rafmacalaba/datause-encoder-data huggingface dataset:
## Figure 10

# GHANA

## Area under crops

<!-- Start of picture text --> Thousand hectares<br>3000<br>2500<br>2000<br>1500L<br>1000<br>500<br>0<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>* _ Cereals Cassava L Yam<br>Other starch Pulses LI Vegetables<br><!-- End of picture text -->
complements, is an empirical issue.<sup>2</sup> Understanding this relationship is extremely important as previous research has shown a very strong connection between educational proficiency and adult income and economic growth, and that proficiency is a stronger determinant than completed years of schooling [see, e.g. Hanushek and Zhang (2009) and Hanushek and Kimko (2000)].

We are aware of three previous studies that have examined the impact of child work on student proficiency. The first paper, closely related to the present study is Bezerra et al. (2009), which uses cross-sectional Brazilian data to test the impact of working on the performance on similar exams. The authors find that working has a negative impact on the performance of participants. Another closely related study is Dumas (2012) which exploits retrospective data from Senegal to examine the e↵ects of child work on the test scores of Senegalese children and finds some evidence of positive e↵ects of child work. Finally Gunnarsson et al. (2006) uses data from nine Latin American countries (including Brazil) and finds negative and significant impacts of working on student test scores. All three studies are constrained by the inherent difficulties in overcoming the potential endogeneity of child labor and all three implement instrumental variables strategies in an attempt to overcome the problem. In all three cases, the challenge of finding sources of variation that are correlated with the decision to work but uncorrelated with the unexplained variation in school performance is severe, leading to questions of the validity of the instruments themselves and thus the results. In our case the use of time-series data presents a huge advantage in our ability to control for both the endogeneity of child labor and the presence of other unobservables (e.g. parental preferences) that are potentially correlated with both the decision to work and the aptitude for, and attitude toward, school. We are also able to explore the lingering and 2 We use the terms ’learning’ and ’proficiency’ synonymously, but for both we are referring specifically to only those aspects of learning that are measured by standardized tests in Portuguese and Math.

6
the household surplus is insignificantly different from zero (equation 5). With the reduced sample there is a significant effect, but it is small (equation 5').

|H-<br>6.51-|0.331 FG||(3)|
|---|---|---|---|
|(18.6)|(4.9)|||
|RSQ-0.171|#of obs-<br>118|(full sample) OLS||
|B-<br>-6.62|-<br>0.637 FG||(4)|
|(17.5|) (8.7)|||
|RSQ-0.397|#of obs-<br>118|(full sample) OLS||
|H-<br>6.07|- 0.105 FG||(5)|
|(13.9|) (0.7)|||
|RSQ-0.09|1 # of obs- 118|(full sample) 2SLS||
|B-<br>-6.07|- 0.917 FG||(6)|
|(12.7|) (6.0)|||
|RSQ-0.32|1#of obs-<br>118|(full sample) 2SLS||
|H-<br>5.61|- 0.184 FG||(3')|
|(19.5)|(3.2)|||
|RSQ-0.09|0#of obs- 108|(reduced sample) OLS||
|B-<br>-5.69|-<br>0.788 FG||(4')|
|(18.2)|(12.7)|||
|RSQ-0.605|# of obs-<br>108|(reduced sample) OLS||
|H-<br>5.55|-<br>0.158 FG||(5')|
|(17.4)|(2.0)|||
|RSQ-0.088|#of obs- 108|(reduced sample) 2SLS||

