rafmacalaba/datause-encoder-data huggingface dataset:
- Holz, C. A., & Sun, Y. (2018). Physical Capital Estimates for China’s Provinces, 1952–2015 and Beyond. China Economic Review, 51(October), 342–57.

- Hu, W., Ho, M. S., & Cao, J. (2019). Energy Consumption of Urban Households in China. China Economic Review, 58(December), 101343.

- IRENA. (2019a). Future of Solar Photovoltaic: Deployment, Investment, Technology, Grid Integration and Socio-Economic Aspects. A Global Energy Transformation: Paper. Abu Dhabi: International Renewable Energy Agency.

- IRENA. (2019b). Future of Wind: Deployment, Investment, Technology, Grid Integration and Socio-Economic Aspects. A Global Energy Transformation: Paper. Abu Dhabi: International Renewable Energy Agency.

- Jomini, P., Zeitsch, J. F., McDougall, R., Welsh, A., Brown, S., Hambley, J., & Kelly, J. (1991). SALTER: A General Equilibrium Model of the World Economy. Database and Parameters, Industry Commission, Canberra.

- State Information Center. (2020). Medium and Long-Term Goals, Strategies, and Paths of China’s Economic and Social Development. Beijing: State Information Center.

- Ross, M. T. (2014). Structure of the Dynamic Integrated Economy/Energy/Emissions Model: Computable General Equilibrium Component, DIEM-CGE. NI WP 14-12. Durham, NC: Duke University.

- RTI International. (2015). The Applied Dynamic Analysis of the Global Economy (RTIADAGE) Model. U.S. Regional Module Final Release.

- Shi, G., Zheng, X., & Song, F. (2012). Estimating Elasticity for Residential Electricity Demand in China. The Scientific World Journal.

- Zhang, L., Brooks, R., Ding, D., Ding, H., He, H., Lu, J., & Mano, R. (2018). China’s High Savings: Drivers, Prospects, and Policies. International Monetary Fund.

67
# **III. Fiscal Accountability and Transparency**

Because tax expenditures are government spending (channeled through the tax system), they should be subject to the same fiscal accountability and transparency criteria as direct expenditures. This section reviews the practice and the extent of the challenges facing governments (of both developed and of developing countries) in applying the principles of fiscal accountability and transparency to tax expenditures.

## **_A. Fiscal accountability_**

Under internationally accepted principles, fiscal accountability entails a state being held responsible by its people and by its elected bodies for its financial choices and for its actions in using public funds. Every government has an obligation to its people and to its elected bodies to provide a full and complete accounting of the way the government has discharged its responsibilities. According to Campos/Pradhan model (Campos and Pradhan, 1996), fiscal accountability consists of three goals or levels of expenditure management for which governments should be accountable:

- Level 1: overall control, within sustainable limits, involving medium-term expenditure frameworks (MTEF), financial discipline and compliance with laws and budgets.

- Level 2: strategic prioritization of resource allocation.

- Level 3: efficient and cost-effective management of programs.

To analyze fiscal accountability under the three levels above, data (on estimated and projected tax expenditures) are essential to enable governments to achieve the accountability criteria on tax expenditure spending. For that purpose, normative or benchmark tax structures and tax expenditures must be identified from the tax structures. Thus, tax expenditures can be estimated and projected.

However, both estimates and projections of tax expenditures, as well as tax benchmarks/norms, are not always available in some countries. These countries have different practices in establishing normative tax structures and estimating and projecting tax expenditures. In this paper, we divided countries into three groups according to country practice. In group 1, countries have identified both normative or benchmark tax structures and tax expenditures, and produced estimates and projections for all tax expenditures. In that group, most countries are high income OECD member states (including Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Republic Korea, the Netherlands, Norway, Sweden, Switzerland, United Kingdom, United States<sup>15</sup> ). In group 2, countries may or may not have identified normative/benchmark tax structures, nor tax expenditures, but they have produced partial estimates of tax expenditures (considered significant either financially or politically), with or without projections. In that group, most countries are transition economies or

> 15 This list may not be complete.

14
| POLICY RESEARCH WORKING PAPER 2467

# Summary findings

Laeven uses panel data on 394 firms in 13 developing countries for the years 1988-98 to learn whether financial liberalization relaxes financing constraints on firms. He finds that liberalization affects small and large firms differently.

Small firms are financially constrained before liberalization begins but become less so after liberalization. The financing constraints on large firms, however, are low both before and after liberalization. The initial difference between small and large firms disappears over time.

