rafmacalaba/datause-encoder-data huggingface dataset:
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<!-- Start of picture text --> el 20132006<br><!-- End of picture text --> <!-- Start of picture text --> TR21<br><!-- End of picture text --> <!-- Start of picture text --> TR22<br><!-- End of picture text --> <!-- Start of picture text --> TR31<br><!-- End of picture text --> <!-- Start of picture text --> TR33<br><!-- End of picture text --> <!-- Start of picture text --> TR41<br><!-- End of picture text --> <!-- Start of picture text --> TR42<br><!-- End of picture text --> <!-- Start of picture text --> TR51<br><!-- End of picture text --> <!-- Start of picture text --> TR52<br><!-- End of picture text --> <!-- Start of picture text --> TR61<br><!-- End of picture text --> <!-- Start of picture text --> TR62<br><!-- End of picture text --> <!-- Start of picture text --> TR71<br><!-- End of picture text --> <!-- Start of picture text --> TR81<br><!-- End of picture text --> <!-- Start of picture text --> TR83<br><!-- End of picture text --> <!-- Start of picture text --> TR90<br><!-- End of picture text --> <!-- Start of picture text --> TRA1<br><!-- End of picture text --> <!-- Start of picture text --> TRA2<br><!-- End of picture text --> <!-- Start of picture text --> TRB1<br><!-- End of picture text --> <!-- Start of picture text --> TRB2<br><!-- End of picture text --> <!-- Start of picture text --> TRC1<br><!-- End of picture text --> <!-- Start of picture text --> TRC2<br><!-- End of picture text --> <!-- Start of picture text --> TRC3<br><!-- End of picture text -->
3 Another, equally important, data problem is that IMF information on the level of “worker remittances” includes only data on official worker remittance flows, that is, remittance monies which are transmitted through official banking channels. Since a large (and unknown) proportion of worker remittance monies is transmitted through private and unrecorded channels, the level of remittances recorded by the IMF underestimates the actual flow of remittances – official and unofficial -- returning to many developing countries.

In view of these problems, this paper constructs a new data set that includes information on official international remittances for 115 developing countries. These countries were selected because they were listed in the latest (2004) edition of the IMF, _Balance of Payments Statistics Yearbook._ In this data set official international remittances are defined as “worker remittances.” The 2004 edition of the IMF _Yearbook_ lists data on official international remittances for 76 of these 115 developing countries (66 percent). The 39 developing countries with no remittance data in this edition of the IMF _Yearbook_ either did not receive worker remittances in 2003 or they did not report such transfers as “worker remittances.” Table 1 shows the regional distribution of official international remittances for these 115 developing countries over the 5-year period, 1998 to 2003. The data show that two regions of the developing world – Latin America and the Caribbean, and South Asia – receive the lion’s share of international remittances. In 2003 these two regions received, respectively, 42.3 percent and 23.9 percent of all official international remittances.<sup>5</sup> By contrast, Sub-Saharan Africa received less than 5 percent of all official international remittances in 2003.

Table 1 reveals that for the developing world as a whole, official international remittances have been increasing at about 8 percent per year. However, in two regions of the world – Europe and Central Asia, and Sub-Saharan Africa – the level of official international remittances actually fell over the last five years.

Table 2 presents another view of the data by showing the distribution of official international remittances by income group of country: low income, lower middle income or upper middle income. In both 1998 and 2003 lower middle income countries – that is, countries with annual GDP per capita between $736 and $2,935 – received the largest
Figure 4: DVAR of China’s Exports to its Top 5 Trading Partners <!-- Start of picture text --> Germany Hong Kong, SAR Japan<br>2000 2002 2004 2006<br>Korea United States<br>2000 2002 2004 2006 2000 2002 2004 2006<br>year<br>Graphs by two-digit iso code<br>.55<br>.5<br>.45<br>.55<br>DVAR of Exports<br>.5<br>.45<br><!-- End of picture text --> Figure 5: DVAR of China’s Aggregate (Processing + Ordinary) Exports <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>year<br>DVAR (Processing + Ordinary Exp) DVAR (Processing Exp)<br>All firms with materials < imp & exp < imp, and processing firms with DVAR>DVAR(25 percentile of Ord Exporters) are excluded.<br>.75<br>.7<br>.65<br>.6<br>.55<br>.5<br>.45<br><!-- End of picture text --> 46
-9-

- (1) The effectn of the increase in fuel prices relative to other lnput prices have been so strong that, despite the very substantial growth in the industrial sector as a whole of 62%, no more fuel was used as an input in 1988 than in 1971. Without the growth in the sector the use of fuel would have declined substantLally.

