rafmacalaba/datause-encoder-data huggingface dataset:
# **XI. Conclusions**

In this paper we have developed an innovative small open economy computable general equilibrium model of the Tanzanian economy that is capable of assessing the impact of the preferential liberalization of barriers against multinational service providers. We have provided a discussion of the welfare economics of services liberalization, in which we argue that it is likely that the gains from preferential reduction of barriers against multinational services will be larger if the partner region is large. We find that Tanzania would obtain small gains from a preferential reduction of barriers in services with the Africa region in our central elasticity case. Gains from a free trade agreement with the EU that includes a preferential 50 percent reduction in the ad valorem equivalents of the barriers in the services sectors will produce significant gains for Tanzania, deriving primarily from services commitments. Gains from liberalization with the EU region are considerably larger because of the larger amount of technology diffusion associated with trade and FDI with large industrialized countries, captured in our model as a relatively large increase in the number of varieties. In addition, when rents are captured initially, the terms of trade loss on partner services and the lost rents on services from the Rest of the World are larger when partners have low supply elasticities. We have conducted extensive sensitivity analysis to determine confidence intervals for the results and found that there is a set of values for key parameters that lead to estimated losses for preferential liberalization of services with the Africa region.

# **References**

Arnold, Jens, Aaditya Matoo and Beata Javorcik (2007), “Does Services Liberalization Benefit Manufacturing Firms: Evidence from the Czech Republic,” World Bank Policy and Research Working Paper Number 4109.

Baldwin, Richard E., Forslid, Rikard and Haaland, Jan (1999), "Investment Creation and Investment Diversion: Simulation Analysis of the Single Market Programme," in R. Baldwin and J. Francois (eds.), Dynamic Issues in Applied Commercial Policy Analysis, Cambridge: Cambridge University Press.

- Balistreri, Edward J. and James Markusen (2009),  “Sub-national differentiation and the role of the firm in optimal international pricing,” _Economic Modeling_ , 26(1), 47 - 62.

50
whereas when individuals are more responsive, the price drops by 13 percent. In terms of welfare gains, they follow the patterns of consumption observed above with richer individuals spending more on telecom services than poorer individuals. Thus, they reap more of the benefits of a decrease in prices. Under the lower elasticity scenario, an individual in a household that uses telecom services in the second quintile is estimated to experience a welfare gain of 828 DJF. In the same scenario, an individual in the top 20 percent would gain 7,347 DJF. Corresponding to the lower price change in the scenario of an elasticity of -2.5, the welfare gains are lower across the distribution.<sup>17</sup>

**Table 5. Results from WELCOM simulations**

||**Elasticity = -1.5**|**Elasticity = -2.5**|
|---|---|---|
|**Price change**|22.2 percent|13.3 percent|
||Welfaregains(D|JFperyear) per user|
|**Quintile 1**|n.a|n.a|
|**Quintile 2**|828|276|
|**Quintile 3**|1,792|597|
|**Quintile 4**|2,290|763|
|**Quintile 5**|7,347|2,449|

Source: own calculations using EDAM 4.

Note: Quintiles based on the distribution of consumption per capita. Results for the bottom quintile were 594 and 198, respectively, but not shown as there is too little information (i.e. a coefficient of variation of 18 percent) to draw reliable estimates.

The welfare gains are low with respect to the overall level of consumption in each quintile, and thus are not conducive to tangible changes in poverty or inequality. It is important to highlight that the results presented are limited as they can simulate the potential welfare gains only among _current_ telecom users. In a context such as Djibouti, an equally important question would be: What would happen to the individuals who are not currently consuming these items (due to several reasons including affordability or coverage). That is, given an expected drop in price and an associated welfare gain, should we expect more people to adopt and use telecom services?

