LXIIe congrès annuel de l’AFSE, 2013

Graph

The image is a line graph depicting the average annual growth rate of labor productivity per hour in the USA from 1891 to 2012. The graph includes both observed and smoothed indicators. The x-axis represents the years from 1890 to 2012, while the y-axis shows the percentage growth rate ranging from -3% to 10%. The observed data is represented by a jagged line, indicating fluctuations in the growth rate over the years. The smoothed data, represented by a smoother line, is derived using Hodrick-Prescott filtering with a lambda value of 500, which helps to smooth out short-term fluctuations and highlight longer-term trends. Key points on the smoothed line are annotated with specific growth rates and time periods: - Around 1915, the growth rate peaks at approximately 4.75%. - Another peak occurs around 1945, also at about 4.75%. - A significant decline is noted around 1973, with the growth rate dropping to around 2.0% by 2012. The graph illustrates notable periods of growth and decline in labor productivity over more than a century, providing insights into economic trends and productivity changes in the USA.

Average annual growth rate of labour productivity per hour in the USA Observed and smoothed indicator Whole economy – 1891-2012 – In %

Smoothed indicator through Hodrick-Prescott filtering (? = 500).

Bergeaud, Cette and Lecat [2014].

Graph 2

Description de l'image par IA : Line graph showing labor productivity growth rates from 1891 to 2012 for USA, Euro Area, UK, and Japan.

The image is a line graph depicting the average annual growth rate of labor productivity per hour for four regions: the United States, the Euro Area, the United Kingdom, and Japan, spanning from 1891 to 2012. The graph uses smoothed indicators through Hodrick-Prescott filtering with a lambda value of 500. The x-axis represents the years from 1891 to 2012, while the y-axis shows the growth rate in percentage, ranging from 0 to 8 percent. Four distinct lines represent each region: - The United States is depicted with a solid line. - The Euro Area is shown with a dashed line. - The United Kingdom is represented with a dotted line. - Japan is illustrated with a dash-dotted line. Key observations from the graph: - The United States shows a relatively steady growth rate with noticeable peaks around the early 20th century and post-World War II era. - The Euro Area exhibits significant fluctuations, with a sharp increase during the mid-20th century, peaking around the 1960s, followed by a decline and stabilization. - The United Kingdom's line shows a moderate growth rate with a peak around the mid-20th century. - Japan's line indicates a rapid growth rate starting from the mid-20th century, peaking around the 1970s, followed by a decline and stabilization. Overall, the graph illustrates the varying trends in labor productivity growth across these regions over more than a century.

Average annual growth rate of labour productivity per hour in the USA, the Euro Area, The United Kingdom and Japan Smoothed indicator Whole economy – 1891-2012 – In %

Smoothed indicator through Hodrick-Prescott filtering (? = 500).

Bergeaud, Cette and Lecat [2014].The Euro Area is here the aggregation of Germany, France, Italy, Spain, The Netherlands, and Finland. These six countries represent together, in 2012, 85¼ % of the total GDP of the Euro Area.

Graph 3

Description de l'image par IA : Line graph showing productivity levels from 1890 to 2012 for Euro Area, UK, and Japan relative to the US.

The image is a line graph depicting the level of productivity per hour relative to the current US level for three regions: the Euro Area, the United Kingdom, and Japan, spanning from 1890 to 2012. The productivity levels are expressed as a percentage of the US level. The x-axis represents the years from 1890 to 2012, marked at intervals of approximately 10 years. The y-axis represents the productivity percentage, ranging from 0 to 140 percent. Three distinct lines represent each region: 1. The Euro Area is depicted with a dashed line. 2. The United Kingdom is shown with a dotted line. 3. Japan is represented with a solid line. Key observations: - In 1890, the Euro Area had a productivity level around 60 percent of the US level. - The United Kingdom started at a similar level, slightly below 60 percent. - Japan began with a significantly lower productivity level, around 20 percent. Over the years, the productivity levels for all three regions show fluctuations: - The Euro Area experienced a general upward trend with some dips, notably around the 1940s and 1970s. - The United Kingdom also shows an upward trend with significant dips around the 1940s and 1970s. - Japan's productivity level shows a steady increase, especially notable from the 1950s onwards, surpassing the other two regions by the 2000s. By 2012, all three regions have converged to similar productivity levels, close to or slightly above 100 percent of the US level.

Level of productivity per hour relative to the current US level, in the Euro Area, The United Kingdom and Japan Whole economy – 1890-2012 – § 2005 ppp – In % of the US level

Bergeaud, Cette and Lecat [2014].The Euro Area is here the aggregation of Germany, France, Italy, Spain, The Netherlands, and Finland. These six countries represent together, in 2012, 85¼ % or the total GDP of the Euro Area.

Figure

Table showing changes in various economic indicators across five time periods from 1959 to 2012.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>1959-2012</th> <th>1959-1974</th> <th>1975-1995</th> <th>1995-2004</th> <th>2004-2012</th> </tr> <tr> <th>Investment</th> <td>-2.27</td> <td>-1.85</td> <td>-1.88</td> <td>-4.11</td> <td>-2.02</td> </tr> <tr> <th>ICT</th> <td>-7.16</td> <td>-6.02</td> <td>-8.26</td> <td>-8.95</td> <td>-4.46</td> </tr> <tr> <th>Computers</th> <td>-18.56</td> <td>-22.71</td> <td>-18.45</td> <td>-19.89</td> <td>-9.93</td> </tr> <tr> <th>Software</th> <td>-4.23</td> <td>-4.45</td> <td>-5.35</td> <td>-2.72</td> <td>-2.22</td> </tr> <tr> <th>Communication equipment</th> <td>-3.33</td> <td>-0.86</td> <td>-2.70</td> <td>-6.52</td> <td>-5.29</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table showing labor productivity growth and contributing factors from 1974-1995, 1995-2004, and 2004-2012.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>1974-1995</th> <th>1995-2004</th> <th>2004-2012</th> </tr> <tr> <th>Growthlabourproductivity（in%)[1]=[2]+[5] +[6] Contributions(inpercentagepoints) Capitaldeepening[2]=[3]+[4] ICT capital[3] Other capital[4] Labourcomposition[5] Totalfactorproductivity[6]=[7]+[10] ICTproducing sectors[7]=[8]+[9] Semi conductors[8] OtherICTsectors[9] Othersectors[10]</th> <td>1.56 0.74 0.41 0.33 0.26 0.56 0.36 0.09 0.27 0.20</td> <td>3.06 1.22 0.78 0.44 0.22 1.62 0.72 0.37 0.35 0.90</td> <td>1.56 0.74 0.36 0.38 0.34 0.48 0.28 0.14 0.14 0.20</td> </tr> </tbody> </table> </div> </body> </div>

Graph 4

Description de l'image par IA : Line graph showing investment growth rates from 1960 to 2010.

The image is a line graph depicting the annual growth rate of investment prices relative to GDP prices in the USA from 1960 to 2012. The graph features four distinct lines, each representing different categories of investments: 1. **Private fixed assets**: Represented by a solid line, this category shows relatively stable growth rates with minor fluctuations throughout the period. 2. **Computers**: Represented by a dotted line, this category exhibits significant volatility, with sharp peaks and troughs, particularly noticeable in the 1970s and 1980s. 3. **ICT (Information and Communication Technology)**: Represented by a dashed line, this category shows moderate fluctuations, generally staying within a narrower range compared to computers. 4. **Software**: Represented by a dash-dot line, this category also shows relatively stable growth rates, similar to private fixed assets, but with slightly more variation. The y-axis represents the annual growth rate in percentage, ranging from -40% to 5%. The x-axis spans the years from 1960 to 2012. The graph highlights the varying trends and volatility in investment prices across these categories over the specified period.

Annual growth rate of investment price relative to GDP price In the USA – 1960-2012 – In %

BEA.

Graph 5

Description de l'image par IA : Graph showing microprocessor performance growth from 1971 to 2009 for INTEL and AMD.

The image is a graph depicting the growth of microprocessor performance from 1971 to 2009. It is divided into three phases: Phase I, Phase II, and Phase III. The x-axis represents the years from 1971 to 2009, while the y-axis represents performance, ranging from 0.01 to 10,000. Phase I covers the period from 1971 to around 1985. During this phase, the performance growth is relatively slow and steady. Two lines represent the performance of microprocessors produced by INTEL and AMD, showing a gradual increase. Phase II spans from around 1985 to 1995. In this phase, the performance growth rate increases significantly. The lines for both INTEL and AMD show a steeper upward trend, indicating rapid advancements in microprocessor performance. Phase III covers the period from around 1995 to 2009. During this phase, the performance growth continues to accelerate, with both INTEL and AMD showing a sharp upward trajectory. The lines for both companies are closely aligned, indicating similar performance improvements. Overall, the graph illustrates the exponential growth in microprocessor performance over nearly four decades, highlighting significant advancements in technology during this period.

Growth of microprocessors performance Microprocessors produced by INTEL and AMD – 1971-2009

Pillai [2011].

Graph 6

Description de l'image par IA : Line graph showing ICT capital coefficient from 1970 to 2009 for the U.S., Euro Area, U.K., and Japan.

The image is a line graph titled "ICT capital coefficient (x100), at current prices (Ratio of ICT capital stock to GDP in current prices)" covering the period from 1970 to 2009. The graph depicts the trends for four regions: the United States, the Euro Area, the United Kingdom, and Japan. - The x-axis represents the years from 1970 to 2009. - The y-axis represents the ICT capital coefficient, scaled by a factor of 100. Four distinct lines represent each region: 1. The United States is represented by a solid black line. 2. The Euro Area is represented by a dashed black line. 3. The United Kingdom is represented by a dotted black line. 4. Japan is represented by a dash-dot black line. Key observations: - The United States shows a general upward trend, starting around 4 in 1970, peaking around 11 in the early 2000s, and then slightly declining towards 2009. - The Euro Area starts at a similar level to the United States in 1970, but with a more gradual increase, peaking around 8 in the early 2000s, and then slightly declining towards 2009. - The United Kingdom starts lower than the United States and Euro Area in 1970, with a steady increase, peaking around 7 in the early 2000s, and then slightly declining towards 2009. - Japan starts at a similar level to the United Kingdom in 1970, with a steady increase, peaking around 8 in the early 2000s, and then slightly declining towards 2009. Overall, the graph illustrates the growth and subsequent stabilization or slight decline in the ICT capital coefficient across these regions over nearly four decades.

ICT capital coefficient (x100), at current prices (Ratio of ICT capital stock to GDP in current prices) Scope: The whole economy – 1970-2009

Cette and Lopez [2012].The Euro Area is here the aggregation of Germany, France, Italy, Spain, The Netherlands, Austria and Finland. These seven countries represent together, in 2012, 88½ % or the total GDP of the Euro Area.

Figure

Description de l'image par IA : A table comparing percentages of US and foreign companies from 1971-1989 and 1990-2008.

Graph 7

Description de l'image par IA : Bar chart comparing ICT capital coefficients among Euro Area, UK, and Japan.

The image is a bar graph comparing the sources of ICT capital coefficient gaps with the US in 2008 across three regions: the Euro Area, the United Kingdom, and Japan. The graph uses three different shades to represent the sources of the gap: user cost, education, and rigidities. 1. **Euro Area**: - The bar is divided into three sections. - The bottom section, in black, represents user cost. - The middle section, in dark gray, represents education. - The top section, in light gray, represents rigidities. - The total height of the bar is slightly above 100%. 2. **United Kingdom**: - The bar is divided into three sections. - The bottom section, in black, represents user cost. - The middle section, in dark gray, represents education. - The top section, in light gray, represents rigidities. - The total height of the bar is slightly above 140%. 3. **Japan**: - The bar is divided into three sections. - The bottom section, in black, represents user cost. - The middle section, in dark gray, represents education. - The top section, in light gray, represents rigidities. - The total height of the bar is slightly above 100%. The graph visually indicates that the United Kingdom has a significantly larger ICT capital coefficient gap with the US compared to the Euro Area and Japan, primarily due to higher rigidities.

Sources of ICT capital coefficient gap with the US in 2008 Whole economy – In % of the gap

Cette and Lopez [2012].The Euro Area is here the aggregation of Germany, France, Italy, Spain, The Netherlands, Austria and Finland. These seven countries represent together, in 2012, 88½ % or the total GDP of the Euro Area.

