rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6. TED spread, risk appetite, and global liquidity**

||Pre-Crisis|Crisis|
|---|---|---|
||TED spread|TED spread|
|State Street risk appetite|0.167|-4.141|
||(0.96)|(2.29)*|
|World Liquidity / World GDP|0.429|-62.33|
||(0.41)|(0.88)|
|Fed Funds Target|6.045|7.042|
||(10.56)**|(0.56)|
|Constant|-8.446|1,281.496|
||(0.33)|(1.24)|
|Observations|91|17|
|R-squared|0.56|0.35|
|Absolute value of t statistics in parentheses|||

- significant at 5%; ** significant at 1%

Policy measures in many advanced countries have also included substantial state intervention in virtually all aspects of the banking industry, including funding, loan portfolio, compensation, and dividend policy. Governments have moved beyond their traditional role of lender of last resort to a new status as “guardian of last resort “ in extending sovereign guarantees to banks’ new debt issuance and risky assets, as well as investing directly in banks’ debt and equity instruments. As of early March 2009, the total amount of cash invested in the banking industry by governments in the US and Europe had reached $538.6 billion, with the US spending $250 billion under the TARP’s Capital Purchase Program, and the UK spending $172.5 billion. Capital infusions had already led governments to acquire large stakes in banks in the United States, Britain, and continental Europe, but even more government financing seemed likely.

# **Other policy measures**

In the last quarter of 2008, attention turned to broader policy measures to provide stimulus to the economy, in particular the use of fiscal policy.<sup>2</sup> Around the world, countries announced their plans. China unveiled a package of new and existing measures totaling some $600 billion, while the U.S. Congress approved, in February 2009, a fiscal stimulus program amounting to $787 billion and involving both tax cuts and increased spending on infrastructure and education, among other items. The fiscal stimulus measures will take time to take effect, especially infrastructure spending, which typically involves long lead times. It is too early to tell whether these measures will have a significant impact on confidence. It is

> 2 The US administration had already implemented fiscal stimulus in the second quarter, 2008, through tax rebates. While they temporarily boosted economic activity, they did not lead to a revival of confidence and the US experienced a sharp fall in GDP in the second half of the year.

19
# **Tables**

**Table 1: Proportion of the labor force engaged in home-based work**

||**Estimated**<br>**proportion**|**Year of**<br>**estimate**|**Year of trade**<br>**liberalization**|
|---|---|---|---|
|Algeria<sup>~~1~~</sup>|3.3|1989|1991|
|Australia<sup>~~1~~</sup>|2.9|1989|1964|
|Brazil<sup>~~2~~</sup>|5.5|1999|1991|
|Ecuador<sup>~~2~~</sup>|17.3|1999|1991|
|India<sup>~~1~~</sup>|2.5|1981|1994|
|Japan<sup>~~1~~</sup>|1.6|1988|1964|
|México<sup>~~2~~</sup>|4.4|1999|1986|
|Philippines<sup>~~1~~</sup>|23.0|1980s|1988|
|Peru<sup>~~1~~</sup>|10.5|1987|1991|
|United Kingdom<sup>~~1~~</sup>|2.3|1981|mid-century|
|United States<sup>~~1~~</sup>|7.53|1985|mid-century|
|1ILO (1995);2author’s calcu|lations|||

**Table 2:  Prevalence of Home-based Work, by Category of Home-based Work and Gender (1999)**

|**Brazil**|**Home-based work**|**ers as a proportion of t**<br>**force**|**he respective labor**|
|---|---|---|---|
||**Male**|**Female**|**Total**|
|Informal self-employed|1.6|6.3|3.6|
|Professional|0.2|0.5|0.4|
|Contract|0.01|0.08|0.04|
|Unpaid|0.2|0.7|0.4|
|Other*|0.3|2.4|1.2|
|**All**|**2.3**|**9.9**|**5.5**|
|**Ecuador**||||
|Informal self-employed|3.4|12.8|7.4|
|Professional|2.05|3.7|2.8|
|Contract|0.1|0.3|0.2|
|Unpaid|2.5|5.9|4.0|
|Other*|1.4|4.8|2.9|
|**All**|**9.5**|**27.6**|**17.3**|
|**México**||||
|Informal self-employed|1.8|5.5|3.2|
|Professional|0.5|0.5|0.5|
|Contract|0.06|0.08|0.07|
|Unpaid|0.2|1.0|0.5|
|Other*|0.08|0.3|0.2|
|**All**|**2.6**|**7.4**|**4.4**|

