rafmacalaba/datause-encoder-data huggingface dataset:
people and other assets from coastal hazards, additional location-specific research is necessary to design and manage these coastal forests. This paper is a step forward in that direction.

This paper evaluates the effect of different species of mangroves, density of trees and width of mangrove forests on cyclone-induced surge height and water flow velocity in coastal Bangladesh.<sup>3</sup> First, location-specific analysis was undertaken to short-list suitable mangrove species for three coastal segments prone to cyclones. Second, the hydrodynamic model for the Bay of Bengal, based on the MIKE21FM modeling system, was run multiple times to simulate the devastating cyclone Sidr that made landfall in Bangladesh in 2007 at the Barisal coast. Third, estimates of surge height and water flow velocity were recorded for different widths of mangroves under different density of planting using specific information on local topography, bathymetry, and root and trunk systems of mangrove species. The analysis is based on estimation of resistance of water flow from mangroves based on field measurements of root and trunk systems.

Bangladesh provides an exemplary backdrop for investigation of coastal protection from mangroves during cyclones, as Bangladesh is the world’s most vulnerable country to tropical cyclones (UNDP 2004). Bangladesh was hit by 48 severe cyclonic storms and 49 cyclonic storms between 1877 and 2016, 20 of which recorded hurricane wind speeds during the more recent period of 1966-2016.<sup>4</sup> On average, a severe cyclone strikes the country every three years (GoB 2009) and records indicate that the greatest damage during cyclones resulted from the inundation caused by cyclone-induced storm surges.<sup>5</sup> Though time-series records of storm-surge 3 Some researchers who are skeptical about the ability of mangroves to protect against tsunamis have noted that mangroves might be more capable of protecting against tropical storm surges (Kerr and Baird 2007; Chatenoux and Peduzzi, 2007).

4 Tropical storms are classified as severe cyclonic storms with hurricane wind speed (wind speed over 118 km per hour), severe cyclonic (89–117 km per hour), cyclonic (62–88 km per hour), deep depression (52– 61 km per hour), and depression (40–51 km per hour) based on the observed maximum sustained surface wind speed measured at a height of 10 m averaged over 3 minutes, (IWM 2009).

5 For details on major cyclones that crossed Bangladesh from 1960 to 2009, see Dasgupta et al., _Vulnerability of Bangladesh to Cyclones in a Changing Climate: Potential Damages and Adaptation Cost_ , Appendices 2 and 3. World Bank, Washington, DC (2010).

3
12 Equation (5), where the market component of non-farm income is denoted _w_<sup>_h_</sup> and the transfer component is denoted _τ_<sup>_h_</sup> , is a comprehensive decomposition of the change in household welfare, taking into account the full compensating variation in “net expenditures” in the last two terms on the RHS, and any additional general equilibrium effects on non-farm incomes, e.g. changes in wages or government transfers, in the first term.

As discussed in Section 2, no study that we are aware of has so far fully captured all of these terms, and ours is no exception. Shortcomings in the Brazilian data, such as the absence of information on household farm production in either the household income or the household budget surveys, preclude us from computing production shares ( ). Neither are we able to estimate the full Slutsky substitution matrix to approximate . In what follows, therefore, our empirical analysis is based on Equation (6) below, where the approximation sign returns: The three terms on the right hand side of (6) correspond to our estimates of the expenditure effect, the market income effect, and the transfer income effect, respectively. The first term is computed, for each household in the POF sample, using expenditure shares from the POF survey, and proportional price increases (for each good i) from the INPC. The second term is computed from wage information in the PNAD, based on the following rule: if the household contains no workers in any agricultural activity if the household contains any individuals _j_ who report positive income from an agricultural activity _i_ , (i.e. that produces good _i_ ).

