rafmacalaba/datause-encoder-data huggingface dataset:
Figure C.1: Growth Accounting: Chile's Growth Acceleration <!-- Start of picture text --> Growth Accounting: Chile 1983−2011<br><!-- End of picture text --> <!-- Start of picture text --> GDP per worker<br>Labor Augmenting TFP<br>K/Y<br>Human Capital Index<br>1985 1990 1995 2000 2005 2010<br>Year<br>0.8<br>0.6<br>0.4<br>0.2<br>Log Difference from 1998 0.0<br>−0.2<br><!-- End of picture text --> Note:(2021).TheWe datadecomposefor therealgrowth _GDP_ accountingper worker,exerciseas<sup>_<u>Y</u>_</sup> _L_<sup>=</sup> stems<sup>_TFP_</sup> from 1 _−_ <u>1</u> _α_ � the _<u>ky</u>_ � 1 Penn _−αα hc_ World, whereTablesreal _GDP_ Databaseis measuredZeileis according to _rgdpna_ in the data, _L_ is the number of employed agents, _k_ is _rkna_ , and _hc_ is the human capital index provided by the data. The labor share, (1 _− α_ ) , is given by the labor share reported in the data, _lbsh,_ for the year 2011.

we t a linear trend to the regression coecients, which we use to extrapolate the elasticities outside the estimation period until 2011, the year in which we assume the reform stalls and distortions stabilize. The result from this calibration strategy is illustrated in gure C.2.

We dispense from prot taxes but continue to rely on xed costs of production to replicate the average rm size in Chile prior to the acceleration. Since we appealed to prot taxes to characterize the egalitarian forces and the barriers to private entrepreneurship that are characteristic of a communist regime, we do not see these taxes as pertinent to think about Chile's acceleration. However, for consistency with a calibration strategy that seeks to start-o the economy at a level of the average rm size that is consistent with the data, we preserve the xed cost specication.

A property of Chile's development dynamics that does not align well with that of the average growth acceleration is the behavior of the investment rate. At the onset of the acceleration, the investment rate declines strongly, constituting a signicant drag on aggregate growth, and then recovers abruptly so that at the point where the _TFP_ impulse stalls, the capital-output ratio starts to increase. This deviation 47
feed industries, chilling oil in leather or rolling industry and even illegal refining for cooking oil. Therefore, waste oil takes market price and takes major share of biodiesel production costs (Table 5). The total supply cost is still higher than that of diesel in most cases. The relative high cost of this feedstock is also contributed by the lack of economic drivers and policy instruments for the construction of collection system.

**Table 5: Typical cost and related assumptions of waste oil-to-biodiesel in China**

|Items|Unit|Waste oil|
|---|---|---|
|**Feedstock cost**|**Yuan/tonne(biodiesel)**|**3,556-5,556**|
|Assumed feedstock pricing mechanism||Market-based pricing|
|Assumed feedstock price|Yuan/tonne|3200-5000<sup>a</sup>|
|Feedstock conversion efficiency|Tonne<br>biodiesel<br>/<br>tonne<br>feedstock|90%|
|**Non-feedstock Cost**|**Yuan/tonne(biodiesel)**|**1444.4**<sup>**a**</sup>|
|Conversion technology||acid catalyzed process|
|Investment cost|Yuan/tonne(biodiesel)|3000<sup>a</sup>|
|**Total production cost**||**5,000-7,000**|
|Share of feedstock cost|%|71.11-79.37|
|Designated biodiesel wholesale price from<br>January 1, 2008 to April 7, 2011<sup>b</sup>|Yuan/tonne||
|- Min<br>- Average<br>- Max||4,425.2 (January 2009)<br>5,644.2<br>7,111.6 (April 2011)|

Notes: a. Survey data of a project with 50,000 tonnes capacity by author in 2010; b. the designated diesel price multiplied by the fixed factor 0.92, which is the suggested factor by Hainan project based on the survey of authors.

# **3.2.3 Challenges**

As mentioned in section 2, the current conversion capacity of waste oil-based biodiesel is more than 1.5 million tonnes in China, but most of them are not operated due to lack of collection system from the residential and commercial sectors (Liu et al.2011).

