rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 15**

<!-- Start of picture text --> Average Distance Measure for Argentina<br>2.35<br>2.25<br>2.15<br>2.05<br>1.95<br>1.85<br>1.75<br>1.65<br>1.55<br>1.45<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Log of Inverse Density<br><!-- End of picture text --> Figure 16 shows how “distance” has changed for the top 10 export products. As can be observed, it is generally flat although with a slight downward trend over the period. Also, notice that the mean distance for these 10 products is around 1.4, except for soy beans which is around 1.1. Finally, notice that the distance of petroleum gases shows a sharp decrease in the late nineties, reflecting a possible “catching up”.

**Figure 16**

<!-- Start of picture text --> Distance to the Argentine Export Basket<br>Top 10 of Argentine Exports 2004<br>1.9<br>1.8<br>1.7<br>1.6<br>1.5<br>1.4<br>1.3<br>1.2<br>1.1<br>1.0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Oil-cake and other solid residues, Soya-bean oil and its fractions<br>Petroleum oils and oils obtained fr Petroleum oils, etc, (excl. crude);<br>Soya beans Wheat and meslin<br>M aize (corn) Petroleum gases and other gaseous h<br>Copper ores and concentrates Leather of bovine or equine animals<br><!-- End of picture text --> In Figure 17, we present “distance” for the high-productivity goods analyzed in the previous section. As can be observed in the graphs, the distance to the Argentine export basket seems to have been stable during the entire period for most products. However, when looking more carefully 21
**Table 11: Main Panel Regressions, El Salvador**

||Percentage<br>perm full-ti<br>since D|change of<br>me workers<br>ec 2019|Decreased to<br>temp w|t number of<br>orkers|Decreased to<br>operat|tal hours of<br>ions|Anticipate<br>arrears on o<br>liabili|falling in<br>utstanding<br>ties|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Received national or local<br>government support|0.148|0.006|-0.174|-0.759|-0.919*|-1.652***|-0.798*|-0.797|
||(0.113)|(0.064)|(0.564)|(0.787)|(0.526)|(0.540)|(0.431)|(0.530)|
|Average change in monthly sales<br>compared to one year ago|-0.160|0.255*|-2.842***|-5.754***|-2.223**|-1.091|-2.591***|-3.228**|
||(0.130)|(0.138)|(0.909)|(1.625)|(0.867)|(1.377)|(0.807)|(1.388)|
|Decreased demand for products or<br>services compar to one year ago|-0.059|0.473***|-0.264|-0.054|1.391***|1.018|-0.606|-0.547|
||(0.077)|(0.154)|(0.695)|(1.755)|(0.493)|(0.853)|(0.784)|(0.671)|
|Decreased supply of inputs<br>compared to one year ago|0.118|-0.326**|2.492**|4.555**|0.414|-0.442|-0.680*|0.240|
||(0.090)|(0.151)|(1.063)|(2.114)|(0.440)|(1.027)|(0.359)|(0.493)|
|Decreased liqdt or cash flow<br>avail. since COVID-19 began|1.156|0.077|-1.487**|-4.727**|-1.178|1.290**|1.050**|0.900|
||(0.771)|(0.128)|(0.721)|(1.911)|(0.816)|(0.611)|(0.468)|(0.979)|
|Overdue on obligations to<br>financial institutions|-0.190|-0.716|-0.183|-1.756**|-0.440|-0.404|1.203*|0.164|
||(0.173)|(0.522)|(0.467)|(0.749)|(0.545)|(0.939)|(0.651)|(0.525)|
|Ever temporarily closed during<br>the COVID-19 outbreak|-0.089|0.069|-0.657|0.011|0.124|-0.508|-0.817***|-0.061|
||(0.127)|(0.082)|(0.510)|(0.656)|(0.553)|(0.610)|(0.261)|(0.589)|
|Adjusting or converting their<br>production or services|0.120|-0.032|0.334|2.015***|-0.121|0.894|-0.192|0.584|
||(0.107)|(0.072)|(0.467)|(0.589)|(0.326)|(0.662)|(0.303)|(0.578)|
|Started or increased online<br>business activity|-0.079|-0.278***|0.222|-1.027*|-0.542*|-0.820**|-0.602|-1.132|
||(0.141)|(0.091)|(0.444)|(0.585)|(0.315)|(0.376)|(0.396)|(0.731)|
|Started or increased delivery of<br>goods, services or carryout|<br>-0.057|<br>0.142**|<br>0.080|<br>0.521|<br>0.343|<br>0.733|<br>-0.219|<br>0.270|
||(0.062)|(0.070)|(0.374)|(0.478)|(0.278)|(0.487)|(0.520)|(0.672)|

