rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 3.00<br>2.50 Manufacturing<br>2.00<br>1.50<br>Food<br>1.00<br>0.50 Total<br>0.00<br>-0.50<br>-1.00<br>Services<br>-1.50<br>Food Manufacturing Services Total<br>% change from BaU<br>2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040<br><!-- End of picture text -->

**Figure 10: CPI by commodity groups of the RNZP relative to baseline**

The impact of the green transition on government revenues depends primarily on the level of the carbon tax. Since liquid fuels are already heavily taxed and are a major source of revenue for the government, reducing fossil fuel use as part of the transition implies significant revenue losses. An adequate level of carbon tax could offset the revenue losses from liquid fuels. Although it would not achieve the net zero trajectory. A carbon tax of $211 in 2040 would generate revenue equivalent to 2.3 percent of GDP and thus provide more fiscal space to manage the transition and its negative effects, including job retraining or unemployment compensation.

The transition would be progressive in terms of household welfare.<sup>13</sup> When carbon tax revenues are used to stimulate investment and thus growth (see the net zero scenario in figure 11), poorer households would be better off due to increased employment and thus wage income, while richer households would be worse off due to lower returns to capital (as using the revenues for investment would stimulate the supply of capital and thus make it cheaper) and higher energy intensity of their consumption. In contrast, shifting carbon tax revenues to households through cash transfers would ease the transition until 2030s, but would reduce and eventually reverse the growth benefits, increase the return to capital, and thus have a regressive welfare impact by 2040. Hence, Türkiye needs to strike a balance between supporting growth in the long run by boosting investments and supporting households in the short run to avoid welfare losses. Carefully designed and targeted social protection programs are hence crucial for a just transition without harming growth.

> 13 The welfare measure we use is equivalent variation, which is the amount of money that must be given or taken from a group of households to make them indifferent between the baseline and the scenario.

19
# **3   Data**

The main data source of firm economic performance is the AMADEUS data set from 2001 to 2009. AMADEUS is a commercial database containing financial information for more than 11 million firms in 41 European countries. Each firm in the database includes a wide range of standardized financial statement information<sup>11</sup> such as asset holdings, turnover, cost of employees, working capital and net income. The comprehensiveness of the data set allows us to control for a number of firm characteristics and examine the effect of program participation in a number of dimensions. In this paper, we focus on three industries (power, cement, and iron and steel) and investigate three different variables that reflect firm competitiveness - unit material cost (which is defined as the ratio of total material costs to turnover), number of employees, and turnover (revenue).

As discussed in section 2, the EU ETS covers installations in nine trading sectors (with specific size thresholds). To locate the participating firms, we first identify firms in the above three sectors in AMADEUS using NACE (revision 2, 4 digit class level) industry code<sup>12</sup> . We then manually match the AMADEUS firms with those in CITL. One difficulty in matching these two databases is that although AMADEUS has a precise sectoral classification (a NACE code), CITL defines sectors based on "activities". For example, the combustion sector defined in CITL includes a number of industries that incur combustion processes, such as power generation, food processing, pharmaceutical, etc. In cases when sector classifications do not match, we use zip code, name of parent company, address and contact information to identify EU ETS firms.

We define program participation based on allowances submission – an installation is a participant if it surrenders a positive number of allowances in that year. Since a firm can have multiple installations and therefore several accounts in CITL, we sum across all the accounts matched with an AMADEUS firm to obtain firm-level allowance allocation and submission. We focus on ten large countries, Austria, Belgium, Czech Republic, France, Germany, Great Britain,

> 11 One potential problem of using financial statement information is the different closing dates of the accounting period. We define the observation as in year _t_ if the accounting period ends on any date from April 1 in year _t_ to March 31 in year _t_ + 1.

> 12 We define the power generation industry as firms in NACE 35.11 – “Production of Electricity"; the cement industry as the combination of NACE 23.51 – “Manufacture of Cement" and NACE 23.52 – “Manufacture of Lime and Plaster". There are multiple sub-sectors under the iron and steel industry with drastically different products and processes, for the purpose of this analysis we define iron and steel firms as those with NACE 24.10 – “Manufacture of Basic Iron and Steel and of Ferro-Alloys".

