rafmacalaba/datause-encoder-data huggingface dataset:
This measure ranges from –1 to +1. Positive values indicate that the importer reports a larger trade flow than the exporter, and negative values indicate the reverse. The index has the maximum value of +1 when the importer reports a trade flow but the exporter does not ( _I_<sup>_R_</sup> _ij_<sup>> 0 and</sup><sup>_ER_</sup> _ij_<sup>= 0) and the</sup> minimum value of –1 when the opposite conditions hold, i.e. the exporter reports a trade flow but the importer does not ( _E_<sup>_R_</sup> _ij_<sup>> 0 and</sup><sup>_IR_</sup> _ij_<sup>= 0). It equals zero only when there is no discrepancy (</sup><sup>_IR_</sup> _ij_<sup>=</sup> _E_<sup>_R_</sup> _ij_<sup>).</sup> This expression can be reorganized to recover the ratio of reported imports to reported exports: For example, reported imports are twice as large as reported exports if ∆ _ij_ = +0.33 and half as large if ∆ _ij_ = –0.33. If ∆ _ij_ = +0.2, reported imports are 50 percent larger than reported exports; if ∆ _ij_ = – 0.2, they are two-thirds as large. If ∆ _ij_ = +0.1, reported imports are about 20 percent larger than reported exports; if ∆ _ij_ = –0.1, they are about 20 percent smaller.

We constructed this relative measure using the cleaned data series in the EFI Database, QUANTITY. We used it in both the descriptive and econometric analyses described below. In both cases, we constructed the relative discrepancy only when reported imports and reported exports were each at least 100 cubic meters (i.e., _E_<sup>_R_</sup> _ij_<sup>≥ 100</sup><sup>_andIR_</sup> _ij_<sup>≥ 100). We did this to ensure greater</sup> precision in our estimates (at least three significant digits) and, in the econometric analysis, to reduce errors-in-variables problems (discussed later). Although this selection criterion reduced the number of bilateral flows included in the analysis, it had a negligible impact on the volume of trade analyzed: bilateral flows of less than 100 cubic meters do not account for much of the total volumes of sawnwood imports and exports reported by the countries in the sample.

# **3 Descriptive analysis**

# **3.1 Romania as an exporter of sawnwood**

According to the EFI Database, Romania exported NC sawnwood to 43 countries during 1982-97 and C sawnwood to 35 countries. European countries accounted for nearly two thirds of the trading partners for both products, 26 and 22 respectively. Moreover, trade tended to occur more regularly with European partners—at least, the database included more years of data on them—and tended to be larger in terms of the physical volumes traded. Appendices 2 and 3 list the importers of Romanian NC and C sawnwood. Appendix 4 lists the countries we considered to be “European.” 4
terms of ambiguity. The choice of a suitable analytical framework for such analysis is, therefore, important. Bank staff need to be exposed to the rar.ge of analytical frameworks available so that appropriate choices can be made and expert assistance sought if necessary.

- The discipline of systematic institutional analysis will become a Bank jp. ztice only when senior managers seek evidence on such analysis whenever ID designs or reforms are proposed in projects. Objectives and scope of the analysis, aspects or elements of the ID issues Investigated, the extent of the borrower's involvement in the diagnosis, analysis of interest groups and their likely impact, and the quality and commitment of the local leadership are the dimensions on which staff should be carefully probed by the management.

- Given the contextual nature of institutional analysis, it may be difficult to specify the approaches to be used, but the feasibility of developing guidelines for the benefit of staff should be explored as cumulative field experience in institutional analysis becomes available in the Bank.

- Institutional diagnosis and design done jointly by the Bank and borrower should be the norm so that a greater sense of ownership and commitment on the part of the latter is created, and a firm foundation for sustainability is laid.

5. Project sustainability can be enhanced if the Bank will change some current practices. Widening the scope of ID to take demand and other client-related issues into account is important, especially in projects that target the poor or deal with social services. Well-focused sector work can facilitate this effort. The quality of supervision and flexibility in the use of resources for this function can have a positive impact on sustainability. Greater attention to project supervision is required because more complex ID issues are being dealt with. Discussions with Bank staff reveal a growing concern that a major casualty of the tightening of supervision will be ID performance. OED data on substantial ID achievement (SID) and negligible ID achievement (NID) cases have clearly demonstrated the key role of supervision and staff continuity.

