rafmacalaba/datause-encoder-data huggingface dataset:
5 losers, uncertainty about the consequences of reform can generate suspicion and hostility or weaken the incentives for potential beneficiaries to organize.

Some investment climate reforms face greater challenges from interest groups than others. Reforms that are more technical or administrative, such as business registration or inspections reform, tend to affect few clearly identifiable groups capable of mobilizing against reform and so may be politically less contentious. The main resistance comes from departments and bureaucrats at different levels of government seeking to preserve their roles. Reforms with wider distributional consequences are more likely to provoke a response from interest groups. These groups can have a positive impact when they mobilize support and help get reform onto the agenda. But more often they act to block reform, seeking to preserve existing benefits. That makes it difficult to obtain support for reform from politicians who benefit from ties to these groups and who are often preoccupied with short-term costs and electoral horizons rather than long-term benefits. Two examples illustrate:

- Labor market reforms often meet resistance from “insiders” such as unions and formal sector workers, who benefit from existing laws and tend to be well organized, compared to new entrants or informal workers who benefit from reform and lack voice. Moreover, labor market reforms may be opaque to ordinary people: voters may not understand or care about their impact and are difficult to mobilize in support of reform. These factors help explain why in Latin America during the 1990s unions and their parliamentary allies repeatedly stalled legislation to extend the use of fixed-term contracts or to reduce severance payments. While 24 countries in the region liberalized trade and the financial sector, only 6 substantially amended labor laws (Gill, Montenegro, and Domeland 2002, p. 5).

- Land reform is similarly contentious. Most large-scale redistributions of land have taken place only under exceptional circumstances: either under pressure of revolt and revolution or in the aftermath of war and foreign occupation. Even where de facto property rights are recognized, formalizing land titles and creating land registries can run into resistance from interest groups. Notaries and other registry personnel may oppose modernization for fear of losing their jobs or opportunities for bribery. In Peru local public officials resisted land titling reform because they saw it as encroaching on their jurisdiction over urban planning (Endo 2004, p. 19).

# _Institutional setting for decision-making_

A second political challenge is the institutional setting in which policy decisions are made. Reforms which entail fundamental institutional change often require legislation to signal government commitment and limit the scope for deviations or reversals in implementation. And securing legislative approval often requires the involvement of a wide range of political institutions and “veto players”—typically the executive (president or prime minister), political parties, the legislature, and the judiciary. (Interest groups
rather than the impact of AD protection in the home market. Prusa (2001) shows that exporters react to a tentative AD duty imposition immediately after an AD investigation is filed and that AD filings reduce the imports of the countries named in a filing even if the complaints are not upheld. From this point of view, beginning an investigation triggers retaliatory incentives for the target country.<sup>3</sup> Additionally, our analysis is based on the trading effects of AD activity in a particular export destination, namely, the country targeted by the domestic industries rather than the entire international market. In this regard, the four-dimensional information on firms, products, and export destinations in the Brazilian firm-level export data fits our research question perfectly.

In addition to its rich firm-level export data, Brazil is a suitable country for our analysis in terms of AD. Brazil implemented an AD law in 1987, and it ranks in the top 10 of all World Trade Organization (WTO) members in terms of both the number of AD petitions filed and the number of AD duties targeted against it in the post-WTO period. Brazil was the defendant country in 26 AD cases during our sample period, which corresponds to 208 unique six-digit Harmonized System (HS) products from 11 different industries.<sup>4</sup> Furthermore, Brazil filed 71 AD cases for 96 six-digit HS products, which is far above the mean and median of worldwide AD activity.

As shown in table 2, Brazil named its major trading partners, which are also very active AD users. Brazil seems to be a member of the “AD club”; its major trading partners also targeted Brazil in several cases after the creation of the WTO. Therefore, the threat of AD retaliation is an important consideration for exporters, not only because of the dramatic impact of AD duties on trade flows, but also because of the target countries’ past AD activities against Brazil. In tables 3 and 4, we document the AD cases filed by and against Brazil by three-digit ISIC industry codes.

