rafmacalaba/datause-encoder-data huggingface dataset:
ladder position of the unsuccessful PAC applicants shows no change (Stillman et al, 2015).<sup>10</sup> Other questions answered at an individual level, in both the short and long surveys, are for education, physical and mental health, and occupations. Mental health was measured using the MHI-5 index of Veit and Ware (1983). Respondents were asked if they had changed their main occupation in the past five years, and what the prior and current occupations were. We code these responses using the New Zealand Socioeconomic Index of Occupational Status (NZSEI) of Davis et al (1997), which gives a continuous measure ranging from 10 (eg. nonordained religious associate professionals) to 90 (eg. health professionals). Scores reflect characteristics of occupations that either translate into observable lifestyle factors, such as incomes, or reflect valued socioeconomic inputs, such as educational levels. An occupational status index for New Zealand should give a better basis for cross-country comparisons with occupations in Tonga than would an explicit international index, such as the ISEI developed by Ganzeboom and Treiman (1996), since an international index is unlikely to have a country as small as Tonga in mind. Moreover, given the role of New Zealand as the major migration outlet and one of the main suppliers of higher education to Tonga, it is reasonable to believe that the same occupations have similar status in both countries.

# **3.  METHODS**

We are interested in the long-term impact of migration on the immigrants themselves and on their families. To deal with self-selection bias, we rely on the PAC lottery randomly choosing a subset of individuals who become eligible to migrate from a larger pool of individuals who wanted to migrate. Given this mechanism, in the absence of non-compliance such as drop-out and substitution, all of the lottery winners would migrate and all of the lottery

> 10 In contrast, Tongan seasonal migrants show significant rises in their ladder position compared to respondents in households without seasonal workers (Gibson and McKenzie, 2014b). These divergent results may be due to frame of reference effects; settlement migrants may use destination country standards to reevaluate their former life in Tonga while seasonal migrants continue to evaluate using Tonga as a reference standard since they are not attempting to integrate into the destination country.

13
choices to engage in corrupt behaviour. The first measure used is the Transparency International Corruption Perception Index (CPI), which provides an independent measurement of perceived corruption in 180 countries, based on 13 different expert and business surveys. Inclusion of country-wide corruption may be interpreted to reflect the extent to which peer effects may be conducive to corrupt behaviour, as in theoretical models that explain the persistence of corruption with social effects, such as Andvig and Moene (1990) and Tirole (1996). The second country-wide measure, used to probe the robustness of the environmental effect of corruption, is the World Economic Forum (WEF) index of the effectiveness of the legal framework in resolving disputes, intended to capture the possibility that a higher likelihood of sanctioning by the legal system may act as a deterrent.<sup>3</sup> Regression analysis shows that firms that do _not_ pay bribes in environments with a high prevalence of corruption and inefficient legal frameworks experience higher productivity. Furthermore, when overall levels of corruption exceed a certain threshold, the total effect of corruption on productivity - i.e. the combination of individual and country effects - is increasingly negative. This indicates that, whereas environmental circumstances are beyond the choice set of individual firms, managers still have some degree of autonomy in deciding whether to recur to bribery or not and this affects firm level productivity.

The remainder of the study is structured as follows. The following section provides an overview of the relevant literature on the possible causes of corruption and on its effects on economic performance. Next is a description of the BEEPS 2009 data, as well as an exposition of the econometric methodology. The fourth section demonstrates the effects of corruption on productivity, both unconditional and conditional on the time tax experienced by individual firms and on country characteristics in terms of prevalence of corruption and efficiency of the legal framework. The final section concludes.

> 3 North (1990) distinguishes between “informal norms,” “formal rules” and “enforcement” as pillars of the institutional framework of an economy. For our purposes, the CPI might also be viewed as a proxy of informal norms of behaviour dictated by social networks, while the WEF index of the efficiency of the legal framework might be interpreted as a proxy for the quality of formal rules or their enforcement.

