rafmacalaba/datause-encoder-data huggingface dataset:
- 40 -

transport documentsl<sup>0 4</sup> to facilitate multi-modal transport arrangements -which is one of the key prerequisites for effective container trades. The intention is to make such arrangements easier to institute by Indian shippers and carriers.<sup>1 0 5</sup> This task was extremely onerous as all existing relevant legislation with regard to liabilities, taxes applicable to trade and transport, foreign exchange regulations, carriage and sale of goods had to be vetted for required amendments. The deliberations resulted in a proposal for a (Multi-modal) Combined Transport of Goods Act. To make it work, substantial changes will have to be effected in several existing laws that govern the conduct of trade and transport. Unfortunately, the proposed act has been before the cabinet for almost two years, without any affirmative action taken yet. This and other similar cases seem to demonstrate how difficult it is for the Indian public administration to face up to today's market realities, and to take corrective measures -in the best interest of the economy.

97. The bottom line is that the public administration has to reconsider national priorities and objectives in light of the changing environments -and notably practicesin the international trade and transport markets. If India wants to catch up with, if not outperform its trade competitors, then there is no alternative to substantial reorganization of its trade logistics system. Of possibly even higher importance is the need to generate enough foreign exchange income through improved trade performance, which is required to ease the transition of the Indian nation into a modern economy based on effective technologies and production as well as distribution processes. This would also reduce the country's vulnerability to shocks in the international economy. At the same time, economic and social services on the domestic scene could be more efficiently provided.

98. Questions that are likely to feature prominently in this debate include:

- _A_ To what extent should the public sector remain involved in the management of trade logistics networks7

- * How much can the rules and regulations that govern the conduct of trade logistics, and their enforcement be relaxed?

- 104/ Which **were drafted** in **conformity** with **standardized forms issued by the International Chamber of Commerce and the International** Federation **of Freight Forwarders Associations.**

- 105/ **A major drawback was** that the national freight **forwarders were** not **involved, and** the **proposals elaborated by** the **Committee reflect** little, if anything, to **improve** the **role** and function **of** Indian **forwarders** in trade **logistics management.**
# **1 Introduction and Motivation**

Countries in South Asia have signed regional trade agreements (RTAs) within the region and with third countries in the rest of the world.<sup>1</sup> These RTAs are expected to contribute to increasing the region’s exports by reducing tariffs and non-tariff barriers, increasing productivity, facilitating technological transfers, and ultimately promoting economic growth and development. Unfortunately, the empirical evaluations of these agreements that take the most recent advances in the literature are limited. This limited evidence is not surprising given that the methodological advances and empirical analyses on RTAs’ effects have usually been conducted using data covering a small set of countries, usually advanced economies. These advances tend to be more demanding in terms of data and this might be the reason why the literature uses data sets with a relatively small number of countries.<sup>2</sup> This makes the evidence of the trade creation effect of trade agreements in developing countries remains not widely available.

South Asia is an interesting case study in terms of potentially heterogeneous effects of FTAs due to the variation in terms of size and economic development in the region. This paper focuses not only on the aggregate effect of trade agreements in South Asia but also pays attention to the heterogeneous effects by sectors, country, country-pairs, types of goods, and the difference between intra-regional RTAs and those with the rest of the world. To empirically identify the RTAs’ effect on South Asia, I use data covering 190 countries over a long period (1990-2015). I use an empirical strategy consistent with theory, in particular, I include domestic trade flows and explore different specifications to capture the heterogeneous effects of trade agreements over different dimensions.

Recent contributions to the literature show that RTAs’ effects can be disappointing and vary widely across different RTAs when these results are available (Baier et al., 2019). Among the recommendations in the literature to properly capture a trade agreement’s effect, the inclusion of domestic trade flows is one of the priorities. Yotov (2022b) review the related literature on this issue and argues that there are significant benefits from adhering to theory by estimating gravity equations with domestic (in addition to international) trade flows. I make use of domestic trade flows to carry out theory-consistent

> 1I use the term Regional Trade Agreements to refer to Free Trade Agreements (FTAs), as done in the literature and as reported to the World Trade Organization (WTO).

> 2The structural gravity model, which is commonly used in evaluating the RTA effects, continues to develop rapidly in the literature and it is now more refined, in particular after Baier and Bergstrand (2007).

2
Federal Ministry of Health Department of Public Health National AIDS/STI Control Programme. 2010. "Technical Report on the 2010 National HIV Sentinel Survey Among Pregnant Women Attending Antenatal Clinics in Nigeria." In.

