rafmacalaba/datause-encoder-data huggingface dataset:
# **2 Conceptual framework**

Although it is the first time that the World Bank tracks an inequality-sensitive growth indicator (based on surveys), the idea of focusing attention on how the poorer segments of every society fare in terms of growth is not new.<sup>5</sup> Basu (2001) proposes to use ‘quintile income’ defined as the growth of the bottom 20% and argues that it is more closely correlated with non-income welfare indicators than growth in the mean. Taking the cutoff at 40%, instead, avoids the measurement problems associated with the lowest percentiles (Basu, 2013). Also, the share of the global poor who live in their respective countries’ bottom 40% is larger than it is at the bottom 20% cutoff.

The degree of overlap between the populations classified as bottom 40% and the extreme poor varies across countries. Figure 1 illustrates how the extreme poor, bottom 40% and top 60% are distributed in 2011 and 2030.<sup>6</sup> The full area represents the world population. In 2011, the extreme poor (areas A1 and A2) cover about 14.5% of the total area. Of this group (the extreme poor), 90.3% are within the bottom 40% of their respective countries (A1) while 9.7% are in the top 60% (A2). In 2011, the bottom 40% in each country amounted to 2.78 billion people (area A1 and B). Of this group, 37% (A1) are classified as extreme poor and 63% (B) are not. This illustrates how the extreme poor are mostly situated within the bottom 40% of their countries’ population. However, it also shows how a large share of the national bottom 40% in the world is not classified as extreme poor. The corresponding illustration for our projected distribution of incomes in 2030 shows a much smaller share of the bottom 40% also being extreme poor.

In contrast to the poverty goal of 3% by 2030, the World Bank Group’s shared prosperity goal does not provide a benchmark (or set a target) in terms of fostering the growth of the bottom 40% of the population. However, a natural and intuitive assessment of progress in this indicator is to compare the growth rate of the bottom 40% to that of the mean in each country (e.g. Basu, 2013). In fact, the World Bank Group’s Corporate Score Card, tracks the share of countries with “growth concentrated in the bottom 40 percent”, measured as the share of countries for which growth in mean real per capita income of the bottom 40 percent is positive and greater than growth in mean real per capita income.<sup>7</sup>

> 5 As pointed out in World Bank (2015), then World Bank President McNamara proposed to use growth in the bottom 40% as an indicator some 40 years ago.

> 6 This illustration is an adaptation of Beegle et al. (2014), updated with data used in this paper.

> 7 <mark>The World Bank Group Corporate Scorecard helps to assess the World Bank Group’s performance toward achieving the two goals. Its indicators cascade into the monitoring frameworks of the three World Bank Group institutions (WB, IFC and MIGA). In:</mark> <u><mark>http://siteresources.worldbank.org/CSCARDEXT/Resources/2014_WBG_corporate_scorecard_e-version.pdf,</mark></u> <mark>accessed on Oct 3, 2014.</mark>

4
Even though the literature on international banking is quite vast, a limitation of existing studies is that the determinants of financial FDI (or local claims) and those driving cross-border claims have been considered largely in isolation.<sup>4</sup> Furthermore, though some studies have documented differences in the behavior of international banks’ cross-border and local claims across countries (see Haas and Lelyveld, 2002, for Eastern Europe, Peek and Rosengren, 2000, for Latin America, and Palmer, 2000, for U.S. bank claims around the world), the implications of the compositions of foreign bank claims have been largely overlooked in the international banking literature and still require a systematic empirical analysis.

