rafmacalaba/datause-encoder-data huggingface dataset:
The country-specific IO tables that we construct partition all sub-Saharan countries exhaustively in three groups, and these three countries/regions are added to the above list:

1. <u>OWN: the country being considered itself</u>

2. <u>CLO (Close sub-Saharan African countries): countries neighboring the country</u> considered 3. <u>FAR (Faraway sub-Saharan African countries): the remaining countries from the</u> region.

Hence, in total there will be 9 industries with 1 final demand vector and 8 countries/regions, which makes for an IO table with approximately 5000 coefficients. While the total amount of information in the 45 country-specific tables with these dimensions is comparable to the single, regional alternative, most of the information in the different tables is identical and will not be used.<sup>13</sup> In our analysis, we will primarily concern ourselves with the interactions between the country of interest and the different regions. It means that we will use the first nine rows and the first nine columns of each country-specific IO table to construct the GVC indicators of interest.

# **3.2 GVC indicators**

From the 45 country-specific IO tables for each of the three selected years, we now proceed to calculate a number of ratios that are of particular interest to illustrate the GVC participation of the different countries. There is still too much information to be able to show results in any exhaustive way. Each metric of interest that we can calculate, we observe in 9,720 cases: (45 sub-Saharan countries) x (8 origin or destination regions) x (9 sectors) x (3 years). In addition to the level of each indicator, we are also interested in another 6,480 changes for the same indicators over the 1995-2005 and 2005-2015 time periods.

To convey as much of this rich information as possible, we report information in three different ways. First, we make available a data file that contains the complete information for all countries, partners, industries, and years for each of the metrics we consider. Second, in the Appendix, we report for each metric one table with the results for all countries and partner-regions but limited to a single industry (manufacturing) and a single year (2015).<sup>14</sup> Third, we discuss below some patterns that we believe are of interest at a more aggregate level, for example the average change over all sub-Saharan countries.

Based on the collapsed country-specific IO tables, we calculate the following four dimensions of GVC participation:

1. Share of imported intermediate inputs: by dividing the sector- and region-specific flows of intermediates in the different rows of the IO table with the total output of each

> 13 The national IO structure in the 5 regions (4-8) and the bilateral interactions between these regions is identical in each of the 45 tables and we only need to calculate that information once.

> 14 For the fourth metric that we consider, the domestic value added in gross exports, there is no partner-country dimension, hence we report results for all 3 years and the 2 decadal changes.

21
The last set of variables represents the role of natural disasters on the growth performance across countries. Data for natural disasters were obtained from the Emergency Disasters Database (EM-DAT) maintained by the Center for Research on the Epidemiology of Disasters (CRED). EM-DAT provides the number of casualties (people confirmed dead, reported missing, and presumed dead), the number of people injured, and the number of people affected. People affected are those requiring immediate assistance during a period of emergency. Also, people reported injured or homeless are aggregated with those affected to produce the total number of people affected (we refer to this number as “total affected”). Throughout the paper, we assume that natural disaster variables are (block) exogenous with respect to the growth variables and shocks to the terms of trade.<sup>1</sup>

# **C. Moderate and severe natural disasters**

As mentioned above, we divide natural disasters into four categories: droughts, floods, earthquakes, and storms. The measure of intensity of natural disasters, _NDtk_ , _i_ , is given by: where

> 1 For the exogeneity of natural disaster variables, see section III.b.i.

12
Table 15. Average Current Timber Rent - Equity Technique (US$/m<sup>3</sup> )

|Datas|et<br>Region<br>Domestic|s<br>Export|NIPIElLesp<br>Domestic|rom<br>Export|
|---|---|---|---|---|
|Revenues(CIFprice)|21.01|52.56|21.01|52.56|
|Costofsales|17.00|34.22|10.21|27.42|
|Normal profit*|1.62|1.62|1.62|1.62|
|Profittax take(notional)|2.34|8.90|3.24|9.82|
|Appropriatedrent|0.04|7.83|5.94|13.70|
|Unrealizedrent|12.00|0.00|12.00|0.00|
|Weightedaverageappropriatedrent|1.8|6|7.76||
|Totalrent appropriated(US$inn)|22|7|944||

* 20% return on equity

2.72 Table 16 identifies the various components of rent averaged across domestic sales and exports, based upon Table 15. It is worth noting that the forest sector generates a large total rent. The government captures about US$ 4 (or 20%) of the total rent, while less than US$ 2 (or 10%) is earned by forest enterprises and intermediaries as normal and excess profit. The largest portion of the rent (US$ 9.69 or 57%) is dissipated through the local economy as unrealized rent.