Finances, Institut National de la Statistique et de la Démographie (INSD) Ministère de l'Économie et des. 2004. "Burkina Faso - Enquête Démographique et de Santé 2003." In.

- ———. 2012. "Enquête Démographique et de Santé et à Indicateurs Multiples (EDSBFMICS IV)." In.

- Geis, S., L. Maboko, E. Saathoff, O. Hoffmann, C. Geldmacher, D. Mmbando, E. Samky, N. L. Michael, D. L. Birx, M. L. Robb, and M. Hoelscher. 2011. 'Risk factors for HIV-1 infection in a longitudinal, prospective cohort of adults from the Mbeya Region, Tanzania', _J Acquir Immune Defic Syndr_ , 56: 453-9.

- Ghana Statistical Service (GSS), Noguchi Memorial Institute for Medical Research (NMIMR), and ORC Macro. 2004. "Ghana Demographic and Health Survey 2003 " In.

- Gomez-Olive, F. X., N. Angotti, B. Houle, K. Klipstein-Grobusch, C. Kabudula, J. Menken, J. Williams, S. Tollman, and S. J. Clark. 2013. 'Prevalence of HIV among those 15 and older in rural South Africa', _AIDS Care_ , 25: 1122-8.

- Government of Botswana Central Statistics Office. 2005. "BOTSWANA AIDS IMPACT SURVEY II 2004." In.

- Government of Botswana, Ministry of Health. 2012. "2011 Botswana Second Generation HIV/AIDS Antenatal Sentinel Surveillance Technical Report " In.

- Government of Botswana Ministry of Health. 2011. "2009 Botswana Second Generation HIV/AIDS Antenatal Sentinel Surveillance Technical Repor." In.

- Gray, R. H., G. Kigozi, D. Serwadda, F. Makumbi, S. Watya, F. Nalugoda, N. Kiwanuka, L. H. Moulton, M. A. Chaudhary, M. Z. Chen, N. K. Sewankambo, F. WabwireMangen, M. C. Bacon, C. F. Williams, P. Opendi, S. J. Reynolds, O. Laeyendecker, T. C. Quinn, and M. J. Wawer. 2007. 'Male circumcision for HIV prevention in men in Rakai, Uganda: a randomised trial', _Lancet_ , 369: 657-66.

- Gregson, S., R. M. Anderson, J. Ndlovu, T. Zhuwau, and S. K. Chandiwana. 1997. 'Recent upturn in mortality in rural Zimbabwe: evidence for an early demographic impact of HIV-1 infection?', _AIDS_ , 11: 1269-80.

- Guwatudde, D., F. Wabwire-Mangen, L. A. Eller, M. Eller, F. McCutchan, H. Kibuuka, M. Millard, N. Sewankambo, D. Serwadda, N. Michael, M. Robb, and Team Kayunga Cohort Research. 2009. 'Relatively low HIV infection rates in rural Uganda, but with high potential for a rise: a cohort study in Kayunga District, Uganda', _PLoS One_ , 4: e4145.

Hallett, T. B., J. Stover, V. Mishra, P. D. Ghys, S. Gregson, and T. Boerma. 2010.

- 'Estimates of HIV incidence from household-based prevalence surveys', _AIDS_ , 24: 147-52.

- Hallett, T. B., B. Zaba, J. Todd, B. Lopman, W. Mwita, S. Biraro, S. Gregson, J. T. Boerma, and Alpha Network. 2008. 'Estimating incidence from prevalence in generalised HIV epidemics: methods and validation', _PLoS Med_ , 5: e80.

73
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# **Table 4: Estimated effects of female and female widow headship on log household consumption per capita with various combinations of controls**

