rafmacalaba/datause-encoder-data huggingface dataset:
the property value; as such, the detection capacity channel is muted. Using a modified version of the Allingham and Sandmo (1972) tax compliance framework, the paper shows that taxpayers’ decision to pay a given tax liability depends not only on their perceived probability of their property being discovered by the tax authority and the associated penalties, but also on the perceived probability that the government will enforce the law and collect the taxes and penalties they owe.

In the first phase of the project, I worked closely with the LRA to conduct a door-to-door property enumeration and develop a new electronic property database. Prior to this exercise, only 5 percent of properties in the city were on the tax register, and many records were not digitized. This exercise thus represented a significant technology investment for the LRA that made the tax base more legible.

Experiment one examines the impact of signaling this newly acquired identification capacity and providing information on penalties to property owners who had not previously paid real estate taxes. The control group received a generic tax notice addressed “Dear Property Owners.” The notice provided information on the procedure for tax payment and requested compliance. A treatment group received a tax notice that, in addition, included identification information from the new database: the name of the property owner and a photograph of the property. This notice also included information on legal penalties for noncompliance. Two additional treatment groups received notices with either the identification information alone or the penalty information alone.

The combination of identification and penalty information significantly increased property owners’ compliance: property owners were more than four times as likely to pay the tax. The payment rate increased from 2.2 percent of property owners in the control group to 9.7 percent in the treatment group. This effect persisted, as the treatment group continued to have a higher payment rate over the next three years of available data. The impact of the combined identification and penalty treatment is statistically different from the identificationonly and penalty-only treatments, neither of which is statistically significant. These results indicate that providing taxpayers with evidence that their non-compliant property is known to the tax authority leads to increased compliance only when this evidence is accompanied 4
17

|Indicator|Definition|Main Data Source|
|---|---|---|
|Blood pressure measured in last 12<br>months (% of population age 18 and<br>older)|Percentage of population over 18 having their blood pressure measured by health professional in<br>the last year|Eurobarometer|
|Cholesterol measured in last five<br>years (% of population at risk of<br>high cholesterol)|Percentage of adult population at risk (overweight or obese and older than 20, male and older<br>than 34) having their cholesterol levels measured in the last 5 years|EHIS|
|Blood sugar measured in last 5<br>years (% of population at risk of<br>diabetes)|Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their<br>blood sugar measured in the last 5 years|EHIS|
at another rate, but the rate needs to be agreed directly with the provider. The default rate for the employee is 3 percent.

Automatic enrollment and automatic escalation in the United States are implemented through safe harbor provisions. In the case of the 401(k) plans, there are no default contribution rates in the voluntary automatic enrollment plans, but safe harbor provisions apply. Plan sponsors may opt for a qualified automatic contribution arrangement (QACA), which allows the plan to automatically pass certain kinds of annual required testing. With a QACA, the initial automatic employee contribution must be at least 3 percent of the salary, and has to increase automatically at 1 percent each subsequent year to reach a minimum of 6 percent and a maximum of 10 percent. The employer may offer a non-elective contribution of 3 percent of compensation to all participants, or a matching contribution of 100 percent for salary deferrals up to 3 percent of compensation and a 50 percent match for salary deferrals for the next 2 percent of compensation.

Safe Harbor rules in the United States are designed for 401(k) (and 403b) plans to give a “free pass” to companies on the non-discrimination test when the rules are followed. These rules call for a company to make a specific contribution on behalf of each participant in the plan. Companies may decide on a non-elective contribution on behalf of all eligible participants of a minimum of 3 percent of wages, which must include wages for the entire plan year regardless of the employee’s entry date in the plan. Alternatively, a matching contribution under one of the formulas listed below (a match is allocated only to employees who make contributions to the plan from their own wages).

- a. Basic Match: 4 percent of pay for participants who defer at least 5 percent of their pay, with 100 percent of the first 3 percent of pay that is contributed; and 50 percent of the next 2 percent of pay that is contributed

- b. The Enhanced Match: 100% of the first 4 percent of pay that is contributed to the plan (this is the minimum required under this option). The maximum allowed to get a free pass on the non-discrimination test is 100 percent of the first 6 percent of pay.

