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45
# _<u>Estimation of resistance to water flow from field measurements</u>_

Understanding the resistance of water flow over surfaces is critical for simulation of cyclone-induced surges as well as for estimation of water flow velocity. Friction between the fluid and a surface is generally represented by Manning’s Number (M). Manning’s Numbers for the Bay of Bengal Model domain for different depths of water are presented in Table 2.

Table 2: Manning Number distribution in the Bay of Bengal Model domain

|Areas with depths|Manning’s number, M (m<sup>1/3</sup>/s)|
|---|---|
|Less than -20 m|32|
|-20 to -15 m|60|
|-15 to -10 m|65|
|-10 to -5 m|90|
|-5 to 0.46 m|100|

Source: IWM, 2013 As expected, Table 2 indicates resistance to water flow increases with reduction in water depth.

Scientific literature to date emphasizes that mangroves can substantially reduce the vulnerability of adjacent coastal lands from storm surges, as the flow of water through the mangrove forest is obstructed by the matrix of roots/trunks and leaves of the mangrove trees. In order to estimate the resistance of water flow from various mangrove species, field visits were made to measure the diameter of the trunks, diameter of the roots and height of the roots from the ground level for each mangrove species short-listed for this analysis<sup>15</sup> (see Table 3). During field visits, the distance between mature plants was measured and local experts were consulted on feasible density of planting and spacing between trees.

> 15 The impact of leaves has not been considered in this analysis.

12
Table A.11: The Impact of Initial Conditions on Employment of Female Refugees by Years Since Migration

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_Variable × Years since migration_|||||||
|Attitudes X 0-2 years|0.00925|0.0132<sup>_∗∗_</sup>|0.0110<sup>_∗∗_</sup>|-0.00115|0.00857|0.00757|
||(0.00569)|(0.00557)|(0.00549)|(0.0114)|(0.0112)|(0.0112)|
|Attitudes X 3-5 years|0.0193<sup>_∗∗∗_</sup>|0.0185<sup>_∗∗∗_</sup>|0.0162<sup>_∗∗∗_</sup>|0.0126|0.0171|0.0164|
||(0.00559)|(0.00600)|(0.00573)|(0.0114)|(0.0119)|(0.0119)|
|Attitudes X 6-10 years|0.0298<sup>_∗∗∗_</sup>|0.0288<sup>_∗∗∗_</sup>|0.0282<sup>_∗∗∗_</sup>|0.0135|0.0101|0.00993|
||(0.00558)|(0.00585)|(0.00585)|(0.0116)|(0.0118)|(0.0118)|
|Attitudes X 11-15 years|0.0343<sup>_∗∗∗_</sup>|0.0301<sup>_∗∗∗_</sup>|0.0293<sup>_∗∗∗_</sup>|0.0263<sup>_∗∗_</sup>|0.0178|0.0177|
||(0.00617)|(0.00662)|(0.00647)|(0.0120)|(0.0126)|(0.0125)|
|Attitudes X 15+ years|0.0430<sup>_∗∗∗_</sup>|0.0308<sup>_∗∗∗_</sup>|0.0289<sup>_∗∗∗_</sup>|0.0271<sup>_∗_</sup>|0.0191|0.0180|
||(0.00748)|(0.00867)|(0.00867)|(0.0146)|(0.0161)|(0.0161)|
|Unemployment X 0-2 years||-0.0242<sup>_∗∗∗_</sup>|-0.0257<sup>_∗∗∗_</sup>||-0.0315<sup>_∗∗_</sup>|-0.0322<sup>_∗∗_</sup>|
|||(0.00805)|(0.00790)||(0.0149)|(0.0148)|
|Unemployment X 3-5 years||-0.0303<sup>_∗∗∗_</sup>|-0.0308<sup>_∗∗∗_</sup>||-0.0382<sup>_∗∗_</sup>|-0.0385<sup>_∗∗_</sup>|
|||(0.00774)|(0.00760)||(0.0156)|(0.0155)|
|Unemployment X 6-10 years||-0.0304<sup>_∗∗∗_</sup>|-0.0312<sup>_∗∗∗_</sup>||-0.0480<sup>_∗∗∗_</sup>|-0.0482<sup>_∗∗∗_</sup>|
|||(0.00776)|(0.00766)||(0.0149)|(0.0147)|
|Unemployment X 11-15 years||-0.0343<sup>_∗∗∗_</sup>|-0.0351<sup>_∗∗∗_</sup>||-0.0546<sup>_∗∗∗_</sup>|-0.0547<sup>_∗∗∗_</sup>|
|||(0.00805)|(0.00788)||(0.0167)|(0.0164)|
|Unemployment X 15+ years||-0.0439<sup>_∗∗∗_</sup>|-0.0460<sup>_∗∗∗_</sup>||-0.0539<sup>_∗∗∗_</sup>|-0.0559<sup>_∗∗∗_</sup>|
|||(0.00876)|(0.00879)||(0.0170)|(0.0175)|
|Network X 0-2 years|||0.00952<sup>_∗∗_</sup>|||0.00206|
||||(0.00369)|||(0.00882)|
|Network X 3-5 years|||0.00299|||0.000676|
||||(0.00398)|||(0.00916)|
|Network X 6-10 years|||0.0159<sup>_∗∗∗_</sup>|||0.00463|
||||(0.00383)|||(0.00944)|
|Network X 11-15 years|||0.0171<sup>_∗∗∗_</sup>|||0.00906|
||||(0.00400)|||(0.00932)|
|Network X 15+ years|||0.0107<sup>_∗∗_</sup>|||-0.000587|
||||(0.00478)|||(0.0105)|
|Current year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country of birth FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Years since migration FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Arrival year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Canton of arrival FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Canton of arrival X Current year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Education FE|No|No|No|Yes|Yes|Yes|
|Observations|278909|278909|278909|55480|55480|55480|