* firm owners, service workers, and agricultural workers

26
<mark>Sallee, James M.</mark> _<mark>Pigou creates losers: On the implausibility of achieving pareto improvements from efficiency-enhancing policies</mark>_ <mark>. No. w25831. National Bureau of Economic Research, 2019.</mark> <mark>Schmalensee, Richard, Thomas M. Stoker, and Ruth A. Judson. "World carbon dioxide emissions: 1950–2050."</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>80, no. 1 (1998): 15-27.</mark> <mark>Severen, Christopher, and Arthur Van Benthem.</mark> _<mark>Formative experiences and the price of gasoline</mark>_ <mark>. American economic Journal, forthcoming</mark> <mark>Sexton, Steven. "Automatic bill payment and salience effects: Evidence from electricity consumption."</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>97, no. 2 (2015): 229-241.</mark> <mark>Stavins, Robert. The Relative Merits of Carbon Pricing Instruments: Taxes versus Trading . Review of Environmental Economics and Policy (2022); 16(1), 62-82.</mark> <mark>van Benthem, Arthur A. "Energy leapfrogging."</mark> _<mark>Journal of the Association of Environmental and Resource Economists</mark>_ <mark>2, no. 1 (2015): 93-132.</mark> <mark>Wackernagel, Mathis, Niels B. Schulz, Diana Deumling, Alejandro Callejas Linares, Martin Jenkins, Valerie Kapos, Chad Monfreda et al. "Tracking the ecological overshoot of the human economy."</mark> _<mark>Proceedings of the national Academy of Sciences</mark>_ <mark>99, no. 14 (2002): 9266-9271.</mark> <mark>Wagner, Gernot, and Martin L. Weitzman.</mark> _<mark>Climate shock</mark>_ <mark>. Princeton University Press, 2016.</mark> <mark>Weitzman, Martin L. "Can negotiating a uniform carbon price help to internalize the global warming externality?."</mark> _<mark>Journal of the Association of Environmental and Resource Economists</mark>_ <mark>1, no. 1/2 (2014): 29-49.</mark> <mark>Weitzman, Martin L. "Voting on prices vs. voting on quantities in a World Climate Assembly."</mark> _<mark>Research in Economics</mark>_ <mark>71, no. 2 (2017): 199-211.</mark> 20
that “the evolution of the current crisis seems remarkably similar to emerging market crises of a decade ago.” Krugman (2009) also notes the role of self-financing in the partial recovery of Japanese firms after the turn of the century. In addition, while the Phoenix Miracles have largely been an emerging market phenomena (Calvo et al., 2004), more recent studies (e.g. Claessens, Kose, and Terrones, 2009; World Economic Outlook, April 2009) have documented a recovery in output ahead of a recovery in credit in recessions and credit crunches even in OECD countries.

# **_5.1 Output versus Credit Recovery – US Case_**

In Figure 6A, we present the macro statistics on US-GDP and credit markets since 2005. The GDP numbers are seasonally adjusted real GDP estimates from the Bureau of Economic Analysis. As a measure of credit, we use Bank Credit from the Federal Reserve Board’s statistical release H.8 on the Assets and Liabilities of Commercial Banks in the US. Bank Credit consists of the aggregate amount of assets held by all US commercial banks and includes both securities held by banks and loans and leases made by banks.<sup>20</sup> We use the December numbers each year from the H8 release which is in monthly data format. Figure 6A shows that both GDP and Bank Credit decline from 2008 to 2009 but begin to recover from 2009 to 2010. The drop in GDP from 2008 to 2009 was 2.63% whereas the change in GDP from 2009 to 2010 was 2.85%. Bank Credit dropped by 3.78% from December 2008 to December 2009 but showed a positive increase of 2.15% from December 2009 to December 2010.

