rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6. Robustness test: Lagged dependent variables in first-stage regression**

||[1]|[2]|[3]|
|---|---|---|---|
|2nd stage: Output informality||||
|Real GDP per capita (in logs)|-3.33**|-3.31**|-2.70*|
||(1.43)|(1.40)|(1.53)|
|Control of Corruption|-0.16|-0.14|-0.05|
||(0.62)|(0.63)|(0.67)|
|Regulatory Quality|-0.05|-0.06|-0.03|
||(0.66)|(0.65)|(0.66)|
|Rule of Law|0.35|0.37|0.41|
||(0.59)|(0.58)|(0.60)|
|Commercial bank branches|-0.16***<br>(0.04)|-0.16***<br>(0.04)|-0.21***<br>(0.06)|
|1st stage: Bankingsector developm|ent|||
|Commercial bank branches in the region (t-1)|0.94***<br>(0.17)|||
|Distance-discounted commercial bank branches (regional; t-1)||0.86***<br>(0.15)||
|Distance-discounted commercial bank branches (global; t-1)|||3.36***<br>(0.72)|
|Observations|2,000|2,021|2,023|
|R-squared|0.99|0.99|0.99|
|Kleibergen-Paap rk LM statistic|24.47***|25.67***|20.89***|
|Cragg-Donald Wald F statistic|29.29|35.16|22.02|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). The data are for the period 2004-18. Commercial bank branches are per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. See sections 2 and 3 for detailed data descriptions and empirical methods. Data are between 2004 and 2018.

23
# **TABLE 3.0: % NON-ANSWER ON SELECTED QUESTIONS**

|**1.Questions on Honesty/Dishonesty of institutions**|**%NA**|
|---|---|
|Direccion Nacional de Immigraciones|10.4%|
|Banco de la Nacion<br>|9.5%|
|Area de rentas de su municipalidad<br>|7.4%<br>|
|Secretaria Municipal de Transito|6.9%|
|ReNIEC|6.7%|
|Registros Publicos|6.0%|
|SUNAT|5.1%|
|Hospitales públicos y Centros de Salud|3.8%|
|Notarias Publicas|3.4%|
|Escuelas o universidades estatales|3.4%|
|EsSalut|3.4%|
|Empresa de Agua y desague|2.4%|
|Policia Nacional de Peru|1.1%|
|**2.Questions onQuality of service**<br>Registro de propiedad inmueble|2.0%|
|Obtencion de Pasaporte|1.8%|
|Tramite licencias de construccion|1.4%|
|Registro de vehiculos|1.3%|
|Tramite para solicitud de instalacion electrica|1.1%|
|Certificado de Antecedentes penales y judiciales|1.0%|
|Obtencion Documento de identidad|0.9%|
|Tramite de impuestos nacionales<br>|0.9%|
|Tramites de licencias de conducir|0.8%|
|Atencion en el seguro social|0.5%|
|<br>Tramite de impuesto predial|0.5%|
|Atencion en establecimientos de salud|0.4%|
|Matriculas en centros educativos estatales|0.3%|
|Tramites en el Banco de la Nacion|0.1%|
|**3. Questions on Corruption**<br>||

|Agree/disgree with reporting corruption act|0.6%|
|---|---|
|Agree/disgree that corruption is natural fact<br>|0.8%|
|Reason for not reporting a corrupt act|1.7%|
|Reason for not reporting a corrupt act|1.7%|
|Reason for not reporting a corrupt act|1.5%|
|Reason for not reporting a corrupt act|3.8%|
|Reason for not reporting a corrupt act|1.9%|
|Reason for not reporting a corrupt act|2.9%|
|Reason for not reporting a corrupt act|4.9%|
|Reason for not reporting a corrupt act|3.1%|
|Reason for not reporting a corrupt act|4.7%|
|Agree/disagree with sentence<br>|0.6%|
|Agree/disagree with sentence|0.5%|
|Agree/disagree with sentence|3.3%|
|Agree/disagree with sentence|0.6%|
|Agree/disagree with sentence|0.9%|
|Agree/disagree with sentence|0.5%|
|Agree/disagree with sentence|1.3%|
|Agree/disagree with sentence|0.7%|
|Agree/disagree with sentence|1.8%|
|Agree/disagree with sentence<br>|1.7%|
|Agree/disagree with sentence|4.0%|