The majority of existing papers on international or foreign banking have examined why international banks enter certain markets. An early strand of the literature on FDI by international banks focused on the experience of developed countries (especially the U.S.) with foreign bank entry and bank internationalization during the 1970s and 1980s (e.g., Goldberg and Saunders, 1980, 1981a, b; Ball and Tschoegl, 1982, Nigh et al., 1986; Cho et al., 1987; Hultman and McGee, 1989; Goldberg and Johnson, 1990; Goldberg and Grosse, 1994; Brealey and Kaplanis, 1996; Fisher and Molyneaux, 1996). More recently, several authors have examined the decision of international banks to establish operations overseas during the 1990s, especially in developing countries (e.g., Buch, 2000; Claessens et al., 2000; Focarelli and Pozzolo, 2001, 2006; Guillen and Tschoegl, 2000; Buch and DeLong, 2001; Moshirian, 2001; Galindo, Micco

> 4 Buch and Lipponer (2004) is an exception. This study recognizes that banks have a choice to lend cross-border or to do so locally in host countries via their foreign affiliates. Using data for German banks, the authors test whether these two forms of financing are complements or substitutes by running regressions of one type of foreign funds visà-vis the other, while controlling for other determinants of international banking activities. They conclude that in the case of German banks both types of claims are complementary. The problem with their approach is that complementarity could be a result of endogeneity, where both types of foreign claims are driven by omitted factors. Also, the authors do not investigate why different countries get different shares of local claims relative to what they receive in cross-border flows.

3
the rest of the population.<sup>22</sup> This in turn means that the poor, especially the bottom decile, may find it harder to escape poverty because with rich’s lack of interest in public health and education, the quality of the services deteriorates while the poor are often fully dependent on them. It is a model of society sketched by Bénabou (2000) where high inequality, combined with credit constraint and influence of the rich on the political process, results in a steady-state of low government spending and persistent high inequality.<sup>23</sup> It is harder to see why inequality among the poor would have a positive effect on the growth rate of the rich. This is a more speculative part. Segmentation among the poor can ensure that the rich are provided some services and amenities at cheaper rates than they would pay if the poor were more homogeneous. In other words, the poor who have a choice between facing destitution or being willing to work for a very low remuneration, may prefer the latter. Segmentation among the poor, some of which may be also due to them being non-documented aliens, creates perhaps what Marx called “the reserve army of the unemployed” which may be conveniently used to improve real incomes of the rich.

> 22 Using cross-country data from the Americas, Sokoloff and Zolt (2005) find empirically that higher levels of inequality are associated with more regressive taxes and consequently less funding for public investments and services. In a study of the US federal tax system, Piketty and Saez (2007) provide evidence that over the last 40 to 50 years, since the 1960s, the federal tax system has evolved from being progressive to being regressive. It is reported that the marginal tax rate on the highest incomes, for example, has declined from 91 percent around 1960 to 35 percent in 2003. This remarkable decline has been accompanied by an equally remarkable upward trend in income inequality. For further empirical evidence that inequality also acts as a barrier to the provision of public goods, see e.g. Araujo et al. (2008) and Easterly (2007).

> 23 See also the studies by e.g. Bénabou (1996b), Alesina and La Ferrara (2000), and Lloyd-Ellis (2000).

23
In Table 18, some scenarios were calculated. Given a base category (male, living in the Center region, age, etc.), the median time for a male graduate to find a job in the 94 Survey is 4 months, and in the 98 Survey 2.8 months. The mean length of time that female graduates search for a job is longer than that for male graduates. Furthermore, female graduates in the 98 Survey found jobs faster than those from the 94 Survey did. Not surprisingly, the job search is longer for graduates without job experience while studying. However, the difference in job search time between individuals with work experience and those without is small.

