rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1.** Equivalency of Units

|Tax per|ton|ofco2|equals:|
|---|---|---|---|
|$10.00||~~C~~<br>15.42||
|$36.67||$84.71|per ton of Carbon|
|$4.77||$11.02|per barrel of oil|
|/c8.80||/c20.33|per gal of gasoline|
|/c2.29||/c5.29|per liter of gasoline|
|/c0.78||/c1.80|per kilowatt-hour of electricity|

Source: Own calculations and Aldy _et al._ (2008).

with respect to key parameters.

We find that, in the benchmark specification, a set of broadly large-emission countries are undertaxing motor fuel. We evaluate the sensitivity of the results to the values of the elasticities and externalities we use in the application of the P-S framework. We find significant effects on the optimal taxes within the level of uncertainty reasonably associated with the values of these parameters. Increasing the shadow value on GHG emissions to $100 per ton of carbon (from the $25 benchmark value) and the size of the labor-leisure substitution response to wages result in more than half the countries undertaxing motor fuel.

Section 2 reviews the issues on optimal indirect taxation. Section 3 applies the P-S framework to a set of countries and discusses the results. Section 4 concludes.

# **2. Optimal Taxation of Petroleum Products**

There are several well-established principles guiding the design of an efficient system of commodity taxes in the absence of market imperfections (see, _e.g._ , Atkinson and Stiglitz, 1980; Myles, 1995). The first principle is that, in the absence of market imperfections, taxation should be restricted to final goods, thus leaving untaxed all intermediate inputs in the production process (Diamond and Mirlees, 1971). By preventing tax-induced distortions in the allocation of resources, this approach maximizes total output, and hence the potential tax base.

A second principle (Ramsey, 1927) dictates that commodity taxes should result in similar reductions in demand for all commodities—where ‘similar’ refers to ‘equally-costly’ from the perspective of the consumers’ welfare. This principle implies higher taxation falling on commodities displaying smaller (substitution) responses to price changes— _i.e._ , on the more price-inelastic goods. The objective is to equalize, across commodities, the proportional reduction in demand relative to the no-tax situation. This proportional reduction in demand is termed the _index of discouragement_ by Mirlees (1976). Moreover, to ameliorate the disincentive effects distorting leisure-work choices (since commodity taxes lower the reward to work), heavier taxes should fall on goods which are more complements to leisure ( _i.e._ , substitutes to work), and lighter taxes should fall on goods that are more complementary to work.

Finally, some degree of differential taxation can also be justified on distributional con3
Table 30. Indonesian Exports, ICT use, and Log of Skills Measures Excluding Industries Using U.S. O*Net database. All workers. 2005-2010

|Dependent variable:||Anal|ytical|||Interp|ersonal|||Ma|nual||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000489||0.000465|0.000159|0.000577||0.000544|0.000396|-0.000512||-0.000468|-0.000297|
||(0.000217)**||(0.000221)**|(0.000316)|(0.000217)***||(0.000221)**|(0.000310)|(0.000248)**||(0.000250)*|(0.000343)|
|Share of hhlds with computer||0.0671|0.0865|0.0988||0.0877|0.115|0.137||-0.121|-0.158|-0.188|
|||(0.0525)|(0.0452)*|(0.0487)**||(0.0667)|(0.0628)*|(0.0719)*||(0.0935)|(0.0879)*|(0.0996)*|
|IDN exports per worker *||||0.00173||||0.000653||||-0.000700|
|Share of hhlds with computer||||(0.00107)||||(0.00105)||||(0.00115)|
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|
|R-squared|0.890|0.889|0.890|0.890|0.656|0.655|0.657|0.656|0.675|0.674|0.676|0.675|

|||Ma|nual|Routine ski|ll measures|Cog|nitive||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|-0.000466||-0.000418|-0.000248|0.000505||0.000468|0.000235|
||(0.000215)**||(0.000218)*|(0.000308)|(0.000240)**||(0.000243)*|(0.000344)|
|Share of hhlds with computer||-0.0901|-0.133|-0.166||0.116|0.146|0.169|
|||(0.0914)|(0.0831)|(0.0978)*||(0.0803)|(0.0748)*|(0.0835)**|
|IDN exports per worker *||||-0.000646||||0.00116|
|Share of hhlds with computer||||(0.00104)||||(0.00113)|
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|
|R-squared|0.962|0.962|0.962|0.962|0.831|0.831|0.832|0.831|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2004 U.S. O*Net database, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and linear province time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

