rafmacalaba/datause-encoder-data huggingface dataset:
14 ˆ **Table 3: Substitution elasticities** (σ _i_ ) **, using human capital related to schooling (HE)**

||**Inputs**|**Substitu**|**tion elasticity**|**R-squared**|**Adj. R-**|**Sample**|
|---|---|---|---|---|---|---|
|||_i_<br>σˆ|**Standard**||**squared**|**size**|
|**A. T**<br>|**wo factors (One leve**<br>|**l CES prod**<br>|**error**<br>**uction function)**<br>||||
|**(1)**|K/HE|1.00*|2.50E-08|0.9061|0.8942|81|
|<br>**B. T**<br>|**hree factors (Two lev**<br>|**el CES pr**|**oduction function**|**)**<br>|||
|**(1)**|(K,HE)/L|||0.9203|0.9076|81|
||� K/HE|1.01*|0.01||||
||� (K,HE)/L<sup>1</sup>|1.00*|2.23E-10||||
|**(2)**|(K,HE)/E|||0.8952|0.8742|67|
||� K/HE|1.65*|0.12||||
||� (K,HE)/E<sup>1</sup>|1.00*|6.76E-11||||
|**(3)**|(K,E)/HE|||0.7674|0.7209|67|
||� K/E|0.17|0.19||||
||�(KE)/HE<sup>1</sup>|100*|822E-08||||
|**C. F**|, <br>**our factors (Three le**|. <br>**vel CES pr**|.<br>**oduction functio**|**n)**|||
|**(1)**|(K,HE,L)/E|||0.9037|0.8081|67|
||� K/HE|1.78*|0.11||||
||� (K,HE)/L<sup>1</sup><br>|1.14*|0.02||||
||� (K,HE,L)/E<sup>2</sup>|1.00*|2.52E-12||||
|**(2)**|(K,HE,E)/L|||0.9059|0.8828|67|
||� K/HE|-8.55|12.61||||
||� (K,HE)/E<sup>1</sup>|0.48*|0.17||||
||� (K,HE,E)/L<sup>2</sup>|1.00*|4.60E-11||||
|**(3)**|(K,E,HE)/L|||0.9062|0.8831|67|
||� K/E|1.57*|0.37||||
||� (K,E)/HE<sup>1</sup>|0.92*|0.02||||
||� (K,E,HE)/L<sup>2</sup>|1.00*|6.41E-11||||

Legend: K – physical capital; HE – human capital related to educational attainment and labor productivity; L – land resources; E – energy resources Notes: Inputs in parenthesis imply that they are nested. 1 two inputs in a nested function 2 three inputs in a nested function (*) denotes statistical significance at 5% level; (**) at 10% level The substitution elasticities and their corresponding standard errors are rounded off to the nearest hundredth.
**—** Fecundity the number of children per mother--dropped from 6.2 in 1980 to 2.4 in 1999. Women’s increased participation in the labor market is an important factor contributing to the reduction in the fertility rate, which also produced a sharp drop in the dependency rate. However, fecundity did not fall for all age groups and fertility rates are much higher for the 15 to 19 and 20 to 24 age groups in the Northeast (0.10 and 0.15, respectively) than in the Southeast (0.07 and 0.12, respectively) according to the 2000 Census. Furthermore, the large reductions in fertility rates since 1980 were lower in the Northeast for each age group than in the Southeast, except for the 15-to-19-year-olds, whose fertility increased, and by far more in the Northeast than in the Southeast.

Total desired fertility rate in the state is reported to be 1.9, close to that of 1.8 for Brazil as a whole. This would indicate that there is still a substantial unmet demand for high quality and reliable family planning services, information, and resources (BEMFAM 1998a). Another important development is the decline in the fertility differential between more-educated (eight years or more of education) and lesseducated (less than four years) mothers, from around 4 children in the Northeast in 1980 to 1.9 children in 1998. This differential declined to 1.3 children in Brazil’s richer southern states. Hence, again education plays a key role both directly (see Sections 5 and 6) and indirectly via the reduced fertility rate in poverty reduction.