Dependent variable is Employment dummy. Network is defined as log (nb of co-nationals+1). All regressions also include age, age squared, gender, marital status. Specifications (1) and (4) also include the variables Unemployment and Network, (2) and (5) also include Network. Standard errors are clustered at the level Canton of arrival _×_ Year of arrival. * p < 0.10, ** p < 0.05, *** p < 0.01 52
Table 5: Market <u>Conditions By State Capital</u>

|**_Market_**|**_Cereal_**<br>**_Production_**|**_Cereal_**<br>**_Surplus_**|**_Area_**|**_Consumption_**<br>**_PC_**|**_Consumption_**|
|---|---|---|---|---|---|
|_Aweil_|60,457|-36,780|30,543|60|48,600|
|_Bentiu_|19,210|-38,793|37,837|72|38,500|
|_Bor_|65,177|-74,615|122,581|98|118,000|
|_Kajok_|107,239|-8,976|45,567|67|80,500|
|_Juba_|100,103|-43,474|43,033|127|140,000|
|_Malakal_|41,044|-45,598|77,283|144|153,000|
|_Rumbek_|70,370|-13,382|43,595|110|75,000|
|_Wau_|42,666|-2,814|91,076|114|35,500|

Note: All figures refer to the states where the towns are located. Cereal production in net, cereal deficit is the difference between production and consumption (and it is reported as the average over 2008-2013), area is expressed in squared kilometers, consumption expresses total consumption in thousands, as indicated in the Household survey for year 2012-2014, consumption per capita is the ratio of consumption and population. Source: Authors’ calculations based on FAO/WFP. 2012-2014

# **3.6. City Characteristics and Price Differentials**

When markets are integrated and well-functioning, the price difference of a homogeneous product across regions converges to the cost of trading that product across the regions through the forces of arbitrage. The costs of trading include, for example, transportation and intermediation costs, and other costs related to, for example, overcoming regulatory obstacles that restrict trade. However, high transport costs, imperfect markets and barriers to trade may prevent arbitrage, resulting in market segmentation and high price differences between geographically separated locations, with associated welfare costs.

When markets are segmented, price dynamics in each region matter for price determination. In that case price differences may be associated with supply or demand conditions (differences in production costs, resource endowments, or with consumer preferences and market size).

To what extent do the heterogeneous characteristics of state capitals related to supply and demand conditions, and to trade costs can explain the observed patterns of price differences in South Sudan?

Methodologically, to identify the effects of state-capital characteristics on the price difference of a variety of products in each of these state-capitals relative to the price in Juba we estimate the following model: 18
and 65 percent boys are approximately 30 percent and 43 percent of the female fetuses aborted.