Safe Harbor rules play an important role in the adoption of contribution rates in the United States. As shown in Figure 6, in the case of Fidelity funds in 2012,<sup>30</sup> approximately 60 percent of the funds offer a 3 percent pay (associated to the non-elective contribution default rates), and the proportion of plan sponsors offering default rates above 6 percent is not significant. Consequently, default contribution rates do matter, even if they are simply suggested through safe harbor provisions.

> 30 Fidelity investment records of 20,500 corporate DC plans and 11.5 million participants as at December 2012.

22
_Brazilian spatial labor dynamics Page 29_ <!-- Start of picture text --> Population Density 2000<br>0.131 - 3.816<br>3.816 - 12.225<br>12.225 - 85.984<br>85.984 - 12878.968<br>N<br>W E<br>1000 0 1000 2000 Miles<br>S<br><!-- End of picture text -->

**Figure 2 Population density,  2000**
**<u>Table 2: Household and individual level summary statistics</u>**

||<br>|**Reserva**|**tion Status**||
|---|---|---|---|---|
||**Total**|**Ever**|**Never**|**Difference**|
|**Panel A: Household characteristics**|||||
|Female head|0.116|0.115|0.117|-0.002|
|Head's age|49.2|49.3|48.9|0.400|
|Head's education|3.800|3.830|3.750|0.080|
|Head married|0.848|0.842|0.855|-0.013|
|Head widowed /separated|0.136|0.143|0.128|0.015|
|Household size|4.480|4.480|4.470|0.010|
|Males 15-65 years|1.650|1.660|1.630|0.030|
|Males 15-65 years|1.560|1.570|1.550|0.020|
|Children <15 years|1.080|1.070|1.110|-0.040|
|Female children <15 years|0.530|0.520|0.540|-0.020|
|Max. educ. in hh (years)|14.260|14.420|14.070|0.350|
|Hindu|0.888|0.882|0.894|-0.012|
|SC/ST|0.419|0.404|0.438|-0.034|
|Owns agricultural land|0.579|0.599|0.555|0.044*|
|Has pucca house|0.476|0.483|0.467|0.016|
|# observations|23,350|12,678|10,672||
|**Panel B: Individual characteristics**|||||
|Female|0.490|0.491|0.489|0.002|
|Age|39.8|39.9|39.7|0.200|
|Educ. primary.|0.213|0.208|0.218|-0.010|
|Educ. up to high school|0.291|0.291|0.292|-0.001|
|...up to graduate|0.110|0.116|0.102|0.014|
|Others|0.015|0.015|0.014|0.001|
|Married|0.748|0.742|0.754|-0.012|
|Unmarried|0.174|0.176|0.172|0.004|
|No. of obs.|66,362|34,707|31,655||

_Note:_ Author’s own calculation from 2014/15 REDS follow-up survey. To test difference in means, p values from regressions with district fixed effects and standard errors clustered by village panchayat are reported in the last column.

17
TABLE 6b. Correlates of Dyadic Differences: Perceived Consequences of Disputes, First Principal Component

||[1] Ethiopia|[2] Sudan|
|---|---|---|
|||_yi_ −_yj_|||_yi_ −_yj_||
|_i_&_j_trade with each other|−0.0757|0.339|
||(0.146)|(0.782)|
|_i_&_j_have common supplier|0.00852|0.0100|
||(0.0841)|(0.151)|
|_i_&_j_have common client|0.0573|0.0579|
||(0.118)|(0.0874)|
|log distance btw_i_&_j_|−0.0152**|0.00614|
||(0.00734)|(0.00704)|
|_i_&_j_belong to same sector|−0.0130|0.0163|
||(0.0258)|(0.0188)|
|Abs diff firm age|−0.000904|−0.00117|
||(0.00168)|(0.000985)|
|Abs diff managers’ education|−0.00478|0.00139|
||(0.0133)|(0.00863)|
|Abs diff managers’ experience|0.00290|0.000487|
||(0.00340)|(0.00169)|
|Owners’ gender differ|0.00676|0.0369|
||(0.0613)|(0.0481)|
|Abs diff log employment|0.00317|−0.0111|
||(0.0164)|(0.0136)|

_Source:_ Authors’ computations based on data described in the text.

_Note_ : The table shows ordinary least squares results. A constant is included in all specifications. The numbers in parentheses are bootstrapped standard errors that are robust with respect to heteroskedasticity and cross-observation correlation in the error terms involving the same firms. Statistical significance at the 5 percent level is indicated by **.