6
<!-- Start of picture text --> g California schools g Education - PISA<br>oa rey2<br>28s . £<br>3 fo ~ S<br>2e 2 oo<br>3 . 328<br>2 : , 2~ +x<br>Bo oan = x0<br>ac a<br>2 woamt ae Mah 28s on athe pn<br>o — - a —— —___—— 7<br>0 5,000 10,000 15,000 20,000 0 5,000 10,000 15,000<br>Inefficiency - one output Inefficiency - one output<br>eS OECD health systems s Vietnam hospitals<br>o 8<br>2 , 28<br>3 . 3s ° :<br>£ : £ :<br>3.8 i 3 Loe<br>o°e©.. o a= 23 :a‘<br>aai ‘peer<br>ogawen ofthe<br>° wet ° yee<br>0 1,000 2,000 3,000 4,000 5,000 0 200 400 600 800 1,000<br>Inefficiency - one output Inefficiency - one output<br>TheNotes:OECDCaliforniahealthdatasystemreferdatato 313referunifiedto 29 countries,school districts,and coverand thecoverperiod the period1960-2005. 2003/2004The Vietnam- 2008/2009.data Therefer PISAto 795 datadistrictreferhospitals to 49 countries,with betweenand cover50 and some500 orbeds,all theand yearscover 2000, the 2003period and1998-2000. 2006.<br><!-- End of picture text -->
connectedness, good relationship with parents, and mental health. The behaviors and outcomes are limited to employment, schooling, sexual health, and participation in activities. There is no information about drug use or violent behavior. We limit the sample to those aged 15-24, giving us a sample size of 5321.<sup>5</sup> Mexico’s National Youth Survey, _Encuesta National de Juventud_ (ENJ) was carried out in 2000.<sup>6</sup> The sample is nationally representative and was performed in two stages: first, the entire household was surveyed and asked basic household characteristics, and later, youth ages 12-29 were asked a separate set of youth-specific questions. Only those aged 12 to 24 were included in this analysis, resulting in a sample of 37,979 respondents.<sup>7</sup> As with Chile, the Mexican dataset includes information on several risk and protective factors and behaviors. However, the Mexican data also include data on attitudes towards alcohol and drugs, parental residence in the household, parental response to their children’s behaviors, attitudes toward school (a proxy for school connectedness) and various proxies for family poverty. Unfortunately, this dataset does not have information about connectedness with other adults, abuse in home (aside from the form used to respond to misbehavior), community violence, ethnicity, or sexual behavior for those aged 12-14.

> 5 For this analysis, 195 observations out of 7,189 (2.7% of sample) for youth aged 15 to 29 were dropped due to missing data.  We then restricted the sample to youth aged 15 to 24 dropping another 1674 observations.  In many cases, missing responses could be coded based on responses to related questions so as to maintain a larger sample size.  For instance, if someone does not respond as to whether they attend church, after they have already indicated that they do not believe in God, then we assume that they do not attend church.

> 6 The survey was repeated in 2005, but the data were not available at the time this paper was under preparation.

> 7 Of the almost 60,000 youth in the original sample, about 10,000 youth aged 12-29 were not surveyed the second time, and were dropped from the sample used for the analysis. The reasons for not interviewing these youth were tracked in the dataset:  they did not want to participate, were not at home at the time of the interview and would not return within the week, were on vacation, were working or at school in another city, were disabled, and other.  Comparing the poverty variables (education level and monthly earnings of heads of households) and rural means of this dataset before and after dropping the data showed no significant difference at α=.01. An additional 5% of the observations were dropped in creation of the variables;  the resulting sample was not statistically different from the original. Finally, the data were restricted to youth aged 12 to 24, thus further decreasing the sample size by 8903.