Next, we look at US firm-level data to understand if we see similar effects in the corporate sector. Using annual financial statements from Compustat for US publicly listed companies, in Figure 6B, we present evidence on sales and short-term borrowing, aggregated across all firms. In constructing the sample, we drop Canadian listings that are included as part of the Compustat U.S. domestic file, firms incorporated outside the U.S., ADRs, and financial services (NAICS 52 or SIC industries 60-64 and 67) resulting in a balanced panel of 3338 firms that had non-missing _Sales_ (Income Statement data item #12) and _Debt in Current Liabilities_

> 20 As alternate measures we also use Loans and Leases by Commercial Banks, Commercial and Industrial Loans, and Consumer Loans. Loans and Leases by Commercial Banks is a sub-category of Bank Credit and includes Commercial and Industrial Loans, Real Estate Loans, Consumer Loans, Fed Funds, Repurchase Agreements with Brokers and other loans; Commercial and Industrial Loans is the corporate lending sub-component of Loans and Leases by Commercial Banks.

23
**Figure 12. Average markup differences by county relative to Bucharest**

_Source:_ World Bank staff calculations based on SBS data set. _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for county fixed effects—relative to Bucharest, the omitted category—in a fixed-effect regression model controlling for firm covariates plus year and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**A variance decomposition analysis suggests that ownership is the most relevant characteristic to explain total variance of markup performance across firms in Romania, followed by capital intensity and export status.** Drawing from estimations of equation (11), it is possible to evaluate the relative importance of each variable to explain the variance of the markup. Letting _d_<sup>_Exp_</sup> represent any of the explanatory variables of equation (11), the contribution to the variance of the markup of that variable can be defined as E = 3<sup>2</sup> <u>� �</u> 3 <u>(</u> 3 <u>)</u> ,                                         (12) δδ VVaarr dd<sup>EEE</sup> +2δδ MMC dd<sup>EEE</sup> ,μμ−δδ dd<sup>EEE</sup> CCTTnnaadd EE VVaarr(μμ)

or as the variance of the variable plus 2 times the covariance of the variable, with the part of the markup not explained by that variable. A similar expression can be derived for any of the remaining explanatory variables. The relative contribution of each variable is computed after discounting the residuals, sector, and year contributions. Results are displayed in figure 13. Ownership structure is shown to be the most relevant feature to explain the variance of markup performance across firms: it accounts for 34.7 percent of the explained variance of markup performance (after removing NACE 4-digit sector and year contributions). The second most important characteristic is capital intensity, corresponding to 21.4 percent of the variance of the markup performance. Export status accounts for the third largest share in explained variance: 11.3 percent.

26
**Table 7: Regressions for log cell expenditure per capita by gender and rural/urban residence**