Table 16. Total Timber Rent in Russia - Equity Technique (US$/m<sup>3</sup> )

||Regions|NIPIEILesprom|
|---|---|---|
|Unrealizedrent(implicitsubsidy)|9.69|9.69|
|Entrepreneurial rent(normalprofit)|1.62|1.62|
|Captured rent (notionaltaxtake)|3.85|4.76|
|Appropriatedrent(excessprofit)|1.86|7.76|
|Totalrent|17.03|23.82|

2.73 Based upon the main usage cut of 121.6 million cubic meters in 1999, the total implicit subsidy to the domestic economy amounted to US$ 900 million. However, total implicit subsidy does not equal foregone revenues. If all the wood sold domestically were in fact exported, some additional revenues would be generated. $9.69 per cubic meter would be the total additional before-tax income to the exporter, of which 30% could be taxed away under the profit tax and 5% withdrawn by the export duty. 35% of the unrealized rent would thus turn into public revenues, _i.e._ $3.40 per cubic meter.

2.74 However, the real effective subsidy to the national economy may still be underestimated. First, the salvage logging and thinnings undertaken by _leskhozy_ do not generate any revenues and are an additional implicit subsidy. In 1999, for example, these cuts amounted to 22.9 million cubic meters, or 19% of the main usage. Second, part of the logging waste should be included as unrealized rent as well. Conservatively, neither salvage logging, thinning nor logging waste are captured in the tables.

31
Kumar, A.K. Shiva, 1996. "UNDP's Gender Related Development Index: A Computation for Indian States," Economic and Political Weedky, April 6, 1996.

Miller, Barbara D., 1982. The Endangered Sex, Neglect of Female Children in Rural North lIndia. Ithaca and London: Cornell University Press.

Miller, Barbara D., 1989. "Changing Patterns of Juvenile Sex Ratios in Rural India, 1961 to 1971," Economic and Political Wekly, June 3, 1989.

Miller, Barbara D., 1993. "On Poverty, Child Survival and Gender: Models and Misperceptions," Third World Planning Review 15(3):6-13.

Murthi, Mamta, Anne-Catherine Guio, and Jean Dreze, 1995. "Mortality, Fertility, and Gender Bias in India; A District Level Analysis," Popuation and Developen Review 21(4):745-782 Pritchett, Lant, and Laurence H. Summers, 1996. "Wealthier is Healthier," Joumnal of Human Resources, 31(4):841-868.

Rosenzweig, Mark R., and T. Paul Schultz, 1982. "Market Opportunities, Genetic Endowments, and Intrafamily Resource Distribution: Child Survival in Rural India," American Economic Review, 72(4):803-815.

Rutstein, Shea Oscar, 1984. "Infant and Child Mortality: Levels, Trends, and Demographic Differentials," World Fertility Surveys Comparative Studies No. 43. Voorburg, Netherlands: International Statistical Institute.

Sen, Amartya, 1989. "Women's Survival as a Development Problem," Bulletin of he American Academy of Arts and Sciences, 43.

Srivastava, Dr. B.P. 1997. Personal communication on work undertaken at the Institute of Population, Environment and Development, Lucknow, India.

Svedberg, Peter, 1990. "Undernutrition in Sub-Saharan Africa: Is There a Gender Bias?" Journal of Development Studies, 26:469-486.

Svedberg, Peter, 1996. "Gender Biases in Sub-Saharan Africa: Reply and Further Evidence" Journal of Development Studies, 32(4):933-943.

Wyon, John B., and John E. Gordon, 1971. The Khanna Study: Population Problems in the Rural Punjah, Cambridge, MA: Harvard University Press.

29
- 4 -

on the measure, and countries affected by it. 4/ The latter is particularly useful for analyzing "discriminatory" measures like bilateral quotas, "voluntary" export restraints, the Multifibre Arrangement, or prohibitions applied against specific countries. In addition, the date(s) that the restriction was first imposed, modified or removed are recorded (if the latter actions occurred) along with the value of imports (by country) in the tariff line item. For some countries like the United States the records are quite detailed and extend back to the mid-1960s. However, full country coverage has only been established in the Data Base for the 1981-86 period.

