rafmacalaba/datause-encoder-data huggingface dataset:
The loan was to be used to complete work started more than 10 years ago but halted because of the State's debt with the Federal Government. This debt had been pardoned as part of the agreement to decentralize the Flumitrens system from the Federal to the State.

# **Privatizing CONERJ**

34. The reform program envisioned selling the assets of CONERJ, the ferry boat company, to the highest bidder. Under the plan, the Secretary of Transport would oversee the private sector concessionaires who would operate the ferry lines in the Guanabara and Sepetiba Bays. The State commissioned a valuation study and set a date of November 1997 for completing the sale.

# **Liquidating CTC and SERVE**

35. The reform program envisioned liquidating CTC and SERVE, the state-owned bus companies. Their assets, mainly buses and garages, were to be sold to the private sector. The routes operated by the companies would be taken over by private bus companies, which would be selected through competitive bidding.

# **Selling Off CODERTE**

36. The reform program also envisioned selling CODERTE, the agency in charge of operating the State's terminals and parking garages. The State commissioned a study to determine the value of the assets involved, obtain all the legal titles of the properties, and prepare terms of reference for selling the agency.

# **Overarching Sector Reforms**

37. Before awarding concessions to the private sector in the urban transport sector, it was important to define a basic sector regulatory framework and the responsibilities of each of the urban transport agencies involved. The State created an independent State Regulatory Commission (ASEP-RJ) which would oversee all the major sectors in the State, including urban transport. This multisectoral commission does not have a transport division. It would use a pool of economists, lawyers and other specialists to deal with all the sectors. Regulation of all modes except buses fall under ASEP-RJ. The bus lobby was able to veto the inclusion of bus regulation in ASEP-RJ's mandate. Instead they continued to be regulated by DETRO, an agency of SECTRAN.

38. Finally, the State created a Regional Transport Coordination Commission (AMTU-RJ) which acts as a policy coordinator for the three levels of government involved in urban transport, as an adviser on tariff and subsidy policy and a main forum to discuss and prioritize the main investments in the RJMR. AMTU-RJ will probably be called upon to make presentations to ASEP-RJ but will not have regulatory power.

