rafmacalaba/datause-encoder-data huggingface dataset:
The results of the analysis – presented in Table 9 - suggest that income shocks via both consumption and production price changes do exert an important influence on the intensity of the Boko Haram conflict. The direction of the effects is consistent with that of the previous analysis, although this time the impact of production shock on conflict events is larger than consumption.

On average a 10% reduction in the value of the production index in a year leads to 2.38 additional conflict events involving Boko Haram in the following year, while the same reduction in consumption prices leads to 0.95 fewer events (column 1). The effects are even larger (3.7 and 2.5 respectively for _PI_ and _CI_ ) when considering only the three Northern regions where Boko Haram activities are concentrated: production (column 2). These results are even more striking, considering that the data on international prices may not reflect accurately local market conditions as argued above. On the other hand, the oil index does not bear any significant relation with Boko Haram conflict activities (column 1).

Interestingly, neither consumption nor production price indices appear to be associated with non Boko Haram conflict events either in the full sample (columns 3) or in the Northern regions (column 4). This suggests that income shocks, at least driven by consumption and production price changes, appear to matter for current conflicts in Nigeria mainly through the Boko Haram insurgency. On the other hand, the oil index is associated with a reduction in non Boko Haram conflict (column 1), confirming our previous results for the post-2009 period.

|**Table 9: The impact of price shocks on the Boko Haram conflict (2010-13)**|
|---|

|Region<br>Dep. variable|(1)<br>All<br>BH event|(2)<br>North<br>BH event|(3)<br>All<br>Non BH<br>event|(4)<br>North<br>Non BH<br>event|(5)<br>North<br>BH fat.<br>event|(6)<br>North<br>Non BH<br>fat. event|(7)<br>North<br>BH<br>fatalities|(8)<br>North<br>Non BH<br>fatalities|
|---|---|---|---|---|---|---|---|---|
|CI<br>Intl_1_|0.046**|0.067***|-0.013|0.014|0.116***|0.010|0.078***|-0.025|
|NAR<br>_(t-)_|(0.020)|(0.018)|(0.011)|(0.013)|(0.018)|(0.016)|(0.024)|(0.022)|
|PI<br>Intl_1_|-0.105**|-0.093**|-0.003|0.008|-0.024|-0.000|0.020|0.050|
|NAR<br>_(t-)_<br>_Oil ind. (t-1)_|(0.043)<br>0.001<br>(0.002)|(0.043)|(0.018)<br>-0.001***<br>(0.000)|(0.023)|(0.026)|(0.023)|(0.038)|(0.034)|
|Observations|148|80|148|80|80|80|80|80|
|Nr. of states|37|20|37|20|20|20|20|20|

_Robust standard errors in parentheses; *, **, *** indicate statistical significance at the 10, 5, and 1 percent level, respectively. All regressions include year and region effects as well as region-year effects and a full set of controls as in Tables 4 and 5. The models are estimated through the population-averaged negative binomial estimator for panel data._ In columns 5-8 we check whether these effects apply also when considering the conflict events with at least one fatality and the number of fatalities. As these variables have a larger share of zeros than conflict events, we can only apply the negative binomial estimator to the 28
the previous estimates for built-up area and vertical growth.<sup>11</sup> Income and population elasticities of total floor area are defined as the following:<sup>12</sup> A measure of area per person is a good proxy of livability for cities. While the slums in Mumbai and the East side of Manhattan have the same population density, the amount of space available per person is the relevant measure to compare these two areas. Based on the previous estimates, income and population elasticities of floor area per person are reported Table 9. The elasticities for total floor area per person with respect to income or population are then derived.<sup>13</sup> Income Elasticities of Floor Area per Capita = γ<sup>b</sup> + γ<sup>heeehtt</sup> Population Elasticities of Floor Area per Capita = β<sup>b</sup> + β<sup>he</sup> – 1<sup>eehtt</sup>

**Table 9: Elasticities of Total Floor Area**

|**ELASTICITIES**|**From**<br>|**the data**<br>|**Inferred from t**<br>|**heprevious elasticities**<br>|
|---|---|---|---|---|
|**Sample of estimation**|**Built-up**<br>**GHSL**|**Height**<br>**DLR**|**TFA**<br>**Combi**|**TFA per person**<br>**nation of both**|
|Population|0.347***|0.251***|0.598|-0.402|
|Income|0.104***|0.185***|0.289|0.289|