7
prices. It can be shown that, in this framework, the pass-through rate depends on the elasticity of the slope of the demand schedule and competitive conditions ( _φd_ ). In our empirical analysis in Section 4, the main trade cost shifter of interest _xod_ is distance. To simplify the exposition, in what follows we set **X** _od_ = _xod_ .

Equation (2) illustrates three sources of bias that can arise when aiming at identifying trade costs from spatial price gaps: (1) incomplete pass-through ( _ρod_ = 1), (2) variation in demand conditions across locations, and (3) variation in the extent of competition across locations. Under additional assumptions, the pass-through rate can control for this sources of bias. In particular, under the assumption that the pass-through rate is independent of quantities _qd_ , equation (2) simplifies to where _ad_ is a local demand shifter. Under this specification, _ρod_ and the local demand shifter _ad_ are sufficient to control for local competitive and demand conditions.

The following subsection explains how this theoretical framework can guide the specification of empirical models to estimate the effect of distance on trade costs exploiting origin-destination price gaps, controlling for other factors that correlate with distance and could affect price differentials through variation of markups across locations. Section 4 describes the methods used to estimate these models.

# **3.2 Estimation strategy**

The estimation strategy exploits price data on multiple products _k_ sourced from locations _o_ and selling at locations _d_ at different time periods _t_ . With variation across products, destinations, origin locations, and time, equation (3) implies the following relationship between product prices at the origin and the destination: where we have further assumed that the pass-through rate remains fixed over time.<sup>6</sup> The latter assumption holds if local demand elasticities and competitive conditions (e.g., entry of new intermediaries) are constant over time.

In equation (4), trade costs _τ_ ( _x_<sup>_k_</sup> _odt_<sup>),pass-throughrates</sup><sup>_ρk_</sup> _od_<sup>anddemandshifters</sup><sup>_ak_</sup> _dt_ are not observable. The strategy aims at identifying _τ_ ( _x_<sup>_k_</sup> _odt_<sup>)and</sup><sup>_ρk_</sup> _od_<sup>basedonvariation</sup> in prices over time and space, and distance between locations, while treating _a_<sup>_k_</sup> _dt_<sup>asun-</sup> observed heterogeneity and capturing it through a set of fixed effects. For this reason, precisely identifying origin locations is key for the accuracy of the estimates.

The estimation procedure involves two steps, as follows:

> 6It is infeasible to estimate separate pass-through rates for each period since the number of parameters to estimate would equal the number of observations.

9
Several years later, Gale and Levine 2010 also find little evidence of impact, stating, “None of the four traditional approaches to financial literacy – employer-based, school-based, credit counseling, or community-based – has generated strong evidence that financial literacy efforts have had positive and substantial impacts.”<sup>9</sup> Still, Gale and Levine 2010 recommend further work in this area, since they find financial planning to be beneficial as well as simplification initiatives to facilitate decision making which public policy can support. Lusardi and Mitchell 2013 provide the most comprehensive and recent of the narrative literature reviews with nearly 200 references. They offer a cautious endorsement of financial education activities but also refer to the issues of endogeneity and omitted variables, “Research on efforts to enhance financial literacy suggest that some interventions work well, but additional experimental work is needed to explore endogeneity and establish causality.”<sup>10</sup> While the search on Econlit was limited to papers that had appeared in peer reviewed journals, this exclusion criteria was not used for the many articles identified through the survey papers or from other key sources (websites, World Bank research) in order to increase the coverage of available research on this topic. Many of the papers included in the analysis have been written in the past few years and thus many (about one-third) are still in working paper format (see Figure 2). Apart from universities, the NBER and the World Bank are the main sources of working papers cited here.