# **3.3 Biodiesel from oil-bearing trees**

# **3.3.1 Resource potential**

There are more than twenty species of oil-bearing tree species that can be used to produce biodiesel feedstock in China. Approximately one-fourth of these species are found in the northern part of the country, while the rest in the southern part, especially in the tropical and subtropical zones (Lv, et al. 2008). The main species include jatropha curcas, xanthoceras sorbifolia, Chinese pisache, varnish tree, sapium sebiferum, S. wilsoniana Sojak, idesia, sumac, euphorbia tirucalli and tung tree. The land productivities of typical oil-bearing trees are shown in Table 6. As in the case of non-grain sugar and starch energy crops, the marginal land available for oil-bearing trees should be in compliance with current land administration policies. It is estimated that, among the available forest barren lands, barren mountains, barren sand areas, and unutilized land resources, there are more than 36 million hectares that 11
A, the marginal contribution of direct taxes to poverty reduction is negative when considering a poverty line of 5.5 dollars per day PPP 2011. The results obtained suggest that there is a possibility for improvement in the Colombian tax system that allows increasing its progressivity and its redistributive effect.

<!-- Start of picture text --> FIGURE V-5: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF TAXES IN LATIN AMERICA<br>Panel A: Direct Taxes<br>0.5 0.021<br>0.018<br>0.4<br>0.015<br>0.3 0.012<br>0.2 0.009<br>0.006<br>0.1<br>0.003<br>0 0<br>Kakwani<br>Coefficient<br>Marginal<br>contribution<br>Panel B: Indirect Taxes<br>0.09 0.015<br>0.06 0.012<br>0.03 0.009<br>0 0.006<br>-0.03 0.003<br>-0.06 0<br>-0.09 -0.003<br>-0.12 -0.006<br>-0.15 -0.009<br>Kakwani Coefficient<br>Marginal contribution<br>Source: The Impact of Taxes and Social Spending on Income Distribution and Poverty in Latin America. An Application of the<br>Commitment to Equity Methodology (CEQ), 2017. Colombia World Bank Group staff estimates based on the ENPH 2017<br>Note: The results for all countries other than Colombia were obtained by applying the old methodology of the CEQ, which<br>among other things did not include indirect effects of indirect taxes, accounted for income in kind differently, while the<br>results for Colombia were obtained by applying the new methodology of the CEQ.<br>Kakwani Coefficient<br>Redistributive effect<br>Regressive <---------> Progressive<br>Chile, 2013 Ecuador, 2011 Brazil, 2009 Colombia, 2017 Uruguay, 2009 Mexico, 2010 Costa Rica, 2010 Peru, 2009 El Salvador, 2011 Unequalizing <---------> Equalizing<br>Kakwani Coefficient<br>Redistributive effect<br>Regressive <---------> Progressive Unequalizing <---------> Equalizing<br>Peru, 2009 Ecuador, 2011 Mexico, 2010 Costa Rica, 2010 Brazil, 2009 El Salvador, 2011 Chile, 2013 Uruguay, 2009 Colombia, 2017 Bolivia, 2009<br><!-- End of picture text --> In contrast to the results of direct taxes, indirect taxes were found to be regressive and contribute to an increase in poverty and inequality. While it is true that regressive taxes may help reduce inequality, this is not the case for Colombia (Figure V-6). In particular, the VAT (value-added tax) implies a very large burden for low-income households, which means that the implementation of this tax increases inequality and poverty. Additionally, taxes on tobacco and alcoholic beverages are also regressive. Of the indirect taxes, the tobacco tax is the most regressive of all, but its impact on poverty and inequality is less than that of VAT. Within indirect taxes, the consumption tax is the least regressive tax and the one that has the least impact on increasing poverty and inequality (see table 1, Annex A).

36
costs—via a reduction in payroll taxes and a compensating increase in sales tax rates—can effectively counteract the negative effects of remittance inflows. We also show that considering the identities of the beneficiaries of remittances matters for the final results. In a simulation where remittances go only to unskilled headed households, as seems to be the case by looking at micro data, the labor market effects are more complex and the corrective policy less effective. This clearly signals the need for additional research aimed at better understanding these distributional effects.