Figure

Table comparing ICP percentages and standard deviations for years 2005 and 2010.

Figure

Table comparing percentages and standard deviations for OCP and JTC in 2005 and 2010.

Figure

Table comparing IT use and workplace factors between 2005 and 2010, including means, standard deviations, and frequency distributions.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th colspan="2">2005</th> <th colspan="2">2010</th> <td></td> <td></td> </tr> <tr> <td></td> <th>Mean</th> <th>SD</th> <th>Mean</th> <th>SD</th> <th>Min</th> <th>Max</th> </tr> <tr> <th>FrequencyofITuse</th> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>Internetuse</th> <td>2.45</td> <td>1.61</td> <td>2.70</td> <td>1.65</td> <td></td> <td>5</td> </tr> <tr> <th>1.Never</th> <td>46.77</td> <td>0.5</td> <td>39.55</td> <td>0.5</td> <td></td> <td></td> </tr> <tr> <th>2.Almostnever</th> <td>11.33</td> <td>0.32</td> <td>11.82</td> <td>0.32</td> <td></td> <td></td> </tr> <tr> <th>3.Around1/4ofthetime</th> <td>12.63</td> <td>0.33</td> <td>13.12</td> <td>0.34</td> <td>0</td> <td>100</td> </tr> <tr> <th>4.Around halfof the time</th> <td>8.86</td> <td>0.28</td> <td>10.04</td> <td>0.3</td> <td></td> <td></td> </tr> <tr> <th>5.Almostallofthetimeorall ofthe time</th> <td>20.41</td> <td>0.4</td> <td>25.47</td> <td>0.44</td> <td></td> <td></td> </tr> <tr> <th>Computeruse</th> <td>2.55</td> <td>1.57</td> <td>2.73</td> <td>1.57</td> <td></td> <td>5</td> </tr> <tr> <th>1.Never</th> <td>38.14</td> <td>0.49</td> <td>32.01</td> <td>0.47</td> <td></td> <td></td> </tr> <tr> <th>2.Less than 1/4ofthe time</th> <td>19.77</td> <td>0.4</td> <td>20.60</td> <td>0.41</td> <td></td> <td></td> </tr> <tr> <th>3.Lessthan3/4ofthe time</th> <td>12.05</td> <td>0.33</td> <td>13.31</td> <td>0.34</td> <td>0</td> <td>100</td> </tr> <tr> <th>4.Almostallofthetime</th> <td>8.92</td> <td>0.29</td> <td>10.70</td> <td>0.31</td> <td></td> <td></td> </tr> <tr> <th>5.Allofthe time</th> <td>21.12</td> <td>0.41</td> <td>23.38</td> <td>0.42</td> <td></td> <td></td> </tr> <tr> <th>NOP</th> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>Workschedulefixedbyemployer</th> <td>16.79</td> <td>0.37</td> <td>22.02</td> <td>0.41</td> <td>0</td> <td>100</td> </tr> <tr> <th>Meeting precise quality standards</th> <td>76.01</td> <td>0.43</td> <td>76.67</td> <td>0.42</td> <td>0</td> <td>100</td> </tr> <tr> <th>Informationabouthealthand safety risks</th> <td>2.24</td> <td>0.69</td> <td>2.35</td> <td>0.66</td> <td>1</td> <td>3</td> </tr> <tr> <th>Qualityofthejobenvironment</th> <td>4.33</td> <td>1.61</td> <td>4.40</td> <td>1.55</td> <td>1</td> <td>6</td> </tr> <tr> <th>Formalassessment</th> <td>40.20</td> <td>0.49</td> <td>43.42</td> <td>0.5</td> <td>0</td> <td>100</td> </tr> <tr> <th>Jobrotation</th> <td>48.82</td> <td>0.5</td> <td>50.36</td> <td>0.5</td> <td>0</td> <td>100</td> </tr> <tr> <th>Telework</th> <td>7.57</td> <td>0.27</td> <td>11.02</td> <td>0.31</td> <td>0</td> <td>100</td> </tr> <tr> <th>Teamwork</th> <td>61.24</td> <td>0.49</td> <td>64.51</td> <td>0.48</td> <td>0</td> <td>100</td> </tr> <tr> <th>#obs.</th> <td colspan="2">11098</td> <td colspan="2">16354</td> <td></td> <td></td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with statistical data comparing IT use, NOP, and employee/job characteristics between 2005 and 2010.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>2005</th> <td></td> <td></td> <th>2010</th> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <th>ICP</th> <th>OCP</th> <th>JTC</th> <th>ICP</th> <th>OCP</th> <th>JTC</th> <td></td> <td></td> <td></td> </tr> <tr> <th>FrequencyofITuse</th> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>Internetuse</th> <td>-0.001 (0.024)</td> <td>0.065*** (0.01)</td> <td>0.144*** (0.03)</td> <td>0.024* (0.014)</td> <td>0.046 (0.016)</td> <td>0.08* (0.031)</td> <td></td> <td></td> <td></td> </tr> <tr> <th>Computeruse</th> <td>0.062 (0.017)</td> <td>0.024 (0.024)</td> <td>-0.068*** (0.020)</td> <td>0.005 (0.019)</td> <td>0.014 (0.016)</td> <td>-0.001 (0.022)</td> <td></td> <td></td> <td></td> </tr> <tr> <th>Workschedulefixed</th> <td>-0.049 (0.057)</td> <td>(0.034)</td> <td>-0.096*%-0.352** (0.067) 0.018</td> <td>-0.102* (0.062) -0.032</td> <td>-0.112* (0.064) 0.117</td> <td>-0.011 (0.045)</td> <td></td> <td></td> <td></td> </tr> <tr> <th>NOP</th> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>byemployer Meeting precise qualitystandards Informationabout health and safety risks Qualityofthejob environment</th> <td>0.084* (0.027) 0.191 (0.012) 0.054*** (0.015) (0.041) 0.246*** (0.044) -0.08* (0.048)</td> <td>0.108* (0.05) 0.342* (0.019) 0.056*** (0.011) 0.12** (0.052) 0.058 (0.042)</td> <td>(0.051) -0.055 (0.037) -0.022 (0.022) 0.086** (0.041) (0.073)</td> <td>(0.037) 0.240** (0.032) 0.032*** (0.007) 0.099** (0.039) 0.128*** (0.013) 0.085**</td> <td>(0.027) 0.302* (0.032) 0.05** (0.01) 0.081* (0.041) 0.03 (0.041) 0.214*</td> <td>0.022 (0.052) -0.052**￥ (0.018) (0.006) 0.105** (0.032) 0.045 (0.033) 0.664***</td> <td></td> <td></td> <td></td> </tr> <tr> <th>Formalassessment Jobrotation</th> <td>0.19***</td> <td></td> <td>0.026</td> <td></td> <td></td> <td>-0.049***</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td>(0.062) 0.005</td> <td>(0.088) -0.039</td> <td>(0.038) (0.033)</td> <td>(0.065) 0.075**</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td>-0.007</td> <td>0.677***</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>Telework</th> <td></td> <td></td> <td></td> <td></td> <td>(0.018)</td> <td>(0.054)</td> <td></td> <td></td> <td></td> </tr> <tr> <th>Teamwork</th> <td>(0.028)</td> <td></td> <td></td> <td></td> <td></td> <td>(0.032)</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td>(0.04)</td> <td>(0.064)</td> <td>0.189**</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Employee charac. Job charac. Firms charac.</td> <td>0.222*</td> <td>Included Included Included Included</td> <td>0.981 (0.651) 1.611** (0.650)</td> <td>0.078 (0.439)</td> <td></td> <td></td> <td></td> <td>Included Included Included</td> <td></td> </tr> <tr> <td>Country charac. Cut1</td> <td>0.448 (0.303) 0.967** (0.308) 1.829*** (0.34)</td> <td>0.016 (0.259) (0.301)</td> <td>1.922***</td> <td>0.733 (0.455) 1.552*** (0.461)</td> <td>Included</td> <td>0.083*** 1.045*</td> <td></td> <td>-0.149</td> <td></td> </tr> <tr> <th>Cut2</th> <td>2.065*</td> <td>0.935** 2.641*** (0.28) 2.958*</td> <td></td> <td>1.82***</td> <td>(0.465) 1.045** (0.473) 2.577*** (0.477)</td> <td>(0.327) 1.847** (0.365) 2.266***</td> <td></td> <td></td> <td></td> </tr> <tr> <th>Cut3</th> <td></td> <td></td> <td>(0.647)</td> <td></td> <td></td> <td>(0.336)</td> <td></td> <td></td> <td></td> </tr> <tr> <th>Cut4</th> <td>(0.35)</td> <td>(0.284)</td> <td></td> <td>(0.452)</td> <td>2.905** (0.476)</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td>0.053</td> <td>11098</td> <td>0.164</td> <td>0.0352</td> <td></td> <td>0.127</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td>0.0721</td> <td></td> <td></td> <td>16354</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>Observations</th> <td></td> <td></td> <td>-10349</td> <td></td> <td>0.0581</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td>-20924</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>-18738</td> <td></td> <td></td> <td></td> </tr> <tr> <th>Log Lik.</th> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>-17263</td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>-26518</td> <td></td> </tr> <tr> <td>Pseudo R-squared</td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>-32812</td> </tr> </tbody> </table> </div> </body> </div>

Figure 1

Description de l'image par IA : A flowchart depicting interactions between two firms, patent processes, and market outcomes.

The image depicts a flowchart illustrating a strategic interaction between two firms, Firm I and Firm E, in a market setting. Here is a detailed description: 1. **Initial State**: - Firm I has the option to either keep a technology secret or offer open access at a price F. - Firm E can either accept or refuse the patent and propose a license contract. 2. **Secrecy Pathway**: - If Firm I chooses secrecy, the process ends with the notation π^m_0. 3. **Open Access Pathway**: - If Firm I offers open access at price F, Firm E has two choices: - Accept the patent and license contract. - Refuse the patent and propose a new license contract. 4. **Firm E Accepts**: - If Firm E accepts the patent and license contract, the process ends with the notation (π^d(γ) + F, π^d(γ) - F). 5. **Firm E Refuses**: - If Firm E refuses the patent, Firm I can either: - Offer no patent. - Patent and propose a new license contract. 6. **No Patent by Firm I**: - If Firm I offers no patent, the process ends with the notation δπ^d(γ) + (1 - δ)π^m_0; δπ^d(γ). 7. **Patent and Propose License Contract**: - If Firm I patents and proposes a new license contract, Firm E has two choices: - Accept the new license contract. - Refuse and leave the market. 8. **Firm E Accepts New Contract**: - If Firm E accepts the new license contract, the process ends with the notation -P(γ, γ) + P, π^E(γ, γ) - P. 9. **Firm E Refuses and Leaves**: - If Firm E refuses and leaves the market, the process ends with the notation π^m_0. The flowchart uses various mathematical notations to represent different outcomes and states in the strategic interaction between the firms.

The game

Graph 1

Description de l'image par IA : Chart showing infringement and trial processes with time intervals and outcomes.

The image is a timeline diagram that illustrates different phases and outcomes related to infringement and trial processes. The timeline is marked with specific points and intervals, indicating various stages and events. Starting from the left, the timeline begins at point 0 and extends to point 1. Key points on the timeline are marked as 22896, 375, and 74597. The timeline is divided into several segments: 1. From point 0 to 22896, labeled as "infringement." 2. From 22896 to 375, labeled as "trial & settlement." 3. From 375 to 74597, labeled as "conditional infringement." 4. From 74597 to 1, labeled as "Lost" and "Profit." Below the main timeline, there are additional annotations: 1. A dashed line segment from 0 to 22896, labeled "infringement," with a sub-label "accommodated." 2. A dashed line segment from 22896 to 375, labeled "infringement," with a sub-label "trial & settlement." 3. A dashed line segment from 375 to 74597, labeled "conditional infringement," with a sub-label "conditional trial." 4. A dashed line segment from 74597 to 1, labeled "conditional infringement," with a sub-label "conditional trial." On the right side of the timeline, there are vertical lines indicating outcomes: 1. "Lost" at point 1. 2. "Profit" at point 1. 3. "Unjust" at point 1. 4. "Enrichment" at point 1. The diagram visually represents the progression and outcomes of infringement cases, including the possibility of accommodation, trial, settlement, and conditional infringement, leading to various outcomes such as lost, profit, unjust, and enrichment.