8
**Table A9: Distribution of gender and high earner/hours status within household**

|||Male|Female|Total|
|---|---|---|---|---|
|_Highest_|_Baseline Earnings in Household_||||
|No|Count|74|139|213|
||Percent|38.7|58.2|49.5|
|Yes|Count|117|100|217|
||Percent|61.3|41.8|50.5|
|Total||191|239|430|
|||100|100|100|
|_Highes_|_t Usual Hours of Work in Household_||||
|No|Count|79|124|203|
||Percent|41.4|51.9|47.2|
|Yes|Count|112|115|227|
||Percent|58.6|48.1|52.8|
|Total||191|239|430|
|||100|100|100|

|_Highest_<br>|_Baseline Earnings in Househ_<br>|_old; Households with 2+ Wor_<br>|_kers Employed at_<br>|_Baseline only_<br>|
|---|---|---|---|---|
|No|Count|74|137|211|
||Percent|51.4|69.2|61.7|
|Yes|Count|70|61|131|
||Percent|48.6|30.8|38.3|
|Total||144|198|342|
|||100|100|100|

|_Highest_|_Usual Hours of Work in Household; Households with 2+ W_|_orkers Employed_|_at Baseline only_|
|---|---|---|---|
|No|Count<br>79|124|203|
||Percent<br>54.9|62.6|59.4|
|Yes|Count<br>65|74|139|
||Percent<br>45.1|37.4|40.6|
|Total|144|198|342|
||100|100|100|

_Notes: Sample includes individuals employed at baseline._
# PPR Working Paper Series

||ima|Author|_Dat_<br>e|Contact<br>forpaer|
|---|---|---|---|---|
|WPS1 17|Europe, Middle East, and North Africa<br>(EMN) Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS1 18|Contract-Plans and Public<br>Enterprise Performance|John Nellis|October 1988|R. Malcolm<br>61707|
|WPS119|Recent Developments in Commodity<br>Modeling: A World Bank Focus|Walter C. Labys|October 1988|A. Daruwala<br>33716|
|WPS1 20|Public Policy and Private Investment<br>in Turkey|Ajay Chhibber<br>Sweder van Wijnbergen|October 1988|A. Bhalla<br>60359|
|WPS 121|CommercialBankProvisioning Against<br>Claims on Developing<br>Countries|Graham Bird|October 1988|I. Holloman-<br>Williams<br>33729|
|WPS122|Import Demand in Developing<br>Countries|Riccardo<br>Faini<br>Lant Pritchett<br>Femando Clavijo|November 1988|K. Cabana<br>61539|
|WPS123|Export Supply, Capacity and Relatve<br>Prices|Riccardo Faini|November 1988|K. Cabana<br>61539|
|WPS124|Intemational Macroeconomic<br>Adjustment, 1987-1992: A World<br>Model Approach|Robert E. King<br>Helena Tang|November 1988|K. Adams<br>33738|
|WPS125|The Effects of Financial Liberaliza-<br>tion on Thailand, Indonesia and<br>the Philippines|Christophe Chamley<br>Qaizar Hussain|October 1988|A. Bhalla<br>60359|
|WPS126|<br>Educating Managers for Business and<br>Goverment: A Review of Intemational||||
||Experience|Samuel Paul|November 1988|E. Madrona|
|||John C. Ickis<br>Jacob Levitsky||61711|
Figure 7. International synchronization of recessions <!-- Start of picture text --> A. Countries in recession, weighted<br><!-- End of picture text --> <!-- Start of picture text --> A. Countries in recession, weighted B. Countries in recession, unweighted<br>Percent Percent<br>100 Advanced economies EMDEs 100<br>Advanced economies EMDEs<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>Sources: Feenstra, Inklaar, and Timmer (2015); Kose and Terrones (2015); World Bank.<br>Note: Panel A shows the proportion of countries (weighted by GDP using market exchange rates) in recession,<br>defined as a contraction in per capita GDP. Panel B shows the unweighted proportion of countries in recession.<br>Shaded areas indicate global recessions in 1975, 1982, 1991, and 2009, and global downturns in 1958, 1998, 2001, and<br>2012. Data for 2019 and 2020 are estimates and forecasts, respectively.<br><!-- End of picture text --> 36
# **1. Introduction**

The need to reconcile rapid economic growth with greater environmental sustainability has led to calls for “greener growth” (World Bank 2013). A key question is how to achieve this greener growth. It is, after all, neither by chance nor by deliberate policy design that the growth of the last 250 years has largely come at the expense of the environment. Market mechanisms, by definition, cannot efficiently manage ill-priced resources. And environmental goods and services are plagued by market failures, such as externalities, and government failures, such as inadequate taxation (Hallegatte et al., 2012).

