rafmacalaba/datause-encoder-data huggingface dataset:
at four separate measures of corruption, the findings in this paper suggest that corruption can act both as a push factor and as a pull factor for migration decisions. Then, tackling corruption may be a viable policy option for reducing outward migration flows, as well as encouraging foreign immigration. Our results open the door to several important questions. What are the most effective ways to design anti-corruption policies that are able to retain – local workers and attract foreign workers? Is it corruption in general or a particular – dimension of corruption that affects migration flows? Does the effect of a reduction in corruption levels vary between different types of workers?

# **References**

- Abdih, Y., R. Chami, J. Dagher, and P. Montiel (2012). Remittances and Institutions: Are Remittances a Curse? _World Development 40(4)_ , 657–666.

- Ahmad, N. and S. Arjumand (2015). Impact of Corruption on GDP per Capita through International Migration: An Empirical Investigation. _Quality & Quantity 50(4)_ , 1633– 1643.

- Anderson, J. E. and E. Van Wincoop (2003). Gravity with Gravitas: A Solution to the Border Puzzle. _American Economic Review 93(1)_ , 170–192.

- Ariu, A. and P. Squicciarini (2013). The Balance of Brains: Corruption and High Skilled Migration. _Discussion Papers IRES - Institut de Recherches Economiques et Sociales_ .

- Auer, Daniel R ̈omer, F. and J. Tjaden (2020). Corruption and the Desire to Leave: Quasiexperimental Evidence on Corruption as a Driver of Emigration Intentions. _IZA Journal of Development and Migration 11(1)_ .

- Banzhaf, H. S. and R. P. Walsh (2008). Do People Vote With Their Feet? An Empirical Test of Tiebout’s Mechanism. _American Economic Review 98(3)_ , 843–863.

- Batista, C. and P. C. Vicente (2011). Do Migrants Improve Governance at Home? Evidence from a Voting Experiment. _The World Bank Economic Review 25(1)_ , 77–104.

- Bauernschuster, S., O. Falck, S. Heblich, J. Suedekum, and A. Lameli (2014). Why are Educated and Risk-loving Persons more Mobile Across Regions? _Journal of Economic Behavior & Organization 98_ , 56–69.

- Beine, M., F. Docquier, and C. Ozden<sup> ̈</sup> (2011). Diasporas. _Journal of Development Economics 95_ , 30–41.

- Beine, M. and K. Sekkat (2013). Skilled Migration and the Transfer of Institutional Norms. _IZA Journal of Migration 2(9)_ .

- Bologna Pavlik, J., E. Lujan Padilla, and B. Powell (2019). Cultural Baggage: Do Immigrants Import Corruption? _Southern Economic Journal 85(4)_ , 1243–1261.

- Borjas, G. J. (1987). Self-selection and the Earnings of Immigrants. _American Economic Review 77(4)_ , 531–553.

- Clark, J. R., R. Lawson, A. Nowrasteh, B. Powell, and R. Murphy (2015). Does Immigration Impact Institutions? _Public Choice 163_ , 321–335.

- Clark, X., T. J. Hatton, and J. G. Williamson (2007). Explaining US Immigration, 19711998. _The Review of Economics and Statistics 89(2)_ , 359–373.

9
Lastly, the survey-to-survey imputation exercise uses household-level data on a range of non-monetary indicators from the GHS panel for Nigeria for 2010/11, 2012/13, 2015/16, and 2018/19. Each wave contains around 5,000 households, and is representative at the national, zone, and urban-rural level. Within each wave, data are collected in two distinct visits to the same set of households: the first “post planting” visit takes place sometime between August and October (of 2010 for the 2010/11 survey, for example) and the second “post-harvest” visit takes places sometime between January and April of the following year.<sup>11</sup> For the purpose of the survey-to-survey imputations, we use information on household demographic characteristics (dependency ratio), dwelling characteristics (main floors material, main source of cooking fuel, availability of toilet facility in the household), household head’s demographics (age, gender) and employment indicators (employment in non-farm activities and in wage-employment), household assets ownership (TV set, air conditioner, generator, microwave, computer, cars and other vehicles, microwave, washing machine), and _frequency_ of consumption – that is dummy variables for whether or not an item was consumed – for some food and non-food items (imported rice, beef, fresh fish, and recharge cards).

