rafmacalaba/datause-encoder-data huggingface dataset:
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE The structure of the regional air transport market has altered significantly since 2001 with the collapse of regional players and the emergence of new carriers (table 2.12). As of 2007 the regional air transport market comprised three major carriers—Air Service, Hewa Bora Airlines, and TAAG Angola Airlines— which together accounted for 65 percent of the regional market. Other carriers—Trans Air Congo, Ethiopian Airlines, and Star Equatorial Airlines—comprised a further 25 percent of the market. Ethiopian Airways is a growing presence in the region. The situation in 2007 was a complete turnaround from 2001, when the three major carriers—Air Afrique, Air Gabon, and Cameroon Airlines—accounted for over 70 percent of the market. European carriers such as Air France, Swiss Air, or Brussels Air SN that were present in 2001 had largely disappeared by 2007.

**Table 2.11 . Measuring progress toward implementation of the Yamassoukro Declaration**

|Community|General status of YD implementation|Status of air services liberalization|Overall<br>implementation<br>score|
|---|---|---|---|
|Arab Maghreb Union|No implementation.|No liberalization within the Arab Maghreb Union<br>initiated, but need is recognized.|1|
|Banjul Accord Group|Principles of the YD agreed upon in a<br>multilateral air services agreement.|Up to fifth freedom granted, tariffs are free, and<br>capacity/frequency is open.|4|
|Economic and<br>Monetary Community<br>of Central Africa|Principles of the YD agreed upon in an<br>air transport program. Some minor<br>restrictions remain.|Up to fifth freedom granted, tariffs are free, and<br>capacity/frequency is open. Maximum two carriers<br>per state may take part.|5|
|Common Market for<br>Eastern and<br>Southern Africa|Full liberalization agreed upon (―legal<br>notice no. 2‖), but application and<br>implementation remain pending until a<br>joint competition authority is<br>established.|Pending. Operators will be able to serve any<br>destination (all freedoms), and tariffs and<br>capacity/frequency will be free.|3|
|East African<br>Community|The EAC council issued a directive to<br>amend bilaterals among the EAC states<br>to conform with the YD.|Air services are not liberalized, as the amendments<br>of bilaterals remain pending.|3|
|Southern African<br>Development<br>Community|No steps taken toward implementation,<br>although the civil aviation policy<br>includes gradual liberalization of air<br>services within the SADC.|No liberalization has been initiated.|2|
|West African<br>Economic and<br>Monetary Union|The YD is fully implemented.|All freedoms, including cabotage, granted. Tariffs<br>have been liberalized.|5|

_Source:_ Bofinger 2009.

29
Niimi, Y., P. Vasudeva-Dutta, and L.A. Winters. 2003. “Trade Liberalization and Poverty Dynamics in Vietnam.” _Poverty Research Unit at Sussex Working Paper_ No: 17.

Reimer, J. J. 2002. “Estimating the Poverty Impacts of Trade Liberalization.” _World Bank Policy Research Working Paper Series_ No: 2790.

Porto, G. 2003. ‘Trade reforms, market access and poverty in Argentina.” _World Bank Policy Research Working Paper Series_ No: 3135.

Van De Walle, D. and D. Cratty. 2003. “Is the Emerging Non-Farm Market Economy the Route Out of Poverty in Vietnam?” _World Bank Policy Research Working Paper Series_ No: 2950.

28
<!-- Start of picture text --> 50<br>45<br>& 40<br>2©35<br>s2§30<br>s&3 2<br>a©8 20<br>S15<br>ss2 510<br>0<br>Jani9 Apr19 Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22<br>—= Youth (15-24 yrs.) Adult (25+ yrs.)<br>Source: CMIE People of India (15 yrs above)<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>45<br>40<br>== 35<br>2£3S 30<br>=<br>© 25<br>=<br>2 2<br>E<br>2 15<br>—) 10<br>5<br>0<br>Jan19 Apri9 Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22<br>— General oBc sT — sc<br>‘Source: CMIE People of india (15 yrs above)<br><!-- End of picture text -->
cost) and the expenses incurred for the rent-seeking activity (indirect cost). As we discuss in the next paragraph, just the direct costs can be substantial and larger than the expenses on electricity. Meeting these expenses is more costly for firms that are financially constrained. For instance, we argued above that corruption hurts firms because it diverts resources from productive activity to rent-seeking activity. Firms that have easy access to finance can compensate for the loss of funds by borrowing. As financial constraints rise, the option of borrowing becomes increasingly more costly and less feasible.