The information on agricultural production activities in the PNAD is much more disaggregated than the 16 food groups used for the INPC – POF concordance, and given in Table 1. An additional concordance was needed, which maps each sector-specific agricultural activity reported in the PNAD to one of the 16 food groups whose prices we follow. The full concordance takes fourteen pages to describe and is available from the authors on request. An excerpt corresponding to the first four INPC food categories is reported in Annex Table A1 for illustrative purposes.
# **3.1 Data**

The surveys, which form the basis for this paper, have been developed as part of a project by the International Trade Department of the World Bank which aims to assist low-income countries (LICs), particularly from Sub-Saharan Africa (SSA), to take better advantage of spillovers from FDI within the context of global value chains. Specifically, the project aims to identify the critical factors for the realization of FDI-related spillovers – including dynamic interactions between FDI and local suppliers.

Acknowledging that the extent and nature of potential FDI-generated spillovers differ importantly by sector and FDI motive, the project focuses not exclusively on manufacturing but includes, besides light manufacturing (apparel) two natural resources-based sectors which are particularly relevant for SSA LICs: mining and agribusiness. Given the share of FDI that goes into natural resources-intensive sectors, particularly in developing countries, understanding better the unique dynamics of FDI linkages and spillovers in sectors like agribusiness and mining represents an important opportunity. In addition, the study includes benchmark countries for these two sectors – Chile (for mining) and Vietnam (for agribusiness) – to be compared with the SSA countries.

Between March and October 2012, three different types of firms have been surveyed by various consultants, namely (i) national suppliers, i.e. firms with a national ownership of at least 75 percent that supply to multinationals in the country, (ii) foreign investors, i.e. firms that have a foreign ownership share of at least 25 percent, and (iii) national producers, i.e. domestic firms that are final goods producers and have a national ownership of at least 75 percent. In cases where reported data seemed unlikely, either consultants or the firms themselves were contacted again to make sure we obtained the correct numbers.

The focus of this section is on foreign investors, but we also compare their characteristics with domestic producers. The foreign investors’ surveys cover 87 firms in Chile (5), Ghana (16), Kenya (20), Lesotho (15), Mozambique (10), Swaziland (11) and Vietnam (10). Table 1 shows that the majority of foreign investors are in apparel (43), followed by agribusiness (30) and mining (14). Domestic producers’ surveys cover 64 firms in Chile (5), Ghana (10), Kenya (26), Mozambique (6) and Vietnam (17). The majority of these firms are in agribusiness (46), followed by apparel (13) and mining (5).

10
# **7. Annexes**

## **_A. Details for measuring accessibility, availability and safety of the public_**

## **_transport network_**

In this annex we provide the technical details for the construction of measures of accessibility, availability, and safety.

### _a. Measuring accessibility_

#### **Definition**

Our first measure of the public transport network focuses on measuring the spatial accessibility of job opportunities throughout the city using either public transport or walking. More precisely, for each residential location, we compute the percentage of total jobs<sup>38</sup> that are accessible within 60 minutes during peak hours (8am to 9am). The measure of accessibility combines the shape of the transport network with the distribution of jobs within the city to measure accessibility— combining the time taken to walk to the public transport stop, time spent in the public transport vehicle, and any time needed to walk to a job opportunity. This approach has strong support in the transport and accessibility literature (see Dijst et al., 2002; El-Geneidy & Levinson, 2006; and Palacios Santana & El-Geneidy, 2022 for a theoretical discussion and Peralta Quiros et al., 2019 for an example of practical application).

#### **Data sources**

We use various sources of data to construct this index.

First, we use a layer of the street composition for each city. All street grid data is taken from OpenStreetMap. For the three cities, there are no pedestrian restrictions, meaning all streets and links in the network are considered accessible to pedestrians.

Second, we use the public transit network that has been collected. The collected frequency of departure, and speeds by which the vehicles travel on any route are used for the analysis (see 2.1. for details and maps about this data).

Third, we use the distribution of employment in each city. We base it on Barzin et al. (2022), which estimates the relative density of employment across the metropolitan area using a machine learning implementation and data on night lights and sites from open street map to create an estimation of concentration of employment opportunities across a metropolitan area

> 38 Out of all the jobs in the region.