The above results come with an important set of caveats. First, one should note that although remittances have some undesirable effects on Jamaica’s competitiveness and labor force participation, they are not themselves undesirable. In fact, remittances represent an important channel of external financing for the economy as a whole, and they also account for a large share of the income of unskilled and unemployed/inactive individuals, who are more likely to be poor. Similarly, while the reduction in labor supply by the remittance recipients has some negative consequences, the decision itself is utility-maximizing. Therefore, the optimal policy response should focus on minimizing the negative indirect impacts rather than minimizing remittances themselves (for example, by taxing them directly). It should also be acknowledged that a move towards more indirect taxation may have some adverse consequences by shifting the tax burden on the poorer households. Although the changes in tax structure implied by our results are mild and the policy is beneficial for the economy as a whole, there may be a need for actions to smooth the transition for the most vulnerable parts of the population.

24
- Browning, M., T.F. Crossley and J. Winter, “The Measurement of Household Consumption Expenditures,” _Annual Review of Economics_ 6, 475–501, 2014. doi:10.1146/annurev- <u>economics-080213-041247</u>

- Bourguignon, F. and C. Morrisson, “Inequality Among World Citizens: 1820-1992,” _American Economic Review_ , 92, 727–744, 2002. doi: 10.1257/00028280260344443

- Browning, M., T.F. Crossley and J. Winter, “The Measurement of Household Consumption Expenditures,” _Annual Review of Economics_ 6, 475–501, 2014. doi:10.1146/annurev- <u>economics-080213-041247</u>

- Buddenberg, D., and W.A. Byrd, _Afghanistan’s Drug Industry: Structure, Functioning, Dynamics, and Implications for Counter-Narcotics Policy_ , United Nations Office on Drugs and Crime and World Bank, New York and Washington, DC, 2006.

- Chandy, L. and B. Seidel, “How Much Do We Really Know About Inequality Within Countries Around the World? Adjusting Gini coefficients for missing top incomes,” Brookings Institution, 2017a. https://www.brookings.edu/opinions/how-much-do-we-really-know- <u>about-inequality-within-countries-around-the-world/</u>

- Chandy, L. and B. Seidel, “Allocating Income from Survey-National Accounts Gap to Top Incomes According to Pareto Distribution,” Brookings Institution, 2017b. <u>https://www.brookings.edu/wp-content/uploads/2017/02/technical-appendix-02-13-17.pdf</u>

- Charmes, J, “The Informal Economy Worldwide: Trends and Characteristics,” _Margin: The Journal of Applied Economic Research_ 6, 103–32, 2012. doi:10.1177/097380101200600202

- Chen, S. and M Ravallion, “The Developing World is Poorer than We Thought, But No Less Successful in the Fight Against Poverty,” _The Quarterly Journal of Economics_ , 125, 1577– 1625, 2010. doi: 10.1162/qjec.2010.125.4.1577

- Damgaard, J., T. Elkjaer and N. Johannesen, “What Is Real and What Is Not in the Global FDI Network?,” _IMF Working Papers_ 19, 2019. doi:10.5089/9781513521527.001

- Deaton, A, “Measuring Poverty in a Growing World (or Measuring Growth in a Poor World),” _Review of Economics and Statistics_ 87, 1–19, 2005. doi:10.1162/0034653053327612

- Deaton, A. and V. Kozel, “Data and Dogma: the Great Indian Poverty Debate,” _The World Bank Research Observer_ , 20, 177–199, 2005. doi:10.1093/wbro/lki009

- Deaton, A., and S. Zaidi, “Guidelines for Constructing Consumption Aggregates for Welfare Analysis.” Living Standards Measurement Study, Working Paper no. 135. World Bank, Washington, DC, 2002.

- Engle-Stone, R., C. Sununtnasuk and J.L. Fiedler, “Investigating the Significance of the Data Collection Period of Household Consumption and Expenditures Surveys for Food and Nutrition Policymaking: Analysis of the 2010 Bangladesh Household Income and

31
# 2 FLOW OF FUNDS DATA

# 2.1 The elements of the flow of funds.

The 1968 United Nations System of National Accounts (SNA)<sup>1</sup> provides a statistical framework for presenting flow of funds accounts. The two main accounts of the SNA that are relevant for our purpose are together referred to as flow of funds accounts. The caRital accumulation account shows, for each sector, the saving of that sector including provision for capital consumption (or depreciation), any capital transfers to the sector, together with the non-financial assets accumulated by the sector, whether through capital formation, land acquisition and purchase of intangible assets. A balancing item of this account is net lending, the breakdown of which is the subject of the other account, namely the caoital finance account..