Before going to the formal empirical analysis, we provide some descriptive analysis about the financial burden that corruption imposes on firms. The Enterprise Surveys (ES) data that we use for our empirical analysis provides some insights into the magnitude of the bribe payment visà-vis other cost elements. We focus here on firms that report paying bribes (about 20 percent of the sampled firms), as the role of access to finance in our model is conditional on payment of bribe. For these firms, Figure 1 shows our main corruption measure, that is the amount of bribes firms pay public officials to “get things done” as a percentage of firms’ annual sales. For comparison, the figure also shows the electricity costs (as percentage of annual sales) for the same firms. All averages shown are median values. Reported bribes are higher, equaling 2.77 percent compared with 0.13 percent of electricity cost. We confirm that the qualitative nature of the finding reported here holds even when we exclude from the sample firms with no electricity cost. Thus, the direct cost of corruption (bribes paid) is significant and likely to put a strain on firms’ financial condition. Information on the indirect monetary cost of corruption is not available.

Another piece of evidence comes from firms’ perception of access to finance as an obstacle to firms’ current operations. If corruption imposes significant costs on firms, then firms that pay bribes should find access to finance as a bigger obstacle to their current operations than firms that 5
paper complements the literature by providing a better understanding of the role of policies in reducing migration barriers.

The remainder of the paper is organized as follows: section 2 describes the data sources, section 3 describes the empirical specification, section 4 describes the results, section 5 presents the potential welfare impacts of the BLAs, and section 6 concludes.

# **2 Data**

## **2.1 Data on migration**

This paper uses the most comprehensive data set on bilateral migration stocks spanning from 1960 to 2020, in ten-year intervals. Migration stocks for years 1960, 1970, 1980, 1990, and 2000 are taken from World Bank (2022) (see Ozden<sup> ̈</sup> et al., 2011, for methodological details). The data for 2010 and 2020 come from UNDESA (2020) which employs similar methodology. These data sets use information collated from censuses and surveys at destination and origin countries to construct a matrix of bilateral migration stock data from each of the origin countries to each potential destination country. This combination of datasets is used widely by many research studies and reports (including in Clemens, 2020, Shrestha, 2023, World Bank, 2023).

A migrant, in these data sets, is typically a person who is born in a country that is different from the country in which they are currently living. To that extent, it includes, in principle, people who have migrated for various reasons and channels, including those who migrated through irregular channels (Ozden<sup> ̈</sup> et al., 2011). However, it is possible that these data sources may misreport irregular migrants for various reasons. To the extent such misreporting exists, measurement errors can mute the estimated relationship.

In addition, this dataset only captures the _stock_ of migrants and does not capture the _flow_ of people between these countries. Migrants who move temporarily, particularly between the census years, will not be fully captured by this dataset. If BLAs increase temporary migration, then the full extent of the impact on migration will not be captured by this dataset. Hence, we interpret our estimates as a lower bound of the migration impacts.

7
# **Figure 12 Public Ownership: Size, Scope and Extent of Control over Public Enterprises**

# **a. Size of the public enterprise sector**

<!-- Start of picture text --> 6<br>5<br>4 OECD average<br>3<br>2<br>1<br>0<br>b. Scope of the public sector<br>6.0<br>5.0<br>OECD average<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>c. Control of public enterprise by legislative bodies<br>5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5 OECD average<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>United KingdomSlovak RepublicBelgiumJapanKoreaNew ZealandIceland DenmarkUnited StatesIreland AustraliaCanadaNetherlandsBrazilRomaniaGreeceMexicoGermanyAustriaFinlandHungaryLuxembourgSpain SwedenCzech RepublicBulgaria PortugalSwitzerlandFrance ItalyNorwayTurkeyPoland<br>AustraliaAustriaJapanIcelandSwedenDenmarkUnited StatesIreland CanadaGreeceMexicoTurkeyKoreaBelgiumFranceNetherlandsCzech RepublicGermanySpainNorwayNew ZealandSwitzerlandUnited KingdomFinlandLuxembourgPolandBrazilSlovak RepublicBulgaria ItalyPortugalRomaniaHungary<br>Slovak RepublicJapan BrazilAustraliaUnited StatesNew ZealandSwitzerlandLuxembourgUnited KingdomPortugalCanadaDenmarkSpainIrelandSweden KoreaIcelandNetherlandsHungaryGermanyCzech RepublicFinlandBelgiumBulgariaMexico ItalyGreeceAustriaNorwayFranceTurkeyPolandRomania<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values are for 2006 for Romania and Bulgaria and for 2003 for all other countries. Romania’s 2002 score was calculated using a different methodology so is not strictly comparable. For full data set see appendix I.