Urban <!-- Start of picture text --> (a) 1972-1984 (N=4,288) (b) 1985–1994 (N=3,664) (c) 1995–2006 (N=1,507)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Rural<br>(d) 1972-1984 (N=20,172) (e) 1985–1994 (N=17,872) (f) 1995–2006 (N=5,823)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> Figure 4: Predicted probability of having a boy and probability of no birth yet from time of marriage for women with no education by quarter. Predictions based on age 16 at marriage. Left column shows results prior to sex selection available, middle column before sex selection illegal and right column after sex selection illegal. N indicates the number of women in the relevant group in the underlying samples.

The most interesting result is how close to the natural sex ratio the predicted percentage boys is for each group and for each period. This is most clearly seen for rural women without education 26
Table 2: Parameter choice and moments to match

|_Parameters:_<br><sup>1</sup>|||_Moments to match:_|||
|---|---|---|---|---|---|
|Average <sup> ̄</sup>_Ai_ = <sup> ̃</sup>_T _<sup>_−_</sup><br>_ξ_|USA|1.000|GDP per capita|USA|35,080|
|by country<br>|SGP|4.768|(PWT)|SGP|32,808|
|(relative to the US)|JPN|1.634||JPN|26,721|
||TWN|4.977||TWN|21,891|
||KOR|4.521||KOR|17,208|
||MYS|14.249||MYS|7,917|
||THA|17.885||THA|5,178|
||IDN|17.657||IDN|2,549|
||CHN|25.049||CHN|2,442|
||PHL|41.881||PHL|2,210|
|Labor supply|USA|53,551|Total value-added|USA|1,878.6|
|in tradeable goods|JPN|41,665|in tradeable goods|JPN|1,113.3|
|(x1000 workers)|SGP|735|(in $M)|SGP|24.1|
||TWN|3,889||TWN|85.1|
||KOR|10,491||KOR|180.5|
||MYS|5,637||MYS|44.6|
||THA|10,410||THA|53.9|
||IDN|36,585||IDN|93.3|
||CHN|266,707||CHN|651.3|
||PHL|13,618||PHL|30.1|
|Average coordination costs <sup> ̄</sup>_θi_|USA|1.161|DX Index|USA|2.555|
|by country|SGP|0.590|(Export weighted)|SGP|2.659|
||JPN|1.029||JPN|2.375|
||TWN|0.692||TWN|2.613|
||KOR|0.825||KOR|2.637|
||MYS|0.695||MYS|2.590|
||THA|0.742||THA|2.360|
||IDN|1.173||IDN|3.002|
||CHN|0.809||CHN|1.973|
||PHL|0.790||PHL|2.508|
|Transaction costs _γi_|USA|0.487|aggregate GO / VA ratio|USA|2.666|
|by country|SGP|0.244||SGP|4.676|
||JPN|0.442||JPN|2.773|
||TWN|0.285||TWN|3.951|
||KOR|0.358||KOR|3.359|
||THA|0.325||THA|3.452|
||MYS|0.383||MYS|3.101|
||IDN|0.740||IDN|2.035|
||CHN|0.343||CHN|3.110|
||PHL|0.464||PHL|2.629|
|Simple average border cost|All|50%|Trade/output ratio|All|26%|

45
# Figure 3. Total factor productivity growth

A. Average TFP growth <!-- Start of picture text --> Percent 2000-2021 average<br>2<br>1<br>0<br>World AEs EMDEs<br>C. Per capita income relative to advanced<br>economies in 2000, 2009, and 2021<br>Percent<br>25 2021 2009 2000<br>20<br>15<br>10<br>5<br>0<br>EMDEs EMDEs EAP ECA LAC MNA SAR SSA<br>excl.<br>CHN<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> C. Per capita income relative to advanced economies in 2000, 2009, and 2021 B. Share of economies and GDP with TFP growth below the previous decade average <!-- Start of picture text --> Percent<br>Share of countries Share of GDP<br>100<br>80<br>60<br>40<br>20<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>World AEs EMDEs<br><!-- End of picture text --> D. Average number of climate disasters and financial crises per year <!-- Start of picture text --> Number of climate disasters Number of financial crises<br>1.0 Climate disasters 0.15<br>0.8 Financial crises (RHS) 0.12<br>0.6 0.09<br>0.4 0.06<br>0.2 0.03<br>0.0 0.00<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Note: AEs = advanced economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, EMDE = emerging market and developing economy, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa, TFP = total factor productivity. A. GDP-weighted arithmetic average of total factor productivity growth. Includes 53 EMDEs and 29 advanced economies.

