rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. A functional tiering of World Bank knowledge products**

|**Tier**|**Knowledge products included**|**P-code**|**Z-code**|
|---|---|---|---|
|Flagships|World Development Report; Doing Business; Global Economic<br>Prospects; regional and sectoral flagships.|132|52|
|Debates|Regional, sectoral and multi-country reports; Doing Business<br>regional reports.|2219|628|
|Extended<br>core|Country Economic Memorandums; Poverty Assessments; Systematic<br>Country Diagnostics; Public Expenditure Reviews; Reimbursable<br>Advisory Services; Financial Sector Assessment Programs; Risk and<br>Resilience Assessments; Country Private Sector Diagnostics; Human<br>Capital Reports; Infrastructure Sector Assessment Programs; Debt<br>Management Performance Assessments; Debt Sustainability<br>Analyses; Agricultural Sector Reviews; Pandemic Preparedness<br>Reports; Environmental Assessments.|1500|6|
|Other<br>country|Policy Notes; Economic Updates; other studies; technical assistance.|6103|1258|
|Research|Journal articles; working papers; impact evaluations; Doing Business<br>country profiles.|1725|3927|
|Capacity|Toolkits; databases; conferences; courses; training.|488|82|
|Internal|International Development Agency papers; strategy documents;<br>briefs; newsletters; evaluations.|665|490|

# 5. Regression analysis

The construction of a consolidated database of knowledge products containing tens of thousands of observations, each with information on its performance and the process through which it was prepared, makes it possible to assess what makes for “good” analytical work at the World Bank using statistical tools. The assessment is in the spirit of a production function, with a measure of performance as the explained variable, and a series of “inputs” to the preparation of knowledge products as the explanatory variables.

The quality of World Bank knowledge products is judged from two complementary perspectives. The rating given to a task by the management of its requesting unit indicates the extent to which it attained the objectives set at its inception, hence of its attainment of expected standards. The number of downloads per million population in the countries or regions that were the focus of the task provides an indication of the visibility it attained, hence of its potential development impact.

19
|**Long-RunResponsivenessof Ml-Demand**|
|---|
|_DepreciationasOpportunityCostVariable_|
|_Elasticity_<br>_Elasticity_|
|**Approach**<br>**RealGDP**<br>**Depreciation**|
|STSM<br>0.68<br>-0.43|
|VECMandMerged<br>0.74<br>-0.26|

At this stage, there are two things to highlight. Firstly, unlike with the other opportunity cost variables, in this case STSM and VECM modeling approaches both yield very similar results for the elasticity of money demand with respect to real GDP. Secondly, the STSM approach consistently yielded an elasticity with respect to real GDP in a relatively "high" range of estimates (0.68, 0.71 and 0.86), regardless of the opportunity cost variable chosen. By contrast, the VECM approach yielded real GDPelasticities of about 0.25 with the inflation rate (in levels or in logs), but an estimate of magnitude about three times higher when depreciation is the opportunity cost variable.

<!-- Start of picture text --> Mil-Demand at the Zero Frequency<br>Depreciation as Measure of Opportunity Cost<br>JtECM, STSM and Merged Approaches *<br>9.45 -<br>9.40 - e%l<br>9.35 V8%<br>9.30 -<br>9.25 1<br>9.20 -<br>9.15<br>9.10<br>9.05<br>9.00 -<br>r- 00~ ~ ~<br>-- - VECM and Merged STSM<br>*Using fitted values of cointegrating equation as VECM long-run<br>component. Using stochastic trend of Ml in the STSM approach.<br><!-- End of picture text --> 26 Money Demand in Venezuela, Mario A. Cuevas
_E_ [ _zjt_ ( _εjt − ε_ ˆ _jt_ ) _|It_ ] = 0. This problem is less of a concern if _zjt_ is included in the first stage of ACF.

