rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1. Technological innovation**

Method of estimation: Ordinary Least Squares with Robust Standard Errors

||Pers<br>(pe|onal Compu<br>r 1,000 peop|ters<br>le)|I<br>(pe|nternet Use<br>r 1,000 peo|rs<br>ple)|
|---|---|---|---|---|---|---|
|Regulatory Freedom|25.48*|-132.01**|-67.26**|26.44**|-77.05**|-12.42|
|(index of Credit, Labor and Business|[1.97]|[3.09]|[2.25]|[2.06]|[2.66]|[0.51]|
|Regulation, by The Fraser Institute: higher,<br>less regulated; country average)|||||||
|Governance|110.07**|-138.22*|102.11**|83.02**|-73.24|81.66**|
|(simple average of ICRG Law and Order,|[7.39]|[1.74]|[6.86]|[5.04]|[1.59]|[5.02]|
|Bureaucracy Quality and Corruption indices:<br>higher, better governance; country average)|||||||
|Schooling|28.96**|33.94**|-41.49|26.08**|28.53**|-4.41|
|(average schooling years in the population aged<br>15 and over, from Barro and Lee (2001);<br>country average)|[3.66]|[4.70]|[1.42]|[3.07]|[3.61]|[0.21]|
|Regulatory Freedom * Governance||39.85**<br>[3.27]|||26.20**<br>[3.44]||
|Regulatory Freedom * Schooling|||12.67**<br>[2.56]|||5.41*<br>[1.68]|
|No. of observations|83|83|83|90|90|90|
|R-squared|0.78|0.82|0.81|0.75|0.77|0.75|

Notes: 1. t-statistics are presented below the corresponding coefficients. * and ** denote significance at the 10 percent and 5 percent levels, respectively. Constant terms are included but not reported. 2. Dependent variables are measured as average of the period 2003-04. Explanatory variables are measured as of mid 1990s. 3. Data on dependent variables are from World Development Indicators and on explanatory variables, as indicated below each variable.

We can use the estimated coefficients to evaluate the effect of an increase in regulatory freedom on technological innovation. For this purpose, we need to consider the coefficients on both the interaction term and regulatory freedom itself.<sup>7</sup> Given that the total impact depends on 7 The regression with interaction terms implies the following formula for the point estimate of the total effect of a change of regulatory freedom on either proxy of technological innovation, Δ _Tech = (βREGFREE + βINT DEV)_ Δ _REGFREE_ Where _Tech_ represents either personal computers or internet users, _DEV_ represents either governance or schooling, _REGFREE_ indicates regulatory freedom, the symbol Δ denotes change, and the parameters _βREGFREE_ and _βINT_ are the estimated regression coefficients on, respectively, regulatory freedom and the 11
change (e.g., household members can die, split off to form a new household, or simply migrate to another place) and it is very costly to track all household members over time. For example, due to attrition, the percentage of households that remain in the panel Russia Longitudinal Monitoring Survey (RLMS) in the first 10 years after it was fielded is around 60 percent; this figure further decreases by half to 29 percent after another 10 years (Kozyreva, Kosolapov, and Popkin, 2016). But as global living standards are rising, this data situation can be improved as more resources may be invested in fielding panel surveys.

# **Imputation Methods**

We briefly show in Table 1 (last column) the imputation methods that can be used to provide poverty estimates in the absence of consumption data. These imputation techniques vary depending on whether the consumption data in need are cross-sectional or panel. For data situations in Group A, the most commonly used method is to generate a wealth index from household assets and the physical characteristics of the house (e.g., the material of the floor or the wall, or which type of toilet is available). For data situations in Group B, techniques have been developed to offer survey-to-survey imputation (i.e., imputation from one survey to another) for sub-cases (i) and (ii), and survey-to-census imputation (i.e., imputation from a survey into a census) for sub-case (iii). Finally, data situations in Group C can be addressed with recently developed methods that construct synthetic panel data from cross-sectional data, which can substitute for actual panel data to some extent. We return to more detailed discussion of each of these imputation methods in the following section.