41
||**Lo**|**g [Self-repor**|**ted Producti**|**vity (XOF/ha**|**)]-Food cro**|**ps**||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
||(0.013)|(0.010)|(0.009)|(0.008)|(0.007)|(0.008)|(0.007)|(0.008)|(0.013)|
|Household Male Labor Use (Number)|0.112**|0.102**|0.095***|0.101***|0.120***|0.123***|0.105***|0.107***|0.115**|
||(0.054)|(0.041)|(0.033)|(0.030)|(0.028)|(0.029)|(0.028)|(0.033)|(0.050)|
|Household Female Labor Use|-0.067**|-0.041**|-0.033**|-0.022**|-0.018*|-0.016|-0.010|-0.000|0.002|
|(Number)|(0.029)|(0.020)|(0.013)|(0.011)|(0.011)|(0.011)|(0.011)|(0.012)|(0.015)|
|Household Child Labor Use (Number)|-0.002|0.000|0.001|0.001|-0.000|0.000|0.000|0.001|0.001|
||(0.003)|(0.002)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|
|Hired Labor Use (Number)|<br>0.004|<br>0.004|<br>0.004|<br>0.003|<br>0.003|<br>0.003|<br>0.002|<br>0.002|<br>0.002|
||(0.004)|(0.004)|(0.003)|(0.003)|(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|
|Number of Fields Managed by the|0.078*|0.053*|0.036*|0.026*|0.025*|0.030*|0.010|0.012|0.009|
|<br>household|(0.043)|(0.030)|(0.021)|(0.016)|(0.015)|(0.018)|(0.010)|(0.013)|(0.010)|
|Total number of crops produced|-0.034|-0.023|-0.015|-0.010|-0.015|-0.023*|-0.003|-0.003|0.014|
||(0.023)|(0.016)|(0.013)|(0.010)|(0.011)|(0.013)|(0.010)|(0.012)|(0.012)|
|Household used any purchased seed|-0.003|-0.006|-0.005|-0.005|-0.005|-0.003|-0.004|-0.004|-0.005|
||(0.003)|(0.004)|(0.003)|(0.003)|(0.003)|(0.002)|(0.003)|(0.003)|(0.003)|
|Plots that Use Chemical Fertilizer (%|-0.009|-0.001|0.009|0.011*|0.014**|0.014**|0.010*|0.002|0.011|
|<br>of total)|(0.010)|(0.008)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.009)|
|Plots that Use Pesticide (% of total)|0.067**|0.056***|0.055***|0.045***|0.041***|0.038***|0.048***|0.056***|0.073***|
||(0.027)|(0.019)|(0.014)|(0.012)|(0.011)|(0.011)|(0.012)|(0.014)|(0.020)|
|**unexplained**||||||||||
|Log [Total land area cultivated in ha]|-0.201*|-0.134*|0.025|0.070|0.100**|0.071|0.062|0.054|-0.149|
||(0.113)|(0.080)|(0.063)|(0.051)|(0.042)|(0.046)|(0.046)|(0.054)|(0.091)|
|Household head age (years)|-0.710**|-0.256|-0.338|-0.167|0.050|-0.048|-0.398|-0.206|-0.134|
||(0.328)|(0.306)|(0.291)|(0.258)|(0.277)|(0.264)|(0.261)|(0.260)|(0.265)|
|Married|<br>0.189|<br>0.116|<br>-0.010|<br>-0.069|<br>0.084|<br>0.026|<br>0.106|<br>0.012|<br>-0.102|
||(0.164)|(0.125)|(0.116)|(0.109)|(0.111)|(0.111)|(0.116)|(0.112)|(0.152)|
|Divorced/Separated|<br>0.026|<br>0.012|<br>-0.026|<br>-0.011|<br>0.054|<br>0.023|<br>0.025|<br>0.021|<br>-0.038|
||(0.062)|(0.050)|(0.047)|(0.046)|(0.043)|(0.043)|(0.046)|(0.046)|(0.063)|
|Widowed|<br>0.231|<br>0.124|<br>0.017|<br>-0.072|<br>0.068|<br>0.046|<br>0.090|<br>0.048|<br>-0.077|
||(0.148)|(0.109)|(0.106)|(0.106)|(0.104)|(0.106)|(0.109)|(0.109)|(0.151)|
|Household head has Primary education|0.006|0.011|-0.011|-0.037|0.002|-0.004|-0.027|-0.018|-0.040|
||(0.043)|(0.034)|(0.029)|(0.029)|(0.025)|(0.023)|(0.023)|(0.027)|(0.031)|

42
29 gain due to the large interhousehold transfers operating in CMte d'Ivoire. Government employees and households in the formal sector lose due to the introduction of the yield neutral alternative tax which they must pay.<sup>23</sup>

# _Subsidies_

The elimination of subsidies, like other experiments considered in Table 3, has a mixed incidence pattern with both positive and negative effects across households. Food croppers tend to lose because of the direct benefits they forgo from agricultural and other subsidies; the inactive lose due to reduced inter-household transfers; and other groups gain due to reduced financing requirements (reflected in a replacement ly a yield-neutral broadly based subsidy).

A theme that strongly emerges from these results is that the Ivorian tax system has large redistributive effects, but they criss-cross the rich-poor spectrum of household mean incomes in a number of ways. Removing import duties substantially benefits export croppers while negatively affecting Government employees and formal sector households. These last two groups gain from removing subsidies to the poor due to lowered taxes, and from removing progressive personal income taxes. Low-income food croppers lose from the effective broadening of the VAT base, and the removal of subsidies. And finally, given the 1986 data we use in which the Stabilization Fund appears as a tax, export croppers benefit 23 Lambert, Schneider and Suwa (1991) also provide evidence that an increase in the receipts of the stabilization fund is distributionally regressive.
political ties have a major or decisive influence on the formulation of laws and regulations governing the business climate (World Bank 2006b).

