rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5.  Burning, stealing and giving by** **<u>treatment</u>**

||(2)|(4)|(6)|
|---|---|---|---|
||Burning|Stealing|Giving|
|Kenya|-0.069|-0.094|0.047|
||(-0.827)|(-1.289)|(1.499)|
|Uganda|-0.108|**-0.147***|0.044|
||(-1.148)|(-1.750)|(1.278)|
|Dummy for optional joining|-0.023|**0.181*****|0.000|
||(-0.344)|(4.674)|(0.053)|
|Kenya x optional joining dummy|0.052|**-0.110****|0.016|
||(0.867)|(-2.095)|(1.524)|
|Uganda x optional joining dummy|0.068|**-0.163*****|0.011|
||(0.918)|(-2.826)|(0.695)|
|γ|-1.865|**-0.319*****|-0.008|
||(-1.033)|(-7.760)|(-0.349)|
|Initial endowment|-0.173|0.096|0.002|
||(-1.440)|(0.866)|(0.349)|
|Kenya x initial endowment|0.046|-0.116|-0.020|
||(0.462)|(-0.868)|(-0.830)|
|Uganda x initial endowment|-0.101|-0.128|0.012|
||(-0.672)|(-0.945)|(0.346)|
|Gain from joining|0.174|**-0.339***|0.003|
||(1.083)|(-1.797)|(0.337)|
|Kenya x gain from joining|-0.013|0.317|-0.023|
||(-0.088)|(1.304)|(-0.864)|
|Uganda x gain from joining|0.396*|0.307|0.012|
||(1.730)|(1.207)|(0.351)|
|Endowment of other player|0.022|**0.055*****|**-0.019****|
||(1.025)|(3.208)|(-2.148)|
|Kenya x endowment of other player|-0.001|**-0.082*****|0.024|
||(-0.032)|(-4.667)|(1.388)|
|Uganda x endowment of other player|0.034|0.012|0.014|
||(0.814)|(0.508)|(0.848)|
|Dummy for rank = 2|-0.003|-0.022|-0.005|
||(-0.230)|(-1.491)|(-1.077)|
|Constant|0.238*|0.635***|0.030|
||(1.707)|(8.501)|(1.459)|
|Observations|1,426|4,112|2,290|
|R-squared|0.045|0.161|0.054|

Source: Authors analysis based on data described in the text. Note: The dependent variable is the share of the endowment of _j_ that is burnt or stolen by _i_ , or the share of the endowment of _i_ that is given to _j_ . Each decision of subject _i_ is a separate observation. Each regression is estimated using a linear probability model. This ensures that each coefficient can be interpreted as the marginal effect of the regressor on average burning, stealing or giving. Gain from joining = (p-1)*e_i. Standard errors are clustered at the session level. _t_ -statistics appear in parentheses. *** p<0.01, ** p<0.05, * p<0.1
– increase in VAT payments by an average of 8 percent an effect highly variable by sector and firm size, with smaller firms as well as firms in computing/printing, construction, and restaurant sectors experiencing larger impacts.

On the other hand, increasing qualitative evidence indicates how taxpayers struggle to optimally use the technology, often with concerning repercussion on equity. For instance, and again in Rwanda, Mascagni, Dom and Santoro (2022) show that the expected benefits of EBM do not always materialize, especially for small taxpayers. The authors first show that the incidence of poor record keeping (as proxied by inconsistencies between VAT returns they submit and data automatically recorded in the EBM) is larger for smaller firms. They then use focus group discussions to demonstrate that taxpayer confusion, complexity and technical problems with the EBM, for example, issues with topping up SIM cards, and with the necessary equipment such as computers and internet connection are particularly severe – for smaller taxpayers. The more advanced version EBM2 seems still expensive especially in terms of the necessary equipment (computers, connection, etc.) - for smaller taxpayers who already face budget constraints in using the traditional EBM.<sup>5</sup> This disproportionate effect brought about by more and more sophisticated technologies (as the ever-evolving versions of EBMs) could exacerbate existing inequalities in the taxpayer population. Similar impediments are witnessed in the deployment process of EBMs in Kenya and Tanzania. Eilu (2018) reviews the evidence from seven studies based on the adoption experiences of these two countries, to conclude that the most-cited challenge with the use of EBMs was the lack of training of VAT-collecting enterprises, mentioned as a hindrance in six of the studies. The second most-cited challenge was the high sunk costs associated with technology adoption.