In 1970, the number of children under 10 was 45 percent of the population. In 1991, it was down to 40 percent and in 2000, 32 percent. At the same time, the number of elderly dependents has not caught up. In 2000, only 5.7 percent of the population was 65 or older (Table 2.2). According to Rebeiro (2001), these trends are likely to continue. This will have a significant effect on the state’s efforts to reduce poverty. For the next few decades, the ratio of children to working age population will decline, while the number of retirees will remain small. As a result, not only will dependency ratios fall, but also the amount the state must spend on expanding the quantity of social services will decline. This will free up resources to spend on improving quality and other poverty reduction efforts.

|**Table 2.2: A**<br>**T**|**ge Gro**<br>**otal Pop**<br>|**ups as**<br>**ulation**<br>|**Percent**<br>**, 1970-**<br>|**age of**<br>**2000**<br>|**Bahia’s**<br>|
|---|---|---|---|---|---|
||**1970**|**1980**|**1991**|**1996**|**2000**|
|0–14|45.4|43.9|39.7|35.0|32.0|
|15–64|51.5|51.9|55.5|59.7|62.3|
|65 +|3.1|4.2|4.8|5.3|5.7|
|**TOTAL**<br>_Source:_SEI.|**100**|**100**|**100**|**100**|**100**|

**<mark>Table 2.2: Age Groups as Percentage of Bahia’s</mark>**

6
Table 1 shows a breakdown of the main tax sources between different spheres of government. To demonstrate their significance, and for comparative purposes user charges levied by municipal government are also shown at the bottom of the Table

**Table 1**

|**Proportion of revenues raised by ea**<br>**spheres ofgovernment 2003/04(revi**|**ch key tax sour**<br>**sed estimate)**|**ce for all**<br>|
|---|---|---|
|_Tax type_|_Amount_<br>_(R million)_|_Proportion of_<br>_total tax_<br>_revenues_|
|**Total taxes raised nationally by**<br>**national and local government**|**317,992**|**100**|
|**Net taxes raised by national**<br>**government **|**294,593**|**92.6**|
|Personal IncomeTax|98,200|30.9|
|CorporateIncomeTax|72,250|22.7|
|VAT|81,000|25.5|
|Excise duties|12,715|4.0|
|Fuel levies|16,350|5.1|
|Customs|8,800|2.8|
|Other|15,000|4.7|
|(less SACustoms Unionpayments)|(9,722)|-3.1|
|**Taxes raised by provincial**<br>**government**|**3,531**|1.1|
|**Taxes raised by local government**|**19,868**|6.3|
|Propertyrates|14,343|4.5|
|RSC/JSB levies|5,525|1.7|
|**_User charges and other non-tax own_**<br>**_recurrent revenue raised by local_**<br>**_government_**|**_45,300_**|**_14.3_**|

Source: South Africa (2004a)

# 4.2. <u>Fiscal transfers</u>

Such an approach to the allocation of tax sources results in significant vertical fiscal imbalances at the provincial level, which in turn lead to the need for a major grant or transfer system. Table 2 shows the flows of revenue between the different spheres.

It also shows that local governments do receive fiscal transfers from national government. However, they collect a substantial proportion of their own revenue and also borrow to finance part of their capital spending. The six metropolitan municipalities are largely self-financing. They receive just 3 per cent to 6.7 per cent of their budgets in grants. On the other hand, smaller and rural municipalities with weaker tax bases are much more dependent upon transfers; they receive up to 87.3 per cent of their resources from transfers.

