rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix 1. Variable Definitions

Table A1.1. Variable definitions

|**Variable**|**Definition**|
|---|---|
|Age of PA|Age of theprincipal applicant(PA)|
|Children under 5|1 if there are one or more children below the age of 5 (inclusive) in the<br>household|
|Disable|1 if there are one or more disabledpersons in the household|
|Education|Categorical variable. We classified education of the PA in three groups: below<br>years,6-11years,and more than 12years of education|
|Elderly|1 if there is one or more persons above the age of 65 (inclusive) in the<br>household|
|Entry status|Categorical variable. ProGres reports 5 entry statuses of which we selected th<br>three categories with the largest number of PAs: Informal, formal, and<br>smuggled.|
|Expenditure|Raw addition of all expenditure categories, which include rent, bills, food,<br>healthcare, education, and others.|
|FamilyType|Categorical variable. For a detailed definition see section 4|
|Household size|Number of people included in the case records of each PA in Individual<br>ProGress dataset|
|Wage Income|1 if the household receives income from employment and/or daily or irregular<br>work|
|Income from remittances|1 if the household receives income from remittances|
|Income per capita|Raw sum of household income from all sources; work, pension, assets in Syria<br>transfers, donations, other organizations' humanitarian aid, and other divided<br>by household size|
|Male Adults|<br>Number of males above 18(inclusive)in the household|
|Marital Status|Categorical variable. The classification includes married PAs with spouse in th<br>household, married PAs without spouse in the household, widowed, single or<br>engaged, and divorced or separated.|
|Proportion of female|Number of female divided bythe household size|
|Location|Categorical variable for 11 Governorates/cities. Ajloun City, Aqaba, Balqa, Irbi<br>Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa.|
|In Camp|<br>1 if the household is located in a refugee camp|
|Poverty before UNHCR and WFP assistance|1 if household expenditure before UNHCR plus WFP assistance is below the<br>povertyline(JD50)|
|Poverty before UNHCR assistance|1 if household expenditure after WFP assistance but before UNHCR assistanc<br>is below thepovertyline(JD50)|

_Source:_ Authors’ elaboration.

32
_Sovereign debt:_ There is the admission that debt is unsustainable—that there is a solvency problem. The country may openly default or begin to seek a debt restructuring. In either case, this point marks the start of the default spell. The eternal divide between the debtors, who want haircuts, and the creditors, who want repayment, ensues. Complicating matters, the awareness of hidden debt problems (as in Box 1) starts to emerge.

Default spells often include “debt with drama” and “debt without drama.” The drama is usually reserved for the outset, as the crisis erupts. But debt crises have lingered without drama for years.<sup>27</sup> Countries shut out of capital markets often face all kinds of derivative problems (lack of domestic credit, buildup of arrears, sluggish growth, reliance on inflationary finance, etc.). Debt without drama may attract little attention if the country(ies) affected are not “systemic,” in terms of impacting the working of global capital markets. Figure 4, from Graf von Luckner, Meyer, Reinhart, and Trebesch (2021), presents the marginal and cumulative frequency distributions of the duration (in years) of 279 external default spells (excluding repudiation cases) in 113 countries over 1800-2020. The average default spell for the full sample lasted ten years, while the median is seven years. These descriptive statistics highlight the slow-moving nature of debt crisis resolution, an issue I will address in Section IV. A significant share of the longest multidecade episodes involved wars or internal civil conflict and predate the 21st century. As shown, since the end of World War II, default spells have become shorter, lasting on average 7.9 years, with a median duration of 5 years.

> 27 See Reinhart and Rogoff (2009).

24
Policy Research Working Paper 9534

# **Abstract**

A forward-looking measure of “vulnerability to poverty” is estimated and a concerted effort is made to understand the sources of vulnerability in the drought-prone lowlands of Ethiopia. Using the Household Consumption Expenditure Survey and the Welfare Monitoring Survey of 2016, the analysis reveals that vulnerability is remarkably higher in the drought-prone lowlands than in the other ecological zones, although differences in poverty rates are modest; the vulnerability rate is more than two times larger than the poverty rate in the lowlands, while the ratio is only 1.6 for the whole country. The analysis also reveals important distinctions in the sources of vulnerability. In the drought-prone lowlands: (i) vulnerability due to aggregate shocks such as droughts is lower than vulnerability due to idiosyncratic shocks in absolute terms, but its relative importance is higher compared with other ecological zones; and (ii) poverty-induced vulnerability is relatively more important than risk-induced vulnerability in contrast to other regions where risk-induced vulnerability is higher than poverty-induced vulnerability. These findings attest to the unique nature of the droughtprone lowlands compared with the other agroecological zones and points in favor of policies and programs tailored specifically to the areas.