43
Research Components of World Bank Edcucation Projects, since 1982.

|AreaX|r--<br>Country and Loan/Credit<br><br>Nlhumber|T<br>r<br>t~Approv<br>1|T-_<br>Reserch Components<br>ed<br><br>Res<br>rh-oonnt|Current Status of Studies, including<br>Completed<br>Documents/Data|
|---|---|---|---|---|
||||secondary<br>schools;<br>(ii)review<br>of curriculum,<br>staff<br>utilization<br>and facilities<br>in vocational schools;|Projects<br>(ii),(iii)<br>and (iv)<br>completed.|
||||(iii) development of trade testingand||
||||(iv) revieu, of inservice training course<br>content (para.3.23<br>p.20).||
|AFRICA|Liberia CR 1306-LBR|1983|Fourkh Education. No research coapcnent.|Confirmed.|
|AFRICA||Madagascar CR 1661-NAG<br>I|1986<br> <br>I|Accounting and Management Training. No research<br>I conponent.|Confirmed.|
|AFRICAI|Malawi CR 1330-MAI<br>l<br>l<br>l<br>l|1983<br> <br> <br> <br>||ifth<br>Education Project. Studies to:(i)<br>evaluate cost-effectiveness of primary school<br>| self-help construction program;<br>**|(i)**<br>to what extent could secondary school<br>boarding facilities be eliminated<br>| (para. 4.18 p.21).|Projects completed by the MOEC, Lilongue.<br>E<br>(i) Ministry of Finance (1983) "Feasibility<br>study of the Need for Boarding Places and<br>Establishment of New Day Schools." (doc. #113.845)<br>(ii)<br>Ministry of Education (1984)"Study on the<br>Development of Private Schools" (doc. # 109.284)|
|AFRICA|Malawi CR 1767-MAI<br>||1987|Education Sector Credit. Feasibility studies tol<br>convert urban secondary schools to double<br>shift institutions (due March 1988),<br>(ii)<br>secondary school mapping survey (March<br>1987).<br>Imptementationl<br>not<br>research,<br>stLAdies.<br>jilplementation,<br>not research, studies.|Feasibility studies completed by the MOEC,<br>Lilongwe. Secondary School mapping survey: r'-aft<br>available from MOEC, Lilongwe.<br>No information on intended studies, however:<br>(i) University of Malawi (1984): Tracer studies of<br>graduates from Chancellor College (doc.#110.592/3)<br>(ii)<br>Feasibility study for establishingday<br>secondary schools in Malawi (doc. # D32608)|

30
<!-- Start of picture text --> ~ Nigeria Ont<br>a / A a<br>a” i i ‘<br>= i ; 1 e<br>8 1 ‘ :<br>= i ~. f<br>an \ [/ ,<br>5 Vig<br>5 fn WS<br>za4 oefNSfr et si)|<br>s , " oa<br>2000 2005 2d 2018 Paral<br>ear<br>——aganes _——-— ge<br><!-- End of picture text -->

<!-- Start of picture text --> Rest of West Africaa(No NGA),<br>-<br>=<br>eat) A<br>2= i\<br>2 _ ~/ \ a<br>ss \ + f<br>g<br>8 ) ey ff<br>& oo",“ : "“<br>By ee La =<br>2000 2005 2010 215 2020<br>ear<br><!-- End of picture text -->
Each inland pixel could be associated with the nearest coastline segment, in a straightline distance. However, in order to better capture the movement of water inland, in this analysis hydrological drainage basins have been used instead. The wave height calculated for the coastline segment closest to the basin outlet was applied to inland areas within that basin.

As a wave moves inland the height is diminished. The rate of decay depends largely on terrain and surface features, as well as factors specific to the storm generating the wave. In a case study on storm surges, Nicholls (2006) refers to a distance decay factor of 0.20.4 m per 1 km that can be applied to wave heights in relatively flat coastal plains. For this analysis, we used 0.3 m per 1 km distance from coastline to estimate the reduction in wave height applied to each inland cell.

The delineation of surge zones was then based on a simple comparison of the calculated wave height, taking into account distance decay, to the SRTM value. If the elevation value of any location is less than the wave height, then the location is part of the surge zone. Low-elevation “coastal zone” was delineated from inland pixels with less than 10m elevation- near the coastline, following McGranahan, Balk, and Anderson (2007).