44
27 labour markets, usually associated with higher skill levels, are better at preserving real salary values. Indexation is less perfect for unskilled, poorer workers. (iv) In addition to financial assets, one can protect the value of one's wealth against inflation by reallocating portfolio from cash to consumption goods. The effectiveness of this strategy declines with the share of goods in one's consumption basket which is perishable, and this is higher for poorer households, due to Engel's law and the fact that a higher share of foodstuffs is perishable than for most other categories of goods. (v) Finally, it also depends on the storage technology available to households. Neri presents evidence on the positive correlation between freezer ownership and household income, which adds another reason why the ability to defend one's wealth against inflation increases with income.<sup>35</sup> Such inequality-increasing effects of high inflation would be felt predominantly within the partition groupings in Table 5, since their impact on household welfare varies with wealth, rather than any other household attribute. Part of it may be captured in partitions by attributes which are strongly correlated with incomes, such as education. But the bulk of the effect is common to all individuals living in the inflationary environment, and would thus be found in the unexplained component of the dynamic decompositions. The regressive nature of the inflation tax may thus provide a candidate explanation for the large “unexplained component” in changes in inequality during the 1980s. After all, it would be almost surprising if the increase in Brazil's inflation rate from 80% p.a. in 1980 to 1509% in 1990 had no distributional effects.

This hypothesis is consistent with the simple correlations one observes between inequality (measured by the Theil index) and (the logarithm of) inflation over the period. Table 7 presents simple correlation coefficients for each of our main sub-periods (1981-1993 and 1993-2004), and both simple and partial correlation coefficients for the period as a whole.<sup>36</sup> The simple correlation coefficient (of 0.5) between inflation and inequality for the whole period is statistically significant

> 35 While the effects of channels (iv) and (v) are not captured by PNAD income data, the first three channels affect capital or labor incomes, and their effects should therefore be registered. 36 The partial correlation coefficient between two variables X and Y, holding a third variable Z constant, is given by

> _rXY_ − _rXZ rYZ rXY_ | _Z_ = ( 1 − _rXZ_ 2 )( 1 − _rYZ_ 2 ) , where rAB denote the simple correlation coefficient between A and B.
Policy Research Working Paper 8639

# **Abstract**

This paper uses Mongolia’s Household Socio Economic Survey for 2016 to estimate the distributive impact of taxes and transfers. The findings show that the system is progressive and contributes to reductions in poverty and inequality. The Gini coefficient of the pre-tax-and-transfer income is 0.4183 and decreases to 0.3507 after-tax-andtransfer. This is a reduction of 6.76 Gini points (around 16 percent). Something similar happens with the poverty rate, which decreases from 47.31 to 31.96 percent. Despite the progressiveness of the whole system, there are some caveats and policy warnings. First, pensions are the most redistributive instrument in the system, but their actuarial and fiscal sustainability is weak. Second, two programs (the child money program and the mortgage subsidy) do little redistribution—the latter is actually regressive—but represent a large share of the budget (around 2.5 percent of gross domestic product). These two factors, and the fact that up to a 35 percent of total expenditures in monetary and in-kind transfers is funded by corporate taxes and royalties—which are highly dependent on volatile commodity prices—make the redistributive impact of the tax-andtransfer system susceptible to fiscal unsustainability.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at sfreijerodriguez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Jansen, K. and E. Roquas 1998, Modernizing Insecurity: The Land Titling Project in Honduras, _Development and Change_ 29: 81-106 Jonakin, Jon. (1996) 'The impact of structural adjustment and property rights conflicts on Nicaraguan agrarian reform beneficiaries', _World Development,_ 24(7): 1179-1191.

Lopez, R. 1997, Land Titles and Farm Productivity in Honduras, World Bank, Washington DC, mimeo Malik, A., and R. M. Schwab. 1991. Optimal Investments to Establish Property Rights in Land. _Journal of Urban Economics_ 29: 295-309.

Merlet, Michel, and Denis Pommier. 2000. _Estudios sobre tenencia de la tierra. Nicaragua._ Managua, Nicaragua: IRAM (Institut de Recherches et d'Applications des Methodes de Developpement).