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 eskoufias@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
# **Box 1: Applying the GIFF – Comparative Value Chain Analysis**

A forthcoming report by the World Bank on Light Manufacturing in Africa implements an innovative form of value chain analysis to both determine the competitiveness of a sector as well as assist governments and the private sector in identifying the constraints which most impact the cost competitiveness of domestically produced products on the global market.

In the usual value-chain analysis, the advantages, bottlenecks and policy issues would be analyzed within the country of study with some comparison between sectors within the economy. In the comparative approach, however, China and Vietnam are being chosen as benchmark countries in order to compare the cost competitiveness of African production of particular products, chosen to be as like-for-like as possible.

After applying the GIFF to arrive at several sub-sectors which could potentially be successfully produced in SSA (Ethiopia, Tanzania and Zambia were the sample countries), in-depth value chain analyses were conducted for particular products in each of those sub-sectors in order to gain a representative view of the competitiveness and constraints of the sub-sector. The analysis included a quantitative breakdown of the proportion and cost of inputs, efficiency input use, logistic costs, labor productivity, production wastage and efficiency etc. This data was gathered from a reasonable sample of firms in all five countries producing similar products in each of the five identified sub-sectors. Each component which impacts the cost and competitiveness of the firms was compared between China, Vietnam and SSA. The results were conclusive in identifying the cost elements which vary significantly between East Asia and SSA, thereby identifying the priority areas for intervention. The results also screened out those sectors where the country does not have comparative advantage by calculating the domestic resource costs.

For Nigeria, value chain analyses have been conducted in recent years for several key sectors which have been valuable in highlighting the constraints and opportunities in the sectors studied. However, the new approach proposed in this paper is to use the GIF framework to identify sectors where Nigeria may have some comparative advantage, latent or revealed. A comparative value chain analysis could then be undertaken in those identified sectors which will provide rigorous evidence and support for a prioritized program by government and private sector to overcome key constraints in targeted sectors. For example, the comparative value chain provides some more conclusive evidence on the wage difference in specific sectors as well as the difference in labor productivity in those sectors. That way, conclusions can be drawn on both the poverty-reducing employment effect of expansion in a sector, as well as the labor cost advantage (or disadvantage) which can be a crucial aspect in determining competitiveness of a sector.

16
Kolodny, Lora (2009) “How to win a business plan competition”, _New York Times_ June 10, <u>http://www.nytimes.com/2009/06/11/business/smallbusiness/11competition.html</u> [accessed March 18, 2014].

Lawless, Martina (2014) “Age or size? Contributions to job growth”, _Small Business Economics_ 42: 815-830.

Lee, David (2009) “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects", _Review of Economic Studies_ 76(3): 1071-1102.

McKenzie, David (2012) “Beyond Baseline and Follow-up: The Case for More T in Experiments”, _Journal of Development Economics_ 99(2): 210-21.

McKenzie, David and Christopher Woodruff (2008) “Experimental Evidence on Returns to Capital and Access to Finance in Mexico”, _World Bank Economic Review_ 22(3): 457-82.

McKenzie, David and Christopher Woodruff (2014) “What are we learning from business training evaluations around the developing world?”, _World Bank Research Observer_ , 29(1): 48-82.

McKenzie, David and Christopher Woodruff (2015) “Business Practices in Small Firms in Developing Countries’, Mimeo. World Bank.

Morris, Rhett (2011) _2011 High-Impact Entrepreneurship Global Report_ . Global Entrepreneurship Monitor, <u>http://www.gemconsortium.org/docs/download/295</u> OECD (2010), _High-Growth Enterprises: What Governments Can Do to Make a Difference_ , OECD Studies on SMEs and Entrepreneurship, OECD Publishing. _<u>http://dx.doi.org/10.1787/9789264048782-en</u>_ Omidyar Network (2013) _Accelerating Entrepreneurship in Africa: Understanding Africa’s Challenges to Creating Opportunity-driven Entrepreneurship._ http://ventureburn.com/wpcontent/uploads/2013/04/Accelerating_Entrepreneurship_in_Africa_source_Ventureburn.pdf Schoar, Antoinette (2010) “The Divide between Subsistence and Transformational Entrepreneurship”, pp. 57-81 in Joshua Lerner and Scott Stern (Eds.), NBER Innovation Policy and the Economy Volume 10, NBER, Boston, MA.

34
From the complete stock of electrotechnical standards, we extracted those standards directly related to electronic goods from 1990 to 2006. In an attempt to consider groups of homogenous goods where standards entail similar technical requirements among products, we distinguish between three groups of electronics: i) Electronic components; ii) consumer electronics and telecom; and iii) Information technology products. Lall et. al (2004) use a similar categorization to study industry fragmentation in electronic products. Table 3 reports the SITC categories within each group.

