rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10014

# **Abstract**

As countries rapidly urbanize, settlements are expanding into hazardous flood zones. This study provides a global analysis of spatial urbanization patterns and the evolution of flood exposure between 1985 and 2015. Using high-resolution annual data, it shows that settlements across the world grew by 85 percent to over 1.28 million square kilometers. In the same period, settlements exposed to the highest flood hazard level increased by 122 percent. In many regions, risky growth is outpacing safe growth, particularly in East Asia, where high-risk settlements have expanded 60 percent faster than safe ones. Developing countries are driving the recent growth of flood exposure: 36,500 square kilometers of settlements were built in the world’s highest-risk zones since 1985–82 percent of which are in low- and middle-income countries. In comparison, recent growth in high-income countries has been relatively slow and safe. These results document a divergence in countries’ exposure to flood hazards. Rather than adapting their exposure to climatic hazards, many countries are actively increasing their exposure.

This paper is a product of the Sustainable Development Chief Economist Office. 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/prwp. The author may be contacted at jrentschler@worldbank.org, pavner@worldbank.org, shallegatte@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
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# B. Conmputable General Equilibrium (CGE) Analyses

The results of studies using CGE models for analysis of the third country effects of NAFTA are quite sensitive to model specifications (particularly, elasticities of demand and supply, market structures, pricing behavior of firms, and assumptions about capital flows). It is also important to noe that assumptions concerning the nature of the preferential liberalization NAFTA will achieve vary significantly from study to study.' Available CGE studies--for a detailed review, see Brown (1992)--., pically find (as expected) that trade expands among the NAFTA member countries as a consequence of the preferential arrangement. Welfare effects are also found to be positive for the participating countries, tending to be larger in models which adopt assumptions of increasing remms to scale vis-a-vis those based on constant returns to scale.

The impact on non-member countries, in turn, may be either negative or positive depending on how the model treats the interplay between trade diversion, capital flows and changes in the terms-of-trade for the rest of the world. It is worth mentioning that the latter is usuaLly introduced in these models via postulated export supply and import demand functions based on relative prices. Table I summarizes the main results for the "rest of the world" (ROW) as established in the models dcveloped by Brown, Deardorff, and Stem (BDS) (1992), Sobarzo (1992), and Cox and Harris (1992). These models allow for increasing returns to scale and assume that imperfectly competitive finns set prices either through a combination of focal 6 This section focuses on CGE models which explicitly address the NAFTA. There are numerous other CGE exercises which focus, for instance, on a US-Mexico FTA. Their results, however, are not at odds with the main conclusions presented here, as far as the economic implications for the third countries are concemed. See, for instance, Hinojosa-Ojeda and Robinson (1991) or Bachrach and Mizrahi (1992).
# 5.5. Net effects

With the estimates of the distributional impact of taxes on processed and ultra-processed foods on the three components analyzed (product expenditure, years of life lost, and medical expenditures) we can estimate the aggregate net effects.

The net effects are a simple exercise where we sum each of the previous analyses and, as each had presented a similar result (a positive impact with a progressive pattern with greater benefits for households at the bottom of the consumption distribution), the net effect maintains these characteristics. Each component is described in terms of changes in income by decile. Variation in medical expenditure and in changes in product expenditure can be understood in terms of increased disposable income, being comparable to the increased income after reduction in years of life lost.

The main component of net effects at the bottom of the distribution is the changes in medical expenditure. For example, of the estimated 14.5% of net effect for the bottom decile, 45% is due to changes in medical expenditure, 29% is due to changes in product expenditure, and 26% is due to changes in years of life lost. Larger consumption deciles have additional income from reduced years of life lost as the main component.