||(1) Rural|(2) Rural|(3) Urban|(4) Urban|
|---|---|---|---|---|
||men|women|men|women|
|Age|0.0239|-0.0170|0.0344|-0.0247|
||(0.0232)|(0.0143)|(0.0212)<br>-|(0.0181)|
|Age squared|-0.000393*|0.000163|0.000343*|0.000300|
||(0.000222)|(0.000148)|(0.000203)|(0.000185)|
|Muslim|-0.265|-0.106|0.473**|0.245|
||(0.290)|(0.233)|(0.194)|(0.163)|
|Serere ethnicity|-0.822***|-0.458***|-0.249*|-0.0306|
||(0.189)|(0.164)|(0.135)|(0.118)|
|Poular ethnicity|<br>-0.353**|<br>-0.129|<br>-0.0922|<br>0.0166|
||(0.149)|(0.133)|(0.126)|(0.0994)|
|Diola ethnicity|-0.532|-0.525|0.159|-0.219|
||(0.547)|(0.673)|(0.237)|(0.146)|
|Mandingue ethnicity|<br>-0.0685|<br>-0.0484|<br>-0.217|<br>-0.137|
||(0.200)|(0.181)|(0.182)|(0.129)|
|Sarakole ethnicity|-0.0465|0.332|0.0841|-0.00152|
||(0.516)|(0.463)|(0.309)|(0.218)|
|Mandiaque ethnicity|<br>-0.553|<br>-0.593***|<br>0.336|<br>-0.363|
||(0.382)|(0.180)|(0.342)|(0.314)|
|Other ethnicity|0.0997|-0.0493|0.136|-0.255|
||(0.253)|(0.225)|(0.215)|(0.157)|
|Brothers same father|-0.00759|-0.00231|0.0186|-0.00920|
||(0.0263)|(0.0182)|(0.0258)|(0.0209)|
|Brothers same parents|0.0302|0.00264|0.0300|-0.00723|
||(0.0315)|(0.0241)|(0.0287)|(0.0252)|
|Sisters same father|-0.0278|0.00545|0.0128|0.00100|
||(0.0295)|(0.0189)|(0.0256)|(0.0196)|
|Sisters same parents|0.0287|0.0516*|0.00979|0.0414*|
||(0.0314)|(0.0275)|(0.0266)|(0.0223)|
|Brothers same mother|-0.0556|-0.0386|0.0858**|0.0382|
||(0.0764)|(0.0528)|(0.0402)|(0.0406)|
|Sisters same mother|0.0814|0.00493|-0.0801*|0.0323|
||(0.0954)|(0.0372)|(0.0440)|(0.0569)|
|First same gender|0.0862|0.0477|0.0910|-0.0959|
||(0.120)|(0.0960)|(0.121)|(0.0937)|
|First of siblings|-0.191|-0.0484|-0.0309|0.160|
||(0.135)|(0.107)|(0.135)|(0.127)|
|First born is male|0.183|0.114|0.00841|0.0584|
||(0.118)|(0.0739)|(0.120)|(0.0870)|
|Father died recently|-0.137|-0.00607|-0.0534|-0.149|
||(0.250)|(0.157)|(0.227)|(0.113)|
|Father in farming|<br>0.114|<br>0.170**|<br>0.407***|<br>-0.0131|
||(0.107)|(0.0852)|(0.131)|(0.126)|
|Mother in farm.|<br>-0.0397|<br>0.147|<br>-0.399***|<br>0.113|
||<br>(0.114)|<br>(0.0899)|<br>(0.148)|<br>(0.130)|
|Father in non-farm|<br>0.443***|<br>0.196*|<br>0.384***|<br>0.148|
||(0.134)|(0.118)|(0.109)|(0.102)|
|Mother in non-farm|-0.0221|0.291**|-0.158|-0.185**|
||(0.143)|(0.140)|(0.130)|(0.0917)|

32
**Table 19: Beijing and Shanghai as location today (%)**

||High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|Satisfied with current location in Beijing|87|65|60|70|
|Intend to relocate elsewhere|13|35|40|30|
|Total|100|100|100|100|
|_Shanghai_|||||
|Satisfied with current location in Shanghai|90|78|90|88|
|Intend to relocate elsewhere|10|22|10|12|
|Total|100|100|100|100|

Table 20 shows the reasons for relocating for those firms which intended to relocate elsewhere. It seems that cost factors are more crucial in Shanghai than in Beijing, particularly for business services companies. Some firms were being induced to relocate due to the urban renewal project of the city, that is, the buildings they are working in will be pulled down. Quite a few respondents said they considered investing in new facilities because their business was growing. And when making decision on the location of new facilities, many firms consider their options elsewhere in China.