8
# **Table 6: The Determinants of Log Real Income in 1993 and 2000**

||`1993`|`1993`|`2000`|`2000`|
|---|---|---|---|---|
|`Age`|`0.096`|`0.092`|`0.103`|`0.098`|
||`(5.78)**`|`(5.44)**`|`(5.58)**`|`(5.28)**`|
|`Age squared`|`-0.001`|`-0.001`|`-0.001`|`-0.001`|
||`(5.47)**`|`(5.22)**`|`(5.85)**`|`(5.61)**`|
|`Sex`|`0.433`|`0.440`|`0.596`|`0.607`|
||`(11.14)**`|`(10.83)**`|`(15.58)**`|`(15.22)**`|
|`Working hours`|`0.002`|`0.002`|`0.001`|`0.001`|
|`per month`|||||
||`(12.21)**`|`(12.16)**`|`(6.70)**`|`(6.87)**`|
|`Wage income`<br>|`0.145`|`0.137`|`-0.024`|`-0.050`|
|`dummy`|||||
||`(4.13)**`|`(3.50)**`|`(0.67)`|`(1.29)`|
|`Years of`|`0.087`|`0.082`|`0.103`|`0.097`|
|`schooling`|||||
||`(20.27)**`|`(17.64)**`|`(25.71)**`|`(22.13)**`|
|`Household size`|`0.073`|`0.074`|`0.058`|`0.057`|
||`(4.86)**`|`(4.94)**`|`(4.52)**`|`(4.50)**`|
|`Number of`<br>|`-0.094`|`-0.094`|`-0.064`|`-0.063`|
|`Children < 5`|||||
||`(3.70)**`|`(3.71)**`|`(2.25)*`|`(2.20)*`|
|`Number of`|`-0.049`|`-0.048`|`-0.037`|`-0.036`|
|`Children < 15`|||||
||`(2.66)**`|`(2.59)**`|`(2.09)*`|`(2.01)*`|
|`Number of Men`|`-0.043`|`-0.045`|`-0.038`|`-0.036`|
|`in hh`|||||
||`(2.29)*`|`(2.39)*`|`(2.09)*`|`(1.99)*`|
|`Urban Dummy`|`0.248`|`0.251`|`0.055`|`0.068`|
||`(7.02)**`|`(7.14)**`|`(1.49)`|`(1.82)`|
|`North Sumatra`|`-0.204`|`-0.198`|`-0.124`|`-0.102`|
||`(2.79)**`|`(2.69)**`|`(1.67)`|`(1.36)`|
|`West Sumatra`|`-0.236`|`-0.240`|`-0.260`|`-0.259`|
||`(3.05)**`|`(3.05)**`|`(2.69)**`|`(2.67)**`|
|`South Sumatra`|`-0.401`|`-0.414`|`-0.189`|`-0.184`|
||`(5.00)**`|`(5.11)**`|`(2.18)*`|`(2.09)*`|
|`Lampung`|`-0.542`|`-0.538`|`-0.305`|`-0.297`|
||`(6.20)**`|`(6.12)**`|`(3.33)**`|`(3.24)**`|
|`West Java`|`-0.307`|`-0.288`|`-0.060`|`-0.054`|
||`(5.17)**`|`(4.78)**`|`(0.96)`|`(0.85)`|
|`Central Java`|`-0.593`|`-0.578`|`-0.304`|`-0.276`|
||`(9.58)**`|`(9.19)**`|`(4.79)**`|`(4.31)**`|
|`Di Yogyakarta`|`-0.700`|`-0.690`|`-0.472`|`-0.456`|
||`(9.83)**`<br>|`(9.78)**`<br>|`(6.21)**`<br>|`(6.01)**`<br>|
|`East Java`|`-0.565`|`-0.554`|`-0.260`|`-0.244`|
||`(9.37)**`|`(9.12)**`|`(4.12)**`|`(3.83)**`|
|`Bali`|`-0.196`|`-0.208`|`-0.149`|`-0.162`|
||`(2.83)**`|`(2.98)**`|`(1.91)`|`(2.05)*`|
|`West`|`-0.462`|`-0.462`|`-0.228`|`-0.220`|
|`NusaTenggara`|||||
||`(5.36)**`|`(5.32)**`|`(3.02)**`|`(2.91)**`|
|`South`|`0012`|`-0006`|`-0115`|`-0126`|
|<br>`Kalimantan`|`.`|`.`|`.`|`.`|

29
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**Table 1 Trade intensity indexes for the** **<u>U.S. and India, 2007</u>**

||i = US<br>j= India|i = India<br> j= US|
|---|---|---|
|Agriculture|0.5|1.0|
|Processed agriculture|0.3|0.8|
|Coal, oil, gas and other mining|0.1|0.0|
|Textiles and apparel|0.9|1.4|
|Other light manufacturing|0.7|0.6|
|Paper and mineral products|1.1|1.8|
|Chemical, rubber and plastic products|0.9|1.2|
|Iron, steel and metal products|0.6|0.9|
|Petroleum and coke products|0.3|0.5|
|Transport equipment|2.8|0.6|
|Electronics and machinery|0.7|1.2|
|Other manufacturing|1.6|1.1|
|Services|1.3|2.0|
|Total|0.9|1.2|

_Source_ : GTAP 8 database.

Table 1 reveals several features of India-US trade patterns. The first is that US exports to India are smaller than would be expected given India’s share in world trade, while India’s exports to the United States are greater than would be expected. Within US exports, the trade intensities are particularly low for agricultural goods (0.5 for agriculture and 0.3 for processed agriculture) and for the products of the mining sector (0.1). Exports of transport equipment and services from the US to India are disproportionately large, with trade intensities of 2.8<sup>4</sup> and 1.3, respectively. Within India’s exports to the U.S., trade intensities are particularly low for mining sector products (0.0), and particularly high for services (2.0).