Epitomizing these challenges, climate change has been called the biggest market failure in the history of humanity (Stern, 2006) _._ Today, the international community has committed to maintain the increase in global temperature below 2 ̊C above pre-industrial levels. Such a commitment entails decarbonizing the world economy by 2100, hence a complete transformation of our economic systems. It also requires a very rapid decline in emissions. Figure 1 shows the speed at which global emissions of greenhouse gases need to decrease to respect this objective, as a function of when global emissions peak. To put this challenge in perspective, the figure also shows the most rapid decrease in emissions ever recorded in a single country over a 5-year period, namely the French shift to nuclear electricity in the 1980s.<sup>2</sup> Even if emissions peak soon, global emission reductions will need to proceed at a pace similar to or quicker than what was achieved in France then.

<!-- Start of picture text --> 10<br>2°C objective,<br>9 pessimistic<br>8<br>7<br>6 2°C objective,<br>optimistic<br>5<br>4<br>3<br>Highest historical<br>2 rate over 5 years<br>(French nuclear<br>1 program, 1980–85)<br>0<br>2010 2015 2020 2025 2030 2035 2040 2045<br>Peaking year for emissions<br>reach 2C objective (%)<br>Rate of emissions decrease after peak to<br><!-- End of picture text --> _Figure 1: Rate of emission decrease that is necessary after the global peak in emissions to keep man-made temperature increase below 2C. Source:_ (Guivarch and Hallegatte, 2013) _._ Government actions are needed urgently to make growth more sustainable. An obvious first step is for policies to price externalities, notably by removing distortive subsidies such as fossil fuel subsidies (IMF, 2013) and imposing carbon prices.

> 2 Excluding the decrease in emission resulting from major economic crisis such as the recent one or the GDP drop in former Soviet Union in the 90’s.

2
4

# **II.   THE DATABASE**

The database builds upon earlier work by Demirgüç-Kunt, Karacaovali, and Laeven (2005). The original dataset covered deposit insurance schemes through 2003. It was constructed through a combination of country sources, as well as earlier studies by Garcia (1999), Kyei (1995), and Talley and Mas (1990), among others.

This version updates the earlier database and extends it to 2013. Whenever possible, we relied on official sources. Our starting point was a comprehensive survey on financial sector regulations conducted by the World Bank in 2010. This survey asked national officials for information on capital requirements, ownership and governance, activity restrictions, bank supervision, as well as on the specifics of their deposit insurance arrangements. These data were combined with the deposit insurance surveys conducted by the International Association of Deposit Insurers in 2008, 2010, and 2011, and in the case of European countries with detailed information on deposit insurance arrangements obtained from the European Commission (2011). Discrepancies and data gaps were checked against national sources, including deposit insurance laws and regulations, and IMF staff reports. Information on government actions undertaken during the financial crisis was collected from Laeven and Valencia (2012), FSB (2010, 2012), Schich (2008, 2009), Schich and Kim (2011), and IMF staff reports.

Our focus is on deposit insurance for commercial banks. For countries with multiple DIS, the data provided relate only to the national statutory scheme. This means that stated coverage levels may understate actual coverage. For example, the complex voluntary DIS for commercial banks in Germany provides insurance of up to 30 percent of bank capital per depositor, essentially offering unlimited coverage for most depositors.

The full database, including information on arrangements other than the national statutory scheme, is available in spreadsheet format as an online Appendix to this paper. The source of the data is indicated in the appendix. The following section describes the variables used in the remainder of the paper.