# **II.2. Analytical Framework**

Following Deaton and Muellbauer (1980), we assume that a household maximizes utility subject to an income budget constraint that includes choice variables such as quantities of goods, durables, and leisure (or labor supply). These in turn are determined by different factors, such as 10
# **V. Conclusions**

Based on our survey of regulatory and supervisory policies in 107 countries, this paper makes two contributions. First, we assess two broad theories of bank regulation and supervision. The helping-hand view holds that governments implement rigorous, official oversight of bank activities to alleviate market failures and thereby enhance bank performance and stability. In contrast, the grabbing-hand view holds that countries that implement rigorous, official oversight of banks produce higher levels of government corruption _without_ a corresponding improvement in bank performance or stability. Second, this is the first paper to examine an extensive list of specific regulatory/supervisory policy debates for a broad cross-section of countries. Since the central issues in bank regulation and supervision are interrelated, our comprehensive dataset makes it possible to conduct a unified assessment of bank regulation and supervision.

The results are generally inconsistent with the helping-hand view of regulation and more consistent with the grabbing-hand view of government. In contrast to the helping-hand view, tighter entry regulations, restrictions on bank activities, powerful supervisory agencies, deposit insurance, and government ownership of the banking industry are not positively associated with bank performance or stability. Indeed, the findings are more consistent with the grabbing-hand view. Regulatory barriers to bank entry, regulatory restrictions on bank activities, greater supervisory power, and government ownership of banks are positively associated with government corruption. This is consistent with the grabbing-hand argument that strong government regulation and supervision will not focus on easing market failures and improving bank performance and stability. Interestingly, greater political openness does mitigate the impact of increasing supervisory powers, both in terms of the impact of the latter on corruption and bank development, but only where openness is particularly advanced; for most countries, greater supervisory powers go with greater corruption and worse outcomes for bank development. This is a nice illustration of the need for caution in exporting 'best practices' from industrial to emerging market countries, as weaknesses in emerging market institutions may literally turn the results on their head.

The evidence suggests that regulatory and supervisory strategies that focus on empowering the private sector and limiting the adverse incentive effects from generous deposit insurance work best to promote bank performance and stability. Countries without excessively generous official deposit insurance regimes have greater bank development and less bank fragility. Countries that 39
Households in the two highest MIPP quintiles pay most of the taxes calculated in this exercise. In fact, the richest 20% of the population pays close to 95 of the observed income tax. This group also pays more than half of indirect taxes (Figure 8). Notably, the observed low concentration of indirect taxes among the poorest households can be consistent with a high incidence of indirect taxes. Although the poorest 20% pay about 4% of these taxes, their share of MIPP is only 1.4%. In addition, indirect taxes are, on average, equivalent to 16.7% of households’ market income plus pensions.

**Figure 7. Incidence of taxes by quintile of per capita market income plus pensions (% of market income plus pensions)**

<!-- Start of picture text --> 0<br>-10<br>-20<br>-18.3 -18.1<br>-19.9<br>-30 -22.7<br>-40<br>-50<br>-52.0<br>-60<br>1 2 3 4 5<br>Quintiles of per capita market income plus pensions<br>Indirect Taxes Direct Tax IPTU/ITR Direct Taxes Total<br>pensions)<br>Incdence (% of Market Income plus<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: IPTU stands for Imposto sobre a Propriedade Territorial Urbana, while ITR stands for Imposto sobre a Propriedade Territorial Rural. Notes: the bars represent the distribution of payments within each quintile.

**Figure 8. Concentration of taxes (% of fiscal interventions)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Quintiles of per capita market income plus pensions<br>Indirect Taxes Direct Tax IPTU/ITR Direct Taxes MIPP<br>Concentration shares (% )<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: IPTU stands for Imposto sobre a Propriedade Territorial Urbana, while ITR stands for Imposto sobre a Propriedade Territorial Rural. Notes: The bars represent the share of each tax paid by a given quintile.