Source: Elasticities of income and population reported in earlier tables Table 9 shows these elasticities and confirms that as cities grow in population, in incomes, or in both, they add floor space by building both upward and outward. These estimates have profound implications for a growing city’s supply of floor area, and also for its tendency to sprawl outward—especially in the absence 11 We do not directly estimate the elasticities using our measures of total floor area as they only cover cities for which we have height data. Instead we use the estimates of elasticities for built-up calculated with the GHSL sample, which includes almost all cities.

sample, which includes almost all cities. 12 These properties come from the log properties. – cst ppPP(T The elasticities for total floor area with respect to income or population are then derived. TT) = ppPP(BBsseepptt−sspp ∗ HHaaeeaahtt/3) = ppPP(BBsseepptt−sspp) + ppPP(HHaaeeaahtt) ⇒ddppPP(TεT TT) = ddppPP(BBsseepptt−sspp) + ddppPP(HHaaeeaahtt)= εb + εhe T b he TT,eeiicc eebbtt−bbuu,eeiicc eehtt,eeiicc 13 The elasticities for total floor area per person with respect to income or population are then derived. The elasticities for total floor area per person with respect to income or population are then derived. TT,uuppuu eebbtt−bbuu,uuppuu eehtt,uuppuu εε = εε + εε

> T b he 13  The elasticities for total floor area per person with respect to income or population are then derived.    The elasticities for total floor area per person with respect to income or population are then derived. T b he T b he − 1 TT uueeppccppuu,eeiicc eebbtt−bbuu,eeiicc eehtt,eeiicc εε = εε + εε TT uueepp ccppuu,uuppuu eebbtt−bbuu,uuppuu eehtt,uuppuu εε = εε + εε

23
**Figure 5.  Selected Countries with Peak in Youth Population after 2030 Population Aged 12-24 (1950=100), U.N. Medium Variant Projections**

<!-- Start of picture text --> 1600<br>Kenya<br>1400 Democratic Republic of the Congo<br>Iraq<br>1200 Senegal<br>Pakistan<br>1000<br>800<br>600<br>400<br>200<br>0<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year<br>Population (1950=100)<br><!-- End of picture text --> 31
<!-- Start of picture text --> egend i<br>* CBD od<br>5km r.<br>10km r<br>15km Q\<br>juilding density 1<br>4532 - 21801 i<br>2938 - 4531 \<br>1701 - 2937 ae |<br>803 - 1700 : \ i<br>0-802 orn Z<br>A OSES REE<br>,4 eeoe<br>oma aa<br>ss i aa<br>1 ; ¢ .<br>cad So pe<br>D275 55 ” 165 2 oes Oo .<br><!-- End of picture text -->

<!-- Start of picture text --> So<br>3<br>x€<br>fomao<br>ae<br>an<br>£5°)<br>2g><br>3S<br>HN<br>oOCc®<br>vo<br>23<br>se<br>Ss<br>a<br>°<br><=5km 5-10km 10-15km >15km<br><!-- End of picture text --> <!-- Start of picture text --> 0 20 40 60 80 100<br>% of Area<br>Strate<br>lp LE ll ly<br>Plow Pmedium Phigh NPlow NPmedium NPhigh Rural<br><!-- End of picture text -->
Policy Research Working Paper 8532

# **Abstract**

This paper contributes to understanding the physical and economic effects of salinity diffusion and planning for appropriate adaptation for managing the Sundarbans in a changing climate, with a focus on the West Bengal portion of the tidal-wetland forest delta. A five-step analysis, using high-resolution spatial assessments, was conducted to get a broader picture of the migration of mangrove species with progressive aquatic salinization in a changing climate. A current (2015) basemap, with overlays of salinity tolerance for various mangrove species, and projected location-specific aquatic salinity for 2050 were used to predict the impacts of salinization on mangrove species by 2050. The results indicate patterns of gains and losses, with dominance of salt-tolerant species at the expense of freshwater species. Overall, the impact of salinity-induced mangrove migration will have an adverse effect on the flow of ecosystem services, ultimately impacting the livelihood options of poor households. Resources should be directed to developing alternative livelihoods for mangrove-dependent households. In addition, efforts are needed to develop sustainable policies that incorporate rising salinity, changes in mangrove dynamics, and the welfare impacts on poor communities.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at sdasgupta@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
reduction (prob.=0.015 for $1 and 0.018 for $2). However, we cannot reject the null hypotheses that γ 1 = γ 2 = δ = 0 for either “$1 a day” (prob.=0.085) or “$2 a day” (prob.=0.160).

