rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4. EU’s import of groundnut oil versus palm and rapeseed oils**

<!-- Start of picture text --> 4000<br>3500<br>Palm oil Groundnut oil Rapeseed oil<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br><!-- End of picture text --> Figures 5a to 5f depict the trends in exports and market shares of raw edible groundnuts prepared groundnuts and groundnut oil since 1976. Consistent with growth in world consumption of raw edible and prepared groundnuts, export of these two products expanded rapidly since the mid-1980s. Exports of edible groundnut increased by 8 percent between the first and second half of 1990s. This growth followed a dramatic increase of over 20 percent over the 1980s. The pattern of growth in prepared groundnut export broadly mirrors that of edible groundnuts signaling the highly integrated nature of these markets as shown in Figure 5c.

China has been the major beneficiary in the expansion of the groundnut markets while the U.S. role has decreased significantly. From barely 1 percent in 1976, China’s market share in the world edible groundnut market increased dramatically to 32 percent in 2001. During the same period, US’ market share dropped from 32 percent to 19 percent (Figure 5b). The emergence of China as a leading groundnut exporter is even more impressive in the prepared groundnut market. Other producers have increased their share from about 20 percent to 35 percent (Figure 5d), also contributing to the erosion of the U.S. export market share.

While the international edible groundnut market has become more concentrated with 61 percent of exports controlled by China, the US and Argentina in 2001, the market for prepared groundnuts has become more fragmented. The concentration of the edible market partially reflects the significant decrease in SSA’s share, from 17 percent to 5 percent. African shares have been quite volatile, as several African countries (including Nigeria, Malawi, and the Gambia) enter intermittently the edible export market depending on their crop quality and world market demand, and are not dependable suppliers in this market.

Over the last 25 years, many countries have exited the groundnut oil market (e.g., Brazil) or have chosen to enter it only when the quality of groundnut harvested cannot be sold in the edible market (e.g., United States). In this rather stagnant groundnut oil world market, Argentina and Senegal remain the world’s leading exporters but the market has significantly fragmented. In 1976, Senegal, Argentina, Brazil, and the United States supplied 85 percent of total exports while in 2001, these four countries represented only 52 percent of total exports. China and Brazil experienced sharp decreases in shares as they elected to exit the groundnut oil market and shift emphasis on edible groundnut and other vegetable oils.

10
<!-- Start of picture text --> o<br>=<br>~<br>o<br>“1 -8 -.8 4 -.2 ) 2 A 6 a a<br>geographic targeting<br><!-- End of picture text --> <!-- Start of picture text -->
- EAP (5) EcAcs) LAG (10)<br>MINA (2) SAR (6) SSA (27)<br>2°<br>2 4 -5 ° 5 7 ala=4 =5 ° 5 4 -1 -.5 o 5 4<br>geographic targeting. by region<br><!-- End of picture text -->
**Policy, Research, and External Affairs**

# **Development Economics**

**WPS** 789

'I'llis papker It product ol' die Research Advisory Staff', Oilice of **Ilhc** Vice Prcsidcit, Development lcnnormi%s - is **part** ot a largcr cl'lort in PRE to understand the design of poverty alleviation policies. Copies are availablM frce fIrom the XVorl'J Biank 1818 **If** Street NW WaW hington D)C 20433. Please coiltact **Janc SweenC\,** rk,om **S3-026,** extension 310(21 **(13** pages). October 1991.

Th,c **te.o 1 ltCatiufcs oll la!tlgt** inand **on** initrahousehold incqualit\ liavc developcd rapidly oVkc, the **pa> I** 13 * cars, but la Rcl\ independent of each other.

T'hc literature on waroetino concerns itself' ; ilt)h Il desi Ln of **t.1a** dld tranlslfcr puitgi slwl M pmo eny alleviation in the presence of'limitcd in tlormiation on l lho the poor are.

intrahiouseihold inequality **anid** allocation mechanisms could affcet the standard analysis of' targeting theory. Thex coniclude with a list of policy questions for further rcsearcih including the following:

* iOW are coiincut ional rules lor indicator targeting modif'ied by different housciold allocatioIn mnechanisms.

**'I'lTh** literaturc oii intrahouschold inequ al ity arosc out ot a dissailst clion wnitih "unilar'' **modf,ls** otheiC houschol(j. es.plecially in explainill_ ilkqUlid t in consumtipion and achievements of (difteellt household mriembcrs, even al;ter allou. **i r tor reie\ adi** illerences **a.imolln tleilm.**

* How far v" roing can onlC go in targetinlg by simpl) assumiing that int-ahouseihold inequality does **n0ot** exist, **when in** fact it does?'

* What sort of intrahouscihold inlonnation s,tould bc collected to best aid tareeting?