**3) In the absence of price data (a problem in many firm-level surveys)** , the output elasticity is inconsistent and tends to become the revenue elasticity. This implies that the estimated markup of Equation 13 converges towards one (see Bond et al. (2020) for an in-depth discussion). Considering the revenue elasticity instead of output, Equation 6 becomes

where _ρ_<sup>_R_</sup> _Xjt_<sup>standsforrevenueelasticity.Thisestimatoriswidelyappliedintheliterature</sup> (see for example Diez, Leigh and Tambunlertchai (2018)). Notice, however, that markups are either increasing when the revenue elasticity is constant, but labor shares are falling, or when labor shares are held constant, but the revenue elasticity increases.<sup>2</sup> The revenue elasticity can be expressed as follows where _ρ_<sup>_P_</sup> _Xjt_<sup>=</sup> _∂Q_<sup>_∂Pj_</sup> _jt_<sup>_t_</sup> _<u>QPjtjt</u>_<sup>represents the price elasticity of demand, and</sup><sup>_ρXjt_is output elasticity</sup> as described in Equation 7. To illustrate the problem raised in Bond et al. (2020), consider the following firm profit maximization problem: where _C_ ( _._ ) is the cost function and the marginal cost is expressed as _MCjt_ =<sup>_∂C_</sup> _∂Q_<sup><u>(</u></sup><sup>_<u>Q</u>_</sup> _jt_<sup>_<u>jt</u>_</sup><sup><u>)</u>.</sup>

> 2Other explanations for higher markups not accounted for directly here include fixed and sunk costs (and thus lower marginal costs), network effects, monopsony power, and mergers and acquisitions (Berry, Gaynor and Morton (2019)).

11
— Table 4: Influence Asymmetry and FO Performance Spatial Matching Strategy

|Land Statistic:|Me|an|Generalized|mean (_q_ = 1_._5)|75<sup>_th_ </sup>pe|rcentile|90<sup>_th_ </sup>pe|rcentile|
|---|---|---|---|---|---|---|---|---|
|**(a) census farm**|**hhs:**||||||||
|_τ_ <sup>1</sup> _× LH_ (_δH_)|0.00552**||0.00225**||0.00487**||0.00195*||
||(0.00225)||(0.00098)||(0.00217)||(0.00115)||
|_τ_ <sup>1</sup> _× LT_ (_δT_)|-0.00512||-0.00174||-0.00489||-0.00175||
||(0.00330)||(0.00113)||(0.00310)||(0.00124)||
|_τ_ <sup>1</sup> _×_(_LH −LT_)||0.00534***||0.00204***||0.00488**||0.00185**|
|||(0.00204)||(0.000733)||(0.00199)||(0.00093)|
|_p−_values:|||||||||
|_H_0 :_λ_= 0|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_H_0 :_δH_ =_−δT_|0.916|—|0.732|—|0.995|—|0.899|—|
|_H_0 :_δH_ =_δT_ = 0|0.024|—|0.023|—|0.038|—|0.125|—|
|_R_<sup>2</sup>|0.567|0.567|0.567|0.567|0.567|0.567|0.567|0.567|
|**(b) all census h**<br>32|**hs:**||||||||
|_τ_ <sup>1</sup> _× LH_ (_δH_)<br>|0.00975***||0.00375**||0.00690**||0.00404***||
||(0.00343)||(0.00158)||(0.00279)||(0.00140)||
|_τ_ <sup>1</sup> _× LT_ (_δT_)|-0.0126***||-0.00426**||-0.00882***||-0.00568***||
||(0.00470)||(0.00167)||(0.00327)||(0.00206)||
|_τ_ <sup>1</sup> _×_(_LH −LT_)||0.0109***||0.00395***||0.00781***||0.00467***|
|||(0.00301)||(0.00120)||(0.00230)||(0.00121)|
|_p−_values:|||||||||
|_H_0 :_λ_= 0|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_H_0 :_δH_ =_−δT_|0.593|—|0.822|—|0.617|—|0.484|—|
|_H_0 :_δH_ =_δT_ = 0|0.001|—|0.003|—|0.004|—|0.001|—|
|_R_<sup>2</sup>|0.566|0.566|0.566|0.566|0.566|0.566|0.566|0.566|