These tests suggest that the main channel connecting population urbanization to poverty is through aggregate economic growth. This was also true for each region separately except for SSA, where for the $2 a day line we could reject the above null, though only at the 2% level (prob.=0.0176).

# **5. On the future urbanization of poverty**

The latest WUP predicts that the urban share of the population of the developing world will reach 60% by 2030 (UN, 2005). Critics of the WUP forecasting methods have argued that they are likely to overestimate the pace of future urbanization (National Research Council, 2003; Bocquier, 2005). This is suggested by Cohen’s (2004) observation that the urban population of the developing world in 2000 was appreciably lower than the WUP predictions for that year made in both 1990 and 1980. Bocquier’s (2005) alternative forecasting method predicts a much slower pace of urbanization, with the urban population share rising to only 49% in 2030.<sup>33</sup> There are reasons to be skeptical of all such forecasts, but this is not the place to dwell on such concerns. All we want to do here is to see what implications current forecasts for urban population growth hold for future trends in the urbanization of poverty, in the light of our new data set. To do so we need to link the growth rate of the urban population to the urbanization of poverty. That link is directly provided by the PUC, _P u_ ( _S u_ ) . Ravallion (2002) proposes the following cubic specification for the PUC:

> 33 The methodological issue raised by Bocquier relates to the extent of nonlinearity in the relationship between the urban-rural growth difference and the urban population share; the UN’s methods assume linearity; Bocquier presents evidence suggesting that it is a nonlinear relationship, which he then allows for in his own forecasting method.

24
|Parameter|Case 1: Average of<br>Industr. Countries|Case 2:<br>Pro-Intensity|Case 3:<br>Pro-Cap|
|---|---|---|---|
|_σE_|0.06|0.11|0.03|
|_σY_|0.11|0.06|0.18|
|_ρ_|0.36|0.44|0.27|
|_σC_|0.66|0.66|0.66|
|_α_|0.5|1|0|
|_C_0(_R_)|R<sup>1</sup><sup>_._5</sup>|R<sup>1</sup><sup>_._5</sup>|R<sup>1</sup><sup>_._5</sup>|
|_Q_=_q_|0.75|0.75|0.75|
|_ρ_minR (from Eq.8)|0_._80|0_._22|_>_1|
|_ρ_minRC (from Eq.18)|0_._96|0_._27|_>_1|
|_m_<sup>_∗_</sup><br>_R_ <sup>(from Eq.9)</sup>|0.22|1.02|0.05|
|_m_<sup>_∗_</sup><br>_RC_ <sup>(from Eq.20)</sup>|0.10|0.89|_<_0|

Table 8: Definition and summary of the three representative cases.

still, with normalized standard deviations always higher than 72%.

A robust pattern emerges: While as the LIT outperforms the quota by a relatively small margin in the pro-intensity case (except of course on emissions), it leads to a medium-to-large increase of uncertainty in the other two cases. Therefore, Table 9 suggests again that adopting a LIT could introduce significant uncertainty into the system.

As expected, an optimal GIT dominates all other instruments on all policy indicators save emissions. Still, because the empirically found linear correlations _ρ_ are not high, in particular always below 1 _/_ 2, the impact of the GIT remains limited, and its performance is on the whole comparable to that of the cap. In addition, given that _ρ_ and _σE/σY_ are uncertain, it is more realistic to assume that the GIT is calibrated for the central ‘average’ case (fourth line for each instrument), and not with the optimal _m_<sup>_∗_</sup> of each representative case. In that case, the GIT no longer outperforms the cap.