Laeven hypothesizes that financial liberalization has little effect on the financing constraints of large firms because they have better access to preferential directed credit in the period before liberalization.

Financial liberalization also reduces imperfections in financial markets, especially the asymmetric information costs of firms' financial leverage.

Countries that liberalize their financial sectors tend to see dramatic improvements in political climate as well. Successful financial liberalization seems to require both the political will and the ability to stop the preferential treatment of well-connected, usually large, firms.

This paper-a product of the Financial Sector Strategy and Policy Department-is part of a larger effort in the department to study the benefits and risks of financial liberalization. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rose Vo, room MC9-624, telephone 202-473-3722, fax 202-5222031, email address hvol@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The author may be contacted at llaeven@worldbank.org. October 2000. (49 pages)

**_The Policy Researcb Working Paper Seoes 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 autbors and sbould 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._**

Produced by the Policy Research Dissemination Center
- 32 -

Table 6 Agricultural Labor Force (AL) **and** Share of Manufactured Zxperts (SM) (percentages)

||19|70|19|80|1985|
|---|---|---|---|---|---|
||AL|SM|AL|SM|SM|
|Latin<br>America||||||
|Argentina<br>|16.0|13.9|13.0|23.2|21.3|
|Brazil|44.9|16.0|31.2|39.2|49.7|
|Colombia|39.3|8.0|34.2|20.4|20.8|
|Chile|23.2|4.4|16.5|9.5|8.5|
|Mexico|44.1|27.6|36.5|14.2|24.0|
|Peru|47.1|1.5|40.0|17.1|21.4|
|Africa||||||
|**Kenya**|84.8|11.2|81.0|15.1|12.8|
|Morocco|57.6|10.0|45.6|13.2|38.9|
|Malaiw|90.5|11.4|83.3|13.4|11.0|
|Nigeria|71.0|1.4|68.1|0.5|0.6|
|Tanzania|n.a.|<br>12.9|**n.a.**|<br>15.8|15.2|
|Zambia|76.6|0.8|73.1|0.9|2.5|
|Zimbabwe|77.3|15.9|72.8|28.4|34.5|
|**Asia**||||||
|India|71.7|52.4|69.7|62.0|70.8|
|Hong Kong|4.4|93.4|2.1|92.0|92.7|
|Korea|49.1|82.1|36.4|90.6|91.4|
|Pakistan|58.9|30.9|54.6|53.7|58.5|
|Singapore|3.4|30.9|1.6|53.7|58.5|
|Sri Lanka|55.3|1.7|53.4|18.8|34.6|
|Europe||||||
|Greece|42.2|35.3|30.9|46.8|49.4|
|Portugal|31.8|64.1|25.8|71.9|77.3|

Note2 There were no data on agricultural labor force AL: proportion of the total labor force.

SM: proportion of total exports.for 1985.

Source: World Tables, 1987, The World Bank.
For the timing of downward mobility, we divide the seventeen-year period into several subperiods and create measures of mobility for each (a larger estimate of downward mobility for later periods, for example, indicates more recent downward mobility affects support for redistribution more than downward mobility a long time ago does). For the persistence of downward mobility, we create measures of mobility by the number of years an individual experienced shocks (a larger estimate for more frequent shocks indicate the more persistent shocks an individual experienced, the stronger her support for redistribution will be).

Because the lack of natural experiments we can exploit and the fact that, to some extent, downward mobility experiences in Eastern Europe and Central Asia in the 1990s and early 2000s are beyond the control of any individual, we use regression-control strategies to estimate the effects of downward mobility on support for redistribution. We include all available individual- and household characteristics in LITS I as well as primary sampling unit (PSU) fixed effects in the regressions, the latter to control for time-invariant, both observed and unobserved, determinants of support for redistribution that may correlate with downward mobility. To the extent that downward mobility is exogenous conditional on these individualand household characteristics and the PSU fixed effects, a regression of support for redistribution on downward mobility and the control variables provides an unbiased estimate of the effects of downward mobility.