With regards to the potential of in-built mechanisms of the VAT to help identify the tax base and monitor compliance, it is still unclear whether African revenue authorities

> 5At least in Rwanda, EBMs need airtime as they transmit information through the mobile network. Taxpayers, especially the smallest ones, easily forget (or are unaware they have) to top up airtime, thereby impeding the EBM from communicating with the authority. In addition, businesses have to buy the machine themselves without any subsidy (Mascagni, Dom and Santoro, 2022).

10
WPS3783

# **PAPER WALLS ARE EASIER TO TEAR DOWN: PASSPORT COSTS AND LEGAL BARRIERS TO EMIGRATION**

David J. McKenzie<sup>1</sup> _Development Research Group, The World Bank_

## **Abstract**

Increased attention to the development potential of international migration has led to calls for greater global cooperation and for developed countries to consider temporary worker programs and other options for increasing the number of immigrants admitted. However, less attention has been devoted to policies that migrant-sending countries pursue which impact on the ability of people to emigrate under the existing system. This paper documents the existence and impact of two such policies: passport costs and legal restrictions on emigration. New data collected on passport costs in 127 countries around the world reveals enormous variation in the cost of a passport from one country to the next. One in every ten countries in our sample are found to have passport costs exceeding 10 percent of annual per capita income. High passport costs are found to be associated with poor governance, especially in terms of the quality of the bureaucracy, and with lower levels of migration. Countries which place legal restrictions on the rights of women to emigrate are also found to have lower migration rates than countries with similar income and population levels. These findings suggest there is scope for some developing countries to receive greater benefits from migration by tearing down the paper walls they place around their own citizens.

## _JEL Classification:_ O15, F22.

_Keywords_ : migration; passport costs; barriers to exit.

World Bank Policy Research Working Paper 3783, December 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._

> 1 Thanks to John Gibson, Maurice Schiff, Çaglar Özden and Alan Winters for useful comments, and to Madhur Arora, Jason Victor, and officials at the various consular departments and ministries for help in collecting the passport data.
Policy Research Working Paper 533

# **Abstract**

About half of Latin America’s youth are considered “at risk,” meaning that they engage in or are at risk of engaging in risky behaviors that are detrimental to their own development and to the well-being of their societies. While child psychologists identify many factors that may cause some youth to engage in at-risk behaviors and others not to, only empirical evidence can identify the set that is relevant to a particular population. This paper uses youth surveys from Chile and Mexico to test which of a large set of potential factors are correlated with a range of risky behaviors among youth. These factors range from relationships with parents and institutions to household behaviors (abuse, discipline techniques) to social exclusion. The authors use stepwise regressions to sort out which variables best explain the observed variance in seven different risky behaviors. They find that higher socioeconomic status, a good relationship with parents and peers, strong connection with local governmental institutions and schools, urban residence, younger age, and spirituality emerge as key explanatory factors for all seven behaviors for boys and girls in both countries. This points to a wider range of policy entry points than currently used, including targeting parents and the relationship with schools.

This paper—a product of the Children & Youth Unit, Human Development Network—is part of a larger effort in the department to understand the factors leading to at-risk behaviors by youth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wcunningham@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 4: The determinants of disaster fatalities - IV-estimates

||Storms|Floods|Earthquakes|
|---|---|---|---|
|Ln(_AIDj,t-1_)|1.270**|0.416|1.280|
||(0.623)|(0.617)|(1.822)|
|Ln(_GDPj,t-1_)|-0.190*|0.101|-0.330|
||(0.111)|(0.141)|(0.232)|
|Ln(_POP jt)_|0.177|0.371**|0.752|
||(0.311)|(0.157)|(0.760)|
|Ln_(POPDENSjt)_|-0.118|-0.010|-1.334*|
||(0.237)|(0.131)|(0.779)|
|_OPENjt_|-0.000|-0.015***|-0.020**|
||(0.006)|(0.003)|(0.008)|
|_MAGNITUDEijt _|0.009***|-0.019|1.186***|
||(0.002)|(0.023)|(0.168)|
|_NATHAZ – RISKj _|-0.100|0.145**|0.345*|
||(0.085)|(0.059)|(0.201)|
|_LATITUDEj_|-0.042***|-0.031***|0.009|
||(0.015)|(0.012)|(0.029)|
|_ELEVATIONj_|-0.001*|0.001***|-0.001|
||(0.000)|(0.000)|(0.001)|
|Obs.|212|818|292|
|Centered R2|0.418|0.340|0.213|
|Prob> F|0.000|0.000|0.000|
|Sargan Test|0.897|0.066|0.144|
|Underid. Test|0.000|0.000|0.001|
|Shea’s R2|0.419|0.114|0.061|

