rafmacalaba/datause-encoder-data huggingface dataset:
landslide results are omitted from the overall index, only one city out of the top 20 cities changes. If the flood index values are omitted, three cities change in the top 20. Removing the cyclones from the overall index, results in a change of six cities and, finally, excluding earthquakes results in a change of seven cities in the top 20 cities. This corresponds to the fact that earthquakes, on average, cause large fatality numbers.

# **7 Future urban hazard risk**

Between 2010 and 2050, urban areas will receive almost 2.7 billion additional residents according to UN estimates. Almost all of this net growth—a result of migration, natural increase and absorption of nearby rural areas—will occur in developing countries. Larger cities also mean greater exposure of people and, because urban dwellers tend to be more productive than rural ones, an even greater increase of exposure of economic assets. We develop a demographic-economic model of city-level population growth to derive an estimate of future population exposure to earthquakes and cyclones up to 2050.

Population projections for countries tend to be more accurate than those for cities. Since international population movements are typically far smaller than within-country migration, demographic models based on fertility and mortality work well to forecast national population totals. But at the city level, migration and the future fertility of these new migrants become more important factors. Migration, in turn, responds to economic dynamics, so commonly used demographic models do not yield reliable predictions (World Bank 2009). Instead, city level projections require consideration of various endogenous and exogenous factors, such as technological change, economic growth and development, as well as national population growth. In order to project future urban growth locations, it is critical to understand the underlying forces that drive this transformation.

We based our projections on a global study of determinants of city growth by Henderson and Wang (2007). This paper empirically modeled the urbanization process between 1960 and 2000. We focus on 30
Dabla-Norris, Era, Jim Brumby, Annette Kyobe, Zac Mills, and Chris Papageorgiou, “Investing in Public Investment: An Index of Public Investment Efficiency,” _Journal of Economic Growth_ , 17 (September 2012), 235-66.

Frankel, Jeffrey A., “The Natural Resource Curse: A Survey,” Working Paper No. 15836, National Bureau of Economic Research (March 2010).

Gelb, Alan, Silvana Tordo, and Havard Halland, “Sovereign Wealth Funds and LongTerm Development Finance: Risks and Opportunities,” Policy Research Working Paper No. 6776, World Bank (February 2014).

Gupta, Sanjeev, Alex Segura-Ubiergo, and Enrique Flores, “Direct Distribution of Resource Revenues: Worth Considering?,” Staff Discussion Note No. 14/05, International Monetary Fund (June 2014).

International Monetary Fund, _Macroeconomic Policy Frameworks for Resource-Rich Developing Countries_ , IMF publications (Washington DC: 2012).

Lundgren, Charlotte J., Alun H. Thomas, and Robert C. York, _Boom, Bust, or Prosperity? Managing Sub-Saharan Africa’s Natural Resource Wealth_ , International Monetary Fund (Washington DC: 2013).

Melina, Giovanni, Shu-Chun S. Yang, and Luis-Felipe Zanna, “Debt Sustainability, Public Investment, and Natural Resources in Developing Countries: The DIGNAR Model,” Working Paper No. 14/50, International Monetary Fund (March 2014).

Rabanal, Pau, and Vicente Tuesta, “Nontradable Goods and the Real Exchange Rate,” _Open Economies Review_ , 24 (July 2013), 495-535.

van der Ploeg, Frederick, and Anthony J. Venables, “Absorbing a Windfall of Foreign Exchange: Dutch Disease Dynamics,” _Journal of Development Economics_ , 103 (July 2013), 229-43.

38
Policy Research Working Paper 9960

# **Abstract**

This paper investigates the labor market outcomes for refugee and urban national communities in Kenya during the COVID-19 pandemic, using five waves of a novel high-frequency phone survey collected between May 2020 and June 2021. Even after conditioning on age, gender, educational attainment, and area of living, only 32 percent of refugees were employed in February 2020 compared with 63 percent of nationals. With the onset of the pandemic in March 2020, the share of employed for both refugees and nationals fell by around 36 percent, such that in May-June 2020, only 21 percent of refugees were still employed compared with 40 percent of nationals. Using a panel setup with wave and location fixed effects, the analysis finds that the recovery in the share of employed, hours worked, and household incomes was slower and often stagnant for refugees compared with the recovery of nationals. These differences cannot be explained by demographic factors, living in an urban or camp environment, having been employed previously, or sectoral choice, suggesting that a third, unobservable “refugee factor” inhibits refugees’ recovery after a major shock and aggravates preexisting vulnerabilities.