Migot-Adholla, S., Hazell, P., Blarel, B., and Place, F. (1991) 'Indigenous land rights systems in Sub-Saharan Africa: a constraint on productivity? _World Bank Economic Review, 5:155-175._ Paige, Jeffery M. 1997, _Coffee and power: revolution and the rise of democracy in Central America,_ Cambridge, Mass.: Harvard University Press Platteau, Jean-Philippe. (1996) 'The evolutionary theory of land rights as applied to Sub-Saharan Africa: A critical assessment', _Development and Change,_ 27:29-86.

Rozelle, S., L. Guo, and L. Brandt. 1998. Land Tenure, Property Rights, and Productivity in China's Agricultural Sector, _Agricultural Economics_ 19(2), 63-71 Sjaastad, Espen, and Bromley, Daniel (1997) 'Indigenous land rights in sub-saharan Africa: Appropriation, security and investment demand', _World Development,_ 25(4):549-562.

Van Donge, Jan Kees 1999, Law and Order as a Development Issue: Land Conflicts and the Creation of Social Order in Southern Malawi, _Journal of Development Studies_ 36(2): 48-70 Wachter, D. 1992, _Farmland Degradation in Developing Countries: The Role of Property Rights and an Assessment of Land Titling as a Policy Intervention,_ LTC Paper 145, Land Tenure Center, University of Wisconsin, Madison Zimmerman, Frederic J.; Carter, Michael R. 1999, A Dynamic Option Value for Institutional Change: Marketable Property Rights in the Sahel, _American Journal ofAgricultural Economics_ 81(2): 467-78 22
**Table A.3. E-commerce development and various categories of household expenditure per capita by region (Extended Model 1)**

||(1)<br>All|(2)<br>bottom 25%|(3)<br>25%-75%|(4)<br>top 25%|
|---|---|---|---|---|
|Total household expenditure|0.257***|0.305***|0.143***|0.053|
||(0.067)|(0.102)|(0.049)|(0.048)|
|Observations|10,872|2,984|5,627|2,232|
|Adjusted R square|0.343|0.416|0.440|0.446|
|Cosmetics and beauty|0.404***|0.643**|0.197|0.164*|
||(0.115)|(0.246)|(0.133)|(0.084)|
|Observations|10,764|2,956|5,578|2,201|
|Adjusted R square|0.344|0.330|0.370|0.423|
|Food|0.311***|0.502***|0.243***|0.108**|
||(0.065)|(0.128)|(0.058)|(0.045)|
|Observations|10,720|2,929|5,558|2,204|
|Adjusted R square|0.345|0.428|0.353|0.445|
|Of which: Food at home|0.345***|0.538***|0.279***|0.177***|
||(0.071)|(0.159)|(0.071)|(0.057)|
|Observations|10,717|2,929|5,555|2,204|
|Adjusted R square|0.389|0.463|0.401|0.391|
|Of which: Dining out|0.424***|0.169|0.144|0.111|
||(0.142)|(0.214)|(0.147)|(0.118)|
|Observations|10,878|2,987|5,630|2,232|
|Adjusted R square|0.332|0.455|0.341|0.380|
|Clothes|0.217**|0.190|0.036|-0.043|
||(0.095)|(0.247)|(0.085)|(0.085)|
|Observations|10,606|2,920|5,475|2,182|
|Adjusted R square|0.325|0.328|0.384|0.348|
|Utilities|0.156***|0.305**|0.118***|0.038|
||(0.035)|(0.131)|(0.039)|(0.031)|
|Observations|10,658|2,956|5,532|2,141|
|Adjusted R square|0.305|0.341|0.311|0.328|
|Communications|0.189***|0.023|0.118**|0.087***|
||(0.044)|(0.137)|(0.049)|(0.032)|
|Observations|10,546|2,913|5,478|2,128|
|Adjusted R square|0.337|0.302|0.382|0.431|
|Local transport|0.353***|0.048|0.148*|0.235*|
||(0.083)|(0.244)|(0.080)|(0.119)|
|Observations|10,490|2,911|5,437|2,113|
|Adjusted R square|0.306|0.338|0.327|0.310|
|Travel|0.713***|0.811**|0.371**|0.411**|
||(0.192)|(0.322)|(0.147)|(0.190)|