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# **Appendix A. Housing types and income groups**

|**Income**<br>**group**|**Annual income range**<br>**in 2011 (ZAR)**|**Average 2011 income**<br>**(ZAR, estimated**<br>**using Census)**|**Percentage of the**<br>**total population in**<br>**2011 (estimated using**<br>**Census)**|
|---|---|---|---|
|1|1 - 38,200|19,580|38.6%|
|2|38,200 - 76,400|57,300|16.7%|
|3|76,400 - 307,600|170,140|28.9%|
|4|> 307,600|780,723|15.8%|

Table A.1: Income groups used in the simulation

|**Housing**<br>**types**|**Income group(s)**|**Location**|**Dwelling size**<br>**(plot size for**<br>**formal**<br>**subsidized)**|**Price**|
|---|---|---|---|---|
|Formal private<br>(FP)<br>Formal<br>subsidized<br>(FS)|1,2,3,4<br>1|Endogenous<br>Exogenous|Endogenous,<br>with minimum<br>dwelling size<br>Fixed (40_m_<sup>2</sup>)|Endogenous<br>Free|
|Informal in|1,2|Endogenous|Fixed (20_m_<sup>2</sup>)|Endogenous|
|backyard (IB)||within the<br>backyards of FS<br>plots|||
|Informal in<br>informal|1,2|Exogenous<br>settlement|Fixed (20_m_<sup>2</sup>)|Endogenous|
|settlement(IS)||locations|||

Table A.2: Modeling assumptions regarding housing 27
<!-- Start of picture text --> Are positions like yours generally advertised to the public? (yes/no)<br>(Bolivia)<br>In your organization, would you say that the employees are hired more often for:<br>1. Performance on an examination<br>2. Personal connections<br>3. Political connections<br>4. Famnily relations<br>5. Payment or gift to some public authorities<br>6. Good CV<br>(Bolivia)<br>Does your organization have Individual Annual Operations Plans (POAI) for its employees? (yes/no)<br>(Bolivia)<br>Can an employee appeal a dismissal? (yes/no)<br>(Bolivia)<br>Does your organization have a Human Resource Management Infornation System (HMIS) containing records of<br>employees? (yes/no)<br>(East Caribbean States)<br>As far as you know, does your organization have a stated performance appraisal system? (yes/no)<br>(East Caribbean States)<br>Are the criteria for appraisal known to you? (yes/no)<br>(East Caribbean States)<br>b. budget management rules<br>For each of the following aspects of budget mnanagement, does your organization have formal, written guidelines<br>describing how these functions are to be canied out. If so, are they carried out in full, in part, or not at all? Please<br>answer on a scale of I to 4, where 1 conresponds to "not at all" and 4 corresponds to "yes, in full"<br>0 budget preparation<br>0 procurement<br>0 budget execution<br>O audit<br>0 production of fnal end-of-year accounts<br>(Macedonia)<br>What is your assessment of the existing formal, written guidelines related to budget managernent? Please tell us the<br>extent to which you agree with the following statements. Answer on a scale of I to 4, where I corresponds to 'strongly<br>disagree' and 4 corresponds to 'strongly agree'<br>* relatively simple to understand<br>* relatively flexible<br>* helpful for effective and efficient planning of programs/activities<br>(Macedonia)<br>Are there rules in place, which govem security, movernent, and storage of financial records?<br>(Macedonia)<br>Does your organization have an intemal audit function? (yes/no)<br>(East Caribbean States)<br>c. general rules<br>Are there rules in place, which govem security, movement, and storage of records? (yes/no)<br>(Macedonia)<br>Is there a fornal system of evaluating programs/ projects being implemented by your organization? (yes/no)<br>(East Caribbean States)<br>Policy Credibility<br>Agencies receive instructions and policies from different parts of the government, some comnplement each other, others<br>conflict. Overall, how do you see the relationship between these various instructions/ policies?<br>I. very consistent<br>2. more consistent than inconsistent<br><!-- End of picture text --> 42
# **Do school facilities matter? The case of the Peruvian Social Fund (FONCODES)**

Christina Paxson* and Norbert Schady** Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08544. Poverty Reduction and Economic Management, The World Bank, Washington, D.C. 20433.

IThis research was conducted as a background paper for the Social Funds 2000 ESW, and was partly funded by the Thematic Group on Impact Evaluation in the Poverty Group of the World Bank. We would like to thank Angus Deaton, Olivier Deschenes, Rutheanne Deutsch, Martin Ravallion, Julia van Domelen, Juliana Weissman, and participants at a seminar held at the World Bank and discussants at the NEUDC Conference held at Harvard on October 8th and 9 h, 1999 for helpful comments. We also wish to thank staff at FONCODES and INFES and Livia Benavides for help providing us with the data. Please address correspondence to the authors at cpaxson@.princeton.edu and nschady@worldbank.org, respectively.