|||**(1)**<br>**R**|**(2)**<br>**ural**|**(3)**<br>**Ur**|**(4)**<br>**ban**|
|---|---|---|---|---|---|
|**Dependent variable**|**Other controls in regression**|**Female head**|**Female widow**<br>**head**|**Female head**|**Female widow**<br>**head**|
|Log consumption per capita|No other controls|0.197***|0.098|-0.017|-0.101*|
|||(2.83)|(1.62)|(-0.33)|(-1.89)|
|Log consumption per equivalent single|No other controls|-0.093|-0.207***|-0.134***|-0.141**|
|person to allow for economies of scale||(-1.53)|(-3.57)|(-2.90)|(-2.45)|
|Log consumption per capita|Log household size|-0.060<br>(-1.00)|-0.175***<br>(-3.01)|-0.118**<br>(-2.52)|-0.135**<br>(-2.41)|
|Log consumption per capita|Above control + geographic &|-0.147***|-0.215***|-0.160***|-0.188***|
||interview month dummies|(-2.60)|(-3.50)|(-3.65)|(-4.19)|
|Log consumption per capita|above controls + marital status|-0.066<br>(-0.89)|-0.189***<br>(-3.43)|-0.078<br>(-0.97)|-0.178***<br>(-3.63)|
|Log consumption per capita|above controls + age & age squared|-0.074<br>(-0.99)|-0.171***<br>(-3.06)|-0.065<br>(-0.80)|-0.201***<br>(-4.11)|
|Log consumption per capita|Above controls + household|-0.057|-0.156***|-0.033|-0.199***|
||composition|(-0.76)|(-2.72)|(-0.46)|(-3.99)|
|Log consumption per capita|Above controls + years of education|-0.058|-0.139**|0.017|-0.067|
||& years squared|(-0.81)|(-2.44)|(0.24)|(-1.52)|
|Log consumption per capita|Above controls + assets|-0.063|-0.129**|0.005|-0.056|
|||(-0.88)|(-2.28)|(0.08)|(-1.50)|
|Log consumption per capita|Above controls + chronic illness of|-0.046|-0.121**|0.012|-0.064**|
||head & of other members|(-0.63)|(-2.24)|(0.18)|(-2.09)|
|Observations||2,797|─ 2772|1,578|─ 1544|

Note: Log consumption per equivalent single person is defined as the log of household consumption normalized by the square root of household size. The rural sample omits 11 households with divorced heads (6 female and 5 male); the urban sample omits the 6 households with divorced male heads. In addition the number of observations varies across the different regressions as noted due to some missing observations for some of the added controls, notably assets and chronic illness of other household members. Robust t- statistics in brackets, clustered at the community level. *** p<0.01, ** p<0.05, * p<0.1. Source: 2006 ELIM.
Table 9. Financial Development (Credit/PIB), Property Rights and Poverty: Instrumental Variable

||Headcount<br>poverty|Income of<br>thepoor|Poverty<br>gap|
|---|---|---|---|
|Private Credit/GDP (log)|1.111|-1.297|1.659|
||(2.04)**|(2.47)**|(1.95)*|
|GDP per capita ($US, log)|-0.853|0.683|-1.204|
||(3.88)***|(3.50)***|(3.60)***|
|Property Rights (log)|-0.995|1.061|-1.491|
||(2.27)**|(2.54)**|(2.17)**|
|Private Credit/GDP (log) * Property Rights (log)|-0.354|0.405|-0.527|
||(2.24)**|(2.65)**|(2.12)**|
|Inflation (log)|0.759|-0.963|1.396|
||(2.00)*|(2.75)***|(2.39)**|
|Trade Openness (log)|0.104|-0.170|0.227|
||(0.47)|(0.84)|(0.67)|
|Constant|11.190|-3.931|13.160|
||(6.06)***|(2.30)**|(4.62)***|
|Observations|45|45|45|
|Number of countries|26|26|26|
|R2|0.48|0.43|0.43|
|Hansen test|0.89|0.62|0.84|

Notes: Data are averaged over five years. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

Table 10. Financial Development (M3/PIB), Property Rights and Poverty: Instrumental Variable

||Headcount<br>poverty|Income of<br>thepoor|Poverty<br>gap|
|---|---|---|---|
|M3/GDP (log)|-1.579|1.757|-3.005|
||(2.07)**|(1.81)*|(2.30)**|
|GDP per capita ($US, log)|-0.411|0.398|-0.517|
||(2.43)**|(1.87)*|(2.07)**|
|Property Rights (log)|-1.079|1.274|-2.030|
||(1.75)*|(1.68)*|(1.94)*|
|M3/GDP (log) * Property Rights (log)|0.351|-0.420|0.658|
||(1.76)*|(1.68)*|(1.92)*|
|Inflation (log)|0.410|-0.688|0.975|
||(1.11)|(1.73)*|(1.79)*|
|Trade Openness (log)|0.040|-0.192|0.148|
||(0.22)|(0.92)|(0.54)|
|Constant|10.608|-4.136|14.039|
||(4.44)***|(1.40)|(3.46)***|
|Observations|50|49|50|
|Number of countries|29|29|29|
|R2|0.51|0.37|0.52|
|Hansen test|0.30|0.19|0.33|

Notes: Data are averaged over five years. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

17
_Figure 12: Vulnerability rate by decile_ <!-- Start of picture text --> 60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>poorest 2 3 4 5 6 7 8 9 richest<br><!-- End of picture text --> Source: Authors’ calculations based on quarterly HBS.