We find downward mobility increases support for redistribution, but only the more intensive one: Downward mobility experience in which an average individual had to respond by taking extreme measures increases support for redistribution by about three percent. We also find the timing and persistence of downward mobility matter: Recent bad years matter more than earlier bad years do; more persistence bad years matters more too. (One bad-year in the last four years during the period of analysis increases support for redistribution about three times as large as the average effect; experiencing nine bad-years or more increases support for 5
In essence, the normative debate and its advances are still fairly far from the actual criteria used by donors to allocate aid. Also, while poverty reduction has increased in importance among the overarching development priorities, the actual allocation of aid is not driven mainly by poverty. These outcomes are partly explained by the fact that the measurement of poverty requires instruments that have only recently become widely available. When the World Development Report on poverty was published in 1990 only a handful of countries carried out and made available household consumption surveys. Today, the World Bank has a database of over 1,700 household surveys many of which are for public use. We are now in a position to make much better use of micro data for allocating aid and targeting the poor whether we believe in efficiency, equity or structural criteria.

The IL methodology proposed makes an optimal use of the micro data available and complies to efficiency, equity and transparency criteria. It improves on the simple measurement of poverty by extending the analysis to the non-poor. The concept can help to improve the policy capacities of countries by highlighting the potential for redistributive policies, it complies to the equal opportunity principle by allocating aid according to the monetary capacity of a country to address its own poverty and it is transparent in that responds to principles well established in the tax and distribution theory literature.

# **3 Framework**

The idea that individuals should contribute _equally_ towards the expenses of the government dates back to Mill (1848) theory of _equality of sacrifice_ .

_For what reason ought equality to be the rule in matters of taxation? For the reason that it ought to be so in all affairs of government. As a government ought to make no distinction of persons or classes in the strength of their claims on it, whatever sacrifices it requires from them should be made to bear as nearly as possible with the same pressure upon all, which, it must be observed, is the mode by which least sacrifice is occasioned on the whole. If any one bears less than his_ fair _share of the burthen, some other person must suffer more than his share, and the alleviation to the one is not, ceteris paribus, so great a good to him, as_ 8
<mark>TABLE 6. Marginal Effects of the Father’s Absence on Children’s Schooling and Work</mark>

||School<br>Overall|School<br>Restri|Work<br>cted|School<br>Overall|School<br>Restri|Work<br>cted|School<br>Overall|School<br>Restri|Work<br>cted|
|---|---|---|---|---|---|---|---|---|---|
|Permanent Absence|−0.0412*|−0.0484*|0.0301+|−0.0422+|−0.0486+|0.0361|−0.0556**|−0.0641**|0.0360+|
||(0.0172)|(0.0216)|(0.0167)|(0.0233)|(0.0289)|(0.0233)|(0.0193)|(0.0234)|(0.0183)|
|Permanent Absence * Girl||||0.0021|0.0005|−0.0122||||
|||||(0.0241)|(0.0275)|(0.0245)||||
|Permanent Absence * Head High Education|||||||0.0742*|0.0851*|−0.0319|
||||||||(0.0313)|(0.0367)|(0.0295)|
|N|32,186|24,531|24,531|32,186|24,531|24,531|32,186|24,531|24,531|
|_Source: Familias en Accion_Evaluation Data.||||||||||

_<mark>Note:</mark>_ Marginal effects are presented from a fixed-effects linear-probability model (equation (1)), controlling for absence of father from household for unknown reason; absence of both parents; time dummies; cubic in child age; sibling composition; dummies for crop losses, illness, and business shocks; and the inverse mills ratio, as computed in equation (2) (see table S3). High education is one for incomplete secondary or more at baseline, zero otherwise. N is the number of children in the sample pooled across three waves. We included 6,146 households in our initial sample, from which 426 fathers have subsequently departed. Schooling is observed for all children in sample ≥7 years (overall sample); work is observed for children ≥10 years (restricted sample). Robust standard errors clustered at the municipality level are in parentheses. + significant at 10 percent; *significant at 5 percent; ** significant at 1 percent.

37
# **Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia**<sup>*****</sup>

Leonardo Iacovone, _World Bank_ William Maloney, _World Bank_ David McKenzie, _World Bank_ _Keywords:_ Management, Employment, Scaling-Up Interventions, Colombia. _JEL codes:_ O14, O32, L2, M2