1
to check if the results hold when the lower threshold for children’s schooling is raised. We thus estimate the IGRC and IGC in a series of samples, starting with 14 years and raising the lower threshold incrementally by one year at a time up to 20 years. The evidence from this exercise is very reassuring; while the magnitudes differ across the samples, the main conclusions reached on the basis of 13-60 years age range remain intact. For the sake of brevity we report estimates for 16 and 20 years as the lower age threshold for children. A 16 year age cut-off implies potentially 10 years of schooling which coincides with one of the most important public examination in both Bangladesh and India (called Secondary School Certificate (S.S.C) or ‘Matriculation’ examination). After 12 years of schooling (18 years of age cut-off), the students sit for a second important public examination, called Higher Secondary Certificate (H.S.C) or Intermediate examination. In our Bangladesh data, about 10 percent of 20 years of age or older has 10 years or more schooling, and 5 percent has 12 years or more schooling.

For each age range, we present the bias estimates and omit the underlying estimates of IGRC and IGC for full and coresident samples. This allows us to reduce the number of tables by putting together the relevant estimates for both Bangladesh and India in a single table. However, all of the underlying estimates are available from the authors upon request. Note that we do not discuss statistical significance of the estimates, all of the estimates are significant at the 5 percent or lower level. But as noted earlier, the estimated standard errors in IGRC and IGC are very small reflecting the large sample size. Thus statistical significance is not very informative, and the focus should be on the magnitude of the bias.

The estimates for three different age ranges for the all children sample including both sons and daughters are presented in Table 4. The estimates, both for India and Bangladesh, lead to the same set of conclusions derived from the 13-60 years age range in section (4). The IGRC estimates, in general, suffer from substantial downward bias because of truncation due to coresidency. In Bangladesh, the bias in the coresident sample estimate of IGRC is more than 10 percent in seven out of nine cases, with an average bias of 15.56 percent. The extent of bias in the case of India is smaller, the average is 14.7 percent, but still the bias 19
where _y_ is the logarithm of output; _l_ and _m_ are the logarithm of the freely variable inputs of labor and materials; _k_ is the logarithm of the state variable capital; _ω_ is the part of the error term that is observed by the firm when decisions on optimal factor input choices are being made, and thus, are correlated with the inputs, _l_ , _m_ , and _k_ ; and _η_ is a true error term uncorrelated with factor input choices that may contain both unobserved shocks (i.e., unpredictable zero-mean shocks realized after inputs are chosen) and measurement errors. As pointed out by Griliches and Mareisse (1998), profit-maximizing firms immediately adjust their inputs each time a productivity shock is observed, resulting in input levels correlated with _ω_ in the regression. This simultaneity violates the OLS conditions for unbiased and consistent estimation.

Olley and Pakes (1996) and Levinsohn and Petrin (2003) have developed two similar semi-parametric estimation procedures to overcome the simultaneity problem when estimating production functions. Olley and Pakes include the investment decision of the firm in the estimation equation to proxy for unobserved productivity shocks. Derived from a structural model of the optimizing firm, the proxy controls for the part of the error correlated with inputs, _ω_ , by “annihilating” any variation that is possibly related to the productivity term. The method suggested by Olley and Pakes, however, generates consistent and unbiased estimates if and only if there is a strictly monotonous relationship between the proxy and output. Consequently, firms that make only intermittent investments will have their zero-investment observations truncated from the estimation routine because the monotonicity condition does not hold for these observations. For AMADEUS, this is a large portion of the data.<sup>6</sup> Given the considerable attrition in the AMADEUS sample when using the Olley and Pakes approach, the paper adopts the method developed by Levinsohn and Petrin (2003) to estimate production functions. Levinsohn and Petrin offer an estimation technique that is very close in spirit to the Olley and Pakes approach but uses intermediate inputs in lieu of investment as a proxy for unobserved productivity shocks. Nearly all firms in the AMADEUS database almost always report positive material costs. Therefore, the Levinsohn-Petrin intermediate input proxy estimator is the optimal choice for the AMADEUS sample.

