rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3.1: Industrialization in China led to a transformation of exports from primary and resource-based products to manufactures which Africa imports from China**

<!-- Start of picture text --> China - exports<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>1980-84 1985-90 1990-95 1995-00 2000-05 2005-08<br>Primary & Resource-based products Light manufactures<br>Medium Tech manufactures High Tech manufactures<br>Source: COMTRADE, 2010<br>Share of total exports (%)<br><!-- End of picture text --> There are several signs to mark slow de-industrialization in Africa in the last quarter of the last century when China and India were emerging as large industrial countries and the African countries were undertaking structural reforms, liberalizing and integrating with global markets. An overview of Africa‘s industrial performance between 1990 and 2010 indicates that 40 out of 45 countries had a manufacturing value added (MVA) per capita of less than $200 and almost half had experienced negative growth in MVA per capita during 1990-2010 (Figure 3.2). The correlation between per capita income and MVA was unmistakable and underscored the centrality of industrialization in Africa‘s catch-up.

The negative growth in MVA per capita leading to de-industrialization occurred for two reasons. One, the removal of protection to the old IS industries causing their collapse established that they were CAD industries that were unviable without protection. And two, the lack of facilitation in the form of the provision of public goods by African governments to the entry of industries in which Africa has a comparative advantage. As in the case of most other developing countries in trying to prop up CAD industries through IS, African governments failed to nurture the emergence of CAF industries. Today, most African countries import even the simplest of consumer goods they once produced.

35
instrumental variables limited information maximum likelihood (IV-LIML) approach, as it is less susceptible than other IV estimators to weak instrument bias, and show results in column 5 (province fixed effects) and column 6 (district fixed effects). Consistent with our expectation of an upward bias in OLS, the IV-LIML estimates suggest that children born to parents in consanguineous unions score 0.83 and 1.09 standard deviations lower, in models with province and district fixed effects, respectively, on Ravens Progressive Matrices tests. The negative effect of consanguinity on cognitive development is significant at the 5 percent level in both models.

The results of the Hansen J-test for over-identification show no evidence against our identification strategy, but the F-statistics on excluded instruments are 8.27 and 5.30 in models with province and district fixed effects. Although jointly significant at better than 1 percent, the F-statistics are below 10, the standard rule of thumb at which researchers should be concerned with possible weak instrument bias (e.g., Stock and Yogo, 2005). Note that we are clustering standard errors at the village, and the “rule-ofthumb” was derived in an environment in which errors can be considered independent, thus standard thresholds may not apply with cluster-corrected F-statistics Cameron and Miller (2011, 2015). Appropriate _F_ -statistic thresholds for the presence of weak instrument bias are likely to vary by application and we thus explicitly test for weak instrument bias. Following the suggestion of Cameron and Miller, we use a cluster robust version of Moreira’s (2003) Conditional Likelihood Ratio test, derived using the method of moments (Finlay and Magnusson 2009). We also use this test statistic to generate weak instrument robust 95 percent confidence intervals around the coefficient estimate on the consanguinity variable. The CLR test shows that the coefficients on parental consanguinity in our IV model are negative and statistically different from zero, in models that alternatively include province and district fixed effects.

The lower bound of the CLR test confidence interval indicates that the negative impact of consanguinity is at least 3 times larger than the estimated effect using Ordinary Least Squares. This underlines the importance of accounting for the endogeneity of consanguinity when attempting to estimate its causal impact on child outcomes, which, to the best of our knowledge, has not been done by previous literature. By failing to control for positive bias due to endogeneity, ordinary least squares approaches underestimate the negative effect of consanguinity on human development outcomes.

Other explanatory variables have expected coefficient signs. The cognitive test scores increase with child age, which is intuitive. Household wealth, as captured by an asset index has a large positive and statistically significant effect on test scores. Male children tend to score higher, although the estimated 14
While this approach was designed primarily to be applied to individual NMHSs for assessing either whole-of-service or specific products, we apply it on a global scale. This results in a less detailed SEB analysis that should be considered a rough estimate of the global potential of GPCs providing forecasting products to LICs.