> 4 This particularly high TII appears to reflect India’s large purchases of transport equipment in 2007.

10
**Table 3. The Informal Sector and Other Determinants of Economic Growth** (t-statistics in parenthesis)

|Dependent Variable|Grow|th Rate of R|eal Per Capi|ta GDP (198|0-92)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Size of the Informal Sector|-1.2482|-1.3165|-1.3303|-1.4555|-1.2245|
||(-3.33)|(4.30)|(-3.79)|(-3.45)|(-3.44)|
|Real Per Capita GDP, 1980|-0.6418|-0.7956|-0.8906|-0.1523|-1.9685|
||(-2.04)|(-2.92)|(-2.80)|(-3.84)|(-4.36)|
|Secondary School||0.4302|0.7341|0.8426|0.7118|
|Attainment, 1980||(1.86)|(2.32)|(2.66)|(2.34)|
|Average Tariff for|||-0.5226|-0.6138|-0.3439|
|Intermediate and Capital<br>Goods, 1985|||(-1.26)|(-1.46)|(-0.86)|
|Average Inflation Rate,||||-0.2497|-0.6027|
|1980-92||||(-0.46)|(-1.60)|
|Public Infrastructure Index,<br>1990|||||1.1966<br>(2.04)|
|P-value (F-Statistic)|0.0046|0.0077|0.0305|0.0724|0.0365|
|Adjusted R<sup>2</sup>|0.4595|0.5119|0.5222|0.4735|0.6490|
|Number of Observations|14|14|12|12|12|

Note: The above t-statistics are computed using heteroskedasticity-corrected standard errors. Regression coefficients are standardized so that they reflect the change in the growth rate produced by a one-standard deviation of the explanatory variable.

-49-
# Risk Management in Border Inspection<sup>∗</sup>

Russell Hillberry<sup>†</sup> Bilgehan Karabay<sup>‡</sup> Shawn W. Tan<sup>§</sup>

## **Abstract**

As part of their commitments under the WTO’s Agreement on Trade Facilitation, many developing countries will adopt risk management, a strategy for selecting import shipments for inspection. In this paper we formalize key enforcement issues related to risk management. We argue that the complexities of international trade oversight mean that inspecting agencies lack certainty about the conditional probability that a given shipment will comply with import regulations. Ambiguity of this sort is likely to be important in developing countries that lack the sophisticated information techOriginally published in the nology (IT) used in advanced <u>Policy Research Working Paper Series</u> risk management. We show empirically <u>on</u> _October 2020._ that infrequent This version is updated on shipments have _June 2021._ conditionally <u>higher inspection rates, a fnding</u> that is consistent with To obtain the originally published version, please email our ambiguity hypothesis. We formalize a role for ambi <u>prwp@worldbank.org guity</u> in a theoretical <u>.</u> model of border inspection. We calibrate the model and shock the ambiguity parameters to illustrate the consequences of an IT-driven improvement in risk management capabilities for search and compliance.

Keywords: Trade Facilitation; Risk Management; Border Inspection; Ambiguity; Choquet Expected Utility.

JEL classification: D73; D81; F18.

> ∗We would like to thank Evgenii Monastyrenko, the participants at the 13th Australasian Trade Workshop (ATW), members of the Purdue University trade brownbag, and two anonymous referees for comments on earlier drafts. Research for this paper has been supported in part by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development, and by the UK Department for International Development (DFID) through the Strategic Research Partnership on Economic Development. This work was also supported by the USDA National Institute of Food and Agriculture, Hatch project 1013622. The views expressed herein are those of the authors and should not be attributed to the World Bank, their Executive Boards, or management. All errors are our own.

