rafmacalaba/datause-encoder-data huggingface dataset:
# **3. Data**

Fiscal incidence analyses such as those presented in this study rely on integrating two sources of data. First, administrative data mainly provide key insights into public revenues and expenditures, but can also provide information on subsidy schemes, transfer systems and users of public services. Second, household survey data is crucial in identifying individuals, both as taxpayers for different kinds of taxes and as users of publicly funded services. The next subsections present details on these datasets used in this study.

# **3.1.  Administrative data: Taxes and the child-relevant budget in Ethiopia**

Administrative data used in this study include the following: (i) public revenue and expenditure data for the 2018/19 fiscal year and regional education and health spending from the Ministry of Finance, (ii) school enrollment information from the Ministry of Education, (iii) kerosene subsidy from the Ethiopian Petroleum Supply, and (iv) wheat subsidy from the Ethiopian Trading Businesses Corporation.

Table 2 shows Ethiopia’s tax revenues in 2018/19. Revenue collection was equivalent to 13.5% of GDP; 43% of those through direct taxes (mainly business profit tax followed by personal income tax) and 57% through indirect taxation. The last two columns show the tax burden per child. Domestic indirect taxes are the most important followed by personal income taxes and business profit tax.

_Table 2: Annual tax revenues, share of GDP and per child burden, 2018/19_

|T|Gove<br>|rnment tax reven<br>|ue<br>|Tax burde|n per child<br>|
|---|---|---|---|---|---|
|ax category|ETB (in<br>millions)|Share of tax<br>revenue (%)|Share of<br>GDP (%)|ETB|Share of tax<br>burden (%)|
|Total taxes|268,457|100.0|13.5|680|100.0|
|Direct taxes|115,858|43.2|5.8|208|30.6|
|Personal income tax|41,203|15.3|2.1|149|21.9|
|Business profit tax|59,407|22.1|3|24|3.5|
|Land use fee and<br>agriculture income tax|708|0.3|0|23|3.4|
|Rental income tax|2,138|0.8|0.1|5|0.7|
|Other direct taxes|12,403|4.6|0.6|7|1.1|
|Indirect taxes|152,600|56.8|7.7|472|69.4|
|Domestic indirect taxes|77,774|29.0|3.9|414|60.9|
|Import duties<br>**_Source:_**_Authors’ calculations; D_|74,826<br>_ata from the Mi_|27.9<br>_nistry of Finance._|3.8|58|8.5|

**_Source:_** _Authors’ calculations; Data from the Ministry of Finance._ **_Note:_** _Import duties include customs, surtax, VAT, and excise on imports. ETB = Ethiopian Birr._

Ethiopia’s 2018/19 public spending with child-relevant components is shown in Table 3. Thirtynine percent of government spending goes towards social development, followed by economic development (33%) and general services.

10
CD

# ,de 4: Commodity Composition of Interstate Trade of Selected FSU Countries, 1993

# vcent of total exports/imports)

||Russia|Ukraine|Belarus|Kazakhstan|Uzbekistan|Turkmenistan|
|---|---|---|---|---|---|---|
|al exports|100.0|100.0|100.0|100.0|<br>100.0|100.0|
|and gas|37.4|_0.5_|8.7|19.9|42.5|78.5|
|iland other fuels|0.5|1.5|.,|21.1|7.7||
|v materials|15.2|35.6|25.6|42.4|41.0||
|riculture and food|4.4|22.6|5.3|11.2|||
|nufactured goods and other|42.4|39.8|60.5|_5.4_|8.9|21.5|
|tal imports|100.0|100.0|100.0|100.0|<br>100.0|100.0|
|and gas|2.5|58.1|59.6|31.4|32.4||
|al and other fuels|_1.5_|1.1|1.6|4.9|3.0||
|w materials|31.6|15.7|24.9|28.5|0.0||
|nculture and food|12.6|_3.5_|9.0|7.8|14.6<br>||
|nufactured goods and other|51.7|21.6|_5.0_|27.4|50.0||

irce: World Bank staff estimates.
line by 2020. The aggregate decline in the consumption of imported consumer goods and services is expected to be much more pronounced at 3.7 percent or US$196 billion relative to the baseline after three years (Table 3).