5
prominent features). As the test operates with the null hypothesis H0: _mean (group 0) – mean (group 1) =0_ , a negative sign (and statistically significant coefficient) should be interpreted as a significantly more positive evaluation given by group 1 on dimension x, than stakeholders who do not belong to group 1. More concretely, one should read the evaluation of the overall quality of the Benefit Plan by government as being significantly more favorable than everybody else who is not a member of the executive, the legislature or the judiciary. In contrast, a positive and significant difference means that the main tested group has a less favorable assessment of dimension x. We can see, for example, that civil society members, overall, assess the Benefit Plan and priority setting strategies designed and implemented by the public sector less favorably than government stakeholders on four different dimensions (overall quality, efficiency and two different measures of equity). The results clearly illustrate a sharp division of perceptions mainly between the representatives of the unified public sector agency (particularly the executive and the legislative branches), on one hand, and patient and civil society groups, on the other hand.

# _Efficiency_

The histogram below plots the distribution of efficiency assessments across two major groups (unified government agencies – 1, and civil society – 0).

**Figure 1: Efficiency bliss points across groups of stakeholders**<sup>**42**</sup>

<!-- Start of picture text --> 0 1<br>1 2 3 4 5<br>Total<br>1 2 3 4 5<br>Efficiency perceptions across groups (public sector versus civil society)<br>Graphs by gov<br>80<br>60<br>40<br>20<br>0<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Likert scale: Very inefficient Inefficient Satisfactory Efficient Very efficient<br>Values: 1 2 3 4 5<br><!-- End of picture text --> 42 The Likert scale provided an additional category “I don’t know” omitted here for clarity of exposition.

23
# **Uganda**

|**Study reference**|**Prevalence**<br>**among females**<br>**15-24(%)**|**Incidence**<br>**rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Ahmed et al.,<br>AIDS2001||1.71|1994-1998|Community HIV<br>prevention trial|Rakai|
|Mulder et al.,<br>BMJ 1995|Ages 13-24:<br>9.9 (1989),<br>7.3 (1994)|0.73<br>(1989-1991),<br>0.71<br>(1992-1994)|1989-1994|Adult general<br>population|Masaka|
|Wawer et al.,<br>AIDS 1997|Adults 15-24: 20.6<br>(1990), 16.45<br>(1992)|2.1<br>(1990-1991),<br>2<br>(1991-1992)|1990-1992|Adult general<br>population|Rakai|
|Kengeya-Kayondo et al.,<br>Int J Epidemiol 1996||0.82 (1990),<br>0.69 (1991),<br>0.57 (1992),<br>0.89 (1993),<br>0.45 (1994)|1990-1994|Adult general<br>population|Masaka|
|Arroyo et al.,<br>J Acquir Immune Defic<br>Syndr 2006||Ages 14-90:<br>1.27|1999-2002|Adult general<br>population|Rakai|
|Brown et al.,<br>AIDS2007||1.6|1999-2004|Women 18-35|Kampala|
|Gray et al.,<br>Lancet 2007||1.33|2004-2006|Control men in<br>circumcision trial|Rakai|
|Tobian et al.,<br>AIDS2009||1.09|2004-2006|Men undergoing<br>circumcision|Rakai|
|Ruzagira et al.,<br>PLoS ONE 2011|Ages 18-24: 10.3<br>(2004)|1.04|2004-2007|Adult general<br>population|Masaka|
|Guwatudde et al.,<br>PLoS ONE 2009|Adults 15-24: 5.8|0.77|2006-2007|Adult general<br>population|Kayunga|
|Seeley et al.,<br>Sex Transm Dis2012||4.9|2009- 2011|Fishing<br>communities|Lake Victoria|
|Baeten et al.,<br>NEJM 2012||2.07|2008-2011|PrEP in discordant<br>couples (placebo|Jinja, Kabwoh,<br>Kampala, Mbale,|
|||||group)|Tororo|
|ALPHA network study||||||