We attempt to answer this question by conducting the following thought exercise. First, we run a probit model to look at the predictors of telecom usage among the Djiboutian population. We use as covariates a series of household characteristics, household head characteristics, dwelling characteristics, location variables, the household’s consumption level, as well as the usage of telecom services among households in the same primary sampling unit (PSU). We estimate the probit via a stepwise regression and find that consumption level and access within the PSU are

> 17 A complementary exercise was run for only the prepaid cards for mobile credit. Results show again that the highest gains are accrued to the top 20 percent of the population (due to their higher expenditures on such items). The welfare gains gap is smaller though. Under an elasticity of -1.5 the top (second) quintile would gain 4,810 (826) DJF per year. Under the high elasticity scenario of -2.5 the richest quintile would gain 1,603 DJF and the second quintile would gain 275 DJF. The small number of consumers of “internet contracts” prevented us from running a simulation for this telecom service.

13
which subsidiaries of the same multinational corporations have been involved in antidumping investigations over similar or related products in multiple emerging-market jurisdictions.

In the first example, the major tire-making multinational corporations Michelin, Bridgestone, Goodyear, and Pirelli all have subsidiaries involved either as petitioners or as targeted firms in antidumping investigations in emerging markets such as Turkey, South Africa, India, Thailand, Brazil, and China.<sup>27</sup> In the second example, an Indian subsidiary of Osram (itself a subsidiary of the German multinational firm Siemens) was part of a petition for an Indian antidumping investigation of compact fluorescent lamps (CFLs) from China in which one of the targeted firms was Osram’s own subsidiary in China. In cases involving Owens Corning, Continental Carbon, Monsanto, and Graftech, the emergingmarket subsidiary of a U.S.-headquartered multinational firm initiated an antidumping investigation against imports from another emerging market. Moreover, not all these examples are of multinationals headquartered in high-income economies. In the last case, subsidiaries in India and Thailand of Indorama Synthetics, an Indonesia-headquartered multinational firm, were among supporters of antidumping investigations of yarn and fiber imports.

Another reason to suspect that recent antidumping actions involving emerging economies may be enforcing rather than combating cartels is that some of these actions have targeted the same products that were identified in the links between antidumping use and cartel behavior in the 1980s and 1990s in high-income economies (see section 3.4). Again, we searched the World Bank’s _Temporary Trade Barriers Database_ (Bown 2012a); this time we looked for cases involving polyvinyl chloride (PVC) – one of the products related to the in-depth examination by Messerlin (1990) in the European Community context—or another industrial chemical, polyethylene terephthalate (PET). As illustrated in

> 27 According to industry reports, Michelin, Bridgestone, Goodyear, and Continental together accounted for almost 55 percent of the world market in 2010 (Datamonitor 2011, 13).  Michelin, Bridgestone, Goodyear, and Pirelli each have subsidiaries in many emerging markets, including China, the latter often through joint ventures.  Not included in the table is the U.S. 2009 China-specific safeguard tariff on tire imports, a case that was _not_ brought by the U.S. domestic tire producers—i.e., U.S.-headquartered firms or by U.S. affiliates of foreign- headquartered multinationals—but by the United Steelworkers on behalf of the industry’s unionized labor force.

24
inequality effects of infrastructure; more comprehensive surveys include Estache (2006), Romp and de Haan (2007) and Straub (2007).

The bulk of the empirical literature on the effects of infrastructure has focused on its long-run contribution to the level or growth rate of aggregate income or productivity. It all starts with Aschauer’s (1989) finding that the stock of public infrastructure capital is a significant determinant of aggregate TFP in the U.S. However, his estimate (based on time-series data) of the marginal product of infrastructure capital —as much as 100% per year— was implausibly high.

The massive ensuing literature on the output effects of infrastructure has employed a variety of data, empirical methods and infrastructure measures. The most popular approaches include the estimation of an aggregate production function (or its dual, the cost function) and empirical growth regressions. Infrastructure is variously measured in terms of physical stocks, spending flows, or capital stocks constructed by accumulating the latter. The majority of this literature finds a positive long-run effect of infrastructure on output, productivity, or their growth rate. This is mostly the case with the studies using physical indicators of infrastructure stocks, but results are more mixed among studies using measures of public capital stocks or infrastructure spending flows (Straub 2007).