Table 3: Impact on household consumption by region

||**Tot**|**al**|**Im**|**ports**|
|---|---|---|---|---|
||Percent|US$ billion|Percent|US$ billion|
|East Asia and Pacifc|-0.9|-21.3|-6.7|-28.3|
|<br>Latin America and the Caribbean|-0.6|-23.3|-7.9|-29.6|
|Middle East and North Africa|-0.5|-10.6|-2.9|-15.0|
|Europe and Central Asia|-0.5|-11.2|-6.1|-22.5|
|Sub-Saharan Africa|-0.4|-6.0|-4.5|-11.1|
|European Union|-0.3|-37.4|-1.9|-30.7|
|<br>Other Advanced Economies|-0.3|-23.5|-4.5|-32.3|
|China|-0.1|-3.7|-3.0|-4.3|
|United States|-0.1|-9.6|-2.4|-19.4|
|South Asia|-0.1|-1.2|-2.5|-3.2|
|**Total**|**-0.3**|**-147.8**|**-3.7**|**-196.3**|

Source: Authors’ simulations.

Note: Results are reported relative to the baseline in 2020 in real consumption.

At the regional level, the distribution of losses follows that discussed in the previous section with respect to impacts on welfare. In general, regions that experience welfare losses are also negatively impacted in terms of real consumption. More specifically, consumers in EAP and LAC are hurt by an aggregate decline in real consumption by 21.3 billion and 23.3 billion, respectively, relative to the baseline by 2020. The decline in the volume of consumer is even more at 7.9 and 6.7 or imported goods pronounced percent percent US$29.6 billion and US$28.3 billion in LAC and EAP, respectively. The aggregate adverse impact on consumers is partly offset by the increase in the consumption of domestically sourced consumer goods.

Apart from their negative impact on aggregate consumption, tariffs have been found to disproportionately impact low-income households as these spend more on traded goods as a share of their income (Fajgelbaum and Khandelwal 2016; Furman et al. 2017). Due to their regressive nature, increases in tariffs are likely to have adverse distributional effects 18
11

**(7) dlnR il** _R_ **_aOL l_** _S_ **_s s_** _aInL ainZ)_ dlnv **1I+fji** **_Ra_ 8lnwI** _s_ **_N_ 1X** **_naL Bsz L)._**

The first right-hand term captures the direct effect of increasing the wage distortion, i.e., the effect of a fall in employment moving the economy further inside the production possibility frontier. The last 2 terms capture the increased losses in efficiency associated with the fall in employment. The employment reduction increases production of capital intensive goods that are protected by the tariff (i.e., [(alnZ)/(8lnL)] **_<_** 0) leading to further efficiency losses.

Next we derive an expression for the social return of capital which we simply define as the elasticity of GDP evaluated at world prices with respect to capital. Totally differentiating (1) with respect to K and expressing this in logarithmic form we obtain, where **_SK_** - [(alnR)/I(lnK)J , is the "private" return of capital, i.e., the percentage change in GDP evaluated at domestic prices due to a one percent increase in the stock of capital.

In order to evaluate expressions (7) and (8) we need to determine expressions for MlnL/8lnt and (dlnL)/(dlnK), respectively. Clearly, [(a1nL)/(alntq)J = **_ji_** [(DlnL)/(alnw)] **_<_** 0 which of course implies that the sign of (7) is minus the sign of the term in square brackets. Thus, one could not in principle rule out the possibility that increasing wage distortions lead to a higher social income if the tariff r is sufficiently large. This would require that the protected sectors be labor intensive (in which case [(alnZ)/(8lnL)J **_<_** 0). As we have seen this is not likely to be the case in LDCs and thus the term in square bracket is likely positive with the effect of the tariff reinforcing the negative effect of i. To obtain an expression for (aInL)/(alnK) we use (2), (3), and (4),
# **Figure B30: Medium-term cycles—Agriculture**

<!-- Start of picture text --> Log scale<br>0.50<br>Maize Wheat Rice Soybean meal Soybean oil Cotton<br>0.25<br>0.00<br>-0.25<br>-0.50<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>0.80<br>Coffee, Arabica Coffee, Robusta Cocoa Tea Natural rubber<br>0.40<br>0.00<br>-0.40<br>-0.80<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 71 —_
liberalization and the development of regulatory frameworks started only in the 1990s, there are substantially fewer observations on the regulatory variables.

