rafmacalaba/datause-encoder-data huggingface dataset:
to the improvements in the rural roads network. Accessibility was measured as the ability of agricultural products and farmers to reach local markets in a given time frame.

3. **Climate resilience.** The analysis for economic growth and human development was combined with climate resilience data, using flood risk maps already available through the Southeast Asia Disaster Risk Insurance Facility (SEADRIF) to assess the vulnerability of the roads to floods and the impact of such floods to accessibility to economic growth and human capital development. Using the water levels from the flood maps and the type and condition of roads, the analysis determined which roads would be impassable during flood events and then assessed which roads would require interventions to enhance climate resilient accessibility to economic growth and human development opportunities.

# **2. Methods**

## 2.1. Accessibility Model

The underlying accessibility model<sup>10</sup> follows different assumptions:

1. Two travel modes are modeled: vehicle and bicycle Each mode has different travel speeds as reflected in Table 1. Based on local consultation, each mode is assigned to a different destination type.

2. Bicycles are used to access schools, high schools, and health centers. Vehicles are used to access markets and referral hospitals.

3. People can walk up to 2 km to road and/or to destination, modeling last-mile connectivity in addition to the main mode of transportation.

4. Roads become impossible to use with more than 0.5 m of water above road design levels for vehicles and 0.25 m for bicycles.<sup>11,12</sup> Design standards are based on local construction practices (Table 1). Walking becomes impossible for any road under the flood zone regardless of water depths. The model does not consider specific design or location of bridges or culverts. Rather, it assumes that a water depth equal to or less than the hydraulic design would not damage the roads.

> 10 The model, written on python ArcGIS algorithms, uses raster-based Cost Distance function to estimate travel time to the closest destination – see Annex 1 for a sample code.

> 11 These values reflect a conservative approach to accommodate uncertainties and lack of information on the road network attributes such as road vertical alignment, embankments or culverts (usual values are 20 to 30 cm).

> 12 The model does not consider reduction of travel speeds for flood levels between 0 to 50cm. It only models passable or impassable.

5
Policy Research Working Paper 10995

# **Abstract**

This paper estimates the differentiated economic impact of natural hazard-related disasters (the specific disasters and climate shocks studied here being floods) when they occur in conflict versus non-conflict affected areas. Existing literature shows that disasters and climate shocks can cause significant distress to countries and people on an institutional and household level. However, assumptions are made that their impact tends to be larger in conflict-affected areas, with little evidence available on the differentiated extent of these damages. This paper investigates whether, and to what extent, the presence of conflicts has amplified the impacts of floods on economic activity and people, and hampered recovery. The paper applies a “top-down” approach to estimating the differential impacts of disasters and climate shocks between conflict and non-conflict affected areas using satellite-derived imagery of nightlight radiance as a proxy for economic activity, along with geospatial footprints of floods. The analysis considers two case studies: the 2019 tropical cyclones Idai and Kenneth and subsequent floods in Mozambique, and the July 2022 floods in Nigeria. Using difference-in-difference estimations, the analysis finds that there are significant differences in disaster and climate shock impacts and recovery between conflict and non-conflict affected areas. Particularly, there is a greater decline in economic activity and a longer recovery time in conflict affected areas, as proxied by the greater change in the intensity of nightlight radiance.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at kbenbih@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> { ag<br>=<br>4<br>Y |<br>ar 3 Changein Poverty Rate<br>4 (Numberof Provinces)<br>: LJ11%-15% decrease (6)<br>: DO 8%- 11% decraase (10)<br>Wi o2%-Sidecrase (75)<br>ae M S%-8 decrease (30)<br><!-- End of picture text -->
**Table 2: Area under biotech cotton varieties (percent of area allocated to cotton)**