- (ii) As between fuels there has bea' a strong move away relatively (and absolutely) from the use of oil in industry as a primary fuel.

# In addition it can be eeon that:

- (iLi) The total use of solid fuel has increased slightly, which has implications for pollution.

- (iv) The use of gas has also increased but only by a small amount, which is interesting given the discoveries of substantial gas resources in several O.B.C.D. countries.

- (v) The main increase in fuel use io from the secondary source of electricity. Without an analysis of the fuel mix for power generatien it is not possible to see whether ln fact there has been substantial switching between primary fuels.

For the public power generation sector in the O.R.C.D. the shares of primary fuels used in 1971 and in 1988 are shown in Table 2.3.

Table 2.3s Shares of Fuel Used for Power Generation in the Public Sector in the O.F.C.D.

||Ot<br>Coal|her Soli<br>Fuel|d<br>Oil|Gas|Nuclear|Hydro|Total<br>MTOE|
|---|---|---|---|---|---|---|---|
|1971<br>|38.6<br>|0.1<br>|19.9|14.1|2.9|24.2|301.0|
|1988|42.4|0.2|7.5|8.3|24.3|17.3|528.6|

source: As for Table 2.1.

In relating this table to the two previous tables it is important to bear in mind two limitations imposed by the nature of the data. Firstly, this **is** for public sector power generation rather than total power generation as in table 2.1. secondly, table 2.3 is for all uses of electricity rather than for tho use by the industrial sector as in table 2.2. However since all end uses of electricity in a glven system can be assumed to use the same fuel mix, the relative shares in table 2.3 can safely be taken as representative of the use by industry. Private sector power generation may have a different pattern of
comprehension test for the guidance manual and paper-based questionnaires at the end of the halfday training. Table 1 summarizes the main questionnaires used to collect data.

Table 1. Summary of data sources

|**Data source/**<br>**Questionnaire**|**Date**|**Sample**|**Type**|**Key information**<br>**collected**|**Response**<br>**rate (%)**|
|---|---|---|---|---|---|
|**SIRE**<br>**administrative**<br>**data**|2016<br>(placebo),<br>2017<br>(placebo /<br>prediction)<br>; 2018<br>(baseline),<br>2019<br>(follow-<br>up)|All<br>schools<br>(4,000)|Online|Key administrative<br>student-level data<br>(including Students’ IDs,<br>detailed grades, age,<br>gender, and dropout<br>status)|100%|
|**Complementary**<br>**baseline data**|May 7<sup>th</sup>-<br>May 29<sup>th</sup>|All<br>schools<br>(4,000)|Online|Perceptions on school<br>dropout, returns to<br>education and influences<br>on the dropout decision,<br>as well as targeted<br>support and tasks<br>prioritized by principals.|70%|
|**Training**<br>**feedback**|May 29<sup>th</sup>-<br>June 6<sup>th</sup>|All<br>treatment<br>schools<br>(3,000)|Paper-<br>based|Feedback on the content<br>and quality of the<br>training received and<br>about the trainer<br>assigned. Confirmation<br>of reception of materials<br>(guide/list), and self-<br>reported level of<br>understanding and|91%|

15
26 with its buyers and suppliers. The second category comprises technological learning that is facilitated by a firm's being embedded in an "information-rich" environment, one replete with other firms engaged in similar activities, with a menu of courses that address its specific business problems, and with access to a network of specialized consultants. The final category comprises "high-intensity" technological support, including formal technology transfer agreements with other private finms engaged in a similar line of business, and sustained joint work with specialist technology institutions. Note that while the channels for leaniing in the first category are entirely private, in the latter two categories the channels might be either private or collective.