_Table 9_ - Net effects after taxation on processed and ultra-processed foods

||**Processed a**<br>|**nd ultra-processed**<br>|**foods**<br>|
|---|---|---|---|
||Low|Medium|Upper|
|**1**|14.58%|14.61%|14.64%|
|**2**|8.17%|8.20%|8.22%|
|**3**|5.68%|5.70%|5.72%|
|**4**|4.41%|4.43%|4.45%|
|**5**|3.16%|3.17%|3.19%|
|**6**|2.30%|2.31%|2.33%|
|**7**|1.47%|1.48%|1.50%|
|**8**|0.79%|0.80%|0.81%|
|**9**|0.47%|0.49%|0.50%|
|**10**|-0.01%|0.00%|0.01%|

Source: Estimates based on POF 2017/2018; Global Burden of Disease 2019; and SIH/SUS. Estimates assume a price shock of 20%.

The estimates of each of the separate analyses and of the aggregated net effects indicate the potential positive and progressive effects of taxes on processed and ultra-processed foods on households` welfare. Figure 6 shows the clear pattern described, with households at the bottom of the distribution having a large positive impact that decreases until the highest consumption decile, which presents zero net effects.

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# **C. Agri-chemicals**

44. The next set of charts in Figure 10 show the impact of different tax rates on agriculture prices by comparing the cost build-up of insecticides and herbicides. In this case, insecticides (which are important inputs for smallholder cotton and tobacco) do not attract import duty or VAT, whereas herbicides (which are not widely used by smallholders, but could substitute for labor spent on weeding) attract 17.5% VAT and 5% customs duty.<sup>10</sup> As shown below, this means that the final price composition of insecticides includes only 5% domestic tax compared to 21% for herbicides.

**Figure 10: Composition of value chain costs for insecticides and herbicides, 2007/08**

<!-- Start of picture text --> Insecticide - Build-up of Farm Gate Price Insecticide - Composition of Farm Gate SV<br>Customs duty & excise0% Clearing fees2% Domestic transport2% Dealer mark-11%ups place of useTransport to 1% costs & mark-Domestic ups8% Official duties and tax5% Additional expenses0%<br>VAT or other<br>direct tax<br>0%<br>Transport to<br>frontier<br>4%<br>Foreign costs<br>Price at origin 87%<br>80%<br>Herbicide<br>Herbicide - Build-up of Farm Gate Price Herbicide - Composition of Farm Gate SV<br>Excess Domestic Transport to Official duties<br>charges transport Dealer mark- place of use and tax Additional<br>0% 2% ups 1%<br>11% Domestic 21% expenses<br>Clearing fees costs & mark- 0%<br>2% ups<br>8%<br>VAT or other<br>direct tax<br>12%<br>Customs duty<br>& excise3% Transport to frontier Price at origin66% Foreign costs71%<br>3%<br><!-- End of picture text -->

## **Herbicide**

45. While the specific question of whether or not it would be an effective strategy for Malawi to reduce the tax rate on herbicides is beyond the scope of the present discussion, these data illustrate how different tax rates can affect farmer decision making and, ultimately, agriculture competitiveness. If an insecticide costs USD 1.00 in the place where it is produced, for example, the final farm gate SV would work out to USD 1.24. On the other hand, herbicide that cost USD 1.00 per unit in the foreign market would have a final SV of USD 1.52 as a result of the additional tax. In this simple example, therefore, the elimination of VAT and 5% import duty on herbicides could result in a 28% savings in unit costs for the farmer and may be a way to encourage growers to adopt this technology.

> 10 In 2008, VAT was reduced to 16%.

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OPTIMAL SIMPLE FISCAL RULES 16 <!-- Start of picture text --> Welfare Loss Consumption of the Ricardain HH<br>6 0.3<br>BBR<br>5 SSR 0.25<br>4 0.2<br>3 0.15<br>2 0.1<br>1 0.05<br>0 0<br>0.45 0.9 0.45 0.95<br>annual persistence of oil-price shock<br>annual persistence of oil-price shock<br>0.3 Consumption of the Hand-to-Mouth HH 4 Public Assets<br>0.25<br>3<br>0.2<br>0.15 2<br>0.1<br>1<br>0.05<br>0 0<br>0.45 0.9 0.95 0.45 0.95<br>annual persistence of oil-price shock annual persistence of oil-price shock<br>annual std deviation<br>% of steady state consumption<br>annual std deviation annual std deviation<br><!-- End of picture text --> Figure 2. **Welfare loss and shock persistence with BBR and SSR:** baseline calibration and constant variance of commodity price shocks.