For only a handful of developing countries are data for these two accounts collected. Even where data exist, they are generally incomplete in certain respects. An exception is Korea, which has detailed statistics presented, generally speaking, in the SNA format and going back a number of years.

Tables 1, 2 and 3 display the Korean flow of funds data for 1984, together with the corresponding stocks. A review of this data set will serve to illustrate many of the characteristics of the flow of funds found in diverse countries, as well as allowing us to fix some conceptual ideas.

Table 1, the capital accumulation account, represents the transition between the national income accounts concepts of savings and capital formation, and the financial accounts concept of net surplus or :.et 1 United Nations, 1968, A Svstem of National Accounts, (Department of Economic and Social Affairs-, Statistical Office of the United Nations, New York), Studies in Methods, Series F, No. 2. A revised methodology for the SNA is in preparation.

4
of new rigidities in the labor market and the potential decrease in employment opportunities may offset the wage distribution gains. The simplest textbook models suggest that putting a wage floor above the equilibrium level will lead to a fall in employment. These effects have traditionally appeared to be weak in the US, perhaps with the exception of young workers (see, for example, Brown 1988, Brown Gilroy a:-d Kohen 1983). However, more recent work by Currie and Fallick (1996), Abowd et. l. (1999) and Neumark et. al. (2000) report sharp disemployment effects for those constrained by the minimum with employment elasticities of between .4 to 1.6 (in absolute value).

The evidence from Latin America suggests large effects. Freeman and CastilloFreeman's (1991) analysis of the imposition of US minimum wage norms on Puerto Rico in 1977 suggest that the weak US evidence results primarily from the fact that the minimum wage is so low as to "nibble" rather than "bite" at the wage distribution. However, when the minimum in Puerto Rico was raised to 63% of the average manufacturing wage, the elasticity of employment to the minimum wage became .91 anld raising the wage led to massive job loss on the island. The same general picture emerges from Bell (1997) in her study using manufacturing panel data from Mexico and Colombia. She finds no impact of the minimum wage in Mexico where it was not binding. However, in Colombia, she finds an employment elasticity of unskilled workers on the order of .15-.33 and for skilled workers .03-.24 with the effect on workers paid near the minimum wage between .55 to 1.22. She concludes that, across the period 19811987, the 10% rise in the minimum wage from 1981-1987 reduced low-skilled, low wage Colombian employment in the range of 2%-12%.

3
Table 3: Robustness II, Survival and Institutional Development.

The dependent variable is the hazard rate of trade relationships for product k of ...rm i operating in country c to destination country j: All regressions are estimated using the Cox Proportional Hazard Model. We control for destination country, year, ...rm and exporting country x HS2 ...xed e¤ects, and allow the baseline hazard to vary across HS8 product (strata). The variables of interest are de...ned in Table 1. Additional controls include the interaction between the sanitary risk and : country c overall economic development (GDPpcc x sanitary_riskc), country c Ease of Doing Business index (EDBcx sanitary_riskc ), country c Logistic Performance index (LPIcx sanitary_riskc), country c level of infrastructure (Infrustructurecx sanitary_riskc), and country c level of trade related infrustructure (TABcx sanitary_riskc). Robust standard errors clustered at (exporting country) � HS4 sector level are in parentheses. *, **, and *** denote statistical signi...cance at the 10%, 5%, and 1% levels, respectively.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|FDcx sanitary_riskk|-0.590**|-0.257**|-0.291**|-0.169*|-0.345***|
||(0.243)|(0.099)|(0.146)|(0.101)|(0.130)|
|initial_exportcikj|-0.088***|-0.088***|-0.088***|-0.088***|-0.088***|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|NProductsci|-0.004*|-0.004**|-0.004**|-0.004*|-0.004**|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|NDestinationscik|-0.057***<br>(0.008)|-0.057***<br>(0.008)|-0.056***<br>(0.008)|-0.057***<br>(0.008)|-0.057***<br>(0.008)|
|Contiguitycj|-0.150|-0.149|-0.150|-0.148|-0.151|
||(0.104)|(0.104)|(0.104)|(0.104)|(0.104)|
|Com_languagecj|-0.125***|-0.126***|-0.126***|-0.125***|-0.125***|
||(0.039)|(0.039)|(0.039)|(0.039)|(0.040)|
|Colonycj|-0.089|-0.088|-0.086|-0.089|-0.087|
||(0.084)|(0.084)|(0.084)|(0.084)|(0.084)|
|Distancecj|0.090*|0.091*|0.090*|0.091*|0.089*|
||(0.048)|(0.048)|(0.048)|(0.048)|(0.048)|
|GDPpcc x sanitary_riskk|0.00016*<br>(0.0001)|||||
|EDBc x sanitary_riskk||0.0001<br>(0.0001)||||
|LPIc x sanitary_riskk|||0.0120<br>(0.036)|||
|Infrastructurecx sanitary_riskk||||-0.052<br>(0.033)||
|TABc x sanitary_riskk||22|||-0.0007<br>(0.0005)|
|Observations|14870|14870|14870|14870|14870|
but does not change the direction or statistical significance of the relationships we identify between participatory institutions and tax outcomes in the broader sample. Municipal tax rates are statistically significant determinants of some tax outcomes (sales, per capita tax revenue) in Arellano-Bond models, but participatory institutions also remain statistically significant. We take these results as good omens for the independence of relationships between participatory institutions and tax outcomes, though we would be more certain with full data on sales tax rates for all Brazilian municipalities.