Figure

Description de l'image par IA : A table with mathematical expressions and corresponding numerical values. The table includes percentages and their respective values.

Figure

Description de l'image par IA : Table with mathematical formula and corresponding values.

Figure

Table of regression analysis results with coefficients, standard errors, and group sizes over different years.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>2003-04</th> <th>2004-05</th> <th>2005-06</th> <th>2006-07</th> <th>2007-08</th> <th>2008-09</th> </tr> <tr> <th>Intercept</th> <td>5.60</td> <td>4.41</td> <td>7.41</td> <td>8.00</td> <td>2.45</td> <td>-3.34</td> </tr> <tr> <th>D-1</th> <td>(3.85) -1.14</td> <td>(4.47) -2.14</td> <td>(2.37) -1.48</td> <td>(2.94) -1.97</td> <td>(3.76) -1.57</td> <td>(2.96) -1.53</td> </tr> <tr> <th>TFP if-1</th> <td>(0.19) -0.97</td> <td>(0.26) -0.93</td> <td>(0.37) -0.86</td> <td>(0.34) -0.86</td> <td>(0.28) -0.89</td> <td>(0.39) -0.75</td> </tr> <tr> <th>Sector;</th> <td>(0.05) -0.12e-3</td> <td>(0.05) -0.10e-3</td> <td>(0.05) -0.42e-4</td> <td>(0.05) -0.62e-4</td> <td>(0.05) -0.52e-4</td> <td>(0.06) -0.23e-4</td> </tr> <tr> <th>Laboru-1</th> <td>(0.48e-4) 0.48</td> <td>(0.49e-4) 0.16</td> <td>(0.53e-4) -0.09</td> <td>(0.51e-4) -0.14</td> <td>(0.56e-4) -0.13</td> <td>(0.68e-4) -0.18</td> </tr> <tr> <th>Squared labori-1</th> <td>(0.19) 0.09</td> <td>(0.20) 0.12</td> <td>(0.25) 0.14</td> <td>(0.28) 0.15</td> <td>(0.23) 0.14</td> <td>(0.24) 0.13</td> </tr> <tr> <td></td> <td>(0.02) -0.54</td> <td>(0.02) 0.46</td> <td>(0.02) -0.66</td> <td>(0.03) -0.74</td> <td>(0.02) 2.13</td> <td>(0.02) 4.33</td> </tr> <tr> <th>Wageit-1</th> <td>(2.04) 0.46</td> <td>(2.51) 0.33</td> <td>(1.28) 0.44</td> <td>(1.54) 0.44</td> <td>(1.92) 0.07</td> <td>(1.52) -0.22</td> </tr> <tr> <th>Squaredwage-1</th> <td>(0.28) 1.14</td> <td>(0.35)</td> <td>(0.18) 1.28</td> <td>(0.21) 1.92</td> <td>(0.03) 1.58</td> <td>(0.20)</td> </tr> <tr> <th>D--TFP</th> <td>(0.36) 0.94e-3</td> <td>2.49 (0.37) 0.75e-3</td> <td>(0.61) 0.22e-3</td> <td>(0.61) 1.19e-3</td> <td>(0.47) 0.18e-3</td> <td>1.42 (0.70)</td> </tr> <tr> <th>D-.Sector,</th> <td>(0.23e-4) 0.44</td> <td>(0.33e-3) 0.63</td> <td>(0.30e-3)</td> <td>(0.33e-4)</td> <td>(0.40e-3）</td> <td>0.83e-3</td> </tr> <tr> <th>Du-1·Labor-1</th> <td>(0.56) -0.06</td> <td>(0.89) -0.17</td> <td>1.68 (1.08) -0.19</td> <td>-1.00 (1.08) 0.04</td> <td>0.31 (0.80)</td> <td>0.38e-3) -0.75 (1.08)</td> </tr> <tr> <th>Du-·Squared labor-1</th> <td>(0.05)</td> <td>(0.09)</td> <td>(0.12)</td> <td>(0.11)</td> <td>-0.05 (0.08)</td> <td>0.04 (0.11)</td> </tr> <tr> <th>Du--.Wageu-1</th> <td>6.30 (5.11)</td> <td>-16.87 (8.16)</td> <td>-2.61 (9.28)</td> <td>-13.98 (6.54)</td> <td>-2.57 (6.53)</td> <td>-6.04</td> </tr> <tr> <th>Du-Squaredwagei-1</th> <td>-0.86</td> <td>1.99</td> <td>0.24</td> <td>1.75</td> <td>0.19</td> <td>0.67</td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> <td>(7.19)</td> </tr> <tr> <td></td> <td>(0.66)</td> <td>(1.07)</td> <td>(1.27)</td> <td>(0.91)</td> <td>(0.83)</td> <td>(0.96)</td> </tr> <tr> <th>SizeofControl group SizeofTreatedgroup</th> <td>3234 220</td> <td>3254 175</td> <td>3141 153</td> <td>2996 175</td> <td>2561 131</td> <td>2120 120</td> </tr> </tbody> </table> </div> </body> </div>

Figure 1

Description de l'image par IA : A horizontal line with markers indicating productivity levels from low to high, labeled for export, domestic producers, and exit production.

Cutoff values and the self-selection

Figure 2

Description de l'image par IA : A graph showing a curve that starts high on the left and gradually flattens out as it moves to the right.

Low entry barrier scheme with fX = 0.1

Figure 3

Description de l'image par IA : Graph showing two curves on a coordinate plane with γ on the x-axis and φ% on the y-axis. One curve is flat, the other rises and levels off.

High entry barrier scheme with fX = 2.1

Figures 1 and 2

Description de l'image par IA : Two scatter plots showing the relationship between term spread and GDP growth rates in EU countries.

The image consists of two scatter plots displayed side by side. Each plot features numerous data points scattered across the graph area. In the left scatter plot: - The X-axis is labeled "the GDP growth." - The Y-axis is labeled "the term spread." - The data points are dispersed, with a concentration of points appearing more densely around the center-right of the plot. In the right scatter plot: - The X-axis is labeled "the term spread." - The Y-axis is labeled "the GDP growth." - Similar to the left plot, the data points are dispersed, with a denser concentration of points towards the center-right of the plot. Both plots use a similar style and color scheme, with the data points represented in black and the axes in a light gray. The overall distribution of points in both plots suggests a relationship between the term spread and GDP growth rates.

The evolution of the term spread and GDP growth rates in EU countries

Note: The Y scale represents the term spread while the X scale represents the GDP growth rates.

Figure

Table comparing statistical data from three studies: Pesarani (2004), Pesarani (2007), and Hadri (2000).

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th colspan="4">Pesaran[2004]</th> <th colspan="2">Pesaran[2007]</th> <th colspan="2">Hadri[2000]</th> </tr> <tr> <th>statistics</th> <td>avgp</td> <td>avglpl</td> <td>CD</td> <td>p-value</td> <td>CIPS</td> <td>p-value</td> <td>LM</td> <td>p-value</td> </tr> <tr> <th>Growthrate</th> <td>0.487</td> <td>0.488</td> <td>34.96</td> <td>0.00</td> <td>0.411</td> <td>0.341</td> <td>11.88</td> <td>0.00</td> </tr> <tr> <th>Term spread</th> <td>0.547</td> <td>0.604</td> <td>39.26</td> <td>0.00</td> <td>2.803</td> <td>0.997</td> <td>49.84</td> <td>0.00</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with statistical values and p-values from a 2007 study by Westerlund.

Figure

Table of statistical data comparing growth rate and term spread across different studies and years.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th colspan="4">Pesaran[2004]</th> <th colspan="2">Pesaran[2007]</th> <th colspan="2">Hadri[2000]</th> </tr> <tr> <td>statistics</td> <th>avgp</th> <th>avgll</th> <th>CD</th> <th>p-value</th> <th>CIPS</th> <th>p-value</th> <th>LM</th> <th>p-value</th> </tr> <tr> <th>Growthrate</th> <td>0.522</td> <td>0.522</td> <td>33.40</td> <td>0.00</td> <td>0.107</td> <td>0.543</td> <td>37.256</td> <td>0.00</td> </tr> <tr> <th>Term spread</th> <td>0.261</td> <td>0.456</td> <td>16.72</td> <td>0.00</td> <td>-0.856</td> <td>0.196</td> <td>15.767</td> <td>0.00</td> </tr> <tr> <td></td> <th colspan="4">Westerlund[2007]</th> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>Statistics-1 lag</th> <td>Gt</td> <td>Ga</td> <td>Pt</td> <td>Pa</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>Value/p-value</th> <td>-3.369 (0.00)</td> <td>24.342 (0.00)</td> <td>13.604 (0.00)</td> <td>26.780 一 (0.00)</td> <td></td> <td></td> <td></td> <td></td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table showing statistical data with periods, terms, and observations.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>Wholeperiod</th> <th colspan="2">Pre-crisis-period</th> <th>Crisis-period</th> </tr> <tr> <th>Long-runterm- Growth</th> <td>-1.684**（ (0.515)</td> <td>-2.109*** (0.645)</td> <td>-0.359***（ (0.092)</td> </tr> <tr> <th>Errorcorrection term(EC)</th> <td>-0.063*** (0.014)</td> <td>-1.327*** (0.012)</td> <td>-0.167*** (0.061)</td> </tr> <tr> <th>Short-runterm 4Growth Constant</th> <td>-0.075**(0.038) 0.172*** (0.041)</td> <td>-0.228*** (0.041)</td> <td>-0.061(0.063) 0.363*** (0.081)</td> </tr> <tr> <td></td> <td></td> <td>-0.398*** (0.040)</td> <td></td> </tr> <tr> <th>No.obs.</th> <td>672</td> <td colspan="2">400</td> <td>272</td> </tr> <tr> <th>No.groups</th> <td>16</td> <td colspan="2">16</td> <td>16</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with statistical data for different periods: Whole, Pre-crisis, Crisis. Includes terms and observations.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>Wholeperiod</th> <th>Pre-crisis-period</th> <th>Crisis-period</th> </tr> <tr> <th>Long-runterm- term spread</th> <td>0.099**(0.042)</td> <td>0.073*** (0.028)</td> <td>0.101*(0.059)</td> </tr> <tr> <th>Errorcorrection term(EC)</th> <td>-0.615*** (0.081)</td> <td>-0.981*** (0.080)</td> <td>-0.606*** (0.071)</td> </tr> <tr> <th>Short-runterm- term spread Constant</th> <td>-0.100**（0.049） 0.144**（ (0.067)</td> <td>-0.173*** (0.052) 0.617***(0.128)</td> <td>0.353**(0.180) -0.187*** (0.064)</td> </tr> <tr> <th>No.obs.</th> <td>672</td> <td>400</td> <td>272</td> </tr> <tr> <th>No.groups</th> <td>16</td> <td>16</td> <td>16</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with statistical data across three periods: Whole, Pre-crisis, Crisis. Includes terms, constants, observations, and Hausman tests.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>Wholeperiod</th> <th>Pre-crisis-period</th> <th>Crisis-period</th> </tr> <tr> <th>Long-runterm- term spread</th> <td>0.247***（ (0.060)</td> <td>0.072(0.048)</td> <td>0.684***（ (0.174)</td> </tr> <tr> <th>Errorcorrection term(EC)</th> <td>-0.670*** (0.078)</td> <td>-1.030***（ (0.082)</td> <td>-0.721*** (0.072)</td> </tr> <tr> <th>Short-runterm- spread Constant</th> <td>-0.192*** (0.060) -0.068(0.112)</td> <td>-0.209**（0.096) 0.642*** (0.192)</td> <td>0.043(0.150) -0.655*** (0.214)</td> </tr> <tr> <th>No.obs.</th> <td>672</td> <td>400</td> <td>272</td> </tr> <tr> <th>No.groups</th> <td>16</td> <td>16</td> <td>16</td> </tr> <tr> <th>Hausmanmgpmg Prob>chi2</th> <td>9.78 (0.002)</td> <td>0.00 (0.991)</td> <td>10.37 (0.013)</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with statistical data and periods.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>Wholeperiod</th> <th>Pre-crisis-period</th> <th>Crisis-period</th> </tr> <tr> <th>Longterm-growth</th> <td>-1.8705**（0.832）</td> <td>-1.021（1.308)</td> <td>3.3776(9.431)</td> </tr> <tr> <th>Errorcorrection</th> <td>-0.0888***（0.018)-0.1375***</td> <td></td> <td>(0.017)-0.2137** (0.075)</td> </tr> <tr> <th>Shortterm-growth Constant</th> <td>0.073***(0.0296) 0.2056***（ (0.031)</td> <td>0.2397***（0.045） (0.051) 0.434**（</td> <td>-0.0757(0.090) 0.3808***(0.093)</td> </tr> <tr> <th>No. obs.</th> <td>672</td> <td>400</td> <td>272</td> </tr> <tr> <th>No.groups</th> <td>16</td> <td>16</td> <td>16</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with statistical data on long-run term growth and error correction term across different periods.