**Countercyclical Transfers** Kumhof and Laxton (2013) find that countercyclical fiscal policy — — where a fall in output leads to an increase in transfers leads to a substantial increase in welfare. Here we add counter-cyclical transfers to both BBR and SSR in Column 4 and 5 of Table 2, and find only a small improvement in welfare (by 0.03-0.05).<sup>19</sup> The reason is that shocks to non-resource GDP are relatively small and not very persistent, and so even with a sub-optimal policy they generate little welfare loss. Countercyclical transfers are optimal because the temporary nature of the non-resource shocks means that they should be smoothed by HtM HHs.<sup>20</sup>

**Optimal Simple Rules (OSR)** The optimal simple rule is shown in Column 6 of Table 2, which is the rule that chooses all six parameters _{θa_<sup>_′, θ_</sup> _y_<sup>_′, θ_</sup> _p_<sup>_′, θ_</sup> _a_<sup>_′′, θ_</sup> _y_<sup>_′′, θ_</sup> _p_<sup>_′′}_tominimizetheweightedaverage</sup> consumption variances in the loss function (Equation 3.10). One can see that the coefficients are _very_ similar to those in the BBR-CCY rule — especially for HtM HHs where the details of the rule have the most effect. The optimal rule suggests that a 10% increase in oil revenues will lead to a 7% increase in transfers to HtM HHs, and so in a sense is a compromise between a BBR and a SSR. The optimal rule also means that temporary non-resource income shocks are almost completely insured for HtM HHs, and that an increase in SWF assets leads to an increase in transfers to HtM HHs by more than the interest earned on those extra assets. For Ricardian HHs, the coefficients

> 19That is, commodity revenues are spent/saved according to the BBR/SSR, but shocks to non-resource GDP are smoothed with counter-cyclical transfers.

> 20The relationship between the persistence of the commodity shock and the ranking SSR-CCY vs BBR-CCY are almost identical to that of SSR vs BBR (not reported).
This paper examines the relationship between regulatory and supervisory policies designed to promote private-sector monitoring and bank development and stability, while controlling for a full range of regulatory characteristics. We also assess whether private monitoring is particularly effective in countries with better-developed institutions.

# **II.G. Government ownership of banks**

Economists hold sharply different views about the impact of govermment ownership of banks on financial and economic development [LaPorta, Lopez-de-Silanes, and Shleifer (2001)]. The helping-hand view argues that govermment ownership of banks facilitates the mobilization of savings and the allocation of those savings toward strategic projects with long-term beneficial effects on an economy. According to this view, governments have adequate information and sufficient incentives to ensure socially desirable investments. Consequently, government ownership of banks helps economies overcome private capital-market failures, exploit externalities, and invest in strategic sectors. Lewis (1950), Myrdal (1968), and Gerschenkron (1962) specifically advocate government ownership of banks to promote economic and financial development, especially in underdeveloped countries.

The grabbing-hand view, in contrast, argues that governments do not have sufficient incentives to ensure socially desirable investments [Kornai (1979), and Shleifer and Vishny (1993,1994)]. Government ownership tends to politicize resource allocation, soften budget constraints, and otherwise hinder economic efficiency. Thus, government ownership of banks facilitates the financing of politically attractive projects, but not necessarily economically efficient projects.

In an influential study, LaPorta, Lopez-de-Silanes, and Shleifer (2000) piece together data on government ownership of banks from an assortment of sources. They find that countries with higher initial levels of government ownership of banks tend to have both slower subsequent rates of financial-system development and slower economic growth. In a related paper, Barth, Caprio, and Levine (2001 a) use data on government ownership from Bankscope. We find that greater government ownership is generally associated with less efficient and less well-developed financial systems. The data used in these papers, however, do not cover all banks operating in an economy and the degree of coverage varies across countries.