_Notes:_ IV-estimates. Dependent variable is Ln(1+ _DEATHijt_ ). The first lags of Gartzke's affinity index with the USA, _AFFINITY USAj,t-1,_ the country’s oil resources _, OIL RESOURCESj,t-1_ and natural gas production, _GAS PRODj,t-1_ , were used as additional instruments for Ln( _AIDj,t-1_ ). Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

23
<!-- Start of picture text --> 3 76<br>64<br>rr}oO<br>2 52<br>2<br>22 *<br>s<br>av<br>oO<br>N 15<br>7° 4<br>2<br>oO<br>Preschool/Primary Secondary University<br>(0 Wage/salaried [EN Employer<br><!-- End of picture text --> <!-- Start of picture text --> 83<br>3<br>68<br>61<br>3<br>8<br>3<br>és 39<br>x 32<br>a<br>o<br>Preschool/Primary Secondary University<br><!-- End of picture text --> <!-- Start of picture text --> b= 4 97<br>~ 1<br>Oo<br>oO<br>e 63<br>£3<br>°<br>=<br>o<br>kg 36<br>o<br>N<br>8<br>3 0 0 1<br>°<br>Preschool/Primary Secondary University<br>MG Public (0 Private<br>[MG International Organization<br><!-- End of picture text -->
Thus, to restate the procedure, suppose there are two groups, A and B. under ˆ consideration. First, to calculate the numerator in _RB_ (  ) , one calculates the standard between-group inequality term,<sup>_I_</sup> _B_ (  ) . Then, second, the household survey data are sorted by income. Starting from the bottom of the income distribution, all incomes are allocated to a group the size of group A, and the remainder are allocated to a second group the size of group B. Between-group inequality is calculated. The exercise is repeated, but now with the first group equal to the size of group B and the second group equal to the size of group A. The largest of the two between-group inequality components is the maximum, and this is then ˆ taken as the denominator in the expression for _RB_ (  ) . If instead of two groups there had been 3, a total of six (3!) between-group calculations would have been necessary to determine which is the maximum.

# **3.  The Story of Caste and Inequality in Palanpur**

Palanpur is a village in Moradabad District of west Uttar Pradesh in north India. The village has been the subject of study since 1957-8, when it was first surveyed by the Agricultural Economics Research Centre (AERC) of the University of Delhi.<sup>ix</sup> The AERC resurveyed the village in 1962-3. In 1974-5 Christopher Bliss and Nicholas Stern selected Palanpur as a village in which to study the functioning of rural markets and the behavior of farmers. They spent just under a year residing in the village and collecting quantitative data, based on a set of questionnaires they designed and fielded, as well as qualitative information emerging out of informal discussion and observation. Bliss and Stern published a book based on their investigations (Bliss and Stern, 1982), which has a primary focus on the 1974-5 survey year.<sup>x</sup> 11
of decline in TAXPAY leads to a roughly 6 percent improvement in tax corruption. The results indicate some differences across regions, as well as income groups. The combined effect of 10 percent declines in TAXTIME and TAXPAY is a 9.6 percent cut in tax corruption.

These empirical findings have important policy implications. There are different ways of reducing tax complexity and simplification of tax systems is useful in the process of fighting tax corruption.

It is worth noting that, in order to draw country-specific, detailed recommendations regarding tax simplification issues, the findings of our cross-country study should be followed by additional country-specific empirical studies, which should consider country specific characteristics that would affect tax corruption and tax simplification at the country level. Our study is not a substitute for such detailed country-level analysis. Each country has different features and it may require country specific analysis to have more detailed conclusions. Unfortunately such country-level studies focusing on the link between tax corruption and tax simplification are very limited, mainly, due to lack of data information on tax corruption. As far as we know, even though it is not perfect, the Enterprise Surveys Database is the only database which includes some data information on tax corruption. Thus, we believe that our panel-data analysis presents useful information on the link between tax corruption and simplification, which has been rarely investigated empirically in the literature.