**Table 18. Cox Regression Model. Job Search (time) after finishing** CONALEP

|**Covariate****|**Survey 94**|**Survey 98**<br>**Difference**|
|---|---|---|
|Male|4.0|2.8<br>1.2|
|Female|4.9|3.0<br>1.9|
|Age (mean=21.5 years)|4.0|2.8<br>*<br>1.2|
|Age (22.5 years)|4.0|2.8<br>*<br>1.2|
|Center Area|4.0|2.8<br>1.2|
|South-East Area|2.9|1.9<br>1.0|
|Center-South Area|2.9|1.9<br>1.0|
|North-East Area|2.0|1.9<br>0.1|
|North-West Area|2.8|1.9<br>0.9|
|Center-North Area|2.8|1.9<br>0.9|
|Pacific Area|2.8|1.9<br>0.9|
|91 Cohort|4.0||
|92 Cohort|4.0||
|93 Cohort|_5.0_||
|94 Cohort||4.9|
|96 Cohort||2.8|
|97 Cohort||2.9|
|None - Primary HH|4.0|2.8<br>1.2|
|Lower-Secondary HH|4.0|2.8<br>*<br>1.2|
|Upper-Secondary HH|4.0|2.8<br>*<br>1.2|
|University - + HH|4.0|2.0<br>*<br>2.0|
|Don't know HH|4.0|2.0<br>*<br>2.0|
|GDP per capita (mean=38.8)|4.0|2.8<br>*<br>1.2|
|GDP per capita|3.0|2.0<br>1.0|
|Worked when studying|4.0|2.8<br>1.2|
|Did not work when studying|5.0|3.0<br>2.0|

** The other covariates are in the base category or at the mean. Base categories: men, Center area (D.F. and Mexico), 91 cohort, none or primary school education of household head (HH), and had worked when he was studying at CONALEP. Means: 21.5 years old, 38.8 thousands of 1998 pesos GDP per capita.

*Not significant at 5 percent.

41
significant. More importantly, the second order effects is positive, smaller than the first order effect, and not as significant, indicating that with increasing levels of decentralization, increased preference cost savings can be achieved even though there are diminishing returns. In contrast to our previous findings, we find that in Korea the joint impact of decentralization is only captured through preference costs, and that it is the decentralization of administration which leads to service quality gains from the point of view of the user.

**Table 12: Impact of Decentralization on Resource and Preference Costs When They are Jointly Determined**

|Variable|Const|ruction|Maint|enance|Admin|istration|
|---|---|---|---|---|---|---|
||Resource<br>|Preference|Resource<br>|Preference|Resource|Preference|
|year|6|-|_1_|-||=|
|per capita GNP|-|-. 8|-|-2,56|-||
|fiscal|5.47|_-0.51_|-1.29|0.84|-0.15|-<br>i|
|decentralization'|(0.33)|(-0.50)|(-1.08)|(1.12)|(-0.35)|(-3.7_|
|fiscal|-0.29|0.02|0.009|-0.03|-0.01|0.013|
|decentralization<sup>2</sup>|(-0.60)|(0.66)|(0.22)|(-1.36)**|**_(-0.95)_**|(1.17)|
|fiscal|0.002|-0.0002|-6.94e-06|0.0002|0.0001|0.00005|
|decentralization<sup>3</sup>|(0.43)|(-0.95)|(-0.02)|(I.I1)|(1.50)*|(0.58)|
|Constant||1|||4523|157|
|R-square|0.67|0.75|0.78|0.86|0.85|0.93|
|Observations|25|25|25|25|25|25|

Fiscal decentralization measured separately as the percentage local government contribution to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at 90% level of confidence.

* significant at 85% level of confidence.

** significant at the 80% level of confidence.

# **_Comparison_** _of_ **_Estimated Resultsfrom Korea and the Cross-Country Panel_**

Table 13 gives a summary view of the impact of decentralization on performance for Korea. Overall, our results for Korea are consistent with our cross-country panel estimations. Despite some sign switches for individual coefficients, the estimated models predict the same point for the optimal level of decentralization.

The results for decentralization of administrative functions (figure 9) have the same pattern as those obtained using the cross-country panel. The only difference is that the results of the cross-country panel seem to overestimate the possible gains from 30
**_Table 1._** _Subjective Assessment Summary_