6. Bank staff working on different sectors do observe the factors which constantly inhibit sustainability. Some of them are country or sector specific, and others are more generic. They may well be aware of the kinds of interventions necessary to remedy them and to strengthen project sustainability. There is, at present, no systematic way in which this Nowledge and cumulative project experience on institutional constraints can ,e tapped and used to evolve selective country strategies in which the Bank and borrowers can work together. Ir-ieed, problems in this category are sometimes addressed through adjustment operations, TA loans, etc. The country implementation reviews being held in recent years seem to focus so much on the current project implementation problems that issues germane to sustainability do not perhaps get addressed adequately. What is required is a special
INTRODUCTION XXXi Table 9. _Long-run and Stationary Population, Momentum; Doubling Year, and Year when Net Reproduction reaches I by World Bank Region, County Department, and Income Group_

||Pop|ulatio|n<br>(milli|ons)|Station<br>populat|ary<br>ion|Year<br>when||
|---|---|---|---|---|---|---|---|---|
||1990|2050|<br>2100|Statio<br>nary|over<br>- 1990<br>popula|Populatio<br>tion<br>momentu|popula<br>n<br>tion<br>n doubtl|-<br>es<br>NRRu1|
|Africa Region|512|1810|2472|2634|<br>5.1|1.5|2016|2055|
|1 Occidental<br>and Central|45|<br>156|<br>208|<br>219|<br>4.9|1.6|2015|2045|
|2 Eastern|126|<br>515|<br>733|<br>781|<br>6.2|1.6|2013|2050|
|3 South-Central<br>and|||||||||
|Indian<br>Ocean|73|<br>260|<br>356|<br>379|<br>5.2|1.5|2015|2055|
|4 Western|119|<br>362|<br>465|<br>499|<br>4.2|1.5|2017|2045|
|5 Sahelian|41|<br>171|<br>248|<br>268|<br>6.5|1.5|2013|2055|
|6 Southern|108|<br>344|<br>461|<br>488|4.5|1.5|2018|2050|
|East Asia and Pacific<br>Region|1619|2669|2847|2911|<br>1.8|1.5|(a)|2040|
|1 Southeast<br>|299<br>|<br>567<br>|<br>622<br>|<br>645<br> <br>|<br>2.3<br>|1.5|2067|2040<br>|
|2 China and Mongolia|1136|1769|1860|<br>1892|1.7|1.4|(a)|2015|
|3Indonesi**a**nd Pacific|184|<br>333|<br>364|<br>375|<br>2.0|1.5|2110|2035|
|South Asia Region|1126|2281|2616|2726|<br>2.4|1.4|2048|2050|
|<br>1 Other South|127|<br>261|<br>302|<br>319|<br>2.5|1.5|2045|2030|
|2 India|850|1599|1790|1855|<br>2.2|1.4|2067|2015|
|3Southwest|150|<br>421|<br>523|<br>553|<br>3.7|1.5|2022|2050|
|Middle East and No.Africa Region|240|834|1144|1199|5.0|1.6|2017|2060|
|<br>1 North Africa|<br>63|<br>165|<br>195|<br>201|<br>3.2|1.6|2025|2050|
|2 Middle<br>East|177|<br>669|<br>948|<br>998|<br>5.6|1.6|2016|2060|
|Europe and Central<br>Asia Region|473|<br>654|<br>694|<br>706|<br>1.5|1.2|(a)|2030|
|1 Southern Europe|99|157|168|171|1.7|1.3|(a)|2030|
|<br>2 Other Europe<br>|85|<br>102|<br>107|<br>108|<br>1.3|1.1|(a)|2030|
|3C.I.S.<br>and Baltic<br>Republic|s 289|<br>395|<br>419|<br>426|<br>1.5|1.2|(a)|2030|
|LAC Region|429|<br>823|<br>900|<br>919|<br>2.1|1.6|2062|2030|
|<br>1 Brazil,<br>Peru, and Venezuel|a 192|<br>360|<br>391|<br>399|<br>2.1|1.5|2081|2010|
|2 Central<br>America|115|<br>242|<br>267|<br>272|<br>2.4|1.7|2041|2025|
|3 Caribbean<br>and other|59|<br>112|<br>124|<br>128|<br>2.2|1.6|2064|2030|
|4 Temperate|63|109|<br>118|<br>120|<br>1.9|1.4|(a)|2030|
|Income group|||||||||
|<br>Low|3039|6268|7358|7765|2.6|1.4|2044|2055|
|Lower-middle|576|1249|1462|1510|<br>2.6|1.6|2039|2055|
|Upper-middle|503|1155|1433|1496|<br>3.0|1.4|2036|2060|
|High|822|<br>930|<br>921|<br>929|<br>1.1|1.1|(a)|2030|
|<br>Monreporting<br>nonmember|328|<br>452|<br>479|<br>486|<br>1.5|1.2|(a)|2030|

Note: World Bank regions and country departments are defined in Box 2. Income groups are defined in Box 1. a. Population will not double with projected rates.
questionnaire in phases over the period 2008-2010,<sup>4</sup> whereas information for OECD countries was obtained from publicly available sources.<sup>5</sup> To ensure data accuracy, the policy information was subjected to review by government officials, which led to confirmation and/or update of the data for most of the OECD countries and a number of developing countries. To the best of our knowledge, no other data source provides similar information on barriers to services trade in a comparable manner for a global cross-section of countries.