Next, we address the possibility of non-linear relationships between participatory institutions and tax outcomes at different levels of taxation and tax shares of revenue. This is because participatory institutions might promote tax collection differently in different socioeconomic environments. For example, participation could be associated with tax compliance in places where tax revenue is low, but face diminishing marginal returns in areas where tax collection is already relatively high.<sup>10</sup> We find that the average treatment effects of participatory institutions on tax outcomes are consistent across municipalities with different levels of tax collected. The one exception is the number of years that PB has been in place, which is not statistically connected to the municipal tax share of revenue or per capita tax revenue in municipalities where tax collection is more than one standard deviation above the mean.

Finally, we address concerns for our matching exercise in general. We use matching because it provides highly-targeted hypothesis tests connecting participatory institutions to tax compliance. However, our goal is not to build comprehensive models of tax compliance because we lack strong, direct measures of local enforcement and facilitation. Instead, we build theory and test hypotheses surrounding alternative, complementary ways to generate compliance using propensity score matching to isolate participatory institutions’ independent effects. Nevertheless, we also provide the results of estimation for tax outcomes using Arellano-Bond Dynamic Panel Estimation, allowing a full range of covariates to vary across our models. Participatory institutions remain statistically significant determinants of tax outcomes in these models, along with measures of local administrative capacity and local GDP per capita. Participatory institutions are positive determinants of tax outcomes. Councils are consistently connected to tax outcomes. Other variables we use in the matching exercises above are not statistically connected to tax outcomes in these models.

# **Conclusion**

Our results demonstrate broad connections between participatory institutions and local tax outcomes in Brazil. Policy management councils and participatory budgeting programs are both statistically significant determinants of a variety of tax measures after accounting for local economic and political conditions as well as proxies for the capacity to enforce tax regulations and facilitate tax collection. Participatory

> 10 Data available in separate appendix.

27
|annual)|||||(0.021)|
|---|---|---|---|---|---|
|Inflation (GDP deflator)|||||0.014<br>(0.013)|
|Rule of Law|||||-0.029<br>(0.261)|
|Control of Corruption|||||0.127<br>(0.275)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|
|Constant|11.080***|5.375***|4.759***|4.511***|5.268**|
||(0.441)|(1.033)|(0.970)|(0.939)|(2.120)|
|Number of observations|25,541|25,541|25,541|25,541|25,541|
|R-squared|0.260|0.311|0.327|0.337|0.345|
|**Panel B: Estimated i**|**mpact of Ethnic Fractio**|**nalization on S**|**MEs and L**|**arge firms**||
|SMEs|-2.291***|-1.357***|-1.262***|-1.187***|-1.127***|
||(0.405)|(0.336)|(0.335)|(0.327)|(0.333)|
|Large (>=100 workers)|-0.637|0.028|0.077|0.207|0.121|
||(0.487)|(0.442)|(0.440)|(0.421)|(0.402)|

Huber-White robust standard errors clustered on country in brackets. Significance is denoted by *** (1%), ** (5%), * (10%) 47