B. Number of economies among 29 advanced economies and 53 EMDEs in which potential total factor productivity growth is lower than the previous decade average.

C. GDP-weighted average of GDP per capita differential to advanced economies between 2009 and 2021. In percent of advanced-economy per capita incomes.

D. Simple average of number of climate disasters (1980-2018) and financial crises (1980-2018) per year in each region.
of being fined. As with the nationwide sample, less than 1 percent reported actually having had to pay any fines or bribes in the last year for operating informally, so the risk of being fined is already low.

These responses suggest that firms have at least somewhat accurate information about some of the advantages and disadvantages of registering. They have much less knowledge of the specifics of how to register. Only 17 percent knew the correct cost of registering at the DS. The most common response when asked how long it takes to register—given by 22 percent of the owners—was ―don’t know‖. The next most common responses were 30 days, 60 days, and 90 days. Firms also lacked knowledge of their income tax responsibilities. Firm owners were asked how much a business owner would have to pay as income tax if their annual income was 100,000 LKR. Only 2 percent got the correct answer of zero, with 55 percent saying ―don’t know.‖ The median answer amongst those professing to know was 2,000 LKR. When asked the same question with respect to an annual income of 400,000 LKR, again 50 percent said they didn’t know, and 88 percent of those answering with a number gave an answer higher than the true rate of 3,400 LKR, with the median answer 8000 LKR.

Finally, firm owners were asked in the baseline a hypothetical question of whether they would register with the DS if someone would pay the fixed cost of registration. Sixty-one percent of firm owners answered yes to this question, suggesting some willingness to formalize if the costs were reduced.

# **3.3 The Intervention**

The baseline survey and hypothetical questions suggest that the informal firms are not perfectly informed about the process of registration or its costs and benefits, and that many express a willingness to register if someone pays the upfront costs. We therefore designed an intervention which provided information about the costs, benefits, and process of registration, and which promised to reimburse firms for the direct costs of registering. If the stylized de Soto/Doing Business view is true, and firms want to formalize but for the costs, then we should expect to see a large response from this intervention. Likewise, if firms really want to formalize but time-inconsistency means they have never gotten around to it, a limited time offer to reimburse registration costs and a nudge from an outsider might be expected to spur registration. In contrast, if firms have decided not to formalize because they don’t think the benefits outweigh the costs, we would expect few firms to be at the margin where just the upfront registration cost alone was enough to change this calculus. Such firms would need additional incentive to register, and so we experiment by seeing how much we need to pay firms to get them to find it worthwhile to formalize. We therefore designed the following four treatments: _Treatment 1:_ Information and Reimbursement. We designed an information brochure in consultation with the Chamber of Commerce and local Divisional Secretariat which clearly set out the advantages and 9
data in later years (since they were later to introduce assessments), this would mean the data that exists is more recent, likely biasing the average up due to testing later rather than stronger performance. Thus, countries that have historically performed poorly might appear to do better than they are. While this bias might exist, it creates a conservative metric. Moreover, we put up with this limitation to enhance data availability and coverage.

Similar limitations exist when using the index to transform across tests as when grouping intervals across grades and time within tests. A regional test might measure a different grade or be administered in a different year than an international test. For example, the regional SERCE test is specific to grade 6, while the international TIMSS test might be specific to grade 8. Furthermore, the SERCE test was conducted in 2006 while the TIMSS test was conducted in 2007. Therefore, even if the mean score for all countries that took a regional test such as SERCE in 2006 is unbiased, when we divide the SERCE 2006 mean by the TIMSS 2007 mean, we might be concerned about the integrity of the index.

This potential bias, however, does not seriously affect the outcome of our methodology for two important reasons. First, we use the index to translate all original scores. Since the same index is used for all original scores, each score is transformed equally. Second, it is unlikely that tests changed from year to year in a way that differentially affected certain countries. For example, even if TIMSS 2007 was made more challenging because of 2006 SERCE test scores, which is relatively unlikely to begin with, this change should not impact Colombia more than Bolivia. Thus, the index we produce can be a powerful and relatively unbiased tool to link international achievement tests with regional tests.