**Table 18. Selected parameters of the business environment**

||Documents<br>for export<br>number|Time for<br>export<br>days|Cost to<br>export<br>(US$ per<br>container)|Procedures<br>for<br>enforcing<br>contracts<br>number|Cost of<br>enforcing<br>contracts<br>% of debt|Time for<br>enforcing<br>contracts<br>Days|
|---|---|---|---|---|---|---|
|Mongolia|11|66|3007|29|17.6|314|
|Cambodia|8|36|736|31|121|401|
|Vietnam|6|35|701|37|31.0|295|
|Azerbaijan|7|69|2275|27|19.8|267|
|Kazakhstan|14|24|720|37|11.5|183|
|Kyrgyz republic|..|..|..|44|12.0|140|
|Uzbekistan|10|44|2550|35|13.5|195|
|Ghana|5|21|822|29|13.0|552|
|Uruguay|9|22|552|39|15.9|655|
|East Asia & Pacific|6.9|23.9|885|31.5|52.7|477|
|Europe & Central Asia|7.4|29.2|1450|31.5|15.0|409|
|L. America &|7.3|22.2|1068|39.3|23.4|642|
|Caribbean|||||||
|Middle East & N.|7.1|27.1|923|41.6|17.7|606|
|Africa|||||||
|OECD|4.8|10.5|811|22.2|11.2|351|
|South Asia|8.1|34.4|1236|38.7|26.4|969|
|Sub-Saharan Africa|8.2|40.0|1561|38.1|42.2|581|

_Source: Doing Business Data, World Bank (2006a)._ The process of acquiring land lease certificates has become a locus of corruption as well. Disputes and lawsuits involving leases are increasing, while disputes over the use of state pastures, especially in the more densely populated areas, are common. Since access to credit depends mainly on collateral in the form of immovable assets (land or buildings), problems with land registration and leases are a constraint to growth in urban and rural areas.

Mongolia’s institutions for contract enforcement though weak are improving and are not a binding constraint to growth (Table 18). More than half of the firms surveyed expressed confidence in the ability of the judicial system to enforce their contractual and property rights in business disputes. However, only 10 percent of all firms have had disputes with clients or suppliers that had to be resolved in the judicial system, and of these firms 70 percent indicate that the mechanisms for dispute resolution are a severe impediment to doing business.<sup>61</sup> Crime has been on the rise but does not appear to be a major obstacle to

> 61 Source: 2004 Productivity and Investment Climate Survey.

33
20 associated with reduced investment ratios.11 An increase of one percentage point in the real interest rate is correlated with **a** fall of .10 percentage points in the investment ratio, while $1 increases in £er capita debt are associated with reductions of the investment ratio of roughly 3.5 to 8.4 percentage points.12 The year-specific dummv variables indicate a samplewide tendency toward higher investment ratios during the pre-debt crisis period. In 1978 through 1981 the average investment ratio was ceteris paribus over 2 percentage points higher than in 1986, while in the 1983-1985 period investment ratios were on average slightly below that of 1986.

It is also instructive to examine the "normal" impact of the external environment on the policy structure of economies. The second half of Table 2 examines these common impacts for current government expenditure (GOV) and money broadly defined (MON) as percentages of GNP as well as for two measures of the real exchange rate (RERA and RERB).

I consider MON an indicator of financial deepening in the economy.<sup>13</sup> As McKinnon (1973) suggested, non-inflationary monetization of the economy may increase efficiency in allocation of goods while increased use of other financial instruments may improve efficiency in allocation of saving. This ratio will register both, but will exclude monetization leading only to 11 Note the importance here of considering lagged per capita debt as the explanatory variable.

12 In the IR and CAR regressions the coefficients of RR are insignificantly different from zero at the 95 percent level of confidence. This insignificance can be explained by the presence of the year-specific dummy variables; these variables are picking up some of the effect properly due to the real-interestrate variables.