47
# **I. Introduction**

What began as a financial crisis in developed countries led to a severe contraction in global output and trade. This turned the financial crisis into a crisis in the real economy, with serious impacts on workers and their families. The global economy, according to the World Bank, shrank by about 2.2 percent in 2009, from a 3.2 percent expansion in 2008—the first time the global economy has shrunk since World War II.<sup>1</sup> The World Bank estimated that 90 million more people would be living in poverty by the end of 2010 than would have been the case without the crisis.<sup>2</sup> Countries in Eastern Europe and Central Asia are the most adversely affected by the crisis and the growth slowdown was thought likely to shrink GDP by about 4.7 percent in 2009, from a 4.2 percent increase in 2008.

Latvia is one of the hardest hit countries in Eastern Europe; its GDP was projected to shrink by 18 percent in 2009. The objectives of this note are: (i) to estimate the distributional impact of the financial crisis on households in Latvia; and (ii) to assess the distributional impact of several policy reforms undertaken in response to the crisis. To do so, we use a methodology that assesses the impact of the growth slowdown or policy reform through its impact on the sources of household income. The impacts quantified are direct short-run impacts; hence, they do not take into account general equilibrium effects.<sup>3</sup> In most countries, measuring real-time impacts of financial crises or economic slowdowns on households is rarely possible due to delays associated with household surveys; consequently simulation tools are often used to analyze welfare impacts. However, among the genre of welfare simulation tools, considerable variation exists in methodology, data requirements, assumptions, and analyst time requirements.

A computable general equilibrium (CGE) model and a micro-simulation (MS) model can be combined in a sequential approach to assess the effects of various macroeconomic policies and shocks on households. For example, Agénor et al. (2006), Cockburn (2006), Cogneau and Robilliard (2006), and Bourguignon and Savard (2008) investigate the distributional impacts of macro-economic structural changes. The CGE models have also been combined with microsimulation models to investigate the impact of macro-economic shocks on households across the

> 1  World Bank (2010) 2  World Bank (2010)

> 3  For example, relative price changes due to changes in domestic demand are not taken into account.

2
# **References** :

Agénor Pierre-Richard (2000). The Economics of Adjustment and Growth. Academic Press.

Agénor Pierre-Richard, Alejandro Izquierdo and Hippolyte Fofack (2003). “The Integrated Macroeconomic Model for Poverty Analysis: A Quantitative Macroeconomic Framework for the Analysis of Poverty Reduction Strategies”. World Bank Policy research Working Paper No. 3092.

Akyuz Yilmaz and Charles Gore (2001). “African Economic Development in a Comparative Perspective”, Cambridge Journal of Economics, Vol. 25, 265-288.

Alesina A. and S. Ardagna (1998). “Tales of Fiscal Adjustment”. Economic Policy: A European Forum, No. 7, 487-546.

Ambler Steve and Alain Paquet (1996). “Fiscal Spending Shocks, Endogenous Growth, Spending and Real Business Cycles”, Journal of Economic Dynamics and Control, 20.

Artadi V. Elsa and Xavier Sala-i-Martin (2003). “The Economic Tragedy of the 20<sup>th</sup> century: Growth in Africa”, National Bureau of Economic Research Working Paper No. 9865.

Bayraktar Nihal and Hippolyte Fofack (2007). “Specification of Investment Function in Sub-Saharan Africa”, World Bank Policy Research Working Paper No. 4171.

Berg Andy and Zia Qureshi (2005). “The MDGs: Building a Momentum”, Finance and Development, The International Monetary Fund. Vol. 42, Number 3.

Blanchard Olivier (1990). “Suggestions for a new set of fiscal indicators”. OECD Economics and Statistics Department, Working Papers No. 79.

Blanchard Olivier, Giovanni Dell’Ariccia and Paolo Mauro (2010). “Rethinking Macroeconomic Policy”. IMF Staff Position Note, SPN/10/03.

Blejer Mario and Ke-Young Chu (1988). “Measurement of Fiscal Impact: Methodological Issues. International Monetary Fund, Washington DC.

Blejer Mario and Adrienne Cheasty (1991). “The Measurement of Fiscal Deficits: Analytical and Methodological Issues”, Journal of Economic Literature, Vol. XXIX, pp. 1644-1678.

Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). _Time Series Analysis: Forecasting and Control_ (3<sup>rd</sup> ed). New Jersey: Prentice Hall.

Chang Ha-Joon (2008). “ _Bad Samaritans: The Myth of Free Trade and the Secret History of Capitalism_ ”, Bloomsbury Press.