Figure

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>Wholeperiod</th> <th>Pre-crisis-period</th> <th>Crisis-period</th> </tr> <tr> <th>Short-runterm- growth</th> <td>0.075*** (0.031)</td> <td>-0.029(0.069)</td> <td>0.151*** (0.056)</td> </tr> <tr> <th>Constant</th> <td>0.694*** (0.153)</td> <td>0.310*(0.175)</td> <td>1.135*** (0.320)</td> </tr> <tr> <th>No.obs.</th> <td>482</td> <td>250</td> <td>182</td> </tr> <tr> <th>No.groups</th> <td>10</td> <td>10</td> <td>10</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with statistical data and coefficients.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>Wholeperiod</th> <th>Pre-crisis-period</th> <th>Crisis-period</th> </tr> <tr> <th>Long-runterm</th> <td>-0.462*** (0.046)</td> <td>-0.130*** (0.031)</td> <td>-0.387*** (0.071)</td> </tr> <tr> <th>Errorcorrection term(EC)</th> <td>-0.690*** (0.091)</td> <td>-0.947*** (0.068)</td> <td>-0.832*** (0.087)</td> </tr> <tr> <th>Short-runterm termspread</th> <td>-0.155(0.169)</td> <td>0.153**(0.068)</td> <td>-0.123（0.205)</td> </tr> <tr> <th>Constant</th> <td>1.256*** (0.223)</td> <td>1.534*** (0.158)</td> <td>0.870***(0.188)</td> </tr> <tr> <th>No. obs.</th> <td>482</td> <td>250</td> <td>182</td> </tr> <tr> <th>No.groups</th> <td>10</td> <td>10</td> <td>10</td> </tr> </tbody> </table> </div> </body> </div>

Figure

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>Wholeperiod</th> <th>Pre-crisis-period</th> <th>Crisis-period</th> </tr> <tr> <th>Long-runterm term spread</th> <td>-0.446***（0.051)</td> <td>-0.155**（0.077）</td> <td>-0.452*** (0.076)</td> </tr> <tr> <th>Errorcorrection term(EC)</th> <td>-0.709*** (0.089)</td> <td>-0.987*** (0.053)</td> <td>-0.877**¥ (0.087)</td> </tr> <tr> <th>Short-runterm term spread</th> <td>-0.152(0.168)</td> <td>0.147*** (0.058)</td> <td>-0.086(0.214)</td> </tr> <tr> <th>Constant</th> <td>1.365*** (0.275)</td> <td>1.551*** (0.188)</td> <td>1.217*** (0.334)</td> </tr> <tr> <th>No.obs.</th> <td>482</td> <td>250</td> <td>182</td> </tr> <tr> <th>No.groups</th> <td>10</td> <td>10</td> <td>10</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with statistical data on economic growth periods.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>Wholeperiod</th> <th>Pre-crisis-period</th> <th>Crisis-period</th> </tr> <tr> <th>Long-runterm growth</th> <td>-1.006*** (0.286)</td> <td>1.354(1.059)</td> <td>-1.558***（ (0.870)</td> </tr> <tr> <th>Errorcorrection term(EC)</th> <td>-0.270*** (0.038)</td> <td>-0.243*** (0.046)</td> <td>-0.405*** (0.083)</td> </tr> <tr> <th>Short-runterm- 4growth</th> <td>-0.058(0.042）</td> <td>-0.053(0.098)</td> <td>0.137***（ (0.042)</td> </tr> <tr> <th>Constant</th> <td>0.791*** (0.197)</td> <td>0.269(0.199)</td> <td>0.274***（ (0.325)</td> </tr> <tr> <th>No.obs.</th> <td>482</td> <td>250</td> <td>182</td> </tr> <tr> <th>No.groups</th> <td>10</td> <td>10</td> <td>10</td> </tr> </tbody> </table> </div> </body> </div>

Figure 1

Description de l'image par IA : Two scatter plots comparing Gender Gap Index with Human Development Index and Total Fertility Rate in 2006.

The image consists of two scatter plots, each depicting a relationship between different indices and rates. Both plots have data points representing various countries, each labeled with a three-letter code. The left scatter plot shows the relationship between the Gender Gap Index and the Human Development Index. The x-axis represents the Gender Gap Index, ranging from 0.45 to 0.80, while the y-axis represents the Human Development Index, ranging from 0.55 to 0.85. A trend line is drawn, indicating a positive correlation between the two indices. Countries are scattered around this line, with some clustering near the middle and others spread out. The right scatter plot illustrates the relationship between the Gender Gap Index and the Total Fertility Rate. The x-axis represents the Gender Gap Index, ranging from 0.45 to 0.80, while the y-axis represents the Total Fertility Rate, ranging from 1.5 to 6.5. A trend line is also present, showing a negative correlation. Countries are dispersed around this line, with some clustering near the middle and others more spread out. Both plots include a total of 114 data points, each representing a different country. The data points are labeled with three-letter country codes, allowing for easy identification of individual countries.

Relation of Gender Gap Index with Human Development Index and Total Fertility Rate in 2006

Note: Data are available for 114 countries.

World Bank

Figure 2

Description de l'image par IA : Six graphs showing economic and social trends in France from 1740 to 2008.

The image consists of six graphs, each depicting various economic and social transformations in France over different periods. Here is a detailed description of each graph: 1. **Top Left (a) GDP per capita, 1740-2008**: - This graph shows two lines representing GDP per capita over time. The x-axis spans from 1740 to 2008, while the y-axis measures GDP per capita. One line shows a steady increase, while the other shows a more pronounced rise, especially after the mid-19th century. 2. **Top Right (b) Demographic Transition, 1740-2008**: - This graph displays birth rates and death rates over time. The x-axis ranges from 1740 to 2008, and the y-axis measures the rates. Birth rates start high and decline significantly, while death rates also decrease but at a different rate, leading to a demographic transition. 3. **Middle Left (c) Median Age at Marriage, 1740-2004**: - This graph illustrates the median age at marriage for men and women from 1740 to 2004. The x-axis represents the years, and the y-axis shows the median age. Both lines show fluctuations, with notable dips and rises, indicating changes in marriage patterns over time. 4. **Middle Right (d) Labor Force Participation, 1856-1996**: - This graph depicts labor force participation rates for men and women from 1856 to 1996. The x-axis represents the years, and the y-axis measures the participation rates. Men's participation shows a general decline, while women's participation increases significantly over time. 5. **Bottom Left (e) Enrollment in Primary School, 1820-1908**: - This graph shows the enrollment rates in primary schools from 1820 to 1908. The x-axis represents the years, and the y-axis measures the enrollment rates. Both men and women's enrollment rates increase over time, with some fluctuations. 6. **Bottom Right (f) Quality of the Labor Force, 1896-1996**: - This bar graph illustrates the quality of the labor force from 1896 to 1996. The x-axis represents the years, and the y-axis measures the quality index. There is a general upward trend, indicating improvements in labor force quality over time. Each graph includes data points and trends that highlight significant transformations in economic, demographic, and social aspects of France over the specified periods.

Transformations of the French Profile during the Development Process

Note: Missing values are interpolated by linear interpolation.

(a) Maddison [2008]; (b) Chesnais [1991]; (c) Henry and Houdaille [1979] – INSEE; (d) Marchand and Thélot [1997]; (e) Diebolt; (f) Own calculations using data from Marchand and Thélot [1997].

Graph 1

Description de l'image par IA : Bar graph showing Brazilian states' gross exports in 2008.

The image is a bar chart titled "Breakdown of Brazilian states’ gross exports (2008)." It displays the percentage contributions of various factors to the gross exports of different Brazilian states. The factors include Tax, Imports from World, Other Brazilian States, and the Added value of the state itself. Each state is represented by a unique code, which is decoded at the bottom of the chart. The chart shows that the contributions from Tax, Imports from World, Other Brazilian States, and the Added value of the state itself are nearly equal across all states, each contributing approximately 25% to the total gross exports. The states listed include Acre (AC), Alagoas (AL), Amazonas (AM), Amapa (AP), Bahia (BA), Ceara (CE), Distrito Federal (DF), Espirito Santo (ES), Goias (GO), Maranhao (MA), Minas Gerais (MG), Mato Grosso Do Sul (MS), Mato Grosso (MT), Para (PA), Paraiba (PB), Pernambuco (PE), Piaui (PI), Parana (PR), Rio De Janeiro (RJ), Rio Grande Do Norte (RN), Rondonia (RO), Roraima (RR), Rio Grande Do Sul (RS), Santa Catarina (SC), Sergipe (SE), Sao Paulo (SP), and Tocantins (TO). The chart is sourced from the 2008 Inter-State Input-Output Table calculated in association with J. Guilhoto (USP) based on the NEREUS database.

Breakdown of Brazilian states’ gross exports (2008)

Acre: AC; Alagoas: AL; Amazonas: AM: Amapa: AP; Bahia: BA; Ceara: CE; Distrito Federal: DF; Espirito Santo: ES; Goias: GO; Maranhao: MA; Minas Gerais: MG; Mato Grosso Do Sul: MS; Mato Grosso: MT; Para: PA; Paraiba: PB; Pernambuco: PE; Piaui: PI; Parana: PR; Rio De Janeiro: RJ; Rio Grande Do Norte: RN; Rondonia: RO; Roraima: RR; Rio Grande Do Sul: RS; Santa Catarina: SC; Sergipe: SE; Sao Paulo: SP; Tocantins: TO.

2008 Inter-State Input-Output Table calculated in association with J. Guilhoto (USP) based on the NEREUS database. Some elements are available on http:// guilhotojjmg.wordpress.com/banco-de-dados/matrizes-nacionais-2/

Graph 2

Description de l'image par IA : Bar chart showing the percentage of directly and indirectly exported value-added by various states.

The image is a bar chart that displays the percentage of directly and indirectly exported added value for various states. The chart is divided into two sections: directly exported added value (represented by dark bars) and indirectly exported added value (represented by light bars). The y-axis represents the percentage, ranging from 0% to 100%. The x-axis lists the abbreviations of different states. Each state has two bars, one for each type of exported value. The chart shows a significant variation in the percentages across different states, with some states having a higher proportion of directly exported added value and others having a higher proportion of indirectly exported added value. The data source is noted as the 2008 Inter-State Input Output Table calculated by J. Guilhoto at USP.

Indirectly and directly exported value-added of states ( %)

2008 Inter-State Input Output Table calculated by J. Guilhoto at USP.

Graph 3

Description de l'image par IA : Graph showing the share of total gross exports and exported added value by states in Brazil, with differences highlighted.