# **6.4 Validity of the ‘Tight Regime’ Assumption**

Table 9 also provides some insights on the validity of the assumptions made in Section 3.4 in order to derive the analytical conditions, namely ‘tight regime’ and ‘limited uncertainty’ on future emissions and GDP. In each cell of the table, the first value is computed based on the approximate analytical formulae derived in Section 4, while the second (between parenthesis) 30
# **Figure 15. Public and private investment**

_In EMDEs, both public and private investment have declined from 2009‐10 peaks. In AEs, post‐crisis public investment contracted as private investment growth stabilized and picked up._

## **A. Public investment growth**

## **B. Private Investment Growth**

<!-- Start of picture text --> Percent 1990-2008 average Percent 1990-2008 average<br>20 2003-08 average 20 2003-08 average<br>15<br>15<br>10<br>10<br>5<br>5<br>0<br>0 -5<br>-5 -10<br>-10 -15<br>AEs EMDEs AEs EMDEs<br>2008 2009 2010 2011 2012 2013 2014 2015 2008 2009 2010 2011 2012 2013 2014 2015 2008 2009 2010 2011 2012 2013 2014 2015 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Eurostat, General Statistics Office of Vietnam, Haver Analytics, International Monetary Fund, Ministry of National Economy of the Republic of Kazakhstan, OECD, Reserve Bank of India, Sri Lanka Ministry of Finance, World Bank. A.B. Public and private investment growth rates are weighted averages of gross fixed capital formation growth rates in the public and private sectors, respectively, in constant 2005 U.S. dollars. The sample includes 20 advanced economies and 99 EMDEs from 1990 to 2015.

# **Figure 16. Public investment and growth**

_Public investment boosts output growth and crowds‐in private investment, but the effects dissipate after about two years._

## **A. Cumulative impact on output of a 1 percent increase in public investment**

<!-- Start of picture text --> Percent deviation from the baseline<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>1 3 5 7 9 11 13 15 17 19<br>Quarter<br><!-- End of picture text -->

## **B. Cumulative impact on private investment of a 1 percent increase in public investment**

<!-- Start of picture text --> Percent deviation from the baseline<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>1 3 5 7 9 11 13 15 17 19<br>Quarter<br><!-- End of picture text --> Sources: International Monetary Fund, World Bank estimates. Notes: The graphs show the cumulative impulse responses (percentage points) of output and private investment due to a positive shock to government investment, based on a sample of 8 EMDEs for 1998Q1‐2016Q2.Variables included are, in this ordering, real government investment, real GDP ̧ real private investment, current account balance, and the real effective exchange rate. The shock size is such that government investment increases by 1 percent from the baseline on impact. Solid lines represent the median, and dotted bands are the 16‐84 percent confidence bands.

37
These results imply that trade responds more sharply to GDP during global slowdowns than during tranquil times. There are a number of potential explanations: (i) firms may draw down accumulated inventories sharply when the forecast worsens in an unexpected and dramatic way. (ii) When global GDP drops sharply, protectionist policies kick in which exacerbate the decline in trade. (iii) During downturns, goods decline by more than services and services make up the bulk of GDP, while goods make up the bulk of trade. Moreover, the share of services in GDP has increased over time magnifying this distinction. (iv) Trade is measured in gross value and GDP in value added. A large decline in trade could reflect a much smaller decline in the value added if production is done across countries at the margin, and as demand falls international production chains break down.<sup>5</sup> And (v) people may tend to source relatively more from home country suppliers during downturns because of trust or financing problems.

On the positive side, we find that trade tends to achieve most of its adjustment in one year also when it rebounds. The quick rebound likely reflects the reversal of many of the conditions above. Figure 3 looks at trade’s share of income over the episodes to see how global openness changes over time. We find that it takes about 4 years for trade to pass pre-downturn levels.

Finally, we examine whether countries with banking crises are affected more severely or differently during a downturn. We focus on three countries, Finland, Sweden,

> would be 23 percentage points.  World real trade growth in 2008 was about 4 percent, yielding a contraction this year of 19 percent.

> 5 An example of this is Porsche which is cutting outsourcing to Finland during the crisis, while maintaining German production (New York Times, April 4, 2009).  In a related point, Yi (2009) argues that vertical specialization leads to a more synchronized transmission of shocks across countries, because trade is directly linked to the production chain. Note that _increasing_ vertical specialization can explain why trade has _expanded_ faster than income in recent years, but a higher _level_ of vertical specialization cannot explain why the elasticity of trade to income is higher.