·· 33
|U.S.|24|-14|-21|24|-14|-21|
|---|---|---|---|---|---|---|
|Canada|-6|-12|-18|35|-12|-18|
|Mexico|60|97|115|60|97|97|
|OECD EU|-8|-16|-24|-8|-16|-24|
|Japan|-6|-12|-18|-6|-12|-18|
|South Korea|120|158|173|120|158|158|
|ANZ|62|0|-10|62|0|-10|
|Russia and OECA|-40|-35|-35|-40|-35|-35|
|China|185|286|336|185|286|286|
|India|118|192|230|118|192|192|
|Other Asia|146|232|266|146|232|232|
|Middle East|119|170|193|119|170|170|
|Africa|59|98|115|59|98|98|
|Brazil|81|125|144|81|125|125|
|OLA|76|125|146|76|125|125|
|Annex IGroup|-5|-21|-26|-5|-21|-26|
|Non-Annex IGroup|132|204|239|132|204|204|
|Global|35|46|52|35|46|42|

Figure 4 illustrates emission paths of total CO2 emissions at Annex I, non-Annex I and Global levels. As can be seen from the figure, Annex I countries’ total CO2 emissions are gradually declining from 15.4 billion tCO2 in 2004 to 11.2 billion tCO2 in 2030 in both scenarios. non-Annex I and global CO2 emissions peak in 2025 with 21.4 billion tCO2 and 33 billion tCO2, respectively under Scenario 1. The global CO2 emissions then drop to 32.6 billion tCO2 by 2030. Under Scenario 2, non-Annex I and global CO2 emissions peak in 2020 with 19.3 billion tCO2 and 31.2 billion tCO2, respectively. The global CO2 emissions drop to 30.8 billion tCO2 in 2025 and 30.4 in 2030.

# **Figure 4: Global, Annex I and Non-Annex I CO2 Emissions under Different Scenarios**

-24-
These methodologies offer some differences. The MT-criteria uses the real credit per capita to identify booms in credit markets whereas the GVL-criteria use the ratio of credit to GDP. Both criteria use the HP-filter to compute the trend in credit and apply a rolling variant of the filter that takes information up to the moment where the deviation is computed. Thresholds are country-specific rather than based on the cross-sectional distribution of countries. Like Calderón and Kubota (2012), we use quarterly information on credit, which, as they argue, is more appropriate to assess cyclical movements and volatility associated to crisis episodes.<sup>4</sup> We collect quarterly information on real credit provided by the banking system to the private sector for 71 countries from 1975q1 to 2010q4 to identify the credit boom episodes according to the two criteria outlined above. Table 1 presents some descriptive statistics for the number of episodes identified (Obs.), their mean duration (Mean), standard deviation (S.D.), minimum (Min.) and maximum (Max.). Industrial and developing countries and different periods of time are also considered in this analysis.

# **<Insert Table 1 around here>**

Using the MT-criteria we are able to identify 123 credit boom episodes over our entire time dimension -- of which 32 episodes took place in industrial economies and 91 in developing countries. Over time, most episodes of lending booms occur in the 1990s (50). On the other hand, when we use the GVL-criteria, we are able to identify a larger number of episodes (231), especially in the period 2000-2010 (98).<sup>5</sup> Most of them also occur in the group of developing countries (180).

> percent. Analogously, the end quarter of the boom is determined if either the year-on-year growth rate of _L/y_ becomes negative, or _L/y_ falls below 3⁄4 times its standard deviation and its growth rate is lower than 20 percent.

> 4 The HP-filter is used to compute the trend, where the value of Lagrange multiplier employed in the maximization problem is _λ=_ 1600 (for quarterly data) rather than the value of 100 used in the MT-criteria to decompose the annual data.

> 5 This means that the GVL criteria allow us to identify many episodes of credit booms in the run-up to the recent global financial crisis.

18
- 32 -

4. Nonetheless, the welfare rankings are transitive so that<sup>if a taxon</sup> commodity B dominates one on commodity C, a tax on commodity<sup>A also</sup> dominates one levied on C.

5. See Yitzhaki (1983) for a complete discussion of the properties of<sup>the</sup> extended Gini.

# 6. Formally we can write

bi (v) - _f_ w (F (y), v) xi (y) dy

- where w (F (y), v) is the weight attached to the marginal propensity of spending on the ith commodity, xi(y). In the case of the Gini v - 2 then

-

- v (F (), 2) F (y) [1 F(y)]

- -- (F tY), 2) -J F (z) [1 - F (z) dz

which means that the highest weight is given to the marginal propensity of the median individual. The higher v, the higher the weight attached to low income groups. See Yitzhaki (1987).

7. The results for the C6te dl'voire also indicate the desirability of taxing gasoline. We have resisted the temptation to recommend ETG (electricity-telephone-gasolin **e** xcises because gasoline taxes in most) countries serve as crude user fees for the consumption of road services and therefore have an efficiency rationale that is quite different from the equity considerations which distinguish ET taxes.