All processing was done by 5o x 5o tile, using aml (ArcInfo Macro Language) for automation.

# **(ii) Datasets**

The following datasets were used to delineate inundation areas:

# _1) Hydrosheds conditioned 90m DEM_

Hydrologically conditioned version of 90 m SRTM data, conditioned to produce a surface that drains to the coast (except in cases of known internal drainages).

7
# **APPENDIX 3: SUPPLEMENTARY SIMULATION RESULTS**

**Table A3.1. Real macro indicators: level in 2012 (constant US$mn) and base growth by period  (% per year**

||**2012**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|
|**Absorption**|2,652.4|1.2|4.7|3.8|
|**Consumption - household**|1,206.6|3.9|5.5|5.1|
|**Consumption - UNMIL**|469.8|-71.6|-0.4|-29.7|
|**Consumption - government**|374.6|10.4|5.5|6.9|
|**Fixed investment - private**|362.2|6.2|1.8|3.0|
|**Fixed investment - government**|239.2|1.0|2.6|2.2|
|**Exports**|590.7|15.5|6.8|9.1|
|**Imports**|1,649.9|1.6|4.8|3.9|
|**GDP at marketprices**|1,593.3|7.1|5.9|6.2|
|**GDP at factor cost**|1,430.2|7.3|6.1|6.5|
|**Total factor employment (index)**|100.0|4.4|3.7|3.9|
|**Total factorproductivity (index)**|100.0|2.9|2.5|2.6|
|**GNI**|1,410.0|6.6|6.4|6.5|
|**GNDI**|2,355.5|0.9|5.7|4.3|
|**GNI per capita (2009 US$)**|0.4|4.1|4.2|4.2|
|**GNDI per capita (2009 US$)**|0.6|-1.5|3.4|2.0|
|**Real exchange rate(index)**|100.0|1.1|2.4|2.0|
|**Unemployment rate (%)**|14.7|13.6||11.0|
|**MDG 1 -- poverty rate (%)**|60.2|57.5||41.8|
|**MDG 4 -- U5MR (per 1000)**|83.4|74.4||58.7|
|**MDG 7 -- Water access (%)**|73.3|75.8||85.5|
|**MDG 7 -- Sanitation access(%)**|17.8|19.4||26.3|

## **Note:**

**Unless otherwise noted, the unit for the 2012 column is millions of US$ (at 2009 prices). Rows for unemployment and MDG indicators show levels in 2012 and at end of each period; the "2018-2030" column is blank since 2030 data is provided in the column 2013-2030. Other rows and columns show average annual real growth during the indicated period.**

32
**Table 7.  Spread Decomposition by Size Quartile, Ranking Based on 2007 Deposits**

||Smallest|||Largest|
|---|---|---|---|---|
||0-25<sup>th</sup><br>Percentile|26-50<sup>th</sup><br>Percentile|51-75<sup>th</sup><br>Percentile|76-100<sup>th</sup><br>Percentile|
|Average Lending Rate|28.97|14.21|18.91|10.46|
|Average Deposit Rate|4.23|3.79|4.63|2.16|
|Spread|24.74|10.42|14.28|8.30|
|Overhead Costs|7.65|4.15|3.96|2.38|
|Loan-loss Provisions|1.33|1.26|1.19|0.45|
|Reserve Requirement|0.47|0.42|0.51|0.24|
|Taxes|4.59|1.38|2.58|1.57|
|Profit Margin|10.70|3.22|6.03|3.66|

Source: Own calculations, based on CBK data. Note: Cooperative, Consolidated, and KCB, and NBK are excluded from these calculations.

The pattern in Table 7 suggests that the largest private banks are more efficient, as reflected in their overhead costs, and more stable, as reflected in their loan-loss provisions. For these reasons, proposals to increase minimum capital requirements and thereby consolidate the sector by creating larger banks are being explored. Section 6 discusses this issue in greater detail.

While the size of private banks appears to have an effect on the efficiency of financial intermediation, bank ownership plays at least as important a role in explaining the relative efficiency of Kenyan banks. Table 8 shows why, namely because the figures for government owned and government influenced banks are not reliable. Recall that our method for calculating spreads is less reliable for banks that have a large stock of non-performing loans. This is likely because interest is accrued on the same non-performing loan multiple times. In short, our method makes sense for banks that are pricing loans to cover the expected future costs of defaults.