This paper is a product of the Poverty and Equity Global Practice. 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 authors may be contacted at upape@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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municipality. In addition, the opening investigation of a specific regulatory barrier in the initial municipality was often prompted by the complaint of a single firm. In other words, the complaint of a single firm in a random municipality, once confirmed, triggered the elimination of similar regulatory barriers across different municipalities across the country. The dependence on such precedents thus led to a ‘roll-out’ of eliminated regulatory barriers across municipalities which can be regarded as exogenous to other contemporaneous changes in municipalities’ local business environment (such as infrastructure investments) that may have promoted higher productivity growth of establishments operating in these municipalities. Moreover, the eliminated barriers were often sector-specific, providing additional exogenous variation in the data.<sup>16</sup> For example, the initial complaint by a second mobile phone operator that had been denied a municipal permit to install antennas in one of Lima’s 42 municipalities enabled INDECOPI to enforce the issuance of permits to install antennas in 3 other Lima municipalities in 2013 and 9 other municipalities in other parts of the country in 2014. Similarly, the refusal of granting an operating license for a passenger transport firm that did not possess the required minimum number of fleet vehicles in one municipality in 2013, led to the elimination of the de facto market entry barrier of minimum fleet size requirements across numerous municipalities in Peru. The province municipality of Chanchamayo, for instance, had to eliminate the requirement to possess at least 10 vehicles to be eligible to obtain an operating license while the district municipality of Puente Piedra had to eliminate the minimum requirement of 30 vehicles.

# **4. Data**

## **4.1 Establishment census data**

The analysis is based on establishment-level data from the Annual Economic Survey (EEA) collected by Peru’s National Institute of Statistics (INEI) for the 2008-15 period. The survey is representative of formal establishments in agriculture, manufacture, commerce, construction, transport & communication and services sectors in all regions. The sample frame comes from SUNAT, the tax administration database. Each year, some establishments are selected with certainty to form the “forzosa” stratum. The forzosa stratum is defined based on net sales, which

> 16 INDECOPI had a limited number of staff available in the Commission of Elimination of Bureaucratic Barriers that was responsible to oversee the business regulations defined in the detailed TUPA’s of over 1,800 subnational jurisdictions. It thus focused its efforts on illegal practices for specific economic activities determined by the initial firm complaints in a given year.

11
**Table 1. The distribution of the sample across administrative categories**

||N|Margin of<br>error|Prior estimate on<br>the probability of<br>being urban|n*|
|---|---|---|---|---|
|Capitals and Municipal Corporations|147|0.05|0.97|34|
|Municipalities|905|0.05|0.90|120|
|Industrial Towns|31|0.05|0.90|25|
|Other Towns|2,769|0.05|0.80|226|
|Census Towns|3,710|0.05|0.71|292|
|Outgrowths|935|0.05|0.60|264|
|Villages|553,127|0.03|0.10|315|
|Total|561,624|||1,277|
|Mean probability of being urban|||0.11||
|Relative standard error|||0.08||

_Note_ : N is the total number of places belonging to an administrative category and n* is the selected sample size for an administrative category.

**Figure 2. The spatial distribution of sample places**

Our final sample with 1,277 administrative units is quite evenly distributed across India’s territory (Figure  2). The selected places cover five of the six regions of the country northern, central, eastern, western, 8
**Figure 3:** Distributions and yield-input regression coefficients for different measures of yield in the first sample split of the test set N = 386. _Notes:_ Left top panel shows the kernel density of the different measures of yield. The right middle panel shows the scatter plots of CC vs ML yields. The right middle panel shows the scatter plots of CC vs SR yields. The bottom left/right panel shows the bivariate/multivariate yield-input regression coefficients for different measures of yield.

<!-- Start of picture text --> G CC<br>ML<br>SR<br>0 500 1000 1500 2000 2500<br>Yield (kg/ha)<br>0.5<br>G<br>G G<br>0.0<br>−0.5<br>Area Seeds HH Labor<br>0.0030<br>0.0025<br>0.0020<br>Density 0.0015<br>0.0010<br>0.0005<br>0.0000<br>β<br><!-- End of picture text --> <!-- Start of picture text --> 0 500 1000 1500 2000 2500<br>SR Yield (kg/ha)<br>0 200 400 600 800<br>ML Yield (kg/ha)<br>0.4<br>0.2 G<br>G<br>0.0 G<br>−0.2<br>−0.4<br>Area Seeds HH Labor<br>2500<br>1500<br>CC Yield (kg/ha)<br>500<br>0<br>2000<br>1000<br>CC Yield (kg/ha)<br>500<br>0<br>β<br><!-- End of picture text --> 24
Δ _W_ = _W_ ( _C B_ ) - _W_ ( _C A_ ) (10) This change in welfare captures both changes in the probability a farm will be a particular type and the conditional value it would have as that type.

