rafmacalaba/datause-encoder-data huggingface dataset:
-17-

TABLE 3(a): INFLATION AND VALUATION OF GOODS SOLD FROM INVENTORY

|Inv<br>A<br>Met|entory<br>ssumption<br>hod|(1) Constant<br>Inventory|(2) Inventory<br>Depletion|(3) Inventory<br>Accumulation|
|---|---|---|---|---|
|I.|Base case<br>with<br>zero<br>inflation<br>**_f_**-<br>0|BI-100<br>P-100 EI-100<br>CGAS-BI+P-<br>200<br>CGS- CGAS - EI<br>_$100|BI-100 P-100 EI-50<br>CGAS- $200<br>CGS- CGAS - EI<br>-<br>S150|BI-100 P-100 EI-150<br>CGAS- $200<br>CGS- CGAS- EI<br>-<br>$50|
|II.||20 percent|<br>Inflation,<br>s-<br>0.2||
|A.|FIF|BI-100 P'-$120<br>EI'-EI(l+w)-120<br>CGAS-$220<br>CGS- 220 -120<br>-<br>$100|BI-100 P'-$120<br>EI'- EI(1+4f)-6C<br>CGAS-$220<br>CGS- 220 -60<br>-<br>$160|BI-100P'-$120<br>EI'-50 +120 -170<br>CGAS-$220<br>CGS- 220 -170<br>_ $50|
|B.|LIFO|BI-100 P'-120 EI-100<br>CGAS-$220<br>CGS- 220 -100<br>-<br>$120|BI-100 P'-120 EI-50<br>CGAS-$220<br>CGS- 220-50<br>-<br>$170|BI-100 P'-120<br>EI'- 100 +60 -160<br>CGAS- $220<br>CGS- 220-160<br>-<br>$60|
|C.|I-FIFO|BI'-BI(1+ir) -120<br>P' -120<br>EI'-EI(1+r) -120<br>CGAS- $240<br>CGS- 240 -120<br>-<br>$120|BI'-BI(l+x)-120<br>P'-120<br>EI'-EI(l+*)-60<br>CGAS- $240<br>CGS- 240 -60<br>-<br>$180<br>_|BI'-BI(l+r)- 120<br>P'-120<br>EI'-120 +50 -170<br>CGAS- $240<br>CGS- 240 -170<br> <br>$70|
|D.|I-LIFO|BI'-BI(l+r) -120<br>P'- 120<br>EI'- EI(l+ff) -120<br>CGAS- $240<br>CGS- 240 -120<br>-<br>$120|BI'-BI(l+w) -120<br>P'- 120<br>EI'- EI(l+,r)-<br>60<br>CGAS- $240<br>CGS- 240 -60<br>-<br>$180|BI'-BI(l+w)- 120<br>P'- 120<br>EI- 120 +60 -180<br>CGAS- $240<br>CGS- 240 -180<br>_ $60|
|Not|ation: BI<br>CGAS|-<br>BeginningInventory,<br> -<br>Cost of goods avai|P-<br>Purchases,<br>EI<br>lable for sale, CGS-|-<br>Ending Inventory<br>Cost of goods sold|

The affect of each method of valuing CGS of a hypothetical firm is shown above. By assuming that revenue (net of other costs) is $500 with **_i_** -0 and $600 with w - 0.2, so that no other effects intrude on the problem, Table 3(b) illustrates the effect on taxable profit of each matrix entry above.
# 10).  Moreover, effects are 10 times smaller than those from BRICS’ FDI. For example, for

every 10 percent increase in FDI stock from G7 countries, employment rate increases by 0.0030.004 percent.

Table 8: SGMM estimates for employment impact of BRICS’ and G7 countries’ direct investment in selected African countries, 2001-2012

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Ln GDP growth|||||-0.0001**<br>(0.0001)|
|Ln BRICS’s direct investment|0.001***|0.004***|0.004***|0.003***|0.004***|
||(0.0003)|(0.001)|(0.001)|(0.0005)|(0.0005)|
|Ln G7’s direct investment|-0.0004|-0.001**|-0.001*|-0.001*|-0.001**|
||(0.0003)|(0.001)|(0.001)|(0.001)|(0.001)|
|Ln Edu||0.037***|0.034***|0.036***|0.030***|
|||(0.005)|(0.005)|(0.005)|(0.009)|
|Ln Fixed-line tel.||||0.001|0.001|
|||||(0.002)|(0.002)|
|Ln openness|||0.002|0.001|0.001|
||||(0.003)|(0.002)|(0.002)|
|No. of observations|160|97|97|96|96|
|No. of countries|16|16|16|16|16|
|No. of instruments|12|13|14|15|16|
|Sargan test (Probability > χ2 )|0.307|0.228|0.258|0.280|0.351|
|Arellano-Bond (Probability > Z)|0.430|0.653|0.638|0.620|0.643|
|Time dummy|Yes|Yes|Yes|Yes|Yes|

