rafmacalaba/datause-encoder-data huggingface dataset:
In absolute terms the fiscal capacity of counties has increased steadily over the last decade. Since 1995, fiscal expenditures of counties (excluding county-level cities) have increased by close to 15 percent annually, but with slower growth in the 1990s and very substantial growth of around 20 percent since 2000. While county budget expenditures were RMB 323 per capita in 2000, they had more than doubled to RMB 731 in 2005 (Table 11). During the same period, though, the share financed from own revenues had fallen steadily, from around half in the mid-1990s to only one-quarter in 2005.

**Table 11.  Revenue and Expenditure of Counties, 1995-2005**

|Year|Budget<br>Revenue<br>(RMB per<br>capita)|Own<br>Revenue<br>(as share of<br>budget rev.)|Budget<br>Expenditure<br>(RMB per<br>capita)|Budget<br>Expenditure<br>(y-o-y<br>increase)|C.V. of<br>revenues per<br>capita|C.V. of<br>expenditures<br>per capita|
|---|---|---|---|---|---|---|
|1995|183|0.47|192|19%|0.70|0.67|
|1996|227|0.50|233|21%|0.72|0.73|
|1997|250|0.50|254|9%|0.71|0.72|
|1998|267|0.53|267|5%|0.80|0.74|
|1999|308|0.49|300|12%|0.88|0.71|
|2000|325|0.46|323|8%|0.84|0.72|
|2001|418|0.40|405|26%|0.87|0.77|
|2002|495|0.34|489|21%|0.87|0.74|
|2003|569|0.34|557|14%|0.90|0.77|
|2004|647|0.31|636|16%|0.91|0.78|
|2005|731|0.25|727|14%|1.04|0.87|

Source: Based on Compendium of Fiscal Statistics for All Prefectures, Cities, and Counties, various years.

There is tremendous variability across counties. This is shown in the last two columns of Table 11, where the coefficients of variation in per capita own revenues and own expenditures are large, and have been growing. Per capita expenditures of the fiscally richest quintile of counties (excluding urban units) exceed those of the poorest quintile by more than 4 (4.2 in 2003),<sup>25</sup> a disparity that is large even in light of cost differences in service delivery and different expenditure assignments across counties. While the ratio has declined somewhat since the beginning of the decade, it is still higher than in 1995 (of 3.9). In fact, this picture understates the real extent of disparity across localities. Since the capacity to raise

> 25 Calculated from Compendium of Fiscal Statistics for All Prefectures, Cities, and Counties, various years.

24
# **Fig. 3c: Urban shares in**

<!-- Start of picture text --> unorganized sector<br>Unorganized Outpu<br>0.80<br>0.70<br>0.60<br>0.50<br>Unorganized Employme<br>0.40<br>0.30<br>Unorganized Plants<br>0.20<br>0.10<br>0.00<br>1989 1994 2000 2005<br>Urban shares<br><!-- End of picture text --> Source: Ghani, Grover, Kerr, 2012 What attracts new entrepreneurs and workers to cities? Ghani et al (2011b) quantify the factors and traits of cities and industries that systematically predict stronger entry levels of new establishments in the formal and informal sectors. They use cross-sectional establishment-level surveys in 630 districts in India spread across 35 states/union territories. They quantify entrepreneurship as young firms that are less than three years old, and define entry measures through employment in these new establishments. They examine the role of demographic traits in cities (age profiles, population, population density), structural traits of cities (education of the local labor force, quality of local physical infrastructure, connectivity to major cities), business climate traits (stringency of labor laws, household banking conditions), and the impact of agglomeration economies in formal and informal sectors. They develop metrics that unite the incumbent industrial structures of districts with the extent to which industries interact through the traditional agglomeration channels (Marshall 1920). This conceptual approach has also been used to describe location choice decisions and city structures in several advanced economies. The first agglomeration channel is proximity to customers and suppliers, which reduces transportation costs and thereby increases productivity. The second is the Chinitz effect. Chinitz (1961) argued that the large, integrated steel firms of Pittsburgh depressed external supplier development. By contrast, New York City's much smaller firms, organized around the decentralized garment industry that then dominated the city, were better suppliers to new firms. The third channel is labor pooling.