Several technical points should be noted concerning the Data Base and its applications. First, the information it contains does not provide any indication of change in the intensity of application of a measure. If, for example, the administration of import licensing requirements, or technical standards, becomes less rigorous it is not possible to incorporate this fact

- 4/ The entries in the Data Base have been compiled from government publications and other official sources like customs schedules and documents, GATT reports, official notifications to GATT as well as documents of other international agencies. Procedures for the periodic verification of entries with member states of UNCTAD have been established to ensure the accuracy of the Data Base. The reliance on official sources for compilation of the Data Base may cause the importance of some NTBs to be understated, especially in cases when there is a lack of "transparency," or where measures like "voluntary" export restraints NVERs) are not reported in official publications. See UNCTAD (1983) (1988) or Laird and Yeats (1988) for technical details involving construction of the Data Base. It should also be noted that problems have been encountered in tabulating Japanese NTBs due to the fact that the barriers sometimes originate in private organizations. For example, in one recent case reported in the Japanese press (see Yomiuri Shimbun, June 24, 1988, p. 2) the Japanese Federation of Cement Users Cooperatives tried to "advise" members not to use cement imported from Korea and Taiwan. Such actions are not adequately incorporated in the Data Base.
income on average. As a consequence, on the income side too, tariff incidence will vary across households.

Having disaggregated and nationally representative household-level data with information on both consumption and income portfolios is a major advantage since it enables policymakers and researchers to quantify the significant heterogeneity in the impacts of trade reforms across households. At the same time, it is worth bearing in mind some of the limitations of household surveys. For instance, they typically fail to capture households at the very top of the household income distribution. In addition, they do not adequately capture capital incomes. Moreover, they suffer from measurement error, especially in incomes, which are often underreported. To minimize the role of measurement error, we dropped households in the top and bottom 0.5% of the expenditure distribution prior to aggregating.

# **2.2 Harmonizing tariff data**

The next step in the analysis is to convert tariffs at the HS6 level to the standardized product classifications from the household surveys. Each group _i_ from the household surveys contains many finer product groups from the HS classification. To arrive at a product level average, we computed weighted average tariff rates _τi_ for each of the groups in the survey classification.

where _n_ is an HS-category that belongs to survey-category _i_ and _m_ ( _c,n_ ) are imports of good _n_ from country _c_ . To calculate (1), we use tariffs from the latest year for which data are available.

Tariffs vary both across countries and across products. The average tariff across countries is 14.2%. Tariffs are highest on average in Bhutan, notably 48.4% on average, and lowest in Iraq (5% on average), and tend to be lower in countries with higher levels of GDP per capita. There is also significant variation in tariffs across the different products in our data. For agriculture, the focus of our applications, Figure 6 depicts both the mean and the maximum tariff for each of the 53 food items in our data. On average, the highest tariff is 39.4%, but 5
-21-

rate tax of 20% or the application of a comprehensive income tax would have a negligible effect on average tax rates. The exception is the highest income decile which would experience an increase of the average tax burden to 37.5% (compared to 35.2% in case of no taxation of capital income). The problem, however, is that the highest income decile undertakes the most ,,tax-planning" activities and that the benefit of using such a tax on high income earners would be nullified by tax avoidance behavior. Evidence from TARKI and from other sources clearly point to a regressive effect of legal and illegal methods of tax planning.<sup>2 8</sup>

**Table 8: Hungary: Simulating the Distributional Impact of Various Capital Income Tax Schemes (using 1994 tax return data)**

||Income|(in HUF)|Income|Tax Payments (in|HUF)*****|A|verage Tax Rate*****|*****|
|---|---|---|---|---|---|---|---|---|
|Decile|Total<br>income|of which:<br>capital income|0% on capital<br> <br>income|20% on capital<br>income|CompIT***|0% on capital<br>income|20% on capital<br>income|CompIT**|
|1|49,202|182|0|36|0|0.00|0.07|0.00|
|2|113,698|91|721|740|740|0.63|0.65|0.65|
|3|159,564|364|10,300|10,373|10,391|6.46|6.50|6.51|
|4|207,073|728|22,086|22,232|22,268|10.67|10.74|10.75|
|_5_|256,917|1,275|37,975|38,230|38,421|14.78|14.88|14.95|
|6|311,389|1,730|56,881|57,227|57,486|18.27|18.38|18.46|
|7|375,386|2,550|78,993|79,503|79,885|21.04|21.18|21.28|
|8|462,086|3,460|112,950|113,642|114,334|24.44|24.59|24.74|
|9|612,991|7,357|173,979|175,450|177,216|28.38|28.62|28.91|
|10|1,424,337|73,374|501,924|516,599|534,208|35.24|36.27|37.51|

***** Composite of PIT on total income (excl. capital income) plus capital income tax.

****** Taxes on income from labor and capital divided by income from labor and capital.

*** Comprehensive Income Tax: capital income is taxed under the personal income tax.

Source: TARKI, 1995; and own calculations.