# 4. Methodology

This analysis uses two approaches to estimate a poverty trend for Nigeria for the period 2009-2019: backcasts and survey-to-survey imputations. This section describes these approaches in detail. One key contribution of the paper is that while the two methods described in this section are very different, they yield very similar results, as we show in section 5.

# _<u>4.1. Backcasting</u>_

For the backcasting exercise, we start with the full consumption vector in the 2018/19 NLSS then construct the consumption vector in each previous year by “rolling back” consumption for each household using sectoral real GDP growth rates and population growth rates. The sectoral GDP data are already in real terms, having been adjusted using the GDP deflator: we therefore do not conduct any additional price adjustments to deflate the consumption vector in each year. Real sectoral GDP growth rates are converted to per capita terms by applying population growth “flat” to each sector: our approach does not, therefore, allow for sectoral switching. However, given the slow pace of structural transformation in Nigeria, this may be a tenable assumption over the period of interest.<sup>12</sup> We use the household head’s employment

> 11 Detailed documentation on the GHS survey can be found in the World Bank Microdata Library, see <u>https://microdata.worldbank.org/index.php/catalog/1002.</u>

> 12 - See Jenq, Lain, and Vishwanath (2021) https://pubdocs.worldbank.org/en/511161631652256763/pdf/Good <u>Jobs-for-a-New-Generation-Delivering-Quality-Jobs-for-Young-Nigerians-After-COVID-19.pdf.</u>

8
Stock, J. and D. Staiger (1997). “Instrumental Variables Regression with Weak Instruments,” _Econometrica_ 65, No 3, pp. 557-586.

Welch, K., M. McMillan and D. Rodrik (2003). “When Economic Reform Goes Wrong: Cashews in Mozambique,” Brookings Trade Forum 2003.

Winters, A., N. McCulloch, and A. McKay (2004). “Trade Liberalization and Poverty: The Evidence So Far,” _Journal of Economic Literature_ , XLII, pp. 72-115.

26
# **2.1 Construction of components**

For each pixel, we construct an estimated value of production based on high spatial resolution information of the five components that serve as priors in the modeling process: crop, livestock, forestry, fishing, and hunting. Given the lack of information on the hunting component, we disaggregate the forestry component into two parts: timber and non-timber products of forestry. The non-timber products of forestry includes an even distribution of hunting. The construction of the five components is described below in four subsections: crop, livestock, forestry (timber and non-timber) and fishing.

## **2.1.1 Crop value of production**

The crop component in the gridded AgGDP is generated by multiplying the quantity of production from the global SPAM 2010 version 1 dataset<sup>2</sup> (You, Wood-Sichra, et al., 2018) with the producer prices at the country level from FAOSTAT (FAO, 2016) for each crop and then summed together.<sup>3</sup> As mentioned earlier, SPAM is a cross-entropy model, which calculates a plausible allocation of crop areas and production to approximately 10 km pixels, based on agricultural statistics at national and sub-national levels, combined with gridded layers of cropland, irrigated areas, population density and potential crop areas and yields (Yu et al., 2020). SPAM’s output distinguishes between 42 crops (33 individual crops, 9 aggregated crops) that together add up to practically all cultivated crops in a country with four parameters including production, yield, physical area and harvest area.

For aggregated SPAM crops (such as other cereals, other pulses, vegetables, fruits, etc.), we computed their prices by taking the weighted average of their components, as follows: where _Jagg_ is the aggregated crop group, _j_ is any crop that belongs to _Jagg_ , _PriceJagg_ is the price of the aggregated crop group, _pricej_ is the price of crop _j_ , and _prodj_ is the production of _j_ .