> * The authors gratefully acknowledge the collaboration of Paula Toro Santana, the staff of CNP and DNP in Colombia, and project management and research assistance provided by Cosma Gabaglio, Camilo Andrés Gutiérrez Silva, Pablo Villar, María Aránzazu Rodríguez Uribe and Innovations for Poverty Action Colombia. Funding is gratefully acknowledged from the DIME i2i Trust Fund, the Knowledge for Change Program (KCP), the World Bank and the IPA SME Initiative, as well as intervention funding from SENA. This study is registered in the AEA RCT registry AEARCTR-0000528. Since no identifying information was collected on human subjects, the study was exempted from the Innovations for Poverty Action IRB. Comments from Miriam Bruhn, Jasmin Chakeri, Siddharth Sharma participants in seminars at George Mason, the IDB, the Management Practices in the Private and Social Sectors conference, MIT Sloan, PEDL, and Williams College are greatly appreciated.
Table 25

|Dependent variable: N|eed of fisc|al adjustme|nt based o|n debt/rev|enue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.003***|0.002***|0.003***|0.001|0.006|0.005|-0.005|0.008|
|(% of GDP)|(0.001)|(0.001)|(0.001)|(0.001)|(0.004)|(0.006)|(0.004)|(0.005)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.568***||-1.939***|-1.862***|-1.923***|-1.890***|-1.918***|
|||(0.352)||(0.361)|(0.370)|(0.363)|(0.369)|(0.359)|
|Inflation (t-1)||-0.065||0.064|0.088|0.066|0.078|0.060|
|||(0.107)||(0.110)|(0.108)|(0.109)|(0.106)|(0.108)|
|U.S. Interest rate||-0.006||-0.005|-0.007|-0.006|-0.007|-0.005|
|||(0.010)||(0.011)|(0.012)|(0.012)|(0.012)|(0.011)|
|**Political factors**|||||||||
|Gini|||0.002|0.002|0.002|0.004|0.002|0.002|
||||(0.002)|(0.002)|(0.001)|(0.003)|(0.001)|(0.001)|
|Rule of law|||0.008|0.016|0.018|0.015|-0.018|0.014|
||||(0.010)|(0.011)|(0.013)|(0.011)|(0.024)|(0.011)|
|Margin of Majority|||-0.046|-0.039|-0.034|-0.033|-0.029|0.154|
||||(0.081)|(0.084)|(0.089)|(0.085)|(0.087)|(0.183)|
|Election's year|||-0.011|-0.017|-0.014|-0.017|-0.015|-0.018|
||||(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|**Interactions**|||||||||
|EMEs|||||0.189<br>(0.129)||||
|EMEs * Rigidity|||||-0.007*<br>(0.004)||||
|Gini * Rigidity||||||-0.000<br>(0.000)|||
|Rule of law * Rigidity|||||||0.001*<br>(0.001)||
|Majority * Rigidity||||||||-0.011<br>(0.008)|
|Observations|3,046|2,911|2,066|2,007|2,007|2,007|2,007|2,007|
|Country FE|No|No|No|No|No|No|No|No|
|r2_p|0.00427|0.0180|0.00586|0.0219|0.0248|0.0221|0.0232|0.0229|
|Robust standard error<br>***p<0.01,**p<0.05|s clustered<br>,*p<0.1|by country|in parenth|eses|||||

- 54 -
|**Measure**|**M1**<br>**M3**|**M4**<br>**Sector**<br>**scope**|
|---|---|---|
|**B.**<br>**Conditions on operations**|||
|**1.**<br>**Conditions on supply of service**|||
|Limits on scope of service (numerical and non-numerical)<br>|x<br>x|x<br>COM<br>|
|Regulations limit the range of products a retailer may carry|x|SS|
|Restrictions on the type of vessels<br>|x|SS<br>|
|Restrictions on the type of cargo transported (other than cargo reservations)|x|SS|
|Restrictions on the quantity of cargo transported (other than cargo reservations)<br>|x|SS<br>|
|Limits oncabotage: Exclusion foreign-flagged ships (orothercriteria, suchasforeign-built ships)|x|SS|
|Licences for department stores or large-store formats are subject to quotas or economic needs tests<br>|x|SS|
|Some banking services reserved for domestic suppliers|x<br>x|x<br>SS|
|Some insurance activities are reserved for domestic suppliers|x<br>x|x<br>SS|
|Restrictions on internet banking|x|SS|
|Repositioning ofequipmentrestricted|x|SS|
|Feedering restricted|x|SS|
|Bilateral/plurilateral cargo sharing agreements|x|SS|
|Limits onexpansionofoperations<br>|x|COM<br>|
|Restrictions on the number of outlets|x|SS|
|Number of branches restricted|x|SS|
|Number of ATMs restricted|x|SS|
|Only domestic banks can establish their own ATM networks<br>|x|SS<br>|
|Quota for private cargo: Bulk|x|SS|
|Quota for private cargo: Liner<br>|x|SS|
|Approval by regulatory authority required for new products or services|x<br>x|x<br>COM|
|Approvalby theregulatory authorityrequiredfor newproducts orservices:compulsoryinsurance<br>|x<br>x|x<br>SS<br>|
|Use of foreign firm names prohibited|x|COM|
|Use of foreign firm names is allowed if alongside that of the local partner<br>|x|SS<br>|
|Conditions onprices/fees/rates|x<br>x|x<br>COM|
|Approval by the regulatory authority required for new rates and fees|x<br>x|x<br>COM|
|Recommended minimum and/or maximum prices|x<br>x|x<br>COM|
|Mandatoryminimumand/or maximumprices|x<br>x|x<br>COM|
|Retail roaming rates are regulated|x|SS|
|Interconnection prices and conditions are regulated|x|SS|
|Local loop unbundling prices regulated|x|SS|
|Wholesale access prices regulated|x|SS|
|Wholesale roaming rates are regulated<br>|x|SS|
|Mobile termination rates are regulated|x<br>x|x<br>SS|
|Vertical agreements: Resale price maintenance is subject to regulation|x|SS|