<!-- Start of picture text --> Figure 2<br>Type of Publication<br>(n=188)<br>38%<br>34%<br>28%<br>Peer-Reviewed Journal Academic Working Paper Non-Academic Publications<br>& Books<br><!-- End of picture text --> There are also numerous papers, briefs, and reports which are released by the many public, private, and non-profit organizations involved in this topic but which are not published through the academic press and subject to peer review. Known as “gray literature” these documents include research reports written by academics and researchers in think-tanks evaluating specific curricula using statistical methods and are thus also included in this literature search. However, since the financial education

> 9 Gale and Levine (2010), page 2.

> 10 Lusardi and Mitchell (2013), page 49.

10
28

# **Economic Regime**

Sachs and Warner (1995) developed a zero-one indicator of trade openness for about 80 developed and developing countries over the period 1960 to 1992. They considered a country to have an open trade policy for a specific year if it had none of the following characteristics during the year:

1. Nontariff barriers (NTBs) covering 40 percent or more of trade

2. Average tariff rates of 40 percent or more 3. A black market exchange rate that depreciated by 20 percent or more relative to the official exchange rate, on average during the 1970s or 1980s 4. A socialist economic system (as defined by Kornai, 1992) 5. A state monopoly on major exports.

The five criteria were chosen in an effort to include all major types of trade restrictions. Tariff and nontariff barriers are the most common and traditional of trade restrictions. Sachs and Warner argue that the black market premium (BMP) is indicative of the presence of exchange rate control and that a large BMP is evidence of the rationing of foreign exchange, which tends to serve as a form of import control. The socialist classification is included as an indicator for the centrally planned imposed trade restrictions such as those of Poland and Hungary. Export controls have the same effects as import controls in terms of closing the economy to trade.

Trade has been widely regarded as a strong engine of economic growth. The direct effects of trade liberalization typically include increased specialization, reduced rent-seeking, enhanced efficiency in resource allocation according to comparative advantage, more widespread diffusion of international knowledge, and heightened domestic competition as a result of international competition. In addition, the endogenous growth literature has emphasized the existence of various mechanisms through which trade openness may lead to an increase in the economy’s rate of growth in the long run. In particular, it has been argued that trade openness may facilitate the acquisition of less expensive or higher quality intermediate goods, and improved technologies, which enhance the overall productivity of the economy. Empirical studies such as Frankel and Romer (1999), Gallup, Radelet and Warner (1999), Irwin and Tervio (2002), and Dollar and Kraay (2001) have shown that countries that are more open to trade indeed also tend to have higher rates of economic growth. Similarly, Sachs and Warner (1995), using the above openness index in cross-section regressions over the period 1970-1989 for 79 developed and developing countries, finds that trade openness, on average, increases economic growth by 2.45 percentage points.

While trade openness is undoubtedly a desired feature of a knowledge economy, we are also interested in the other features of a healthy economy, such as macroeconomic balance and reliance on the private sector as the main engine of growth. However, Sachs
# **Box 1: What is Crisis Volatility?**

We define “crisis volatility” as the portion of the standard deviation of per capita GDP growth that corresponds to downward deviations below a certain threshold (in our case, the world average standard deviation of output growth; see Figure 6). Crisis volatility is designed to capture major drops in output growth, in contrast with the standard deviation of output growth that also captures small variations. To put it differently, while the standard deviation of output growth (i.e. “volatility”) may capture mostly high frequency variations of limited magnitude, the crisis volatility only captures major output drops (for more details see also Hnatkovska and Loayza, 2004).

**Figure 6: Computing crisis volatility for Indonesia**

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005<br>-5<br>-10<br>"Crisis Volatility"<br>-15<br>-20<br>Indonesia Growth Rate World average growth (mu)<br>Upper Bound Lower Bound<br><!-- End of picture text --> Sources: author’s own calculations using WDI data. Crisis volatility measures the portion of the standard deviation of per capita GDP growth that corresponds to downward deviations below the world average.

10
# **5. Discussion**

Results from this study indicate that high aid levels tend to undermine the quality of tax policy and administration, potentially aggravating long-run dependence on foreign aid to finance public expenditures. Inefficient tax systems can also reduce the level and productivity of private investment, slowing growth in incomes. Results for natural resource rents were not as robust as for aid. However, to the extent dependence on fuel exports also makes tax systems less efficient, negative effects on private investment and growth can be expected, as in aid-dependent countries.

