rafmacalaba/datause-encoder-data huggingface dataset:
forgotten issues include urban employment and labor markets, urban transport and the future of small and medium‐size cities.

A plethora of new instruments are available today to deal with old and new issues, including comprehensive slum upgrading schemes, land tenure, titling and regulatory reform, micro‐credit finance, private sector engagement, community driven development, multi‐stakeholder alliances, conditional cash transfer programs and other incentives based approaches, and climate change funding. There also remain a number of instruments available that were already in play 30 years ago, such as slum upgrading and sites and service projects, capital subsidies for the urban poor, special assessment and land‐value based revenue tool, sub‐ national borrowing and urban/municipal development funds.

In addition a whole ream of new analytical and planning tools are now available: happiness and life satisfaction analysis, urban poverty assessments, asset‐based policy analysis and longitudinal studies, GIS mapping, impact evaluation, metrics of the investment climates, ICT in public administration and various national, urban and city planning tools. Many of these analytical tools are the result of four relatively recent developments in analytical methods which have been developed to underpin evidence‐based policy making: “development of survey techniques, including large‐ scale, even world‐wide surveys; development and management of comprehensive data bases; development and application of advanced statistical and econometric methods; and ease of access by analysts to much enhanced computing capacities.” (Linn, 2010, p. 15) In addition, data and benchmarks have become more important in urban policy making. This includes a systematic focus on socio‐economic and demographic information, measurement of poverty according to a broad spectrum of dimensions, tracking of the achievement of MDG targets, and measuring urban land use, prices and regulatory interventions.

A number of new institutional issues are now the center of attention: decentralization of responsibility to sub‐national levels of government, coordination of ministerial competencies in country, and effectiveness of aid to urban areas. New institutions have appeared on the scene focusing on urbanization in developing countries at the international level (U.N.‐Habitat, the Cities Alliance), and manifold civil society organizations (CSOs) have sprung up involved in supporting especially the poor communities in cities.

Finally, the framework allows for a consideration of the geographic dimension in which urbanization takes place. Thirty years ago, urbanization in Latin America was already relatively far advanced and much of the urban policy debate focused on the experience of that continent. Since then Africa, China and India have moved into the forefront of the debate, along with a focus on fragile and conflict‐ridden states; moreover, selected cities (among them Bogota, Mumbai, and others), have been subjected to much study and analysis.

7
# **4. Data**

We use the comprehensive data set available at PovCalNet from the World Bank as of January 2013.<sup>11</sup> The unit of observation is country-year. This data set contains information on poverty measures, inequality (as measured by the Gini coefficient), and mean income for over 100 countries. Poverty and inequality measures are based on comparable household surveys and mostly based on consumption rather than income. We include in our analysis countries that have data available for at least two surveys. The data spans from 1980 to 2011.<sup>12</sup> The mean income measure refers to the average monthly income in 2005 prices and at Purchasing Power Parity (PPP) following World Bank (2008). Given data availability, our data set is an unbalanced panel with “spells” (i.e. time gap between two observations for the same country) ranging from one to 15 years. The growth rate of country _i_ between time period _t_ and time period _t - d_ for measure _x_ is calculated as _gr(xit) = ln (xit / xit-d )_ . While we focus on one measure of poverty, the headcount index (H), we will estimate equation (7) for three poverty lines as they help answer different questions. One analysis is performed using the updated $1 a day poverty line, that is, a $1.25 a day at 2005 PPP. A second set of results are obtained for the $2 a day poverty line. This line is a better representation of poverty standards in developing and transition countries.<sup>13</sup> Finally, we present results for headcount rates for a $4 a day poverty line, closer to moderate poverty lines in middle-income countries. Summary statistics for the spells and for all regions included in the regressions are presented in Table 1.

As noted in Ravallion (2012), we are cautious about the inclusion of countries in the Eastern Europe and Central Asia (EECA) region. These countries “started their transitions from socialist command economies to market economies with very low poverty rates, but poverty measures then rose sharply in the transition.” Their experience is clearly not typical of the developing world. We take a conservative approach and present results both including and excluding countries from this region.