_Notes:_ Robust standard errors in parentheses (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1), clustered on FO/distributary. Number of observations (clusters) [FOs]: 586,417 (892) [247] in panel (a); 602,425 (909) [252] in panel (b). All specifications use spatial matching with 60 km buffer. Dependent variable is tail shortage _DPR_<sup>_H_</sup> _− DPR_<sup>_T_</sup> . Channel-years in 2nd FO tenure dropped ( _τ_<sup>1</sup> is indicator for first FO tenure). All specifications include channel fixed effects, circle-specific time trends, and buffer-year fixed effects. _Lj_ denotes land ownership statistic at position _j_ of channel computed over census farm households (panel (a)) or all census households (panel (b)). _τ_<sup>1</sup> is interacted with _Z_ variables: log channel length, log number of outlets, whether channel is on head or middle of parent channel (tail omitted category), and whether channel is minor or sub-minor (distributary omitted category), the coefficients on which are denoted by _λ_ .
**Table 3: T-Test of Difference within the Same PSU and During the Same Reference Period.**

|**Mean**<br>**Std.**<br>**Err.**|**Std.**<br>**Dev.**|**[95**<br>**I**|**% Conf.**<br>**nterval]**|
|---|---|---|---|
|**1. Yesterday (Any Outage)**||||
|Survey<br>0.184<br>0.003|0.387|0.178|0.190|
|Boxes<br>0.288<br>0.003|0.453|0.281|0.296|
|_diff_<br>_-0.104 ***_<br>_0.004_|_0.537_|_-0.113_|_-0.095_|
|**2. Day Before Yesterday (Any Outage)**<br> <br> <br>||||
|Survey<br>0.191<br>0.003|0.393|0.184|0.198|
|Boxes<br>0.284<br>0.003|0.451|0.276|0.292|
|_diff_<br>_-0.092 ***_<br>_0.004_|_0.542_|_-0.101_|_-0.083_|
|**3. Past 10 Days (Any Outage)**||||
|Survey<br>0.287<br>0.003|0.452|0.279|0.295|
|Boxes<br>0.602<br>0.004|0.489|0.593|0.610|
|_diff_<br>_-0.314 ***_<br>_0.005_|_0.657_|_-0.325_|_-0.303_|
|**4. Yesterday Hours of Outage (Out of 24)**<br>||||
|Survey<br>1.851<br>0.041|4.745|1.769|1.933|
|Boxes<br>2.193<br>0.053|6.041|2.088|2.297|
|_diff_<br>_-0.341 ***_<br>_0.057_|_6.523_|_-0.453_|_-0.228_|
|<br>**5. Day Before Yesterday Hours of Outage (O**<br> <br> <br>|**ut of 24)**<br>|||
|Survey<br>1.940<br>0.043|4.893|1.856|2.025|
|Boxes<br>2.180<br>0.054|6.064|2.076|2.285|
|_diff_<br>_-0.239 ***_<br>_0.058_|_6.627_|_-0.353_|_-0.125_|
|<br>**6. Yesterday Hours 18:00-22:00 (Out of 4)**<br> <br> <br>||||
|Survey<br>0.074<br>0.003|0.454|0.066|0.082|
|Boxes<br>0.317<br>0.008|1.017|0.299|0.334|
|_diff_<br>_-0.242 ***_<br>_0.009_|_1.086_|_-0.261_|_-0.247_|
|<br>**7. Day Before Yesterday Hours 18:00-22:00 (O**<br> <br> <br>|**ut of 4)**<br>|||
|Survey<br>0.080<br>0.004|0.488|0.072|0.088|
|Boxes<br>0.319<br>0.008|1.022|0.302|0.337|
|_diff_<br>_-0.239 ***_<br>_0.009_|_1.106_|_-0.258_|_-0.220_|
|**8. Past 10 Days (Out of 10)**||||
|Survey<br>0.665<br>0.012|1.449|0.640|0.690|
|Boxes<br>2.921<br>0.029|3.365|2.863|2.979|
|_diff_<br>_-2.256 ***_<br>_0.031_|_3.605_|_-2.318_|_-2.194_|
|**9. Past 10 Days: Hours (Out of 24)**||||
|Survey<br>2.002<br>0.044|5.035|1.915|2.088|
|Boxes<br>2.280<br>0.049|5.664|2.182|2.377|
|_diff_<br>_-0.278 ***_<br>_0.064_|_7.318_|_-0.404_|_-0.152_|