23
**Table 3A: Export share and total private credit as a share of GDP**

|Dep. Var.:|2SLS|2SLS|2SLS|2SLS-FE|
|---|---|---|---|---|
|export|(1)|(2)|(3)|(4)|
|ln_totalcredit|17.8468***|22.3807***|21.0350***|18.8406***|
||(0.004)|(0.000)|(0.000)|(0.002)|
|fdi|2.3396***|2.2713***|2.2427***|2.2316***|
||(0.005)|(0.004)|(0.004)|(0.004)|
|ln_initialgdppc|-6.8612**|-7.7754**|-6.8356*|-5.0834|
||(0.050)|(0.029)|(0.062)|(0.174)|
|financialopenness|0.0019|0.0020|0.0020|0.0021|
||(0.392)|(0.312)|(0.308)|(0.296)|
|tot|-0.2225*|-0.3597**|-0.2688|-0.3277*|
||(0.073)|(0.014)|(0.111)|(0.071)|
|financialcrisis||-18.4777**|-19.1788**|-25.1928***|
|||(0.020)|(0.015)|(0.008)|
|commodityexporter|||-9.0356*|-8.6162|
||||(0.100)|(0.125)|
|Constant|-5.1132|-6.2392|-9.3134|7.7618|
||(0.809)|(0.789)|(0.695)|(0.786)|
|Observations|141|138|138|138|
|R-squared|0.433|0.457|0.467|0.491|
|Prob > F|0.000|0.000|0.000|0.000|

Regressions (1) – (3) are 2SLS regressions with legal origin and religious composition as instruments. Regression (4) is a 2SLS regression with the same instruments adding with year fixed effects. Credit variables and initial GDP per capita are in logs. All the other variables are in levels. Five-year average data are used. Robust p-values are reported in parentheses. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.

12
29

# Table 3

Comparison of Actual and Gravity Model Predicted Value of lnUs-SSA Trade Shares for Nineteen SSA Countries; Average Values for 1980-82

||||Non-oi<br>Share<br>|l imDorts<br>s<br>'||||Non-oil<br>Shares<br>|ExportS||
|---|---|---|---|---|---|---|---|---|---|---|
||Total<br>Imports<br>|ROWY|All<br>SSAY|Sample<br>Actual|SamploI<br>Predicted|Total<br>Exports|ROW"|All<br>SSAY|Sample<br>Actual!v|Sample#<br>Predicted|
|Country|mnS S|%|%|%|%|mile S|%|%|%|%|
|COTE<br>D'IVOIRE|6,003|95.3|4.7|3.9|2.3|7,826|92.55|7.45|5.3|2. I|
|CAMEROO<br>N|3,990|97.3|2.7|1.3|11.7|2,494|94.14|5.86|4.5|10.1|
|CONGO|1,782|96.3|3.7|4.0|5.4|536|98.07|1.93|2.7|11.6|
|ETHIOPIA|1,725|98.6|1.4|1.4|0.4|1,200|92.85|7.15|1.0|0.4|
|GABON|2,302|98.5|1.5|1.5|4.1|829|96.69|3.31|3.5|12.6|
|GHANA|2,552|92.8|7.2|6.5|7.1|2,317|99.82|0.18|0.2|4.9|
|KENYA|4,183|96.2|3.8|1.6|1.8|2,767|84.40|15.60|7.7|2.1|
|LIBERIA|1,073|97.3|2.7|1.8|2.7|1,606|98.30|1.70|1.1|1.6|
|MADAGAS<br>CAR|1,356|98.3|1.7|0.7|0.9|977|98.92|1.08|0.3|08|
|NIGERIA|43,799|99.3|0.7|1.0|1.0|1,874|94.74|5.26|7.4|2.8|
|NIGER<br>|1,398|85.3|14.7|17.1|12.5|1,446|88.51|11.49|11.7|8.0|
|SUDAN|3,966|98.2|1.8|1.8|0.4|1,606|99.59|0.41|0.4|0.2|
|SENEGAL|2,411|94.0|6.0|4.3|1.7|1,429|80.48|19.52|15.9|1.4|
|SOMALIA|1,297|92.3|7.7|6.3|0.3|428|98.8S|1.12|0.8|0.0|
|TOGO|1,240|94.6|5.4|5.7|5.7|805|87.24|12.76|15.8|4.0|
|TANZANIA|2,299|98.47|1.6|1.5|2.2|1,560|92.76|7.24|3.4|2.5|
|ZAIRE|2,655|97.7|2.3|3.1|_3.5_|5,366|99.02|0.98|1.9|5.2|
|ZAMBIA|2,324|97.1|2.9|2.6|2.4|3,188|99.03|0.98|0.7|1.9|
|ZIMBABWE|1,158|94.6|5.4|0.3|2.5|1,811|94.55|5.45|2.5|1.8|
|Average<br>Median||95.9|4.1|3.5<br>1.8|3.6<br>2.4||94.2|5.8|4.6<br>2.7|3.9<br>2.1|