> 11 <u>http://iresearch.worldbank.org/PovcalNet/index.htm</u>

> 12 Appendix table A1 presents the distribution of observations by region and data availability. In our analysis, we include all countries with mean income, headcount rate and Gini coefficient data available. The total number of countries available is 102. Our preferred measure of welfare is consumption. Thus, if a country had headcount rates data available for both income and consumption we keep only the consumption-based measure. These countries include Mexico, Jamaica, Nicaragua, and Peru. The dataset is available from the authors upon request.

> 13 This line is the median poverty line found in Ravallion _et al._ (2009).

11
34 rate in excess of 3.5% per year in spite of the attrition in firm numbers. In retailing, the corresponding figures are over 60% attrition through liquidation, and growth by a factor of only about 1.6, so there was net job loss. Since retailing entrants were substantially more numerous and slightly larger at birth, the employment in the cohort declined in the two sectors combined at a rate of 3.7% per year.

Similar patterns have been found in a variety of data sets. Dunne, Roberts and Samuelson (1988) reported an extensive analysis of entry and exit into 387 manufacturing industries, using data from the Census of Manufactures. Here, "entry" and "exit" refer not to the birth or death (liquidation) of firms, but to their appearance and disappearance from an industry."<sup>0</sup> Averaging rates across 387 four-digit SIC industries they found, for example, exit rates over the five year inter-census interval to be over 50% regardless of the type of entry (new firm or diversifying); over ten year intervals the cumulative rates are all above 65%. Persistent industry effects are prominent in virtually all measures considered; for example, across 20 twodigit industries the average market share of entrants correlates with the average market share of exiters at the level .92. Across the 387 industries, intertemporal correlations of exit rates are substantial even over long time intervals; for example, the exit rate over 1977-82 correlates with that over

**10** Also, ownership changes do not produce entries or exits under the definitions used by Dunne, Roberts and Samuelson.
**Figure 3: Education requirements of the SDS’s 30 occupations by skill level** _Share of workers reporting the level of education is required (%)_

<!-- Start of picture text --> Elementary occupations<br>Plant and machine operators and assemblers<br>Craft and related trades workers<br>Service and sales workers<br>Technicians and associate professionals<br>Professionals<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%<br>Less than secondary Secondary Certificate I-II VT Certificate III-IV VT<br>College Bachelors Masters<br>Low<br>Mid<br>High<br><!-- End of picture text --> Note: Certificate 1-II VT and Certificate III-IV VT refer to secondary-level vocational training. College refers to tertiary-level vocational training. Source: SDS.

**<mark>Field of study</mark>** <mark>. Engineering, manufacturing, and construction is a cross-cutting field of study important at all skills levels: a third of occupations covered by the SDS require this field (</mark> **<mark>Figure 4</mark>** <mark>). These are mostly engineers (professionals), electrical technicians, and technical craft and related workers (for example, assemblers, repairers, and fitters). In general, field of study requirements depend on the specific field of specialization of the occupation. For example, health and welfare are important to nursing associate professionals and elementary occupations such as freight handlers and construction laborers. However, 20 percent of occupations do not require a field of study. These are mostly less-skilled occupations such as elementary occupations, drivers, and sales workers.</mark>

**Figure 4: Field of study requirements of the SDS’s 30 occupations**

_Share of occupations with at least half of workers reporting the field of study is required (%)_ <!-- Start of picture text --> None<br>Services<br>Health and welfare<br>Agriculture and veterinary<br>Engineering, manufacturing, and construction<br>Natural science and environmental knowledge<br>Science, mathematics, and computing<br>Social sciences, business, and law<br>Humanities and arts<br>Education<br>0% 5% 10% 15% 20% 25% 30% 35%<br>Source: SDS.<br><!-- End of picture text --> 12
# **Investing Back Home: Return Migration and Business Ownership in Albania**<sup>**1**</sup>

Talip Kilic, American University Gero Carletto, World Bank Benjamin Davis, FAO Alberto Zezza, FAO JEL Codes: C25, J23, 015 Keywords: Albania, International Migration, Return Migration, Non-farm Business Ownership, Instrumental Variables Probit.