43
MINIMUM WAGES IN CAMBODIA 17 Table 2. Impact of minimum wage on worker composition and compensation

||Age<br>(1)|Educ-<br>ation<br>(2)|Log(<br>mthly wage)<br>(3)|Log(<br>hrly wage)<br>(4)|Log(<br>hours)<br>(5)|over-<br>time<br>(6)|
|---|---|---|---|---|---|---|
|||_A_|_ll workers_||||
|1 quarter before|0.111|-0.012|0.047*|0.042*|0.001|0.005|
||(0.190)|(0.093)|(0.025)|(0.024)|(0.022)|(0.018)|
|1 quarter before x|0.066|-0.135|-0.090***|-0.113***|0.022|0.059|
|Garments|(0.373)|(0.136)|(0.033)|(0.032)|(0.029)|(0.038)|
|0-1 quarters after|-0.117|-0.144**|-0.011|-0.032**|0.020|-0.002|
||(0.149)|(0.071)|(0.017)|(0.015)|(0.015)|(0.014)|
|0-1 quarters after|0.243|0.091|0.018|0.041|-0.021|-0.008|
|x Garments|(0.267)|(0.120)|(0.028)|(0.025)|(0.018)|(0.027)|
|2+ quarters after|0.026|-0.034|-0.008|-0.015*|0.004|0.003|
||(0.067)|(0.031)|(0.009)|(0.008)|(0.008)|(0.007)|
|2+ quarters after|0.034|0.146|0.018|0.044***|-0.024|-0.038*|
|x Garments|(0.196)|(0.091)|(0.017)|(0.016)|(0.015)|(0.020)|
|Average|34.699|5.523|12.906|7.802|5.100|0.407|
|||_Fem_|_ale workers_||||
|1 quarter before|0.388|-0.041|-0.026|0.024|-0.052|-0.033|
||(0.269)|(0.097)|(0.046)|(0.051)|(0.036)|(0.021)|
|1 quarter before x|-0.320|-0.180|0.016|-0.078|0.092**|0.120***|
|Garments|(0.479)|(0.153)|(0.053)|(0.058)|(0.042)|(0.042)|
|0-1 quarters after|-0.250|-0.120|-0.027|-0.030|0.001|-0.016|
||(0.199)|(0.079)|(0.025)|(0.022)|(0.022)|(0.017)|
|0-1 quarters after|0.314|0.026|0.038|0.033|0.008|0.027|
|x Garments|(0.330)|(0.130)|(0.033)|(0.030)|(0.024)|(0.030)|
|2+ quarters after|0.035|-0.041|-0.041***|-0.031**|-0.012|-0.002|
||(0.091)|(0.034)|(0.015)|(0.013)|(0.013)|(0.011)|
|2+ quarters after|-0.168|0.148|0.057**|0.064***|-0.006|-0.029|
|x Garments|(0.237)|(0.096)|(0.023)|(0.021)|(0.020)|(0.022)|
|Average|35.686|4.792|12.695|7.668|5.023|0.356|

_Source_ : Author’s calculations from Cambodia SES 2007-2015. _Note_ : The table presents the estimate of _α_ 0 _, β_ 0 _, α_ 1 _, β_ 1 and _α_ 2 _, β_ 2 from Equation (6). The sample is restricted to working age population, and those observations surveyed within 8 quarters of any of the minimum wage hikes. Columns (3)-(5) restrict the sample to wage-workers. The sample is further restricted to women in the bottom panel. In addition to controls mentioned in the notes for Table 1, the estimates also control for garment sector specific time trend for each location, and garment sector specific global recession shock. Individual and household level controls omitted from the first two columns. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.

wages falls by 4.3 percent.<sup>14</sup> However, in the subsequent sectors, as Table 2 (column (3))

> 14I refer to _αt_ + _βt_ , the sum of the coefficients reported in the tables, while referring to the impact on the garment sector. The diff-in-diff estimates are always referred to as an impact relative to non-garment sector.
# **Introduction**

IMore time will be needed for more definitive results<sup>as well as to sort out unresolved</sup> factual issues. The experience of the more market-oriented<sup>economies of the post-FSU period</sup> should serve as a reality check on historical estimates.<sup>But tentative judgements must be made</sup> now, in deciding the terms under which FSU economies<sup>may borrow from the Bank. Since the</sup> results are inevitably subjec **t** o a large margin of<sup>error, the goal of this report is to allocate FSU</sup> economies to income brackets or categories used<sup>for the Bank's operational guidelines.</sup> The most difficult aspect of assigning FSU economies<sup>to Bank lending categories is</sup> finding a ruble-dollar conversion factor that is broadly in line<sup>with usual Bank practices, or the</sup> so-called _Atlas_ method. Well-known studies can be<sup>quoted that place per capita income of the</sup> FSU over $9000 or under $2000 per capita in 1989-90,<sup>primarily because of differing</sup> approaches to the ruble-dollar conversion factor.<sup>This arises mainly from oft-discussed</sup> 3 issues about how best to convert income estimates from national<sup>currencies to a common numeraire.</sup> A strong ruble and high $GNP per capita for the FSU<sup>(over $9000) emerges from official US</sup> assessments of the FSU (see US ACDA)<sup>4which are based strictly on a bilateral study of</sup> purchasing power parities (PPPs); a weak ruble and a low<sup>$GNP per capita ($1780 in 1989) was</sup> posited by the Houston Summit report of the Bank<sup>and other international agencies, based on</sup> a "back-cast" of the so-called commercial exchange<sup>rate, intrduced in late 1990 (see IMF,</sup> World Bank, OECD and EBRD 1991).5 The bulk of the<sup>report, Section I-r, is devoted to</sup> taidng a position on this issue.