Reproducing the results reported in Ghani et al. (2011b) Table 1A below provides basic spatial results for the organized manufacturing sector. The first column includes just district populations, district-industry employments, and industry fixed effects. Not surprisingly, existing district-industry employment strongly shapes the 12
- 44 -

Prior to the RPTA project, the property discovery process was based upon owner declarations. Property owners were required to submit periodic statements to local assessors, declaring the properties under their ownership and providing basic data on each property's physical characteristics. Valuations were calculated using a mass appraisal technique: standard unit cost factors, calculated by the assessor, were applied to the descriptive data supplied by property owners to yield an estimate of each property's value. This procedure permitted two forms of evasion: property owners could either understate the characteristics of their properties, or they could neglect to declare their properties entirely.

The RPTA project was designed to eliminate these opportunities. It intended to do so by fundamentally changing the procedure used to discover property, from one based on owner declarations to one based on field-verified inventories of properties, a procedure termed "tax mapping".

This conversion of systems was accomplished by special tax mapping teams, recruited and led by the local assessor. The work of each team comprised three phases:

- the prefield tie-up, where the RPTA team compiled a working parcellary map of the jurisdiction, drawing on whatever maps and cadastral records were available, and then matched owner declarations to mapped parcels as far as data permitted;
# **_3.6 Robustness of Adequacy Dimensions_**

We perform several robustness checks at dimensions level to test the robustness of our coding framework against macro-level data for all 64 emerging economies (Annex 6).

First, we compare the weighted survey average of each of the adequacy dimensions with the macro variable that best matches the adequacy concept. As the definitions of the seven adequacy dimensions for data available at macro-level and the respective HCES variables’ definitions do not exactly match, some smaller deviations between macro-level data and weighted survey average are to be expected.

For WASH-related data, we compare the country averages obtained by coding access to water and access to sanitation obtained from HCES against macro-level data from JMP which is jointly managed by WHO and UNICEF. An alternative data source would be WDI, which classifies access to water by only considering “drinking water from an improved water source which is located on premises, available when needed and free from faecal and priority chemical contamination”; hence WDI does not capture water that is considered improved by JMP but located outside the HH’s premises (e.g., Tubewells). For the robustness check on water, we therefore revert to JMP data and collapse the first three categories of the adequate drinking water ladder: safely managed, basic, and limited access to secure water.<sup>12</sup> Further, we check our coding of the cooking and electricity dimension against macro data from the IEA, which has published databases on electricity access as well as on the reliance on traditional biomass for cooking since 2000 in their World Energy Outlook (WEO).<sup>13</sup> As we combine PRINDEX data with the HCES ownership variable, we already implicitly include a macro-level variable for the tenure security dimension and therefore abstain from an additional macro-check.

To the best of our knowledge, there are no comparable data sets at macro-level for overcrowding and structural quality for a large enough sample. The results [ _available in supplementary material_ ] show negligible differences which can likely be attributed to the differences in definition across surveys and macro-level data. Hence, we are comfortable that our harmonization efforts across the 64 household surveys measure the concept accurately.

> 12 JMP provides an adequacy ladder of drinking water which includes safely managed drinking water located within the premises of the HH; basic drinking water, which is an improved source within 30 minutes roundtrip; and limited drinking water for which collection time exceeds 30 minutes. Since not all HCES data allow for the calculation of time to the nearest water source, we collapse these three categories of JMP for our robustness check instead of reverting to WDI data, which only includes the first category. For more information, see: washdata.org.