42
# **Are Psychometric Tools a Viable Screening Method for Small and Medium-Size Enterprise Lending? Evidence from Peru**<sup>**1**</sup>

Irani Arráiz,<sup>2</sup> Miriam Bruhn,<sup>3</sup> Claudia Ruiz Ortega,<sup>4</sup> Rodolfo Stucchi<sup>5</sup>

## **JEL Classification:** D82, G21, G32

**Keywords:** Asymmetric information, psychometrics, credit risk, access to credit.

> 1 We thank the Entrepreneurial Finance Lab and our partner bank for sharing their data and information, as well as Pierre Freundt from Equifax Peru for patiently answering our questions. We are also grateful to Asim Khwaja and Bailey Klinger for their valuable comments. Alejandra Arrieta provided excellent research assistance. Financial support for this project was provided by the Strategic Research Program. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, Inter-American Investment Corporation, or the World Bank, their Boards of Directors, or the countries they represent.

> 2 Inter-American Development Bank; e-mail: iarraiz@iadb.org

> 3 World Bank; e-mail: mbruhn@worldbank.org

> 4 World Bank; e-mail: cruizortega@worldbank.org

> 5 IDB Invest; e-mail: rstucchi@iadb.org
inequality within the GIDD, this section starts with two simulations that illustrate, in a simple way, the expected effects of a global agricultural trade reform. Focusing only on low and middle income countries in our data, both simulations raise the average income in the developing world by 1 percent. In the first instance, this occurs due to an increase in incomes of agricultural households only, while in the second exercise, the increase is due entirely to an expansion in non-agricultural incomes. The results of these examples are shown with two growth incidence curves (GIC)<sup>21</sup> in Figure 8. The thin GIC captures the effects of assigning income gains only to agricultural households, while the thick GIC raises incomes only for those households whose head works in non-agricultural activities.

**Figure 8 Growth Incidence Curve of a 1% Increase in Incomes**

<!-- Start of picture text --> 1% Increase in Non-<br>Agricultural Incomes<br>1% Increase in<br>Agricultural Incomes<br>0 20 40 60 80 100<br>Percentiles<br>Vertical line represents the extreme poverty line<br>1<br>.8<br>.6<br>.4<br>% Change in Per-capita Income .2<br>0<br><!-- End of picture text -->

# **4.2.1 Poverty and Inequality Impacts: A Global View**

Translating the shocks from the LINKAGE model into poverty and inequality outcomes with the GIDD shows that the effects of a full removal of agricultural trade distortions on global poverty are close to zero. This limited impact is explained by several factors. First, the growth effects of the reform (i.e., changes in per capita consumption) are very small.

> 21 The GIC is shows the changes in welfare along the entire income distribution, therefore capturing, in a single graph, the growth and distributional components of overall welfare changes. For a detailed description of the properties characterizing the growth incidence curves see Ravallion and Chen (2003).

17
54 Our general approach is to estimate industry-specific and overall (sample-wide) production functions of the form

# InQ = g(Z)f(X),

where Q is value added in constant domestic or world prices, X is a vector of inputs, and Z is a vector of structural and policy variables that may affect productive efficiency and lend themselves to or become potential tools in a policy reform. The function f(X) is hence the input function, and g(Z) is the productive efficiency function.

The X variables that we use include the gross capital stock, the number of permanent workers in the various national and skill groups,<sup>1</sup> and the defiated wage bill of temporary workers. The latter variable is used as a measure of the temporary labor input. Apart from the time trend, which captures the evolution of productive efficiency over time, the Z variables include enterprise ownership measured by the percentage of enterprise capital owned by (a) Senegalese state--referred to in the tables as % Senegalese State," (b) French citizens or companies-referred to as % French," (c) Syro-Lebanese nationals or companiesreferred to as "% Syro-Lebanese," and (d) individuals, companies or governments of other countries-referred to as "% Other Foreirn." The Z vector also includes the percentage of output that is exported (capturing efficiency differentials between exporting and nonexporting firms), and the effective rate of pr otection (reflectmg2productivity differences between more and esprotected firms).' The latter three variables (ownership, export orientation, and effective protection) are of particular relevance to public policy. Although econometric evidence is scarce,3 institutional knowledge has led numerous governments and international organizations,

1. In the few instances when no workers of a given nationaiy were found in a given group (cell), we set the value of the natural logarithm of this labor input variable equal to 0.001. The results are not sensitive to variations in this number.

