rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2: Iron ore Prices (1951 – 1985)**

<!-- Start of picture text --> Liberia: Iron Ore Prices<br>US$<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985<br>Years<br>Price US Deflated Price Linear (Price US) Linear (Deflated Price)<br><!-- End of picture text --> Source: Author’s calculation

# **_Production Costs_**

Data on the production costs for iron ore for the period between 1951 and 1985 are scarce. However, 1984 estimated production costs of sinter feed for a selected group of countries including Liberia compiled by Toweh (1989) suggest that Liberia is a relatively high-cost producer or iron ore. Of the eight countries for which production cost data were available, Liberia had the 4<sup>th</sup> highest total cost of production (US$16.96 per ton of sinter feed), after India, Sweden and Canada. Furthermore, the estimated total cost of production in Liberia was higher than the average of US$16.27 for the selected group of countries (see Annex 2). In terms of the detailed operating costs, Liberia’s labor, energy and raw material costs were higher than the average for the selected countries. However, Liberia’s transport and handling costs were lower than the average for the group. Liberia also showed the third highest costs related to royalty and management fees.

A breakdown of the production costs showed that operating costs accounted for nearly 75 percent of the total cost while capital costs, royalty, taxes and management fees accounted for the remaining 25 percent of total cost. Of operating cost, the cost of raw material accounted for about 28 percent. Labor cost accounted for nearly 17 percent and energy cost accounted for 12 6
Figure A – Reported innovation in the short, long and long‐clean questionnaire <!-- Start of picture text --> Short questionnaire<br>NO YES<br>Long questionnaire Long questionnaire<br>No No<br>Yes<br>no>no Yes yes>no<br>(1) (2)<br>Cleaned Cleaned<br>Yes No Yes No<br>no>yes>yes no>yes>no yes>yes>yes> yes>yes>no<br>(5) (6) (3) (4)<br><!-- End of picture text --> The percentage of firms in each of the six categories by country is presented in Table 1. Column (7) shows the percentage of answers that could be verified, which is equal to the share of reported innovators in the long questionnaire, since these are the firms for which the description of innovations is available. In total 70 percent of the answers were verified with the share of verified answers ranging from less than 40 percent of firms in Nepal, Pakistan, and Sudan to almost all firms in Bangladesh and India. Column (8) shows the percentage of firms that provided a correct and consistent answer in the short questionnaire. On average, 66 percent of the responses were correct, which means that in 34 percent of the cases innovation was not measured correctly when using the short questionnaire. In some countries such as India, South Sudan, and Zambia the short questionnaire provided a correct measure of product and process innovations for more than 75 percent of firms, but in countries like Nepal or Sudan the short questionnaire was successful in measuring innovation in less than 50 percent of the sample. Note, however, that this is based on the assumption that the firms answering no in both surveys (1) or reporting 15
Table 5: Impacts of program on forest cover loss, 25-point and 10-point windows

||(1)<br>**Full**|(2)<br>**2011-2012**|(3)<br>**2013-2014**,|(4)<br>**Full**|(5)<br>**2011-2012**|(6)<br>**2013-2014**|
|---|---|---|---|---|---|---|
|||**Perc. defore**|**sted**||**Defor** _>_**2 ha**||
||||**Panel A: 25-**|**point wind**|**ow**||
|Above threshold|-0.125|-0.285|-0.016|-0.029|-0.042|-0.028|
||(0.109)|(0.230)|(0.054)|(0.032)|(0.049)|(0.038)|
|Control Mean|0.619|0.956|0.319|0.062|0.093|0.033|
|Control SD|3.385|4.138|2.495|0.240|0.291|0.179|
|MDE|0.304|0.644|0.151|0.089|0.138|0.106|
|Observations|14119|7019|7127|14119|7019|7127|
||||**Panel B: 10-**|**point wind**|**ow**||
|Above threshold|-0.151|-0.324|-0.025|-0.072*|-0.070|-0.062|
||(0.160)|(0.336)|(0.059)|(0.039)|(0.060)|(0.049)|
|Control Mean|0.634|0.934|0.329|0.060|0.084|0.036|
|Control SD|3.507|4.158|2.654|0.238|0.277|0.186|
|MDE|0.448|0.940|0.164|0.109|0.168|0.136|
|Observations|11030|5856|5195|11030|5856|5195|