The baseline of this study is the status quo of weather and climate forecasting quality in LICs. Here, it is assumed that in the status quo, LICs lack full access to global forecasting products from GPCs. The relevant change is the gaining of full access to forecasting products available from GPCs.

# 3.1 Conservative approach

A critical challenge in assessing costs and especially benefits in this field is that it is difficult to attain comprehensive, robust and accurate figures. Firstly, it is somewhat ambiguous as to what exactly should be included under costs and socioeconomic benefits. Secondly, assuming the first issue is solved, it is not possible to obtain data on every aspect. This issue is difficult to solve, but its consequences can be controlled. In consideration of these limitations, we assess ranges of benefit and cost estimates, rather than singular values. Furthermore, the trend of potential biases can be managed; when confronted with data gaps, socioeconomic benefits are intentionally systematically understated, while the more robust cost data are considered more comprehensive.

Under such a conservative approach, the resulting net benefits likely underestimate the true net benefits. Assessment biases therefore affect only the magnitude and not the overall trends of the results, increasing confidence in the conclusions.

# 3.2 Costs

Costs are defined as the total amount of sacrifices made to accomplish a task or produce a product or service (WMO et al 2015). In the case of weather forecasting, costs occur at different stages:

- Service production and delivery (GPCs, RSMCs, NMHSs, commercial weather services, etc.) including staff, forecasting infrastructure investment and maintenance as well as the costs of buildings, utilities, etc. Taking a wider perspective, the use of public goods should be included as well. Apart from the public good use, these costs can be estimated from the balance sheets of the GPCs, RSMCs, NMHSs or commercial weather services.

- Users incur costs when accessing forecasting products and adapting their decisions accordingly. These costs are more difficult to assess quantitatively and are not relevant for this research, as we will only consider GPC costs.

As such, costs for our purposes occur at four different stages:

1. Costs of the GPCs for research, development, production and dissemination.

2. Costs of the RSMCs and NMHSs to use and adapt these forecasting products.

3. Costs of commercial weather services to process forecasting information, tailor forecasting products for the end user and communicate these.

4. Costs of users of weather services for information retrieval, interpretation as well as the cost of taking action according to the forecasting information.

11
The variables under the _knowledge about BOS_ are ordered by difficulty, or specificity of the knowledge. For example, the first question asks whether the respondent has ever heard of BOS, the second question asks whether the respondent knows what BOS stands for (“School Operational Assistance”) and the third question asks the amount of the grant. In the endline survey, a question about the uses of funds was added. In addition, in the endline, we can assess whether the responses (on the amount and uses) were correct.

**Table 1: Availability of variables used to measure knowledge** **<u>and participation at baseline and endline</u>**

|Variable|Baseline|Endline|
|---|---|---|
|**_Knowledge index BOS_**|X|X|
|Heard of BOS|X|X|
|Knows what BOS stands for|X|X|
|Knows amount|X|X|
|Gets amount right||X|
|Knows negative list of uses||X|
|**_Knowledge index BOS in school_**||X|
|Knows of BOS plan document||X|
|Knows BOS reporting documents||X|
|Knows BOS use in school||X|
|Sees BOS use in notice board|X|X|
|Knows School Committee role||X|
|**_Participation Index_**|X|X|
|Visits at least 3 times a year|X|X|
|Thinks BOS use is transparent|X|X|
|Involved in BOS plan||X|
|Communicates with School Committee|or principal|X|
|Contribution (cash)||X|
|Contribution (in kind)||X|
|Note: The composition of the indices is dif<br> <br>Contribution(labor)|ferent at baseline a<br>|nd endline,<br>X|