The vulnerability measure allows identifying the most vulnerable groups of population (see Annex, Table A4 for details). Those residing in small cities and rural areas have average vulnerability levels of 27.0 and 37.5 percent correspondingly. During the crisis vulnerability levels in those areas surpassed 40 percent. Despite low pre‐crisis vulnerability levels, residents of large cities also saw their vulnerability increase to over 30 percent of that group. Vulnerability is naturally high for those partially employed or non‐employed, and it increased to the 50 percent level during the peak of the crisis. Similar vulnerability levels and dynamics can be observed among individuals with low levels of education. Across the age distribution, children are the most vulnerable group, with average vulnerability of 51.3 percent; and going as high as 74.3 percent at the peak of the crisis. Accordingly, households with children, especially households with two children, households with three and more children, and households with children and a single adult also have high vulnerability levels – 42.3, 89.7 and 65.1 percent, going up to 60.7, 90.6 and 72.7 percent correspondingly during the crisis. Retirees and retiree households do not fall into the vulnerable category as the minimal pension is above the official poverty line.

Following Lopez‐Calva and Ortiz‐Juarez (2013), we also estimate the vulnerability threshold in disposable income. We estimate the following regression (see Table A17 for full regression output): log �Disposable income��f��residence�, age�, occupational status�, household type�, education�� where the set of the explanatory variables is the same as in the probit regression above, and the functional form is linear. We use the average values of the explanatory variables in case the probability of poverty is equal to 10 percent and the coefficients from the second regression to estimate the corresponding vulnerability threshold in the disposable income. The vulnerability threshold can be seen to track, roughly, the evolution of the minimum consumption basket threshold throughout the 2014‐2017 period, and, while being a bit higher at the beginning of 2014, by the end of 2017 the monetary values of the MCB and the vulnerability threshold are roughly the same (Figure 13). This can be seen to suggest that the MCB threshold is not just a threshold corresponding to a broader measure of welfare, vis‐à‐vis the official poverty line; it is also congruent with an empirical concept of vulnerability of falling below the official poverty line, as measured by the probability of falling below the official poverty threshold of above 10 percent.

11
to the importance of factors such as attitudes toward risk, access to assets, vulnerability, and institutional influences, in determining household livelihood strategies (Ireland, 2004).

Case study evidence exists from specific project experience showing that new sources of employment and income can be created as a result of efforts to promote these AIGA (Ireland, 2004). Tanzania indeed has already had some successful experience with the introduction of AIGA in coastal areas. Activities such as handicrafts, beekeeping, and seaweed farming at the MIMP generate income of about US$10 per day for participating members (Ruitenbeek and others, 2005). Programs in Tanga have promoted organic vegetable gardening to fulfill the untapped high demand for vegetables in local markets. While so far the number of households involved in this vegetable growing pilot is small and the amount of income generated not enough to completely disengage households from fishing activities, the potential for profitable scaling up is significant given market demand for vegetables in the area (Ireland, 2004).

However, the empirical evidence on whether or not these AIGA, and other MPAs activities generally, are successful in reducing pressure on fisheries is scarce and shows mixed impacts. A comparative study of costal resource management in the Pacific islands found that most AIGA have not been successful in reducing pressure on coastal resources (World Bank, 1999). By contrast, an analysis of MPAs in the Philippines shows that integrating conservation with promotion of livelihood opportunities is a significant factor explaining MPA’s success (Pollnac and others, 2001). As Tanzania embarks in its commitments to expand protection of coastal and marine environment through the use of MPAs, it is important to assess the impact of existing MPAs. This paper seeks to fill the knowledge gap on the determinants of destructive fishing gear use and what impact, if any, MPAs have had on the use of destructive fishing gear.

10
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**Figure 5: Change in the index of private monitoring from Surveys I to IV**