Note _: Reports are based on two-step SGMM. Standard errors are in parentheses. *** p<0.01, ** p<0.05,_ _* p<0.1. Arellano-Bond test that average auto-covariance in residuals of order 2 is 0 accepts H0: no autocorrelation_ The importance of infrastructure development in raising absorptive capacity of FDI, and in turn leading to job creation in African countries is apparent, especially in the case of G7 countries’ FDI. As stated above, accounting for infrastructure development stimulates the employment generating effects of G7 countries’ FDI. In fact, in models where infrastructure 24
<!-- Start of picture text --> Middle-class [ 6.7 14.6<br>15<br>w<br>S Wulnerable 18.2 13.8<br>N<br>0 10 20 30 40 50<br>% of total population<br>@ 2012 Poor 2012 Vulnerable 2012 Middle-class<br><!-- End of picture text --> <!-- Start of picture text --> Middle-class | 5.5 9.1<br>OF<br>uw<br>S Vulnerable 26.6 12.1<br>ws<br>0 10 20 30 40 50<br>% of total population<br>@ 2012 Poor 2012 Vulnerable 2012 Middle-class<br><!-- End of picture text -->
# **Figure 1: Reallocation of Exports Across SITC One-Digit Industries**

<!-- Start of picture text --> Agriculture and Raw Mat. Misc Manuf Manuf Materials Machinery Chemicals<br>(SITC 1-4) (SITC 8) (SITC 6) (SITC 7) (SITC 5)<br>1992 2005<br>.4<br>.3<br>.2<br>Share of Total Exports<br>.1<br>0<br><!-- End of picture text -->

# Note: Column headings include the following industries:

- SITC 1-4: Beverages, tobacco, raw materials, mineral fuels, oils and fats.

- SITC 5: Chemicals, dyes, pharmaceuticals, and perfumes.

- SITC 6: Leather, rubber, cork and wood products, textiles, metallic and non-metallic manufactures.

- SITC 7: Industrial machinery, office machinery, telecommunications equipment, electrical machinery, transportation equipment.

- SITC 8: Prefabricated buildings, furniture, travel goods, clothing, footwear, professional and scientific equipment.

20
and biophysical studies assessing the potential for closing yield gaps as well as attaining further advances in potential yields through plant breeding.

We solve the model over the period 2005-2204, with an emphasis on the first century. The model baseline accurately reflects developments in global land use over the 10 years that have already transpired, while also incorporating projections of population, income and demand growth from a variety of international agencies. The results of the perfect foresight model, based on expected realizations of climate and technological impacts on crop yields, predict a significant expansion in the area dedicated to food crops due to rapid population growth and increasing per capita demand for food at lower income levels. This expansion results in significant losses of unmanaged natural forests, while managed forest lands are little changed. This trend is reversed in the second half of the coming century due to slower population growth, and technology improvements in crop yields and food processing. The resulting crop land expansion is modest by 2100. Rising real incomes, and growing demand for timber products and ecosystem services bring a significant expansion in managed and protected natural forest land areas, which grow significantly by the end of the century. When we factor in the potential for future tipping points in agricultural productivity, due either to adverse climate impacts or barriers to further technological progress, we find relatively modest increases in cropland amounting to roughly two percent of current cropland area (30 Mha) by 2100.

# **References**

- Alexandratos, N., 2011. Critical Evaluation of Selected Projections. In: Conforti, P. (Ed.), Economic Analysis of Land Use in Global Climate Change Policy. Food and Agriculture Organization of the United Nations, Rome, pp. 465–508.

- Alston, J. M., Pardey, P. G., Jan. 2014. Agricultural R&D, Food Prices, Poverty and Malnutrition Redux. Staff Paper P14-01, University of Minnesota, Department of Applied Economics.

- Bateman, I., Mace, G., Fezzi, C., Atkinson, G., Turner, K., 2011. Economic Analysis for Ecosystem Service Assessments. Environmental and Resource Economics 48 (2), 177–218.

- Bellman, R., 1957. Dynamic Programming. Princeton University Press.

- Benayas, J., Newton, A., Diaz, A., Bullock, J., 2009. Enhancement of Biodiversity and Ecosystem Services by Ecological Restoration: a Meta-analysis. Science 325 (5944), 1121–1124.