34
# **TableA1. Factors affecting the household's probability to live in the GMA and, once in** **<u>the GMA, the probability to participate in the CRB/VAG</u>**

|||Probitmode|lsfortheh|ousehold's proba<br>Participate|bility to<br>in the|
|---|---|---|---|---|---|
|Variable|Variable description|Liveinthe|GMA|VAG/CR|B<sup>_a_</sup>|
|||(1)<br>||(2)<br>||
|cons|Intercept|0.707<br>|**|-2.612<br>|***|
|||(0.279)||(0.262)||
|park2|Kafue dummy variable|-1.099|***|-0.247||
|||(0.332)||(0.190)||
|park3|Lower Zambezi dummy|-0.523<br>(0.330)||-0.123<br>(0.106)||
|park4|Luangwa dummy|-0.512||-0.336|***|
|||(0.340)||(0.121)||
|hage|Age of the household head in years|-0.006<br>(0.003)|*|0.001<br>(0.006)||
|fhhh|Female-headed household dummy|0.193|**|-0.018||
|||(0.088)||(0.138)||
|maxedu|Education of the most educated household<br>member in years|-0.033<br>(0.013)|**|0.034<br>(0.015)|**|
|c0to14|Number of children below 15 years|-0.026<br>(0.019)||0.015<br>(0.030)||
|f15to60|Number of female members 15-60 years old|0.009<br>(0.039)||0.071<br>(0.076)||
|m15to60|Number of male members 15-60 years old|0.058<br>(0.036)||0.055<br>(0.071)||
|m61plus|Number of members older than 60 years|-0.062<br>(0.075)||-0.125<br>(0.158)||
|kroad|Distance to the nearest main road in km|0.008<br>(0.004)|*|0.008<br>(0.002)|***|
|kbsch|Distance to the nearest basic school in km|0.004<br>(0.010)||-0.011<br>(0.010)||
|kheal|Distance to the nearets health centre in km|-0.002<br>(0.004)||0.006<br>(0.004)|*|
|vacast|Value of consumer durable assets in ZMK|-0.020<br>(0.008)|**|-0.003<br>(0.018)||
|dcoop|Participation in cooperatives dummy|-0.388<br>(0.098)|***|0.552<br>(0.157)|***|
|nproj|Number of projects in the community|0.042<br>(0.019)|**|0.055<br>(0.017)|***|
|dcfnd|CRB funded dummy variable|0.617|***|0.829|***|
|||(0.159)||(0.155)||
|npart|Number of participants in the CRB/VAG|0.097<br>(0.058)|*|0.203<br>(0.019)|***|

> _a_ Based only on the sub-sample of households that are located in the GMAs Level of significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

16
Figure 4. Inequality extraction ratios with b=0 and b=0.5 <!-- Start of picture text --> Inequality extraction ratio, Inequality extraction ratio, USA<br>England/UK 1688-2010 1774-2009<br>100 100<br>90 90<br>80 With s=300 and b=0.5 80 With s=300 and b=0.5<br>70<br>70 With s=300 and b=0<br>60<br>60<br>50<br>50<br>With s=300 and b=0<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10 0<br>1750 1800 1850 1900 1950 2000 2050<br>0<br>1600 1700 1800 1900 2000 2100 Year<br>Year<br>Inequality extraction ratio Inequality extraction ratio<br><!-- End of picture text --> Source: See notes to Table 1.

13