# **3. Description of the Data**

The data for this study came from the recently completed GEF/World Bank project in Africa (Dinar et al. 2008). The surveys asked questions about both crop cultivation activities and livestock management during the farming period from July 2001 to June 2003. The countries were chosen to reflect a wide range of agro-ecological systems in Africa. Niger, Burkina Faso, Senegal, and Ghana were chosen from West Africa; Kenya, Ethiopia, and Cameroon from East Africa; South Africa and Zambia from Southern Africa; and Egypt from North Africa. Zimbabwe was also surveyed but the turmoil in that country during the survey period forced us to drop the observations. The number of surveys varied from country to country (Dinar et al 2008).

In each country, districts were chosen to get a wide representation of farms across climate conditions in that country. In each chosen district, a survey was conducted of randomly selected farms. The sampling was clustered in villages to reduce sampling costs. After cleaning, over 9000 observations remained.

Data on climate were gathered from two sources. We relied on temperature data from satellites operated by the US Department of Defense (Basist et al. 2001). These polar orbiting satellites pass above each location on earth between 6am and 6pm every day. These satellites are equipped with sensors that measure surface temperature by detecting microwaves that pass through clouds (Weng and Grody 1998). The precipitation data come from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association’s Climate Prediction Center, is based on ground station measurements of precipitation.

Soil data were obtained from FAO (2003). The FAO data provide information about the major and minor soils in each location as well as slope and texture. Data concerning the hydrology were obtained from the University of Colorado (Strzepek and McCluskey 2006). Using a hydrological model for Africa, the hydrology team calculated flow 6
exist. However, if after taking into account the influence of other variables, the negative association exists, then per-capita incomes tend to converge conditionally, the so-called conditional convergence.

In absolute convergence, the inverse relationship originates in a production function with diminishing returns: regions with lower per capita output would grow faster than regions with higher per capita output because the marginal product of capital would be higher in the former. A study by Cavalcanti and de Goes (1996) of absolute convergence for Brazilian states concludes that, on average, there is convergence, although it is very slow. In fact, the study finds that it would take about 90 years for Brazilian states to reach their steady state level of GDP, longer than what has been found, for example, for the states of the United States or for Spanish autonomous communities ( _comunidades autônomas_ ).

Findings reveal that poor municipalities are not catching up with high-income municipalities in RN (Table 5.1). The relationship between 1970–96 growth and initial per-capita income based on 150 municipalities is hazily negative and reveals a very small rate of convergence in RN (Table 5.1). The estimates of the speed of convergence is incredibly small and implies that it takes about 170 years to eliminate one-half of the initial gap in per capita GDP.<sup>6</sup> Hence, even if RN as a whole is catching up with other states in Brazil, there are strong forces within the state itself that leave poor municipalities behind in the growth process.

**– Table 5.1:  Convergence in RN (1970 96) Dependent Variable: GDP Growth Rate**

|**Parameter**|**Coefficient**|**Std. Error**<br>**t-Statistic**|**Prob.**|
|---|---|---|---|
|αˆ|0.066|0.0027<br>23.07|0.00|
|β<sup>ˆ</sup>|0.004|0.0004<br>8.43|0.00|
|R-sq|0.35|Mean dependent var|0.03|
|Adjusted R-sq|0.34|S.D. dependent var|0.02|
|S.E. of regression|0.017|Akaike info criterion|-5.28|
|F-statistic|0.04|Schwarz criterion|-5.24|
|Prob(F-statistic)<br>_Source:_Estimations|398.1<br>based on IPEA|Durbin-Watson stat <br>’s municipal dataset.|1.69|

**Figure 5.2 Convergence Across Municipalities, Rio Grande do Norte**

> 6 The half-life is calculated using the formula: t≈0.69/β ≈ 172.5 ( see Romer, 2001 .pp. 25)

12
centers, especially the capital Amman (see Table 2 for population distribution changes); the changes in sector productivity and employment perspective, especially those with large concentration in rural areas (see Table 10 for a breakdown of sectoral employment by region); and finally the public sector transfers and government programs that focused extensively on rural regions. These arguments are discussed further throughout the paper.

Looking at welfare growth from a governorates perspective, the Northern governorates saw the best performance in the Kingdom. Table 1 reveals that the largest rates of welfare growth in 2002-2010 were depicted in the Northern and North-Eastern governorates, mainly Zarqa (52.7 percent), Jarash (48.9 percent), Mafraq (38.5 percent), Irbid (25.9 percent), and Balqa (23.5 percent)<sup>13</sup> . One important contributor to this increased welfare is their proximity with Syria; these governorates have benefitted from the economic and trade openness that this neighboring country saw in the past decade. This opened further opportunities for cross-border trading with Syria,<sup>14</sup> pushing residents of these areas to import cheaper Syrian goods and export both goods and services with higher value added. As a result household real per capita consumption increased at a faster rate than in other parts of the Kingdom. Additionally, household consumption in these areas has indirectly benefitted from several special economic zones that were set up during this period in those governorates; and that employed many of their residents<sup>15</sup> . On the other hand, Amman, with the biggest population in the Kingdom, registered one of the lowest rates of welfare increase (8<sup>th</sup> of the 12 governorates in Jordan). While this increase remains significant (double digit figure) and average per capita household consumption remains the largest (in real terms) in Jordan, Table 1 indicates a noteworthy catching-up effect between the governorates and the capital in the past decade<sup>16</sup> .

