the cost of increased benefit payments would offset those improvements. 

Mandatory coverage would also be fairer.  Other workers pay, via payroll taxes, the “legacy” costs associated with the creation of Social Security as a pay-as-you-go system.   Early generations of Social Security beneficiaries received far more in payouts than they contributed to the system and those net costs are now being paid by current and future generations.  There appears to be no convincing reason why certain state and local workers should be exempt from this societal obligation.  As a result of this fact and the short-term benefit to the program’s finances, most major proposals and commissions to reform Social Security and all commissions to shore up the long-term federal budget have included the idea of mandatory coverage of newly hired SLGWs.

While these issues are long-standing, recent developments concerning state and local pensions have raised the issue of mandatory coverage in a new light. Linking the funding status of state and local pension plans and the potential risk faced by those employees with the mandatory coverage question is a principal goal of this paper.  One factor is that many state and local government pension plans are facing significant underfunding of promised pension benefits.  In a few municipal bankruptcy cases, the reduction of promised benefits for both current employees and those who have already retired has been discussed.  The potential vulnerability of these benefits emphasizes the importance of Social Security coverage, and naturally invites a rethinking of whether newly hired SLGWs should be required to join the program.  On the other hand, the same pension funding problems imply that any policy that adds newly-hired workers to Social Security, and thus requires the state to pay its share of those contributions, would create added overall costs for state and local governments at a time when pension promises are already hard to meet.  The change might also divert a portion of existing employee or employer contributions to Social Security and away from the state pension program. 

We provide two key results linking state government pension funding status and SLGW coverage. First, we show that states with governmental pension plans that have greater levels of underfunding tend also to have a smaller proportion of SLGW workers that are covered by Social Security.  This tends to raise the retirement security risks faced by those workers and provides further fuel for mandatory coverage.  While one can debate whether future public pension commitments or future Social Security promises are more risky, a solution resulting in less of both is the worst possible outcome for the workers in question.  Second, we show that state pension benefit levels for career workers are somewhat compensatory, in that states with lower rates of Social Security coverage for SLGWs tend to have somewhat higher pension benefit levels.  The extent to which promised but underfunded benefits actually compensate for the higher risk to individual workers of non-Social Security coverage is an open question, though.  

Mandatory coverage of newly hired SLGWs could improve the security of their retirement benefits (by diversifying the sources of their retirement income), raise average benefit levels in many cases (even assuming significant changes in state and local government pensions in response to mandatory coverage), and would improve the quality of benefits received, including provisions for full inflation indexation, and dependent, survivor and disability benefits in Social Security that are superior to those in most state pension plans.  The ability to accrue and receive Social Security benefits would be particularly valuable for the many SLGWs who leave public service either without ever having been vested in a government pension or having been vested but not reaching the steep part of the benefit accrual path.  

Just as there is strong support for mandatory coverage in the Social Security community and literature, there is strong opposition to such a change in elements of the state and local government pension world.  The two groups that are most consistently and strongly opposed to mandatory coverage of newly hired SLGWs are the two parties most directly affected – state and local governments that do not already provide such coverage and their uncovered employees.  Opponents cite the higher cost to both employees and the state and local government for providing that coverage and the potential for losing currently promised pension benefits. They note that public pensions – unlike Social Security – can invest in risky assets and thus can provide better benefits at lower cost.  This, of course, is a best-case alternative as losses among those risky assets could also increase pressure on pension finances.

There is nothing inconsistent about the two sides of these arguments; one set tends to focus on benefits, the other on costs.  They can be, and probably are, all true simultaneously.  There is also a constitutional issue that used to hang over the whole debate – whether the federal government has the right to tax the states and local government units in their roles as employers – but that seems resolved at this point.

Section II of this paper discusses the history and current status of Social Security coverage for SLGWs.   Section III discusses mandatory coverage in the context of Social Security funding and the federal budget.  Section IV discusses the issues in the context of state and local budgets, existing pension plans, and the risks and benefits to employees of those governments.  Section V concludes.



[1] Earlier surveys of these issues provide excellent background.  See Government Accountability Office (1998), Munnell (2005), and Congressional Research Service (2011).

[2] A variety of related issues are beyond the scope of the paper, including in particular how best to close gaps between promised benefits and accruing assets in state and local pension plans and the level of those benefits.   


Note: A revised version of this paper is forthcoming in The Journal of Retirement.

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