Note: The composition of the indices is different at baseline and endline, using the available variables indexed below A second set of questions asks about the _knowledge of BOS in own school_ . This is an important distinction to make: While disseminating information about the program as a whole may have inherent benefits in terms of increased awareness of, or interest in, education, and public support for the program, this information is unlikely to have an impact on participation and schooling outcomes of interest unless it is translated into knowledge about the program at the school level. This set of questions includes whether the respondent knows the roles of the School Committee, 12
# Table 1. Excess Demand in Supply-Use Table, 2007

|Sector|Demand<br>(Million Drams)|Supply<br>(Million Drams)|Excess Demand<br>(Million Drams)|Excess Demand<br>(% of total supply)|
|---|---|---|---|---|
|Agriculture, hunting, forestry and fishing|667674|946682|-279008|-29|
|Mining and quarrying|140383|265814|-125431|-47|
|<br>Manufacturing|2628702|1696509|932192|55|
|Electricity, gas and water supply|309011|167184|141827|85|
|Construction|1199096|1349184|-150089|-11|
|Wholesale and retail trade and repair|462281|511197|-48917|-10|
|Hotels and restaurants|103346|138806|-35460|-26|
|Transport, storage and communications|328311|436416|-108105|-25|
|Financial intermediation|69604|109189|-39585|-36|
|Real estate and professional services|95704|175056|-79352|-45|
|<br>Public administration and defence|176585|162771|13814|8|
|Education|203286|120656|82631|68|
|Health and social work|157127|129786|27341|21|
|Other social and personal services|29630|84707|-55077|-65|
|Source: Authors' calculations based on data fro|m National Statistical Of|fice of the Republic|of Armenia||

Table 2. Total GDP, 2007 (million Drams)

|GDP<br>Official Estimate|Supply-Use<br>Table|Authors'<br>balanced table|
|---|---|---|
|Income<br>3149283|2860584|3044640|
|Expenditure<br>3149283|3137365|3044640|
|Production<br>3149283|2906200|3044640|
|Source: Authors' calculations|based on data fr|om National|
|Statistical Office of the Repu|blic of Armenia||

Table 3. Composition of GDP by expenditure category, 2007

|Total<br>Household|Government|Investments|Net Exports|
|---|---|---|---|
|(Millon Dram)<br>(%)|(%)|(%)|(%)|
|Supply-Use Table<br>3137365<br>72|10|38|-20|
|Authors' balanced table<br>3044640<br>67<br>Source: Authors' calculations based on data from National St|10<br>atistical Office of t|42<br>he Republic of Ar|-19<br>menia|

109
Policy Research Working Paper 7548

# **Abstract**

Using all the household survey data available in Latin America during the period 1992 to 2013, this paper estimates that in 2015, 20 million youth ages 15 to 24 years in the region were out of school and not working (making them _ninis,_ for “ _ni_ estudian _ni_ trabajan”). The share of out-ofschool, out-of-work youth in Latin America, at about 19 percent, is roughly equal to the global average of 22 percent. Although women make up over two-thirds of the ninis in the region, the number of male ninis grew by 46 percent between 1992 and 2010. As a result, the absolute number of ninis rose over the two-decade period, even as women’s education and employment rates were improving. Global comparisons show that Latin America is the region of the world with the largest concentration of ninis among households in the bottom 40 percent of the income distribution. Coupled with the long-lasting harm it causes to the youth’s future labor-market outcomes, the high incidence of ninis among the poorest households tends to lock in income disparities from one generation to the next, obstructing social mobility and poverty reduction in the region.

This paper is a product of the Education Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at rdehoyos@ 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
with _ε_<sup>_b_</sup> _jt_ +1<sup>_∼N_(0</sup><sup>_, σ_</sup> _b_<sup>2)and</sup><sup>_εa_</sup> _ijt_ +1<sup>_∼N_(0</sup><sup>_, σ_</sup> _a_<sup>2).We further assume that the shocks</sup><sup>_bjt_are independent</sup> across sectors and the shocks _Aijt_ are independent across rms. Since _bjt_ and _Aijt_ are not observable, we infer them from data on operating prots as follows. From the denition of instantaneous prots we can write the combination of sector-level and rm-level protability shocks as Let _xijt_ denote the product of the rm and sector level shocks, with _xijt_ = _bjtAijt_ . We compute the right-hand size of (17) from data ( _πijt_ , _Kijt_ , _wjt_ ) and the estimates from the previous step ( _αL_ , _αK_ , _vM_ ). This gives us estimates for _xijt_ . To estimate the _A_ parameters, we rst compute deviations of _xijt_ from sector-year means (to get rid of _bjt_ ) and then regress this on one lag (no constant) To estimate the _b_ parameters we compute the sector-year means of _xijt_ and run the regression using the means, at the sector-year level In Table 1 in the paper, we report _ρa_ = 0 _._ 902 , _σa_<sup>2= 0</sup><sup>_._118,</sup> _b_ = 0 _._ 318 , _ρb_ = 0 _._ 777 , and _σb_<sup>2= 0</sup><sup>_._044.</sup>

