rafmacalaba/datause-encoder-data huggingface dataset:
discuss how these effects will net out and affect on poverty and income distribution in developing countries.

# **5.2 Effects on Global Poverty and Income Distribution**

The CGE results on key economic variables were incorporated into the GIDD model to simulate the distributional and poverty effects of expansion in biofuels. In the poverty analysis, two poverty threshold levels were applied: $1.25 per day and $2.50 per day. In Table 5, we present the GINI coefficient, the poverty headcount, and the poverty incidence of major regions in _BaU_ in 2005 and 2020.

**Table 5: Poverty and income distribution in** **_BaU_**

||GINI|Population|PovertyHeadc|ount(million)|PovertyIncide|nce(%)|
|---|---|---|---|---|---|---|
|Regions|Coefficient|(million)|Poor-1/a/|Poor-2/b/|Poor -1|Poor-2|
||||BaU2005|/c/|||
|East Asia|0.4190|1,805|280|898|15.49|49.73|
|Industrial Countries|0.3905|711|-|3|0.00|0.42|
|East Europe and Central Asia|0.3911|449|34|85|7.51|18.95|
|Latin America|0.6055|492|48|115|9.70|23.40|
|Middle East|0.3972|205|8|56|3.98|27.47|
|South Asia|0.2893|1,439|538|1,231|37.41|85.56|
|Sub-Sahara Africa|0.5584|445|234|355|52.54|79.73|
|ALL|0.7017|5,546|1,141|2,743|20.58|49.46|
||||BaU202|0|||
|East Asia|0.38443|2,014|111.7|605.1|5.54|30.04|
|Industrial Countries|0.40234|744|-|3.0|0.00|0.40|
|East Europe and Central Asia|0.36617|453|14.3|26.9|3.15|5.93|
|Latin America|0.57415|591|49.5|137.3|8.38|23.24|
|Middle East|0.40380|257|0.4|22.5|0.16|8.75|
|South Asia|0.31384|1,735|211.7|1,131.3|12.21|65.21|
|Sub-Sahara Africa|0.55093|567|176.4|356.0|31.09|62.74|
|ALL|0.65466|6,361|564.1|2,282.0|8.87|35.87|

/a/ Poor-1 threshold is $1.25 (PPP) per day /b/ Poor-2 threshold is $2.50 (PPP) per day /c/ Business as usual The GDP growth projection incorporated in the _BaU_ scenario results in falling poverty incidence, poverty headcount and GINI coefficient in all regions between 2005 and 2020. In the $1.25 per day poverty threshold, the global poverty incidence declines from 20.58 percent in 2005 to 8.87 percent in 2020. In the $2.50 per day poverty threshold, the global poverty incidence declines from 49.46 percent in 2005 to 35.87 percent in 2020. The GINI coefficient 26
**III. Consumption Taxes**

Consumption tax rates in South Asia are not high by international standards (Figure 2). The average VAT/sales tax rate for Bangladesh, India, Pakistan and Sri Lanka is 14.1% compared to the global average of 15.8%. There has been little change in VAT rates in South Asia and globally since 2006.

**Figure 2. Standard VAT/Sales Tax Rates, 2014**

<!-- Start of picture text --> 30 27<br>25 23 21 21.5<br>19 19 19.2<br>20 17 18 18 17<br>15 14 15.1 13.6 15.8<br>15 12.5 12 12.2<br>10 10 10 10 9.6<br>10 7 7<br>5<br>5<br>0<br><!-- End of picture text --> Source: Author’s calculations based on KPMG. 2014. Global Indirect Tax Rates Table Note: the average for Middle East is based on data for four countries that have VAT/sales taxes. In India, standard VAT rates vary from 12.5 to 15% depending on the state.

South Asian countries have been actively trying to reform their consumption taxes over the past decade. India introduced VAT legislation throughout the country by 2008 to replace the old fashioned sales tax, which did not offer input credit. Pakistan made some progress in broadening its Sales Tax base; the Sales Tax in Pakistan is charged in VAT mode and is hereinafter referred to as VAT. Sri Lanka unified the VAT rate at 12%<sup>19</sup> and extended VAT coverage to wholesale and retail trade in 2013.<sup>20</sup> Bangladesh developed new VAT legislation based on international standards in 2012, although implementation has been postponed until 2016.

Despite the reform efforts, VAT in all four countries has a narrow base because of a long list of items eligible for exemptions or reduced rates. As a result, VAT in the region functions as a highly differentiated tax and distorts consumption and production decisions. Another important problem is cascading of indirect taxes, when the same product or service is taxed more than once as it passes through the various stages of production‐distribution chain. This increases the final cost of goods and makes South Asian producers less competitive in domestic and foreign markets.