||~~East~~|Plots with|Objective Soil|Analysis||Full S|ample|
|---|---|---|---|---|---|---|---|
||Wellega|Borena|West Arsi|To|tal|To|tal|
|~~N~~|589|496|592|1677|100%|4149|100%|
|_Soil_ _Quality_||||||||
|Goo~~d~~|128|276|304|708|42%|1458|35%|
|Fa~~ir~~|419|193|274|887|53%|2445|59%|
|Po~~or~~|42|27|14|83|5%|246|6%|
|_Soil_ _Color*_||||||||
|Blac~~k~~|97|128|413|638|38%|1362|33%|
|Re~~d~~|403|270|87|761|45%|2170|52%|
|White/Lig~~ht~~|88|84|92|264|16%|592|14%|
|Yello~~w~~|1|14|0|14|1%|24|1%|
|_Soil_ _Type_||||||||
|Sand~~y~~|143|123|93|360|21%|803|19%|
|Cla~~y~~|291|250|360|901|54%|2292|55%|
|Mixture of Sand/Cla~~y~~|92|120|139|351|21%|785|19%|
|Oth~~er~~|63|3|0|65|4%|269|6%|
|_Soil_ _Texture°_||||||||
|Very Fin~~e~~|4|26|26|56|3%|87|2%|
|Fin~~e~~|240|225|404|870|52%|2070|50%|
|Between Coarse and Fin~~e~~|259|187|140|586|35%|1584|38%|
|Coars~~e~~|81|56|21|158|9%|390|9%|
|Very Coars~~e~~<br>*"~~"~~|5<br>""|2<br>|1<br>|8|0%|18|0%|

* Categories "White/Light ~~<u>" a</u>~~ nd "Yellow" combined for analysis ° Categories "Very Fine" and "Fine" were combined for analysis, as were "Coarse" and "Very Coarse"

While subjective assessments of soil quality are both cost- and time-efficient, the quality of results may be questionable. Summary statistics of the subjective questions included in the LASER study, found in Table 1, reveal little discrimination by respondents.<sup>3</sup> The table focuses on the sample of plots for which spectral soil analysis was completed, as this is the sample that will be compared to the laboratory results in subsequent sections, but there is also a column for the full sample of plots. When asked about the quality of soil on a particular plot, 94 percent of all plots were reported to have either _good_ or _fair_ soil (35 percent _good_ , 59 percent _fair_ ). On the whole, only 6 percent of plots were reported to have _poor_ soils. This heavy-tailed distribution holds across administrative zone and agroecological zone, with no more than 8 percent of plots in a single agroecological or administrative zone reported as _poor_ . This finding is not unique to the LASER data set. In nationally representative LSMS-ISA surveys from Uganda (201314), Malawi (2013), and Tanzania (2012-13), only 3 percent, 12 percent, and 6 percent of plots were reported as having poor soil, respectively (UBOS, 2013; Malawi NBS, 2013; Tanzania NBS, 2012). In

> 3 The sample is limited to plots in which a topsoil sample was tested in the laboratory. Due to mislabeling of soil samples and/or transportation between the field and the laboratories, 120 plots with subjective measurements do not have matching objective measurements. These observations have been dropped. The number of samples lost to mislabeling was significantly reduced by the use of barcoded labels in a replication study.

7
60 pp.121-30.

United Nations. Economic Commission for Europe. Economic Survey of Europe in 1948. Geneva: United Nations.

- Uzawa, H. 1962. "Optimal technical change in an aggregative model of economic growth." International Economic Review 6: 18-31.

- van Wijnbergen, S. 1985. ‘Trade reform, aggregate investment, and capital flight. On credibility and the value of information.” Economic Letters 17.

- . 1989, *Extema! debt, inflation, and the public sector: Toward fiscal policy for sustainable growth." World Bank Economic Review 3(3): pp. 297-320.

- Von Pischke, J. et al. 1983. Rural Financial Markets in Developing Countries. Baltimore: Johns Hopkins University Press.

- Wallace, Cynthia Day and contributors. 1990. Foreign Direct Investment in the 1990s: A New Climate in the Third World. Dordrecht: Martinus Nijhoff.

- Wang, Jian-Ye. 1990. "Growth, technology transfer, and the long-run theory of international capital movements." Journal of International Economics 29: pp. 255-271.

- Weitzman, Martin L. 1990. Comment on G. Ofer "Macroeconomic issues of Soviet reforms." tn O.J. Blanchard and S. Fischer, eds. NBER Macroeconomics Annual 1990. Cambridge, MA: MIT Press.