For each grid, the value of crop production is thus: where _Cropvali_ is the value of total crop production in pixel _i_ , _prodi,j_ is the production of

> 2Available at www.mapSPAM.info

> 3As for the producer price, ideally, we need sub-national level figures, but such a dataset is not available globally. Therefore, we use the FAOSTAT’s national producer prices.

6
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- [11] Dupas, Pascaline (2007). "Relative Risks and the Market for Sex: Teenage Pregnancy, HIV, and Partner Selection in Kenya." mimeo, Dartmouth College.

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27
|(11)<br>(12)|-1.102_∗∗∗_<br>-0.432_∗∗∗_<br>(0.1780)<br>(0.2012)|-0.119_∗∗∗_<br>-0.035_∗∗_<br>(0.0133)<br>(0.0061)|0.199_∗∗∗_<br>0.021<br>(0.0316)<br>(0.0144)|X<br>X|Emp<br>K|133<br>122|0.19<br>0.14|27522<br>92058|5<br>6|and country fxed|ermany, Italy,|Private LLCs. The|ifcation 3 includes|and 5000 employees||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|(10)|-0.462_∗∗∗_<br>(0.1247)|-0.149_∗∗∗_<br>(0.0122)|0.087_∗∗∗_<br>(0.0293)|X|Emp|133|0.16|99474|4|as 3 digit SIC|a, Finland, G|egistered as|other”. Spec|between 50|e frm size|
|(9)|-0.349_∗∗∗_<br>(0.0752)|-0.133_∗∗∗_<br>(0.0111)|0.097_∗∗∗_<br>(0.0250)|X|Emp|133|0.20|336086|3|cated, as well|govina, Estoni|0 employees r|s other than “|des frms with|bor to measur|
|(8)|-0.328_∗∗∗_<br>(0.1161)|-0.110_∗∗∗_<br>(0.0090)|0.053_∗∗_<br>(0.0240)|X|Emp|133|0.24|140002|2|is college edu|ia and Herzo|en 10 and 500|gistration type|fcation 5 inclu|) instead of la|
|(7)|-0.576_∗∗∗_<br>(0.1032)|-0.140_∗∗∗_<br>(0.0085)|0.117_∗∗∗_<br>(0.0209)|X|Emp|133|0.17|105120|1|pulation that|ulgaria, Bosn|h frms betwe|cludes all re|oyees. Speci|eus database|
|(6)|-0.158_∗∗∗_<br>(0.0653)|-0.028_∗∗_<br>(0.0022)||X|K|122|0.14|92058|6|a, share of po|e consists of B|cifcation wit|ecifcation 2 in|and 250 empl|d in the Amad|
|(5)|-0.127_∗∗∗_<br>(0.0455)|-0.041_∗∗∗_<br>(0.0038)||X|Emp|133|0.18|27522|5|gdp per capit|e. Full sampl|baseline spe|s follows. Sp|h between 10|ets, as defne|
|(4)|-0.167_∗∗∗_<br>(0.0646)|-0.116_∗∗_<br>(0.0050)||X|Emp|133|0.16|99474|4|, regional log|gistration typ|fcation 1 is the|the baseline a|des frms wit|capital (or ass|
|(3)|-0.158_∗∗∗_<br>(0.0653)|-0.095_∗∗_<br>(0.0040)||X|Emp|133|0.20|336086|3|log frm age|control for re|raine. Speci|es relative to|cation 4 inclu|ation 6 uses|
|(2)|-0.135_∗∗∗_<br>(0.0573)|-0.091_∗∗_<br>(0.0038)||X|Emp|133|0.24|140002|2|de controls for|nally includes|witzerland, Uk|lternate sampl|oyees. Specif|rame. Specifc|
|(1)|-0.160_∗∗∗_<br>(0.0638)|-0.096_∗∗_<br>(0.0032)||X|Emp|133|0.17|105120|1|ations inclu|on 2 additio|nd, Spain, S|ations use a|n 5000 empl|S sampling f|
|Dependent: FM|ln_Size_|Trust|Trust_×_ln_Size_|Controls|Size Measure|No. Clusters|_R_2|_N_|Specifcation|Notes: All specifc|effects. Specifcati|Netherlands, Pola|remaining specifc|frms with less tha|similar to the WM|