# **A.   Variable Definitions**

# **Type of deposit insurance**

We follow Demirgüç-Kunt, Kane, and Laeven (2006) in arguing that a country may be assumed to offer implicit deposit insurance, given the strength of governmental pressures to provide relief in the event of a widespread banking insolvency, unless the country has passed formal legislation or regulation outlining explicit deposit coverage. Indeed, implicit coverage always exists regardless of the level of explicit coverage. Countries may have an explicit deposit insurance scheme without specifying an institution or fund to carry out powers laid out in statutes or regulation, but the issuance of temporary blanket guarantees by the government is not sufficient to qualify as having explicit deposit insurance. Hence, we assume that any country that lacks an
Table 7: Regression results for CDM projects cooperation level (Dependent variable is number of projects)

|Equation|1<sup>a</sup>|2|3<sup>b</sup>|4|
|---|---|---|---|---|
|Estimationprocedure|Poisson|Poisson|Poisson|Poisson|
|Dependet variable|_Number of_<br>_Projects_|_Number of_<br>_Projects_|_Number of_<br>_Projects_|_Number of_<br>_Projects_|
|_hst_Averag GDP_|7.47E-06***<br>(24.79)||8.35E-06***<br>(24.56)||
|_inv_Averag GDP_|8.46E-07***<br>(20.03)||1.06E-06***<br>(20.24)||
|_hst_inv_Averag GDP_|||-1.07E-12***<br>(-6.16)||
|_hst_Average Annual Energy Use_||2.98E-03***<br>(17.72)||3.18E-03***<br>(17.39)|
|_inv_Average Annual Energy Use_||7.95E-03***<br>(31.52)||8.60E-03***<br>(26.67)|
|_host_inv_Average Annual Energy Use_||||-1.70E-06***<br>(-2.58)|
|_hst_Ease of Doing Business_|-9.92E-03***<br>(-10.71)|-7.81E-03***<br>(-7.62)|-8.40E-03***<br>(-5.99)|-7.47E-03<br>(-4.79)|
|_inv_Ease of Doing Business_|-0.016***<br>(-5.63)|-2.13E-02***<br>(-7.64)|-1.33E-02**<br>(-1.96)|-1.92E-02***<br>(-2.96)|
|_host_inv_Ease of Doing Business_|||-5.32E-05|-2.55E-05|
||||(-0.76)|(-0.32)|
|_hst_Governance_|0.082***|0.160***|0.050|0.088*|
||(6.74)|(14.08)|(0.99)|(1.78)|
|_inv_Governance_|0.301***|0.290***|0.292***|0.303***|
||(15.42)|(16.74)|(14.34)|(16.36)|
|_host_inv_Governance_|||2.99E-03|6.47E-03|
||||(0.66)|(1.45)|
|_hst_Renewable Energy_|1.09E-03***|1.56E-03***|1.12E-03***|1.66E-03***|
||(12.84)|(17.55)|(12.76)|(18.38)|
|_inv_Renewable Energy_|-8.31E-04***|-1.89E-03***|-3.65E-05|-0.01***|
||(-5.25)|(-10.94)|(0.17)|(-4.74)|
|_host_inv_Renewable Energy_|||-1.28E-06***<br>(-4.72)|-1.35E-06***<br>(-4.50)|
|_hst_Impact Vulnerability_|0.074***<br>(25.03)|0.063***<br>(21.41)|0.114***<br>(14.11)|0.110***<br>(12.74)|
|_inv_Impact Vulnerability_|0.018***<br>(8.18)|0.023***<br>(9.20)|0.087***<br>(6.42)|0.104***<br>(7.18)|
|_host_inv_Impact Vulnerability_|||-1.22E-03***|-1.40E-03***|
||||(-5.32)|(-5.80)|
|_Trade1_|82.15***|35.95***|132.63***|54.82***|
||(6.94)|(2.96)|(8.46)|(3.37)|
|_Constant_|-8.72***|-8.01***|-11.26***|-11.08***|
||(-25.49)|(-25.38)|(-19.46)|(-18.52)|
|Observations|2771|2771|2771|2771|
|LogLikelihood|-2760.34|-2630.94|-2715.74|-2596.95|
|LR<br>|10076.69***|10335.49***|10165***|10403.48***|
|Pseudo R<sup>2</sup>|0.646|0.662|0.651|0.667|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10).