**_Massll4/Wlfoar_** Dominance & **the** Design of Exciso Taxation in **the** Coto D Ivoire/WThiLrk:ab/5/18/88
the final disposition of the case (Finger 1993; OECD, 1997), the main indicator used is the number of anti-dumping investigations that had been initiated as of the time of the TPR. For safeguards and countervailing, the actions were so infrequent , that the data shown refer to the number of specific products, usually narrowly defined, on which there were either affirmative actions in place or **(in** going investigations as of the time of the most recent TPR, using both TPR data and WTO notification ;. Thus, while the two sets of data in Table 11 are not directly comparable, the frequency of safeguard and countervailing actions is likely to be much smaller than those for anti-dumping.

The data verify and provide detail on the well known increasing use of antidumping actions, especially by higher middle income developing countries and the emergence of antidumping as the

**Table 11**

# **Trade Remedies by Developing Countries, 1989-1998**

(in % and Number of Products)

|**COUNTRY**|**Antidu**<br>**(1)**|**mping***<br>|**Counter**<br>**(2**|**vailing**<br>**)**|**Safegu**<br>**(3)**|**ards"**<br>|
|---|---|---|---|---|---|---|
||**1989-94**|**1995-98**|**1989-94**|**1995-98**|**1989-94**|**1995-98**|
|Argentina<br>|0.70%<br> <br>|1.12%<br>|2|3||**2**|
|Bangladesh<br>|L<br>I|I|||||
|Benin|_<br>_|<br>|_<br>_<br>.||||
|Bolivia<br>_<br>||<br>r||||||
|Brazil|1.28%|**0.54%**|2|2||1|
|Cameroon|||||||
|Chile|0.01%|0.74%|**2**|5|||
|Colombia||0.14%°|||||
|Costa Rica||0.23%|||||
|**C6te**d'lvoire|||||||
|Cyprus<br>X|=|________|||||
|Dominican<br>Rep.|||||||
|Egypt||x|_||||
|El Salvador|||||||
|Fiji<br>_||||.|||
|Ghana|||||||
|Hong<br>Kong,<br>China|||||||
|India|**0.07%**|0.72%°_|||||
|Indonesia||0.16%°|/||||
|Kenya|||||||
|**Korea**|**0.27%**|0.53%||||**3**|
|Malaysia|_|0.16%°||_|||
|Mauritius|||||||
|**Mexico**|16.31%|28.12%||**_**<br>**31**|||
|**Morocco**|||||||

28
consumption data are comparable and ignores the country-specific information on the levels in surveys. As noted above, that is a questionable assumption. However, unlike all the other examples of this second class of methods, Karshenas assumes that the surveys are correct on <u>average</u> and focuses instead on the problem of survey comparability, for which purpose the poverty measures are anchored to the national accounts data.

Where we depart from the Karshenas method is that we do not ignore the countryspecific survey means. When one has two measures of roughly the same thing, but neither is ideal, it is natural to combine the two measures. For almost all developing countries, surveys are less frequent and more recent than NAS data. It is natural then to think of the NAS consumption series as being the basis for setting a Bayesian prior for average consumption, while treating the survey as new, posterior, data. But how should we combine the two in a mixed-method? A result from Bayesian statistics provides an interpretation of a mixing parameter under the assumption that consumption is log normally distributed with a common variance in the prior distribution as in the new survey data. In particular, the Appendix proves the following claim: _<u>Proposition 2: Suppose that the prior is the expected value of the survey mean,</u> conditional on national accounts consumption, and consumption is log normally distributed with a common variance. Then the posterior estimate is the geometric mean of the survey mean and its expected value. Over time, the relevant growth rate is the arithmetic mean of the growth rates from the two data sources._ These assumptions can certainly be questioned. As noted, it is unlikely that the prior based on the NAS would have the same relative distribution as the survey. However, this proposition does at least offer a clear foundation for a sensitivity test, given the likely heterogeneity in surveys.

# **5. Benchmark estimates**

We report aggregate results over 1981-2005 for the regions of the developing world and (given their populations) China and India. Jointly with this paper, we have updated the website _PovcalNet_ to provide public access to the underlying country-level data set, so that users to replicate these results and try different assumptions, including different poverty measures, poverty lines and country groupings, including deriving estimates for individual countries.<sup>35</sup>

> 35 See http://econ.worldbank.org/povcalnet. The process of updating the _PovcalNet_ web site to incorporate the 2005 PPPs will be complete by September 16 2008.

21