1 Covariates include: point score, threshold x point score, ejido indicator, ln(polygon ha) percent forest in 2000, cohort indicators, ecosystem indicators, and state dummy variables. Standard errors are clustered at the ejido level for ejidos, and at the level of the municipality for private properties. Sample is limited to polygons greater than 5 ha and with more than 50% forest in 2000. *** p _<_ .01; ** p _<_ .05; * p _<_ .10 Table 6: Heterogeneity by level of deforestation risk (% Forest Cover Loss)

||(1)<br>**Full**|(2)<br>**2011-2012**|(3)<br>**2013-2014**|
|---|---|---|---|
|Threshold|0.006|-0.002|-0.002|
||(0.103)|(0.220)|(0.050)|
|Threshold x high defor risk|-0.256***|-0.491**|-0.049|
||(0.091)|(0.028)|(0.028)|
|Control mean (high risk)|0.865|1.294|0.421|
|Observations|14119|7019|7127|

1 Covariates include: point score, threshold x point score, ejido indicator, ln(polygon ha) percent forest in 2000, cohort indicators, ecosystem indicators, and state dummy variables. Standard errors are clustered at the ejido level for ejidos, and at the level of the municipality for private properties. Sample is limited to polygons greater than 5 ha and with more than 50% forest in 2000. Deforestation risk indicates that the level of risk using the INECC layer is higher than the median in the sample. *** p _<_ .01; ** p _<_ .05; * p _<_ .10 38
ownership. Confirming widespread anecdotal evidence on the positive impact of working abroad in fostering the establishment of microenterprises back home, our findings are indicative of a strong, positive relation between past (return) migration and business ownership. The propensity of being involved in small family businesses is highest among households with migration experience in countries other than Greece. This may reflect the higher earning potentials in countries like Italy or the United Kingdom, _vis a vis_ Greece, as well as differences in the type of skill acquisition and human capital accumulation in the different host countries.

It is becoming increasingly evident how the initial flood of migrants to neighboring countries is slowly generating a stream of returnees who, often after multiple moves back and forth, have decided to settle back in Albania. The importance of return migrants, particularly the financial and possibly human capital that they bring back, is grossly overlooked by policy makers and researchers alike, in part due to the fact that return migration is still a relatively recent phenomenon. Our estimates seem to suggest the existence of a multi-episode migration cycle, with migrants investing back home only after completion of a process of accumulation of cash and skills required to successfully run a business. Pushing the argument somewhat further, it may even be that facilitating the development of new businesses could actually provide further impetus for more international migrants to return to the homeland where some of the difficulties associated with starting microenterprises could be lifted. Other factors such as location and availability of services and infrastructure also play a role in fostering business development.

Despite much debate about the undisputable importance of an active non-farm business sector, Albanian microenterprises still face significant financial, fiscal and institutional constraints that limit their development. A cursory look at the results of a recent investment climate survey (Table 9), for instance, suggests that while some features of the Albanian economy are fairly conducive to successful entrepreneurship, notable improvements can be made in several areas (e.g. the provision of basic services such as water and electricity) where Albania lags behind countries in its region or elsewhere. Taken together with the results presented in this paper, this suggests that improvements in these critical areas would also foster a better use of the capital accumulated over the last decade and a half by the numerous Albanian diaspora, and in particular by the increasing number of return migrants.

Migration can and should be seen as a resource that can be put to work to foster national development. Remittances have played a most important role in fuelling the impressive growth

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47

# Table Ba

lIprovements in Resource Balance (as 2 of GDP) With 1969 to 1978 Average Terms of Trade