# Table 4 here

# Figure 1 here

Table 4 and figure 1 show the evolution of total number of product standards in each category. In average, IT products accounts for 11 percent of the overall number of standards. The main standardization activity has taken place in categories 1 and 2, in average they account for 36 percent and 53 percent of the total number of standards, respectively.

# Table 5 here

# Figure 2 here

The share of internationally harmonized standards (IEC standard) has increased in the sample period for electronic products as shown in table 5 and figure 2. This pattern differs among subsectors. In 1990, categories 1 and 2 began with a high share of international harmonization (almost 90 percent), partly due to the small number of standards in those categories. While the EU standardization process increased over time, the share of internationally harmonized standards temporarily decreased reaching its lowest level in 1993 for category 1 (36 percent) and in 1996 for category 2 (69.2 percent). From that point on, the share of international harmonization has remained constant for category 2 whereas it has increased in category 1 to reach 70 percent in 2006. Category 3 has a lower initial value (30 percent) in 1990, it reached it lowest point in 1992 (20 percent) to begin increasing until reach a level of 23 percent in 2006 (figure 3).

# Figure 3 here

It is worth mentioning that the percentage of international harmonization tends to converge on the 70% to 80% range for the stock of standards of each sub-sector. This pattern may be the result of the implementation of the Dresden Agreement in 1996, which lays down a set of parameters to expedite the collaboration between IEC and CENELEC in the preparation and publication of international standards.

# **4.  Model and Estimation Results**

In this section, we provide the basic intuition for the empirical question we examine in this research. Next, we present an empirical model and discuss the econometric strategy as well as our estimates. Since we attempt to make the basic mechanisms underlying the model as clear as possible, we keep the theoretical presentation highly stylized.

10
observations, and qualitatively we obtain the same results. In column 7, the analysis is restricted to the sample for which we have mortality data to show that results when using European settlement in 1900 are similar to when using mortality data. In column 8 we use all observations. The first stage relationship between institutions, colonial origin and European settlement in 1900 is stronger, and the F test for excluded instruments is larger than when using mortality data (F=12.19). In all specifications, the results of the civil war regression show the negative and significant effect of protection against expropriation risk on the probability of civil war.

In columns 9 to 11 we perform the same analysis but include per capita income and population. The results of civil war regression show that protection against expropriation risk affects the probability of civil war negatively and significantly. Improving the institutional framework of a country from the value of the median to the percentile 75, the probability of civil war is reduced by 38 percentage points. When we include the institutional variable in the regression, per capita income becomes insignificant in all specifications. As before, first stage results show that colonial origin matters in explaining institutional development. In this specification, the log of European settler mortality is not significant. However, in columns 10 and 11 we substitute the log of European settler mortality with the percentage of Europeans or European descent in 1900. In this case settlement is not significant either when we restrict the analysis to the sample for which there are mortality data (column 10). However in column 11, when we use all the data available, European settlement in 1900 has a significant and positive effect in explaining institutional development.

Notice that while per capita income does not have any direct effect on civil wars, it has an indirect effect through worsening institutions, when we use the reduced sample in columns 9 and 10. However, once we include European settlement in 1900 with all the data available (column 11), 12
**Internet Job Search, Employment, and Wage Growth: Evidence from the Arab Republic of Egypt**

# **Nelly El-Mallakh**<sup>**1**</sup>

Keywords: Internet, job search, unemployment, wages, Egypt. JEL codes: J31, J64, L86.

> 1 Paris School of Economics. An earlier version of this paper was commissioned by the World Bank Chief Economist Office of the MENA region for its flagship report on the new digital economy. The author wishes to thank all the participants of the 2019 World Bank’s workshop on the digital economy in MENA, and in particular Daniel Lederman, Federica Saliola, Ana Paula Cusolito, and Nista Sinha, for their valuable feedback.
Galor, O. and D. N. Weil. (1996), ‘The Gender Gap, Fertility and Growth,’ _American Economic Review_ , **86** , 374-387.

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Krueger, A. B. and L. H. Summers. (1988), ‘Efficiency Wages and the Inter-Industry Wage Structure,’ _Econometrica_ , **56** , 2, 259-94.

Lemieux T., B. W. MacLeod and D. Parent. (2009), ‘Performance Pay and Wage Inequality,’ _The Quarterly Journal of Economics_ , **<mark>124</mark>** <mark>, 1, 1-49.</mark> Lipsey, R. E. (2004), ‘Home and Host Country Effects of Foreign Direct Investment,’ in R. E. Baldwin and L. A. Winters (eds.), _Challenges to Globalization: Analyzing the Economics_ , 333379. Chicago and London: University of Chicago Press.

Lipsey, R. E. and F. Sjöholm. (2004), ‘Foreign Direct Investment, Education, and Wages in Indonesian Manufacturing,’ _Journal of Development Economics_ , **73** , 1, 415-422.