ROW - Rest of the World. 100 - ROW Share Trade with the SSA countries in the sample as a percentage of trade with all partner countrieS in the Sample. Predicted share of **trade** with the SSA countries in the sample; all trade data from COMTRADE.
**Figure 4: Distribution of Employment (2009-2012)**

<!-- Start of picture text --> ZEWAY/BATU<br>60<br>SODO<br>Addis Ababa SHASHEMENE<br>SEBETA<br>50 NEKEMTE<br>NEGELE<br>METU<br>MEKELE<br>40 JIMMA<br>JIJIGA<br>HOSAENA<br>HARAR<br>30 GONDER<br>GIMBI<br>DIRE DAWA<br>DILA<br>20 DEBRETABOR<br>DEBERE MARKOS<br>BUTAJIRA<br>BEDELE<br>10<br>AXUM<br>ASELA<br>AMBO<br>0 AGARO<br>ADWA<br>2009 2010 2011 2012<br>ADIGRAT<br><!-- End of picture text --> Source: UBAEUS, CSA When the focus is on employment by MLEs (Medium and Large Enterprises) only, Addis contributes an even larger share. Figure 5 summarizes the share of employment by the top 1%, 5%, 10% and bottom 80% of cities, across different years. Employment is extremely skewed towards Addis, which accounts for almost three-fourths of employment in any given year. The smallest cities account for less than 10% of total employment across Ethiopia.

**Figure 5: Distribution of Employment (by Cities)**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Top 1% Top 5% Bottom 80%<br>Employment Distribution (%)<br><!-- End of picture text --> Source: LMMIS, CSA 7
# **5. Aggregate Effects of Bribing**

This section compares the effects of rent-seeking in the form of bribes relative to an economy without bribing (the benchmark economy). Table 7 shows selected statistics on aggregate variables (such as aggregate output, TFP, and aggregate innovation), firms (such as average productivity per firm and output per firm), and bribing behavior. All variables, except for those in the last two rows, are displayed relative to the benchmark economy, in which _τ_ = 0 for all firms.

**Table 7. Quantitative effects of bribing**

||**Bribing**<br>**economy**<br>**(****_hτ_ = 0.002)**|**Bribing**<br>**economy**<br>**(****_hτ_ = 0.001)**|**Bribing**<br>**economy**<br>**(****_hτ_ = 0.005)**|
|---|---|---|---|
|**_Aggregate_**||||
|**_statistics_**||||
|Output|95.60|90.42|97.41|
|TFP|96.99|91.34|98.89|
|Consumption|94.05|88.78|96.03|
|Aggregate|71.3|65.21|80.28|
|innovation||||
|**_Firm-level_**||||
|**_statistics_**||||
|Average|97.28|93.14|98.25|
|productivity||||
|Average size|98.12|92.59|98.93|
|Output per firm|88.81|84.71|93.21|
|Number of firms|111.43|119.21|104.72|
|Std (TFPR)|104.13|108.36|102.77|
|_Bribing statistics_||||
|Percentage of<br>|10|22|6|
|firms bribing<br><br>||||
|<sup>brbb</sup><br><br>|0.07|0.13|0.02|
|aa~~�~~<br>salbs<br>�<br>_Note:_All results are relat<br>productivity; TFPR = total facto|ive to the benchmark econ<br>r revenue productivity.|omy in which there is no|bribing. TFP = total factor|

_Note:_ All results are relative to the benchmark economy in which there is no bribing. TFP = total factor productivity; TFPR = total factor revenue productivity.