> 13 <u>https://www.iea.org/articles/defining-energy-access-2020-methodology</u>
# **3.2.1 Electricity prices**

To construct electricity prices, we combine EAIM information on plant-level electricity expenditure with state-level data on number of users, values and volumes of electricity sales, and state-month-specific electricity tariffs from the CFE. First, using the information on value and volume of consumption at the tariff-municipalitymonth level from Secretar ́ıa de Energ ́ıa (Ministry of Energy, SENER), we calculated average tariffs paid as value over volume (pesos/KwH) and average sales as sales over number of users (pesos/user). Secondly, we analyzed this information and defined a lower and an upper limit of electricity bills for each municipalitytariff level. On the basis of these lower and upper limits, we assigned a tariff level to each establishment based on its expenditure on electricity. We assigned four different electricity tariffs (two for medium-sized companies and two for large companies). Once we defined the tariff-level that each plant faces, we used that to compute a weighted price using the plant-specific monthly production shares from the EMIM in 2009 as weights. For firms that entered the survey after 2009, we used the first year in which the firm appears to compute weights.<sup>26</sup> where _Pit_<sup>_e_istheaverageelectricitypricethatplant</sup><sup>_i_facesinyeart;</sup><sup>_yim_0isthe</sup> revenue of _i_ in month _m_ in year 0 and _Pimt_<sup>_e_isthepricethat</sup><sup>_i_facesinmonth</sup><sup>_m_</sup> and year _t_ .<sup>27</sup>

# **3.2.2 Fuels prices**

To construct plant-level fuel prices, we followed four steps. First, using consumption data from SENER, we identified the four main types of fossil fuel used in manufacturing. These are natural gas, diesel, petroleum coke, and coal and account for 85% of other fuels consumption, and 68% of total energy consumption

> 26To test the robustness of these weights, we used alternatively current year weights and the results do not change.

> 27 Thus, we assume that energy consumption is proportional to production volumes over the year. For each plant we compute shares in 2009 to avoid potential endogeneity of time-varying proportions.

18
Czubala, Witold, Ben Shepherd, and John S. Wilson. 2007. “Help or Hindrance? The Impact of Harmonized Standards on African Exports.” Policy Research Working Paper 4400, World Bank, Washington, D.C.

- de Melo, Jaime, and Alberto Portugal-Perez. 2008. “Rules of Origin, Preferences and Diversification in Apparel: African Exports to the US and to the EU.” CEPR Discussion Paper No. 7072.

- Disdier, Anne-Célia, Lionel Fontagné, and Mondher Mimouni. 2007. “The Impact of Regulations on Agricultural Trade: Evidence from SPS and TBT Agreements.” Working Paper No. 2007-04, CEPII.

- Deardorff, Alan V. 1998. “Determinants of Bilateral Trade: Does Gravity Work in a Neoclassical World?” In J.A. Frankel (ed.), _The Regionalization of the World Economy_ .  Chicago: University of Chicago Press.

- Djankov, Simeon, Caroline Freund, and Cong S. Pham. 2008. “Trading on Time.” _Review of Economics and Statistics_ , forthcoming.

- Dutt and Traca. 2007. “Corruption and Bilateral Trade Flows: Extortion or Evasion?” NSEAD - Economics and Political Sciences and Universite Libre de Bruxelles - Solvay Business School. Mimeo.

- Eaton, J., and S. Kortum. 2002. “Technology, Geography, and Trade.” _Econometrica_ 70: 1741–79.

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- Francois, Joseph, and Miriam Manchin. 2007. “Institutions, Infrastructure, and Trade.” Policy Research Working Paper No. 4152, World Bank, Washington, D.C.

- Faze, McArthur, Sachs, and Snow. 2004. “The Challenges Facing Landlocked Developing Countries.” _Journal of Human Development_ 5(1,March).

- Fisman, Raymond, and Shang-Jin Wei. 2004. “Tax Rates and Tax Evasion: Evidence from ‘Missing Imports’ in China.” _Journal of Political Economy_ 112(2, April): 471-500.

- Freund, Caroline, and Diana Weinhold. 2004. “The Effect of the Internet on International Trade.” _Journal of International Economics_ 62(1): 171-89.

- Gatti, Roberta. 2004. “Explaining Corruption: Are Open Countries Less Corrupt?” _Journal of International Development_ 16: 851-61.

- Helble, Matthias, Ben Shepherd, and John S. Wilson. 2007. _Transparency and Trade Facilitation in the Asia Pacific: Estimating the Gains from Reform_ , Washington, D.C.: World Bank.