**Table 20: Reasons to relocation (%)**

||High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|Cost factors – relocate to cheaper areas|-|29|-|13|
|Market factors – relocate to access large|50|29|-|20|
|emerging market|||||
|Resources – manpower, talents|50|-|33|13|
|Other reasons|-|57|67|67|
|_Shanghai_|||||
|Cost factors – relocate to cheaper areas|21|100|40|46|
|Market factors – relocate to access large|26|-|40|26|
|emerging market|||||
|Resources – manpower, talents|-|-|20|26|
|Other reasons|53|-|-|-|

_Note_ : the denominator in calculate percentage is the number of firms that intended to relocate elsewhere, and percent may not add to 100% as multiple responses are included.

32
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Table 5: Early Life Exposure to Coffee Mills and Test Scores: Effects by Location

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||||Rural|||||
|CatchmentArea X Post|0.0799***|0.0890***|0.0730***|0.0604**|0.0585***|0.0450**|0.0867***|0.1027***|0.0860***|
||(0.0273)|(0.0229)|(0.0217)|(0.0241)|(0.0211)|(0.0203)|(0.0286)|(0.0239)|(0.0225)|
||[ 0.005 ]|[ 0.001 ]|[ 0.001 ]|[ 0.015 ]|[ 0.008 ]|[ 0.009 ]|[ 0.004 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3112|0.3588|0.3588|0.3017|0.3450|0.3451|0.2925|0.3423|0.3424|
|Observations|1314120|1313807|1313807|1314123|1313810|1313810|1314119|1313806|1313806|
||||||Urban|||||
|CatchmentArea X Post|0.1524|0.1509|0.1525|0.1562|0.1357|0.1339|0.1458|0.1584|0.1615|
||(0.1363)|(0.1245)|(0.1295)|(0.1335)|(0.1331)|(0.1367)|(0.1328)|(0.1174)|(0.1225)|
||[ 0.275 ]|[ 0.242 ]|[ 0.241 ]|[ 0.256 ]|[ 0.309 ]|[ 0.309 ]|[ 0.28 ]|[ 0.196 ]|[ 0.194 ]|
|R-squared|0.5513|0.5717|0.5718|0.5355|0.5554|0.5556|0.5254|0.5474|0.5475|
|Observations|172407|172371|172371|172407|172371|172371|172407|172371|172371|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|

Notes: Dependent variable is the standardized aggregate test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. CatchmentArea X Post is an indicator variable equal to 1 if a student was born within the vicinity ( _≤_ 2.5km) of an operational coffee mill and 0 if otherwise. Controls include gender of the student, average annual temperature and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at school level in parenthesis. False discovery rate (FDR) adjusted p-values for CatchmentArea X Post are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 35
|Travel Time to<br>Markets –<br>International Food<br>Policy Research<br>Institute (IFPRI)|The data set includes five data layers<br>representing travel time to the nearest market<br>of five sizes (population of 20,000, 50,000,<br>100,000, 250,000, and 500,000) respectively, on<br>five arc-minute grids (~9 km) across SSA (2016-<br>01-29).|https://dataverse.harvard.edu/file.xhtml<br>?persistentId=doi:10.7910/DVN/YKDWJD<br>/UFOGZS&version=2.2|HarvestChoice, International Food<br>Policy Research Institute (IFPRI). 2016.<br>“Travel Time to Markets in Africa<br>South of the Sahara.”<br>https://doi.org/10.7910/DVN/YKDWJD<br>,Harvard Dataverse,V2.|
|---|---|---|---|
|Global Agro-<br>Ecological Zones<br>(GAEZ)|GAEZ generates large databases of (i) natural<br>resource endowments relevant for agricultural<br>uses; (ii) spatially detailed results of individual<br>land utilization type assessments in terms of<br>suitability and attainable yields; (iii) spatially<br>detailed results of estimate/actual yields of main<br>food and fiber commodities for all rain-fed and<br>irrigated cultivated areas; and (iv) spatially<br>detailed yield and production gaps, also for main<br>food and fiber commodities.|http://pure.iiasa.ac.at/id/eprint/13290|Fischer, G., F. O. Nachtergaele, S<br>Prieler, E. Teixeira, G. Toth, H. van<br>Velthuizen, L. Verelst, and D. Wiberg.<br>2012._Global Agro-Ecological Zones_<br>_(GAEZ v 3.0)—Model Documentation_.<br>Laxenburg, Austria: IIASA<br>(International Institute for Applied<br>Systems Analysis) and Rome, Italy:<br>FAO.|
|Livelihood zones|Map of farming systems in Africa in 2015. The<br>farming systems analysis integrates an extensive<br>range of spatial data, administrative statistics,<br>assessment reports, and expert knowledge in<br>order to update the African component of the<br>2001 FAO/World Bank farming systems analysis.<br>Data sources include GAEZ FAO/IIASA, FAOSTAT,<br>and HarvestChoice.|Personal copy of data from Christopher<br>M. Auricht(https://auricht.com/)|Dixon, John, Dennis P. Garrity, Jean-<br>Marc Boffa, Timothy O. Williams,<br>Tilahun Amede, Christopher Auricht,<br>Rosemary Lott, and George Mburathi,<br>eds. 2019._Farming Systems and Food_<br>_Security in Africa: Priorities for Science_<br>_and Policy Under Global Change._<br>Routledge.(See figure 2.2b.)|