# **Zambia**

||**Prevalence amo**|**ng females 15-24(%)**|**Spectrum e**|**stimates**|**Indicator 1.6 -**<br>|
|---|---|---|---|---|---|
||||||**HIV**|
||**National Antenatal**<br>**Sentinel Surveillance**<br>**Surveys **|**National Demographic and**<br>**Health or AIDS Impact**<br>**Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|**prevalence in**<br>**young people**<br>**15-24(%)**|
|**1990**|-|-|2.50|-|-|
|**1991**|-|-|2.53|-|-|
|**1992**|-|-|2.59|-|-|
|**1993**|-|-|2.51|-|-|
|**1994**|-|-|2.43|-|-|
|**1995**|-|-|2.33|-|-|
|**1996**|-|-|2.23|-|-|
|**1997**|-|-|2.10|-|-|
|**1998**|-|-|1.98|-|-|
|**1999**|-|-|1.88|-|-|
|**2000**|-|-|1.79|-|-|
|**2001**|-|-|1.75|-|-|
|**2002**|16.3|11.1|1.66|-|-|
|**2003**|-|-|1.59|-|-|
|**2004**|11.5|-|1.49|7|-|
|**2005**|-|-|1.38|16|-|
|**2006**|14.6|-|1.35|26|-|
|**2007**|15.6|8.5|1.27|46|16.8|
|**2008**|-|-|1.15|-|-|
|**2009**|-|-|1.02|81|13.8|
|**2010**|-|-|0.99|72|-|
|**2011**|-|-|0.96|82|-|
|**2012**|-|-|079|79|-|
|<br>ANC da<br>HIV and<br>Nationa<br>**Zamb**|<br>ta sources: Zambia UNGA<br>AIDS and the Universal A<br>l DHS and AIS data source<br>**ia**|<br>SS Report 2008, Zambia Country<br>ccess 2010, ANC Sentinel Surve<br>s: Zambia DHS 2001-2002 and 2|.<br>Report: Monitoring the<br>illance of HIV/Syphilis t<br>007|<br>Declaration of Co<br>rends in Zambia 19|<br>mmitment on<br>94-2002|

65
<!-- Start of picture text --> Real Dry Bulk Index, 1850-2020 (1850=100)<br>200<br>150<br>& 100<br>50<br>1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->
Table 4: The strong industrial productivity growth

||Value-added/worker<br>growth 1993-97 (%)|
|---|---|
|Total|<br>39.8|
|Agriculture|20.8|
|Industry|54.0|
|Industry less construction|<br>63.0|
|<br>Services|<br>19.2|

> _Source:_ Calculations based on GSO (1998) for output in current prices and on VLSS I and 2 for employment data

# _b) The Industrial Sector_

Table 5 shows that in industry both the State and the domestic non-state sectors have grown rapidly (by 10.2% and 9.2% per annum respectively) over the period 1995-98. In terms of size, however, the State sector still dwarfs the private sector. In 1998, its contribution to industrial value-added was more than twice as large as that of the domestic private sector. Accordingly, a large share of the recent industrial expansion is due to State enterprises. Indeed, table 5 shows that between 1995 and 1998 State companies have accounted for 37.2% of Vietnam's total industrial growth, compared to 16.2% for domestic private companies (foreign investment is discussed in the next section).

The State sector's contribution to employment, however, is much smaller. In 1997/98, it provided jobs for less than 25% of industrial workers. This contrasts with the domestic nonstate sector which, despite its smaller size, employed more than 64% of industrial workers. As can be seen in Table 5, a large proportion of these workers are in household enterprises. But registered "private and mixed companies" are also much more labor-intensive than the State sector. Although accounting only for 7.9% of industrial GDP in 1998, these companies employed more workers than State-owned enterprises.

This picture shows that Vietnam's slow employment growth reflects the large State sector's strong growth but low labor intensity, and the private sector's small size (but not slow growth). One may thus look at Vietnam's poor employment performance as being the byproduct of a transition period in which both the State sector and private enterprises grow, albeit from a different base. On the other hand, State enterprises, which are still concentrated in import-substituting activities and sheltered from both foreign and domestic competition, typically draw capital and other resources away from the private sector. This may have prevented higher rates of private sector growth (and thus employment growth), and/or may do so more substantially in the future.

6
from a Cash Transfer Project.” <mark>Policy Research working paper; no. WPS 7505. Washington, D.C.: World Bank.</mark>

- 36) 2014. Townsend, Robert M. 1994. “Risk and Insurance in Village India.” _Econometrica_ 62 (3): 539–91.

- 37) United Nations. 2003. “State of World Population 2007: Unleashing the potential of urban growth.” New York. Accessed September 23.

<u>http://www.unfpa.org/publications/state-world-population-2007</u>

- 38) Wolpin, Kenneth I. 1982. “A New Test of the Permanent Income Hypothesis: the Impact of Weather on the Income and Consumption of farm Households in India.” _International Economic Review_ 23(3): 583–594.