- Helpman, Elhanan, and Paul R. Krugman. 1985. _Market Structure and Foreign Trade: Increasing Returns, Imperfect Competition, and the International Economy_ . Cambridge, Mass.: MIT Press.

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- Hoekman, Bernard, and Alessandro Nicita. 2008. “Trade Policy, Trade Costs, and Developing Country Trade.” Policy Research Working Paper No. 4797, World Bank, Washington, D.C.

- Hummels, David. 2006. “Transport Costs and Trade over Time.” In _OECD Report of the One Hundred and Thirtieth Round Table on Transport. Round table 130, Transport and International Trade._

35
# **<u>Measuring the Effects of Choice on Achievement</u>**

Controlling for a series of institutional, school, student and family characteristics, and running a series of OLS regressions, it turns out that private school attendance is a positive and significant explanatory variable, though the effect is very small, except in the case of science, where it has no effect (Table 3). Private school attendance increases test scores in math by no more than 5 points and 4 points in reading. Given the PISA scale – mean OECD score of 500 and standard deviation of 100 points – these are very small size effects. In the case of science, the effect is not significant.

However, other institutional variables have a strong effect, such as firing teachers, especially in science. The effect varies from 20 points in reading, or 0.20 of a standard deviation, to 43 in math and 73 in science. However, most schools report that they can fire teachers in the Netherlands. Assessment is important in the Netherlands and many schools use achievement information to assess progress. This has a small but positive and significant effect on outcomes, about twice the size effect of private school attendance. The same goes for schools setting teacher salary increases. Overall, 8 to 10 points increase is associated with schools setting teacher increases. Parental involvement in school budget decisions is associated with higher scores, raising achievement by 11 points in math, to 14 points in science, and by 16 points in reading. Therefore, the OLS analysis suggests that institutional factors are more important than private school attendance in determining achievement in the Netherlands. It is important to bear in mind however that the OLS results are correlations, and not causal relationships. We turn now to the identification problem.

13
of salary rather than dividends, given that the corresponding tax brackets of the personal income taxation were equal or higher compared to the dividend tax (i.e. 15%-25%-35% versus 15%).<sup>2</sup> _Fourth_ , the elimination of the ‘Allowance for Corporate Equity’ (ACE) scheme in 2001 does not adversely affect our results. On the contrary, since the ACE allows neutrality between finance by debt and finance by equity (Devereux and Sorensen, 2005; Keen and King, 2002), the elimination of ACE would create a debt-bias, typical of standard tax systems, thus allowing a stronger test of the corporate tax reform’s positive effect on the equity choice of Croatian firms. Another important issue is that the elimination of the ACE after 2001 did not broaden the tax base substantially; Klemm (2007) suggests that there is weak evidence that ACE in Croatia had an effect on tax revenues, while in Keen and King’s (2002) review of Croatia’s tax system, it is reported that 86.3% of the population of profitable firms in 1998 was subject to taxation after estimating the profit tax base with ACE. This could be attributed to the fact that the imputed rate on the equity under the ACE scheme was equal to 5 percent plus the inflation rate of industrial product prices, a rate that was deemed as rather low by many tax experts in Croatia (Svaljek, 2005), since the protective interest rate should ideally be equal to the interest rate on risk-free investments which is difficult to estimate in the absence of developed capital markets in Croatia in the 1990s.<sup>3</sup> _Fifth_ , Croatia’s banking system quickly recovered after the brief crisis of 1998, resulting in increased credit availability and decreased loan rates that enhanced the access and use of debt by firms (Kraft, 2000). In particular, the ratio of private credit to GDP increased from 38% in 1998 to 54% in 2004, while bank long-term loan rates for enterprises decreased from 14.5% in 1998 to 6% in 2004.<sup>4</sup> Also, the rapid development of the capital markets in Croatia provided equity capital to listed firms, thus easing bank-based financing for non-listed firms, which are the sole focus of this study. In this context of substantial financial developments, a possible decrease in the debt levels of Croatian firms would strengthen our argument of a negative effect on debt financing due to the corporate tax reform. Furthermore **,** it is easier to investigate the effect of taxation changes in private Croatian companies because they did not have the size,

> 2 Similarly, the introduction of the dividend tax does not necessarily lead corporations to finance their activities largely through retained earnings (Poterba and Summers, 1985; Zodrow, 1991). 3 Keen and Kling (2002) acknowledge this criticism, but suggest that the rate was about right (namely “not too high, not too low”).