2. The effective rate of protection is defined as the percentage differential between a fin's value-added calculated with domestic and world prices.

3. **See** for example Aharoni (1986) and Svejnar and Hariga (1987) for a dision of the misting studies.
Policy Research Working Paper 6497

# **Abstract**

This paper first reviews the history of social insurance policy and coverage in urban China, documenting the evolution in the coverage of pensions and medical and unemployment insurance for both local residents and migrants, and highlighting obstacles to expanding coverage. The paper then uses two waves of the China Urban Labor Survey, conducted in 2005 and 2010, to examine the correlates of social insurance participation before and after implementation of the 2008 Labor Contract Law. A higher labor tax wedge is associated with a lower probability that local employed residents participate in social insurance programs, but is not associated with participation of wage-earning migrants, who are more likely to be dissuaded by fragmentation of the social insurance system. The existing gender gap in social insurance coverage is explained by differences in coverage across industrial sectors and firm ownership classes in which men and women work.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at jgiles@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
<!-- Start of picture text --> Machinery /<br>Mineral fuel / Crude materials Transport Animal / veg Chemicals / Manufactured Beverages and Food and Live Miscellaneous Commodities<br>lubricants excluding fuel Equipment oil / fat / wax Products n.e.s. goods tobacco Animals Manuf. Articles n.e.s.<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>-60 Total<br>-70<br><!-- End of picture text --> Note: Data is from UN Comtrade database. Export growth is in nominal USD. Years are calendar years. The bars denote the difference between the average growth rate between 2003 and 2008 and the average growth rate between 2012 and 2016.

- India’s export destinations are diversified too. The largest share of exports from India is destined for the Middle East (approximately 20 percent in 2016), and within it the largest share is exported to the United Arab Emirates. The United States is the second largest destination, accounting for 16 percent of India’s exports, followed by China (including Hong Kong SAR, China), Sub-Saharan Africa and Europe. We disaggregate Indian exports across its 8 main trading partners and the rest of the world for 2003 to 2017.<sup>48</sup> We note that the export slowdown experienced by India after 2013 was across destinations, as exports slowed to most of India’s main trading partners in the Middle East, the UAE and Saudi Arabia, but also to the US, China and Singapore.

**Figure 28: Destinations of Indian Exports**

28 A: Contribution to Export Growth 28 B: Contribution to Export Growth: Middle East and the USA

> 48 India’s main trading partners, in order of share in total exports, are the USA, the United Arab Emirates, Hong Kong SAR, China, China, the United Kingdom, Singapore, Germany, and Saudi Arabia.

45
<!-- Start of picture text --> Youth not employed baseline: out of labor force later<br>05<br>E on 3 02 0073<br>= 0 0028<br>a<br>=!<br>oO<br>2<br>&<br>E5 > -035<br>“= -.05<br>o<br>5<br>ie) $058 & -.068<br>@ Pre-covid cohort<br>“1 $086 ®@ Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later***<br><!-- End of picture text -->
19 include mixed socialist countries: Madagascar, Rwanda, Sudan, Burkina Faso, Zambia, Congo, Mali, Somalia, and Togo.

(4) The political rights and civil and economic liberties in the recipient country may influence the donor community. Emergency aid may also depend on the form of government. To test these hypotheses, we used Gastil's (1984) ranking of African countries according to political rights, civil liberties, and state of freedom. The first two indices rank countries from one (best) to seven (worse), while the third index characterizes countries as not free, partially free, or fully free. Most countries in Africa are between five and seven. The exceptions are Botswana, Ghana, Mauritius, Nigeria, Senegal, and Zimbabwe. Countries ruled by a military regime are: Togo, Chad, Liberia, Niger, Ghana, Central African Republic, Mauritania, Burkina Faso, and Ethiopia.