Another strand of recent literature has examined the effects of infrastructure on income inequality. The rationale is that infrastructure provision may have a disproportionate effect on the income and welfare of the poor by raising the value of the assets they hold (such as land or human capital), or by lowering the transaction costs (e.g., transport and logistical costs) they incur to access the markets for their inputs and outputs. These effects may occur through a variety of mechanisms documented in the empirical literature —see for example Estache, Foster and Wodon, (2002), Estache (2003), and Calderón and Servén (2010). Of course, for infrastructure development to reduce income inequality, the key ingredient is that it must help expand access by the poor, as argued for example by Estache et al. (2000).<sup>13</sup>

> 13 There may be two-way causality in this relationship, that is, income inequality may prevent the access of poorer people to infrastructure services. For example, Estache, Manacorda and Valletti (2002) show that income inequality adversely affects access to internet, while Alesina, Baqir and Easterly (1999) argue that more unequal societies devote less effort to the provision of public goods, including infrastructure.

13
The further use of implicit stratification proposed by the “Poverty map” design is able to further reduce the design effect to 1.87. This could lead to a reduction in the needed sample size (and costs) and the desired maximum percentage error.

**Figure 2. Sample sizes (left) and gains in cost (right) as function of expected maximum error**

<!-- Start of picture text --> 40000 6<br>35000 5<br>4<br>30000<br>3<br>25000<br>2<br>20000<br>1<br>15000<br>0<br>10000<br>-1<br>5000 -2<br>0 -3<br>4.5 5 5.5 5.7 5.9 6 7 8 9 10<br><!-- End of picture text --> Based on the right column of Table 5, the INS has decided to adopt a final size of 21,600 households selected from 1,800 PSUs. This choice, also seen in Figure 2, is based on two main considerations: i) this size not only permits getting maximum percentage errors less than 10%, which can also reach 5.7%, which is even below the estimated maximum value in the EBCNV 2015; ii) thus, the total cost of the survey will be about 5.2 million Tunisian dinars, with a 1.7 million dinar increase in cost when compared with the initial estimated budget.

# **5. Discussion, further research, and concluding remarks**

In this paper we have proposed an original methodology for improving the design effect of household surveys in general and Household Budget Surveys in particular. This is carried out by using the results of a poverty mapping exercise implemented by the method developed by the World Bank. Moreover, we have described how the new JRR method, proposed by Verma and Betti (2011), has been implemented for the first time in a developing country by a National Statistical Office for estimating standard errors for _complex measures_ from _complex surveys_ .

19
# Appendix I.A.4 Historical Demographic Data, Brazil 1872-1970

|Year|1872|1890|1900|1920|1940|1950|1960|1970|
|---|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|7.78|12.91|16.17|13.17|23.01|22.75|20.10|17.40|
|Population aged 20 to 59/<br>population over 60 years old|6.39|10.83|12.85|9.76|10.47|10.17|8.94|8.24|
|Percentage<br>of population<br>over 60 years old|7.63|4.41|3.24|4.04|4.06|4.25|4.73|5.07|
|Percentage<br>of population<br>under 15 years old|32.97|38.63|44.34|42.78|42.54|41.86|42.75|42.10|
|Women over 60 years old/<br>Menover60yearsold|0.90|-|0.94|1.03|1.13|1.09|1.00|1.05|

Ratios calculated using data found in Mitchell, B. R. 1983 "Intemational Historical Statistics: The Americas and Australasia".

# Appendix I.A.5 Historical Demographic Data, Bulgaria 1888-1985

|**Year**|1888|1900|1910|1926|1946|**1965**|**1985**|
|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|8.40|10.66|10.46|11.01|11.37|7.84|5.92|
|Population aged 20 to 59/<br>population over 60 years old|4.56|4.82|5.08|5.64|5.49|4.10|3.08|
|Percentage<br>of population<br>over 60 years old|9.02|8.39|8.30|8.14|9.59|13.25|17.78|
|Percentage<br>of population<br>under 15 years old|41.45|40.21|39.76|34.93|27.88|23.85|20.83|
|Women over 60 years old/<br>Men over60 years old|0.96|0.88|0.88|0.97|1.11|1.15|1.16|