- Wheeler, David. 1984. "Sources of stagnation in Sub-Saharan Africa." World Development 12 (1): pp. 1-23.

- Wood, A. 1988. "Global trends in real exchange rates, 1960-1984." World Bank Discussion Paper No. 35. Washington, DC: World Bank.

- World Bank. 1987. World Development Report 1987. New York: Oxford University Press.

- . 1988. World Daveloprnent Report 1988. New York: Oxford University Press.

- . 1989a. “Adjustment lending: An evaluation of ten years of experience.” Policy and Research Series 1. Washington, DC: Wortd Bank.

. 1989b. Sub-Saharan Africa: From Crisis to Sustainable Growth. Washington, DC: World Bank.

- . 1989c. World Development Report 1989. New York: Oxford University Press.

- . 1990a. “Adjustment lending policies for sustainable growth.’ Policy and Res arch Series 14. Washington, DC: World Bank.

. 1990b. World Development Report 1990. New York: Oxford University Press.

Yugoslavia. Federal Statistical Office. 1978, 1987. Statistical Yearbook of the Socialist Federal Republic of Yugoslavia. Belgrade: Federal Statistical Office.

Zodrow, G.R. and C.E. McLure. 1988. "Implementing direct consumption taxes in developing countries." PPR Working Paper No. 131. Washington, DC: Word Bank.
_Brazilian spatial labor dynamics Page 27_

|Dependent Variable:<br>Delta ln Employment|2SLS.Est.|2SLS.SE|Spatial GMM<br>Est.<br>(Cutoff = 0.5)|Spatial GMM<br>SE<br>(Cutoff = 0.5)|
|---|---|---|---|---|
|Intercept|-.95503801|.15016687|-.9338026|.21124536|
|ln Wage in 1991|.19092101|.0156507|.18901078|.02148466|
|ln 5 to 15 over 15 to 55 yrs Ratio|<br>.1983383|.03620152|.19401434|.05055868|
|Proportion of Natives in 1991|.28972116|.04454679|.3009288|.06903289|
|Delta ln Market Potential|.30840242|.03091629|.30340591|.05130368|
|ln Teacher Qualification in 1991|-.00807114|.00235408|-.00812438|.00351447|
|Homicides in 1991|-6.9250641|4.325995|-8.436305|11.13018|
|Employment in Farming in 1991|-.46696147|.16523983|-.50415081|.25446269|
|Bank Dummy|.00072828|.00950343|.00397452|.01257344|
|ln Population in 1991|-.02531787|.00636568|-.02616172|.00975625|
|Mean Temperature|-.00678719|.00265857|-.00660142|.00409379|
|Total Rainfall|-.00005046|.00001317|-.00004882|.00002093|
|Rainfall - 1o Princ. Component|.00971466|.00196289|.00973935|.00324289|
|Rainfall - 3o Princ. Component|.02245587|.00465159|.02228627|.00712442|
|Employment rate in 1991|.15742175|.14161712|.17703837|.2183038|
|crit. fn. test of overid. restrictions|----|----|3.819309|3.819309|
|Dependent Variable:<br>Delta ln Employment|Spatial GMM<br>Est.<br>(Cutoff = 1)|Spatial GMM<br>SE<br>(Cutoff = 1|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|Intercept|-.91925794|.23347463|-.8959102|.25081733|
|ln Wage in 1991|.18708293|.02332669|.183212|.0248753|
|ln 5 to 15 over 15 to 55 yrs Ratio|<br>.19600923|.05612046|.19536939|.06081697|
|Proportion of Natives in 1991|.30085957|.07981306|.29273868|.09316006|
|Delta ln Market Potential|.30237946|.05999995|.30237827|.06949838|
|ln Teacher Qualification in 1991|-.008132|.00415266|-.0078909|.0047722|
|Homicides in 1991|-9.6619034|15.947038|-12.576632|24.664917|
|Employment in Farming in 1991|-.52499872|.29569033|-.53298384|.3420592|
|Bank Dummy|.00465381|.01249154|.00546396|.01298614|
|ln Population in 1991|-.02584312|.01101769|-.02346004|.01342835|
|Mean Temperature|-.006529|.00497929|-.00703587|.00608096|
|Total Rainfall|-.00004341|.0000258|-.00003918|.00003218|
|Rainfall - 1o Princ. Component|.00924989|.00368997|.00853621|.00395734|
|Rainfall - 3o Princ. Component|.02213984|.00880441|.02222459|.01032099|
|Employment rate in 1991|.18801478|.2473453|.18705417|.27703223|
|crit. fn. test of overid. restrictions|3.0782778|3.0782778|2.4566422|2.4566422|
Other studies, such as Fischer et al. (2009), also find similar results from CGE analyses. They show that the implementation of announced targets for biofuels would release 4.1 to 5.3 billion tons of CO2 equivalent cumulative GHG emissions during the 2000-2020 (20 years) period. In order to meet the same targets, our study finds a net release of about 1.2 billion tons of CO2 over the 2009-2020 period (11 years). The different estimates can be attributed to a number of differences in these two studies. First, Fischer et al. (2009) freeze production of biofuels at the current level (i.e., 2008 level) in the baseline. We do not artificially freeze biofuel production but instead assume that biofuel production will continue in the baseline following market signals and existing policies. Second, the period for the accumulation of emissions is different: while the cumulative GHG emissions referred to above is for 20 years (2000-2020) in Fischer et al. (2009), it is for 11 years in our study. Due to these differences, Fischer et al. (2009) find the emission payback period or carbon debt of implementing announced targets to be 30 to 50 years whereas our study finds it to be about 17 years.