||**United**<br>**States**|**Australia**|**Mexico**|**China**|**South**<br>**Africa**|**Argentina**|**India**|**Colombia**|**Brazil**|**Burkina**<br>**Faso**|**WORLD**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1996/07|12.7|7.7|0.8|—|—|—|—|—|—|—|**2.0**|
|1997/08|25.5|14.0|7.8|0.7|—|—|—|—|—|—|**4.4**|
|1998/09|45.0|15.4|14.3|2.4|12.0|0.8|—|—|—|—|**6.6**|
|1999/00|58.7|22.7|12.5|14.2|28.0|3.9|—|—|—|—|**12.1**|
|2000/01|71.1|30.0|33.4|25.0|24.0|6.1|—|—|—|—|**15.7**|
|2001/02|76.7|30.0|27.4|32.0|74.0|4.6|—|—|—|—|**18.1**|
|2002/03|75.4|30.0|37.6|48.7|84.0|8.0|0.5|—|—|—|**20.2**|
|2003/04|75.1|60.0|41.4|51.6|86.0|10.0|1.1|0.5|—|—|**20.8**|
|2004/05|78.0|60.0|60.6|59.1|75.0|10.0|6.1|23.0|—|—|**24.3**|
|2005/06|81.0|90.0|57.4|62.2|84.0|20.0|14.1|40.0|—|—|**28.4**|
|2006/07|85.4|90.0|59.0|66.6|91.0|25.0|41.5|44.0|0.5|—|**36.5**|
|2007/08|90.2|95.0|60.0|61.0|95.0|25.0|66.3|57.0|13.0|—|**43.5**|
|2008/09|92.6|95.0|65.0|65.7|95.0|25.0|74.0|71.0|20.0|1.6|**47.1**|
|2009/10|95.0|95.0|62.0|68.0|95.0|85.0|79.3|61.0|20.0|30.5|**52.0**|

**Source:** International Cotton Advisory Committee **Notes:** ‘—‘ indicates that no biotech cotton was used.

_— 14 —_
the Busung Paper company has received the largest number of government actions with a total of 84.

Since 1990, the Korea Press Foundation has operated a comprehensive online news database service known as KINDS (Korean Integrated News Database System). This system is the largest service of this nature in Korea. It covers national and economic daily newspapers in both Korean and English, news bulletins, local daily newspapers, magazines, and foreign newspapers. It provides the complete text of 10 major national daily newspapers.<sup>10</sup> On-line users can further search articles in 23 local daily newspapers in Korea. Since its inception, it has cumulated a total of over 3 million articles, and continues to add to its database approximately 2,000 articles each and every day. The KINDS database was searched by entering keywords such as environment, violation, and accident, searching for articles related to environmental news.

Over the period of observation, approximately 11% of the total number of violation events that have appeared on the MVR have been covered by printed news media. In Table 1, note that of the 756 events covered in the printed news media, 40% of them involved traded companies. Given that traded companies represent only 17% of the total number of violation events, it would appear that news media show a particular attention to traded companies (which may also be larger and more prominent companies) in their news coverage of the MVR.

> 10 The Kyunghyang Shinmun, the Kukmin Daily, the Korea Daily News, the Dong-A Ilbo, The Numhwa Ilbo, the Segye Ilbo, the Chosun Ilbo, the Joongang Ilbo, the Hankyoreh, and the Hankook Ilbo.

9
# **Table 20a.  Impact on Output by Sector and Regional Market of WTO Accession and Simultaneous Energy Intensity Standards to Reduce Carbon Emissions by 20 Percent Impact. (Results are Percentage Change from Base Year)**