> 6 Calculating investment as the year-to-year change in the real value of tangible fixed assets, only 1,947 (8.5 percent) of the 22,004 manufacturing firms in the final sample used for the econometric analysis in this paper have strictly positive investment in years 2001 through 2004.

10
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 12. Illustrative investment targets for infrastructure in Liberia**

||Economic target|Social target|
|---|---|---|
|ICT|Install fiber optic links to neighboring capitals and submarine cable|Provide universal access to GSM signal and<br>public broadband facilities|
|Irrigation|Develop additional 221 hectares of economically viable small scale<br>irrigation|n.a.|
|Power|Develop 258 MW inter-connectors (no-trade scenario). Could be<br>replaced under trade expansion with 369 MW of generation<br>capacity.|Raise electrification to 66 percent (100<br>percent urban and 6 percent rural)|
|Transport|Achieve regional (national) connectivity with good quality 2-lane (1-<br>lane) paved road.|Provide rural road access to 66 percent of<br>the highest-value agricultural land, and urban<br>road access within 500 meters|
|WSS|n.a.|Achieve Millennium Development Goals,<br>clear sector rehabilitation backlog|

Sources: Mayer and other 2009; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. n.a. = not applicable.

Meeting these illustrative infrastructure targets for Liberia would cost between $615 to $345 million per year through 2015, depending on the standards applied. Capital expenditure would account for 80 percent of this requirement. Power and transport each account for about one-third of the total (table 10). The pragmatic scenario—based on lower standards for meeting similar infrastructure targets—costs almost half as much as the base scenario, which highlights the importance of careful consideration about which standards to adopt.

**Table 13. Indicative infrastructure spending needs in Liberia for 2006 to 2015**

|US$ million per year||||
|---|---|---|---|
|Sector|Capital expenditure|Operations and<br>maintenance|Total needs|
|_Base scenario_||||
|ICT|31|14|45|
|Power|233|18|251|
|Transport|157|40|198|
|Water supply and sanitation|82|40|122|
|Total|503|112|615|
|_Pragmatic scenario_||||
|ICT|31|14|45|
|Power|79|43|122|
|Transport|61|39|100|
|Water supply and sanitation|51|26|77|
|Total|222|122|344|

Sources: Mayer and other 2009; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. Derived from models that are available on-line at http://www.infrastructureafrica.org/aicd/tools/models.

Liberia’s infrastructure spending needs are high relative to the size of its economy —a staggering 70 percent of GDP under the base scenario and 40 percent under the pragmatic scenario (figure 13). This is much higher than any other country in West Africa and exceeds the 20 percent of GDP spent by China 26
**_Energy demand models for policy_** **_<u>formulation</u>_**

# **_5.1 Brief descriptions of selected energy demand models_**

# **5.1.1 Country-specific models**

# **UK energy forecast model**<sup>34</sup>

For its energy forecasts and future carbon emission estimations, the Department of Business, Enterprise and Reform of the United Kingdom relies on an econometric model. Although the model covers both supply and demand sides, the demand is fairly elaborate. It contains 150 econometric relationships to determine the demand in various sectors of the economy. The model follows the Error Correction Modeling approach and uses price and economic activity as main variables although time trends are used in some sectors. The model has 13 final users who are then grouped into four major sectors, namely industry, transport, services and domestic. Each final user sector is further disaggregated by fuels. The model structure of the model is shown in Fig. 6, while the main demand drivers are indicated in Table 5.

# Fig. 6: DTI energy model overview

<!-- Start of picture text --> Other assumptions GDP and activity World Fuel<br>assumptions prices<br>UK Fuel<br>Cost of electricity<br>prices<br>generation<br>Final energy<br>demand<br>Electricity prices<br>Fuel demand by sector<br>Electricity demand<br><!-- End of picture text --> Source: Based on DTI (2005)<sup>35</sup> However, a comparison of the forecasts made in DTI (2000) with the actual data shows that the model did not succeed in making correct projections (see Table 6). In fact, the

> 34 Based on DTI (2000), 35 See (http://www.berr.gov.uk/files/file26611.ppt).

77