Iladdad and Kanhur hbein **io** tforge **ihe** link hetu.cen the tvo literattu-es, so thex canl address issues policxmakeurs face around iltle u.old, Alter a hrief reprise of **ilic** key featurcs of **thc** t\ko litclrture.u **tilc** indicale ho% tile pilesence ol'

* **f-lo%** do the "bargaining" versus the "common prefcrence" views of tfhe liousciold **irillu-** encc our evaluation of alternative transfer prograrms?

I11<1[iSl \\, 'i,} . I' 1 **l'l** S, t :. **;. ":.,..** 1:t' tl'i2u:1,wl>. c 1 kertk un,Lr '\ y **'! I** iI3. tld1k '. **jlii** . **Rcxr.tr 'e (in~t7** f\WflUi, **'c** p'llvi, tit no: **flCk c-.I!i\ c''n o1h j' ii H.,i~k poli**

**Pict .:.! 1,% tl:,** **_I'R_ L Di;senn;:m.,r-.** Cc **fitcr**
The third question we address is: What factors have caused the recent changes in child mortality and any changes in inequalities in child mortality? What role, for example, has economic growth played? Have changes in the health care sector affected child survival prospects, and if so to what extent? Have changes impacted equally on all groups? To answer these questions we estimate child survival models linking child survival to the underlying determinants of survival, such as the child's age and gender, the education level of the mother, the income of the household, its water and sanitation, and variables capturing the success of local health services in delivering key maternal and child health interventions such as immunization and antenatal care. We then undertake a decomposition analysis to see how different factors contribute to the overall decline in mortality and to the changing inequalities.

The fourth question we ask is: How are child survival prospects likely to evolve over the next 15 years. Vietnam is currently preparing a poverty-reduction strategy paper (PRSP) to qualify for continued development assistance through the International Development Association (IDA). In the PRSP, countries set out not only their povertyreduction strategies but also commit themselves to achieving targets for poverty and human development indicators. l Among the targets being set by other countries include rates of infant and under-five mortality, since these are among the seven key international development goals (IDGs).<sup>2</sup> The latter involve _inter alia_ a reduction by all countries of infant and under-five mortality rates by two-thirds between 1990 and 2015. We address the issue of whether such a reduction is possible for Vietnam, and whether commitment to such a path would be realistic for Vietnam's PRSP. To do this, we use the survival model described briefly above, and project forward to 2015, making assumptions about income growth, as well as the evolution of water and sanitation, and health services.

The plan of the paper follows these four questions. Section 2 reviews the evidence on recent trends in child survival at the population level. Section 3 explores trends in socioeconomic inequalities in child survival. Section 4 outlines the empirical model we use in the remainder of the paper. Section 5 presents and discusses the parameter estimates, and then uses these parameter estimates to explain recent changes in the levels of and inequalities in child mortality. Section 6 uses the model to explore likely trends in child survival up to 2015-the year by which the IDGs are supposed to be reached. Finally, section 7 contains a discussion of the issues raised by our findings and discusses some policy options.

' See http://www.worldbank.org/povertv/strateLies/review/index.htm.

2 See httD://www.developrmentgoals.org/.

2
**Figure 1. Business Cycle Synchronization and Tr ade with the US**

<!-- Start of picture text --> Business Cycle Syncronization and Trade with the US<br>0.600<br>Can<br>Argentina<br>0.500 Costa Rica<br>El Salvador<br>France<br>0.400 Mex<br>Honduras<br>0.300<br>Guatemala<br>Nicaragua<br>0.200<br>0.100 y = 0.1055x + 0.3873<br>R 2 = 0.1389<br>0.000<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8<br>bilateral Exports/GDP<br>Figure 2. Business Cycle Synchronization and Intra-industry Trade<br>Intra-Industry Trade and Business Cycle Synronization<br>0.600<br>Can<br>Argentina Mexico<br>0.500<br>0.400 Costa Rica France<br>0.300 y = 0.2164x + 0.376<br>R 2 = 0.5433<br>0.200<br>0.100<br>0.000<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>IIT<br>Syncronization<br>Syncronization<br><!-- End of picture text -->

**Figure 2. Business Cycle Synchronization and Intra-industry Trade**

As in Shin and Wang (2003) and Garnier (2003), the link between intra-industry trade and business synchronization is found to be stronger.

13
total, we tracked down 87 of the households. These households gave our enumerators the information that was needed to complete the employment sections of the survey form.

The second type of household is a household that at one time was living in the village (during our 2000 survey) and during 2008 was living out of the village in the city. In total, there were 89 such households (out of our entire sample of 1160) that fit into this category. In this case, there was no one in the village that knew on a day to day basis what the individuals in the household were doing employment-wise. In the case of these households, we tracked them to the city. In total, we tracked down 87 of the households. These households gave our enumerators the information that was needed to complete the employment sections of the survey form.