The image is a graph that displays data on the share of total gross exports and the share of total exported added value for various states in Brazil. The graph has two primary axes: the vertical axis on the left represents the percentage share, ranging from 0% to 35%, while the vertical axis on the right represents the difference, ranging from -0.02 to 0.03. Three different data series are plotted: 1. **Share in total gross exports**: Represented by black bars, this series shows the percentage of total gross exports attributed to each state. 2. **Share in total exported added value**: Represented by a gray line, this series indicates the percentage of total exported added value for each state. 3. **Difference**: Represented by a dashed line, this series shows the difference between the share in total gross exports and the share in total exported added value. The horizontal axis lists various states in Brazil, each represented by an abbreviation. The states are ordered from left to right, presumably based on their share in total gross exports. Key observations from the graph include: - São Paulo (SP) has the highest share in total gross exports, with a peak value close to 35%. - The share in total gross exports for most other states is significantly lower, with values generally below 10%. - The share in total exported added value for most states is relatively stable, hovering around 10% to 15%. - The difference between the share in total gross exports and the share in total exported added value varies across states, with some states showing positive differences and others showing negative differences. The graph provides a comparative view of the economic contributions of different states in Brazil in terms of gross exports and added value.

States’ gross exports and value-added share in Brazilian total ( %)

2008 Inter-State Input Output Table calculated by J. Guilhoto at USP.

Figure

Table listing variables for efficiency scores and main explaining variables with definitions and sources.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Variable</th> <th>Definition</th> <th>Source</th> </tr> <tr> <td>H/L</td> <td>Variablesfor efficiency scores Yearsofschoolingpercapita divided World by theworkingagepopulation</td> <td>Development Indicators (WDI) and Easterly and Levine [2001]</td> </tr> <tr> <td>K/L</td> <td>CapitalstockpercapitainconstantEasterly 2000USdollarsdividedbythework-[2001],WDlandcalcula-</td> <td>and Levine tions</td> </tr> <tr> <td>Y/L</td> <td>ing agepopulation GDPpercapita in constant2000USWDI dollars dividedby theworkingage population</td> <td></td> </tr> <tr> <td>Aid Bilateralaid</td> <td>Mainexplaining variables GrossODAasashareofGDP Gross ODA transactionsundertakenOECD-DAC and WDI by a Donor Assistance Committee donor directly with arecipient</td> <td>OECD-DACandWDI</td> </tr> <tr> <td>ELF</td> <td>dividedbyGDP Ethno-linguisticf fractionalizationisAlesinaetal.[2003] theprobabilitythattwoindividuals fromapopulationbelong totwodif- ferent groups</td> <td></td> </tr> <tr> <td>Finance</td> <td>Domestic credit to private sectorWDI dividedbyGDP</td> <td></td> </tr> <tr> <td>Inflation</td> <td>LogofoneplustheGDPinflationWDl growthrate</td> <td></td> </tr> <tr> <td>Democracy</td> <td>Sumof“civil liberties"and“political FreedomHouse rights".Lower numbers stand for</td> <td></td> </tr> <tr> <td>Military</td> <td>greaterdemocracy Log ofmilitaryexpendituresdividedStockholmInternational by GDP</td> <td>PeaceResearch Insti- tute,Yearbook:Arma- ments, Disarmaments andInternationalSecu- rity</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table listing variables, definitions, and sources for a study. Includes variables like Multilateral aid, Openness, Catholic share, and more.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Variable</th> <th>Definition</th> <th>Source</th> </tr> <tr> <td></td> <td>MultilateralaidGross ODAfrominternationalagen-OECD-DAC ciesdivided byGDP</td> <td></td> </tr> <tr> <td>Openness</td> <td>Exports plus importsat constantUnited Nations Statistics 2005pricesdividedbyGDP Additionalvariables</td> <td>Division[2012]</td> </tr> <tr> <td></td> <td>Catholic sharePercentageof Catholics in thepopu-ClA-Factbook lationin1996</td> <td>and La Portaetal.[1999]</td> </tr> <tr> <td>Colonial tory</td> <td>his-Tenfold classificationof theformerTheQualityofGover- colonial ruler.The categorization is nance[2013] as the following:(O)Never colonized byaWestern overseas colonial power,(1)Dutch,(2)Spanish,(3)Ital- ian,(4)US,(5) British(6)French,(7) Portuguese,(8)Belgian,(9) British- French,(10)Australian</td> <td></td> </tr> <tr> <td>Continent</td> <td>Continentclassificationasfollows:TheQualityofGover- (0)Pacific,(1)Africa,(2)America,（3)nance[2013] Asia,(4)Europe</td> <td></td> </tr> <tr> <td>Latitude</td> <td>LatitudeinabsolutevaluedividedbyLaPortaetal.[1999] 90</td> <td></td> </tr> <tr> <td>Protestant share</td> <td>Percentageof Protestantsinthe ClA-Factbook populationin1996</td> <td>and La Portaetal.[1999]</td> </tr> <tr> <td>UNGAVotes</td> <td>PercentoftimesacountryvotesinDreherandSturm[2012] linewith theaverageof theG7coun- tries intheUNGeneralAssembly.All votes are included(definitionaccord- ing to Thacker)</td> <td></td> </tr> <tr> <td>UNSCdummy</td> <td>DummyequalstooneiftherecipientDreherandSturm[2012] countrybelongs totheUNSecurity Council</td> <td></td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with variables, observations, means, and standard deviations.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Variable</th> <th>Observations</th> <th>Mean</th> <th>Standard Deviation</th> </tr> <tr> <th>H/L</th> <td>1.142</td> <td>6.154</td> <td>2.498</td> </tr> <tr> <th>K/L</th> <td>1.142</td> <td>9.323,91</td> <td>11.800,39</td> </tr> <tr> <th>Y/L</th> <td>1.142</td> <td>3.697,52</td> <td>4.531,49</td> </tr> <tr> <th>Efficiency</th> <td>1.142</td> <td>58.92</td> <td>24.46</td> </tr> <tr> <th>Aid</th> <td>1.142</td> <td>7.68</td> <td>11.45</td> </tr> <tr> <th>BilateralAid</th> <td>1.142</td> <td>4.21</td> <td>5.94</td> </tr> <tr> <th>MultilateralAid</th> <td>1.142</td> <td>3.32</td> <td>6.82</td> </tr> <tr> <th>Finance</th> <td>1.142</td> <td>35.29</td> <td>31.01</td> </tr> <tr> <th>Inflation</th> <td>1.142</td> <td>0.13</td> <td>0.34</td> </tr> <tr> <th>Democracy</th> <td>1.142</td> <td>7.69</td> <td>3.31</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table comparing statistical models for different types of aid.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th colspan="3">2SLSFixedEffects</th> <th colspan="2">One-stepGMM</th> <th colspan="2">Two-stepGMM</th> </tr> <tr> <td>Aid</td> <td colspan="2">0.052***</td> <td></td> <td>0.009**</td> <td></td> <td colspan="2">0.008***</td> </tr> <tr> <td></td> <th colspan="2">(2.68)</th> <td></td> <th>(2.49)</th> <td></td> <th colspan="2">(2.59)</th> </tr> <tr> <td>BilateralAid</td> <td></td> <td>0.613**</td> <td></td> <td></td> <td>0.015*</td> <td></td> <td>0.015*</td> </tr> <tr> <td></td> <td></td> <td>(2.52)</td> <td></td> <td></td> <td>(1.89)</td> <td></td> <td>(1.83)</td> </tr> <tr> <td>MultilateralAid</td> <td></td> <td></td> <td>0.026*</td> <td></td> <td>0.005**</td> <td></td> <td>0.004**</td> </tr> <tr> <td></td> <td></td> <td></td> <td>(1.87)</td> <td></td> <td>(1.97)</td> <td></td> <td>(1.97)</td> </tr> <tr> <td>Observations</td> <td>1095</td> <td>1095</td> <td>1095</td> <td>1142</td> <td>1142</td> <td>1142</td> <td>1142</td> </tr> <tr> <td>Testa</td> <td></td> <td></td> <td></td> <td></td> <td>0.328</td> <td></td> <td>0.201</td> </tr> <tr> <td>Hansen Jtestb</td> <td></td> <td></td> <td></td> <td>0.129</td> <td>0.265</td> <td>0.129</td> <td>0.265</td> </tr> <tr> <td>AR(2)test°</td> <td></td> <td></td> <td></td> <td>0.086</td> <td>0.291</td> <td>0.054</td> <td>0.262</td> </tr> <tr> <td>Lag restriction?</td> <td></td> <td></td> <td></td> <td>No</td> <td>Yes</td> <td>No</td> <td>Yes</td> </tr> <tr> <td>Instruments/ countries</td> <td></td> <td></td> <td></td> <td>54/67</td> <td>66/67</td> <td>54/67</td> <td>66/67</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table comparing statistical effects of aid on finance, democracy, and inflation.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th colspan="3">2SLSFixedEffects</th> <th colspan="3">One-StepGMM</th> <th colspan="3">Two-StepGMM</th> </tr> <tr> <td>Aid</td> <td>(2.62)</td> <td>0.053**0.081*** (3.38)</td> <td>0.051** (2.66)</td> <td>(1.47)</td> <td>0.0080.031*** (4.17)</td> <td>0.012** (2.55)</td> <td>(1.45)</td> <td>0.0080.029*** (3.92)</td> <td>(2.57)</td> </tr> <tr> <th>AidxFinance</th> <td>-0.000 (-0.23)</td> <td></td> <td></td> <td>-0.000 (-0.16)</td> <td></td> <td></td> <td>-0.000 (-0.14)</td> <td></td> <td></td> </tr> <tr> <th>AidxDemocracy</th> <td></td> <td>-0.004* (-1.99)</td> <td></td> <td colspan="2">-0.003*** (-4.21)</td> <td></td> <td colspan="3">-0.003*** (-4.06)</td> </tr> <tr> <th>Aidxlnflation</th> <td></td> <td></td> <td>0.001 (0.23)</td> <td></td> <td></td> <td>-0.004* (-1.88)</td> <td></td> <td colspan="2">-0.004* (-1.91)</td> </tr> <tr> <th>Observations Hansen J testa</th> <td>1095</td> <td>1095</td> <td>1095</td> <td>1142</td> <td>1142</td> <td>1142 0.253</td> <td>1142</td> <td>1142</td> <td>1142</td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td>0.262</td> <td>0.310</td> <td></td> <td>0.262</td> <td>0.310</td> <td>0.253</td> </tr> <tr> <th>AR(2) testa</th> <td></td> <td></td> <td></td> <td></td> <td>0.227</td> <td>0.261</td> <td>0.116</td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td>0.147</td> <td></td> <td></td> <td></td> <td>0.161</td> <td>0.377</td> </tr> </tbody> </table> </div> </body> </div>

Figure 1

Description de l'image par IA : Graph showing trends in droughts, floods, and affected people in Malawi from 1970 to 2011.

The image is a composite graph depicting trends related to natural disasters in Malawi from 1970 to 2011. It consists of two main sections: a line graph on the left and a bar graph on the right. The left section, a line graph, shows the number of people affected by floods and droughts over different decades. The x-axis represents the time periods: 1970-79, 1980-89, 1990-99, 2000-2009, and 2010-2011. The y-axis indicates the number of people affected, ranging from 0 to 16 million. The graph has two lines: one representing the number of people affected by droughts and the other by floods. Both lines show an increasing trend over the decades, with a significant spike in the 2000-2009 period, indicating a peak in the number of people affected by these natural disasters. The right section, a bar graph, provides additional data on the number of droughts, floods, and the number of people adversely affected. The x-axis is labeled with the same time periods as the line graph. The y-axis on the left side of this section shows the number of events, ranging from 0 to 12 million. The y-axis on the right side shows the number of people affected, ranging from 0 to 10 million. The bars indicate the number of droughts and floods for each period, with a notable increase in the number of events during the 2000-2009 decade. The number of people adversely affected also peaks during this period, aligning with the trend observed in the line graph. Overall, the image illustrates a significant increase in the frequency and impact of droughts and floods in Malawi, particularly between 2000 and 2009.

Droughts and floods trends between 1970 and 2011 in Malawi

“EM-DAT: the OFDA/CRED International Disaster Database, Université Catholique de Louvain, Bruxelles. www.emdat.be.