_Source:_ World Bank compilation.

# Appendix B. Hazard Data

**Table B.1. Information on hazard data**

||**ERA5**|**CHIRPS**|**SPEI**|**WRSI**|**SWI10**|**NDVI**|
|---|---|---|---|---|---|---|
|**Hazard**<br>**measure**|Precipitation<br>(modeled)|Precipitation|Evapotranspiration|Availability of water for<br>crops|Soil moisture|Vegetation<br>(greenness)|
|Full name|European Centre for<br>Medium-Range<br>Weather Forecasts<br>(ECMWF) Reanalysis<br>5th Generation|Climate Hazards Group<br>InfraRed Precipitation<br>with Station data|Standardized<br>Precipitation-<br>Evapotranspiration<br>Index|Water Requirement<br>Satisfaction Index|Average of Soil Water<br>Index over a 10-day<br>period|Normalized<br>Difference Vegetation<br>Index|

37
applying best managerial practices (“within” component). A decline in TFP dispersion<sup>7</sup> may indicate reallocation of factors of production, economic activity, and market shares towards more efficient firms (“between” component).

# **The positive increase in aggregate productivity tends to be accompanied by higher TFP**

**dispersion** (Figure 14) **.**<sup>8</sup> This points to an improvement of the "within component" accompanied by a worsening of the "between component". Put differently, the analysis suggests that productivity has risen within firms because of improved capabilities (“within component”), but capital adjustment costs or other allocative distortions prevented productivity improvements across sectors (“between component”). Yet, there is heterogeneity within countries at the sector level. For example, the food, beverage and tobacco industry in the Philippines has witnessed a decline in median productivity in parallel with an increase in dispersion. This suggests a worsening of both the within and the between components. Distortions in the factor allocation within the firm and across sectors have prevented the materializing of productivity improvements. Over time, the economy has become less efficient, and the market share of less productive firms seems to have increased.

**Figure 14 Evolution of TFP in Indonesia: Median and Dispersion**

<!-- Start of picture text --> Panel A: Indonesia<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Indonesia Food Textiles, Wood, Media and Chemical, Basic and Electrical Machinery<br>-0.1 beverage apparel and paper and other rubber, fabricated and optical and<br>and tobacco leather furniture manuf. plastics and metals equipment transport<br>other<br>minerals<br>∆(TFP, 2000-07, 2008-15) ∆(TFP Dispersion, 2000-07, 2008-15)<br><!-- End of picture text -->

**<u>Panel B: Malaysia</u>**

> 7 Two definitions of dispersion are considered: (i) the standard deviation of TFP which summarizes the entire distribution, and (ii) the interquartile difference (75<sup>th</sup> -25<sup>th</sup> ) which focuses on half of the distribution and is less sensitive to extreme values. The results tend to be qualitatively similar.

> 8 We present the aggregate and sectoral breakdown of productivity evolution.

23