|**Good**|**`Central`**|**`Siberia`**|**`South`**|**`North`**|**`Urals`**|**`Far East`**|**`Moscow`**|**`St. Pete.`**|**`Tumen`**|**`Northwest`**|
|---|---|---|---|---|---|---|---|---|---|---|
|**`NFM`**|25.4|11.4|39.1|13.1|38.8|17.6|63.6|32.3||85.9|
|**`FME`**|12.6|-7.1|10.8|0.5|-3.1|7.5|15.0|170.0||2.2|
|**`TMS`**|11.1|5.5|10.4|17.5|-1.6|19.1|-3.5|19.1|9.5|19.6|
|**`COL`**|5.3|2.7|4.5|4.9|3.3|5.6|||||
|**`SCI`**|2.6|2.6|0.4|7.2|0.4|6.4|2.0|9.0|-6.6|8.2|
|**`CRU`**||-1.5|0.0|-0.4|-1.0|0.4|||-1.1|-0.5|
|**HEA**|-0.6|-0.6|-0.8|-0.9|-0.8|-0.8|-1.2|-0.7|-0.8|-0.3|
|**PST**|-1.3|-2.1|-0.6|-1.1|-2.4|-0.2|-1.6|-1.0|-0.7|-0.6|
|**`TRK`**|-2.1|-4.5|-1.4|-0.6|-6.0|1.3|-6.0|1.6|0.1|1.8|
|**`CON`**|-0.7|-3.3|-1.5|-2.8|-1.9|-1.8|0.1|-2.7|-4.4|-3.6|
|**TRD**|-4.8|-4.1|-2.3|-2.3|-3.7|-2.2|-3.5|-2.4|1.4|-1.9|
|**`RLW`**|-3.3|-6.3|-5.2|-5.9|-6.4|-3.6|-5.8|-1.7|-5.1|-2.6|
|**HOU**|-5.2|-5.4|-5.4|-6.0|-6.2|-5.2|-5.8|-5.2|-7.1|-4.5|
|**`OTI`**|-4.7|-9.1|-4.8|-7.6|-6.2|-6.4|-1.8|-4.1|-10.1|-5.3|
|**`AGR`**|-2.9|-6.0|-4.7|-6.3|-3.6|-6.1|-4.1|-9.3|-11.1|-9.0|
|**`GAS`**||-7.3|-2.9|-4.4|-3.9|-3.7|||-15.7|-8.0|
|**`FIN`**|-6.4|-7.1|-6.6|-7.6|-7.2|-6.5|-6.9|-6.1|-8.8|-5.0|
|**`MWO`**|-8.7|-10.0|-5.9|-17.8|-10.9|-3.9|2.9|-1.0|-11.1|-6.0|
|**CLI**|-5.4|-7.7|-6.7|-8.3|-6.9|-7.3|-6.9|-9.4|-11.9|-8.6|
|**`FOO`**|-9.1|-9.8|-8.2|-8.2|-9.1|-8.5|-11.5|-10.3|-10.5|-11.2|
|**`OTH`**|-6.0|-12.2|-7.3|-11.4|-6.3|-11.2|-7.2|-14.8|-10.6|-15.4|
|**`TPP`**|-12.1|-16.1|-17.5|-11.1|-8.5|-1.2|-19.4|-14.0|5.2|-10.4|
|**`TRO`**|-12.0|-17.4|-13.4|-10.0|-21.8|-7.8|-24.5|-7.4|-19.4|-7.0|
|**`CNM`**|-11.5|-18.5|-10.7|-13.9|-12.3|-13.9|-18.9|-18.4|-11.6|-18.9|
|**`MAR`**|-13.7|-19.4|-15.8|-14.4|-13.7|-13.9|-17.2|-16.2|-12.6|-13.3|
|**`PIP`**|-19.8|-20.7|-12.0|-9.3|-13.9|-15.8|-23.4|-19.2|1.3|-21.6|
|**`CHM`**|-13.2|-20.5|-15.0|-20.2|-16.0|-15.9|-17.8|-17.1|-22.4|-3.6|
|**`OIL`**|-14.8|-24.0|-16.8|-18.1|-21.6|-14.9|-20.2|-16.1|-27.0||
|**ELE**|-26.6|-24.1|-24.3|-24.0|-26.6|-22.3|-22.3|-24.2|-18.5|-25.0|
|**`AIR`**|-32.5|-32.1|-31.9|-37.8|-29.0|-37.4|-28.6|-38.0|-35.9|-37.0|

See table 1 for sector codes. **Source: Authors’ estimates.** 64
**Figure 8**

**Share of Children who had a Migrant Parent During Their Childhood**

<!-- Start of picture text --> 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>Year<br>7 to 12 Year Olds 13 to 15 Year Olds<br>.1<br>.08<br>.06<br>Share<br>.04<br>.02<br>0<br><!-- End of picture text --> Source: RCRE Supplemental Household Survey (2004) 37
-----. March 2007b. "Entrepreneurial Geographies: Support Networks in Three High-Tech Industries." _Economic Geography_ 81(2): 201.

Lall, Somik V., Zmarak Shalizi, and Uwe Deichmann. 2004. "Agglomeration Economies and Productivity in Indian Industry." _American Economic Review_ 73(3): 643-673. "Malaysia/Singapore: Deal with Spur". 2006. "Malaysia/Singapore: Deal With Spur".

= <u>http://www.oxfam.com/display.aspx?ItemID DB124874,</u> Meijers, Evert. April 2005. "Polycentric Urban Regions and the Quest for Synergy: Is a Network of Cities More Than the Sum of the Parts?" _Urban Studies_ 42(4): 765781.

Mellander, Charlotta and Richard Florida. December 2006. "The Creative Class or Human Capital?" _CESIS Working Papers_ 79.