All are observations in or after 2016, Nov 1.

10
representative at the regional level in Haiti. The dataset includes 7,186 households. It is the first time in Haiti’s history that a survey of this magnitude has been conducted.<sup>9</sup> The household survey consists of 15 SPSS files (these files are dated 10.06.2004 and named Base de Données Mar). The Bank obtained them directly from the Haitian statistical agency via FAFO the Norwegian institution that has worked with the statistical office. We have discovered a number of serious flaws. The most important flaw relates to the variable describing the metropolitan-urban-rural status of a household/individual, which is different in the different files. After discussions with the IHSI the only reliable data for metropolitan, urban, and rural levels are those based on the file with household information, therefore this data is used throughout the paper.

To calculate poverty, income including self-consumption has been used. Income is for the past 12 months based on a number of individual income sources and selfconsumption is estimated value of consumption (and barter) of household production of crops, meat, and fish during the last week. First, respondents answered questions on consumption of own production, and the market value of it. Second, an average unit price of each type of good was calculated for the whole sample and multiplied by the quantity consumed last week and multiplied by 52 weeks.

# **Methodology**

The income-poverty measures are designed to count the poor and to diagnose the extent and distribution of poverty. The income-poverty measures proposed by Foster, Geer, and Thorbecke (1984) are used throughout the paper. These are the headcount rate (P0), poverty gap (P1), and squared poverty gap (P2) measures. The former measures the magnitude of poverty and the latter two poverty measures assess both poverty magnitude and intensity.

The headcount rate is defined as the proportion of household heads (not the whole population) below the poverty line. One concern applying the P0 measure is that each individual below the poverty line is weighted equally and, therefore, the principle of transfers is violated. A limitation of the measure is illustrated by the fact that it would be possible to reduce the P0 measure of poverty by transferring money from the very poor to lift some _richer_ poor out of poverty, hence increasing social welfare according to the measure. P0 takes no account of the degree of poverty and it is unaltered by policies that lead to the poor becoming even poorer.

One measure of poverty that takes this latter point into account (at least in weak form) is the poverty gap measure (P1). P1 is the product of incidence and the average distance between the incomes of the poor and the poverty line. It can be interpreted as a per capita measure of the total economic shortfall relative to population. P1 distinguishes the poor from the not-so-poor and corresponds to the average distance to the poverty line of the poor. One problem with the poverty gap, as an indicator of welfare is that, poverty

> 9 See FAFO for more information. www.fafo.no.

14
<!-- Start of picture text --> re Je sa ciaeee , vy J<br>= 1 es 5) ae *<br>values, below the 25th percentile . 4%oY .<br>. I values equal or greater than 25th and less than 50th percentile.<br>~ Blvalues equel or greater than 50th and less than 75th percentile ©<br>Bh values chove the 75th percentile<br><!-- End of picture text -->