When bribing is introduced (column 2), output and TFP decrease by 4.4 percent and 3 percent, respectively (both relative to the benchmark economy with no bribing). This decrease is due to 28
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# **TABLE OF CONTENTS**

|**ACRONY**|**MS....................................................................**|**ii**|
|---|---|---|
|ABSTRAC|T....................................................................|i|
|I|INTRODUCTION**.1**||
|II|OBJECTIVESANDFocusOF**THE**ASSESSMENTANDCOMPARATIVE**ANALYSIS**|**1**|
|**III**|**NEW**DIRECTIONSINPROVIDINGMICROFINANCESERVICES:INTERNATIONALBEST<br>PRACTICE............................................................|2|
|IV|IMPACTOFTHELEGALANDREGULATORY FRAMEWORKONSUSTAINABLE<br>**MICROFINANCE.**|**4**|
||_TheRationaleforRegulationandSupervision._|**_4_**|
||_GlobalExperienceinRegulating Microfinance._|_5_|
||_FinancialIntermediationActivitiesastheBasisforRegulation._|_7_|
|V|BACKGROUNDONMICROFINANCE**IN**GHANA.|**9**|
||_MacroeconomicandPolicyEnvironment._|_9_|
||_PovertyintheGhanaianContext._|_9_|
||_StructureoftheMicrofinance_**_Sector._**|_9_|
||_LegalandRegulatoryFrameworkforMicrofinance .12_||
|VI|ASSESSMENTOFGHANA**'S**REGULATORY FRAMEWORK FORMICROFINANCE|**15**|
|**VII**|BACKGROUNDONMICROFINANCEINTHEPHILIPPINES**.15**||
||_MacroeconomicandPolicyEnvironment.15_||
||_PovertyinthePhilippineContext.16_||
||_StructureoftheMicrofinanceSector_**_.16_**||
||_LegalandRegulatoryFrameworkforMicrofinance_**_.18_**||
|**VIII**|ASSESSMENTOF**PHILIPPINE**REGULATORYFRAMEWORK FORMICROFINANCE|21|
|**IX**|**LESSONS**LEARNED FROMEXPERIENCESOFGHANAANDTHEPHILIPPINESIN<br>MICROFINANCE**REGULATION.**|22|
||_TieredStructuresandInstitutionalTransformation_**_.23_**||
||_PrudentialStandards:CapitalAdequacy; AssetQuality;Provisioning_**_.25_**||
||_OtherRegulatoryFrameworkIssues_**_.27_**||
||_RegulatingCooperativeFinancialInstitutions_**_.27_**||
||_OwnershipandControlIssues.30_||
||_Self-RegulatoryOrganization.30_||
||_LegalandJudicialSystemsforLoanContractsandSecurityInterest_|31|
|ANNEX|OVERALLREGULATORYFRAMEWORKFOR RURAL/MICROFINANCEINSTITUTIONS-|32|
||INDONESIA**.**||
|REFEREN|CE....................................................................|**34**|
DGP, an additional equation determines which observations of the outcome equation are included. We also analyze a data set generated for consistency with the Helpman, Melitz and Rubinstein (2008) model.

In our initial simulations, we follow SST in using Monte-Carlo simulations with a log standard normal distribution of the continuous driving variable. These tests confirm the importance of heteroscedasticity and the prevalence of zeros as sources of bias with many estimators. However, estimators such as the censored PPML that are known to perform well in the presence of heteroscedasticity are found to be potentially susceptible to limiteddependent variable bias when a substantial fraction of the observations are censored.

When the data are generated using an Eaton-Tamura Tobit model, the smallest biases were sometimes found with Eaton-Tamura Tobit estimators consistent with the data generating process, but only when the functional form was consistent with the form of heteroscedasticity, or an appropriate correction had been made to deal with heteroscedasticity. On average across the four cases considered, the truncated PPML had the lowest mean absolute bias, including relative to the explicitly limited-dependent estimators consistent with the data-generating process.