# **5. Can Land-use Emissions be Reduced?**

The key challenge to the expansion of biofuels from a climate change mitigation perspective is the emissions released to the atmosphere through land-use change, particularly deforestation. Table 1 presents deforestation caused by the expansion of biofuels to meet announced and enhanced targets in various countries or regions. While meeting the announced targets would cause deforestation of about 5 million hectares in 2020, deforestation would increase by more than 5 times that amount if the targets are doubled. Most of the deforestation would occur in Brazil, Canada, India, Thailand and Eastern Europe.

Some results look rather surprising, for example, huge deforestation in Canada, though not in percentage, and reverse deforestation in Russia. The reason might lie in the data as the model uses a huge database including 113 countries, 57 sectors, and 18 agro-ecological zones in each country. it is thus possible that there might be some issues with the data. Nevertheless, small errors in data for a country are not expected to affect the big-picture results. In fact, the 19
Table 4: Market Access and Sale at the market

|(1)|(2)|(3)|(4)|
|---|---|---|---|
|Travel time to market<br>-0.82741<br>(2.01)**||||
|Market Access Index|0.01722|0.01440|0.01166|
||(2.93)***|(2.24)**|(4.25)***|
|Land owned(log)||-0.08306|-0.06087|
|||(4.01)***|(3.68)***|
|Quantity sold (log)||0.04852|0.00746|
|||(2.77)***|(0.37)|
|Household size(log)|||-0.02016|
||||(0.51)|
|share of adult female|||0.18329<br>(1.79)*|
|Share of kids (5yr & Older)|||0.17663<br>(1.69)*|
|Share of old (>65 yr)|||-0.03716<br>(0.45)|
|Dummy if member of Scheduled|||0.03917|
|Caste/Scheduled tribe|||(1.08)|
|Age of household head|||-0.08291<br>(1.05)|
|Education of Household Head|||0.01167<br>(2.10)**|
|Number of adult male|||0.02703<br>(1.70)*|
|Residual from 1st Stage||||
|Quantity sold||-0.00976|0.02942|
|||(0.32)|(0.99)|
|Pseudo R<sup>2</sup><br>0.35|0.4|0.34|0.5199|
|LogLikelihood<br>-144.77|-132.31|-145.99|-105.6724|

Robust z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Note: Coefficients are marginal effects from Probit estimation All regressions include dummies for crop, month of sales and district