Ratios calculated using data found in Mitchell, B. R. 1990 "International Historical Statistics: Europe 1750-1988".
||**Table5.The PowersoftheIntegratedSuperviso**<br>|**ryAgencieso **<br>|**verBanks**<br>|
|---|---|---|---|
||**RegulatoryandSupervisory Powers**|**Number**<br>**ofagencies**<br>|**As% ofall**<br>**agencies**|
|1.|Conducton-site examinations|15|100|
|2.|Conductoff-site exarninationsand surveillance|15|100|
|3.|Imposesanctionsandfinesfor non-compliance with<br>rulesandregulations|15|100|
|4.|Setprudential regulationsonmarket,credit,<br>operationalandliquidityrisks|12|80|
|5.|Setaccounting rulesand information disclosure<br>requirements|11|73|
|6.|Setrulesonthecompositionofcapital|11|73|
|7.|Approveandrevokealicensetoafinancial<br>intermediary|11|73|
|8.|Setminimumcapital requirements|10|66|
|9.|Setlicensingrequirements|9|60|
|10.|Consumer protection(assisttoresolveclaimsfor<br>abuses against usersoffinancialservices)|9|60|

In terms of regulatory powers, 12 out of 15 of the agencies can set or amend prudential rules on credit, market and liquidity risks. In countries where the supervisor can not directly set or amend these prudential rules, it is normally the Ministry of Finance who has the prerogative to do it. Hungary is an interesting case, because the integrated supervisor was established without giving it any of the aforementioned regulatory powers. The Hungarian Financial Supervisory Authority was conceived as purely a supervisory entity. Setting or amending financial sector regulations in Hungary is the exclusive prerogative of the Ministry of Finance.'<sup>4</sup> As shown in the Table 5, 11 out of 15 agencies can directly set or change the accounting and disclosure rules applicable to financial institutions. In countries where the unified agency does not have this prerogative, such as Australia, there is normally an independent body, usually a professional association of accountants that is responsible for setting the rules. However, in practically all countries in which an independent entity is responsible for setting the accounting rules, the supervisory agency has the ability to request a review of accounting practices and influence changes in the desired direction.

The survey also revealed that 11 out of 15 agencies have powers to establish the rules on the composition of capital. Interestingly, only 10 agencies can set the minimum capital requirements that financial intermediaries must observe. In cases where the supervisor can not set this type of prudential rule directly, it is the Ministry of Finance that normally does so.

Similarly, nine out of 15 agencies have the power to set the minimum licensing requirements and 11 to approve or revoke a license to a financial intermediary. Traditionally, granting or revoking 14 Laws, however, grant the Hungarian Financial Supervisory Authority (HFSA) the right to express its opinion when laws affecting the financial system or the institutions and persons under its control are being prepared. Moreover, the President of the HFSA has the right to participate in government sessions on issues affecting the regulation and supervision of the financial system.

16
# **Works Cited**

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2. WHO Ebola Response Team. Ebola Virus Disease in West Africa -- The First 9 Months of the Epidemic and Forward Projections. _The New England Journal of Medicine_ . 2014:1481-1495.

3. United Nations. _World Population Prospects: The 2012 Revision._ : United Nations, Department of Economic and Social Affairs, Population Division; 2013.

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9. BBC News Africa. In pictures: Ebola orphans. _BBC News Africa_ . November 2014:http://www.bbc.com/news/world-africa-30019892.

10. UNICEF. More than 16,000 children lost parents or caregivers to Ebola - many are taken in by the communities: UNICEF. February 2015.

11. WHO. Ebola Situation Report. _World Health Organization_ . February 11, 2015.

- http://apps.who.int/iris/bitstream/10665/152219/1/roadmapsitrep_11Feb15_fre.pdf?ua=1&ua=1. Accessed February 12, 2015.

12. Liberia Institute of Statistics and Geo-Information Services (LISGIS); Ministry of Health and Social Welfare [Liberia]; National AIDS Control Program [Liberia]; ICF International. _Liberia Demographic and Health Survey 2013_ . Monrovia, Liberia 2014.

13. Statistics Sierra Leone (SSL); ICF International. _Sierra Leone Demographic and Health Survey 2013_ . Freetown, Sierra Leone and Rockville, Maryland, USA 2014.