20
_The Negative Binomial Regression Model_ We now address over-dispersion in our sample. The PRM accounts for observed heterogeneity (i.e., observed differences among sample members) by specifying the rate _μi_ as a function of observed _xk_ ’s. In practice the PRM rarely fits due to _over-dispersion_<sup>_23_</sup> . That is, the model underestimates the amount of dispersion in the outcome. The negative binomial regression model (NBRM) addresses the failure of the PRM by adding a parameter _α_ that reflects _unobserved_ heterogeneity among observations. For example, with three independent variables, the PRM is: The NBRM adds an error _ε_ that is assumed to be uncorrelated with the _x_ ’s, where the second step follows by basic algebra, and the last step simply defines _δ ≡_ exp ( _ε_ ). To identify the model, we assume that _E_ ( _δ_ ) = 1 which corresponds to the assumption _E_ ( _ε_ ) = 0 in the PRM. With this assumption, it is easy to show that: Thus, _the_ PRM _and the_ NBRM _have the same mean structure_ . That is, if the assumptions of the NBRM are correct, the expected rate for a given level of the independent variables will be the same in both models. However, the standard errors in the PRM will be biased downward, resulting in spuriously large _z_ -values and spuriously small _p_ -values (Cameron and Trivedi 1998).

The distribution of observations given both the values of the _x_ ’s _and δ_ is still Poisson in the NBRM. That is, Since _δ_ is unknown, we cannot compute Pr ( _y |_ **x** ). This is resolved by assuming that _δ_ is drawn from a gamma distribution (see Long (1997: 231-232) or Cameron and Trivedi (1998:70-79) for details). Then we can compute Pr ( _y |_ **x** ) as a weighted combination of Pr ( _y |_ **x** _, δ_ ) for all values of _δ_ , where the weights are determined by Pr ( _δ_ ). This leads to the negative binomial distribution:

The larger value of _α,_ the greater spread in the data; indeed, if _α_ = 0, the NBRM reduces to the PRM, which turns out to be the key to testing for over-dispersion.

> _23 Using STATA 8.2 SE, we fit the Poisson regression for our basic model. As with most Poisson regressions applied to real-world data, we find a poor fit. Various measures of pseudo and adjusted R-squared are suspiciously high and the Chi-Square test for the goodness of fit of the regression forces us to reject the null hypothesis that our distribution is a Poisson distribution. The bad fit of the PRM is not a surprise since the standard deviation of our distribution is about three times the mean. This is partly due to the very high number of PSP projects in China. Even after for correcting for outliers (China), the SD of the distribution (9.34) is still almost twice the mean (5.6) – See Annex B for detailed descriptive statistics._

22
# FIGURE 8. URBAN AND RURAL INFANT MORTALITY TRENDS IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986)

<!-- Start of picture text --> ew~~~O<br>------- ~EsRural<br>(60% of the pop.)<br>All<br>o<br>o 0 Urban<br><!-- End of picture text --> <!-- Start of picture text --> °<br><!-- End of picture text --> Year (1) NOTE: (1) DMR r -5 MOVING
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 32 Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H. and Ward, P. J. (2016) A global reanalysis of storm surges and extreme sea levels. _Nature Communications_ **7** , Article number: 11969, doi:10.1038/ncomms11969 Nicholls, R.J. (1995) Coastal megacities and climate change. GeoJournal, 37, 369–379.

Nicholls, R. J. (2010). Impacts of and responses to sea-level rise. In J.A. Church, P.L. Woodworth, T. Aarup, and W.S. Wilson (Eds.), Understanding Sea-Level Rise and Variability. (pp. 17-51). Chichester, GB: Wiley-Blackwell. DOI: 10.1002/9781444323276.ch2 Nicholls, R.J. (2018) Adapting to Sea-Level Rise. Resilience: The Science of Adaptation to Climate Change, Elsevier, Alverson, K., Zommers, Z. (eds) Paperback ISBN: 9780128118917, in press.