||1978|1979|180|1981|1982|1983|1984|1985|1926|
|---|---|---|---|---|---|---|---|---|---|
|Resource ga1 CFI gain<br>6DP Share||||||||||
|Oil Exporters|-2.91|-5.4%|-9.6%|-9.8%|-8.71|_-7.2%_|-7.2%|-5.72|-0.4%|
|Manufacturers|0.5%|0.92|3.0%|4.2%|4.1%|4.8%|4.72|5.3l|.SX|
|Prisary|-0.7%|-0.1%|1.5%|2.3%|2.0%|1.52|).0.|0.3%||
|Lao<br>Income|1.0%|0.5%|1.1%|3.0%|3.9%|4.82|2.3%|2.22|2.:.|
|MiddleIncome|-1.0%|-1.52|-2.1%|-1.8%|-1.2%|-0.72|-1.02|00%|:.n2|
|Non Oil Exporting|0.0%|0.6%|<br>2.77.|3.7%|3.4X.|3.4%|3.1%|3.32|3.4',|
|Africa|-2.6%|<br>-0.1%|-2.8%|<br>-4.9%|-2.32|-0.3%|-3.3%|-2.5%|<br>l2|
|Asia|-1.6%|-7.6%|-4.4|-3.2%|<br>-2.2%|-2.5%|<br>-3.0%|-2.12|-1.52|
|India|0.0%|1.02|3.5%|3.0%|2.6%|1.81|2.5%|2.4%|2.1|
|ENENA|1.2%|<br>-0.5%|<br>-0.6%|<br>0.47|0.8%|1.3%|1.7%|<br>2.3%|3T'.|
|LAC|-0.9%|<br>-1.1%|<br>-1.11|<br>-0.7%|<br>-0.5%|<br>0.32|0.0%|1.0%|<br>3. 1|
|HICS|-1.4%|-1.1|-1.5%|-1.50||-0.4%|-0.8%|0.Z<br>.||
|SAL Countries|-0.6%|-0.3%|-0.4%|-0.4%|<br>0.0%|0.8%|9.42|1.12.||
|Non SAL Countr:es|-1.b6|<br>-6.2%|-8.6%|<br>-6.5%|-4.3%|-4.02|<br>- .12|-3.6X||

.dote: Asia excludes .ndia and China
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All five surveys measured mental health for multiple adults within the household, enabling us to examine the co-prevalence of mental health. There are several possible channels through which such a correlation may occur. It may reflect the effect of omitted household-level variables, such as household-specific shocks or lack of available health services. It could also reflect unobserved individual-level traits, if assortative mating leads those with poor mental health to marry and perhaps pass on genetic factors influencing mental health to other family members. However, it is also plausible that the relationship could be causal, whereby the presence of one household member with poor mental health creates a poor mental health environment for other household members, i.e. a “contagion” effect. Tables 2 and 3 show that there is indeed a strong and significant positive relationship between an individual’s mental health and that of his or her family.<sup>10</sup> A one standard deviation change in the mental health of household members is associated with a 0.22 to 0.59 standard deviation change in own mental health.

Finally, in BiH, Mexico and Indonesia we are able to look at the relationship between individual mental health and the mental health of others in the surrounding community. We see that there is a significant and positive association, even after controlling for household average mental health, with the size of the coefficient up to twice that at the household level. Some of the explanations that account for the withinhousehold clustering of poor (or good) mental health can also apply at the community level.

# <u>3.2 Mental health and shocks</u>

The cross-sectional relationships examined above show no significant relationship between mental health and poverty, but do find strong clustering of mental health outcomes within households and, to a lesser extent, within communities. One potential interpretation of this intra-household clustering is that they reflect, in part, the effect of common household-level (or community-level) shocks. The three countryspecific studies produced by the Development Research Group’s comparative project provide detailed study of the impact of shocks on mental health, and provide some support for this interpretation. We summarize the three studies and their implications for the impact of shocks here.

Two of the papers demonstrate that shocks that involved large changes in income led to changes in mental health. Friedman and Thomas (2007) consider the impact of a negative shock, the Indonesian financial crisis, finding that the crisis worsened mental health of households, and more so for households that were more greatly affected by the crisis. Moreover, they find this effect persists up to three years after the onset

> 10 The household level mental health measure is the average across household members (excluding the individual respondent). Similarly we can define the average community mental health score as the average across the community (excluding the household in question).

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6 26th level corresponding to a female life expectancy of 82.5 was added. The appropriate level for each time period was chosen given life expectancy, with interpolation if necessary between adjacent levels. Choice among life table families was based on region and mortality level, as follows: Region and mortality level Life table family Sub-Saharan Africa Below 12 South 12-17 North 18 and above West North Africa, the Middle East, Latin America, and the Caribbean Below 18 South 18 and above West Northern America, Europe, Asia, and Oceania All levels West Over time, countries in particular regions may therefore switch from one life table family to another. To avoid discontinuities, the switch is made gradually, using life tables linearly interpolated between the two families involved for five intervening periods.