We can conclude by saying that neither theoretical considerations nor empirical evidence build a strong case for more taxation of capital income for high income earners in Hungary. The simulations above show that the distributive impact would be small. Even if we are optimistic about APEH's tax collection rate, the tax office is severely limited in its ability to collect revenue, 28 For example, the TARKI study shows that 82% of all accounted expenses happen to be in the highest income decile. This source of income is the most unevenly distributed of all. The results show that, first, evasion of income taxes is highest and, second, by far the most tax evasion takes place in the highest income decile (probably as much as 90%).
GDP per capita independent of a relationship between schooling and the size of government.

Table 9 in Brueckner and Lederman (2015) shows estimates of an econometric model where initial (1970) average years of schooling in the population are interacted with income inequality. The estimated coefficient (standard error) on the interaction term between average years of schooling and the Gini coefficient is -0.49 (0.09), see column (1). This suggests that the effect of income inequality on transitional GDP per capita growth is significantly decreasing in countries' initial level of human capital.

To illustrate the implied difference in effects, it is useful to consider some specific values of the average years of schooling in the sample. At the 25th percentile, the average years of schooling is around 4.2 years. Plugging the value of 4.2 into the estimates shown in column (1) of Table 9 yields a predicted effect of 0.5 with a standard error of 0.2; that is, a one percentage point increase in the Gini coefficient increases GDP per capita by around 0.5 percent. Consider now the sample median of average years of schooling. The sample median is around 6.4 years. The predicted marginal effect (standard error) at the median value of schooling is -0.56 (0.22). It is also instructive to consider the effect at the 75th percentile. At the 75<sup>th</sup> percentile the value for average years of schooling is around 8.6 years. The predicted marginal effect (standard error) is in that case -1.64 (0.39).

Table 10 of Brueckner and Lederman (2015) shows that the interaction between initial years of schooling and inequality is robust to restricting the sample to: (i) Asia (column (1)); (ii) Latin America and the Caribbean (column (2)); (iii) the pre-1990 period (column (3)); and (iv) the post1990 period (column (4)). As can be seen from Table 10, the coefficient on the Gini is significantly positive while the coefficient on the interaction between the Gini and schooling is significantly negative.

Table 11 of Brueckner and Lederman (2015) reports estimates from an econometric model that includes an interaction between income inequality and schooling as well as an interaction 21
|**Co**|**ntents**||
|---|---|---|
|**A **|**Data Description**|**48**|
||A.A Characterizing Refugees in Tajikistan. . . . . . . . . . . .|. . . . . . . . . . .<br>48|
||A.B Characterization of Treatment Variables . . . . . . . . . .|. . . . . . . . . . .<br>52|
|**B**|**Description of Variable’s Construction**|**57**|
||B.A Outcome Variables<br>. . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . .<br>57|
||B.B<br>Treatment Variables . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . .<br>58|
||B.C Social Desirability Bias . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . .<br>59|
|**C **|**Robustness Tests**|**61**|
|**D **|**Additional Exercises**|**66**|
||D.A Impacts by Gender . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . .<br>66|
||D.B Impacts byAge . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . .<br>69|

47
gasoline). The average prices of alcohol and gasoline were 0.89 and 1.70 BRL/liter respectively, resulting in a price for E25 gasohol of 1.50 BRL/liter. The congestion function coefficients were set as _c_ 0  1/ 80, _c_ 1  0.15, _c_ 2  2.0 . Aggregate highway capacity, _Z_ , was calibrated in such a way that, given the 2002 car-equivalent traffic from (4) and (5), the car speed from (7) is the 24.10 km/hr reported for 2002.<sup>10</sup> The trips were distributed among the six income groups and the three modes, according to the data shares in Table 2. Tables 1 and 2 show all the basic data that was used in calibrating the model.