The stronger results for aid rents than for resource rents appears inconsistent with Collier’s (2006: 1485) conclusion that aid is better than oil: Like resource rents, aid reduces the need for taxation and so increases sovereign rents [relative to “scrutinized revenues”]. However, offsetting this, aid comes with various donor-imposed mechanisms of scrutiny, which may spill over onto other expenditures, and so substitute for reduced pressure from citizens.

A possible explanation for this inconsistency is that available data more accurately measure aid rents than resource rents flowing to governments. Aid data compiled by the OECD-DAC are far from perfect, but they capture most aid, and most aid recorded as going to a recipient country is spent by its government, including even most aid that never enters government budgets. Natural resource exports are a relatively poor measure of resource rents. Production costs cannot be netted out very accurately.<sup>8</sup> Total revenues from resource sales may exceed export revenues by a substantial margin in some countries. If resource rents are measured less accurately in the analysis, estimates of their impact on quality of tax systems may be less accurate than estimates for

> 8 Hamilton and Clemens (1999) construct estimates of natural resource rents for 1985-94, prior to the period covered in this study.  They outline a number of limitations to their methods.

14
Vietnam. We discuss the estimation method and provide the empirical results, respectively, in Sections 4 and 5. We conclude in Section 6.

# **<mark>2. Data sets</mark>**

The main data set used in this study is the most recent Vietnam Household Living Standards Surveys (VHLSS) from 2010 to 2016. The VHLSSs have been conducted every two years by the General Statistics Office of Vietnam (GSO) with technical support from the World Bank in Vietnam since 2002. The latest VHLSS was conducted in 2016. In this study, we use the 2010-2016 VHLSS instead of those carried out before 2010, since the more recent surveys contain more information on employment consisting of monthly wages and formal employment.

The 2009 Population and Housing Census is used as the sampling frame. Of 10,896 communes, 3,133 were chosen as the primary sampling units. A village was randomly selected from each commune, and around 15 households were selected randomly from the village. Each VHLSS covers around 46,000 households selected from 3,063 areas of the master sample frame, and is divided into 2 types: (1) The sample for the income survey includes 36,756 households and collects information to assess non-monetary living standards at the national, regional and provincial/city level; (2) The sample for the income-expenditure survey includes 9,189 households and collects sufficient information for further assessment and analysis of monetary living standards at the national and regional level.

In this study, we used the full sample of the VHLSS to increase the number of children born in January and February. The total number of households and household members sampled in VHLSS are as follows:

- VHLSS 2010: 46,995 households with 185,696 household members.

- VHLSS 2012: 46,996 households with 182,042 household members.

- VHLSS 2014: 46,335 households with 178,267 household members.

- VHLSS 2016: 46,380 households with 175,340 household members.

The VHLSS contain very detailed data on individuals, households and communes. Household data include durables, assets, production, income and expenditures, and 5
**Table 1: Prevalence of High Growth Firms across Economic Sectors**

||**High-Growth**<br>|**Other firms**|**Share of high-growth**<br>|<br>**High-Growt **|<br>**h Firms**|<br>**Other F**|<br>**irms**|
|---|---|---|---|---|---|---|---|
|**ISIC Codes - Sector**|**Firms**<br>**[1]**|**[2]**|**firms in the sector**<br>**[3]=1/(1+2)**|**Employment**<br>**Growth**|**Output**<br>**Growth**|**Employment**<br>**Growth**|**Output**<br>**Growth**|
|**Manufacturing**|**1,608**|**27,188**|**5.6%**|**127%**|**38%**|**0%**|**14%**|
|17-- Textiles|158|2,414|6.1%|124%|43%|0%|13%|
|29-- Machinery and equipment|123|1,972|5.9%|100%|33%|0%|15%|
|18-- Apparel and fur|226|3,574|5.9%|141%|40%|0%|14%|
|28-- Metal products|180|2,938|5.8%|150%|47%|0%|17%|
|15-- Food products and beverages|393|6,508|5.7%|133%|35%|0%|14%|
|36-- Furniture|114|1,927|5.6%|150%|52%|0%|15%|
|24-- Chemicals|150|2,789|5.1%|132%|35%|0%|17%|
|26-- Non-metallic mineral products|144|2,684|5.1%|130%|34%|0%|11%|
|25-- Rubber and plastic|120|2,382|4.8%|100%|33%|0%|14%|
|**Services**|**1,479**|**24,446**|**5.7%**|**133%**|**40%**|**0%**|**13%**|
|64 & 72-- ICT|116|1,319|8.1%|115%|53%|9%|17%|
|45-- Construction|173|2,463|6.6%|115%|53%|0%|12%|
|50-52-- Wholesales and retail trade|929|14,845|5.9%|133%|39%|0%|13%|
|60-63-- Transport and storage|109|2,251|4.6%|150%|34%|0%|11%|
|55-- Hotels and restaurants|152|3,568|4.1%|130%|33%|0%|9%|