We first review comparatively the empirical results from the four country studies as towhich sources proved most useful in helping finns to acqWire technological capability. Thereafter, we evaluate in some depth the specific role of collective technological support. M. 1 Leading Sources of Technological Capability Five of the subsectors (garments in Indonesia and Colombia, both fiurniture subsectors in Indonesia, and leather products in Colombia) use craft-based technologies; two (woven textiles in Korea and Japan) use simple, engineering-based technologies; and five (machinery in Colombia, automotive components in Korea and Japan, and electronic products and factory automation equipment in Korea) are examples of more complex, engineering-based technologies.2=/ While craft-based and engineering-based activities pose distinct technological challenges, each can be met in distinctive ways. Indeed, as the comparative review which follows will highlight, and for reasons that are closely analogous to those identified earlier for export marketing, there turn out to be distinctive national patterns in the external mechanism of technological support for both craft- and engineering-based activities.

Japan. As Table 12A reveals, the leading external sources of technological capability are remarkably similar for Japan's silverware, woven textile and auto parts SMEs, despite very substantial subsectoral differences in the technological challenges. Paralleling export marketing,

- 22/ Japan's silverware subsector is a hybrid, with strong roots as a craft-based industry but with increasing application of science-based technologies to -sustain competitive advantage.
unit of investment basis, the capacity for small enterprises to absorb labor could be as much as 14 times the capacity of large enterprises.39 However, at current stage, a large number of SMEs, notably the TVEs, are facing many constraints and challenges in a more and more competitive environment. The majority of TVEs have limited technological and human resources to become internationally competitive,40 and most SMEs in general have difficulties accessing financing, credits, technical assistance, market information, training and so on. In order to fully unleash the dynamism of SMEs for economic growth and employment generation, the government should undertake proactive strategies to help the development of SMEs in all sectors, from agriculture to services: 41

- Drastically reducing the many regulatory hurdles for establishing and operating new private enterprises.

- Ensuring that small and medium-size enterprises have access to bank credit.

- Promoting informal lending schemes to small start-up businesses, such as group lending and special micro-venture schemes.

- Providing small and medium enterprises access to market and technical information.

- Setting up “small business incubator” to assist start-ups in finding appropriate technologies, skills, and venues/equipment, setting up managerial and accounting systems, and preparing relevant documents. 42

- Developing formal and informal basic business and accounting skill training aimed at SMEs.

- Strengthening university and continuing education offerings relating to business and entrepreneurial skills.

- Drawing on extensive global experience on small and medium enterprise support programs, adapting the most relevant to the Chinese context (box 2).

# **Box 2 Government Support for SMEs**

Small and medium-size enterprises account for significant economic activity in all high-income countries. In the EU they make up 93% of firms, with nearly two-thirds of employment (more than 70 million

> 39 See Mo Rong (2001), “Some Effects of China’s Joining the WTO on Employment, and Certain Policy Proposals in Response to the Problem”, _The Chinese Economy_ , V34, No. 5, pp. 5-6. The paper was translated by M.E. Sharpe, Inc., from original Chinese text.

> 40 Yao, Shujie, “China’s Rural Economy in the First Decade of the 21st Century: Problems and Growth Constraints”, _China Economic Review_ , V13, 2002, pp. 354-360.

> 41 This section draws on World Bank, _China & Knowledge Economy: Seizing the 21st Century_ , 2001, the World Bank, Washington, DC.

> 42 Jun, Tang, “Joining the WTO, the Employment Problem, and How to Deal With It”, _The Chinese Economy_ , V34, No. 3, May-June 2001.

18
# **Decomposing the Labor Productivity Gap between Upper-Middle-Income and High-Income Countries**

By: Mohammad Amin*, Asif Islam**, and Usman Khalid*** Enterprise Analysis Unit, World Bank & Department of Economics and Finance, College of Business and Economics, United Arab Emirates University, Al Ain, United Arab Emirates _Keywords_ : Upper middle-income, High-income, Labor productivity, Oaxaca-Blinder decomposition, Middle-income trap _JEL_ : D24, J24, O53 ____________________ We would like to thank Jorge Luis Rodriguez Meza for providing useful comments.