- 39) World Bank, 2014. Accessed July 7. <u>http://data.worldbank.org/country/niger</u>

40
substantially higher for middle-income households than either for the households in the bottom three or in the top decile of total per capita expenditures. However, these averages hide substantial variation by region. As shown in Table 4, rice production revenues constitute the highest share of total income in the Northwest, North Central and South Ganges Floodplain regions.<sup>4</sup>

**<u>Table 4. Spatial Profile of Rural Poverty and Dependence on Agriculture.</u>**

||North-<br>west|South<br>Ganges<br>FPLN|North<br>Central|Meghna<br>FPLN|East<br>Hills|Coastal<br>Area|
|---|---|---|---|---|---|---|
|Headcount rates,|||||||
|UPL, 2000 (%)|63|61|49|49|47|37|
|Headcount rates,|||||||
|LPL, 2000 (%)|49|49|37|26|21|25|
|_(percent of total hous_|_ehold gross_|_income)_|||||
|Agricultural wage|22|18|15|10|11|14|
|income|||||||
|Farm income|27|28|22|14|13|18|
|Crop production|24|22|18|11|11|13|
|revenues|||||||
|Rice production|17|13|14|9|9|10|
|revenues|||||||
|Non-agricultural<br>income|30|34|38|37|40|36|

Note: Poverty headcount rates are individually weighted, and income structure statistics are weighted using the household weight. Agricultural income includes agricultural wages and production revenues. Nonagricultural income includes non-farm wage income and non-agricultural enterprise income. UPL denotes the upper poverty line, and LPL denotes the lower poverty line as defined by the Bangladesh Bureau of Statistics and reported in World Bank (2002a). Source: HIES 2000, rural households.

Given the importance of rice in the income and consumption structure of Bangladeshi households, in particular in the main regions of rice cultivation, a productivity improvement and a price change of rice have a significant effect on the welfare of rice producing and rice consuming households alike. Indeed, the rapid expansion of irrigation and _boro_ rice cultivation led to significant growth or rural per capita domestic product, particularly in the Northwest, South Ganges and North Central regions (World Bank 2002b). Before turning to the ex-post analysis, it is useful to

> 4 In terms of the share of total production value three regions with the highest rice shares are the Meghna Floodplain, Northwest and North Central regions, according to HIES 2000. The South Ganges region has a somewhat lower share of rice in total production value (around 30 percent compared to between 43 and 51 percent in the other three regions).

14
- Galiani, S., P. Gertler, and E. Schargrodsky, 2005, “Water for Life: The Impact of the Privatization of Water Supply on Child Mortality”, _Journal of Political Economy_ 113, 83-120.

- Gertler, P., 2004, “Do Conditional Cash Transfers Improve Child Health?  Evidence from PROGRESA's Controlled Randomized Experiment”, _American Economic Review_ 94, 331-336.

- Heckman, J. and D. Masterov, 2007, “The productivity argument for investing in young children”, IZA Discussion Paper No. 2725.

- Hesham, M.S., A. Edariah, and M. Norhayati, 2004, “Intestinal Parasitic Infections and Micronutrients Deficiency: A Review”, _Medical Journal of Malaysia_ 59, 28493.

- Jiménez, E., 1983, “The Magnitude and Determinants of Home Improvement in SelfHelp Housing: Manila’s Tondo Project”, _Land Economics_ 59, 70-83.

- Kahneman, D. and A. Krueger, 2006, “Developments in the Measurement of Subjective Well-Being”, _Journal of Economic Perspectives_ 20, 3-24.

- Katz, L., J. Kling, and J. Liebman, 2001, “Moving to Opportunity in Boston: Early Results of a Randomized Mobility Experiment”, _Quarterly Journal of Economics_ 116, 607-54.

- Kaufmann, D. and J. Quigley, 1987, “The Consumption Benefits of Investment in Infrastructure: The Evaluation of Sites-and-Services Programs in Underdeveloped Countries”, _Journal of Development Economics_ 25, 263-84.

- Kremer, M. and E. Miguel, 2004, “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities”, _Econometrica_ 72, 159-217.

- Nokes, C., S. Grantham-McGregor, A. Sawyer, E. Cooper, and D. Bundy, 1992, “Parasitic Helminth Infection and Cognitive Function in Schoolchildren”, _Proceedings of the Royal Society of London, Series B, Biological Sciences_ 247, 77-81.

- Melvin, D.M., 1982, <u>Laboratory Procedures for the Diagnosis of Intestinal Parasites,</u> U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, Laboratory Improvement Program Office, Laboratory Training and Consultation Division, Atlanta, GA.

- Muñoz F., C. Quilodrán, P. Velásquez, C. Niedmann, A. Baeza, G. Silva, and M. Osorio, 1989, “Acquirement of the Spanish Vocabulary among Rural and Urban Students of the 9th Region”, _Revista Chilena de Pediatria_ 60, 354-358.