> 1 We are grateful to Alan de Brauw, Thomas Hertz, Mieke Meurs, Martin Ravallion, and Paul Winters for helpful discussions or comments. We also thank the participants of the workshop “Migration, Transfers, and Household Economic Decision Making” organized by the Food and Agriculture Organization of the United Nations in Rome, Italy. We are also grateful to the Albanian Institute of Statistics (INSTAT) for granting access to the data. We dedicate this work to the memory of Dini Llagami of INSTAT. The usual disclaimer applies: the views expressed are the sole responsibility of the authors and not of the institutions they represent.
Policy Research Working Paper 10667

# **Abstract**

Despite the efforts made to increase women’s inclusion in the economy, they are still underrepresented in trade in general and in global value chains in particular. Thus, this paper aims at examining the impact of global value chains on women’s trade participation as entrepreneurs and employees. It also analyzes how this effect is moderated through external (gender provisions in trade agreements) and internal (investment climate variables) factors. The analysis uses firm-level data for 154 developing economies and emerging markets with a special focus on the Middle East and North Africa region, being one of the regions with the lowest female labor force participation. The main findings show that global value chains integration increases the likelihood of being a female owner and the share of female employees, especially production ones. A less robust negative effect is found for the impact on being a female top manager. These effects are moderated by the inclusion of gender provisions in trade agreements and by the characteristics of the investment climate (especially tax policy, access to finance, and corruption). These results remain robust after controlling for the endogeneity of global value chains using an instrumental variable approach and a propensity score estimation method where the treatment is being part of a global value chains. Thus, global value chains can be perceived as a tool that boosts women’s empowerment in emerging economies, especially in the Middle East and North Africa region.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 chahir.zaki@feps.edu.eg.

_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
# Table 1: Measures of liquidity needs for 4 digit ISIC industries

Different measures of liquidity needs are reported in columns (1) to (4). Columns (1) and (2) (I/S and I/A) show the median of total inventories (Compustat #3 )over annual sales (Compustat #12) and over total assets (Compustat #6) across U.S. firms in each sector. Column (3) (CCC) is the cash conversion cycle, in hundreds of days, which is defined as: (inventories*365/(cost of goods sold))+(account receivables*365)/(total sales)-(account payables*365)/(cost of goods sold). The values in the table are the median of this measure across US firms in each sector. Finally, column (4) (wL/S) shows the median of average yearly short term debt (Compustat #104) over annual sales (Compustat #12). The universe of firms was all the manufacturing firms included in Compustat during the period 1980-1989. The SIC code, used to determine the industry in which a firm operated, was obtained from CRSP. The correspondence between SIC and ISIC classification systems was made by the author.

|||Mea|sures o|f liquidi|ty needs|
|---|---|---|---|---|---|
|ISIC|DESCRIPTION|I/S|I/A|CCC|wL/S|
|3112|Dairy products|0.05|0.15|0.20|0.06|
|3530|Petroleum refneries|0.06|0.08|0.19|0.02|
|3134|Soft drinks|0.06|0.10|0.41|0.12|
|3117|Bakery products|0.06|0.12|0.44|0.16|
|3133|Malt liquors and malt|0.07|0.12|0.27|0.09|
|3111|Slaughtering and preserv. meat|0.08|0.23|0.43|0.10|
|3420|Printing, publishing and allied|0.08|0.12|0.78|0.28|
|3411|Pulp, paper and paperboard|0.11|0.12|0.68|0.16|
|3121|Food products n.e.c|0.11|0.17|0.55|0.09|
|3116|Grain mill products|0.11|0.19|0.54|0.07|
|3513|Synthetic resins and plastic|0.11|0.19|0.73|.|
|3843|Motor vehicles|0.11|0.22|0.56|0.18|
|3699|Non-metallic mineral prod. n.e.c.|0.12|0.12|0.84|0.19|
|3419|Pulp, paper and paperboard n.e.c|0.13|0.21|0.83|0.17|
|3551|Tyre and tube industries|0.13|0.20|0.88|0.20|
|3529|Other chemical products|0.13|0.17|0.96|0.13|
|3412|Boxes of paper and paperboard|0.13|0.22|0.74|0.17|
|3119|Chocolate and sugar confect.|0.14|0.23|0.82|0.10|
|3311|Sawmills and other wood mills|0.14|0.22|0.78|0.19|
|3512|Fertilizers and pesticides|0.14|0.18|0.96|0.10|
|3560|Plastic products n.e.c|0.14|0.19|0.84|0.18|
|3511|Basic industrial chemicals|0.14|0.13|0.96|0.09|
|3115|Vegetable and animal oils|0.14|0.26|0.82|0.05|
|3559|Rubber products n.e.c.|0.15|0.20|0.98|0.25|
|3720|Non-ferrous metal basic ind.|0.15|0.21|0.89|0.17|