* Corresponding Author. Senior Economist, Enterprise Analysis Unit, DECEA, World Bank Country Office in Kuala Lumpur, Malaysia, World Bank **.** Email: <u>mamin@worldbank.org</u> ****** Economist, Enterprise Analysis Unit, DECEA, World Bank **.** Email: <u>aislam@worldbank.org</u> ******* Assistant Professor of Economics, Department of Economics and Finance, College of Business and Economics, United Arab Emirates University, Al Ain, United Arab Emirates. Email: <u>usman.khalid.lu@gmail.com</u>
# **I. Introduction**

Recent events, including the Arab Revolutions and protest movement of unemployed youths in OECD countries, have contributed to the popular sentiment that access to good jobs is an important driver of social cohesion. The OECD, in its recent report on social cohesion, argues that labor market outcomes are critical determinants of social stability, both because they influence the level and distribution of labor earnings and because jobs are critical loci of social interaction (OECD 2011: 154). In a similar vein, the World Bank’s 2013 World Development Report on Jobs identifies the link between labor markets and social cohesion as one of the central pillars of its multidimensional approach to jobs and development.

While economic dimensions of labor market outcomes are relatively well documented, evidence on the link between social cohesion and employment conditions is still surprisingly scarce. Studies of insider-outsider conflicts over labormarket policies are typically limited to high income countries or they only consider economic outcomes (see among others Rueda 2005, 2006, Shayo 2009, Freeman 2008, Heckman and Pages 2000, Botero et al. 2004). On the other hand much of the cross country literature on social cohesion focuses on aggregate social outcomes – average levels of trust and civic attitudes or the quality of political institutions for instance (see Knack and Keefer 1997, 1995, Easterly et al. 2006, Easterly and Levine 1997, Rodrik 1998). However, few of these studies analyze specifically how social cleavages and distributional conflicts within and around national labor markets influence individual and aggregate outcomes.

This paper, a background report for the forthcoming _WDR 2013 on Jobs,_ presents descriptive evidence that illustrates possible linkages between labor market outcomes and social cohesion. The first link we consider involves associations between work and wellbeing. We focus on self-reported job preferences and the effect of unemployment on life satisfaction, assuming that these measures provide an indication of how labor marker-related priorities vary across countries at different stages of development. Second, we explore correlations between individual employment status and job quality and other more widely studied indicators of social cohesion, including self-reported levels of trust, political activism and social associations. Throughout, our analysis focuses on outcomes in middle and low income countries, given the relative dearth of information on social cohesion and labor market outcomes in this part of the world. However, for comparison purposes our study also report results from a set of high income countries.

It is important to stress from the outset limitations of our data and analytical strategy. Data for this analysis are taken from the World Value Survey (WVS) project. To our knowledge this project provides the largest available number of 2
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Table 49

|Dependent variable:|Fiscal adjus|tment whe|n needed b|ased on La|vigne(2011|)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|-0.002|-0.003|-0.005*|-0.007*|-0.008|-0.005|0.000|-0.005|
|(% of GDP)|(0.001)|(0.002)|(0.003)|(0.003)|(0.005)|(0.005)|(0.004)|(0.003)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.022||-0.057|-0.081|-0.056|-0.095|-0.055|
|||(0.130)||(0.175)|(0.166)|(0.175)|(0.175)|(0.175)|
|Inflation (t-1)||-0.279||-0.389|-0.387|-0.387|-0.397|-0.389|
|||(0.209)||(0.260)|(0.262)|(0.261)|(0.260)|(0.260)|
|U.S. Interest rate||-0.006||-0.010|-0.010|-0.010|-0.010|-0.010|
|||(0.007)||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|**Political factors**|||||||||
|Gini|||0.000|-0.000|-0.000|0.000|-0.001|-0.000|
||||(0.000)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Rule of law|||-0.016|0.000|0.001|-0.000|0.050|0.001|
||||(0.018)|(0.025)|(0.024)|(0.026)|(0.040)|(0.025)|
|Margin of Majority|||-0.067|-0.071|-0.065|-0.074|-0.054|-0.028|
||||(0.050)|(0.055)|(0.053)|(0.061)|(0.052)|(0.072)|
|Election's year|||-0.017*|-0.023**|-0.023**|-0.023**|-0.023**|-0.023**|
||||(0.010)|(0.010)|(0.010)|(0.010)|(0.010)|(0.009)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||0.002<br>(0.006)||||
|Gini * Rigidity||||||-0.000<br>(0.000)|||
|Rule of law * Rigidity|||||||-0.002<br>(0.001)||
|Majority * Rigidity||||||||-0.002<br>(0.004)|
|Constant|0.073**|0.135**|0.250*|0.328**|0.326**|0.311**|0.158|0.305**|
||(0.033)|(0.061)|(0.130)|(0.141)|(0.142)|(0.128)|(0.125)|(0.126)|
|Observations|687|677|485|482|482|482|482|482|
|R-squared|0.002|0.016|0.013|0.041|0.041|0.041|0.046|0.041|
|Number of ifscode|88|87|65|64|64|64|64|64|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard erro<br>*** p<0.01, ** p<0.0|rs clustere<br>5, * p<0.1|d by countr|y in parent|heses|||||