Figure

Table showing incidence and intensity of economic and domestic activities across different demographics.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <td></td> <th>Urban</th> <th>Rural</th> <th>Boys</th> <th>Girls</th> <th>No climate shock(a)</th> <th>Climate shock(a)</th> </tr> <tr> <th rowspan="2">Economic activities</th> <th>Incidence (%)</th> <td>9.35</td> <td>24.51</td> <td>22.87</td> <td>21.37</td> <td>22.13</td> <td>22.10</td> </tr> <tr> <th>Intensity (hours)</th> <td>9.41</td> <td>10.00</td> <td>10.37</td> <td>9.53</td> <td>9.22</td> <td>10.87</td> </tr> <tr> <th rowspan="2">Domestic tasks</th> <th>Incidence (%)</th> <td>5.86</td> <td>12.60</td> <td>6.01</td> <td>16.90</td> <td>9.50</td> <td>14.01</td> </tr> <tr> <th>Intensity (hours)</th> <td>9.43</td> <td>11.26</td> <td>10.78</td> <td>11.22</td> <td>10.48</td> <td>11.63</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table with coefficients for economic and household activities.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th colspan="2">Economic activities</th> <th colspan="2">Household chores</th> </tr> <tr> <th>VCIndex</th> <th>Incidence (a)</th> <th>Intensity (b)</th> <th>Incidence (a)</th> <th>Intensity (b)</th> </tr> <tr> <td>Coefficient</td> <td>-0.128*** (0.035)</td> <td>-5.736*** (1.501)</td> <td>0.004 (0.025)</td> <td>0.722 (2.016)</td> </tr> <tr> <td>Level</td> <td>-0.125*** (0.024)</td> <td>-8.239*** (1.266)</td> <td>-0.033* (0.018)</td> <td>-2.930 (1.788)</td> </tr> <tr> <td>BVIndex</td> <td>-0.178*** (0.030)</td> <td>-9.675*** (1.317) -1.206</td> <td>0.019 (0.021) -0.000</td> <td>-1.772 (1.820) -2.782</td> </tr> <tr> <td>SRIndex</td> <td>0.017 (0.029)</td> <td>(1.332)</td> <td>(0.022)</td> <td>(1.893)</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table listing variables, descriptions, sources, means, and weights for biophysical vulnerability and social resilience indices.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Nameofthevariable</th> <th>Description</th> <th>Sources</th> <th>Mean(St. Dev.)</th> <th>Weight(MCA, Burt)</th> </tr> <tr> <th>Biophysicalvulnerabilityindex (BV)9</th> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>1.Climatevolatility</td> <td>1ifanormalfloodsbetween 2009-2010,0otherwise 1iftheaverage totalrainfallin</td> <td>Malawianmeterologyinstitute /FAO</td> <td>0.41(0.49)</td> <td>0.296</td> </tr> <tr> <td>2.Climatevariability</td> <td>wettestquarterbetween2001 and2010isextremelylowor extremelyhigh</td> <td>Malawianmeterologyinstitute /FAO</td> <td>0.43(0.50)</td> <td>0.324</td> </tr> <tr> <td>3.Landscapetypology</td> <td>Percentunderagriculturewithin 1kmbuffer</td> <td>Malawianmeterologyinstitute /FAO</td> <td>1.88(0.75)</td> <td>0.137</td> </tr> <tr> <td>4.Soilpracticability</td> <td>Landworkability(1=Noor slight constraint/7=Water)</td> <td>Malawianmeterologyinstitute /FAO</td> <td>1.95 (1.37)</td> <td>0.243</td> </tr> <tr> <th>Socialresilienceindex(SR)</th> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>1.lrrigationsystem</td> <td>1if the communityhasan irriga- tionscheme,0otherwise</td> <td>IHS3-Community questionnaire</td> <td>0.19(0.39)</td> <td>0.127</td> </tr> <tr> <td>2.Fertilizer sellers</td> <td>1ifthecommunityhasfertilizer sellers,Ootherwise</td> <td>IHS3-Communityquestionnaire</td> <td>0.16(0.36)</td> <td>0.265</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table listing community resources with descriptions, sources, means, and weights.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Nameofthevariable</th> <th>Description</th> <th>Sources</th> <th>Mean(St. Dev.)</th> <th>Weight(MCA, Burt)</th> </tr> <tr> <td>3.Hybridmaizesellers</td> <td>1if thecommunityhashybrid maize sellers,0otherwise</td> <td>IHS3-Community questionnaire</td> <td>0.18(0.39)</td> <td>0.260</td> </tr> <tr> <td>4.Localwarehouse</td> <td>1if thecommunityhasa local warehouse,0otherwise</td> <td>IHS3-Community questionnaire</td> <td>0.04 (0.19)</td> <td>0.001</td> </tr> <tr> <td>5.Healthfacilities</td> <td>1ifthecommunityhasahealth clinic,Ootherwise</td> <td>IHS3-Community questionnaire</td> <td>0.28(0.45)</td> <td>0.034</td> </tr> <tr> <td>6.Supportgroup</td> <td>1ifthecommunityhasNGO supportgroup,0otherwise</td> <td>IHS3-Community questionnaire</td> <td>0.56(0.50)</td> <td>0.02</td> </tr> <tr> <td>7.Bank</td> <td>1if thecommunityhasa com- mercialbank,0otherwise</td> <td>IHS3-Communityquestionnaire</td> <td>0.05 (0.21)</td> <td>0.163</td> </tr> <tr> <td>8.Microfinance institutions</td> <td>1ifthecommunityhasmicrofi- nanceinstitutions,Ootherwise</td> <td>IHS3-Communityquestionnaire</td> <td>0.09(0.23)</td> <td>0.128</td> </tr> <tr> <td>9.School facilities</td> <td>1ifthecommunityhasprimary schoolfacilities,0otherwise</td> <td>IHS3-Communityquestionnaire</td> <td>0.94(0.24)</td> <td>0.002</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table listing variables of interest with descriptions, means, and standard deviations.

Figure

Table listing variables with descriptions, means, and standard deviations for social and economic data.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Nameofthevariable</th> <th>Description</th> <th>Mean</th> <th>St.Dev</th> </tr> <tr> <th>3.Socialresilienceindex</th> <th>SR=indexof9socialsub-variablesatcommunitylevel (see Table A1)</th> <td>0.303</td> <td>0.175</td> </tr> <tr> <th>Child characteristics</th> <td></td> <td></td> <td></td> </tr> <tr> <th>Age</th> <th>Ageinyear</th> <td>10.284</td> <td>2.283</td> </tr> <tr> <th>Age_sqr</th> <th>Ageinyear*Ageinyear</th> <td>110.97</td> <td>47.631</td> </tr> <tr> <th>Boy</th> <th>1ifthechild isa boy,0if she isa girl</th> <td>0.496</td> <td>0.500</td> </tr> <tr> <th>Firstborn</th> <th>1ifthechildisafirstborn，0otherwise</th> <td>0.359</td> <td>0.480</td> </tr> <tr> <th>Household head characteristics</th> <td></td> <td></td> <td></td> </tr> <tr> <th>Educationlevel</th> <th>Householdnumberofyearsofschooling</th> <td>0.268</td> <td>0.648</td> </tr> <tr> <th>Employed</th> <th>1isthehouseholdheadiseconomicallyactive</th> <td>0.707</td> <td>0.455</td> </tr> <tr> <th>Woman</th> <th>1ifthehouseholdheadisawoman</th> <td>0.210</td> <td>0.408</td> </tr> <tr> <th>Household characteristics</th> <td></td> <td></td> <td></td> </tr> <tr> <th>land area</th> <th>land area inacres</th> <td>0.925</td> <td>2.542</td> </tr> <tr> <th>logexpenditures</th> <th>logoftotalexpenditures</th> <td>5.691</td> <td>2.681</td> </tr> <tr> <th>Rural wealthindex</th> <th>Indexofassets</th> <td>0.160</td> <td>0.134</td> </tr> <tr> <th>Socialnetwork</th> <th>Numberofyearsinthehousehold</th> <td>0.114</td> <td>0.318</td> </tr> <tr> <th>Remittancesreceipt</th> <th>1ifthehouseholdreceivesremittances</th> <td>0.230</td> <td>0.421</td> </tr> <tr> <th>Distanceto thenearestroad</th> <th>Distanceto thenearestroad[km]</th> <td>8.414</td> <td>10.638</td> </tr> <tr> <th>Distance to thenearestmarket</th> <th>Distance to thenearestmarket[km]</th> <td>7.180</td> <td>6.238</td> </tr> <tr> <th>Credit access</th> <th>1ifthehouseholdhasacreditaccount</th> <td>0.117</td> <td>0.321</td> </tr> <tr> <th>Food security</th> <th>1ifthehouseholdhashadlessfoodthanneeded during thereferenceweek</th> <td>0.288</td> <td>0.453</td> </tr> <tr> <th>Cantril ladder</th> <th>Subjectivewell-being(from 1to7)</th> <td>1.638</td> <td>1.058</td> </tr> <tr> <th>Programme</th> <th>1ifthehouseholdparticipatesinasocialprogramme</th> <td>0.190</td> <td>0.392</td> </tr> <tr> <th>Crops diversification</th> <th>1ifthehouseholdcultivateshybridmaize</th> <td>0.337</td> <td>0.473</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table showing statistical data for US, UK, and Euro area with W, Y, and ADF t-statistic values.

Figure

Table showing statistical data for US, UK, and Euro area with FW, HW, Y, and ADF t-statistic values.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th>FW</th> <th>HW</th> <td></td> <th>ADFt-statistic Lags:1</th> </tr> <tr> <td>US</td> <td>0.25*** (15.41)</td> <td>0.12** (4.82)</td> <td>0.70** (23.84)</td> <td>-3.15***</td> </tr> <tr> <td>UK</td> <td>0.10** (7.08)</td> <td>0.07*** (4.18)</td> <td>0.71*** (12.07)</td> <td>-4.45***</td> </tr> <tr> <td>Euroarea</td> <td>0.13*** (10.15)</td> <td>0.01 (0.78)</td> <td>0.70*** (15.86)</td> <td>-2.83***</td> </tr> </tbody> </table> </div> </body> </div>

Figure 1

Description de l'image par IA : Three line graphs showing trends over time with shaded confidence intervals.

The image consists of three separate line graphs stacked vertically, each depicting data trends over a range of values on the x-axis. Each graph has shaded regions around the lines, likely representing confidence intervals or variability in the data. 1. **Top Graph:** - The y-axis ranges from approximately -0.5 to 0.5. - The x-axis ranges from 0 to 1. - The line fluctuates, showing peaks and troughs, with the shaded area widening and narrowing at different points, indicating varying levels of uncertainty or variability. 2. **Middle Graph:** - The y-axis ranges from approximately -0.5 to 0.5. - The x-axis ranges from 0 to 1. - The line shows more pronounced fluctuations compared to the top graph, with significant peaks and valleys. The shaded area is wider in some sections, suggesting higher variability. 3. **Bottom Graph:** - The y-axis ranges from approximately -0.5 to 0.5. - The x-axis ranges from 0 to 1. - This graph also shows fluctuations, but the pattern appears less erratic compared to the middle graph. The shaded area varies, indicating different levels of uncertainty. Each graph appears to represent different aspects of the same dataset, possibly related to economic indicators such as consumption, asset wealth, and labor income, as suggested by the context. The lines and shaded areas provide a visual representation of trends and variability within the data.

OLS and quantile regressions: Evidence for the US (consumption, asset wealth and labour income).

Figure 2

Description de l'image par IA : Four graphs showing regression trends for consumption, financial wealth, housing wealth, and labor income.

The image consists of four separate line graphs arranged in a 2x2 grid. Each graph displays data trends over a range of values on the x-axis, which spans from 0.2 to 1.0. The y-axis values vary for each graph, indicating different scales of measurement. 1. **Top Left Graph (labeled "consumption")**: - The y-axis ranges from approximately -1.5 to 1.5. - The line fluctuates, showing a general downward trend from left to right. - Shaded areas around the line indicate confidence intervals, which widen as the x-axis value increases. 2. **Top Right Graph (labeled "FYI")**: - The y-axis ranges from approximately -1.5 to 1.5. - The line shows an upward trend, starting low and rising steadily. - The shaded confidence intervals are relatively narrow and consistent throughout. 3. **Bottom Left Graph (labeled "HWI")**: - The y-axis ranges from approximately -1.5 to 1.5. - The line exhibits a downward trend initially, then stabilizes and slightly rises towards the end. - The shaded confidence intervals are wider at the beginning and narrow as the x-axis value increases. 4. **Bottom Right Graph (labeled "Y")**: - The y-axis ranges from approximately -1.5 to 1.5. - The line shows a slight upward trend, with minor fluctuations. - The shaded confidence intervals are relatively narrow and consistent throughout. Each graph includes a title at the top, and the overall layout suggests a comparative analysis of different variables over the same range of x-axis values.