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**Table 6:  FE estimates of household participation in microenterprise activity**

|**Explanatory variables**|**Thana**|**Thana**|**Mean of**|
|---|---|---|---|
||**FE**<br>**(restricted**|**times year**<br>**FE**|**explanatory**<br>**variables**|
||**to 2010)**|||
|Year (1=2000, 2=2005, 3=2010)|-|0.023|2.07|
|||(0.03)|(0.82)|
|Head’s sex (1=male, 0=female)|0.102|0.087|0.883|
||(5.36)|(6.89)|(0.32)|
|Head’s age (years)|0.0003|-0.0002|45.96|
||(0.71)|(-0.66)|(14.00)|
|Head’s education (years)|0.002|0.001|4.91|
||(0.92)|(0.80)|(2.11)|
|Household size|0.018|0.016|4.91|
||(4.87)|(7.39)|(2.11)|
|Log of HH land asset (decimal)|-0.046|-0.046|117.38<sup>†</sup>|
||(-9.10)|(-17.26)|(470.96)|
|Log of HH non-land asset (Tk.)|0.086|0.101|192,119.8<sup>†</sup>|
||(17.12)|(28.31)|(552,265.9)|
|Village has electricity|0.061|0.058|0. 683|
||(1.54)|(1.74)|(0.466)|
|Proportion of irrigated land in village|-0.058|0.005|0.655|
||(-0.42)|(0.06)|(0.289)|
|Village has agricultural banks (1=yes, 0=no)|0.013|0.001|0.255|
||(0.29)|(0.03)|(0.436)|
|Village has commercial banks (1=yes, 0=no)|0.005|-0.001|0.247|
||(0.10)|(-0.03)|(0.431)|
|Village has microcredit programs (1=yes, 0=no)|-0.044|-0.059|0.931|
||(-0.66)|(-1.03)|(0.253)|
|Village has Food for Work program (1=yes, 0=no)|-0.012|-0.021|0.430|
||(-0.19)|(-0.51)|(0.495)|
|Village has Vulnerable Group Feeding program (1=yes,|0.030|0.011|0.580|
|0=no)|(0.40)|(0.22)|(0.494)|

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Corporation ( _IFC_ ) of the World Bank launched a project which addresses the private investments of various developing countries from 1970 to 1999. Studies using this disaggregated data on private investment show that private and public investment can have very different determinants (Aizenman and Marion, 1999). Therefore, disaggregated data need to be utilized in order to capture the investment-conducive factors in today’s globalized economies. Building on the IFC series, our new data set covers 99 countries (63 for high quality data) over the period 1970-2002.

Finally, as previously mentioned, our empirical model allows for the simultaneous estimation of private investments and of various types of governance institutions. This model is also justified by the fact that -- in addition to economic policies -- changes in private investment can influence the quality of governance. Moreover, some hidden factors are likely to affect in parallel private investment and governance institutions. Our empirical results show that governance institutions play a significant role in private investment decisions. This result is particularly true in the case of “Administrative Quality” ( _QA_ ) in the form of control of corruption, bureaucratic quality, investment friendly profile of administration, and law and order. Similarly, “Political Stability” ( _PS_ ) significantly generates higher private investment. Evidence in favor of “Public Accountability” seems, however, less robust.

This paper is organized as follows. The second section introduces our classification of governance institutions to determine which institutions are detrimental for entrepreneur investments. The third section presents the other determinants of private investment which will be taken into consideration in our empirical analysis and highlights the importance of these factors for the MENA countries. The fourth section presents the 7
Table D.7. Impacts of PEP Program on Electoral Outcomes by Treatment Intensity Mayoral Elections