When the data are generated by a Heckman sample-selection DGP, the truncated PPML estimator outperformed all other models that do not explicitly account for the limited-dependent nature of the data set. The Zero-inflated Poisson (ZIP) model appeared to have the lowest bias of the models considered, and particularly for the continuouslydistributed variables that are frequently of greatest interest in a gravity-model context.

Applying a range of estimators to the SST empirical dataset on international trade resulted in a number of surprising findings. In particular, we found very little difference between the PPML estimator and its truncated version. Further, the results from the truncated PPML estimator were identical with those from the ZIP estimator. To try to better understand the relationship between our Monte Carlo results and our empirical findings, we repeated the Monte Carlo analysis with a continuous explanatory variable distributed like a composite of the key explanatory variables in the gravity model, with a much higher fraction of the explanatory variables clustered near zero. This analysis helped explain two key findings in the empirical analysis—that the PPML and truncated PPML estimates were similar, and the truncated PPML and the ZIP were almost identical.

27
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> Table 5b: _Change in trade (in US Dollars) associated with 1 US Dollar increase in A4TF (by type of aid, trade flows 2007)_

||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
|Aid<br>Category|Baseline<br>– all<br>positive<br>trade|Baseline<br>with per<br>capita<br>GDP|Only<br>countries<br>receiving<br>A4TF /1/|Recipients<br>only;<br>trade with<br>recipients<br>& ROW|Recipients<br>with<br>developing<br>world only|Recipients<br>with<br>developing<br>& bilateral<br>donor; no<br>intra-<br>donor<br>trade|Base-line<br>(2) with<br>lags|
|Narrow|696.64|696.64|-|821.06|-|558.06|-|
|Broad|9.66|9.66|-|8.82|3.52|7.83|8.78|
|Soft|7.88|7.88|20.63|27.85|-4.40|-|47.25|
|Hard|6.21|-|-|13.73|-|-|26.41|
|All A4TF|5.21|4.34|6.37|5.58|0.81|3.52|-|

The key results of Table 5 can be summarized as follows:

- In 2007, aid for trade facilitation amounted to about US$ 10.5 billion.  If this level of aid flow increased by 1% (US$ 105 million), the increase in global trade could be about US$ 545 million (see column 1, Table 5a). This yields a ‘rate of return’ on each 1US$ aid extension of US$ 5 (US$ 545 million divided by US$ 105 million) (see column 1, Table 5b).

- Considering the much narrower definition of aid for trade facilitation, that is considering only the extensions of aid to _trade policy reform and regulatory reform_ , based on aid from 2007 data of US$ 117 million, a 1% increase in aid (US$ 11.7 million) could generate a trade increase of about US$ 818 million (see column 1, Table 5a).  This yields a ‘rate of return’ on every additional dollar of aid of about US$ 697 (see column 1, Table 5b).

Within the context of the global financial crisis, these findings are particularly noteworthy. They suggest that economic growth could be effectively stimulated through a targeted aid-for-trade agenda that emphasizes trade facilitating investments with the highest returns – specifically those reforms associated with trade policy and regulation. Furthermore, ongoing dialogue to address coordination and monitoring of trade-related aid might consider our results, in regard to strengthening aid effectiveness.

18
agriculture to non-agricultural activities have been an important contributor to poverty reduction (for a summary see for example World Bank, 2007a). Households that run a nonfarm enterprise in rural Tanzania have an income that is about one-third higher than that of those without (Sunderam-Stukel, Deininger and Jin, 2007).

# **3. A profile of rural nonfarm enterprises**

Rural nonfarm enterprises are thus of crucial importance for Tanzanian rural population. With very low capitalization, Tanzanian rural nonfarm enterprises differ from their urban counterparts. The main activity is rural trade and the average productivity is low but registered enterprises exhibit higher labor productivity. The rural nonfarm enterprises sector in Tanzania is quite heterogeneous. Nevertheless, some characterization is possible.