Note:<sup>a</sup> See Appendix 3, equation 1 for regression results of 3411 observations;<sup>b</sup> See Appendix 3, equation 2 for regression results of 3411 observations.

31
||**Table 3: Fina**|**ncial depend**|**ence of secto**|**rs**|||
|---|---|---|---|---|---|---|
|Dependent variable: Woman top<br>manager Y:1 N:0|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Bank credit (ratio of|3.030***|3.106***|2.971**|2.682**|2.725**|2.806**|
|GDP)*Financial dependence|(1.080)|(1.097)|(1.275)|(1.310)|(1.310)|(1.303)|
|Bank credit (ratio of GDP)|1.155**|1.098**|1.794**|1.707**|1.853**|24.394*|
||(0.487)|(0.537)|(0.741)|(0.792)|(0.797)|(13.903)|
|Financial dependence|0.044|0.041|0.339|0.478|0.498|0.459|
||(0.495)|(0.504)|(0.689)|(0.718)|(0.729)|(0.719)|
|GDP per capita (logs)||0.168|-0.454|-0.262|-1.131|-1.300|
|||(0.751)|(1.043)|(1.021)|(1.069)|(1.052)|
|Number of workers (logs)||-0.208***|-0.228***|-0.194***|-0.189***|-0.000|
|||(0.049)|(0.062)|(0.061)|(0.061)|(0.153)|
|Age of firm (logs)|||-0.096|-0.124|-0.131|-0.327|
||||(0.110)|(0.115)|(0.116)|(0.307)|
|Labor productivity (logs)|||-0.006|0.019|0.004|0.135|
||||(0.047)|(0.048)|(0.045)|(0.093)|
|Women owners Y:1 N:0|||2.600***|2.700***|2.703***|2.715***|
||||(0.134)|(0.148)|(0.149)|(0.150)|
|Women workers (proportion)|||1.444***|1.446***|1.482***|1.497***|
||||(0.205)|(0.202)|(0.205)|(0.207)|
|Exports (direct, share of sales)|||-0.125|-0.122|-0.133|-0.096|
||||(0.252)|(0.248)|(0.248)|(0.245)|
|Foreign ownership|||0.259|0.298|0.314|0.299|
||||(0.297)|(0.296)|(0.296)|(0.291)|
|Quality certification Y:1 N:0|||0.082|0.108|0.107|0.117|
||||(0.142)|(0.142)|(0.141)|(0.141)|
|Overdraft Y:1 N:0||||-0.095|-0.102|-0.105|
|||||(0.126)|(0.126)|(0.125)|
|Multi establishment firm Y:1 N:0||||0.127|0.091|0.105|
|||||(0.178)|(0.181)|(0.179)|
|Obtaining license is a major||||0.055|0.059|0.055|
|obstacle Y:1 N:0||||(0.185)|(0.186)|(0.186)|
|Losses from power outages||||1.538*|1.671**|1.841**|
|||||(0.802)|(0.779)|(0.799)|
|Firm audited Y:1 N:0||||<br>-0.084|<br>-0.087|<br>-0.076|
|||||(0.119)|(0.119)|(0.119)|
|Registered when started Y:1 N:0||||<br>-0.019|<br>-0.028|<br>-0.065|
|||||<br>(0.214)|<br>(0.214)|<br>(0.210)|
|Publicly listed company Y:1 N:0||||0.607|0.564|0.627|
|||||(0.585)|(0.595)|(0.597)|
|Privately held Limited Liability||||0.853*|0.838|0.878*|
|<br>company Y:1 N:0|||||||

40