Mellinger, Andrew D., Jeffrey Sachs, and John L. Gallup. 2003. "Climate Coastal Proximity and Development," in Clark, Gordon L., Maryann P. Feldman, and Meric S. Gertler (eds.), _The Oxford Hand Book of Economic Geography_ . Oxford, U.K.: Oxford University Press.

- Midelfart-Knarvik, K. H., H. G. Overman, S. J. Redding, and A. J. Venables. 2000. "The Location of European Industry." _European Economy - Economic Papers_ 142.

- "Multimedia Super Corridor". 2006. "Multimedia Super Corridor".

<u>http://en.wilipedia.org/wiki/Multimedia_Super_Corridorr1,</u>

- O'Mara, Margaret Pugh 2005. _Cities of Knowledge: Cold War Science and the Search for the Next Silicon Valley_ . Princeton, NJ: Princeton University Press.

- OECD 2006. _Competitive Cities in the Global Economy_ . Paris:

Overman, Henry G. and Anthony J. Venables. 2005. "Cities in the Developing World." CEPR Discussion Paper. London: Centre for Economic Policy Research.

Peri, Giovanni. 2004. "Catching-Up Foreign Technology? Evidence on the "VeblenGerschenkron" Effect of Foreign Investments." NBER Working Paper. Cambridge, MA: National Bureau of Economic Research.

Pinchot, Gifford and Ron Pellman 2000. _Intrapreneuring in Action: A Handbook of_ _Business Innovation_ . San Francisco, CA: Berrett-Koehler .

- "PricewaterhouseCoopers". 2007. "Largest City Economies in the World in 2005 and

2020". http://www.pwc.com/extweb/pwcpublications.nsf, 21
<!-- Start of picture text --> (la) (I) (.FDI 1 pp -AFD1, ) )a+p ODp+X GDP E-XIM+ p pO+ INV, pp+XR+lI CON, E M PIV hcEXp +FR7ZX+Xi +<br>(2a) (2) (EDI, ( - PEDI,) lx) +p~o GDP, i +X EXYIM, pp - +6p INVI +0POp + CONI 9MY, + GDP,*d+ , ApRt + PRIv vp!I EX +e<br>X(FDI -PFDI) POP POP POP POP, POP, PRIV,<br><!-- End of picture text --> with PFDI representing FDI resulting from privatization sales, PRIV standing for total privatization revenues, and FEX being the foreign exchange obtained through privatizations (in the form of FDI as well as portfolio equity investments).

The estimation of these equations indicates that privatizations are a very important element in the foreign direct investment decision (see Table 5). On both variables, the coefficients are positive and, compared to the other regressors, quite large in both regressions. While they are only moderately significant in the share estimation, they show a high statistical significance in the per capita regression. Both variables also prove to be very robust, barely changing size or statistical significance in different specifications of the model. While such interpretations always have to be taken with a grain of salt, the coefficients indicate that each dollar it, privatization revenue generates an additional 38 cents in new FDI, and that a one percentage increase in foreign participation adds another 50 cents. Note also that the regression fit improves drastically in both cases, with the adjusted R<sup>2</sup> jumping to 0.97 in the per capita case and to 0.77 in the share estimation.

Table 5 Privatizations as a Determinant of FDI <!-- Start of picture text --> Dependent FDI pr aepita FDI stcr<br>Vetiab.<br>Constant -6.481 1.4084<br>(11,525) 1.406)<br>GDP per capita -7.73E-05 -0.002<br>1-0.025) 1 1.8818)<br>Trade 0.0283 -040005<br>(8.7059) (-0.4598)<br>Investment 0.0023 0.01f1<br>(0.0907) (2.9511)<br>Goveonment 0.013 -0.0128<br>Consumption (-0.5el) i-2.2473)<br>Exchange Rate 0.1238 0.0002<br>(2.5407) (0.014)<br>Roa GDP R 2.38E-05<br>(4.1269)<br>Foreign Exchango 0.497 0.0258<br>Ratio (7.125) (1.4107)<br>Privatization 0.3804 0.0854<br>Revenue (2.7324i 01.889)<br>Adj. R-Squared 0.9724 0.7878<br><!-- End of picture text -->

**t-statistioc** in **parenthaeee; indicates statistical** significance **at the** 98%-levol, **indicate** statistical significance **at the** 95 **%-level, indicates statistical** significanos **at the 90%-levol.**