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are strongly concentrated (in terms of employment) in Yaoundé and Douala and, more generally, along the region surrounding these economic and political capital cities, respectively. These two cities which represent less than 0.2 percent of Cameroon’s total surface account for more than 60 percent of employment. One of the key reasons for that strong overall clustering is plausibly the uneven distribution of the population arising from colonial settlement patterns. Geographical variations in industrialization are the primary factor affecting the spatial variations in incomes. We use the 2009 General Enterprise Census data to get a more precise picture of the extent and the strength of industrial localization of industries as well as the specialization of cities in Cameroon.

Table 1: Sectoral distribution of formal firms, informal firms and jobs in Cameroon

||**Establi**|**shments**|**Jo**|**bs**|
|---|---|---|---|---|
|**Industries**|**Freq.**|**Percent**|**Freq.**|**Percent**|
|Agriculture|145|0.16|26,530|6.87|
|Breeding|86|0.10|549|0.14|
|Forestry|47|0.05|6,787|1.76|
|Fishingand fsh farming|6|0.01|39|0.01|
|**Sub-total Primary**|**284**|**0.32**|**33,905**|**8.78**|
|Mining|25|0.03|1,162|0.30|
|Food industry|695|0.79|19,447|5.03|
|Other Manufacturing|10,227|11.60|49,993|12.94|
|Electricity water and gas|74|0.08|8,530|2.21|
|Construction|664|0.75|8,757|2.27|
|**Sub-total Secondary**|**11,685**|**13.26**|**87,889**|**22.75**|
|Commerce|46,788|53.08|105,458|27.30|
|Transport|518|0.59|15,041|3.89|
|Bank and insurance|596|0.68|12,584|3.26|
|Other tertiary|26,963|30.59|128,844|33.36|
|**Sub-total Tertiary**|**74,865**|**84.93**|**261,927**|**67.81**|
|Unknown|1,310|1.49|2542|0.66|
|**Total**|**88,144**|**100.00**|**386,263**|**100.0**|

# **2.1.2 Cameroon National Surveys on Employment and Informal Sector**

We use data from the Cameroon National Surveys on Employment and Informal Sector (eesi) to empirically investigate the characteristics of the Cameroon informal sector and its most salient features. The eesi surveys were conducted in 2005 and 2010 by the 6
readily available from the UN and other agencies and which, for African countries, rely heavily on imputations and interpolations.

We collected urban and rural population measures for sub-national regions (provinces and districts) from census reports. We include countries with at least two available censuses with the relevant information for a complete or nearly complete set of sub-national units, where either district boundaries changed little or common units over time can be defined. The data were extracted mostly from hardcopy census publications obtained from the U.S. Census Bureau library, the U.S. Library of Congress, the LSE library, and the British Library. The collected sample covers 32 countries but Namibia and Congo are dropped because of data problems with urban or district definitions.<sup>16</sup> We limit the panel sample to intercensal periods ( _L_ ) of less than 20 years, so Liberia is included in long difference specifications only, because its two available censuses were 34 years apart. We have information from 2 to 5 censuses between 1960 and 2010 for each remaining country (Figure 3 and the Data Appendix). Kenya is effectively treated as two countries, before and after rapid redistricting and urban redefinition of the 1990s. Each country is divided into a number of sub-national units we call districts. The 369 districts used in panel estimation are outlined in Figure 3.