||Election|Sha|re of Votes|for|Electoral|
|---|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|Competition<br>(5)|
|_Panel A. Tercile 1 - PEP Hold_|_ers Municip_|_alities_||||
|PEPm_×_I(Post2018)t|-0.000|-0.004|0.006|-0.002|0.005|
||(0.002)|(0.011)|(0.016)|(0.013)|(0.009)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.876|0.482|0.382|0.439|0.398|
|Observations|3,107|3,107|3,107|3,107|2,977|
|_Panel B. Tercile 2 - PEP Holde_|_rs Municipa_|_lities_||||
|PEPm_×_I(Post2018)t|-0.002|-0.012|0.026|-0.014|0.001|
||(0.002)|(0.012)|(0.017)|(0.014)|(0.009)|
|q-values|[0.763]|[0.763]|[0.763]|[0.763]|[0.763]|
|R-squared|0.867|0.456|0.375|0.441|0.411|
|Observations|3,134|3,134|3,134|3,134|3,005|
|_Panel C. Tercile 3 - PEP Holde_|_rs Municipa_|_lities_||||
|PEPm_×_I(Post2018)t|-0.017|-0.002|0.048|-0.048|0.064**|
||(0.009)|(0.028)|(0.037)|(0.035)|(0.024)|
|q-values|[0.124]|[0.310]|[0.166]|[0.166]|[0.042]|
|R-squared|0.880|0.451|0.383|0.436|0.420|
|Observations|3,126|3,126|3,126|3,126|3,002|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE x Year|Yes|Yes|Yes|Yes|Yes|
|Municipal controls x Year|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results of our main specification in equation 1. I(PEPm) is a discrete variable that takes the value of one if the municipality had positive take-up rates and PEPm is the standardized value of the ratio of PEP migrants to population. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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provinces, which can be divided into 8 main groups, as shown in Table 7. The sample statistics for soils presented in the last column of Table 7show the percentage of district area covered by each soil type. As with other variables, endowment of soil resources differs from one province to another. The means show that about 50% of the sampled provinces in Kenya are covered by nitosols and ferralsols. All other soils cover relatively small proportions. Nitosols are well-structured soils which vary from deep to very deep and are well drained. These soils are concentrated in the more arable districts. Central Province has 34% of its total land covered by nitosols, Eastern Province has 23%, Nyanza and Rift Valley Provinces each 31%, while Western Province has the highest coverage at 38%. Ferralsols are strongly weathered and leached soils with a low chemical fertility. They are mostly found in Nyanza and Western Provinces where they cover 50% and 57% respectively. Other important soils include luvisols, mostly found in Coast Province (68%) and Eastern Province (23%), andosols (covering 22% of Central Province) and lithosols (covering 20% of Rift Valley Province).

# _Household level data_

Here the key variables of interest for the Ricardian analysis include net crop revenue, wage rates and a few other household variables. We define net revenue as gross revenue less all total variable costs, costs of hired labor, farm tools, machinery, fertilizers and pesticides. Costs of household labor are not netted due to difficulties of accurate measurement. Instead we introduce household wage rates for adults and children as independent variables in the net revenue regression. Other household variables used are summarized in Table 8. Other than for precipitation, farm size, household male wage rates and irrigation, there are no significant differences in the means of variables in medium/low and high potential zones. For the full sample, further analysis suggests that there is little variation across provinces/zones in the mean number of family laborers for both children and adults. On average, each household uses four adult workers, two of each gender and two children, one of each gender. The data across provinces further shows that about 70% of all household heads have farming as their main occupation and about 20% have farming as their secondary occupation. This implies that on average about 90% of all Kenyans are engaged in farming, which is consistent with countrywide statistics on the percentage of the population relying on agriculture.

# **4. Literature review**

# **_4.1 Introduction_**

There are four ways in which climate affects agriculture (Kurukulasuriya & Rosenthal 2003):

- (i) Changes in temperature and precipitation directly affect crop production and can even alter the distribution of agro-ecological zones.

- (ii) Increased CO2 is expected to have a positive effect on agricultural production due to greater water use efficiency and higher rates of plant photosynthesis.

- (iii) Runoff or water availability is critical in determining the impact of climate change on crop production, especially in Africa.

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