Table 1. Data sources

|Variable|Datasource|
|---|---|
|RealGDP|WBWorldDevelopmentIndicators|
|Investmentdeflator|WBWorldDevelopmentIndicators|
|GDPdeflator|WBWorld Development Indicators|
|Interestrate|IMFIFS|
|Realpublicinvestment|Calderon and Easterly, 2001|
|Realprivate investment|Calderonand Easterly, 2001|
|Regulatoryvariables|Guasch,2001|
|Physicalcapitalstock|CalderonandEasterly,2001|

Appendix I, Table 3a is the correlation matrix for the complete dataset and indicates how different determinants of private investment flows in infrastructure hang together. Public and private investment are significantly negatively correlated, supporting the idea that they are overall substitutes. Private investment is also significantly positively correlated with the dummy for the passage of reform legislation and with the existence of a regulatory body. The passage of reform legislation and the existence of a regulatory body are highly positively correlated but not perfectly so - reform legislation had been passed in only 40% of sector-country combinations prior to the establishment of a regulatory authority. Appendix I, Table 4a is the correlation matrix for the set of variables measuring aspects of the regulatory regime. The correlation between private investment levels and the passage of legislation opening the sector to private investment is significantly more positive in this subset of the data.

12
42 Table 3: Resources, (De-)Industrialization & Urban Employment, Cross-Section, c. 2000

|Dep.Var. =|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|**Empl. Sh. of:**|MFG|FIRE|SUM|NTR|NTR2|NTR3|GOVT|GOVT2|NRX|CONST|
|MFGSERV|0.18**|0.10**|0.28**|-0.21*|-0.29*|-0.23|-0.02|-0.01|0.05|-0.02|
||[0.09]|[0.05]|[0.11]|[0.12]|[0.14]|[0.15]|[0.03]|[0.06]|[0.10]|[0.04]|
|NRXGDP|-0.02|0.03|0.01|0.09|0.16|0.22|-0.13**|-0.09|0.11|-0.17**|
||[0.11]|[0.07]|[0.15]|[0.15]|[0.20]|[0.26]|[0.06]|[0.13]|[0.16]|[0.07]|
|DEINDU|-0.73*|0.09|-0.64*|0.38|0.88*|1.15*|-0.23|-0.20|0.20|-0.40*|
||[0.37]|[0.11]|[0.36]|[0.36]|[0.50]|[0.63]|[0.15]|[0.29]|[0.29]|[0.21]|
|**NRXGDP**|-0.20**|-0.07*|-0.27**|0.29*|0.45***|0.44**|-0.11**|-0.08|0.06|-0.15**|
|**- MFGSERV**|[0.10]|[0.04]|[0.11]|[0.15]|[0.15]|[0.20]|[0.05]|[0.10]|[0.14]|[0.06]|
|**DEINDU**|-0.91**|-0.01|-0.92**|0.58|1.17**|1.38**|-0.22|-0.18|0.15|-0.37*|
|**- MFGSERV**|[0.35]|[0.10]|[0.35]|[0.38]|[0.48]|[0.61]|[0.14]|[0.28]|[0.28]|[0.21]|
|Mean; Ctrls|20.7; Y|5.0; Y|25.7; Y|20.7; Y|25.3; Y|25.4; Y|6.5; Y|13.9; Y|13.0; Y|6.8; Y|

_Notes:_ Observations = 61 countries. This table shows the correlation between the employment share of each sector in urban areas c. 2000 and measures of natural resource exports, industrialization/FIRE-ization, and deindustrialization, also defined with respect to 2000. (3) SUM = MFG + FIRE. Robust SE.

Table 4: Resources, (De-)Industrialization & Urban Informality, Cross-Section, c. 2000

|Dep.Var. =|Empl. S|h. in Urba|n Empl.|Empl. S|h. in Urba|n MFG|Empl|. Sh. in Ur|ban NTR|
|---|---|---|---|---|---|---|---|---|---|
|Type of<br>Employment:|Wage<br>Work|Self<br>Empl.|Unpaid<br>Empl.|Wage<br>Work|Self<br>Empl.|Unpaid<br>Empl.|Wage<br>Work|Self<br>Empl.|Unpaid<br>Empl.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|MFGSERV|0.54*|-0.62**|0.08|1.00***|-0.91***|-0.09|0.63*|-0.84***|0.21|
||[0.28]|[0.24]|[0.07]|[0.30]|[0.25]|[0.07]|[0.31]|[0.26]|[0.14]|
|NRXGDP|0.06|-0.22|0.16|0.56|-0.52|-0.05|-0.14|0.00|0.14|
||[0.37]|[0.28]|[0.12]|[0.37]|[0.32]|[0.09]|[0.43]|[0.40]|[0.13]|
|DEINDU|-0.33|0.19|0.15|-0.47|0.31|0.16|0.52|-0.88|0.36*|
||[0.69]|[0.57]|[0.17]|[1.08]|[0.90]|[0.21]|[0.88]|[0.80]|[0.21]|
|**NRXGDP**|-0.48|0.40|0.08|-0.44|0.39|0.05|-0.77**|0.84**|-0.07|
|**- MFGSERV**|[0.31]|[0.24]|[0.09]|[0.35]|[0.29]|[0.08]|[0.33]|[0.32]|[0.11]|
|**DEINDU**|-0.88|0.80|0.07|-1.47|1.22|0.25|-0.10|-0.05|0.15|
|**- MFGSERV**|[0.72]|[0.62]|[0.15]|[1.07]|[0.90]|[0.20]|[0.83]|[0.79]|[0.15]|
|Mean|58.0|34.8|7.2|61.2|33.9|5.0|35.8|58.4|5.8|
|Obs.; Ctrls|55; Y|55; Y|55; Y|54; Y|54; Y|54; Y|54; Y|54; Y|54; Y|