8
77 TABLE 18: Agricultural Credit Trends - 1980-88 8ANRURAL A FIRA: Annual Lending **and** Portfolio (In billions of 1988 Mex8)

|BANRURAL|1980|1981|1982|1983|1984|1986|1988|1987|1988|
|---|---|---|---|---|---|---|---|---|---|
|Balances at year-end 1/|6,829|6,641|4,441|3,663|3,922|4,380|3,329|2,774|4,036|
|I.<br>Working Capital<br>Loans<br>II.<br>Investment Loans|2,806<br>2,824|3,416<br>3,228|2,209<br>2,233|1,860<br>1,702|2,282<br>1,641|2,847<br>1,533|2,149<br>1,180|1,988<br>788|2,996<br>1,039|
|LOANSGRANTED<br>DURINGTHE YEARV|6,337|6,996|4,802|3,806|4,314|4,093|4.076|8,710|3,943|
|I. Working Capital Loans<br>II.<br>Investment<br>Loans|4,060<br>1,287|4,843<br>1,362|3,004<br>998|3,061<br>744|3,636<br>779|4,023<br>070|3,499<br>676|3,270<br>434|3,422<br>622|
|FIRA||||||||||
|Balances**at**year-end/|3,667|3,616|2,710|2,687|2,983|3,262|2,134|1,886|2,889|
|I. WorkingCapital<br>Loans<br>II.<br>Investment Loans|1,766<br>1,881|1,976<br>1,641|1,811<br>1,100|1,729<br>867|2,066<br>928|2,113<br>1,139|1,624<br>010|1,262<br>632|1,732<br>968|
|NEW DISCOUNTS DURING THEYEAR_V_|3,610|3,679|3,464|2,962|3,274|3,700|2,686|2,882|2,689|
|I. Working Capital Loans<br>II.<br>Investment Loans|1,727<br>1,883|2,010<br>1,6d9|2,062<br>1,401|1,973<br>978|2,266<br>2,019|2,407<br>1,208|1,918<br>709|1,762<br>890|1,732<br>968|
|BANRURAL<br>& FIRA||||||||||
|Balances at Year-end|9,196|10,167|7,161|6,139|6,905|7,632|6,403|4,869|8,724|
|I. Working Capital Loans<br>II.<br>Investment<br>Loans|4,612<br>4,886|6,390<br>4,767|3,819<br>3,332|3,680<br>2,680|4,336<br>2,689|4,960<br>2.872|3,873<br>1,790|3,240<br>1,419|4,728<br>1,996|
|NEW LOANS/DISCOUNTS<br>DURING THE YEAR|8,947|9,674|8,260|8,767|7,686|8,398|6,781|6,362|6,632|
|I. Working Capital Loans<br>II.<br>Investment Loans|6,777<br>3,170|6,653<br>2,920|6,867<br>2,399|6,036<br>1,722|8,790<br>1,708|6,430<br>1,969|6,418<br>1,343|6,038<br>1,324|6,164<br>1,478|

1/ Portfolio balances **were** inflated according to the<sup>end-of-year</sup> CPI.
E-6

|

.

Carter Brandon and Ramesh Ramankutty, Agia:Environmentand Revelorpment (1993); and Richard Calkins, et. al., Indonesia: Environmentand Development (1994). Inside the Bank, sector reports for Mexico, Malaysia and several Middie Eastern countries have also used IPPS-based estimates. IPPS has been used to produce the first comprehensive cross-country estimates of toxic pollution in World Resources 1994-95 (Table 12.4) published by the World Resources Institute. Recent work on trade and the environment by the OECD has also been based on IPPS, most notably the paper by David Roland-Holst and Hiro Lee: "International Trade and the Transfer of Environmental Costs and Benefits" (OECD, December 1993).

During the next year, we anticipate very rapid movement on Phase II of IPPS development: adjustment to conditions in other economies. At the conclusion of Phase I, we can offer a massive database of pollution parameters which are immediately usable for environmental planning and analys*s. Complete 2-, 3-, and 4- digit ISIC pollution intensities are available on diskette from the authors.
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27