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26
very close to that of total expenditure. Expenditures on electricity and nonfood items are more concentrated in high income households.

Figure 2.2. Guinea 1994: Concentration Curves of Cereals and Beverages with respect to Total Expenditure <!-- Start of picture text -->
-<br>1.0<br>-<br>0.9<br>0.8-<br>0.7-<br>0.6-<br>0.5-<br>0.4  Tta<br>0.3 - Cereals<br>Beverages<br>0.2-<br>0.1<br>-<br>0.0<br>0.0  0.1  0.2  0.3  0.4 0.5  0.6  0.7  0.8  0.9  1.0<br><!-- End of picture text -->

# _3. Truncation by a Poverty Line_

The standard approach to poverty analysis is to partition the population exhaustively into two mutually exclusive groups on the basis of a poverty line, and to assign a weight equal to zero to the wellbeing of anybody whose living standard is above the poverty line. Such a partition raises at least two interesting issues. The first one relates to the decomposability of the Gini family of indices, and the second to the interpretation of some well known poverty indices within the framework defined by (2.1). Before considering these issues, we first examine some implications for the aggregate distribution of the social weights defined by (2.4).

16
- 43 -

# Tablo_Z: Share of Nonfarm Earnings in Total Rural Income in Sub-Saharan Africa (percent)

|Country<br>A. Total Rural|Agricultural<br>Income<br>Income|Nonfarm<br>Income|
|---|---|---|
|Botswana (1974-75)|56-75|24-44|
|Ghana, Akwapim (1965-66)|58-72|14-28|
|Ghana, Eastern Region (1967-68)|58-82|18-42|
|Ghana, Heads of Household in E. Region|25-50|50-75|
|Ghana, Five Villages|70-84|16-30|
|Ivory Coast, South East Region (1963)|71|29|
|Kenya (1969)|69|31|
|Kenya (1976)|72|28|
|<br>Lesotho (1977-78)|23|77|
|Northern Nigeria, Kano Region|72|28|
|<br>Northern Nigeria, Zaria Region|78|22|
|Sierra Leone (1974)|64|36|
|Sierra Leone (1975-76)|81|19|
|Tanzania (1969)|79|21|
|Tanzania (1975)|77|23|
|B. Rural Cash<br>Ghana, Akwapim (1965-66)|Income<br>55-78|22-45|
|<br>Ghana, Cocoa Farmers (1968-70)|77|23|
|Ghana, Eastern Region (1967-68)|45-68|32-55|
|<br>Ghana, Heads of Household in E. Region|17-45|55-83|
|<br>Ivory Coast, Bouake Region (1970)|58-76|24-42|
|<br>Liberia, Western Region (1973)|69|31|
|<br>Tanzania (1969)|64|36|
|Tanzania (1975)|57|43|
|Zambia, Luapuala Province (1977)|79|21|
|Zambia, Three Provinces (1982)|76|24|

A/ Remittances account for 2/3 of rural income. Domestic nonfarm income is 12 percent of total, or 34 percent of domestic rural value added.

Source: Dotawna: Chernihovsky ot *l. (1985) p.51 _Mxans: ILW_ (192f), pp. 51, 53, **54,** 57; **_IW_** (1085.) Ivory Coast: Ivory Coast (1067) **_Kenya:_** 1W (1982s) Lesotbo: National Accounts, cited In IW (198Sb) p.15 **Liberia:** Ministry of **Planing** and Zoomoic **Affaira** (Liberia). cited **In** IL (l9821) p.58 **Nigeria, Kmno** Region: Matlon (1070); Zarl **Region: Norxma, Pryor nd Oibbs (1079) 8ierrs Leone:** Chuta and Liadhola (1979) **Tanzania:** IL (1982a) Uganda: **Nassel and** Parnes **(1969)** p.<sup>3</sup> 15 Zmbia, **Luapaula:** Mlabo **(Z_bia),** cited **In** **_IW_** _(19C2S)_ Zmbil, **Three** Provinces: **Due nd Nindunda** (1985)
Enterprise Recovery following Natural Disasters Suresh de Mel, David McKenzie and Christopher Woodruff<sup>*</sup> _JEL Classification Codes:_ O12, Q54, C93.

_Keywords_ : Microeconomic Impact of Aid, Enterprise Recovery Following Natural Disasters.

> * University of Peradeniya, World Bank, and University of Warwick, respectively. The authors thank Susantha Kumara and Jayantha Wickramasiri and Michael Callen for outstanding research assistance and Kathleen Beegle, Saroj Jha and Apurva Sanghi for comments. AC Nielsen Lanka administered the surveys on which the data are based. Financial support from NSF grant # SES-0523167, and the World Bank GFDRR and Norway Governance Trust Fund is gratefully acknowledged.