# _<u>Preferential treatment of certain products and services</u>_

VAT legislation in South Asia suffers from a number of exemptions and reduced rates. Some of the exemptions, for example, for food items are used to protect consumption of the poor, which is common throughout the world. However, rather than granting VAT exemptions to broad food categories (e.g. all

> 19 Sri Lanka will reduce its VAT rate to 11% in 2015 budget

> 20 International Monetary Fund. 2014. Sri Lanka. Article IV Consultation. IMF Country Report No. 14/285. September 2014

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(income effects), greater empowerment and bargaining power within the household, complementarities with household characteristics and community services, access to information (i.e., exposure to media) and information processing (Aslam and Kingdon, 2012). ECD is defined to include the literacy/numeracy, as well as physical, social, emotional and learning/cognitive development of the child. Parental investments in the quantity and quality of ECD form both the basis for readiness as well as performance and achievement in education, leading to higher productivity. Thus, they are critical for the quantity and quality of the human capital of future generations (Daelmans et al., 2017).

Chronic malnutrition as measured by stunting is one important factor associated with early childhood development. Impaired growth in early life - particularly in the first 1,000 days from conception until the age of two - has adverse functional consequences on the child as these years contain the most rapid changes in brain development. Stunting is associated with concurrent and later cognitive delay or deficit (Powell, et al 1995; Crookston et al, 2011; Berkman et al. 2002), poor cognition (Shonkoff, 2003, and Shonkoff et al., 2012; Prado and Dewey, 2014) and educational performance (Glewwe et al., 2001), low earnings in adulthood (Hoddinott et al., 2008), a variety of adverse consequences over the life course on human, social, and economic capital (Hoddinott et al., 2013), and, when accompanied by excessive weight gain later in childhood, an increased risk of nutrition-related chronic diseases in adult life (Wells et al., 2020).

Given the difficulties involved in the measurement of developmental delays and some empirical evidence on the causal effect of stunting on early childhood development, stunting, which is more easily measured, is frequently used as a stand-in for developmental delay in cross-sectional population health (e.g. Jeong et al., 2019; and Kang et al., 2018). However, there is an ongoing debate about the relationship between child linear growth (height gain) and child development, especially based on the evidence from cross-sectional data. On the one hand, some researchers argue that there is a causal relationship between linear growth or stunting and childhood development, and especially their cognitive ability (Sudfeld et al., 2015a and 2015b). On the other hand, studies such as Tran et al (2019), based on data from 178,393 children aged 36 to 59 months from 55 countries, conclude that the “association between growth and development in early childhood appears to be primarily a cooccurrence because the magnitude of the association varies among settings from no association in higher-income countries to a moderate level in low-income countries, and that.....overall, growth is not a sensitive and therefore suitable indicator of child development.” 3
this work by merging issues of poverty with issues of inequality. Furthermore, we have a somewhat narrower focus, as we are mainly interested in commodity prices, but we take a somewhat deeper look at some of these issues, all in the context of a general framework.

The rest of this paper is organized as follows. Section 2 presents an overview of household commodity dependence in Latin America and Africa. Using various household income and expenditure surveys from Latin American and African countries, we estimate the average budget shares spent on commodity-related sectors (mostly food and energy), and the average income share derived from commodity-related sectors (agriculture, forestry, fishing, mining, food manufactures, and energy). The data reveal strong commodity dependence in both continents. Section 3 introduces the analytical framework and reviews the literature that measures those effects using data from developing countries. The implications of price changes for net-consumers and net-producers are central in this analysis, with labor markets, various types of spillovers across markets, and imperfect price transmission of border prices playing fundamental supporting roles in the story. Section 4 turns to departures from first-order effects and studies the role of household adjustments in production and consumption. Section 5 presents a simple example of the magnitudes of the different channels identified in the paper using Mexican data. Section 6 concludes.

# **2 Commodity Dependence: Stylized Facts**

This section characterizes the dependence on commodities of households in developing countries. To study dependence on consumption, we focus on expenditure shares spent on items related to commodities, mainly food and energy. Households’ dependence on commodities on the income side is then measured by income shares in agriculture (including forestry and fishing), mining, food manufactures (beverages, food, and tobacco), and energy. An important aspect of the analysis is related to poverty and inequality, because dependence on commodities either on the consumption or the income sides can vary across quintiles of the income distribution in a given point in time.