<!-- Start of picture text --> France India<br>Belarus<br>United States<br>Mexico<br>Ghana<br>Bangladesh<br>Trinidad and Tobago<br>Slovakia<br>Malawi<br>Ireland Italy<br>Indonesia<br>Greece<br>Chile<br>Austria<br>Thailand<br>Spain<br>South Africa<br>Russia<br>Romania<br>Poland<br>Netherlands<br>Lesotho<br>Kenya<br>Jamaica<br>Iceland<br>Hungary<br>Germany<br>China<br>Cayman Islands<br>Belgium<br>Venezuela<br>United Kingdom<br>Switzerland<br>Sri Lanka<br>Singapore Peru<br>Morocco<br>Moldova<br>Lithuania Luxembourg<br>Liechtenstein<br>Jordan<br>Israel<br>Honduras<br>Guyana<br>Gambia<br>Bulgaria<br>Botswana<br>Bahrain<br>Argentina<br>Virgin Islands, British<br>Slovenia<br>Panama<br>New Zealand<br>Mauritius<br>Macao, China<br>Lebanon<br>Latvia<br>Kyrgyz Republic<br>Guatemala<br>Denmark Egypt<br>Canada<br>Brazil<br>Bhutan<br>Belize<br>Armenia<br>Suriname<br>Portugal<br>Oman<br>Malta<br>Malaysia<br>Gibraltar<br>El Salvador<br>Bosnia and Herzegovina Australia<br>Burundi<br>Finland<br>-3 -2 -1 0 1 2 3 4<br><!-- End of picture text --> We will now turn our attention to a more systematic extension of our earlier research to gauge the impact of the aforementioned changes in the regulatory environment on the development of the banking sector, its fragility, and other outcomes of interest.

# **IV. Bank Regulation and Supervision around the World: What the Data Mean**

# **IV. A. Variable Definitions**

Since BCL (2006) provides information on the data, sources, and specific survey questions used to construct the variables for this paper, we only briefly define them here in the text.

1. _Bank Activity Regulatory Variables_ . We measure the degree to which national regulatory authorities allow banks to engage in the following three fee-based rather than more traditional interest-spread-based activities:
# _‐_ _<u>Figure 5: Wage and Child Labor Meta Analysis Results</u>_

<!-- Start of picture text --> (a) Child & Wage Labor Transfers Estimates (%)<br>100%<br>50%<br>0%<br>‐50%<br>‐100%<br>‐150%<br>‐200%<br>Effect Mean Effect CI Child labor Wage employment<br>(b) Child & Wage Labor Transfers Impacts (%) and Transfer Sizes<br>1 300<br>0.8<br>250<br>0.6<br>0.4<br>200<br>0.2<br>0 150<br>‐0.2<br>100<br>‐0.4<br>‐0.6<br>50<br>‐0.8<br>‐1 0<br>Baseline Mean Estimate Monthly HH Transfer (2011 PPP USD)<br>Percentage of baseline<br>Monthly HH Trasnfer<br>Baseline Mean and Estimate<br><!-- End of picture text --> 35
asset returns falling above or below a pre-specified level.<sup>3</sup> Although the exceedance correlation is easy to understand and simple to calculate, Ang and Chen (2002), Campbell et al. (2002), and Longin and Solnik (2001) show that the exceedance correlation has a conditioning bias. For example, no matter how strongly two assets following a multivariate normal distribution are correlated, the exceedence correlation in the tails equals 0. Because of the conditioning bias, the exceedance correlation needs to be adjusted before measuring correlation asymmetries.

The tail dependence coefficient calculates the asymptotic probability of one asset providing extremely small or large returns given another asset provides extreme returns.<sup>4</sup> See Garcia and Tsafack (2011), Patton (2006), and Fortin and Kuzmics (2002) for example. One advantage of the tail dependence coefficient is that it does not need to choose a threshold as other conditional correlations do. However, this also induces a drawback: as a measure of dependence under very extreme circumstances, the tail dependence coefficient is realized infrequently. In addition, the tail dependence coefficient does not provide information on the dependence during normal times. For example, the tail dependence coefficient equals 0 when returns follow multivariate normal distribution.

Different with the exceedance correlation and tail dependence coefficient, the VaR-implied correlation does not have a conditioning bias. The VaR-implied correlation is conditional not only on individual assets’ returns falling beyond a given threshold, but also on returns of a portfolio composed of the assets falling beyond the threshold. The additional condition on the portfolio counteracts the conditioning bias from truncating individual asset returns and thus makes the VaRimplied correlation free of conditioning bias.

Campbell et al. (2002) proposed the VaR-implied correlation and showed that it outperformed the linear correlation using data of the US, the UK, France, and Germany. Cotter and Longin (2011) investigated the impact of portfolio weights, the type of position, the frequency of data and the probability level on VaR-implied correlations by using the equity indexes of the US and the UK. Mittnik (2014) extended the pairwise method used in these papers to joint estimation.

> 3A general form of the exceedance correlation between two variables X and Y at thresholds δ1 and δ2 is

4The coefficient of upper tail dependence is and the coefficient of lower tail dependence is 3