- Bentsen, M., Bethke, I., Debernard, J., Iversen, T., Kirkev ̊ag, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I., Hoose, C., et al., 2012. The Norwegian Earth System Model, NorESM1-M-Part 1: Description and Basic Evaluation. Geoscientific Model Development Discussions 5, 2843–2931.

28
Southeast, benefited more from primary public spending (12 percent) than its share of school-age children (10 percent).

**Table** 17: **Incidence of public subsidies for primary and lower secondary education by region, 1993 and** 1998 (as % of public spending for education, adjusted using regional unit cost)

||Northern<br>Uplands|RedRiver<br>Delta|NorthCentral<br>Coast|Central<br>Coast|Central<br>Highlands|Southeast|Mekong<br>Delta|Vietnam|
|---|---|---|---|---|---|---|---|---|
|_1993_|||||||||
|_%ofpublicspending_|||||||||
|Primary|22|19|11|11|3|17|17|100|
|Lowersecondary|10|30|19|13|1|12|14|100|
|_%ofpop._|16|22|13|12|3|13|22|100|
|%_ofschool-age_|||||||||
|Prinary|17|23|14|11|3|12|20|100|
|Lower secondary|14|20|12|13|3|13|24|100|
|_1998_|||||||||
|_%ofpublicspending_|||||||||
|Primary|24|16|14|9|5|12|19|100|
|Lowersecondary|24|17|13|11|4|11|20|100|
|_%ofpop._|18|20|14|11|4|13|21|100|
|_%ofschool-age_|||||||||
|Primary|17|17|14|12|8|15|17|100|
|Lowersecondary|16|17|13|12|8|17|18|100|

_Source:_ Author's estimates based on 1992-93 and 1997-98 VLSS This partially reflects a result of recent decentralization process in Vietnam which allow more authority at local level to make decision on allocation of local budget and has resulted in an increased inequality in education opportunities. In fact, the provinces which may have more public funding resources and thus can spend more on education in terms of absolute per student level and / or in terms of share of total public expenditure spent on education. In contrast, poorer provinces and districts have fewer funding resources which in turn limited their capability to spend more on education. There is a need to review and improve system of budget planning and management in general and mechanism for budget allocation in particular which allow opportunities to redirect more public resources towards poorer provinces and towards those which need more resources for social services such as education and health for the poor.

# **4. Returns to education**

This section looks at the recent changes in rate of returns to education, focusing on wage earners in the private sector. The Vietnamese labor force is relatively well educated given its low income level, and there was a clear improvement over the five years from 1993 to 1998 (Table 20
**Table 5: Results of 1,000 Replications—Cumulative Means and Standard Errors**

|Approaches|Areas|First<br>replica|30<br>tions|Following<br>replicat|1,000<br>ions|95% CI from<br>Direct Es|Full Sample<br>timation<sup>a</sup>|
|---|---|---|---|---|---|---|---|
||Subsa|mple size =|640 house|holds||Lower|Upper|
||National|39.4|(2.0)|38.4|(2.4)|37.8|42.2|
|Subsample<br>Imputation|Rural|43.1|(2.5)|41.8|(2.9)|41.3|46.3|
||Urban|28.2|(3.1)|27.9|(3.2)|24.6|32.1|
||National|40.2|(2.7)|40.4|(2.9)|||
|Subsample Direct<br>Estimation|Rural|43.8|(3.2)|44.3|(3.5)|||
||Urban|29.0|(4.5)|28.4|(4.6)|||
||Subsa<br>|mple size =<br>|320 house<br>|holds<br>||||
||National|39.1|(2.1)|38.5|(3.5)|||
|Subsample<br>Imputation|Rural|42.1|(3.1)|42.6|(4.2)|||
||Urban|30.0|(3.6)|29.0|(4.3)|||
||National|40.3|(3.5)|40.6|(4.1)|||
|Subsample Direct<br>Estimation|Rural|44.0|(4.3)|44.1|(5.1)|||
||Urban|29.2|(5.9)|29.8|(6.9)|||

_Source_ : Authors’ estimations using 2005 HIES data. _Notes_ : Numbers in parentheses are SEs. For each replication, a model is estimated using OLS and it keeps variables with significance levels higher than 0.001 and 0.0001 in rural and urban areas, respectively. More details are available in appendix tables A2.4–A2.6. a. The 95% CIs are presented in the first three rows only because these numbers remain the same irrespective of approaches or subsample size.