Regarding commodity-dependence on the consumption side, Table 1 presents the average 3
<!-- Start of picture text --> 80<br>70<br>60<br>50<br>o<br>6<br>F 40<br>30<br>20<br>10<br>0<br>QPAO > PPPDP BD HKPPPSPM PLDP EPPLP PLP D PLMHP PEPEHP KKHLP EEEKKM ODES MH<br>Year<br>—@-Cocaine — Heroin -—#—Marijuana<br><!-- End of picture text --> <!-- Start of picture text --> 2.60<br>2.30<br>2.00<br>x 170<br>2<br>a 1.40<br>$<br>c<br>1.10<br>0.80<br>0.50<br>75 78 77 78 79 80 81 82 83 84 85 86 87 88 BD 90 91 G2 93 94 95 96 97 98 99 OO 01 O2 03<br>Year<br>—e— real cigarette price —#-— real beer price —4—real wine price —# real spirits price<br><!-- End of picture text -->
the coverage of the Bankscope financial institutions with maturity and purpose of credit information and the final number of countries, by region, that surpass the mentioned criteria.<sup>19</sup> The region and country classification data are from the World Bank.<sup>20</sup> The total number of countries in the raw sample in 2014 is 160; therefore, the maturity loan data only have 51 percent country coverage while the loan purpose data have 52 percent. As can be seen earlier, the country coverage improves dramatically between 2004 and 2009. Overall, the country coverage for high income is the best for both maturity and purpose of loan data. This is followed by Europe and Central Asia (ECA) and Africa (AFR) for maturity and ECA and LAC for loan purpose. As can be seen, the LAC maturity coverage is very poor, and this was the primary reason for embarking on the World Bank-ASBA questionnaire. The first rows of **Error! Reference source not found.** 3 show that almost double the number of financial institutions report loan sector data; however, this difference does not mirror the country coverage as much as the difference is likely within countries above the 30 percent representative threshold and not across countries.

# **3. Representativeness**

The data set assembled in the paper uses information on loan shares by maturity and purpose obtained from supervisory sources and Bankscope to disaggregate IFS data on credit to the nonfinancial private sector. The data set is constructed using a variety of sources for which the credit definition or the universe of reporting entities does not coincide with IFS credit to the nonfinancial private sector. Hence, it is important to compare the data we used to calculate the shares of credit by maturity and loan purpose with IFS credit data to assess the overall representativeness of the data produced.

Figure 2 shows the representativeness of the maturity and loan purpose data. For the maturity data, the top dark blue line is the total credit in trillions of U.S. dollars over the sample period. The line below that is the same volume data, but only for the country-years that are present in our data. The red line is the constructed data for maturity pairs while the yellow line is the actual data for maturity triples. The sampling within countries—which is equivalent to the red line divided by the light blue line—averages 97 percent. However, it is much lower from 2009 onward

> 19 Three LAC countries (The Bahamas, Chile, and Uruguay) that are classified as High Income are reclassified to LAC. 20 https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

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How do we determine the BWR is in practice? Here we only attempt to estimate the − mean BWR ( β ), so we re-write (1) as:

* Here ε _i_ = ( β _i_ − β ) _Yi_<sup>*</sup> + α _i_ − α and _E_ ( ε _i_<sup>_Y_</sup> _i_ ) = 0 by construction. We cannot estimate (2) as a linear regression since<sup>_Y_</sup> _i_ *<sup>is unobserved. Common practice in benefit incidence studies is (in</sup> effect) to replace<sup>_Y_</sup> _i_ *<sup>by actual (observed) income net of transfers received from the program.</sup> Mean transfer receipts (or tax payments) are then tabulated against net income. A linear “benchmark model” for estimating the BWR consistent with this practice would then entail running an ordinary least squares (OLS) regression, giving transfer receipts conditional on income net of transfers:

Here<sup>_Y_</sup> _i_<sup>denotes the observed (survey-based) total income. Equation (3) can be interpreted as the</sup> linear regression corresponding to the long-standard non-behavioral method of assessing benefit incidence, as described in the introduction.

Equation (3) can be derived from (2) by postulating a behavioral model for incomes. As noted in the Introduction, past non-behavioral benefit incidence studies for developed countries have argued that the rigidity of working hours makes an incentive effect on labor supply unlikely (see, for example, Kakwani, 1986). By similar logic, measurement errors are not presumably of much concern. As we have noted, this fixed-income assumption is questionable with reference to all income sources, though defensible for some sources. This is a clue for identification. In particular, we postulate two components of income net of transfers: Component 1 comprises those income sources that are unaffected by the program and measured accurately, while <u>Component 2 comprises sources that are influenced by behavioral responses to the transfers and</u> are also measured with error. Candidates for Component 1 are formal (regular salaried) income and property income while Component 2 includes earnings from casual work (not regular salaried work), self-employment income, and private transfers.