OLS and quantile regressions: Evidence for the US (consumption, financial wealth, housing wealth and labour income).

Figure

Table with statistical data including quantiles, W, Y, FW, HW, and corresponding p-values.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Quantile</th> <th>W</th> <th>Y</th> <th>FW</th> <th>HW</th> <th>Y</th> </tr> <tr> <th>2.5%</th> <td>0.33 0.35 0.36 0.37 0.38</td> <td>0.68 0.70 0.71 0.72 0.74</td> <td>0.24 0.25 0.26 0.26 0.28</td> <td>0.12 0.13 0.14 0.14 0.16 0.13</td> <td>0.64</td> </tr> <tr> <th>25%</th> <td></td> <td></td> <td></td> <td></td> <td>0.66</td> </tr> <tr> <th>50%</th> <td></td> <td></td> <td></td> <td></td> <td>0.67</td> </tr> <tr> <th>75%</th> <td></td> <td></td> <td></td> <td></td> <td>0.68</td> </tr> <tr> <th>97.5%</th> <td></td> <td></td> <td></td> <td></td> <td>0.70</td> </tr> <tr> <th>OLS</th> <td>0.36</td> <td>0.71</td> <td>0.26</td> <td></td> <td>0.68</td> </tr> <tr> <td>Khmaladze[1981]andKoenker andXiao[2002]Test(p-value):</td> <td colspan="2">0.01***</td> <td></td> <td colspan="2">0.00**</td> </tr> </tbody> </table> </div> </body> </div>

Figure 3

Description de l'image par IA : Three line graphs showing trends over time, with shaded areas indicating variability.

The image consists of three separate line graphs arranged vertically. Each graph displays data trends over a period of time, represented on the x-axis, which ranges from 0 to 1. The y-axis measures a variable that ranges from -0.2 to 0.8. 1. **Top Graph:** - The line fluctuates around the 0 mark initially, showing minor variations. - Around the 0.4 mark on the x-axis, there is a noticeable upward trend. - The line continues to rise steadily, with some fluctuations, until it sharply increases towards the end of the period. 2. **Middle Graph:** - Similar to the top graph, the line starts with minor fluctuations around the 0 mark. - There is a slight upward trend starting around the 0.4 mark on the x-axis. - The line shows more pronounced fluctuations compared to the top graph but follows a generally upward trajectory. - Towards the end of the period, there is a significant increase in the line's value. 3. **Bottom Graph:** - The line begins with minor fluctuations around the 0 mark. - Around the 0.4 mark on the x-axis, there is a noticeable upward trend. - The line shows more variability compared to the other two graphs but maintains an overall upward trajectory. - Towards the end of the period, the line sharply increases, similar to the other graphs. Each graph includes shaded areas around the lines, likely representing confidence intervals or variability in the data. The overall trend in all three graphs indicates an increase in the measured variable towards the end of the period.

OLS and quantile regressions: Evidence for the UK (consumption, asset wealth and labour income).

Figure 4

Description de l'image par IA : Four line graphs showing trends for consumption, financial wealth, housing wealth, and labor income in the UK.

The image consists of four separate line graphs arranged in a 2x2 grid. Each graph displays data trends with shaded confidence intervals. 1. **Top Left Graph (labeled "Consumption")**: - The x-axis ranges from 0.0 to 1.0. - The y-axis ranges from -0.5 to 0.5. - The line shows an upward trend, indicating a positive relationship. - The shaded area around the line represents the confidence interval, which widens as it moves along the x-axis. 2. **Top Right Graph (labeled "Financial Wealth")**: - The x-axis ranges from 0.0 to 1.0. - The y-axis ranges from -0.5 to 0.5. - The line shows a downward trend, indicating a negative relationship. - The confidence interval is relatively narrow throughout. 3. **Bottom Left Graph (labeled "Housing Wealth")**: - The x-axis ranges from 0.0 to 1.0. - The y-axis ranges from -0.5 to 0.5. - The line shows a downward trend, indicating a negative relationship. - The confidence interval is wider at the beginning and narrows as it progresses. 4. **Bottom Right Graph (labeled "Labour Income")**: - The x-axis ranges from 0.0 to 1.0. - The y-axis ranges from -0.5 to 0.5. - The line shows an upward trend, indicating a positive relationship. - The confidence interval is relatively narrow throughout. Each graph appears to represent regression analysis results, likely Ordinary Least Squares (OLS) and quantile regressions, for different economic variables in the UK.

OLS and quantile regressions: Evidence for the UK (consumption, financial wealth, housing wealth and labour income).

Figure

Table showing statistical data with quantiles, W, Y, FW, HW, and Y values. Includes p-values from tests.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Quantile</th> <th>W</th> <th>Y</th> <th>FW</th> <th>HW</th> <td></td> </tr> <tr> <th>2.5%</th> <td>0.13</td> <td>0.63</td> <td>0.07</td> <td>0.04</td> <td>0.68</td> </tr> <tr> <th>25%</th> <td>0.16</td> <td>0.69</td> <td>0.08</td> <td>0.06</td> <td>0.73</td> </tr> <tr> <th>50%</th> <td>0.17</td> <td>0.72</td> <td>0.09</td> <td>0.06</td> <td>0.75</td> </tr> <tr> <th>75%</th> <td>0.18</td> <td>0.75</td> <td>0.10</td> <td>0.07</td> <td>0.78</td> </tr> <tr> <th>97.5%</th> <td>0.21</td> <td>0.80</td> <td>0.11</td> <td>0.09</td> <td>0.83</td> </tr> <tr> <th>OLS</th> <td>0.18</td> <td>0.71</td> <td>0.10</td> <td>0.07</td> <td>0.73</td> </tr> <tr> <td>Khmaladze[1981]andKoenker and Xiao[2002] Test(p-value):</td> <td colspan="2">0.00***</td> <td colspan="3">0.00***</td> </tr> </tbody> </table> </div> </body> </div>

Figure 5

Description de l'image par IA : Three line graphs showing trends over time.

The image contains three line graphs arranged vertically, each depicting data trends over a range of values on the x-axis. The y-axis measures different variables in each graph. 1. **Top Graph:** - Title: "Bilateral" - X-axis: Values range from 0 to 1. - Y-axis: Values range from -0.1 to 0.1. - Description: The graph shows a fluctuating trend with a shaded confidence interval. The line starts around -0.1, rises sharply to around 0.1, and then fluctuates around this value with a slight upward trend towards the end. 2. **Middle Graph:** - Title: "W" - X-axis: Values range from 0 to 1. - Y-axis: Values range from -0.1 to 0.1. - Description: This graph shows a downward trend initially, dipping to around -0.1, then gradually rising and stabilizing around 0.05 towards the end. The shaded area indicates a confidence interval. 3. **Bottom Graph:** - Title: "Y" - X-axis: Values range from 0 to 1. - Y-axis: Values range from -0.1 to 0.1. - Description: The graph shows an upward trend starting from around -0.1, gradually increasing and stabilizing around 0.05 towards the end. The shaded area represents a confidence interval. Each graph includes a dotted line representing the trend and a shaded area indicating the confidence interval, which shows the variability or uncertainty around the trend.

OLS and quantile regressions: Evidence for the euro area (consumption, asset wealth and labour income).

Figure 6

Description de l'image par IA : Four line graphs showing trends over time for consumption, financial wealth, housing wealth, and labor income in the euro area.

The image contains four line graphs arranged in a 2x2 grid. Each graph displays data trends over a range of values on the x-axis, which spans from 0.2 to 1.0. The y-axis measures different variables, each ranging from 0 to 100. 1. **Top-left graph (labeled "Consumption")**: - The y-axis ranges from 0 to 100. - The line shows an upward trend, indicating an increase in consumption as the x-axis value increases. - The shaded area around the line represents the confidence interval, which widens as the x-axis value increases. 2. **Top-right graph (labeled "FVW")**: - The y-axis ranges from 0 to 100. - The line shows a downward trend, indicating a decrease in the variable as the x-axis value increases. - The confidence interval is relatively narrow and consistent throughout the graph. 3. **Bottom-left graph (labeled "HW")**: - The y-axis ranges from 0 to 100. - The line shows a downward trend, indicating a decrease in the variable as the x-axis value increases. - The confidence interval is wider at the beginning and narrows as the x-axis value increases. 4. **Bottom-right graph (labeled "Y")**: - The y-axis ranges from 0 to 100. - The line shows an upward trend, indicating an increase in the variable as the x-axis value increases. - The confidence interval is relatively narrow and consistent throughout the graph. Each graph includes a dashed line that represents the trend line, and the shaded areas around the lines indicate the confidence intervals for the data.

OLS and quantile regressions: Evidence for the euro area (consumption, financial wealth, housing wealth and labour income).

Figure

Table with statistical data and p-values.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Quantile</th> <th>W</th> <th>Y</th> <th>FW</th> <th>HW</th> <th>Y</th> </tr> <tr> <th>2.5%</th> <td>0.04 0.06</td> <td>0.83 0.87</td> <td>0.09 0.11</td> <td>0.01 0.02 0.02 0.03 0.04</td> <td>0.65 0.69</td> </tr> <tr> <th>25%</th> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>50%</th> <td>0.07</td> <td>0.89</td> <td>0.11</td> <td></td> <td>0.72</td> </tr> <tr> <th>75%</th> <td>0.08</td> <td>0.91</td> <td>0.12</td> <td></td> <td>0.74</td> </tr> <tr> <th>97.5%</th> <td>0.10</td> <td>0.94</td> <td>0.13</td> <td></td> <td>0.78</td> </tr> <tr> <th>OLS</th> <td>0.09</td> <td>0.85</td> <td>0.12</td> <td>0.03</td> <td>0.69</td> </tr> <tr> <td>Khmaladze[1981]andKoenker and Xiao[2002] Test(p-value):</td> <td colspan="2">0.00**</td> <td colspan="3">0.00***</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table categorizing ownership types of firms with main characteristics, degree of involvement, and time horizon.