40
51 From the case study by Nemova and Lippolt (1995) of Russian Federal Employment Services (FES) it is known that many of the tools to combat unemployment used in the cited transition economies of Central Europe have also been employed in the Russian Federation. However, the net effects of the application of e.g. training schemes and public works cannot be inferred from their study<sup>4</sup> l. Also, their study does not embed the discussion of ALMPs into the above presented context of the Russian labour market. Combining the presented stylised facts of the Russian labour market with the evidence on the effects of ALMPs in Central Europe one can draw some general, very tentative lessons for policy makers in the Russian Federation.

From the discussion on the age structure of Polish unemployment it should be clear that it is a rather complex undertaking to identify problem groups with certainty. Participation rates of such groups need to be analysed and compared to economies with similar development and age structure. Relatively low participation rates, especiallv of the young, might hint at greater opportunities in the rather large shadow economy than in the formal sector. As a counterweighing argument, Russian employment legislation is rather restrictive in its recognition of benefit entitlement and, given often high transportation costs, might produce a strong downward bias for the groups who are especially vulnerable. One of the major tasks of the FES at this point in time, i.e. before registered unemployment will reach levels of other transition economies, should be to promote employment legislation and to develop an infrastructure which both allow the identification of the true problem groups among the unemployed.

With continuous labour hoarding in state enterprises, wage subsidy programmes seem an absurd variant of ALMPs which always should be considered flanking measures for the economic transformation towards a market economy. Given the political structures at the local and regional level, it seems rather an illusion to try to restrict such subsidies to de novo private firms which could be targeted without retarding the restructuring efforts of policy makers.

The stylised facts, presented above, point, at least for the reported period (beginning of reform programme to the middle of 1994), to a differently performing labour market than in e.g. Hungary and Poland. Overall oufflow from unemployment, using the LFS measure, is much higher and problem groups cannot be made out, at least if one uses as an identifier the outflow rate from unemployment. Nevertheless, there 41 Unfortunately,they do not discuss possible distortive effects of the various labour market programmes.
of specific ratings and the willingness of business people to criticise state institutions. Fries et al. (2003) check for such perception bias in the BEEPS 2002 by statistically comparing measures obtained from the aggregation of survey responses to related objective measures and find no significant perception biases across the countries in the sample. Since the BEEPS 2009 follows a similar methodology, we may be reasonably confident that perception bias will not affect the results of the analysis. However, as a further control, the analysis that follows will make use of sector and country level fixed effects.

# **_3.1 The Bribe Tax and Productivity_**

The aim of this study is to evaluate the extent to which institutional inefficiencies experienced by firms –namely corrupt practices - may be a drag on their productivity. At the micro level, there are a number of reasons for expecting negative consequences of corruption for productivity. Firstly, as discussed in the previous section, corruption distorts the allocation of scarce resources away from the most productive use. This, all other things equal, should have a negative effect on productivity. Secondly, corruption may decrease firms incentives - or increase costs - of expanding productive capacity or investment (as in Alesina et al., 2005), which, again, would have a negative impact on productivity. On the other hand, as foreseen in the “efficient grease” hypothesis previously discussed, corruption may help a firm to cut through red tape and hence increase productivity. At the same time, both the occurrence and the effects of corrupt behaviour for individual firms may be linked to the quality of the institutional environment in the country. In this sense, a crucial role may be played by the degree to which corruption is a widely recurrent and accepted phenomenon, as well as by the ability of legal structures, such as courts or administrative recourse mechanisms within the public administration, to enforce contracts between individuals and the state and sanction deviant practices.

In order to capture the complexity of the phenomenon of corruption and its potentially varied effects on the performance of individual firms, the empirical methodology will proceed in three steps. First, is an analysis of the effects of the bribe tax and of the time tax on individual firms, controlling for firm, sector and country characteristics that may influence both phenomena.