Assuming that transfer receipts displace Component 2 linearly at a rate π _i_ for household _i_ and allowing for classical measurement errors we can write: 8
It is common in the literature to classify skilled and unskilled workers either by educational attainment or by occupation (white- vs. blue-collar). There is some difficulty in differentiating Vietnamese individuals into a skilled and unskilled worker dichotomy by educational attainment, however, since it is actually the intermediately educated who are most likely to be engaged in export-oriented production.<sup>29</sup> I therefore use the occupation codes available in the VHLSS which are similar to the International Standard Classification of Occupations (ISCO) by the International Labour Organization (ILO) to categorize individuals into skilled and unskilled workers.<sup>30</sup> The summary statistics of demographic and economic characteristics for skilled and unskilled workers are reported in Appendix Table A.7 along with all the other variables used in my regressions. For my sample of the panel individuals for the period 2002-2004, the average wage growth rate of 27 percent for skilled workers was larger than that for unskilled workers (18 percent), suggesting that the skill premium increased for Vietnam’s economy as a whole. (ii) Export index I first develop an index to represent the province-specific labor demand induced by exports as a mechanism through which trade affects wages. The increase in production for exports may increase wage levels in the location where export firms operate by raising the demand for labor in that location. In addition, it may influence wage levels indirectly, for instance, by increasing 29 Winters (2002) points out that it is not clear that the least skilled workers are the most intensively used factor in the production of tradable goods in developing countries. Thus, it might be the case that the wages of workers with primary education increase with trade liberalization, those of the least educated workers may be left behind (Winters, 2002). The information on whether or not individuals work for exports is not available in the VHLSS. Using production workers who worked for formal firms in export-oriented sectors (namely apparel and textiles, footwear and leather, furniture and miscellaneous manufacturing and electronics) in 2004 as a proxy for those working for export-oriented production, a large majority of these workers were intermediately educated, as 28 percent, 39 percent and 26 percent of them have upper-secondary, lower-secondary and primary education respectively. The workers in these fields who had tertiary education and those who had no school degree were relatively small at 2 percent and 5 percent respectively.

> 30 “Skilled” workers include leaders (11-19), top- and mid-level professionals (21-34), staff and other white-collar occupations (41-52), and skilled workers in agriculture, sylviculture, and aquaculture (61). “Unskilled” workers consist of production workers (71-83) and those generally classified as “unskilled workers” (91-93). The skill category is not assigned for those who served in “Army force” (00). Between parentheses are the occupation codes available in the VHLSS 2004.

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- Komives, K., Foster, V., Halpern, J., Wodon, Q., et al. (2005). _Water, electricity, and the poor: Who benefits from utility subsidies?_ World Bank Publications.

- Mulder, N. (2020). The impact of the covid-19 pandemic on the tourism sector in latin america and the caribbean, and options for a sustainable and resilient recovery.

- Saltane, V., Ereshchenko, V., Hovhannisyan, S., and Mensah, J. T. (2022). Responses of the electricity sector in 120 economies to the covid-19 pandemic.

- Schmidt, E., Dorosh, P., and Gilbert, R. (2021). Impacts of covid-19 induced income and rice price shocks on household welfare in papua new guinea: Household model estimates. _Agricultural Economics_ , 52(3):391–406.

- Younger, S. et al. (2016). The impact of reforming energy subsidies, cash transfers, and taxes on inequality and poverty in ghana and tanzania. Technical report, Tulane University, Department of Economics.

- Zhang, R., Li, H., and Khanna, N. (2021). Environmental justice and the covid-19 pandemic: Evidence from new york state. _Journal of environmental economics and management_ , 110:102554.

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# IV. CONCLUSION

This paper analyses returns and risks from investing in an international portfolio composed of assets from developed and emerging stock markets and the new opportunities for investment in commodity market assets as represented by the GSCI. The optimal composition of the international portfolio and the mean-standard deviation frontier for different investment sets were calculated. These results are potentially useful because the irnter-temporal stability of crossmarket corre!ations endow ex-post portfolios with good ex-ante predictive ability.

The results of this paper point out to the important role commodities and emerging market stocks could play in an investor's portfolio. The results from the optimal composition of an international portfolio also indicate that the shares of commodities in an investor's portfolio could well increase above of what is typically today is invested in commodities. Our results indicate that commodities could account for between 20-40% of an international investment portfolio. For commodity markets the implication of these results is the increased participation of investors, mainly investment funds, into commodity markets could increase the liquidity and the possibilities for arbitrage in these markets. The increased liquidity is likely to bring more opportunities for large commodity producers to enter and exit commodity futures markets without affecting prices.<sup>12</sup> However, the role of the investment funds in commodities has caused some skepticism among certain analysts. It is felt that because investment funds trade commodities mainly based on technical analysis rather than fundamentals and also because of their size, they could result in short-lived deviations between futures and spot prices, causing 12Large developing country producers that need significant liquidity in commodity futures/options markets are Venezuela and Mexico for oil; C6te d'Ivoire and Ghana for cocoa; and Brazil and Colombia for coffee, amongst others.
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