> 4 _Sources_ : World Bank, Croatian National Bank

4
- Taheripour, F. and Tyner, W.: 2011, Introducing First and Second Generation Biofuels into GTAP Data Base Version 7, GTAP Research Memorandum 21, Center for Global Trade Analysis, Purdue University.

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- USGAO: 2003, Natural Gas: Domestic Nitrogen Fertilizer Production Depends on Natural Gas Availability and Prices, Report to the Ranking Democratic Member, Committee on Agriculture, Nutrition and Forestry, U.S. Senate GAO-03-1148, United States General Accounting Oce.

- Weitzman, M. L.: 2010, What Is The "Damages Function" For Global Warming - And What Dierence Might It Make?, Climate Change Economics 1(01), 57 69.

- Winston, A.: 2009, Enhancing Agriculture and Energy Sector Analysis in CGE Modelling: An Overview of Modications to the USAGE Model, Centre of Policy Studies/IMPACT Centre Working Paper G-108, Monash University, Centre of Policy Studies/IMPACT Centre.

- Yu, W., Hertel, T., Preckel, P. and Eales, J.: 2004, Projecting World Food Demand Using Alternative Demand Systems, Economic Modelling 21(1), 99 129.

26
Table 2: Percent distribution of jobs by occupation at place of work and place of residence: Tokyo’s central city (23 wards), Tokyo, greater Tokyo, national capital region and Japan

||Central City<br>(23 wards)|Tokyo|Greater Tokyo<br>(3 prefectures<br>only*)|National<br>Capital Region<br>(4 Prefectures<br>only**)|Japan|
|---|---|---|---|---|---|
|_At Place of Work_||||||
|Production|16.8|18.4|30.3|34.5|29.2|
|Professional & technical|16.5|16.8|16.7|12.1|13.5|
|Managerial|4.2|3.9|2.5|2.5|2.9|
|Clerical|27.6|26.1|19.5|16.9|19.2|
|Sales|19.8|19.0|13.7|12.4|15.1|
|Services|8.2|8.6|8.6|8.7|8.8|
|Security|1.5|1.5|1.7|1.2|1.6|
|Agriculture, forest & fishery|0.1|0.3|5.1|7.9|5.1|
|Transport & Telecommunication|3.0|3.1|3.0|3.3|3.5|
|Unclassified|2.3|2.3|1.7|0.5|1.1|
|Total %|100.0|100.0|100.0|100.0|100.0|
|Total No (in million)|7.0|8.5|8.5|4.0|62.9|
|_At Place of Residence_||||||
|Production|20.1|20.9|26.5|34.0||
|Professional & technical|15.9|16.6|14.4|12.1||
|Managerial|3.9|3.7|2.9|2.5||
|Clerical|23.4|23.2|21.9|17.2||
|Sales|18.1|17.6|16.2|12.6||
|Services|10.8|10.3|8.5|8.6||
|Transport & telecommunication|3.3|3.2|3.6|3.4||
|Safety|1.2|1.4|1.8|1.3||
|Agriculture, forest & fishery|0.2|0.5|2.5|7.8||
|Unclassified|3.1|2.8|1.7|0.5||
|Total %|100.0|100.0|100.0|100.0||
|TotalNo. (in million)|4.2|6.2|10.8|4.0||

*3 prefectures include Kanagawa, Saitama and Chiba surrounding Tokyo. **4 prefectures include Gunma, Ibaraki, Tochigi, and Yamanashi around greater Tokyo. Source: Statistics Bureau, _Population Census 2000._ Tokyo. 2003.

77
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