The most notable omission is Nigeria, Africa’s most populous country, because of concerns over the quality of census figures (see, e.g., Okafor, Adeleke and Oparac 2007). Other Sub-Saharan African countries are missing because either they had no censuses with needed information or in a few cases because we were unable to obtain the printed volumes. Finally, we do not include South Africa because it is more developed, province maps were redrawn post-Apartheid, and pre-Apartheid migration restrictions make it a special case.

With few exceptions, most studies of climate impacts on agriculture focus exclusively on precipitation. However, moisture available for plant growth is also a function of evapotranspiration. Thus, dividing precipitation by potential evapotranspiration (PET), which is a non-linear function of temperature, increasing in the relevant range, is viewed as a better measure of climatic agricultural potential. Although this measure is often called an aridity index and used to define aridity zones (UNEP 1992), we call it a moisture availability index, because larger values indicate relatively greater water availability, with values above one indicating more moisture than would be evaporated given prevailing temperature. Precipitation and temperature data are from the University of Delaware gridded climate data set (Willmott and Matsuura 2012). We estimated monthly PET from 1950 to 2010 using the

> 16 For Namibia, the problem is changing district boundaries and urban definitions. For Congo most districts were originally drawn to be either wholly urban or wholly rural, making within-district analysis impossible.

13
# **5.6 Investment Performance**

Asset allocation changed significantly when the new strategy decided in fiscal 2005 was put under implementation (Table 5.3). Investments in global equities, especially in larger cap equities, declined rapidly from 51 percent in 2004 to 40 percent in 2006. The longterm target of the current strategy is to lower this further to 34.5 percent. The allocation to emerging markets has been reduced in the long-term strategy despite the recent strong performance of emerging markets and the promise of higher returns in the future as developing countries strengthen the regulation of their markets and seek to maintain financial stability. However, for the time being the share of emerging market equities has remained stable at 3.6 percent of total assets.

A major expansion of investments in alternative assets from 3 to 20 percent of the total portfolio took place in fiscal 2006. This is expected to rise further in fiscal 2007 in implementation of the new target of 35 percent for this asset class.

Table <u>5.3: Asset Allocation, 2004-2006</u>

|percent of total assets|2004|2005|2006|
|---|---|---|---|
|**New Zealand Equities**|**9.9**|**8.4**|**7.6**|
|Large Cap Global Equities|50.7|43.2|40.2|
|Small Cap Global Equities|11.2|9.3|8.6|
|Emerging Market Equities|2.0|3.6|3.6|
|**Total Global Equities**|**63.9**|**56.2**|**52.4**|
|**Total Equities**|**73.8**|**64.6**|**60.0**|
|New Zealand Fixed Interest|12.1|16.0|14.4|
|Global Fixed Interest|14.1|16.1|5.7|
|**Total Fixed Interest**|**26.2**|**32.1**|**20.1**|
|Property||2.9|7.2|
|Timber|||3.6|
|Infrastructure|||3.8|
|Other Alternative Assets||0.4|5.2|
|**Total Alternative Assets**||**3.3**|**19.8**|

Source: NZSF Investments in timber (forestry) and infrastructure grew rapidly in 2006. The first involves the NZSF in a principal capacity.<sup>26</sup> The 2006 annual financial statements report major timber operating expenses, amortization of goodwill, and depletion of forestry

> 26 McCulloch and Frances (2004:185) note the NZSF is intended to be a portfolio of financial investments, not an operator of businesses. They also state that section 59 of the NZSF Act precludes it from taking a controlling interest in other entities. It is not clear if the timber operations would classify as a business operation. It is also not clear under what conditions would an expanded involvement in principal investing infringe the spirit of section 59.

59