Chiogna Monica, Carlo Gaetan and Guido Masarotto (2008). “Automatic Identification of Seasonal Transfer Function Models by Means of Iterative Stepwise and Genetic Algorithms.” Journal of Time Series Analysis, Vol. 29, Issue 1, pp. 37-50.

Cochrane John H. (2009). “Fiscal theory, and fiscal and monetary policy in the financial crisis”, NBER Working Paper No. xxxx. National Bureau of Economic Research.

Easterly William and Sergio Rebelo (1993). “Fiscal Policy and Economic Growth: An Empirical Investigation”, Journal of Monetary Economics, 32 (1993) 417-458.

Easterly William and Klaus Schmidt-Hebbel (1993). “Fiscal Adjustment and Macroeconomic Performances in Developing Countries”. The World Bank Research Observer, Vol. 8, No. 2. The International Bank for Reconstruction and Development.

23
**_Value-added for domestic markets (VA_D)_** is defined as the value-added in production of final products to the domestic market directly **_._**

**_Value-added for GVC (VA_GVC)_** is the value-added in production of intermediate exports, in other words, exports used as inputs abroad.

**_Share of value-added for domestic markets (VD_share)_** is defined as the ratio of **_VA_D_** over the total value added ( _VA)_ created by the country-sector.

**_Labor productivity of country i in sector j (Yij)_** is defined as the value-added of the country-sector per worker in that country sector.

Appendix table 1 shows that a heterogeneous set of mostly capital-intensive sectors (Real estate activities, Electricity and water, Mining and quarrying) have the highest values of **_Y_** . Summary statistics for other variables are reported in Table 2.

**Table 2: Summary Statistics**

|Variable|Number of<br>Observations|Mean|Standard<br>deviation.|
|---|---|---|---|
|_ln FDI_|15,090|4.06|2.283|
|_ln labor productivity_|7,848|3.76|1.462|
|_DVA share_|15,090|0.76|0.162|
|_GVC_backward_|15,090|0.24|0.162|
|_GVC_forward_|15,089|0.25|0.314|
|_Logoutput_|14,954|8.62|2.282|
|_ln DVA_|14,953|8.31|2.388|
|_ln FVA_|14,943|6.94|2.079|
|_ln VA_|15,089|8.76|2.23|
|_ln VA_GVC_|15,087|6.83|2.305|
|_ln V_D_|14,944|8.04|2.846|
|_VD_share_|15,089|0.60|0.424|

**Notes** : Asian Development Bank estimates based on Wang, Wei, Yu and Zhu (2017) using Asian Development Bank Multi-Regional Input Output Tables (2011 - 2017) and Financial Times fDi Markets Database.

# 5 Direct FDI Effects and GVC Participation

## 5.1 Empirical strategy

To identify the direct effects of FDI on the labor productivity of a country-sector, we specify the regression equation as follows:
_Africa’s Macroeconomic Story_

# _Growth in fragile states has picked up recently_

Up until the mid-1970s, trend growth in fragile states was very close to other countries (Figure 7).<sup>7</sup> Per capital real GDP in fragile states declined at an average rate of 2 percent a year from the early 1980s up until the early 2000s, underperforming other countries by a wide margin. However, the differential has narrowed significantly over the past few years. The growth surge mainly reflects transition from conflict in three resource-abundant countries--Sierra Leone, Liberia and the Democratic Republic of Congo--where economic activity has been bolstered by an expansion in mining activity following years of civil war and political/economic instability.<sup>8</sup> There is currently wide diversity across fragile states, with trend growth in those three countries well above other countries (Figure 8).

**Figure 7: Trend Real GDP per Capita Growth – Fragile States vs. Other Countries, 1962-2012**

# _Percent_

<!-- Start of picture text --> 4<br>2<br>0<br>-2<br>-4<br>1962 1972 1982 1992 2002 2012<br>Fragile States Other<br><!-- End of picture text --> _Source:_ World Bank _Global Economic Prospects_ January 2013.

7Our analysis is based on the World Bank definition of a “fragile state” as a low income country or territory, IDA eligible (including those countries which may currently be in arrears), with a CPIA score of 3.2 or below. 8 Sierra Leone and Liberia resumed iron ore mining in 2011; the DRC has recently agreed to a number of joint ventures that will expand production of copper and cobalt substantially.

15
alternatives, and/or for whom walking to the newly opened station is not an option (either because of safety or distance considerations).