_Notes:_ This table shows the correlation between the employment share of each type of employment in urban areas or specific sectors in urban areas c. 2000 and measures of natural resource exports, industrialization/FIRE-ization, and deindustrialization, also defined with respect to 2000. NTR = nontradables (domestic wholesale and retail trade). Robust SE. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
# PRE Workiog Paper Series

||iwa||Dthor|Contact<br>papr|
|---|---|---|---|---|
|WPS388|The Distortionary Effects of Tariff<br>Exemptions in Argentina|Faezeh Foroutan|March 1990|S. Fallon<br>38009|
|WPS389|Monetary Cooperation In the CFA<br>Zone|Patrick Honohan|March 1990|Wilai Pitayato-<br>nakam<br>37666|
|WPS390|Price and Monetary Convergence<br>in Currency Unions: The Franc and<br>Rand Zones|Patrick Honohan|March 1990|Wilai Pitayato-<br>nakam<br>3766R|
|WPS39,|An Acounting Framework to Assess<br>Wealth Effects of Voluntary Debt<br>R&duction: Some Applications to<br>Latin America|Dariel Oks|||
|WPS392|Institutional Development at the<br>Sectoral Level: A Cross Sectoral<br>Review of World Bank Projects|Samuel Paul|||
|WPS393|Debt-for-Nature Swaps|Michael Occhiolini|March 1990|S. King-Watson<br>31047|
|WPS394|Threshold Effects in International<br>Lending|Mark M. Spiegel|||
|WPS395|The Rope and the Box: Gambian<br>Saving Strategies and What They<br>Imply for International Aid in the<br>Sahel|Parker Shipton|||
|WPS396|Strategic Trade Policy: How New?<br>How Sensible?|Max Corden|||
|WPS397|Antidumping Regulations or<br>Procartel Law? The EC Chemical<br>Cases|Patrick A. Messerlin|||
|WPS398|Agricultural Extension for Women<br>Farmers in Africa|Katrine A. Saito<br>C. Jean Weidemann|||
|WPS399|Macroeconomic Adjustment,<br>Stabilization, and Growth in<br>Reforming Socialist Economies:<br>Analytical and Policy Issues|Andres Solimano|||
|WPS400|Macroeconomic Constraints for<br>Medium Term Growth: A Model for<br>Chile|Andres Solimano|||
|Rural Poor – Chriqui|105.6|3.5%|132|1.1%|1,250|
|---|---|---|---|---|---|
|Rural Non-Poor Chiriqui|120.0|3.9%|359|3.1%|2,993|
|Rural Poor – Other Panama|513.9|16.8%|520|4.5%|1,012|
|Rural Non-Poor Other Panama|236.8|7.7%|584|5.1%|2,468|
|Urban Poor|370.9|12.1%|575|5.0%|1,550|
|Urban Non-Poor|1,483.9|48.5%|8,941|77.5%|6,025|
|Rural Poor|757.5|24.8%|800|6.9%|1,055|
|Rural Non-Poor|447.3|14.6%|1,220|10.6%|2,728|
|**All Panama**|**3,059.6**|**100.0%**|**11,536**|**100.0%**|**3,770**|

_Source:_ Authors’ calculations from 2003 Panama SAM.