And so the enormous lending rates and spreads calculated for government banks is the first tip-off that they are not adhering even remotely to commercial banking principles, though we concede that the difficulties appear to be more severe for government-owned than government-influenced banks. At the same time, our spreads were increasing for all government banks from 2000 to 2005. During this same period, spreads were declining substantially for the 15
WPS3774

# **Innovation and Development around the World, 1960-2000**

Daniel Lederman � Laura Saenz Office of the Chief Economist Latin America and the Caribbean The World Bank

## _Abstract_

This paper presents a database of indicators of innovative activity around the world since the early 1960s. The data include measures of innovation outcomes as well as variables related to innovation effort. The main indicator of innovation outputs is patents. The main variables related to innovation inputs are investment in research and development (R&D) and technical personnel (engineers, scientists) working in R&D activities. The sources of these data are publicly available (OECD, UNESCO, etc.), yet there have been few attempts at double checking the consistency of these data and digitizing observations dating back to the 1960s. After discussing the sources and definitions of the data, the paper examines trends and patterns of innovation outputs and inputs by looking at the over-time behavior of the relevant series and comparing the performance of developing and high-income countries. The authors also provide cross-regional comparisons and a detailed examination of trends in selected countries. In turn, the authors provide estimates of the impact of innovation on long-run development by following an emerging empirical literature on the determinants of levels of GDP per capita. The econometric results suggest that innovation might indeed have strong positive effects on long-run development, which might be stronger than the direct effects of institutions. The analysis pays close attention to issues related to the potential endogeneity of innovation (and institutions) with respect to the level of development.

World Bank Policy Research Working Paper 3774, November 2005 _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._ � I am grateful to Laura Saenz for her dedicated research assistance. She patiently constructed the database from original and computerized sources. I am also grateful to William F. Maloney for his guidance and invaluable suggestions. José L. Guasch, Lauritz Holms-Nielsen, Guillermo Perry, Andrés Rodríguez-Clare, and Luis Servén provided helpful discussions and comments at various stages.
proposed by Imai and Ratkovic (2014).<sup>41</sup> This strategy enables the analysis of a pseudo-sample of youths in which the characteristics of their households of origin, prior to the intervention of the program, are not associated to the different intervention levels to which they were exposed (Ridgeway et al. 2015).<sup>42</sup> Appendix B shows the variables used in the construction of the weights and the favorable balance achieved through their application.

The weights obtained were used in the construction of the mobility tables for the groups with low and high treatment intensity, and for the purpose of estimating the effect of the program on both absolute occupational mobility (through the mobility rates) and on relative mobility (through the use of log-linear models).

# D. _Methods and variables for the study of intergenerational occupational mobility_

This section describes the methods and variables used to answer the first set of questions posed. This requires the analysis of both the absolute mobility and the relative mobility of the youths of the sample and the relevant subgroups. First, we present the scheme of the occupational strata used, followed by the mobility tables and log-linear models.

# _D.1 Occupational strata scheme_

For the analysis of intergenerational occupational mobility, we used an approach to occupational stratification that seeks to establish a relation between intergenerational mobility and the improvement of the socioeconomic status of the beneficiary youths of PROSPERA. This scheme was used by Yaschine (2015) for the analysis of intergenerational occupational mobility with data of ENCEL 2007 and is based on the scheme proposed by Solís and Cortés (2009) for Mexico, which, in turn, used elements of the CASMIN classification.<sup>43</sup> An ordinal scheme is proposed, which assumes that a position in the highest occupational stratum grants greater access to life opportunities and, therefore, allows the identification of upward and downward movements of the youths. In table 4 our approach to occupational stratification is presented, which is ordered

> 41 In methodological literature, the causal inference methods based on matching are often introduced as (1) a quasiexperimental technique to contrast sample treatment and control cases, or otherwise as (2) a non-parametric method of fit for systematic patterns of assignment to treatment when it is not reasonable to rely on the estimators of a simple parametric regression (see Morgan and Winship, 2015).

> 42 The estimations based on inverse probability weights, introduced by Rubin (1985) and extended to the marginal structural models in Robins et. al (2000), have been extensively used to control the selection bias in treatment variables.

> 43 The Solís and Cortés classification (2009) was the first in the country validated with representative data at the national level, using the ENIGH 2004. Yaschine (2015) used and adapted this classification for the analysis of the ENCEL 2007 data.

24