Given the large multicollinearity between all these variables, their impact on B had to )e estimated one at a time. When the effect was suspected to originate from a correlation with a third variable, that variable was added as a control in the regression. Table 7 summarizes the results of including z as an interaction with the output shocks in the aid equation. The table indicates the sign of the effect and whether it is significantly different from zero at the 5 percent significance level. The first two columns present the results for emergency aid in the form of cereals and noncereals. The signs of the coefficients are the same for the two types of food aid, not surprising given the high correlation between the two. Almost all the coefficients are statistically different from zero and generally they are more precise in the noncereal equation (lower standard errors for the coefficients).

With regard to economic determinants, the hypothesis that the response is
where _PCE_ ( _i_ , _t_ ) denotes the monthly PCE of household i in the month of interview t divided by the value of the consumer piece index for Southern Mexico in the month of interview of the household35, _z_ is the poverty line used (in June 2002 Pesos), α takes on the values 0, 1, and 2, and _Poor_ ( _i_ , _t_ ) is a binary variable taking the value of 1 if _PCE_ ( _i_ , _t_ ) ≤ _z_ , and equal to 0 otherwise. Based on the specification of the regression equation (2), the intercept term β 0 is the estimate of the poverty rate (headcount ratio, poverty gap, or the severity of poverty) in the _J_ control localities in the baseline round, while β 0 + β _T_ is the corresponding estimate of poverty in the three treatment localities of type J (where J=1,2,3) (in the baseline round).36. The estimates _J_ of the parameters γ are the DiD estimates of the impact of the program on poverty in round 2 (follow-up) of the survey.

In Table 5, the constant term summarizes the poverty rate in the control localities in the baseline while the estimates of the coefficients of T1, T2 and T3 show the baseline differences in the poverty rate between the treatments groups (T1, T2 and T3). To calculate baseline poverty rates between treatments, one adds the constant term to each of the treatment coefficients. For example, the food poverty line yields a baseline headcount poverty rate P(0) of 63.5% in the control localities and a headcount poverty rate of 67% in group T2 and 65.7% in group T3.

> 35 The national consumer price index (base year June 2002) was obtained from Banco de México for the baseline survey months between October 2003 and April 2004 and for the follow-up survey from October to December 2005. <u>http://www.banxico.com.mx/polmoneinflacion/estadisticas/indicesPrecios/indicesPreciosConsumidor.html</u>

> 36Along similar lines, β 0 + β _R_ 2 is the poverty rate in control localities in round 2 and

> β 0 + β _T_ + β _R_ 2 +γ is the poverty rate in treatment localities in the same round.

28
# **TABLE OF** CONTENTS

|SUM|MARY||1|
|---|---|---|---|
|1.|Struct|ure of Labor Markets in Ghana|2-5|
||1.1<br>|Introduction<br>|2|
||1.2|Multiple Occupations|2-4|
||1.3|The Impact of Government Policies on the Labor Market|4-5|
|2.|Econo<br>5-7|mic Policies and Basic Trends in the Economy||
||2.1|Pre-1983|5-6|
||2.2|The Economic Recovery Program (ERP) and After|6-7|
||2.3|Recent Shocks|7|
|3.|The F|ormal Sector|8-19|
||3.1|The Pre-ERP Period|8-9|
|||3.1.1 Employment|8-9|
|||3.1.2 Wages|9|
||3.2|Post-ERP Developments|10|
|||3.2.1 The Public Sector|10|
|||3.2.2 The Private Sector|10|
||3.3|Evaluation|11-19|
|||3.3.1 Is the Size of Employment in the Public Sector too Large?|11-12|
|||3.3.2 Are Public Sector Wages too High?|12-18|
|||3.3.3 The Distribution of Earnings|18-19|
|4.|Firm|Size and Wage Levels in Ghana's Manufacturing Sector|20-28|
||4.1|Analysis by Teal|20|
||4.2|Analysis by Mazumdar|21-23|
||4.3|Causes of the Increase in Earnings with Firm Size|23-24|
||4.4|Inelastic Supply of Labor|24-25|
||4.5|Efficiency Wage|25-26|
||4.6|Increasing Returns to Scale and Profit Sharing|26-27|