Table 6 shows the effects of a 1 dollar exogenous increase in value added from the various tradable goods sectors. For example, on average, a US$1 increase in the output of goods and services from the Zone Colon leads to an additional US$0.42 increase in value added from non-traded goods and services (national economy simulation).<sup>17</sup> This gain is due mainly to consumer spending effects as incomes earned in various activities are spent in the domestic economy. Multipliers are smallest (1.30 to 1.64) in sectors such as the Panama Canal sector, mining and textiles) where there are few production linkages (much of the inputs are imported; much of the outputs are exported) and much of the income accrues to formal capital (enterprises).<sup>18</sup> In contrast, the multipliers for the fruits, shellfish and other agricultural exports are especially large because much of the income earned accrues to rural households who spend a high proportion of their incomes on nontradable goods and services in the local economy. The multiplier for the tourism industry (hotels and restaurants) is the largest of all the sectors: an additional US$1 in value added (approximately US$2.80 in total tourism spending) results in a total US$2.87 in total incomes. This large multiplier is due to strong backward linkages in terms of demand for local food products as well as forward linkages of household spending.<sup>19</sup>

> 17 The value added multipliers vary little across region because the input-output structure and patterns of factor payments to households are assumed to be the same across regions (since regional input-output tables are not available).

> 18 In these multiplier simulations, investment is held constant and income gains to enterprises are not distributed as dividends (income) to domestic households.

> 19 These simulations may understate the multiplier somewhat because the hotels and restaurants is exogenously fixed in supply. Thus, there are no demand linkage multiplier effects for this sector (apart from the initial simulated increase in demand and output). Note that in the regional simulations, it is assumed the all demand for elastically supplied goods and services are assumed to be met from firms within the region.

14
WPS3856

# **Creating an Efficient Financial System: Challenges in a Global Economy**

## **Thorsten Beck**

**Abstract:** Financial sector development fosters economic growth and reduces poverty by widening and broadening access to finance and allocating society’s savings more efficiently.  This paper first discusses three pillars on which sound and efficient financial systems are built: macroeconomic stability and an effective and reliable contractual and informational frameworks. The paper then describes three different approaches to government involvement in the financial sector: the laissez-faire view, the market-failure view and the market-enabling view.   Finally, the paper analyzes the sequencing of financial sector reforms and discusses the benefits and challenges that emerging markets face when opening their financial systems to international capital markets.

### World Bank Policy Research Working Paper 3856, February 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* The author is with the World Bank’s research department. This paper was written for the G20 Seminar on Economic Growth in Pretoria, August 2005. The author is grateful to Robert Cull, Asli Demirguc-Kunt and Patrick Honohan for useful comments and Ed AlHussainy for outstanding support.

1
**The control group** did not receive any intervention under this study, including information and pharmacy vouchers. However, the ongoing mass media campaign funded through the MOH provided information on the prevalence of diabetes and hypertension and encouraged adults to screen for free for hypertension and diabetes in the local primary health care clinic. These campaigns involved broadcasts on public and private television stations, billboards on major roads, posters in health facilities and post offices, and text messages from the MOH.

# **_Experimental Methods_**

# A. **_<u>Study sample and randomization</u>_**

The experimental sample drew on the administrative records of the PHC clinics in the public sector Armenia. Health clinic administrative records in Armenia are relatively complete and of high quality, as the e-health system is nationwide in scope and is updated following the patients’ visits. The health sector is dominated by public health facilities, particularly outside Yerevan. In addition, 100 percent of the rural population and 85 percent of the urban population is registered for care at the PHC level. Hence, the e-health database is likely to have information on most of the diabetes and hypertension screenings conducted in Armenia, particularly at the PHC level.

After selecting public health facilities in 4 provinces (Ararat, Armavir, Kotayk and Lori) chosen on the basis of their infrastructure and NCD prevalence and local culture affecting behavior and screening rates and which were sufficient to reach the desired sample size of 2,000 individuals, the experimental sample was randomly drawn from the patient records, proportionally to the size of the catchment population of the health facilities. A total of 6,934 individuals were selected from the registered population of the selected PHC facilities. The inclusion criteria of subjects selected from the e-health database were individuals between the age of 35 and 68 years old, and who had not been screened for diabetes or hypertension in the last 12 months. From this sampling list, individuals were randomly assigned to the 5 study arms of the study.

After the randomization, potential study participants were contacted in two phases. The first phase, conducted by phone, was the eligibility verification. The second phase, conducted face-toface, was the informed consent. These two phases took place between June and September 2019 for the 4 intervention groups and between January 10 and February 20, 2020, for the control group.

For the eligibility verification, the fieldworkers contacted by phone all 6,934 individuals to verify whether they could be reached, were available and eligible for the study. The results of the verification indicated that: 1,966 (28.4 percent) were out of reach, had the wrong contact information or could not be located, 1,893 (27.3 percent) were temporarily out of their residence, 386 (5.6 percent) had already been screened at one of the selected PHC facilities during the 11