In the trinomial logit (2), the dispersion parameter,  , was set to 0.25, the coefficient _b_ in the value of time was set at 0.5, and the mode-specific constants for walking set to zero, while set to replicate the public transit and car mode shares for _f_ in Table 2. This calibration resulted in car choice elasticity with respect to travel time and monetary cost shown in Table 3, the former elasticity increasing with income, the latter decreasing.

**TABLE 1: Basic data and coefficients for the modes of travel**

||**Walk+**<br>_m_=1|**Bus**|**Rail**|**Public**<br>**transit**<br>_m_=2|**Car**<br>_m_=3|
|---|---|---|---|---|---|
|Average trip length (2 way km),<br>_n_<br>_d_<br>_a_|2.56|35.43|n/a|n/a|20.73|
|Trip times (2 way hrs.),<br>_n_<br>_G_ <sup>_a_</sup>|0.54|2.10|1.50|1.80 <sup>_d_</sup>|0.86|
|Speed (km/hr),<br>3<br>3<br>0.7<br>ˆ ˆ<br>ˆ<br>,<br>_bus_<br>_s s_<br>_s_<br><br>_b_|4.83|16.87|n/a|n/a|24.10|
|Fuel expenditure,<br>3<sup>ˆ</sup><br>(<br>)<br>_f s e_, miles/gallon|n/a|n/a|n/a|n/a|24.18|
|Fuel price, (BRL/liter),<br>_F_<br>_p_<br>(E25 gasohol: 25% alcohol at 0.89, 75%<br>gasoline at1.70BRL/liter) <sup>_c_</sup>|n/a|n/a|n/a|n/a|1.50|
|Persons per vehicle,<br>1<br>_n_<sup> </sup><sup>_b_</sup>|n/a|37.36|n/a|n/a|1.75|
|Traffic load per vehicle,_h_|n/a|4 <sup>_e_</sup>|n/a|n/a|1.00|
|Average monetary cost of trip per worker<br>(BRL/2-way trip),<br>_n_<br>_g_|0|n/a|n/a|0.77 <sup>_b_</sup>|1.73|

> 10 Source: International Association of Public Transport (2007).

11
# Table 8a: District-industry-year regressions for share of financial loans for young <u>plants</u>

|||<br>Pa|nel estimation|<br>with district-ind|<br>ustry,state-year|and industry-year|<br>FE||
|---|---|---|---|---|---|---|---|---|
|||Using district||Using state-|Adding||Misallocation|Misallocation|
||Baseline<br>estimation|<br>covariates<br>instead of FE|Dropping<br>weights|<br>industry-year<br>FE|<br>employment<br>control|Using<br>balanced panel|without industry<br>aggregation step|using OLS<br>TFP metric|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||A. Using l|and and labor mi|sallocation in o|rganized sector|||
|Land misallocation index|0.008|0.012+++|0.009|0.008|0.008|0.010|0.015+++|0.015++|
||(0.006)|(0.004)|(0.006)|(0.006)|(0.006)|(0.007)|(0.006)|(0.006)|
|Labor misallocation index|-0.003|-0.009++|-0.003|-0.004|-0.003|-0.005|0.001|-0.001|
||(0.005)|(0.004)|(0.005)|(0.006)|(0.005)|(0.007)|(0.006)|(0.006)|
|Log employment in|||||-0.004||||
|district-industry-year|||||(0.004)||||
|Observations|14053|14053|14053|14053|14053|6439|14053|14053|
|Adjusted R-squared|0.148|0.066|0.173|0.148|0.148|0.089|0.149|0.149|
|<br>|||B. Using land|and labor misallo|cation in total m|anufacturing sect|or||
|Land misallocation index|-0.005|0.004|-0.005|-0.003|-0.005|-0.006|0.008|0.012+|
||(0.006)|(0.005)|(0.006)|(0.006)|(0.006)|(0.008)|(0.007)|(0.007)|
|Labor misallocation index|0.014++|0.002|0.014++|0.013++|0.014++|0.018++|0.006|-0.004|
||(0.006)|(0.004)|(0.006)|(0.006)|(0.006)|(0.008)|(0.007)|(0.007)|
|Log employment in|||||-0.004||||
|district-industry-year|||||(0.004)||||
|Observations|14053|14053|14053|14053|14053|6439|14053|14053|
|Adjusted R-squared|0.148|0.066|0.173|0.148|0.148|0.090|0.148|0.148|

Notes: See Table 6a.