# **_For subsamples of 640 households_**

The cumulative means of poverty rates and SEs after 1,000 replications are both close to those based on the first 30 replications for both approaches. As shown in table 5, the cumulative means of the Subsample Imputation are 38.4, 41.8, and 27.9 percent for the national, rural and urban levels/areas, respectively. These are slightly underestimated compared with the poverty rates of the first 30 replications and the Full Sample Direct Estimation. However, all estimates from the Subsample Imputation are in the 95 percent CIs of the Full Sample Direct Estimation. The poverty estimates of the Subsample Direct Estimation are 40.4, 44.3, and 28.4 percent for the national, rural and urban levels/areas, respectively. They are very close to those of the Full Sample Direct Estimation. Like the results from the first 30 replications, the SEs of poverty estimates from the Subsample Direct Estimation are higher than those of the Subsample Imputation, and particularly higher for urban areas.

# **_For subsamples of 320 households_**

The results on the cumulative means of poverty rates are similar to those of the subsample with 640 households. Also, they do not change much from the results of the first 30 replications. The SEs of poverty rates increased significantly by reducing the size of subsamples to 320 households. But, the simpler and less accurate estimation process used for the 1,000 replications resulted in much bigger SEs of the poverty rates when the sample size is reduced. For example, when the Subsample Imputation approach is used, the SE of the national poverty rate increases from 2.2 percent to 3.5 percent after switching from a more accurate estimation method used in the first 30 replications to a less accurate and 13
practice guidelines, medical school training, and international scientific evidence. Before the experiment, only one‐third of Plan Nacer beneficiaries were initiating care in the first trimester (National Ministry of Health, 2009 and 2010). The experiment randomized temporary financial incentives to primary health care clinics in which treatment clinics were paid a 200% premium for early initiation of prenatal care, i.e. before week 13.

Table 1 presents the payment schedule for the periods before, during and after the intervention. Prior to the intervention period, the province paid facilities $40 ARS for each prenatal visit regardless of when it occurred or whether it was the first or a subsequent visit.<sup>15</sup> During the intervention period the fee was increased to $120 ARS for 1<sup>st</sup> visits that occurred before week 13 but remained at $40 ARS for subsequent visits. After that, the intervention period fees reverted to the original payment of $40 ARS for all visits. The modification amounted to a 3‐fold increase in the fee for 1<sup>st</sup> visits before week 13. The modified fee structure was implemented for 8 months ‐ from May 2010 to December 2010. Facilities selected to receive the modified fee structure were invited to participate and notified of the time‐limited implementation on April 14, 2010. Facility directors were required to sign a formal modification of their existing contract with Plan Nacer in order to receive the modified fee structure.

The study design included 37 clinics out of 262 primary care facilities of the province, of which 18 were randomly assigned to the treatment group and were offered the modified fee schedule. The other 19 formed the control group. Table 2 shows that compliance with treatment assignment was not perfect: out of 18 facilities assigned to the treatment group, 14 were actually treated as three refused to sign the agreement and a fourth closed before the intervention started. In addition, one of the facilities originally assigned to the control group was mistakenly offered the treatment and agreed to the modified fee structure. In the end, there were 36 facilities in the study excluding the one that closed.

# **4 DATA**

The Province of Misiones maintains a well‐developed and long‐established automated medical record information system managed by the provincial authorities. Personnel at public primary health clinics and hospitals digitize a record of each service provided to each patient. The data are of unusually high quality in that key outcomes such as dates of visits, services delivered, and birth weight are recorded at the time of care by the provider; therefore we do not need to rely on maternal

> 15 The exchange rate for $1 ARS was around $0.25 USD between 2009 through 2011.

8
The income earning potential of group activities deserves careful scrutiny. Agricultural activities undertaken by women's groups for incomegeneration purposes often suffer from marketing problems as well as from lack of skills, equipment and capital. Projects that involve the small-scale cultivation of known crops are likely to be the most successful since they can be carried out with no, or limited, outside assistance. Non-agricultural activities aimed at generating income also have not met with uniform success due to a lack of consideration for market conditions or organizational requirements (Feldman, 1982).

Overall, many income-generating activities undertaken by women's groups (with donor support) have not shown themselves to be economically viable but rather appear to be more income-consuming than income-generating (ILO, 1986). Factors that contribute to the limited success of projects from an incomegeneration perspective include a lack of organizational and administrative capacity, an ineffective support structure and lao-k of training, a lack of market feasibility studies and identification of market outlets, and an underestimation of costs and labor input required (the latter generally on a voluntary basis).