6
# Table A2.1: COMPARISON BETWEEN VV ESTIMATES AND THIS PAPER

||All Em|ployees1/|Gov<br>Emp|ernment<br>loyees 2/|Private<br>Empl|<br>Sector<br>oyees2/|
|---|---|---|---|---|---|---|
||W|This Paper|W|This Paper|W|This Paper|
|Years of Primary Schooling<br>|na<br>|.042<br>|ns|.095|ns|.059|
|Years of Lower Secondary Schooling<br>|.088<br>|<br>.126<br>|.205|<br>.142<br>|ns|.109<br>|
|Years of Higher Secondary Schooling<br>|<br>ns<br>|]<br> <br>|ns|J|ns|IJ|
|Years of Higher Schooling<br> <br>|.208|<br>.123<br>|.205|<br>.082|.300|<br>.196|
|Years of Secondary<br>VTE<br> <br>|,<br>|.069<br>||ns||.139|
|Years of Post-Secondary<br>VTE<br>|.072|<br>ns|ns|n|.098|<br>ns|
|Years of Teacher Training<br>||as|_|ns||ns|
|Years of Apprenticeship<br>|ns<br>|ns<br> <br>|.067|<br>ns|ns|.053|
|Years of Current Job Experience<br> <br>2|.107<br>|<br>.092<br> <br>|.087|<br>.047|.116|<br>.084|
|(Years of Current Job Experience)<br><br>|-.0019<br>|<br>-.0017<br>|ns|ns|-.023|<br>-.0018|
|CEPE<br>|.494<br>|<br>ns<br>|.801|<br>ns|.395|<br>no|
|BEPC<br>|.594|<br>no|.424|<br>ns|.617|<br>ns|
|Secondary VTE Diploma<br>Post-Secondary<br>VTE Diploma<br>|_<br>ns|nso<br>.876|ns|no<br>no|ns|ns<br>ns|
|Teacher Diploma<br>||ns<br>o<br>||ns|_|ns|
|Other Diploma<br>|.536|<br>.508<br>|.621|<br>ns|ns|no|
|Female<br>|ns|-.204|ns|-.444|ns|-.255|
|Non-Ivorian|ns|-.275|_|ns|.285|<br>ns|

ns - Not significant at .10 level. 1/ OLS estimates. 2/ Selection corrected estimates.
likelihood or the two-step method proposed by Heckman (1979). We report results from the former method only, but have veri...ed that the latter yields very similar estimates. As before, the results point to a positive and statistically signi...cant relationship between export revenue and migrant networks. Results from additional estimations (not reported but available upon request) con...rm that these estimates remain very similar when log employment is used as a proxy of ...rm e¢ ciency.

Overall, thus, these results suggest that, conditional on a ...rm serving a market, the presence of emigration stocks appears to be an important determinant of how much it sells there. This ...nding is in line with the theory presented in section 2, which suggests that migrant networks are a source of ...rm-destination demand shocks.

# 7 Concluding remarks

We have used detailed ...rm-level data from Portugal to examine the e¤ect of migrant networks on export participation and intensity. In doing so, we have exploited unique features from Portugal –notably stable, historically-determined stocks of emigrants with whom some ...rms are more likely to have close ties.

While accounting for the role of ...rm productivity heterogeneity and the customary gravity-type regressors, we have found that larger stocks of Portuguese migrants in a given destination increase the likelihood of export participation. This e¤ect tends to be signi...cantly larger for ...rms that are more likely to have a larger number of contacts among the emigrants –i.e., ...rms currently located in the North of the country; and ...rms that were already born when the bulk of emigration ‡ows took place. Conditional on a ...rm serving a market, the presence of migrant networks appears to be an important determinant of how much it sells there. Taken together, these ...ndings suggest that migrant networks are an important source of market and ...rm-speci...c heterogeneity in entry costs and demand, thereby complementing and extending recent work by Eaton et al. (2011).

By way of conclusion, we would like to note that although our empirical ...ndings are consistent with a relatively parsimonious static model, a framework highlighting dynamic interactions between the formation of social networks, entry in export markets and ...rm growth would likely provide important additional insights. We regard the development of such a framework as an interesting avenue for future research.

# References

> [1] Angelini, P. and Generale, A. (2008). "On the evolution of ...rm size distributions." American Economic Review, 98(1): 426-438.