# **Location and activity**

According to the 2001 HBS, Tanzania’s rural nonfarm sector includes about 1.2 million rural enterprises. Regionally, there are large differences. The highest enterprise density (the number of nonfarm enterprises per 1,000 households) is around the Lake region and in Central Tanzania (Figure 1). This pattern tends to mirror the concentration of roads or railways and associated economic activities. However, the concentration is not related to one single factor. For example, high densities can be found in regions with high agricultural productivity, but also in regions that tend to perform poorly. About one-half of the enterprises are located in rural areas, while the other half is located in small rural market towns.<sup>5</sup> While the overall landscape of nonfarm enterprises in Tanzania is diverse, the predominant entrepreneurial activity of rural nonfarm enterprises across all regions is trading. About 57% of rural

> 5 As defined by NBS geographical classification. Rural towns have higher population densities than rural areas and usually have their own markets or social service providers, such as schools and health centers.

7
Table 4: EFFECTS ON FOOD TRANSFERS OF ULTRA-POOR HOUSEHOLDS

||Transfers|received|Transf|ers given|
|---|---|---|---|---|
||(Yes=1)|(No.)|(Yes=1)|(No.)|
||(1)|(2)|(3)|(4)|
|Treatment|0.013**|0.097*|0.069**|0.229***|
||(0.005)|(0.050)|(0.026)|(0.065)|
|Mean in control|0.936|2.340|0.560|1.284|
|Adjusted R-squared|0.069|0.169|0.260|0.314|
|Observations|13464|13464|13453|13464|
|_Panel B:_||_Within-vi_|_llage Transf_|_ers_|
|Treatment|0.020***|0.136**|0.068**|0.227***|
||(0.006)|(0.051)|(0.026)|(0.063)|
|Mean in control|0.907|2.199|0.547|1.236|
|Adjusted R-squared|0.078|0.193|0.259|0.318|
|Observations|13464|13464|13464|13464|
|_Panel C:_||_Outside-vi_|_llage Transf_|_ers_|
|Treatment|-0.027**|-0.034**|-0.003|-0.001|
||(0.012)|(0.015)|(0.004)|(0.006)|
|Mean in control|0.107|0.141|0.037|0.047|
|Adjusted R-squared|0.065|0.074|0.037|0.036|
|Observations|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers from other households The dependent variable in Panel A, column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers; in column 2 it is the number of households (capped at 3) that the respondent’s household receives food transfers from; in column 3 it is a DV equal to 1 if the respondent’s household ever gives food transfers to other households; in column 4 it is the number of households (capped at 3) that the respondent’s households gives food transfers to. All dependent variables in Panel B (C) are identical to those in Panel A, except they refer to within (outside) village food transfers.

39
relative to the 2050 baseline scenario, but a decrease of about 9% relative to the population at risk currently. (See Table 3.) Most people living in inundated areas, however, are likely to be exposed to higher inundation depths under the climate change scenario. The estimated population exposed to inundation depths greater than 0.9 meter is expected to increase from 13.6 million under the baseline scenario to 16.6 million under the climate change scenario, implying an increase of 22%. Correspondingly, this is offset by a 19% decrease in the population living in areas with inundation depth less than 0.9 meter.

It should be noted that the estimates of population exposed in 2050 are based on 1.15% annual expected growth rates<sup>21</sup> in the rural and urban populations, which are applied uniformly across the country. These projections do not account for a) autonomous adaptation by rural households in response to changing flood risk conditions or b) the implementation of the land use and coastal zone management policies which the government has started to put in place over the past decade.

# 5. Infrastructure Exposed to Additional Inundation Risk and Adaptation Cost

The infrastructure assets considered for the analysis includes: national highways, regional highways, feeder roads-type A, feeder roads-type B, railways, bridges, embankments and drainage systems. In order to estimate the cost of climate proofing infrastructure, the additional flood inundation risk due to climate change has been determined for each 300m x 300m grid by taking the difference in inundation depth between the baseline and climate change scenarios using GIS software. Figure 3 shows a map of the areas with additional inundation risk.<sup>22</sup> Visual inspection suggests the areas with large expected changes in inundation depth are spread out across the country. (See Figure 3.)

> 21 Projection of population is based on the assumption that Bangladesh is expected to attain replacement fertility in 2021 (BBS, 2007)

> 22 These represent just the additional risk due to climate change and not the total risk from flooding.

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