- Olsen, E., 2003, “Housing Programs for Low-Income Households”, in R.. Moffitt (ed.), <u>Means-Tested Transfer Programs in the United States, National Bureau of</u> Economic Research.

- Paxson, C. and N. Schady, 2005, “Cognitive Development Among Young Children in Ecuador: The Roles of Wealth, Health and Parenting”, World Bank Policy Research Paper 3605, The World Bank.

26
||**Co**|**mplaints V**|**Table 8**<br>**s All other ne**|**gative event**|**s16**||
|---|---|---|---|---|---|---|
|**Da**|**v -1**<br>**I**|<br>**Da**|**v 0**|**Da**|**y +1**|**Window**|
||**C**<br>|**omplaints (**<br>|**Government**<br>|<br>**and Citizens**<br>|<br>**)**<br>||
|**AAR**<br>|**CAAR**<br>|**AAR**<br>|**CAAR**|**AAR**|**CAAR**|**CAAR**|
|-1.405|T30.209|*3.137|-27.331*|-1.244|-24.473-|-36.014-|
|(-0.343)|(-2.335)|(0.767)|(-2.014)|(-0.304)|(-1.727)|(-1.921)|
||<br>|**All ot**<br>|**her negative**<br> <br>|**events**<br>|<br>||
|-2.751|T-1.274<br>[|0.524|[-1.489<br>|12.889<br>|]<br>2.680<br> <br>|11.1687<br>|
|(-0.988)|<br>(-0.146)|<br>(0.190)|<br><br>(-0.162)|(1.047)|<br>J(0.280)|J(0.092)|
||<br>**Co**<br>|<br>**mplaints V**<br>|<br>**s All other n**|<br>**egative even**|<br>**ts**||
|1.347<br>|-28.934 <br>|2.613|-25.842-|-4.133|-27.152.|-37.182*|
|(0.273)|<br>j(-1.853)|<br>(0.530)|(-1.578)|(-0.838)|(-1.587)|(-1.643)|

We mav interpret this result by noting that the filing of a complaint can provide _unanticipated_ news to markets leading them to expect further actions, yet unknown, to be undertaken. Reductions in market values range on average from 4% to 15%. These losses are much greater in magnitude than any losses observed in previous studies conducted in developed countries. "7

# **V. Conclusion**

In this paper. we have shown that despite a generallv acknowledged poor enforcement of environmental regulations, capital markets in Argentina, Chile, Mexico and the Philippines appear to react to the announcement of environmental events involving publicly traded companies. While fines and penalties used by the environmental agencies of these countries may have fallen short of creating incentives for pollution control, capital markets have penalised firms suffering from adverse environmental events, and rewarded firms with positive environmental news. While we

**16** See Foomote **15** for details of computation. **17** See Lanoie et al. (1997) for more details.

24
# **Figure 14: Effect of interest rate caps in WAEMU**

<!-- Start of picture text --> Average interest rate on loans Interest margin<br>11.5 1.2<br>11 1.1<br>10.5<br>1<br>10<br>0.9<br>9.5<br>0.8<br>9<br>8.5 0.7<br>8 0.6<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>Return on assets Bank credit to deposits<br>1.2 1.1<br>1.1 1.05<br>1 1<br>0.9 0.95<br>0.8 0.9<br>0.7 0.85<br>0.6 0.8<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>WAEMU SSA WAEMU SSA<br><!-- End of picture text --> _Notes:_ Charts 2 to 4 plot the average level of the respective variable for the WAEMU member countries in dark blue and the average for all other countries in the Sub‐Sahara Africa (SSA) region in light blue (dotted lines). The series are standardized to 1 in 2013 by dividing them by their respective values in 2013. _Sources:_ IMF, World Bank The figures above show that following the decision to reduce the rate ceiling in early 2014 average lending rates in the region dropped, suggesting that the lower ceilings were binding. The average rate on loans declined from 10.7% in 2013 to 9.1% in 2014 and continued to fall to 8.8% in 2015.<sup>30</sup> Lower lending rates resulted in decreasing interest margins for banks. The average interest rate margin in WAEMU in 2014 was more than 20% lower than in the previous year and continued to fall in 2015. Parallel to falling interest rate margins, the profitability of the banking sector took a hit after the ceilings on interest rates were lowered.

> 30 Data based on IMF Country Report No. 17/99. BCEAO data are slightly different.

29
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