16
# **Does Collective Action Sequester Carbon? The Case of the Nepal Community Forestry Program**

Randy Bluffstone Eswaran Somanathan Prakash Jha Harisharan Luintel Rajesh Bista Naya Paudel Bhim Adhikari Key words: Carbon Sequestration, Forests, Collective Action, Nepal JEL Codes: D79, H41, Q01, Q23, Q54,
cumulative impacts of child labor as well as explore the heterogeneous e↵ects on student of di↵erent ages and abilities. In addition, through the time-use information available to us, we are able to explore the potential channels through which child labor may interact with the process of learning and we are thus able to shed light on the mechanisms involved.<sup>3</sup> This paper proceeds as follows: In section 2 we describe the data used in the study. In section 3 we describe the general child labor and educational environment in the City of S ̃ao Paulo. In section 4 we explain the empirical strategy, how we identify our model and the robustness checks we employ. In section 5 we present and discuss the results of the empirical investigation. In section 6 we summarize the paper and discuss the policy implications of the results of the empirical investigation.

# **2 Data, Sample Selection and Descriptive Statistics**

In 2007, the City of S ̃ao Paulo started an evaluation system for students enrolled in municipal schools involving a set of proficiency exams in mathematics and Portuguese. These exams were accompanied by a questionnaire that was given to each student taking the exams as well as an additional questionnaire that was given to the parents of the students taking the exams about the socio-economic characteristics of the family, although the parents questionnaire was not administered in 2008.<sup>4</sup> The exam is called the _Prova S ̃ao Paulo_ (S ̃ao Paulo Exam) and was implemented annually until 2013 when it was discontinued. The microdata from the 2007 to 2010 exams are available and used in this paper. In 2007 all students in the even grades (2<sup>_nd_</sup> _,_ 4<sup>_th_</sup> _,_ 6<sup>_th_</sup> and 8<sup>_th_</sup> ) took the exam. In 2008, all students in the 2<sup>_nd_</sup> _,_ 4<sup>_th_</sup> and 6<sup>_th_</sup>

> 3Also related are studies from cross sectional data such as Heady (2003) which finds large and negative impacts of child work on educational achievement and Akabayashi and Psacharopolous (1999) which examines the correlation between work and subjective measures of ability, and finds a negative impact from child work. 4Questionnaires were also given to the principals which asked them to answer questions about themselves, their school, the teachers and supervisors, and the student population, but it was not administered in 2009.

7
products,<sup>10</sup> certain glass products and certain headwear.<sup>11</sup> The average duty on textiles is over 8 percent and the duty on certain textile products reaches almost 30 percent.

In terms of its impact on total trade, AGOA adds 1790 lines of preferences to the 3,635 lines already liberalized under the GSP for non-LDCs.<sup>12</sup> With eligibility for clothing preferences adding a further 557 lines. For the LDCs which are not eligible for clothing benefits only an additional 225 lines are liberalized under AGOA. For the LDCs without clothing benefits 23 percent of dutiable lines remain excluded from preferences, whilst for non-LDCs with clothing benefits 16 percent of dutiable lines remain without preferences.

So, in terms of the number of tariff lines liberalized the principal impact of AGOA falls on the non-LDC Sub-Saharan African countries. This reflects that, with the exception of clothing, most of the products liberalized under AGOA had already been liberalized under the GSP for the LDCs. For the LDCs what matters is whether they are able to access the preferences on clothing products. We shall also discuss below that a key factor determining the ability of LDCs to exploit the preferences on clothing are the rules of origin. Current liberal rules of origin for many countries have helped to stimulate exports. However, much more restrictive rules of origin will be imposed in 2004 which will seriously constrain the ability of LDCs to benefit from AGOA.

> 10 AGOA does provide preferences for specific textile items which are hand loomed, handmade or a folklore article of a beneficiary country, but in practice this has had no impact on trade. No imports into the US have been recorded of such products. A number of countries have been approved to export such products but the impact on trade will be minor.