13 Use of quasimoney alone as an alternative measure of financial deepening yields qualitatively results.
# **B. End-Use Certificates**

End-use certificates were the result of deep controversy between the parties which led to a dispute because of the differences in the technical specifications of the product under investigation, the several uses of the product depending on the industry involved, and the domestic industry’s capacity to supply each specific type of product.<sup>68</sup> In some cases related to steel, imported goods were usually replaceable by other domestically manufactured products, but that was not possible in the case of some specific end uses. Therefore, the Mexican authority sought to prevent these inputs —so essential for important manufacturing and exporting sectors in Mexico, such as the automotive, household appliances, capital goods, and engineering industries— from becoming unjustifiably expensive. The case of the cold-rolled sheet illustrates the rationale for these end-use certificates and how they were applied.<sup>69</sup> The clearest example showing the origin of this measure was that of the cold-rolled sheet thicker than 52 inches. According to exporters’ allegations, domestic manufacturers were incapable of manufacturing cold-rolled sheet thicker than 52 inches —a fact that was entirely taken for granted by the requesting parties, since local production facilities had technical limitations to manufacture the product in this thickness range, though it was not so with regard to all other specifications of the product. Domestic manufacturers, in turn, insisted on the fact that there was practically no demand from the market to produce cold-rolled sheet of such thickness and that imported sheets of such dimensions were acquired by distribution and service centers simply to be cut and sold in the sizes that were manufactured at the domestic level. Thus, manufacturers asked the authority not to exclude the sheet thicker than 52 inches from the antidumping duty.<sup>70</sup> Some users, such as the car and tubing industries, proved that sheets thicker than 52 inches were required for several production processes. The investigating authority also admitted that,

> 68 Paragraph 7, ibid.

> 69 This case is based on information from the final determinations in antidumping and anti-subsidy investigations involving cold-rolled sheet imports, merchandise under tariff items 7209.12.01, 7209.13.01, 7209.22.01 and 7209.23.01 of the General Import Duty Law, from Canada, United States of America, Australia, the Republic of Korea, Venezuela, the Federal Republic of Germany and Brazil. Determination published in the DOF, December 27th 1995 (Final determination on cold-rolled sheet).

35
Table A23: Indexes of conformism, loading factors

|**_Loading factor_**|
|---|
|**_Conformity index_**|
|He/she believes that people should do what they’re told. He/she thinks people<br>should follow rules at all times, even when no-one is watching.<br>0.4832<br>It is important to him/her to always behave properly. He/she wants to avoid<br>doing anything people would say is wrong.<br>0.5541|
|He/she believes he/she should always show respect to his/her parents and to<br>older people. It is important to him/her to be obedient.<br>0.4393<br>It is important to him/her to be polite to other people all the time. He/she<br>tries never to disturb or irritate others.<br>0.5162|
|**_Tradition index_**|
|He/she thinks it’s important not to ask for more than what you have. He/she<br>believes that people should be satisfed with what they have.<br>0.5239|
|Religious belief is important to him/her. He/she tries hard to do what his/her<br>religion requires.<br>0.5127|
|He/she thinks it is best to do things in traditional ways. It is important to<br>him/her to keep up the customs he has learned.<br>0.4236<br>It is important to him/her to be humble and modest. He/she tries not to draw<br>attention to himself/herself.<br>0.5321|
|**_Self-Direction index_**|
|Thinking up new ideas and being creative is important to him/her. He/she<br>likes to do things in his/her own original way.<br>0.4765<br>It is important to him/her to make his own decisions about what he/she does.<br>He/she likes to be free to plan and to choose what to do himself/herself.<br>0.5445<br>He/she thinks it’s important to be interested in things. He/she likes to be<br>curious and to try to understand all sorts of things.<br>0.4678|
|It is important to him/her to be independent. He likes to rely on himself.<br>0.5076|

xxx
where _gi_ is the growth rate of real per capita GDP for country _i_ , _yi0_ is initial income (possibly in log form), _K_<sup>_I_</sup> _i_<sup>is a measure of infrastructure capital, and</sup><sup>_Z_</sup> _i_<sup>is a vector of covariates as</sup> mentioned above.

# **3.2. Data**

We opt for physical infrastructure indicators. Three specific reasons support this choice.<sup>11</sup>

1. As mentioned above, public investment data are subject to a lot of problems, which make them unlikely to capture infrastructure stock or availability properly.

2. Physical indicators allow for a longer time frame and a higher number of countries.

3. They will allow for direct comparisons with the results from the growth accounting exercise.

Physical indicators for three different sectors (telecom, energy and transport) are taken from Canning’s database, covering the 1971-1995 period. Specifically, we use the following series:

- Main telephone lines per 1,000 people. This series is extended up to 2002/2003 using Estache and Goicoechea (2005).

- Electricity generating capacity in million kilowatt per 1,000 people.

- Rail route length in km per 1,000 people.