28
# **Appendix A. Additional Tables**

# **Table S1. An Overview of the World Bank’s Statistical Capacity Index (SCI) in Selected Recent Studies**

|1|Angrist, Goldberg &<br>Jolliffe (2021)|_Journal of Economic_<br>_Perspective_|Global analysis|Measuring economic growth in<br>developing countries|Poorer countries have lower statistical capacity,<br>which can severely bias their reported<br>measurements of economic growth.|
|---|---|---|---|---|---|
|2|Anderson & Whitford<br>(2017)|_Review of Policy_<br>_Research_|100 countries|Technological attainment and<br>statistical capacity|Countries with greater levels of technological<br>attainment have greater national statistical<br>capacity.|
|3|Goren & Winkler (2022)|_Journal of African_<br>_Economies_|57 African<br>countries|Low-quality statistics, slave trades<br>and development|Replacing mismeasured GDP per capita by<br>nighttime light intensity per capita significantly<br>reduces the impact of the slave trade on<br>economic development by a factor of 2 to 4.|
|4|Hanson & Sigman (2021)|_Journal of Politics_|139 countries|Measuring state capacity in<br>political science research|The SCI is most strongly correlated with state<br>capacity compared to other indicators in<br>bureaucratic quality, public administration, law<br>and order ratings, or state fiscal capacity.|
|5|Henderson, Storeygard &<br>Weil (2012)|_American Economic_<br>_Review_|113 countries|Better measuring income growth<br>with night lights data|SCI can help provide more accurate estimates of<br>country income growth.|
|6|Hu & Yao (2022)|_Journal of_<br>_Econometrics_|162 countries|Estimating the relationship<br>between nighttime light growth<br>and GDP growth|SCI can help provide more accurate estimates of<br>country GDP growth.|
|7|Jacob (2017)|_World Development_|145 countries|Impact of data gaps on<br>Millennium Development Goals<br>achievement (MDG)|Stronger country statistical capacity increases the<br>probability of MDG success.|

37
Poland and Spain, respectively. Conversely, occupational shifts at the top half of the distribution account for much smaller shares (see the P90/50 ratios in column (1) of table 1).

There are two reasons for these differential polarization impact on the top and bottom part of the earnings distribution. The first is technical and has to do with the fact that the occupational change simulated with a change of the parameters is not equivalent to the full change, and the second is because most of the displaced middle skilled workers tend to move down in the distribution. A more detailed explanation is provided below.

<u>Table 1 – Decomposition results</u>

|||<br>|<br> <br>Percen|<br> <br>tage explain|<br> <br>ed by:|
|---|---|---|---|---|---|
|||Change|**Occup.**|**Charact.**|<br>**Returns**|
|Country|Inequality measure,<br>labor income|1990s‐<br>|<br>(C1,<br>|(C2,<br>|<br>(C3,<br>|
|||2013|section 2.2)|section 2.2)|section 2.2)|
||||(1)|(2)|(3)|
||Gini coefficient|0.058|‐1|20|30|
|Germany|P90/10|3.148|44|14|185|
||P90/50|0.404|‐6|13|21|
||P50/10|0.311|211|23|818|
||Gini coefficient|0.078|42|‐4|27|
|Poland|P90/10|0.938|91|‐10|59|
||P90/50|0.303|26|‐9|30|
||P50/10|0.208|184|‐4|79|
||Gini coefficient|0.068|92|‐21|51|
|Sain|P90/10|2.672|312|‐10|158|
|p|P90/50|0.466|39|‐19|60|
||P50/10|0.442|772|86|114|

Source: Authors’ calculations based on LIS harmonized surveys. Note: the percentages shown in columns (1), (2) and (3) are derived using the distributions of earnings simulated according to the methods described in the respective sub‐sections of section 2.2. The change in the inequality indicators corresponds to the ones estimated in the working sample and may differ from those of the whole sample of wage workers. Observations with missing data in some of the relevant variables are excluded. The change in the Gini coefficient of labor income for the whole set of wage workers (including those with missing information) was 0.065 in Germany, 0.060 in Poland, and 0.071 in Spain The education, age and gender structure of the labor force underwent significant transformations between the early 1990s and 2013. For example, in our sample, the share of individuals with tertiary education expanded substantially in Poland, where it went from 7% to 20%, and in Spain where it went from 12% to 32%; but not so much in Germany where it increased from around 22% to 27%. Another scenario, where only assets/characteristics of individuals are changing, is therefore simulated to account for the distributional impact on earnings of these transformations. Note that, when characteristics are varying, there are two effects on earnings. One effect is direct; for example, a larger number of well‐ 21
proaches from the ones explored in this study.