It is also worth noting, however, that our study has several limitations. Most importantly, perhaps, while our analysis focuses on the impacts of the opening of the MRT system on demand for ride-hailing services in the immediate neighborhoods of newly opened MRT stations, it is silent on impacts elsewhere in the city. In other words, our findings should strictly speaking be taken as strong evidence for _local complementarity_ . The opening of the MRT may, for example, have decreased ride-hailing activity to/from locations near the newly opened MRT stations to locations further away, as people now take an MRT ride, (possibly using a _Gojek_ ride for the first/last mile only) to get to that location, instead of, as before, using ride-hailing for the entire journey.

In this respect it would be particularly interesting for future research to be able to trace back how/where people who are now using ride-hailing services for their first and/or last-mile connectivity to the MRT were using ride-hailing services, possibly in combination with other forms of public transport, before the MRT system opened. Another fruitful direction of future research would be to estimate the impacts of the MRT system on local levels of pollution, greenhouse gas emissions, traffic congestion, and the use of other forms of public transport, as well as to replicate our analysis for other cities in Indonesia, and elsewhere in the developing world where the rise of ride-hailing services predates the construction of rapid mass public transit options, fewer people own their own transport, and where ride-sharing is predominantly motorbikebased.

# **REFERENCES**

- Babar, Y., and G. Burtch. 2020. “Examining the Heterogeneous Impact of Ride Hailing Services on Public Transit Use,” _Information Systems Research_ , 31 (3), 820–834.

- BPS, 2020. Jabodetabek Commuter Statistics (Statistik Komuter Jabodetabek: Hasil Survei Komuter Jabodetabek) 2019. Badan Pusat Statistik (BPS).

- Cairncross, J., J.D. Hall, and C. Palsson. 2021. “VancUber: The effect of ride-hailing on public transportation, congestion, and traffic fatalities,” Working Paper. <mark>University of Toronto.</mark> <u><mark>http://individual. utoronto. ca/jhall/documents/Vancouver_and_Uber. pdf</mark></u>

- Diao, M., H. Kong, and J. Zhao. 2021. “Impacts of transportation network companies on urban mobility,” _Nature Sustainability,_ 4, 494-500. 10.1038/s41893-020-00678-z.

Erhardt, G.D., R.A. Mucci, D. Cooper, B. Sana, M. Chen, and J. Castiglione. 2021. “Do Transportation Network Companies Increase or Decrease Transit Ridership? Empirical Evidence from San Francisco,” _Transportation,_ 49, 313-342.

26
Diamond, D. W., and Rajan, R. G. (2001). Liquidity risk, liquidity creation, and financial fragility: A theory of banking. _Journal of Political Economy_ , 109(2):287–327.

Duchin, R., Ozbas O., and Sensoy, B. A. (2010). Costly external finance, corporate investment, and the subprime mortgage credit crisis. _Journal of Financial Economics_ , 97:418-435.

Duygan-Bump, B., Levkov, A., and Montoriol-Garriga, J. (2015). Financing constraints and unemployment: Evidence from the Great Recession. _Journal of Monetary Economics_ 75:89-105.

Fan, J. P., Titman, S., and Twite, G. (2012). An international comparison of capital structure and debt maturity choices. _Journal of Financial and Quantitative Analysis_ , 47(1):23.

Gale, D., and Hellwig, M. (1985). Incentive-compatible debt contracts: The one-period problem. _Review of Economic Studies_ , 52(4):647–663.

George, T. J., and Hwang, C.-Y. (2010). A resolution of the distress risk and leverage puzzles in the cross-section of stock returns. _Journal of Financial Economics_ , 97:56-79.

Gertler, M., and Gilchrist, S. (1994). Monetary policy, business cycles, and the behavior of small manufacturing firms. _Quarterly Journal of Economics_ , 109(2):309–340.

Hart, O. and Moore, J. (1995). Debt and seniority: An analysis of the role of hard claims in constraining management. _American Economic Review_ , 85(3):567–585.

Holmström, B. and Tirole, J. (1997). Financial intermediation, loanable funds, and the real sector. _Quarterly Journal of Economics_ , 112(3):663–91.

Jaramillo, F., and Schiantarelli, F. (1997). Access to long term debt and effects on firms’ performance: Lessons from Ecuador. _Research Network Working Paper_ R-460, InterAmerican Development Bank, Washingon, DC.

Jensen, M. C., and Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. _Journal of Financial Economics_ , 3(4):305–360.