Nicholls, R.J., Hanson, S., Herweijer, C., Patmore, N., Hallegatte, S., Corfee-Morlot, J., Château, J. and Muir-Wood, R. (2008) Ranking port cities with high exposure and vulnerability to climate extremes: exposure estimates. Paris, France, Organisation for Economic and Co-operative development (OECD), 62pp. (Environment Directorate Working Papers, (ENV/WKP(2007)1) ).

Nicholls, R. J., Brown, S., Hanson, S., & Hinkel, J. (2010). Economics of coastal zone adaptation to climate change. (World Bank Discussion Papers; No. 10). Washington, US: International Bank for Reconstruction and Development / World Bank.

Nicholls, R.J., Brown, S., Goodwin, P., Wahl, T., Lowe, J., Solan, M., Godbold, J.A., Haigh, I.D., Lincke, D., Hinkel, J., Wolff, C., Merkens, J. (2018) Stabilisation of global temperature at 1.5°C and 2.0°C: Implications for coastal areas. Philosophical Transactions of the Royal Society, A 376: 20160448.http://dx.doi.org/10.1098/rsta.2016.0448 O’Neill, B.C., Kriegler, E., Riahi, K., Ebi, K.L., Hallegatte, S., Carter, T.R., Mathur, R., van Vuuren, D.P. (2014) A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change 122, 387–400. doi:10.1007/s10584-0130905-2 Peltier, W. R. (2004) Global glacial isostasy and the surface of the ice-age Earth: The ICE5G (VM2) Model and GRACE. Annual Review of Earth and Planetary Sciences, 32(1), 111149. doi:10.1146/annurev.earth.32.082503.144359 Ranger, N., Reeder, T. & Lowe, J. 2013 Addressing ‘deep’ uncertainty over long-term climate in major infrastructure projects: four innovations of the Thames Estuary 2100 Project. EURO Journal on Decision Processes 1, 233–262. (DOI:10.1007/s40070-013-0014-5). Rabus, B., Eineder, M., Roth, A., Bamler, R. (2003) The shuttle radar topography mission: a new class of digital elevation models acquired by spaceborne radar. ISPRS Journal of Photogrammetry and Remote Sensing 57, 241–262.

Sadoff, C.W., Hall, J.W., Grey, J.C.J.H., D. an. Aerts, Ait-Kadi, M., Brown, C., Cox, A., Dadson, S., Garrick, D., Kelman, J., McCornick, P., Ringler, C., Rosegrant, M., Whittington,
- 20 -

**Flgure** 7

<!-- Start of picture text --> GDP GROWTH RATE<br>CArvuw I av ) 6a<br>7<br>5.4~~~~~~~~~~.<br>4A~~~,<br>4<br>6-S 3<br>|~~~~~~~~~~~4 1 9 1S 4X> 2<br>'1<br>Olu na Kanlya ~lawl Targamla L.ganda<br><!-- End of picture text -->

Source: IMF and World Bank data, and authors' estimates.

## _V.3_ **_Performnance_** _Indicatorn_

# **V.3.1** GDP **Growth Rate**

GDP growth rates for the five countries under comparison are shown in Figure 7. It reveals that the rate of economic growth in all countries except Malawi increased, in some cases markedly, during the period of reform. In Tanzania, the rate of growth of GDP increased sharply, from **0.5** percent per annum in the pre-reform period, to 4.2 percent per annum in the period of reform. Nevertheless, two questions arise related ta the performance of each country's economy. Firstly, was the level of investment adequate in each country, or was the country consuming 'too much' and investing "too little" ? Secondly, did the investment that took place provide an adequate "rate of
arguments about diversity in nursery cities and innovation, we need to understand the role of diversity outside the most prominent clusters. This is key to refining our conclusion about diversity’s role for inclusive growth. The ability to observe inputs into plants with the Indian data may provide an empirical foothold. The sub-contracting of work between organized and unorganized sectors might also be at play (e.g., Mukim, 2013). It is also worth investigating whether onerous land-use regulations and building codes in India cities (Sridhar, 2010) is pushing some of the growth linked to diversity outwards, especially as local infrastructure improves connectivity between urban and rural areas within districts.