> †Department of Agricultural Economics, Purdue University, Krannert Bld., Room 564, 403 W. State Street, West Lafayette, IN 47907, USA. Tel: +1 (765) 494 4249. E-mail: rhillber@purdue.edu. URL: http://www.russellhillberry.net.

> ‡School of Economics, Finance and Marketing, RMIT University, 445 Swanston Street, Bld. 80, Level 11, Melbourne, VIC 3000, Australia. Tel: +61 (3) 9925 5867. E-mail: bilgehan.karabay@gmail.com. URL: http://bilgehan.karabay.googlepages.com.

> §The World Bank. 1818 H Street, N.W. Washington, DC 20433 USA. Tel: +1 (202) 473 7621. E-mail: swtan@worldbank.org.
Gains from targeting also vary with the choice of poverty line. The higher the poverty line, the less need for targeting, as leakage to the non-poor diminishes to zero. In this study, we select as benchmark the a poverty line that yields a poverty rate of exactly 20% in both 1999 and 2009, respectively.

# **_7.3. Simulating the impact of uniform transfers_**

Our policy simulation in the benchmark case of uniform transfers is calculated in a very straightforward manner. Budget _S_ is divided by total population _N_ . The resulting transfer _a_ is ( _r_ ) added to each predicted expenditure in our database, to yield _ych +a_ . For each replication _r_ we estimate post-transfer national poverty. The average across the _r_ replications of the estimated post-transfer poverty rates yields our expected poverty rate associated with the benchmark, untargeted lump-sum transfer scheme. This new estimated poverty rate can be compared to the original national-level poverty estimate from the poverty map to gauge the impact of the transfer.

# **_7.4. Simulating the impact of “optimal” geographic targeting_**

Simulating the impact of the “optimal” targeting scheme is a bit more complicated. Following Kanbur (1987) we want to equalize the following expression across the poorest locations of a country: which is _z_ times the poverty gap in location _c_ , after every person in the location has received a transfer _ac. Fc(y)_ is the average of the R simulated expenditure distributions of _c_ . The function ( _x_ )<sup>_+_</sup> gives the ‘positive part’ of its argument, i.e. ( _x)_<sup>_+_</sup> _=x,_ if _x_ is positive, otherwise 0. Transfers _ac_ (which must be nonnegative) add up to a given budget _S_ : where _Nc_ is the population size of location _c_ . After transfers there is a group of locations all sharing the same (maximum) poverty gap rate in the country. These are the only locations 26
**_Energy demand models for policy_** **_<u>formulation</u>_**

Pachauri, S., 2004, An analysis of cross-sectional variations in total household energy requirements in India using micro survey data, Energy Policy, 32(15), pp. 1723-35. Paga, E. and F. Birol, 1994, An empirical analysis of oil demand in developing countries, OPEC Review, Spring, pp. 25-46.

Pandey, R., 2002, Energy Policy modeling: Agenda for developing countries, Energy Policy, 30, pp. 97-106.

Parikh, J., 1981, Modelling energy demand for policy analysis, New Delhi: Planning Commission, Government of India.

Pesaran, M. H., R. Smith, and T. Akiyama, 1998, Energy Demand in Asian Developing Countries, Oxford University Press, Oxford, UK.

Pilavachi, P.A., Th. Dalamaga, D. Rossetti di Valdalbero, and J.-F. Guilmot, 2008, Expost evaluation of European Energy Models, Energy Policy, 36(5), pp. 1726-35.

Pindyck, R. S., 1979, The structure of world energy demand, The MIT Press, Cambridge, Massachusetts.

Pokharel, S., 2007, An econometric analysis of energy consumption in Nepal, Energy Policy, 35, pp. 350-61.

Polemis, M. L., 2006, Empirical assessment of the determinants of road energy demand in Greece, Energy Economics, 28, pp. 385-403.