Note: This table shows the total number of firms by type and their associated median employment and output growth across economics sectors. Sectors with fewer than 100 enterprises are dropped. Sectors are ranked by the importance of HGFs (column 3). The data uses the revision 3.1 of the International Standard Industry Classification (ISIC). The table present the median standard growth rate within each cell. Source: Author computation using data from the World Bank Enterprise Surveys.

# **b. Measuring Two Channels of FDI Spillovers**

I construct measures of the linkages and demonstration channels at the country-sector level using information from the WBES. The _linkages_ channel (����������) is defined as the average share of inputs that foreign firms in sector j acquire in the host economy. Foreign firms are identified as enterprises with at least 10 percent of foreign ownership. Specifically, this variable is constructed as: where ����������� represents the value of inputs sourced locally by foreign firm _i_ in sector _j_ in ��� country _c_ , and �������� corresponds to firms _i_ 's total value of inputs, regardless of their origin. The total number of foreign firms in country _c_ , sector _j_ is _n_ . This approach, which focuses on the demand for inputs from foreign companies, is used in Sanchez-Martin et al (2015) and is complementary to that in Javorcik (2004) and Blalock and Gertler (2008), who adopt the perspective of the local supplying sector and look for foreign presence downstream in the supply chain.

- 11 -
30 Table 1: Estimates of fungibility and naïve diffs-in-diffs estimates of project impact

||(1)||(2)<br>Provincial|<br>governme|(3)<br>nt health expen|<br>ditures|(4|)|(5)<br>Infant|<br>mortality|
|---|---|---|---|---|---|---|---|---|---|---|
||coef|t|coef|t|coef|t|coef|t|coef|t|
|GDP|-0.003**|-2.158|0.000|0.613|-0.004**|-2.555|-0.000|-0.331|||
|ODA|1.275***|5.939|1.060***|6.671|||||||
|Net prov. revenues|0.006**|2.414|||||||||
|Net prov. expenditures|||0.046***|7.970|||||||
|Prov. revenues|||||0.009***|2.711|||||
|Prov. revenues x Bank project|||||0.001|0.255|||||
|Prov. expenditures|||||||0.057***|7.412|||
|Prov. exp. X Bank project|||||||-0.002|-0.511|||
|2002 dummy|||||||||-0.002|-0.333|
|Bank project|||||||||0.012*|1.825|
|Bank project x 2002 dummy|||||||||-0.008|-0.884|
|Constant|36.284***|9.877|9.537***|3.727|43.598***|9.455|8.632***|2.623|0.018***|4.460|
|Additional t-tests:|||||||||||
|ODA diff. from own resources||5.90||6.32|||||||
|ODA diff. from 1||1.28||0.38|||||||

Notes: .01 - ***; .05 - **; .1 - *. t-statistics in column (5) reflect standard errors adjusted for clustering at the province level. ODA is overseas development assistance.
<!-- Start of picture text --> 6.0%<br>4.0%<br>orbs —SS<br>- =<br>4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>2003-2014 2014-2020 2012-2014 2014-2019 2019-2020<br>TM Capital Stock @ labor Human Capital TM Total factor productivity © GDP growth<br><!-- End of picture text -->