A smaller margin of error is though **t** o surround the underlying<sup>ruble estimates of 1990</sup> GNP for each FSU economy (see page 18). However, subtle<sup>points of methodology become</sup> important at this level since the GNP per capita of the<sup>richest FSU economy is about four times</sup> that of the poorest. For practical reasons, this study<sup>assumes that the same conversion factor</sup> can be applied to each FSU economy. And several<sup>themes are developed in Section IV to</sup> suggest why a less sanguine view may be appropriate<sup>for more recent estimates and projections.</sup> Section V considers the relative position of the FSU and<sup>other economies, as presented here and</sup>

> 3See, for example, a Bank working paper, **_Estimadng_** _Per Capita_ **_Income,_** available on request.

4Data accord with CIA methodology. CIA continues to report<sup>growth rates but no longer publishes dollar e_tmates</sup> for the FSU, given concerns about the resuls<sup>in the present context.</sup>

> 5The Summary and Recommendations to **_the Economy_** _of_ **_de USSR,_** _by_ the _M,_ World Bank,<sup>OECD, and BRD,</sup> report $1780 for 1989 (page 51). Further detail was<sup>provided in the IMP, World Bank, OECD and EBRD 1991 study;</sup> hereafter referred to as JSSE.
|**Study**|**Method**|**Sample**|**Intervention(s)**|**Impact**|
|---|---|---|---|---|
|McKenzie et<br>al.<br>(forthcoming)|RCT|1,256 current<br>and potential<br>female<br>entrepreneurs<br>in Sri Lanka|1) Eight-day ILO course on<br>generating and starting<br>business idea for potential<br>entrepreneurs; six-day<br>course for improving<br>business for current<br>entrepreneurs<br>2) Cash grants to half,<br>conditional on completing<br>training|+ Training has significant impact on business practices for<br>current entrepreneurs; training and grant together also has<br>significant impact on profits<br>+Training and training and grant together for potential<br>entrepreneurs speeds up starting an enterprise, though it<br>doesn’t improve ultimate entry rate|
|Calderon et al.<br>(forthcoming)|RCT|850 female<br>micro-<br>entrepreneurs<br>in rural Mexico|Four hours of class for six<br>weeks with homework|+Large impact on revenues, number of clients, profits, and<br>formal accounting, 7-8 months after intervention|

35
offerings such as electronic payments and mobile banking remain largely unexploited, particularly when compared to peers like Thailand and Malaysia.

In terms of physical access to outlets, Sri Lanka also enjoys high levels of banking penetration, with bank branch density at 17 per 100,000 adults, compared to the South Asia regional average of 10.2. Interestingly, countries like Malaysia (10.2) and Thailand (11.7) do not fare much better than even the South Asian average when it comes to physical branch penetration. However, they are far ahead in terms of digital financial inclusion<sup>15</sup> (the upper middle-income average was at 62 percent) which is a more efficient and flexible mode of banking compared to physical financial access. Overall, when considering Sri Lanka’s level of financial inclusion, there is evidence to show that ease of physical access to accounts has not translated into full financial inclusion, particularly with respect to usage.

# **3. Data and Methodology**

Information from the recently concluded National Financial Inclusion Survey (NFI survey) for Sri Lanka is used in our analysis, given its national coverage and data quality.<sup>16</sup> The NFI survey was initiated by the CBSL and the International Finance Corporation. It was the first such focused survey on financial inclusion for Sri Lanka. The data indicators capture Sri Lankans’ behavior relating to saving, borrowing, making payments, and managing risks, as well as with respect to financial capability.

# **3.1. NFI Survey Data**

The survey data are based on face-to-face interviews with 4,791 nationally representative and randomly selected Sri Lankans over 18 years of age, stratified by geographic areas. Fifty-six percent of the sample respondents were female, and 83 percent lived in rural areas. The age of respondents varied between 18 and 97 years, with a mean of 45 years.

> 15 “Digital financial inclusion” can be defined broadly as digital access to and use of formal financial services by excluded and underserved populations (CGAP, 2015).

> 16 The sample was stratified at 2 levels (stratified by households in urban/rural sector within each of the 25 administrative districts) maintaining 95% confidence at each stratum.

9