The entire database, including all qualitative and quantitative information, annotated descriptions of policies as well as detailed documentation, is available at this stage at <u>http://iresearch.worldbank.org/servicestrade (after clearance, it will be placed on an</u> externally accessible website). The remainder of this section outlines essential features of the database, such as coverage of sectors and measures, quantification of policy measures and relationship to other databases. The Database Guide and the website contain a host of additional information, e.g. on the data collection process, the full list of policy measures covered, a list of governments that confirmed policy information, a detailed documentation of scoring rules, and the questionnaire used in the data collection.<sup>6</sup> The five major services sectors covered in the Database, namely financial services (banking and insurance), telecommunications, retail distribution, transportation<sup>7</sup> and professional services, are further disaggregated into subsectors as shown in Table 1. The choice of sectors was based primarily on our assessment of their economic importance from a development perspective, on the existence of meaningful restrictions on services trade, and the feasibility of collecting relevant policy data.

> 4  The questionnaires were completed by local law firms familiar with the policy regimes in the respective countries and sectors.  While most of the surveys were conducted in phases over the period 2008-2010, an effort was made to update the information for some of the countries that were surveyed at earlier stages.  The information on policies received was evaluated, and its restrictiveness assessed, by a team of World Bank economists.  The consistency of information was cross-checked in consultation with industry experts, private sector representatives and lawyers.

> 5  These sources include WTO Trade Policy reviews, countries‘ most recent offers submitted in the WTO‘s Doha negotiations, the AXCO insurance database, OECD reports on ―Exceptions to National Treatment for Foreign-Controlled Enterprises‖, and the IMF‘s annual reports on exchange arrangements and exchange restrictions.

> 6  Any additional inquiries or feedback regarding the database can be sent to servicestrade@worldbank.org.

6
Fujita, Masahisa, Paul Krugman and Anthony J. Venables (1999), _The Spatial Economy: Cities, Regions, and International Trade_ , Cambridge MA: MIT Press.

Harris, Richard (1084), ―Applied General Equilibrium Analysis of Small Open Economies with Scale Economies and Imperfect Comp,‖ _The American Economic Review,_ Vol. 74 (5), December, 10161032 Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997), ―Quantifying the Uruguay Round,‖ _Economic Journal,_ Vol 107 (444), September, 1405-1430.

Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1993), "Trade Reform in the Partially Liberalized Economy of Turkey," _The World Bank Economic Review_ , May.

Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr. (1996), AIncreased Competition and Completion of the Market in the European Community: Static and Steady-State Effects,@ _Journal of Economic Integration_ , 11(3), September 1996, 332-365.

Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997a), AEconomic Implications for Turkey of a Customs Union with the European Union,@ _European Economic Review_ , 41(3-5), 861-870.

Harrison, Glenn H., Thomas F. Rutherford, David G. Tarr and Angelo Gurgel (1997a) ―Trade Policy and Poverty Reduction in Brazil,‖ _The World Bank Economic Review_ , Vol. 18, 289-317, 2004.

Helu, Samuel (2007), ―Maritime Services in Kenya,‖ paper presented at the Trade in Services Workshop in Nairobi, Kenya, sponsored by the Ministry of Industry and Trade of Kenya and International Lawyers and Economists against Poverty, Nairobi, March 26, 27.

Heston, Alan, Robert Summers and Bettina Aten (2006), Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September. Holmes, T. (1995), ―Localization of Industry and Vertical Disintegration,‖ Federal Reserve Bank of Minneapolis.

Hummels, D (1995), ―Global Income Clustering and Trade in Intermediate Goods,‖ Graduate School of Business, University of Chicago.

Ikiara, Gerrishon K., Moses I. Muriira and Wilfred Nyangena (2000), ―Kenya’s Trade in Services: Should the Country Fully Liberalize,‖ in Services in the International Economy, Robert Stern (ed.), Ann Arbor: University of Michigan Press.

Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2007), ―The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,‖ _Review of Development Economics,_ Vol 11 (3), August, 2007, 482-506.

Jensen, Jesper and David G. Tarr (2010), ―Can Preferential Liberalization of Services Reduce Welfare: The Case of Tanzania,‖ The World Bank, draft mimeo.