# **5.2 Heterogeneous Effects of the ASP’s Socio-Emotional-Learning Component**

We study the differential impacts of the SEL component on behavior following the machinelearning approach discussed in Section 4.2. Table 4 presents the first-difference CATE for behavior at school, for the _“Strong"_ and _"Weak"_ groups, as defined in Section 4.2. Additionally, we characterize the groups using a balance test for the baseline variables. We present the differences shown by the balance tests (column (3)) along with the corresponding _p_ -values adjusted for the Benjamini-Hochberg procedure for multiple-hypothesis testing for that difference across these groups (column (4)).

Overall, the results indicate that the most vulnerable students are benefiting the most from the SEL component—that is, they are overrepresented among students with a higher CATE. For example, the program has a larger impact on male and older students, on those with worse behavior at school before the intervention, and on those enrolled in a higherlevel course (which is related to age). Moreover, students with greater exposure to risks are also benefiting more from the intervention. Specifically, students who are enrolled in the afternoon shift (and thus less likely to have adult supervision during the morning), or who are less likely to be working<sup>49</sup> are overrepresented in the Strong CATE group.

Household and school/neighborhood characteristics support the result that the most vulnerable students benefit the most from this intervention. First, the _Strong_ group is overrepresented by students living only with their mothers (and underrepresented by students living with both parents), who are less likely to have adult supervision compared to those living with both parents. This result aligns with existing evidence from Chile that the effects of an ASP are larger for children who at baseline spent after-school hours at home alone (Martínez and Perticará, 2020). Second, the average number of years of education

> 49Based on our discussions with the NGO, we interpret this result as follows: if students are working, they are busier (have less unsupervised time) and probably are less exposed to risks. Also, working students attend fewer sessions, which makes them less likely to benefit from the curricula.

30
|tions b|_tnI_|6.52|6.52|6.42|6.34|6.53|6.49<br>6.54|6.53|mating steady|
|---|---|---|---|---|---|---|---|---|---|
|an Dura|_tnF_|6.52|6.50|6.67|6.73|6.53|6.48<br>6.49|6.45|approxi|
|Me|_tu_|69|70|53|47|70|76<br>73|78|ors for|
||_σ_|0.089|0.090|0.059|0.064|0.091|0.098<br>0.085|0.085|nitial vect|
|Efects|_σBS_|0.043|0.037|0.004|0.000|0.039|0.044<br>0.031|0.028|(guessed i|
|ibutional|_σW S_|0.052|0.053|0.057|0.064|0.052|0.054<br>0.053|0.056|conditions|
|Distr|_μlnWF −μlnWI_|0.40|0.41|0.14|-0.06|0.42|0.44<br>0.40|0.40|changes in initial|
|ghtness|_θ_|0.30|0.32|0.10|0.04|0.29|0.31<br>0.37|0.45|robust to|
|LM Ti|_θ_|0.67|0.62|1.69|2.46|0.51|0.717<br>0.43|0.39|esults are|
|ects|_nI_|52.23|53.94|25.73|11.71|54.11|50.51<br>58.37|61.28|ulation r<br>ess.<br>s.|
|regate Ef|_nF_|29.66|27.51|59.90|75.35|27.51|29.74<br>22.68|18.70|points. Sim<br>pling proc<br>on in year|
|Agg|u|17.86|18.28|14.11|12.68|18.12|19.49<br>18.69|19.76|ercentage <br>of the sam<br>ent durati|
|Valuations|_τ_, _μ_|_τ_ = 0, _μ_= 0_._5|_τ_ = 0|_τ_ = 0_._5|_τ_ = 1_._0|_μ_= 0_._5|_μ_= 1||ployment rates in p<br>ultiple repetitions <br> in weeks, employm<br>nts _μ_= 0_._5.<br>nts _τ_ = 0.<br>nts _τ_ = 0, _μ_= 0_._5|
|||**Benchmarck** a|∆**Labor Taxes**c<br>_δ_1 _↑_4_._46%, _δ_2 _↑_7_._53%|||∆**SA**<br>d<br>_δ_3 _↑_by 159_._74%|∆**Relative Prices**e<br>_PF /PI ↓_by 5%|_PF /PI ↓_by 10%|a Unemployment and em<br>state equilibrium) and m<br>b Unemployment duration<br>c In these set of experime<br>d In these set of experime<br>e In these set of experime|

41