|=<br>|re Je<br>a<br>sa<br>ci<br>1<br>es<br>,<br>valuesbelow<br>.<br>I values equal <br>~<br>Blvaluesequel <br>Bhvalueschove<br>|e<br>ee<br> the25th pe<br>or greater t<br> or greater t<br> the 75th pe<br>|5)<br>.<br>rcentile<br>han 25th and <br>han 50th and <br>rcentile|lessthan 50<br>lessthan 75|,vy<br>ae<br>4<br><br>th<br>th percentile <br>|J<br>*<br>%<br>oY<br>percentile.<br> ©|.||
|---|---|---|---|---|---|---|---|---|
|Accountsperthousanda|dultsfrom|Number of|||Standard|||Weighted|
|commercial banks||countries||Average|| Median ||deviation|||average®|
||World|79|904.7}|584.2]|1,147.3|2.4|| 7,185.2]|1,339.0|
|By developed/devel|oping economies||||||||
||Developed economies|18 ||2,004.3]|1,311.2]|1,766.1]|121.8 ||7,185.2]|3,761.8|
||Developing economies|61|580.2]|395.8}|598.2|24|| 3,176.4]|691.5|
|Byincome level|||||||||
||High income|18<br>||2,004.3]|1,311.2]|1,766.1]|121.8|| 7,185.2]|3,761.8|
||Upper middle income|21|921.1}|902.7)|534.1]|38.0|| 2,015.2]|997.9|
||Lower middle income|24|570.1]|437.3]|664.1]|16.1<br>||3,176.4]|725.9|
||Low income|16|147.9||128.9]|112.0|24|365.5]|222.5|
|Byregion|||||||||
||High income:OECD|12<br>||2,320.2]|1,581.8]|1,945.7]|513.6 ||7,185.2]|3,933.9|
||High income: non-OECD|6 ||1,372.5]|878.1]|1,248.0]|121.8|| 3,561.8]|1,082.9|
||EastAsia& Pacific|7|668.6]|431.6]|630.3]|443|| 1,570.3]|799.3|
||Europe & Central Asia|13.|[1,047.8]|909.2]|811.2}|38.0|| 3,176.4]|1,645.5|
||LatinAmerica&Caribbean|7|873.6||667.2)|587.6]|329.4|| 2,015.2]|967.0|
||Middle East & North Africa|d|385.9]|343.3]|295.6]|77.4|873.0)|384.7|
||South Asia|5|506.7}|365.5]|429.7]|71.1<br>|| 1,130.0]|531.7|
||Sub-SaharanAfrica|22|261.0]|150.3}|294.5|2.4||1,132.0]|281.1|