# A.3 SMM estimator

The adjustment costs parameters are given by the vector Γ = ( _γ_ 1 _, γ_ 2 _, γ_ 3; _Ce, Cu_ ) , which we estimate by simulated method of moments (SMM). The SMM estimator is based on comparing a vector of empirical moments computed from rm and worker actual data, denoted by Ψ , with moments computed from rm and worker simulated data, denoted by Ψ<sup>_s_</sup> (Γ) . The simulated moments depend on the parameters Γ through the investment policy function and the employment transition probability policy function, given that the moments are based on rm-lelve investment and labor allocations across sectors. The estimator for the adjustment costs minimizes the weighted distance between the empirical and simulated moments. Formally, 43
Table 2: Prevention: caretaker’s knowledge and behavior.

||Care|taker’s||Water d|isinfection||Malaria|
|---|---|---|---|---|---|---|---|
||kno|wledge|sel|f-reported||measured|protection|
||Knows<br>ORS|Knows age<br>of excl.|Given<br>last|Used<br>last|Used<br>today|Water<br>chlorine|Bed net<br>hung|
||recipe<br>|breastfeeding|year|7 days|c|ontent (ppm)|correctly|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|CHW visits|0_._330<sup>_∗∗∗_</sup>|0_._096<sup>_∗_</sup>|0_._230<sup>_∗∗∗_</sup>|0_._125<sup>_∗∗∗_</sup>|0_._095<sup>_∗∗∗_</sup>|0_._056|0_._146<sup>_∗∗∗_</sup>|
||(0_._044)|(0_._053)|(0_._030)|(0_._038)|(0_._030)|(0_._040)|(0_._046)|
|Subsidy|0_._067<sup>_∗_</sup>|_−_0_._057|0_._023<br>|_−_0_._013|0_._006|0_._020|0_._082<sup>_∗_</sup>|
||(0_._037)|(0_._054)|(0_._022)|(0_._035)|(0_._027)|(0_._043)|(0_._045)|
|Full program|0_._498<sup>_∗∗∗_</sup>|0_._046|0_._357<sup>_∗∗∗_</sup>|0_._169<sup>_∗∗∗_</sup>|0_._083<sup>_∗∗∗_</sup>|0_._027|0_._141<sup>_∗∗∗_</sup>|
||(0_._040)|(0_._052)|(0_._037)|(0_._038)|(0_._028)|(0_._048)|(0_._044)|
|Control group mean|0_._167|0_._554|0_._008|0_._120|0_._058|0_._170|0_._662|
|N<br>|1016|1016<br>|1015<br>10|15<br>10|15|1016|1649|
|p-value: CHW = full|0_._00|0_._33|0_._00|0_._26|0_._69|0_._51|0_._91|
|p-value: C+S = full|0_._10|0_._93|0_._03|0_._27|0_._65|0_._43|0_._17|

_Notes:_ All regressions include stratum fixed effects, control for covariates, and cluster standard errors at the compound level. “Knows ORT recipe”: mother can explain how to make oral rehydration treatment for diarrhea. “Knows age of exclusive breastfeeding”: mother answers “6 months” when asked how long a baby should be exclusively breastfed. “Given last year”: reports receiving water disinfectant tabs in the last year. “Used last 7 days”: self-reported use of water disinfectant. “Water chlorine content” measured chlorine in parts per million. “Any chlorine detected”: dummy for nonzero chlorine content. “Bed net hung correctly”: the enumerator asked to see the bed net under which the child sleeps (if any) and noted whether it was hung correctly or could be hung easily, vs. stored or not available. Significance levels: * 0.1, ** 0.05, *** 0.01.