Price, L., D. Phylipsen, and E. Worrel, 2001, Energy use and carbon dioxide emissions in the steel sector in key developing countries, LBNL – 46987, Lawrence Berkeley National Laboratory, California.

Ramanathan, R., 1999, Short and long-run elasticities of gasoline demand in India: an empirical analysis using cointegration techniques, Energy Economics, 21, pp. 321-30.

116
sending an average of $US2,638 annually – a sizeable amount relative to Sub-Saharan Africa’s GNI per capita of $1082.<sup>2</sup> Compared to other developing country immigrants in the OECD we find African migrants to be both more likely to remit, and to remit more. Within Africa we find a strong negative association between the likelihood of remitting and country income.

We use this microdata to explore the determinants of remitting as a function of individual characteristics which theory suggests should matter. We focus in on four areas of particular interest to recent policy debates. The first is gender. There has been an increasing feminization of global migration flows, and there is often a belief that women are better and more reliable remitters. For example, Lindley (2007, p. 12) remarks that in Somali communities it is sometimes said that “women are ‘better’ remitters than men (even that it is better to have one daughter abroad than ten sons)”, although in practice she finds Somali men to remit more. We find for Africans as a group that female migrants are less likely to be remitting, and that men remit more than women when they do remit. This is particularly the case when comparing individuals with a spouse remaining at home – men with a spouse outside the country remit $US3,879 more per year than women with a spouse remaining at home.

Second and third, we focus on the roles of education and income. The issue of brain drain is always quick to arise when discussing migration out of Africa. From the remittances standpoint, there is a fear that more-educated migrants will send less remittances, perhaps because they have less intention to return, and are more likely to move their whole families with them. With this same database, we have recently shown that for developing country emigrants as a whole, more educated migrants in fact remit more (Bollard et al, 2009). We find here, if anything, this relationship is stronger when we just consider African emigrants. Furthermore, we show there is a strong non-linearity among African migrants, with the amount remitted relatively constant with respect to education until completion of high school, and then strongly rising with education thereafter. Similarly, we find a strong positive association between income earned abroad and amount remitted, with this relationship quite flat over middle ranges of income and

> 2 Source: World Bank Gross National Income per capita. http://siteresources.worldbank.org/DATASTATISTICS/Resources/GNIPC.pdf

3
migration patterns demonstrate how neighbouring countries in the Global South actually host the majority of forced migrants worldwide (UNHCR 2020). The relatively few studies involving host communities in the Global South observe how, despite obvious differences in the scale of forced migratory flows, most published work focuses on the experiences of higher-income destination countries (Alrababa’h et al. 2020).

Third, we extend and improve upon prior work that considers key moderating factors for migration policy preferences. Specifically, we consider the role of humanitarian and economic values in impacting preferences for specific policies. This is relevant in a context in which the saliency of the economic and humanitarian crisis is particularly strong in explaining the driving forces of the migration inflows. We also revisit the role of social contact with migrants by moving beyond approaches used in existing observational studies (e.g., measures of distance to the border or migrant density as an approximation of exposure), instead using a detailed battery of questions regarding self-reported encounters with Venezuelans.

The paper is organised as follows: section 2 of the paper presents the conceptualisation of the different dimensions of policy; section 3 reviews the context of Venezuelan migration into Colombia; section 4 discusses the research design for the conjoint survey and the analysis and estimation strategies; section 5 presents the results; section 6 presents a series of robustness tests; and section 7 contains the conclusion and policy implications.

# **2. Policy Preferences and Dimensions of Policy Responses**

# **2.1 Dimensions of Policy Responses to Large Migration Inflows**

Policymakers in host countries must decide among several options when setting migration policies. In this section, we describe the six dimensions that we included in our hypothetical policy packages: (1) labour market access, (2) location restrictions, (3) public service access, (4) family reunification, (5) numerical limits, and (6) length of residency. Then, we show how they theoretically relate to economic and humanitarian priorities.