Source: Global Financial Development Database, 2008-10 data. Note: OECD = Organisation for Economic Co-operation and Development. a. Weighted average by total adult population.
_Table 6: Regression of incidence of disease and mortality on gas flaring controlling for individual and household characteristics: individual level_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|Child has<br>cough|Child has respiratory<br>issues symptoms|Child has fever|Child has<br>diarrhoea|Child is<br>stunted|Child is wasted|Child is<br>underweight|
|Flaring exposure|0.0316***|0.0300***|0.0299***|0.0012|0.0063|0.0111**|0.0093*|
||[0.0077]|[0.0077]|[0.0086]|[0.0038]|[0.0067]|[0.0044]|[0.0048]|
|Boy Child|0.0101|0.0102|-0.0041|0.0009|0.0214*|0.0117|0.0185**|
||[0.0094]|[0.0093]|[0.0093]|[0.0055]|[0.0113]|[0.0081]|[0.0084]|
|Mother Education (years)|0.0021|0.0018|0.0041**|0.0018|-0.0031|-0.0019|-0.0033*|
||[0.0019]|[0.0019]|[0.0018]|[0.0013]|[0.0025]|[0.0015]|[0.0018]|
|1.age (years)|0.1050***|0.1059***|0.1070***|0.0409***|0.0045|0.0344*|0.0052|
||[0.0162]|[0.0165]|[0.0157]|[0.0108]|[0.0204]|[0.0208]|[0.0190]|
|2.age (years)|0.0685***|0.0615***|0.1049***|0.0061|0.0954***|-0.0378*|0.0080|
||[0.0169]|[0.0164]|[0.0155]|[0.0096]|[0.0217]|[0.0215]|[0.0185]|
|3.age (years)|0.0589***|0.0547***|0.0642***|-0.0063|0.0817***|-0.0870***|-0.0163|
||[0.0155]|[0.0157]|[0.0156]|[0.0105]|[0.0233]|[0.0196]|[0.0190]|
|4.age (years)|0.0073|0.0009|0.0446***|-0.0268***|0.0384*|-0.0789***|-0.0138|
||[0.0165]|[0.0171]|[0.0140]|[0.0088]|[0.0228]|[0.0195]|[0.0205]|
|5.age (years)|-0.0115|-0.0147|0.0410**|-0.0181|0.0438*|-0.0821***|0.0013|
||[0.0189]|[0.0189]|[0.0187]|[0.0115]|[0.0263]|[0.0220]|[0.0236]|
|Age mother first child|-0.0005|-0.0004|-0.0009|-0.0005|-0.0009|0.0003|0.0012|
||[0.0013]|[0.0013]|[0.0013]|[0.0007]|[0.0013]|[0.0009]|[0.0010]|
|Mother’s partner education (years)|0.0042**|0.0046***|0.0003|-0.0008|-0.0042**|-0.0013|-0.0024*|
||[0.0017]|[0.0017]|[0.0019]|[0.0013]|[0.0019]|[0.0014]|[0.0014]|
|Has TV|-0.0199|-0.0151|0.0007|-0.0087|0.0252|-0.0057|0.0147|
||[0.0168]|[0.0163]|[0.0151]|[0.0085]|[0.0188]|[0.0125]|[0.0149]|
|Poorer Wealth quintile|-0.0135|-0.0212|-0.0020|-0.0731**|-0.0365|0.0033|0.0251|
||[0.0385]|[0.0355]|[0.0549]|[0.0367]|[0.0623]|[0.0288]|[0.0367]|
|Middle Wealth quintile|-0.0203|-0.0251|-0.0335|-0.0790**|-0.0229|0.0048|-0.0024|
||[0.0388]|[0.0373]|[0.0565]|[0.0386]|[0.0598]|[0.0283]|[0.0363]|
|Richer Wealth quintile|-0.0214|-0.0307|-0.0446|-0.0648|-0.0957|-0.0167|-0.0604|
||[0.0437]|[0.0423]|[0.0601]|[0.0416]|[0.0616]|[0.0314]|[0.0386]|
|Richest Wealth quintile|-0.0510|-0.0595|-0.0760|-0.0901**|-0.0999|-0.0268|-0.0626|
||[0.0499]|[0.0488]|[0.0623]|[0.0427]|[0.0635]|[0.0347]|[0.0411]|
|Kerosene cooking fuel type|-0.0474|-0.0454|-0.0014|-0.0056|0.0110|0.0119|0.0166|

15
The period of duration in each state can then be generated as 1/pii. So, for example, the period of duration in unemployment is given by 1/puu and the period of duration in any non-employment state is given by 1/pii where i≠unemployment.

Finally, the propensity to move out of a certain sector can be calculated as the number who leave the sector as a share of the total number who move: and the associated matrix is: where each rij is the transitional probability if we assume that all workers were to leave their initial sector at the same rate. We are particularly interested in those rij where i=unemployment or j=unemployment.

# _Data_

Data from urban Argentina, Brazil, and Mexico are used. These countries were selected for the analysis since they are the only countries in Latin America with panel labor force surveys that allow us to follow the behavior of individuals over time. They also have recent labor market histories that are instructive to understanding youth unemployment. Namely, Mexico has the lowest unemployment rates in Latin America while Argentina has some of the highest rates. Brazil has moderate levels of unemployment. The structure of each country’s data provides us with some lessons, as well.

The Argentine _Encuesta Permanente de Hogares_ for 1995-2003 is used. The national survey is carried out in urban areas and is designed such that a sample is selected in period t and their labor market status is observed. Six months later, the same sample is interviewed again. This process is repeated two more times which gives us, in the end, the labor market status at four points in time over a one and a half year period (t, t+6, t+12, t+18). In each period, there are four consecutive cohorts at different phases of the interview process. To limit attrition bias, we only track one transition. Further, we only track May to September transitions to avoid the summer vacation which falls during the September to May transition.