21
**Table 16 OLS: Village fixed effects regression of teacher effort, UP** (Dependent variable has four observations per teacher: Attendance=1 if teacher present, 0 otherwise: Activity=1 if teacher actively <u>engaged in teaching, 0 otherwise)</u>

||**Attendance**|**Attendance**|**Activity**|**Activity**|
|---|---|---|---|---|
|**Whether teacher is contract**<br>**teacher (****_shiksha mitra_) **|**0.12**|**0.10**|**0.07**|**0.07**|
||**(0.00)****|**(0.05)**<sup>*****</sup>|**(0.01)****|**(0.04)***|
|Age|-0.003|-0.004|-0.004|-0.004|
||(0.12)|(0.11)|(0.00)**|(0.05)*|
|Caste (1 if non-SC)|-0.03|-0.03|0.01|0.01|
||(0.34)|(0.31)|(0.65)|(0.63)|
|Gender(1 if male)|-0.01|-0.02|-0.09|-0.09|
||(0.70)|(0.57)|(0.00)**|(0.00)**|
|College educated|-0.08|-0.07|-0.04|-0.04|
||(0.02)*|(0.04)*|(0.12)|(0.13)|
|Graduate degree|-0.15|-0.13|-0.07|-0.07|
||(0.00)**|(0.00)**|(0.01)**|(0.01)*|
|Pre service training||-0.04||-0.01|
|||(0.39)||(0.88)|
|Years ofservice||0.00||0.00|
|||(0.22)||(0.69)|
|Days of inservice training||0.00||0.00|
|||(0.90)||(0.63)|
|Village/school fixed effect<br>(200 villages)|YES|YES|YES|YES|
|R<sup>2</sup>|.17|.18|.23|.23|
|Observations|2417|2416|2416|2415|
|Robust p valuesinparentheses c|lustered at teach|er level|||
|*significant at 5%;**significa|nt at1%||||

30
_Policy Options for Reaping the Benefits of S&T_ The effects of S&T on poverty and economic growth underlie their importance for development. However, achieving progress in S&T capacity depends on good policies and practices that foster the appropriate environment implemented consistently over the long term. The framework for these policies must recognize the interconnectedness of the areas upon which S&T bears. Policies that affect human resources development, demand for knowledge from the private sector, public support for and management of knowledge institutions, and access to information and communication technologies (ICT) infrastructure must be coordinated and harmonized in order to create the conditions in which S&T capacity deepens and consolidates. Each of these areas, which are detailed in brief in the following pages, entail a set of specific sub-policies, yet success depends on integrating them harmoniously. Different countries, of course, face different challenges that require emphasis in one area or another.'I

> Policies for iRlHnman Resources DeveRoplment Policies concerned with human

> resource development seek to accomplish four major goals that are briefly highlighted below.

- Provide the broad basic science education that makes a human resources base scientifically literate, imparting both everyday skills and intellectual abilities needed for an informed citizenship

- Stimulate interest and prepare adequate numbers of young people to pursue careers in science and technology as well as provide opportunities for life-long learning and skill renewal

- Educate a diverse labor force and develops skills for various purposes at various levels of sophistication

- Encourage the conduct of research and advanced training that creates the knowledge and highly trained specialists needed to advance the frontiers of knowledge and applications

> Sound human resources development for S&T begins with science education at the primary and secondary levels. Student achievement outcomes in science in much of the

> developing world are very low." Of course, many of the problems of science education

> at primary and secondary levels cannot be separated from the more generalized

> challenges in basic education, such as teacher training and recruitment, stable education

> finance, availability of teaching materials, etc. Other challenges are specific to the

> domain of S&T. These include curricula that are appropriate and science programs tailored to the developmental needs of students and their societies, the use of goals and

> standards for student achievement to guide the design, implementation, and assessment of

> all elements of the science program, and provision of support systems for teachers that

> align with the goals of the science program.

> **_10_** _Appendix 2_ summarizes how the policy area of emphasis for S&T growth differs with the varying

> capacities of developing countries.

> See the results from the TIMSS for a more detailed description at www.timss.bc.edu.

12