# Fertility level and trend

Base-period total fertility rates (TFRs) are derived from the same mix of sources as mortality rates, including the U.N. 1988 revision; official sources, as cited in Population and Vital Statistics Reports or in government publications; the U.N. 1984 assessment; the U.S. Bureau of .he Census (1985); Eurostat (1987);-and Celade (1987). We relied on two sources more frequently for information on fertility than mortality: various surveys, especially Demographic and Health Surveys and World Fertility Surveys; and the Institut National d'ttudes Demographiques (Monnier 1987). Often, all these sources give fertility for a different period or in terms of the crude birth rate, and corresponding TFRs had to be estimated using available data on age-sex composition and age-specific fertility, or obtained from a projection using an estimated TFR for an earlier period. Where no reliable source was available, an unattributed Bank estimate may be indicated in the Appendix.

Future fertility trends are specified by selecting a year when the net reproduction rate (NRR) reaches unity, i.e., when the average woman's fertility results in exactly replacing herself. (This is referred to here as the replacement year.) We explain below how the replacement year was selected; what was done for countries with NRR already below replacement; how the path of fertility betweer the present and the replacement year was specified; and what patterns of fertility by age were applied.

For all countries, the replacement year is assumed to be before
_property rights protection index_ , which is based on a firm’s answer to the following question: Of all the commerce or other conflicts that the company has experienced, what is the likelihood that its contractual or property rights are protected by the legal system? This variable ranges from 0 to 1, with a higher value implying better protection of property rights. A related measure is court development, proxied by the share of a firm’s disputes that are resolved by the court system. A higher value of _court development_ implies a stronger legal system and better protection of property rights. As a proxy for corruption and government expropriation, we rely on the share of entertainment and travelling costs over sales (ETC). Cai, Fang and Xu (2011) provide evidence that this is a good proxy for corruption in China: ETC is higher in regions that feature higher tax burdens and worse government services, presumably because it was spent to reduce tax burdens and to improve government services; the average ETC level in a locality also has a negative relationship with firm efficiency, but the effect differs across regions, with less pronounced negative effects in regions with higher taxes and worse government services.

Since the efficiency of government bureaucracy may also affect firm efficiency, we include three additional control variables. We use three variables to measure local government efficiency.<sup>7</sup> _Government Efficient Services_ is an index that reflects the manager’s assessment about the share of government officials that offer efficient services. _Official Ability_ is an index that reflects the manager’s assessment of the share of government officials that are competent. _Official Helping Firms_ is an index that reflects the manager’s assessment of the share of government officials that help rather than hinder firm development. Finally, leadership turnover has been frequent in Chinese local governments—the typical party secretary, the top leader of a locality, only has a 3 to 4 year tenure. This may have an effect on local economic development (Lu and Liu 2013). To the extent that longer horizons for local government leaders may lead to more efficient government policies and actions that internalize the benefits of current efforts or investments, or alternatively behave as “stationary bandits” in the words of Mansur Olson (Olson 2000), it may be important to capture the time horizon of local party secretaries. We thus create

> important than contracting institutions in facilitating economic development. Their interpretation is that it is easier for private parties to use alternative mechanisms to get around the contracting issues, but it is harder to avoid government expropriation.

> 7 All three variables are based on direct survey questions posed in the survey.

6
# Main Legsonp

Nonformal education projects have had more than the usual share of implementation problems. This is not surprising since the appropriate method for delivering these components on a large scale is not well developed. Four lessons emerge from the limited Bank experlence in this area. Figure 3 summarizes the objectives, strategies and lessons of Bank investment in this area.

First, small components have at best yielded small benefits; often they have not been given the attention in preparation and supervision that they needed. The best results have been achieved in free-standing projects or projects where at least 30% of the funds was allocated to NFET. Running NFET programs as an appendix to formal programs jeopardizes potential success.

Second, experimentation and piloting of delivery strategies, programs, and materials are essential. Evaluation, including assessment of learning achievement, is critical in a systematic process of learning about what works and what does not work. Unless outcome evaluations will be systematically developed, the renewed interest in NFET will not be sustained. If outcomes are not measured, reductions in budget allocations have no known cost.

Third, the most successful programs have built-in mechanisms to respond flexibly to requests for training from the beneficiaries, on the basis of clearly spelled out procedures and criteria. Also, when income generating training is included in the package, chances for success increase. Fourth, attending to management and institutional issues is very important. Where programs have worked, there have been significant investments in building administrative and managerial capacity.

30