7
# **Table 13: Entry of Brazilians to Japan**

|Year|Total|First comer|Repeater|
|---|---|---|---|
|<br>Number|||<br>|
|1999|70,794|38,275|32,519|
|2000|101,513|58,577|42,936|
|2001|81,800|39,533|42,267|
|2002|71,763|33,296|38,467|
|2003|79,692|41,101|38,591|
|<br>Share (%)|||<br>|
|1999|100.0|54.1|45.9|
|2000|100.0|57.7|42.3|
|2001|100.0|48.3|51.7|
|2002|100.0|46.4|53.6|
|2003|100.0|51.6|48.4|

Source: Japanese Ministry of Justice.

35
households. At the core of this reframed question, is the acknowledgment that there is a trade‐off in urban areas between minimizing damages and lowering housing costs.

To illustrate this point, Table 3 compares subsidized insurance and risk‐based insurance for a set of floods, looking at the impact on land values or landowners’ profits (LO) and on rents for tenants (T). It shows that subsidized insurance makes a transfer of wealth from landowners in the safe areas to landowners in the flood‐prone area. This is the direct effect of the subsidy.

But the subsidy also results in a decrease in rent in the safe and flood‐prone areas of the city. The decrease is around 5 percent if 40 percent of the city is affected every 20 years, or 20 percent of the city is affected every 5 years. Importantly, this benefit goes toward all tenants in the city, not only those living in the flood‐prone area, since rents are not affected by floods.

**Table 3: Variation (%) between subsidized and risk‐based premium insurance for Landowners’s profits (LO) and Tenants’s rents (T) in flood prone and safe areas for various flood return periods and flood‐prone land fraction, assuming a 40% damage fraction**

||Flood<br>return<br>period<br>(years)|5 ye|ars|10 ye|ars|20 ye|ars|
|---|---|---|---|---|---|---|---|
|Flood<br>prone<br>area<br>(%)||LO|T|LO|T|LO|T|
|10%|Safe|‐9.9|21|‐7.0|18|‐4.6|125|
||Flood|306|‐.|130|‐.|59|‐.|
|20%|Safe|‐19.3|44|‐13.9|37|‐9.1|256|
||Flood|263|‐.|113|‐.|51|‐.|
|30%|Safe|‐28.3|69|‐20.7|57|‐13.5|39|
||Flood|223|‐.|96|‐.|44|‐.|
|40%|Safe|‐37.0|97|‐27.2|79|‐17.9|53|
||Flood|184|‐.|80|‐.|36|‐.|

# **4.2 Optimal strategy and distance to second best**

The optimal flood management that delivers the highest utility to households is unambiguously the risk‐based premium insurance. It dominates subsidized insurance and zoning for any type of flood, irrespective of the return period of the flood ( τ ), of the fraction of flood prone urban area ( θ ), and of the share of damage inflicted to buildings when it occurs ( δ ). Figure 4, shows how risk‐based premium insurance compares to other flood management options, for various metrics, and for an estimated damage to structures of 40% in case of floods. For all of these (utility and income equivalent utility differentials), risk‐based insurance compares favorably.

The distance between the 1<sup>st</sup> and 2<sup>nd</sup> best option however varies dramatically depending on how frequent the floods are ( τ ), how much of the urban area they impact ( θ ), and what share of the installed capital they destroy ( δ ). For frequent floods that affect a very large share of the urban area, the gaps are very substantial. Consider for example flood events that return every year and affect 99% (the near totality) of the urban area – graphically, at the bottom right of each of Figure 4 quadrants. In this extreme case, households’ utility would be 13% higher with risk‐based insurance rather than with the second best. In income equivalent terms, this would represent close to 7,800 euros or 13% of 19