8
website at <u>www.govindicators.org. These disaggregated underlying indicators are</u> presented for the entire time period covered by the aggregate indicators, from 1996 to 2008.

As in past years, the WGI are based exclusively on subjective or perceptionsbased data on governance reflecting the views of a diverse range of informed stakeholders, including tens of thousands of household and firm survey respondents, as well as thousands of experts working for the private sector, NGOs, and public sector agencies. We rely on the reports of these stakeholders, which reflect their judgments and perceptions, for three main reasons.

First and most basic, perceptions matter because agents base their actions on their perceptions, impression, and views. If citizens believe that the courts are inefficient or the police are corrupt, they are unlikely to avail themselves of their services. Similarly, enterprises base their investment decisions - and citizens their voting decisions - on their perceived view of the investment climate and the government's performance. Second, in many areas of governance, there are few alternatives to relying on perceptions data. This is most particularly so for the case of corruption, which almost by definition leaves no 'paper trail' that can be captured by purely objective measures. Third, we note that even when objective or fact-based data are available, often such data may capture a _de jure_ notion of laws 'on the books' that differs substantially from the _de facto_ reality that exists 'on the ground'. In fact, in Kaufmann, Kraay and Mastruzzi (2005) we document sharp divergences between _de jure_ and _de facto_ measures of business entry regulation and find that corruption explains a good deal of the extent to which the former are subverted in practice.

And finally, we note, as discussed in more detail in Kaufmann and Kraay (2008), that virtually all measures of governance and the investment climate rely on judgment in some measure, so that the distinction between 'subjective' and 'objective' data is somewhat of a false dichotomy. Rather, a more useful distinction is between efforts to measure formal rules as distinct from their implementation in practice, recognizing that changes in formal rules (often associated with so-called 'actionable' indicators) need not lead to desired changes in outcomes.

4
|**COUNTRY**|Antidum<br>**(1)**|ping'<br>|Counter<br>(|vailing<br>2)|Safeguar<br>(3)|ds|
|---|---|---|---|---|---|---|
||1989-94|1995-98|1989-94|1995-98|1989-94|1995-98|
|**Pakistani_**|||||||
|Paraguay||.<br>|||||
|**Poru**<br>|**0.05**|<br>3.50%||.|||
|Philippines|0.06%|0.51%|||||
|**Seegal=**|||||||
|**Singapore**||0.02%|||||
|**South Africa**||0.76%°|||||
|**Sn Lanka**|||||||
|**Thailand**|0.05%|0.05%|||||
|**Tunisia.**||__<br>|||||
|Turkey|**0.45%**|<br>0.22%|_||||
|Uganda|||||||
|Uruguay|||||||
|Venezuela<br>|0.03%|0.12%|_|1|||
|Zambia||.|||||
|Zimbabwe|||||||

Explanation:

* Investigations based on WTO database. Frequency ratio relative to total tariff lines

** Number of Products, based on TPR's and WTO notifications

x = 2 tariff lines Blanks means zero

** _Source: GA1T, TPR; wTO, TPR; WTO. Antidumping Measures Database._

most frequent used remedy both by developed and developing countries (Miranda et. al.1998; Finger and Schuknecht, 1999). The countries in our sample taking such action increased from 11 in the period 19891994 to 18 in 1995-1998. They include most of the developing country members of the WTO which have taken anti-dumping actions.)<sup>0</sup> The Table also shows that only four developing countries out of the 42 in our sample have used safeguard actions and only six countries have taken countervailing actions, mostly in the period since 1994; and in each instance affecting only a few isolated products.

The average frequency ratio for anti-dumping actions taken by developing countries, which had taken action in both periods, also increased over the period, from an average of 1.75 per cent of tariff lines to 3.29 per cent. Despite increases in anti-dumping actions by developing countries, a comparison with Table 10 and Table A-2 in the Appendix (keeping in mind some of the methodological and data

> <sup>0</sup> ° Recent notifications for 1998 sugges **t** hat the number is increasing. In addition to the countries above, Guatemala, Trinidad and Tobago, Nicaragua, Equador and Panama have initiated investigations since 1996, the last three for the first time in 19971998, (WTO, 1999a).

29