King, R. G., and Levine, R. (1993a). Finance and growth: Schumpeter might be right. _Quarterly Journal of Economics_ , 108(3):717–737.

King, R. G., and Levine, R. (1993b). Finance, entrepreneurship and growth. _Journal of Monetary Economics_ , 32(3):513–542.

Kiyotaki, N., and Moore, J. (1997). Credit cycles. _Journal of Political Economy_ , 105(2):211–248.

Levine, R. (2005). Finance and growth: theory and evidence. _Handbook of Economic Growth_ , 1:865–934.

Levine, R., Loayza, N., and Beck, T. (2000). Financial intermediation and growth: Causality and causes. _Journal of Monetary Economics_ , 46(1):31–77.

Levine, R., and Zervos, S. (1998). Stock markets, banks, and economic growth. _American Economic Review_ , 88(3):537–558.

Rajan, R. G. (1992). Insiders and outsiders: The choice between informed and arm’slength debt. _Journal of Finance_ , 47(4):1367–1400.

Rajan, R. G., and Zingales, L. (1998). Financial dependence and growth. _American Economic Review_ , 88(3):559–586.

24
# **5.  Financial exclusion – the effects of banking barriers**

This section provides back-of-the-envelope calculations of the effects of barriers in terms of the percentage of the population in a country that cannot afford banking services. Specifically, we combine income and income distribution data with our information on annual fees to maintain checking and savings accounts to compute the share of the population that does not earn enough to afford using checking and saving accounts (see methodological explanation in the appendix). Using the latest income distribution data from UNU-WIDER (2005), we utilize information on the Gini coefficient to compute percentiles of income distribution and combine this with income data to compute income per capita data at different percentiles of the income distribution.<sup>12</sup> We follow Genesis (2005b) and assume that people cannot afford to spend more than 2% of their annual household income on financial services.<sup>13</sup> We adjust income with the average household size for every country.<sup>14</sup> These calculations provide us with a cut-off percentile of a country’s income distribution below which the use of checking and saving accounts is not affordable.

Table 7 shows that while in terms of fees checking and savings accounts are affordable for almost the entire population in many countries, there are significant outliers. In ten countries at least 30% of the population cannot afford checking accounts and in several African countries, more than 50% of the population is priced-out of using these services. Specifically, 54% of the population in Cameroon, 81% in Kenya, 40% in Madagascar, 94% in Malawi, 89% in Sierra

> 12 Calculations are based on Dollar and Kraay (2002) and Lopez and Serven (2006).

> 13 According to Genesis (2005b), the 2% limit is based on unpublished research by the South African Universal Services Agency in the context of mandated rolling-out of telecom service to lower-income families.  As both financial transaction accounts and telecom service can be considered network products, similar assumptions on affordability for both services seem reasonable.

> 14 Household size is expected to vary with income level within countries.  As we do not have data available on household size distribution, we are not able to adjust for this effect.  Again, our numbers are indicative and a more detailed analysis would require richer country-level information on the variation of household size distribution with income distribution.

18
Table 6 follow the same pattern, but our multivariate regressions find no statisticallysignificant difference between dung and woodfuels. Our results suggest that kerosene is somewhat more pollution-intensive than gas, but the difference is much less than the difference in the India study.

# **Table 16:  Comparative Air Pollutant Concentrations (ug/m**<sup>**3**</sup> **)**

# 16a: India (RSPM ≈ .61 PM10)

|Area|Fuel|Inside<br>Kitchen<br>With<br>Partition|Inside<br>Without<br>Partition|Detached<br>Kitchen|Open<br>Air<br>Kitchen|
|---|---|---|---|---|---|
|Kitchen|Solid|666|652|575|297|
||Gas|70|70|86||
|Living|Solid|357|559|280|215|
||Gas|70|76|96||
|||A|mbient|||
||||91|||

Source: World Bank (2002), p. 32

# 16b: Bangladesh (PM10)

|Area|Fuel|Inside<br>Kitchen|Detached<br>Kitchen|Open<br>Air<br>Kitchen|
|---|---|---|---|---|
|Kitchen|Solid|313|248|182|
||Gas|134|||
|Living|Solid|286|189|155|
||Gas|129|||
|||Ambient|||
|Ur|ban||Peri-Ur|ban|
||89||48||

# **9.  Use of Improved Stoves**

New biofuel stove designs offer the prospect of reduced indoor air pollution, along with more efficient combustion. Air-monitoring research in Guatemala has indicated that improved stoves can lower indoor PM10 concentrations by 50% or more (Smith, et al., 33