- 78 -
# **Remittances and the Brain Drain Revisited:**

**The microdata show that more educated migrants remit more**<sup>**#**</sup>

Albert Bollard, _Stanford University_ David McKenzie, _World Bank, BREAD, IZA and CReAM_ Melanie Morten, _Yale University_ Hillel Rapoport, _Bar-Ilan University, EQUIPPE and CID, Harvard University_ _Keywords:_ Remittances, Migration, Brain Drain, Education.

_JEL Codes:_ O15, F22, J61.

> # We are grateful for funding for this project from the Agence Française de Développement (AFD). We thank the various individuals and organizations who graciously allowed us to use their surveys of immigrants and Michael Clemens for helpful comments. All opinions are of course our own and do not represent those of AFD, or of the World Bank.
Page 2 Families do not act as one, as a single "agent" (Schultz 1989; Sen 1987). And the paradigm inadecuately captures the links between the growing human population, **itb** economic production, and the natural environment. Failure to recognize and adjust to the more diverse and complex character of reality carries a cost, in both economic and social terms. Failure to consider women is a case in point. Part of the cost comes in forgone production; part in diminished family welfare, particularly for the poor; part in rapid population growth; and part in a worrisome environmental threat.

6. This paper focuses on the economic case for specific attention to women in development. The case rests on three main points:

- - Women already contribute heavily to the economy and family, more than is generally recognized or reflected in official GNP.

- - But women face special disadvantages in gaining access to education and training, to information and the means of production, and to product markets and jobs. Both factor and product markets often work more imperfectly for women.

- - Investments to improve women's human capital and expand their economic opportunities can pay off not only for women, but for their families and the economy.

7. According to rough estimates from FAO, women account for more than half of the labor required to produce the food consumed in the developing world, and perhaps three-fourths in Africa (FAO 1985). For example, aggregate rough data suggest that African women provide about 90 percent of the labor for processing food crops and providing household water and fuelwood; 80 percent of the work in food storage and transport from farm to village; 90 percent of the work in hoeing and weeding; and 60 percent of the work in harvesting and marketing. About one-fourth of the industrial labor force of the developing world is female (Anker and Hein 1986). And, although data are sketchy, women work increasingly in vast informal sectors in rural and urban areas.

# Women's invisibility

8. Much of women's economic output is produced and consumed at home, so it goes unrecognized. Unpriced, it is difficult to value; and it is often consumed almost immediately. Thus, it is seldom reflected in estimates of GNP. Many studies show that women account for much subsistence production for home consumption -- especially in Africa but substantially in Regions. Almost everywhere it is primarily women who store and prepare the family's food. Women also fetch and use most household water and household fuelwood (90 percent of the household energy consumed in Africa), sometimes spending 2-4 hours daily in this work. (World Bank OED 1988; World Bank 1989a, 1989b; Acharya and Bennett 1982; Jiggins 1987; Anker 1983; Recchini de Lattes and Wainerman 1982). And it is primarily women who care for children and see to the family's basic needs.

9. Economic and demographic surveys of households arA bringing to light women's full contribution (Scl-ultz 1989; Binswanger and others 1980; Acharya and