> 4 Enumerators attempted to ask the employment histories from each individual. If a household member or one of the children of the household head was not present, the respondent (which was almost always the household head or spouse of the household head) answered. Extensive pre-testing found that the data are fairly accurate. In addition, we conducted a practical test to see whether or not a respondent bias problem exists in the employment history part of our data. We replicated the analysis after excluding observations on individuals whom we did not interview directly and found that the results did not change.

In addition, we were worried about recall bias. Fortunately, we have data on the exact same households from an earlier wave of the survey in 2000. Because of this we are able to compare the household’s estimate of labor market participation in 2000 from the 2008 survey versus the information provided by the household from the 2000 survey). With this unique set of data, we are able to judge if there was a recall bias. As it turns out, there is almost none. Household participation in the off-farm labor market in 2000 was estimated to be 34.5 percent in 2008 CNRS survey; the off-farm labor market in 2000 was estimated to be 35.4 percent when using the 2000 survey itself.

5 Whether the counterfactual method overestimates the quantitative impact of the global financial crisis largely depends on how to isolate the shock of the global financial crisis from other domestic and exchange 37
_ oboth of these projects later developed a means of having the community involved in planning, decision making and implementation because the program staff believed that this would make the program str-ger.

Community based nutrition programs of whatever kind tend to be small and often are organized and implemented by a non-governmental entity. Shrimpton (1989) in a review of community based nutrition programs worldwide concludes that there are few examples of community based programs that are government run. This is not easy to explain.

Given th*. many people, including most Africans outside of government surveyed in this exercise, believe that community participation is essential, little is apparently part of government-run nutrition program. Part of the explanation is provided from a review of the Iringa Nutrition Project in Tanzania. This community based project was started as part of the UNICEF/WHO Joint Nutrition Support Program (JNSP) carried out in conjunction with the Government of Tanzania. This program was very effective in getting the program started at the grass roots level; a recent external evaluation indicated that the Iringa project has been associated with a significant decrease in the levels of malnutrition in preschoolers (UNICEF/WHO, 1988). Although tne active participation of the local community is credited to be one reason for success, the meaning of community participation goes beyond the village level. For community based programs to be successful, even with active local level involvement some implementors suggest that there needs to be an awareness and commitment of the leadership at higher levels of government. (Yambi, 1989).

In addition, the historical perspective is often lost in an evaluation of programs. The ease with which community participation was effected in Iringa relates to Tanzania's political history. President Nyerere's Arusha Declaration in 1967 emphasized that people's participation in the development process is critical; this declaration was reinforced by a political ideology that supported a community orientation and perhaps more importantly, an
# **Figure B29:  Medium-term cycles—Precious metals and fertilizers**

<!-- Start of picture text --> Log scale<br>0.80<br>Gold Platinum Silver<br>0.40<br>0.00<br>-0.40<br>-0.80<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>0.60<br>Phosphate Potassium TSP Urea<br>0.30<br>0.00<br>-0.30<br>-0.60<br>-0.90<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 70 —_
|Supplementarymaterials S1. Comparis<br>|on of full sample and sa<br>|mple with labor ma<br>|rket information<br>|||
|---|---|---|---|---|---|
|Variable|Mean full sample|N full sample|Mean sample<br>with labor market<br>information|Nsample with<br>labor market<br>information|Two-sample t-<br>test P-value|
|Female|0.486<br>(0.009)|3078|0.488<br>(0.018)|770|0.897|
|Age|36.748<br>(0.219)|3082|36.499<br>(0.433)|770|0.609|
|Household dependencyratio|1.090<br>(0.033)|3080|1.090<br>(0.016)|771|0.818|
|Head of household|0.590<br>(0.008)|3080|0.585<br>(0.018)|772|0.825|
|Completed secondaryschool|0.337<br>(0.009)|3080|0.334<br>(0.017)|772|0.896|
|Main source of income: Business|0.583<br>(0.028)|3080|0.588<br>(0.018)|772|0.812|
|Asset score|0.355<br>(0.003)|3080|0.357<br>(0.006)|772|0.853|

_Source_ : Authors’ calculations from CIMS (2020). Note: Standard errors in parentheses.

39
Policy Research Working Paper 5358

# **Abstract**

This paper analyzes the link between firm size and investment in job training by employers. Using a large firm level data set across 99 developing countries, the analysis shows that a strong and positive correlation in investment in job training and firm size is a robust statistical finding both within and across countries with very different institutions and level of development. However, the findings do not support the view that this difference is mostly driven by market imperfections disproportionally affecting small and medium enterprises. Rather, the evidence is supportive of small and medium enterprises having a smaller expected return from the investment in job training than larger firms. Therefore, the findings call for caution when designing pro-small and medium enterprises policies fostering investment in on-the-job training.