<div> <body> <div class="page"> <table> <tbody> <tr> <th>Ownershiptype</th> <th>Main characteristics</th> <th colspan="2">Hypothesis</th> </tr> <tr> <td></td> <td></td> <th>Degree of involvement</th> <th>Time horizon</th> </tr> <tr> <td>(1)CEO-controlled (2)Heirfamily(CEO</td> <td>No separationbetweenshareholders and CEO.</td> <td>N/A</td> <td rowspan="3">Long-term</td> </tr> <tr> <td>(3)Activefamily (hired CEO)</td> <td>The founderofthecontrolledfirm,or aheirformer topmanager,isa mem- berof theBoard.</td> <td>High</td> </tr> <tr> <td>(4)Passivefamily (hired CEO)</td> <td>Heirs without experience ofmanage- mentin thecontrolled firm (member ornot of the Board）;include family conglomerates when thelisted firm hasbeencontrolledformorethan10 years.</td> <td>Low</td> </tr> <tr> <td>(5)Publicsector</td> <td>ControlbytheStateoffirmswhich activityisofgeneralorstrategicinter estforthecountryinthelongrun.</td> <td>High</td> <td>Long-term</td> </tr> <tr> <td>(6)Parentcompany</td> <td>Parent company from the same or complementaryindustry(controlaim- ingatachieving industrialsynergies, a critical size ina given market,etc.)</td> <td>High</td> <td>Medium/ Long-term</td> </tr> <tr> <td>(7)Investment company</td> <td>Portfolio companies,mainly private equities.Includefamilyconglomer- ates when the firm has been con- trolledforlessthan10years.</td> <td>Medium</td> <td>Short/ Medium- term</td> </tr> <tr> <td>(8)Noblockholder (diffuseownership)</td> <td>Minorityshareholders.</td> <td>Low</td> <td>Short-term</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table showing compensation data for various sectors and roles.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <td></td> <th>Total</th> <th colspan="2">Cashcompensation</th> <th>%Equity-</th> <th>Sales</th> <td></td> </tr> <tr> <td></td> <th>Num</th> <th>compensation</th> <td></td> <th>%Bonus</th> <th>based comp.</th> <td></td> <th>Market</th> </tr> <tr> <td></td> <td></td> <td></td> <th>Salary</th> <th>/Salary</th> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <th>/Total comp.</th> <td></td> <th>capitalization</th> </tr> <tr> <th>Public sector</th> <td>11</td> <td>1257 (1226)</td> <td>659 (620)</td> <td>64% (67%)</td> <td>3% (7%)</td> <td>17290434 (26149177)</td> <td>7011497 (19524794)</td> </tr> <tr> <th>CEO-controlled</th> <td>9</td> <td>911 (1001)</td> <td>466 (465)</td> <td>70% (57%)</td> <td>9% (14%)</td> <td>1727659 (7238290)</td> <td>2544871 (3377802)</td> </tr> <tr> <th>Heirfamily(CEOmemberoffamily)</th> <td>8</td> <td>2288 (1946)</td> <td>999</td> <td>94%</td> <td>6%</td> <td>6411382 (7283107)</td> <td>4240366 （10131634)</td> </tr> <tr> <th>Activefamily(hiredCEO)</th> <td>13</td> <td>1752 (1503)</td> <td>(1006) 513 (506)</td> <td>(76%) 99% (83%)</td> <td>(11%) 25% (21%)</td> <td>1505919 (2582241)</td> <td>3174704 (3999284)</td> </tr> <tr> <th>Passivefamily(hired CEO)</th> <td>12</td> <td>2416 (3350)</td> <td>827 (961)</td> <td>83% (84%)</td> <td>27% (32%)</td> <td>2922959 (11308017)</td> <td>4476591 (8696047)</td> </tr> <tr> <th>Parentcompany</th> <td>14</td> <td>1532</td> <td>634</td> <td>76%</td> <td>26% (25%)</td> <td>3495512</td> <td>3290808</td> </tr> <tr> <th>Investmentcompany</th> <td>8</td> <td>(1778) 2919 (2451)</td> <td>(560) 905 (753)</td> <td>(82%) 102% (100%)</td> <td>30% (27%)</td> <td>（15833179) 10872706 (13525479)</td> <td>(7794853) 8505248 (7367763)</td> </tr> <tr> <th>No blockholder(diffuseownership) Total panel</th> <td>29</td> <td>2857 (3049)</td> <td>806 (828)</td> <td>100% (102%)</td> <td>35% (34%)</td> <td>6480426 (19227497)</td> <td>6579862 (17085790)</td> </tr> <tr> <td></td> <td>104</td> <td>1930 (2220)</td> <td>662 (723)</td> <td>88% (85%)</td> <td>24% (24%)</td> <td>4260433 (14209361)</td> <td>4081052 (11009767)</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table showing compensation data and its sensitivity to annual performance, categorized by ownership sectors.

<div> <body> <div class="page"> <table> <tbody> <tr> <td></td> <th colspan="2">Levelof compensation</th> <th colspan="2">Structureofcomp.</th> <th colspan="5">Sensitivitytoannualperformance</th> </tr> <tr> <td></td> <th>(1)</th> <th>(2)</th> <th>(3)</th> <th>(4）</th> <th colspan="3">(5)</th> <th colspan="2">(6)</th> </tr> <tr> <td></td> <th>log(Total comp.)</th> <th>log(Sal- ary)</th> <th>%Bonus/ Salary</th> <th>%E-b/ Total comp.</th> <th colspan="3">d(%Bonus/Salary)</th> <th colspan="2">d(%Equity-based/Total compensation)</th> </tr> <tr> <th>log(Sales)</th> <td>2.20*** (0.48)</td> <td>1.32*** (0.31)</td> <td>1.09** (0.47)</td> <td>0.17 (0.16)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>log(Sales)²</th> <td>-0.07*** (0.02)</td> <td>-0.04*著 (0.01)</td> <td>-0.04*关 (0.02)</td> <td>-0.01 (0.01)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>log(MarketCapitalization)</th> <td>0.23*** (0.05)</td> <td>0.05 (0.03)</td> <td>0.21关* (0.05)</td> <td>0.05关关* (0.02)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>d(Operatingincome)x“Ownership" d(ROA)x“Ownership"</th> <td></td> <td></td> <td></td> <td></td> <td>X X</td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <th>%Totalsharereturnx"Ownership” d(ROA(-1))x"Ownership" d(Salespershare)x“Ownership” d(Tobin'sQ)x"Ownership" Ownership:</th> <td></td> <td></td> <td></td> <td></td> <td></td> <td>X</td> <td>X</td> <td>X</td> <td>X</td> </tr> <tr> <th>Publicsector</th> <td>omitted</td> <td>omitted</td> <td>omitted</td> <td>omitted</td> <td>9.5E-08 (6.3E-08)</td> <td>1.41 0.00 (1.31) (0.11)</td> <td>0.31 (0.89)</td> <td>2.5E-03 (4.7E-03)</td> <td>0.13 (0.19)</td> </tr> <tr> <th>CEO-controlled</th> <td>0.10 (0.22)</td> <td>0.15 (0.13)</td> <td>0.04 (0.22)</td> <td>0.05 (0.07)</td> <td>4.0E-07￥￥ (2.0E-07)</td> <td>0.96 0.05 (1.22) (0.11)</td> <td>0.82 (0.84)</td> <td>4.6E-04 (3.0E-03)</td> <td>0.07 (0.08)</td> </tr> <tr> <th>Heirfamily(CEOmemberoffamily)</th> <td>0.62 (0.22)</td> <td>0.83*** (0.14)</td> <td>0.03 (0.21)</td> <td>-0.03 (0.07)</td> <td>-8.1E-08 (2.0E-07)</td> <td>1.52 0.29￥ (1.12) (0.12)</td> <td>0.72 (0.78)</td> <td>-1.4E-03 (2.0E-03)</td> <td>-0.03 (0.12)</td> </tr> <tr> <th>Activefamily(hiredCEO)</th> <td>0.92*** (0.22)</td> <td>0.37** (0.14)</td> <td>0.42* (0.21)</td> <td>0.13* (0.07)</td> <td>1.6E-07 (3.2E-07)</td> <td>1.25 -0.05 (1.04) (0.11)</td> <td>-0.30 (0.63)</td> <td>-1.6E-03 (4.6E-03)</td> <td>0.09* (0.06)</td> </tr> <tr> <th>Passivefamily(hired CEO)</th> <td>1.28** (0.20)</td> <td>0.80*** (0.13)</td> <td>0.17 (0.19)</td> <td>0.19* (0.06)</td> <td>1.1E-07 (9.3E-08)</td> <td>1.95 0.20￥ (1.47) (0.10)</td> <td>0.82 (0.85)</td> <td>1.7E-03 (2.5E-03)</td> <td>0.23￥* (0.07)</td> </tr> <tr> <th>Parentcompany</th> <td>0.76*** (0.19)</td> <td>0.24* (0.13)</td> <td>0.30 (0.19)</td> <td>0.20￥ (0.06)</td> <td>1.9E-07* (3.1E-08)</td> <td>0.76 0.23￥ (1.61) (0.08)</td> <td>3.01￥美 (1.10)</td> <td>5.9E-04 (1.1E-03)</td> <td>0.20 (0.14)</td> </tr> </tbody> </table> </div> </body> </div>

Figure

Table of statistical data with coefficients and significance levels for various corporate and financial metrics.

<div> <body> <div class="page"> <table> <tbody> <tr> <td>Investmentcompany</td> <td>1.03*** (0.23)</td> <td>0.50*** (0.15)</td> <td>0.29 (0.23)</td> <td>0.22*** (0.08)</td> <td>-2.1E-07* (1.2E-07)</td> <td>2.54￥ (1.10)</td> <td>0.22* (0.12) 0.13</td> <td>0.29 (0.75)</td> <td>5.0E-03 (6.2E-03)</td> <td>-0.05 (0.06)</td> </tr> <tr> <th>Diffuse</th> <td>1.11*** (0.17)</td> <td>0.52*** (0.12)</td> <td>0.75*** (0.21)</td> <td>0.22号 (0.06)</td> <td>1.2E-07￥ (2.0E-08)</td> <td>2.05 (0.46)</td> <td>(0.05)</td> <td>0.44* (0.27)</td> <td>3.4E-03￥ (1.2E-03)</td> <td>0.11* (0.06)</td> </tr> <tr> <th>CEOand firm characteristics: %Freefloat*(1-Diffuse)</th> <td>ns</td> <td>ns</td> <td>0.80￥* (0.29)</td> <td>ns</td> <td></td> <td>ns</td> <td></td> <td></td> <td>ns</td> <td></td> </tr> <tr> <th>S.d.(ROA)</th> <td>ns</td> <td>2.47** (1.21)</td> <td>ns</td> <td>ns</td> <td></td> <td>na</td> <td></td> <td></td> <td>na</td> <td></td> </tr> <tr> <th>CEOisBoardchair</th> <td>0.25** (0.11)</td> <td>0.10 (0.07)</td> <td>0.16 (0.10)</td> <td>0.06* (0.04)</td> <td></td> <td>ns</td> <td></td> <td></td> <td>ns</td> <td></td> </tr> <tr> <th>AgeofCEO</th> <td>ns</td> <td>0.01** (0.00)</td> <td>ns</td> <td>-5.7E-03** (2.4E-03)</td> <td></td> <td>ns</td> <td></td> <td></td> <td>ns</td> <td></td> </tr> <tr> <th>Tenure (years)</th> <td>0.01 (0.01)</td> <td>ns</td> <td>-8.0E-03 (7.7E-03)</td> <td>ns</td> <td></td> <td>ns</td> <td></td> <td></td> <td>ns</td> <td></td> </tr> <tr> <th>Ageoffirm(yearslisted)</th> <td>5.6E-03 (3.7E-03)</td> <td>2.8E-03** (2.4E-03)</td> <td>ns</td> <td>2.9E-03￥ (1.2E-03)</td> <td></td> <td>ns</td> <td></td> <td></td> <td>ns</td> <td></td> </tr> <tr> <th>ChangeofCEO</th> <td>na</td> <td>na</td> <td>na</td> <td>na</td> <td></td> <td>0.31￥美美 (0.04)</td> <td></td> <td></td> <td>0.25** (0.04)</td> <td></td> </tr> <tr> <th>Renunciation tobonus</th> <td>na</td> <td>na</td> <td>na</td> <td>na</td> <td></td> <td>-0.89*** (0.09)</td> <td></td> <td></td> <td>na</td> <td></td> </tr> <tr> <th>Intercept</th> <td>(3.85)</td> <td>(2.49)</td> <td>-14.98***-6.87*关-10.87*** (3.76)</td> <td>-1.63 (1.26)</td> <td></td> <td>-0.03 (0.04)</td> <td></td> <td></td> <td>0.04 (0.02)</td> <td></td> </tr> <tr> <th>Industryfixedeffects</th> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td></td> <td>na</td> <td></td> <td></td> <td>na</td> <td></td> </tr> <tr> <th>Annualindustryfixedeffects</th> <td>na</td> <td>na</td> <td>na</td> <td>na</td> <td></td> <td>Yes</td> <td></td> <td></td> <td>Yes</td> <td></td> </tr> <tr> <th>adj.-R2</th> <td>0.75</td> <td>0.77</td> <td>0.29</td> <td>0.47</td> <td></td> <td>0.39</td> <td></td> <td></td> <td>0.11</td> <td></td> </tr> <tr> <th>Period</th> <td>2005-12</td> <td>2005-12</td> <td>2005-12</td> <td>2005-12</td> <td></td> <td>2006-2012</td> <td></td> <td></td> <td>2006-2012</td> <td></td> </tr> <tr> <th>Num.ofcompanies</th> <td>102</td> <td>103</td> <td>102</td> <td>102</td> <td></td> <td>104</td> <td></td> <td></td> <td>104</td> <td></td> </tr> <tr> <th>Num.ofobservations</th> <td>102</td> <td>103</td> <td>102</td> <td>102</td> <td></td> <td>708</td> <td></td> <td></td> <td>710</td> <td></td> </tr> </tbody> </table> </div> </body> </div>

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