# **Labour market access**

Restricting labour market access is a popular policy in the forced migration context. This policy intends to reduce concerns about labour market competition between hosts and the 5
The impact of the unreliability of the power supply in Pakistan is also confirmed by data obtained between 2013 and 2015 from the World Bank’s Enterprise Survey (2013) as shown in Table 7.<sup>28</sup>

**Table 7: Comparative Performance of Pakistan on power supply reliability**

||**Pakistan**|**South Asia**|**All**|
|---|---|---|---|
|Firms experiencing electrical outages (percent)|81|66|59|
|Number of outages in a typical month|75|24|6|
|Average duration of outage (hours)|17|5.3|4.5|
|Average losses as percent of annual sales|34|11|5|
|Percent of firms owning or sharing a generator|65|45|34|
|Average percent of electricity from generator if available|41|24|21|
|Firms identifying electricity as a major constraint<br>(percent)|75|46|31|

Source: World Bank Enterprise Surveys.

Putting all these facts together it appears that, although Pakistan was able to attract a modest amount of private investment into the generation sector, this was inadequate given the rate of growth of demand. The costs of the supply unreliability were high. With such an evident gap between existing supply and demand there were clearly important factors discouraging the entry of more IPPs. The GENCOs were not able to finance much expansion given the poor performance of the sector, while the magnitude of “circular debt” and the inability of some IPPS to be paid fully or on time, which in 2011 had led some to threaten to call in sovereign guarantees, is likely to have played an important role in deterring the entry of more IPPs. The persistence of different governments in continuing to set the actual tariffs below those calculated by the regulator to consumers so as to limit price rises through the provision of subsidies, despite pressure from multilaterals to reform this aspect of the management of the power sector, made it clear that there could be substantial risks of entering the Pakistan power sector. As is demonstrated below, the DISCOs had been operating inefficiently for a long period, thus throwing more burden on the government which wished to hold tariffs below costs. This too, may have limited the enthusiasm of possible new entrants.

However, the predictions for the next five years show the capacity deficit disappearing in 2018, and a substantial surplus of nearly 5GW appearing by 2021, and this is in the context of demand growing by around 5 percent per annum (equivalent to an increase of about 5GW). Since the government has not changed its policies towards the sector with respect to price setting or the management of the public-sector utilities, some other explanation must be sought. In April 2015 the China-Pakistan Economic Corridor (CPEC) initiative was announced. The original valuation of the infrastructure projects included was $46 billion, and the total has since risen to $62 billion.

> 28 Data for Pakistan are for 2013-2015, while data for other countries are based on the most recent year in which a survey was undertaken.

28
**Table 3b. Heterogeneous Impacts of EPAG on Participation in income generating activities—by community**

|||OLS<br>||
|---|---|---|---|
|Communities|AnyIGA|Observations|Controls|
|All|0.181***<br>|||
||(0.026)|3200|Yes|
|West Point|-0.109**|||
||(0.041)|252|Yes|
|Battery Factory|0.058|||
||(0.065)|314|Yes|
|New Kru Town|0.141|||
||(0.117)|338|Yes|
|Doe Community|0.245*|||
||(0.112)|358|Yes|
|Bassa Community|0.347***|||
||(0.075)|321|Yes|
|Red Light|0.121*|||
||(0.063)|558|Yes|
|Old Road|0.239***|||
||(0.032)|429|Yes|
|Bentol|0.354**|||
||(0.087)|184|Yes|
|Kakata|0.184***|||
||(0.056)|446|Yes|

Controls used are household size, sex of household head, asset based quintiles, age of adolescent, educational status of adolescent, marital status of respondent, parental status of respondent (indicator variables for adolescent having a child and being pregnant), orphan status of respondent (one parent or both parents being deceased), and month of interview at midline; all controls are from baseline data except for month of midline interview.

35