> 11 Although there is a quota on exports of clothing under AGOA we do not add the out of quota rates to the group of lines excluded from preferences, as we did for the agricultural products subject to TRQs, for a number of reasons. First, the clothing quota is not specific to particular tariff lines but covers all clothing products. Second, the quota is applied to AGOA countries, whereas most of the agricultural TRQs have global quotas so that the quota can be filled by any country or countries. Finally, the clothing quota is specified as a (growing) share of (the expanding) value of total US imports of clothing rather than a more narrow and restrictive quantitative limit.

> 12 Many publications which discuss AGOA, see USTR (2003) for example, suggest that the GSP covers 4650 lines. This is a very dated figure and does not capture that for a large number of tariff lines duties have been subsequently reduced to zero under the commitments entered into under the Uruguay Round and therefore there are now no GSP preferences. We also exclude the General Note 15 products.

12
However, in that case our number of observations—whose low number is already an obstacle to better estimation—drops precipitously and the quality of results deteriorates. We thus assume that one or two years (to the extent that _dtarf_ includes also some twoyear lagged observations) are a sufficient period of time for changes in protection to work their way through wage distribution. Endogeneity is unlikely in levels, and particularly so in a first-difference formulation as here, since _change_ in inter-occupational inequality is not likely to have much to do with _change_ in protection. We therefore do not use instruments.<sup>32</sup> Furthermore, the use of first-differences implies that idiosyncratic country effects are included.<sup>33</sup> Table 5 gives the results of the regressions for inter-occupational wage inequality. We begin with a very parsimonious formulation where change in inter-occupational inequality ( _dginioww_ ) is explained by change in average tariff rate ( _dtarf_ ) and income. None of the variables is found significant at the 5 percent level; however _dtarf_ is negative and significant at 10 percent level. The situation changes when we introduce the interaction term between the change in average tariff rate and level of income, and trade union membership or percentage of workers covered by collective bargaining agreements. Now, decrease in protection is strongly pro-inequality, with 1 point decrease in average tariff rate associated with 5.7 percent annual increase in inter-occupational inequality. This amounts to an annual increase of 1.2 Gini points for a country with the average inter-occupational Gini of about 24. However, this pro-inequality effect is reduced the richer the country (because of the positively signed interaction effect, see regression 3), and even for the very poor countries is less than it appears at first sight. Thus, in a very poor country with an income of $PPP 1,000, a one point decrease in the average tariff rate will be associated with a Gini increase of only 1 percent. Around

> 32 It is also difficult to find reasonable and workable instruments. We tried initial tariff  level, on the assumption that reduction in tariffs bears some proportion to their initial levels, but the results were disappointing.

> 33 Birdsall, Behrmann and Szakely (2003) have the same formulation as here but present also the first difference formulation of policy changes, or in other words the difference of differences formulation (with distributed lags over seven periods on the right-hand side). Their first difference in levels formulation (Table 2) is the same as our equation (14).