Additional assumptions and limitations revolve around data availability and coverage. There is a trade-off between coverage and disaggregation. For example, while constructing averages across subjects would increase data coverage, it also makes it harder to interpret the meaning of each score by eliminating the ability to differentiate math and reading scores. To this end, we construct disaggregated measures as well as aggregated ones. This enables us to conduct analyses with each, considering the trade-offs.

# _3.2.3. Specific choices made in the anchoring process_

We use all available information on performance for each country to obtain the most precise and accurate panel dataset between 1965 and 2015.

For linking early time intervals, where some countries took part in the FISS and SISS without participating in FIMS and SIMS, we estimate a countries’ performance by regressing their 20
<u>Table 2 (Cont.)</u>

||(1)<br>Wome|(2)<br>n 25-55|Women with<br>kid 4-6<br>(3)|<br>Latino-<br>barometro 2018<br>(4)|Urban areas<br>only<br>(5)|
|---|---|---|---|---|---|
|_Local labor market variables_||||||
|Male/Female employment ratio in the municipality|-0.0155|-0.0157|-0.0144|-0.0142|-0.0157|
|Log of municipality GDP per capita|[0.00374]***<br>0.0475|[0.00366]***<br>0.0487|[0.00374]***<br>0.0443|[0.00246]***<br>0.0342|[0.00398]***<br>0.0412|
||[0.0245]*|[0.0245]**|[0.0255]*|[0.0130]**|[0.0329]|
|Crimes in the municipality / Total population in the municipality|-1.55|-1.865|-1.768|-1.523|-2.37|
||[1.743]|[1.769]|[2.081]|[1.369]|[1.970]|
|Share of male employment in primary (municipality)|0.00393|0.00526|-0.0344|-0.00259|0.0198|
||[0.0541]|[0.0539]|[0.0528]|[0.0459]|[0.0680]|
|Share of male employment in construction (municipality)|-0.0837|-0.0848|-0.204|-0.123|-0.0933|
||[0.0784]|[0.0780]|[0.0812]**|[0.0841]|[0.0883]|
|Share of male employment in white services (municipality)|0.00477|0.00716|-0.0368|0.029|0.0336|
||[0.0819]|[0.0826]|[0.0818]|[0.0785]|[0.106]|
|Share of male employment in other services (municipality)|0.0132|0.00382|-0.0509|0.00711|0.0535|
||[0.0621]|[0.0624]|[0.0591]|[0.0676]|[0.0859]|
|Constant|-0.738|-0.733|-0.644|-0.72|-0.881|
||[0.232]***|[0.231]***|[0.240]***|[0.131]***|[0.308]***|
|Observations|1,983,873|1,983,873|580,115|1,696,260|961,743|
|R-squared|0.247|0.248|0.228|0.255|0.206|
|Department fixed effects|Yes|Yes|Yes|No|Yes|
|Clustered std. errors|Municipality|Municipality|Municipality|Department|Municipality|

Notes: Robust standard errors in brackets clustered at the municipality/department level. *** p<0.01, ** p<0.05, * p<0.1. Table A1 in the Appendix provides details on variables’ definitions.

29
CONTENTS

|Ackn<br>Exec|owledgments<br>utive Summary|
|---|---|
|I.|Introduction-Globalization through Trade Integration .................................<br>1|
|II.|Developments in World Trade in the Early 1990s<br>-Decoupling of Trade and Output Growth? .-.<br>7|
|III.|New Waves of Liberalization in the Mid 1980s and Early 1990s<br>-Trend Acceleration in Speed of Trade Integration? ....................... 19|
|IV.|Balanced vs. Unbalanced Integration ...................<br>........................<br>28|
|V.|Concluding Remarks ...........................................<br>37|
|APP|ENDIX A: Classification of Economies<br>..<br>40|
|APP|ENDIX B: Prospects for Japan's Trade-Market<br>Opening?<br>.<br>.<br>42|
|APP|ENDIX C: Openness Rankings<br>..<br>48|
|Refe|rences<br>..<br>54|