- Paved road length in km per 1,000 people.

Additionally, we perform some tests with alternative variables: Telephone mainlines (per 1,000 people) from the World Bank World Development Indicator (WDI), fixed line and mobile phone subscribers (per 1,000 people) also from WDI, to capture the rise in mobile connections in the second half of the 1990s, roads total network and percentage of paved roads from WDI, which is used here as a quality proxy. We introduce additional proxies for

> 11 See Straub (2007) for a more general discussion of public investment versus physical infrastructure indicators.

16
Making a global value chain greener raises specific challenges.<sup>2</sup> Who are the participants in such a chain, how do they interact with each other, and how do they respectively and collectively affect the environment? What incentives should be set across the chain in order to foster collective environmental performance while maintaining individual competitiveness? How can the chain’s members coordinate their actions effectively? This note’s objectives are to briefly cover state-of-theart knowledge on these questions, and to point out certain relevant considerations that have received little weight so far. In accordance with the Green Growth Knowledge Platform’s intended outlook, special attention will be given to how public policies and business strategies can support each other in meeting those challenges, particularly in developing countries.

# **2.  Getting the right picture**

Like any practical goal, the greening of GVCs first relies on information. This section will accordingly consider the generic components of a GVC, the milieu in which its activities take place, and the extent of environmental data each entity should have or generate.

# **●  Who is involved?...**

Building on Klassen and Vachon (2012), Abbey and Guide (2012) and the DCED (2012) document, Figure 1 provides a broad framework to consider greening a global value chain.

In most industries – electronics, automotive, agro-food, aerospace, etc. – the typical GVC is seen as a sequence of raw materials extraction, components making, assembly, retailing, and customer utilization and disposal. As far as production is concerned, these activities are _complementary_ , so any constraint that is put on one of them will affect the others, be they located upstream or downstream. **_This calls for a systemic approach to greening supply chains._** As Klassen and Vachon (2012, p. 269, 271) indicate: “(...) firms in a supply chain do not work in isolation; (...) greener supply chain management is defined as the strategic and transparent integration of material, information, and capital flows to achieve environmental and economic objectives through the systemic coordination of key inter-organizational business processes.” The considered system will naturally comprise a number of environmental tasks, for example: site cleanup, water treatment, air pollution abatement, cleaner production, resource recovery, refurbishment and manufacturing, and recycling. Nowadays, these tasks are often outsourced, in whole or in part, to specialized firms forming the so-called environmental goods and services (EGS) industry. Alas, this industry is often overlooked in environmental policy discussions. Some precisions on it seem therefore in order at this point.

> 2 Global value chains are also transforming international trade, putting into question current trade data and the usual ways to assess trade policies. Taking on those issues goes beyond the scope of this paper. The interested reader may look at the recent studies by De Backer and Yamano (2012) and Van Assche (2012).

3
members were classified as being in a split-off household (or households) and their current locations and other basic information collected. Split-off households that had moved anywhere within the four study municipalities were tracked for interviewing. Using this protocol, we followed 181 split-off households, interviewing a total of 1,308 distinct households in 2012. At least one adult member from 1,127 (of the 1,222 originally surveyed households) was reinterviewed in 2012. Consequently, attrition at the household level over the four-year interval was 7.8 percent. In section 4.4.3, we document individual child-level attrition, which was higher, and consider its potential influence on the results.

Modeled on the comprehensive questionnaire used in the Guatemalan ENCOVI, the survey also incorporated a specially designed module (Section 2.2) to collect migration histories for all current household members, as well as residential location and other information for all adult children and siblings of the household head or spouse. In 2008, anthropometry was collected for children under six years old, and in 2012, the cut-off age was increased to ten in order to include all individuals measured in 2008. Enumerators used regularly standardized equipment to weigh and measure children. We then converted the measures into standardized HAZ, WAZ and weight- for-height (WHZ) z-scores, replacing flagged values with missing as recommended (WHO 2006; de Onis et al. 2007). Consequently, all anthropometric outcome measures examined are standardized and age-adjusted so that differences over time for the same individuals reflect changes in nutritional status rather than, for example, linear growth rates. We focus on HAZ as a long-term indicator but also examine the effect of the economic shock on WAZ and WHZ, which better capture current conditions and in combination with results for HAZ can be used to explore the time patterns of effects. We also consider commonly used binary indicators for undernutrition, including stunting, underweight and wasting (WHZ < -2), to assess changes in the tails of the distributions and to understand whether any observed average changes also affect the worst-off children in the sample.

_2.2 Measuring migration_