41
<!-- Start of picture text --> a. Travel times between firms in Inner Kampala b. Journey time increase due to flooding<br>Pa) Pa]<br>c= 2<br>Cc Cc<br>oO oO<br>xe) eo)<br>> ><br>.e) 1S)<br>Cco CcoO<br>a a]<br>oOoO / o OO<br>5 15 25 35 45 (0) 50 100 150 200 250 300 350 400<br>Minutes Time travel increase (%)<br>No flood —— 10-year flood —— 50-year flood<br><!-- End of picture text -->
|**Table 3: Pr**<br>**across firms**<br>Ranking in<br>the DB<br>distribution|**edicted va**<br>**from the**<br>Country|**lues of var**<br>**ES at vari**<br>Actual<br>DB<br>value|**ious perce**<br>**ous levels o**<br>Percentile<br>firms in th<br>spline reg|**ntiles of th**<br>**f the coun**<br>s of the dis<br>e ES (pred<br>ression)|**e distribut**<br>**try DB va**<br>tribution a<br>icted using|**ion**<br>**lue**<br>cross<br>two|
|---|---|---|---|---|---|---|
|across|||10th|25th|75th|90th|
|countries|||||||
||||Imports<br>||||
|Min|EST|4|1.1|1.6|4.4|10.5|
|25th|MEX|15|1.4|2.2|9.8|19.4|
|50th|NPL|23|1.6|2.8|12.0|23.2|
|75th|BDI|34|1.9|3.7|13.7|26.3|
|Max|UZB|84|3.7|6.3|19.6|37.9|
|Difference|||||||
|Max-Min||80|2.6|4.7|15.2|27.4|
|||Constr<br>|uction Per<br>|mits<br>|||
|Min|LBR|77|4.5|7.0|38.7|29.1|
|25th|CHL|155|6.4|16.2|66.3|160.6|
|50th|PER|210|7.7|18.7|77.0|178.7|
|75th|GTM|286|8.7|18.3|81.0|166.5|
|Max|LSO|601|7.9|8.5|65.0|168.3|
|Difference|||||||
|Max-Min||524|3.4|1.4|26.3|139.2|
|||Business/<br>|Operating L<br>|icense<br>|||
|Min|GEO|3|3.9|8.6|31.4|54.3|
|25th|SVK|16|3.4|8.6|31.3|63.5|
|50th|NPL|31|2.6|6.6|29.1|65.2|
|75th|BOL|50|1.9|3.9|26.5|62.4|
|Max|GNB|183|4.9|11.3|38.5|79.4|
|Difference|||||||
|Max-Min||180|0.9|2.6|7.2|25.1|

# **V. Variability in the ES results**

The next question is whether the variance across firms in the ES surveys increases or decreases with the increase in the DB indicator. Figures 5a, 5b, and 5c show the 90<sup>th</sup> -10<sup>th</sup> percentile spread for each country for imports, construction permits, and operating license. In this case, the indicators show different patterns. For construction permits the cross-country variance increases with DB at first, then flattens out entirely (consistent with the results in Table 2 showing that the 21
# **4.2 Data**

The structure of the MPSGE framework and its compatibility with the SAM data format makes it suitable for use with the GTAP Data Base Version 6 (Dimaranan, 2006)<sup>10</sup> . The input‐ output structure provides the core of the database to be used with the general equilibrium framework described above. Since, the full GTAP Data Base has more information than is necessary for the analysis in this paper, I adjust for savings‐investment and trade protection data to construct a consistent global SAM using cost structure, factor income, and final demand data. This is an accurate, yet frictionless, delineation of the input‐output structure of the integrated world economy (Table A‐1, Appendix A).

The GTAP services data are based on the 2003 IMF Balance of Payments Statistics<sup>11</sup> . Mode 3 (commercial presence) services have no coverage in the GTAP database, freight data are incorporated into the margin services data, and travelers’ expenditure can be considered to be GATS Mode 2 (consumption abroad). That leaves the other non‐margin services as an amalgamation of Mode 1 and Mode 4 (temporary movement of natural persons) services. The service trade data from the IMF, and built into the GTAP services trade data, are thus already parsed to separate Mode 1 services from the other service data, making the GTAP Data Base best suited to the task of examining the trade in services.

As mentioned earlier, Armington product differentiation is not assumed, and imported intermediate inputs are considered to be the same as domestically produced intermediates. An exception is the difference between services used as intermediate inputs and Mode 1 services. Mode 1 services are produced when a fraction of the exporting country’s services output are transformed into Mode 1 services and exported as intermediate inputs. They then enter the production framework of an importing country’s industry as an input in a nest distinct from domestically produced services. Domestically produced services, on the other hand, are used in fixed proportions in the intermediate inputs nest. The composite of these intermediate inputs is in turn used in fixed proportions in the top‐level of the CES production relationship. Domestically produced services thus do not substitute for any factor endowment, while Mode 1 services compete (or complement) skilled labor in the valued added nest of the production structure.

> 10 Base year  2001

> 11The service sector is an aggregate of the following GTAP sectors: Electricity, Gas Distribution, Water, Construction, Trade, Transportation, Sea Transport, Air Transport, Communication, Financial Services, Insurance, Business Services, Recreation and Other Services, Other Government Services, Dwellings.

12