Finally, we would like to evaluate the longitudinal changes highlighted in this study in terms of the impact of specific infrastructure projects (e.g., the Golden Quadrangle project) or trade liberalizations. These factors have been shown to play an important role for Indian manufacturing (e.g., Goldberg et al., 2010), but we do not know their role in terms of the specialization and diversity of India’s districts. Tracing out the economic geography of these economic shocks is important, and the rich longitudinal data for India provide a unique laboratory for doing so in a developing economy. This would be useful for understanding the allocation of activity in the Indian economy and how it can be improved (e.g., Hsieh and Klenow, 2009; Ghani, Goswami and Kerr, 2012a,b; Desmet et al., 2011). Likewise, Ellison, Glaeser and Kerr (2010) consider coagglomeration and the inter-linkages of industries within a local area. It would be interesting to quantify what happens to related industries located in specialized or diversified districts when these dramatic changes occur. It would also be important to depict how the traded versus non-traded nature of manufacturing and services industries modulates or amplifies these external shocks.

24
Table 4 explores differences in banks’ behavior across regions. Much has been discussed about the foreign bank lending contraction in ECA, but we find that our results are not driven by the behavior of foreign banks in ECA. In fact, we find that foreign banks in EAP and the European countries in ECA did not behave differently than foreign banks in LAC, as interactions between _Foreign_ and regional dummies are not significant (column 1).<sup>10</sup> At the same time, foreign banks in the former soviet republics (FSU) seem to have expanded credit (relative to domestic banks) by more than foreign banks in the other regions. Consistent with De Haas et al. (2012), we also find evidence that the Vienna Initiative worked; foreign banks in Vienna initiative countries expanded credit more than other foreign banks. When controlling for government bank ownership, we still find the same results (column 2). The behavior of foreign banks vis-a-vis private domestic banks is similar in all regions **(** now also including FSU), with the exception of Vienna Initiative countries. We also find that government-owned banks behaved in a similar counter-cyclical role in all regions in 2009.

To assess whether there is heterogeneity in the reaction to the crisis among foreign banks depending on their balance sheet characteristics, we include interactions of the foreign ownership dummy with our measures of bank size, capitalization, liquidity, and deposit funding structure. (Table 5). We find, as Claessens and Van Horen (2013) and De Haas and van Lelyveld (2013), that foreign banks with a higher deposit funding base exhibited a faster credit growth rate than other foreign banks in 2009 (column 1). However funding structure does not completely explain the behavior of foreign banks as the foreign bank dummy continues to be negative in 2009.<sup>11</sup> We

> 10 To avoid multicolinearity problems we introduced interaction terms for foreign ownership and region where the bank operates for all regions in the sample but one, LAC. Thus, the negative sign for foreign bank ownership in 2009 reflects the behavior of LAC banks.

> 11 Ideally, we would also like to explore if foreign bank behavior was affected by whether or not foreign banks relied on cross-border lending or foreign currency funding, but unfortunately Bankscope does not include data on funding by origin (i.e. domestic or foreign) or currency denomination.

12
employees 10% (Giles et al, 2013). Workers may be skeptical about whether the benefits from such schemes are equal to the contribution value, especially migrant workers who are young, healthy, may get health insurance coverage in their home counties. Further, migrants may have well-founded fears that benefits will not be portable if they move to other cities in the future, and may doubt the value of future pension benefits. In such cases, both sides may agree that paying higher wages is more desirable than enrolling in social insurance programs.

Regression analysis provides some evidence that rural migrant workers did sacrifice pension benefits for higher pay. Using the 2005 and 2010 survey data, we regress the log of rural migrant workers’ monthly wages (for wage employees only) on indicator variables for whether the worker received pension benefits from their employer and whether the worker signed a labor contract, as well as controls for age, age squared, years of education, whether the worker completed higher education, marital status, gender, and city dummy variables. On the one hand, one might expect that better working conditions and higher wages are both positively associated with higher quality jobs; on the other hand, workers could negotiate higher wages in return for sacrificing non-wage benefits (such as having a labor contract or receiving pension benefits).

We find that in 2005, having pensions is associated with wages that are nearly 16% lower while having a labor contract increases wages by nearly 17%. However, in 2010, having a labor contract is associated with wages that are 11% lower, while receiving pension benefits is not significantly associated with wage levels (the coefficient is close to zero) after controlling for whether one has a labor contract (full results available from the authors upon request).<sup>11</sup> These results suggest that employers shifted from rewarding higher wages to rural migrants who gave up pension benefits to those who gave up having a labor contract. This could be due to the increased costs to employers of signing labor contracts, reduced

> 11 For local residents and urban migrants, both pension benefits and having a labor contract are positively associated with wages, suggesting that these workers demand these benefits for better jobs.

26