> 13 Karak in the center of the country has also recorded a strong growth in per capita consumption of 32.1 percent. 14 Economic growth in Syria for the period 2002-2010 averaged 4.9 percent. Syrian imports to Jordan increased from US$97 million in 2002 to US$308 in 2010, while Jordanian exports to Syria augmented from US$69.8 million to US$233.5 over the same period (Source: Jordan Department of Statistics).

> 15 Jordan has 18 special economic zones out of which 6 are in the Northern governorates (Zarqa, Jarash, Mafraq, Irbid and Balqa).

> 16 The ratio of real per capita household expenditure in Amman to the average per capita expenditure in the Kingdom (excluding the capital) has decline from 1.55 percent in 2002 to 1.42 percent in 2010.

6
|111.<br>CONCLUSIO|NS AND RECOMMENDATIONS<br>.............................|55|
|---|---|---|
|A.<br>TERM|S OF THE INSURANCE PLAN ...........<br>..<br>.................|55|
|1.<br>|The Insurance Schemes Cover Selective Types of Services<br>.......<br>|55<br>|
|2.<br> <br>|The Most Successful Plans Have Modest Premiums<br>....<br>.........<br> <br>|55<br>|
|B.<br>ORG<br>|NIZATION<br>AND MANAGEMENT ......<br>....<br> <br> <br> <br> <br>|56|
|1.|An Acceptable<br>Quality<br>of Services<br>Is a Precondition<br>for<br>||
|2.|Insurance .56<br>Voluntary<br>Schemes Have Found It Important<br>to Sensitize the<br>||
|3.|Population .57<br>Committed, Decentralized Management Provides Flexibility and<br> <br>||
|4.<br>|Accountability<br>.57<br>Simple Control Methods Can Reduce Risks of Error or Fraud<br> <br> <br>|57|
|5.|Appropriate<br>Investment<br>Strategies Can Preserve the Value of<br>||
|6.|Premium Income over the Year .58<br>Financial<br>Analysis of the Insurance<br>System Requires<br>Better<br>||
||Accounts .58<br>||
|7.|A Financial Guarantor Can Help Build Confidence in Launching<br>||
||an Insurance System .......................................|59|
|C<br>RESO|URCE MOBILIZATION|59|
|.<br>|<br>.................||
|1.|Access and Resource Mobilization Motivated Health Insurance||
||Sh|5|
||cemes .<br>.................................................|9|
|D.<br>UTILI<br>|ZA.ION<br>AND ACCESS ................<br>....................<br>|59|
|1.|Evidence of Adverse Selection and Moral Hazard wag Found, But<br>||
|2.|Plans Minimized Their Impact ..............................<br>Systems of Utilization and Cost Control Can Help Make Insurance<br>|59<br>|
||Affordable<br>..............<br>................................|60|
|E.<br>INFO|RMAL ASSOCIATIONS ................|60|
|1.|Informal<br>Associations<br>Financed<br>Health<br>Care Only Through<br>d|6|
|<br>|Emergency Loan Funs ....................................<br> <br>|0<br>|
|F.<br>STRE<br>|GTHENING<br>EXISTING INSURANCE SYSTEMS .....<br>.........<br>|61<br>|
|1.<br>|Training<br>.<br>...............................................<br>|61<br>|
|2.<br>|Exchange Visits...........<br>........................<br>|.......<br>61<br>|
|3.<br>|Information Systems.......................................<br>|61<br>|
|4.<br> <br>|Technical Assistance<br>......................................<br>|61<br>|
|G.<br>ESTA|BLISHMENT OF ADDITIONAL INSURANCE SYSTEMS ........|62|
|1.|Rural Health Insurance Systems .............................|62|
|2.|Urban Health Insurance Systems ............................|62|
|REFERENCES .63|||
|ANNEXES .65<br>|||
|ANNEX I|Description of the Zaire Health Zone Structure And Management<br>||
|ANNEX II|Organization .66<br>List of Prices 1988 and 1989 Bwamanda Reference Hospital and<br>||
||Health Centers .71||
|ANNEX III|Exchange and Inflation Rates .74||
|ANNEX IV|<br>Documentation<br>for Bwamanda Health Insurance Plan .75||

ii
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