As pointed out however, the existence of such problems and the lack of income generated from an activity does not necessarily imply a project's failure. If the activity helps the group members' subsistence and/or provides mutual labor assistance, this can be considered positive in terms of overall socio-economic benefits (Monsted and Riunge, 1987).

Posho mill projects have often been undertaken by women's groups as income-generation activities. From a strictly income-earning perspective, there are a number of questions about the viability of such operations and potential problem areas need to be carefully explored. But these mills also have been shown to have other benefits. The site of a women's group posho mill operation, for example, can be a good location for women to hear other information provided by extension agents. Women's groups can be an effective channel of communication. As mentioned above, the extension system is already working through women's groups with some signs of success in terms of the overall number of women farmers reached and the adoption of extension messages (World Bank, 1989). Because of Kenya's tradition of group self-help efforts, it has been relatively easy for agents to identify and work with women's groups in rural areas. Extension agents prefer to work with women's groups as a composite contact farmer, and this approach offers a cost-effective way of reaching a relatively large number of farmers (World Bank, 1989). Male agents appear to be able to work effectively with women's groups and to address most issues (childcare and food preparation being the exceptions) (Muzaale and Leonard, 1985).

28
Markusen, J. and Maskus, K, 1999. “Multinational Firms: Reconciling Theory and Evidence”, NBER Working Paper 7163, June 1999.

Mukherjee, A. and Suetrong, K., 2009. "Privatization, strategic foreign direct investment and host-country welfare," _European Economic Review_ , Elsevier, vol. 53(7), pages 775-785, October.

Rodrik, D., Subramanian, A. and Trebbi, F., 2004. "Institutions Rule: The Primacy of Institutions Over Geography and Integration in Economic Development," Journal of Economic Growth, Springer, vol. 9(2), pages 131-165, 06.

Sánchez-Martín, M.E., de Arce, R., and Escribano G., 2014. "Do changes in the rules of the game affect FDI flows in Latin America? A look at the macroeconomic, institutional and regional integration determinants of FDI," _European Journal of Political Economy_ , Elsevier, vol. 34(C), pages 279-299.

Sánchez-Martín, M.E., Escribano G., and de Arce, R., 2015. "How regional integration and transnational energy networks have boosted FDI in Turkey (and may cease to do so): a case study: how geo-political alliances and regional networks matter," _Turkish Studies_ (upcoming publication).

Tong, H. And Wei, S-J., 2011. "The Composition Matters: Capital Inflows and Liquidity Crunch During a Global Economic Crisis," _Review of Financial Studies_ , Oxford University Press for Society for Financial Studies, vol. 24(6), pages 2023-2052.

Wolf, M., 2005. "Will Globalization Survive?," _World Economics_ , World Economics, Economic & Financial Publishing, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 6(4), pages 1-10, October.

Wooldridge, J. M. (2009). “On estimating firm-level production functions using proxy variables to control for unobservables.” _Economics Letters_ , 104(3), 112-114.

32
reforms in Honduras had a greater positive effect on women in the _maquila_ sector (making their real wages increase faster than wages in other categories over time), λ _tk_ should be significantly different from zero and positive.

# IV. Results

The poverty effects of a boom in the _maquila_ sector are estimated using data from the Encuesta Permanente de Hogares de Propósitos Múltiples (EPHPM)—Honduras’ nationally representative household survey—for 1991, 1995, 2001, and 2006. The time span covered by these surveys (1991–2006) captures a period of significant tariff reduction and strong growth of the _maquila_ industry (see figure 1).

All of the regression results for the four specifications of equation (6) account for EPHPM’s survey design (that is, clustering, stratification, and expansion factors on point estimators and standard errors); all of the results presented show heteroskedastic-robust standard errors and control for year and industry fixed effects (table 6).<sup>10</sup> The first specification shows the result of a standard human capital equation, with the log of wages a function of years of schooling and its squared form, experience and experience squared, and a dummy variable for urban workers. The results of this specification are as expected. One additional year of schooling yields a 10 percent increase in wages; experience has a positive, though marginally decreasing, effect on earnings; and urban wages are about 16 percent higher than rural wages. Female wage-earners in Honduras earn 27 percent less than men, once observable characteristics are taken into account. Regardless of their gender, _maquila_ workers earn a conditional wage premium of 31 percent over workers outside the sector. These results are robust, both qualitatively and quantitatively, to differences in model specification.

> 10. The industries included are mining; manufacturing; electricity, gas, and water; construction; commerce; restaurant and hotels; transport and communication; financial services; and other services. The excluded category is the agricultural sector.

11