38
Policy Research Working Paper 8736

# **Abstract**

In discussing the inordinately low employment of Indian women in urban areas, several studies have argued that culture and attitudes have created a labor market that is inherently discriminatory. The unsaid corollary is that culture is slow and hard to change and so, women will stay out of the labor market until social change occurs. The empirical evidence on the role of culture is slim at best. This paper fills the void in the policy literature, as it assesses the relative role of culture, as signified by attitudes of employers, and firm characteristics in hiring women. The paper is based on a unique survey of 618 firms in three of the largest cities in the state of Madhya Pradesh (India)—Bhopal, Indore, and Gwalior. Using detailed descriptive, bivariate and multivariate analysis at the firm level, the hiring process, and attitudes toward male and female workers, the paper addresses the issue of culture and firm characteristics, while noting that the two are not necessarily in binary opposition. The results reinforce the conventional wisdom in some ways and are surprising in others. The most salient result is that employer attitudes matter much less for the chance that women will be hired, than do firm and location characteristics. This has significant policy implications, the most important of which is that female employment in urban India is amenable to policy intervention, and that it is not necessary to wait for culture to change.

This paper is a product of the Social, Urban, Rural and Resilience 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/research. The authors may be contacted at mdas@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
More generally, Panel B of Figure 7 presents the cross national partial correlation<sup>13</sup> of the Gini coefficient and share of people below age 14. This panel indicates that the correlation between these two variables is negative (and significantly different from zero). That is, younger societies have lower Gini coefficients.

What does this mean in practice? Well, if Latin America had the demographic structure of aging Europe, its Gini coefficients could be higher by 4 percentage points. Moreover, in some of the comparatively young countries such as Bolivia, Guatemala or Honduras the Gini coefficient could be up to 7 percentage points higher. Thus everything else equal and to the extent that the region starts aging, it is possible that income inequality levels become worse before getting better.

# _II. 6 Beyond income inequality_

Income measures are highly correlated with most aspects of household welfare. However, it must be recognized that in some important dimensions of welfare, such as health and life expectancy, there has been significant progress and convergence among income groups. This is illustrated in figure 8 where we plot the distribution of income (Panel A) and the distribution of life expectancy (Panel B) across Brazilian municipalities in 1970 and 2000. Inspection of this figure indicates that whereas on the income front there has been some increased dispersion and the emergence of a bimodal distribution, this has not been the case with life expectancy, which if anything has experienced a decline in dispersion (i.e. a decline in inequality). In other words, regional distribution trends in some welfare indicators may be improving, and this would suggest an important role for some policies that fight poverty independently of those aimed to growth.

**<u>Figure 8. The distribution of municipal incomes and life expectancy in Brazil</u>**

<!-- Start of picture text --> Panel A. Income Panel B. Life expectancy<br>0.45 0.40<br>0.40 Distribution 1970 0.35<br>0.35<br>0.30 Distribution 2000<br>0.30 Distribution 2000<br>0.25 Distribution 1970<br>0.25<br>0.20<br>0.20<br>0.15<br>0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>-4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4<br>Country Relative Income Country Relative Life<br><!-- End of picture text --> Source: Perry et al. (2006)

> 13 We control for level of development.

12
(5.6). An activity factor for travel of 3 lies between these extremes, and it is in the same range as “walking slowly” and “driving a motorcycle.” Based on NSS data, the car ownership rate in rural areas as late as 2005 was only 1%, and in urban areas was only 3%. Motorcycle and bicycle ownership rates are higher in 2005 (8% and 48% respectively in rural areas, 25% and 44% in urban areas). The approach used here still allows better-off households (proxied by per capita expenditure) to economize on travel time by using faster modes of transport, which leads to lower activity levels since travel is fairly energy-intensive compared to most other activities, but does not allow for potentially higher or lower activity factors associated with different modes of transport for a given amount of travel time measured in the TUS.

To impute activity levels to individuals in the NSS-E, individual activity levels in the TUS are regressed on a set of common variables to generate predictive equations. Separate equations are used for each combination of rural/urban, male/female, under 15 and over. Appendix Table S.3 presents the set of variables and regression coefficients for these regressions, which include detailed age and household demographics, education levels, land, per capita expenditure, household and individual two-digit NIC (1987 National Industry Classification) and NCO (1968 National Classification of Occupations), and work status variables (e.g. self-employed, casual worker, salaried worker, student, domestic duties, etc.). Dummy variables are also constructed for the TUS to match the detailed agricultural task variables (based on the previous week) and domestic chores (for women with primary status as domestic), based on whether households report any time spent during the last week on activities like weeding, harvesting, caring for animals, collecting firewood or water, food collection and preparation, etc. As with REE, values outside of the 1% of the tails of the distribution are replaced with the cutoff values prior to estimation. For individuals younger than 6, activity levels are set equal to the sample mean for 6 year olds, which is 1.46.

# **C Estimation of caloric intake**

The alternative, independent caloric intake estimates reported here, the NSS consumption survey (schedule 1) is used for the same years as the NSS-E. For 2010and 2012 the main results use a comparable 30-day recall period. Household respondents are asked to recall the total quantity and expenditure for each food item from a detailed list over the previous 30-days. The standard 22