This paper—a product of the Social Protection & Labor, Human Development Network—is part of a larger effort in the department to promote labor markets programs that foster job creation. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ralmeida@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
O.A.M.D.G.

limit of common delinquent activity. However, we still prefer this violence dataset, assembled by Colombian violence historians and economists, to geographically more complete official sources, because it contains vital control variables, such as the rate of captures under the drug act by the police.

Household level variables from the _Familias_ survey cover day-to-day decision-making, the socio-economics of members and consumption information. The dataset also covers whether and how many family members have left the municipality. It also covers the political activity of the household expressed as membership in groups such as parties, religious groups, pro-peace and other groups.

At the municipality level, we chiefly have two sets of variables: violence variables, which are discussed in more detail in the next section, and other municipality characteristics, some of which may facilitate violence. Here, the empirical literature on Colombia has pointed to geography, judicial efficiency and welfare, respectively. Regarding geography, from _Familias_ we obtain the altitude in meters, a dummy for three different degrees of rurality and a dummy for one of four regions. In our context, geography is important for two reasons. First, a municipality with difficult physical access will naturally enjoy less governmental protection against armed groups such as guerrillas and paramilitaries (compare Vargas, Restrepo and Spagat (2004) and Reynal-Querol (2005)). Access in Colombia can be difficult through dispersion of settlement (rurality), rainforest/mangrove swamps (Pacific region), or mountainous terrain (intermediate to high altitudes). Second, coca plants, a major source of income for the armed groups as well as common delinquents, are best planted in certain areas. While the actual plantations are endogenous to the protection by armed groups, the geography apt to their cultivation, i.e. Andean altitudes between 1,000 and 2,000m, is exogenous. Regarding judicial efficiency, our municipal database contains the arrests under the Drug 11
# **WORKING LONG HOURS AND HAVING NO CHOICE: TIME POVERTY IN GUINEA**

Elena Bardasi and Quentin Wodon<sup>1</sup> World Bank Key words: Time Use, Employment, Underemployment, Poverty, Guinea JEL codes: J22, I32, O15

> 1 The views expressed here are those of the authors and need not reflect those of The World Bank, its Executive Directors or the countries they represent.  The authors benefitted from comments from Diane Elson, Maria Floro, and Caren Grown, as well as participants to a workshop on unpaid work, time use, poverty and public policy held at American University in March 2009, and anonymous peer reviewers.  A revised version of the paper is to be published in Feminist Economist.

1
Bishkek, the country’s capital.<sup>27</sup> These records contain crucial information for the design of our sampling procedure. The data identify the address of each of the households in the district as well as the transformer through which each of them is served.

Within the district for which we have utility data, seven villages (comprising 248 eligible transformers)<sup>28</sup> were chosen due to their accessibility from Bishkek during the winter months.<sup>29</sup> Given that the area surrounding the capital tends to be better off than those in the country’s other regions, the district in which the study occurs is not representative of the entire country. In order to improve external validity, the study sample was restricted to the 124 eligible transformers with below median monthly household electricity consumption in the year prior to the intervention. Such households and transformers are more representative of the country as a whole. To complete the sampling process, 20 percent of the households from each transformer were randomly selected to participate in the study.<sup>30</sup> Because the number of households per transformer is heterogeneous, the exact number sampled in each transformer also varies.

# **3.2 Experimental saturation design**

The randomized saturation design varies household exposure to the CFL technology to test for evidence of a household energy efficiency effect, spillovers in take-up, and a technological externality due to aggregate impacts. Transformers are first randomized to differing treatment saturations and then households within those transformers are randomized either to receive CFLs or to control status, according to the saturation previously assigned.

Figure 1 depicts how treatment status was randomly assigned in two stages. In the first stage, the 124 sampled eligible transformers were randomized into three groups: control, lower treatment saturation, and higher treatment saturation. Due to funding constraints, households in 14 control transformers were not surveyed. This resulted in only 110 eligible transformers being finally included in the study, with 25, 45, and 40 transformers in control,

> 27The data were provided by the electricity utility through a Data Use Agreement. The utility, however, was not informed as to which transformers or households were being treated through the experiment.

> 28Transformers providing at least 5 households with electricity were eligible. According to the utility, transformers serving fewer households likely also serve industrial consumers.

> 29This was to ensure that survey enumerators could reach households in March, when weather conditions can make transportation challenging.

> 30Due to funding constraints, households in only 25 of the 39 control transformers were surveyed, resulting in households in 110 transformers being surveyed.

11