14
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26
_Figure 13 Main source of drinking water in DRM and GLSS6_ <!-- Start of picture text --> GLS DRM<br>70%<br>Sachet water<br>54%<br>Pipeborne outside 17%<br>dwelling 22%<br>Pipeborne inside 9%<br>dwelling 12%<br>0% 20% 40% 60% 80%<br><!-- End of picture text --> _Figure 14 - Access to sanitation service in DRM and GLSS6_ <!-- Start of picture text --> GLS DRM<br>32%<br>Public toilet and other<br>67%<br>20%<br>KVIP<br>14%<br>10%<br>Pit latrine<br>3%<br>34%<br>WC<br>16%<br>4%<br>No facility 0%<br>0% 20% 40% 60% 80%<br><!-- End of picture text --> _Figure 13 Garbage disposal system for DRM and GLSS6_ <!-- Start of picture text --> GLS DRM<br>1%<br>Dumped indiscriminately<br>2%<br>16%<br>Public dump<br>23%<br>17%<br>Burned by household<br>1%<br>66%<br>Collected<br>74%<br>0% 20% 40% 60% 80%<br><!-- End of picture text --> In the DRM survey, more households report having their waste collected and using a public dump than the GLSS6 (see Figure 13). Most households – 74%, reported having their waste collected. Various waste collection options are available to households in slum settlements. In Accra, the mode of waste collection, at the household level, depends on the neighborhood. In high income neighborhoods, a franchise system is operated where private companies collect waste from house to house on a weekly basis and charge for their services. In slum settlements, results from the community survey report different types of waste 39
**Table 2.3: Net Financial Inflows Resulting From Emissions Trading US$billion, 2000 Prices. Long-run concentration target 450 ppm CO2.**

|Region|Emission<br>populatio|s rights depend<br>n|ing on|Emissio|ns rights by|baseline|
|---|---|---|---|---|---|---|
||2020|2060|2100|2020|2060|2100|
|Africa|64|278|211|-1|-9|-28|
|China|14|-14|-29|15|89|22|
|FormerS. U.|-18|-16|16|6|22|31|
|EastEurope|-6|12|19|-1|25|18|
|India|62|210|244|1|147|250|
|Japan|-18|-39|-29|-2|-5|-15|
|Latin<br>America|20|-37|-149|-3|-118|-175|
|United States|-25|-236|-333|-20|-61|-109|
|Western<br>Europe|-36|-58|10|-5|13|50|
|OECD|-83|-415|-383|-29|-64|-101|

Source: IMF (2008a)

**Table 2.4: Net Financial Flows From Emissions Trading US$billion , 2000 Prices. Long-run Concentration Target 550 ppm CO2.**

|Region|Emission<br>populatio|s rights depend<br>n|ing on|Emissio|ns rights by|baseline|
|---|---|---|---|---|---|---|
||2020|2060|2100|2020|2060|2100|
|Africa|15|193|269|0|4|-8|
|China|0.5|-30|-21|-1|0|-4|
|Former S. U.|-5|-25|4|0|1|-23|
|East Europe|1|8|1|0|9|16|
|India|15|58|103|1|11|163|
|Japan|-3|-26|-29|0|0|-1|
|<br>Latin<br>America|5|36|-86|0|-6|-105|
|United States|-19|-115|-143|-1|-7|-50|
|Western<br>Europe|-7|-56|-46|-1|-8|-2|
|OECD|-30|-203|-223|-3|-17|-74|

Source: IMF (2008a).

Table 2.5 presents an alternative calculation of net cumulated trading volumes (nondiscounted), up to 2100, using a different modeling framework developed by IIASA, and utilized in a study by WBGU (2003). Also here the basis is population-related emissions rights, but in a less absolute sense than the calculations in Tables 2.3-2.4, using now a “contraction and convergence” assumption whereby allocations converge only gradually over 9