rafmacalaba/datause-encoder-data huggingface dataset:
commonalities across countries and regulations. Francois concludes that accession is unlikely to exert significant pressure on Turkey to restructure as a result of either general market access conditions or regulatory convergence requirements.

# **6. Conclusion**

The literature on trade in services has grown significantly since the late 1980s. The factors that determine trade and investment flows, the effects of such flows, their importance and the types of policies that affect them are now much better understood. That said, the current state of knowledge still leaves much to be desired. Much uncertainty continues to prevail regarding the determinants of trade in services, the extent to which countries restrict trade and the impact of past or prospective liberalization – the magnitude of (potential) net gains, their distribution, and the associated adjustment costs.

The lack of comparable cross-country, time series data on services output and prices, the limited availability of bilateral trade and investment flow statistics, and the very patchy coverage of data on “foreign affiliate trade in services” and mode 4 transactions explain the paucity of empirical research and policy analysis. The fact that most statistical resources continue to go towards the measurement of goods producing sectors is an anachronism. Time series, bilateral data on trade in goods is available for thousands of product categories. In contrast, comprehensive datasets on the origin and destination of trade in services – for whatever mode of supply – does not exist for any level of aggregation. The same is true for information on policies affecting services trade and investment. Given that services generate some 20 (40) percent of total foreign exchange flows at the global level if modes 3 and 4 are ignored (included), this would appear to be a misallocation of statistical resources. A concerted, internationally cooperative effort to rectify this situation through the launching of surveys to collect data on a regular basis is a precondition for more rigorous empirical analysis. Progress has been made on the methodological front in the last 20 years, including the development of an international manual and methodology to collect services trade statistics. What is needed now is implementation—which in turn will depend on advocacy from and willingness by the private sector to support such a global venture.

> 61 They found little impacts on the markups of other sectors, except mining and quarrying.

48
<!-- Start of picture text --> Response in key variables to shocks<br>GDP, in percent compared to baseline Consumption , in percent compared to baseline Prvt. Investment , in percent compared to baseline<br>00s 005 00s<br>100 —~ 200 = 000<br>~005 Ne 205 x -005 »<br>010 ‘\ “i -010 x \ ~010 *. :<br>015 — Sconaria: \REZ. -015 ~ ~ -p1s s ~.<br>— Seanaria: RP: 8.35. -020 Th ‘\.<br>~020 hs <p — ~020 bs<br>~025 —— a0 2 -025 ae<br>-030 “035 -030 eo<br>2020 2030 ©2040 «©2050 «2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Government revenue, in percent compar ed to baseline Government expenditure, in percent comparedto baseline Government debt, percentagepoint deviation from baseline<br>004 005 08<br>000<br>mee a » -005200 TS LL 006oa Fs TM x<br>-012 q‘-010 \ , 002 Z} \\<br>016 \~ “015 se . 900 \<br>-020 ie sa -020 aa 002 Ne ae —Taie<br>~024 ~025 -004<br>2020 ©2030 «2040 «62050 «2060 «2070 ©2080 2090 2100 2020 2030 «2040 «62050 «2060 «2070 +2080 2090 ©2100 2020 2030 42040 ©2050 ©2060 2070 2080 2090 2100<br>Consumer prices, difference in growth rates Real consumer wage, difference in growth rates Real producer wage, difference in growth rates<br>00025 a 0001 0001<br>iat / a 2000 =. 2000 =><br>00015 / \ ~0001 Pe ~9001 we<br>0010 / at = -0002 we -0002 in<br>‘ooes /} mo ~ ~0-2 00 34 \\ ~~A - 0o 00 43 \\. 4) p<br>ioe ~0005 \ / 0005 * 7<br>-90005 —_ j oad \ 7 -0006 ‘ 3<br>~90010 2020 2030~~ S2040 ©2050 2060 2070 2080 2090 2100 ~0007~n0082020 2030\ =2040 20504 2060 2070 2080 2090 2100 ~0007~20082020 2030seBe2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->
20 seen in the example on **Egypt.** This does not detract from the fact that there is a substantial differential in the **GNP** per capita estimate for the Bank's Operational Guidelines.

Table **5:** SYRIA **-** Derivation of Composite Exchange Rate

|**1983**|1984|**1985**|**1986**|**1987**|**1988**|
|---|---|---|---|---|---|
|MultileRates||||||
|**1.**<br>PrincipalRate**/1**<br>**3.95**<br> <br> <br>|**3.95**<br>|**3.95**|**3.95**|**3.95**|**11.225**|
|**2.**<br>SecondaryRate:**/1**<br>5.45<br> <br> <br>|5.45<br>|5.45<br>|5.45<br>|5.45<br>||
|**3.**<br>TertiaryRate<br>**5.70**|**7.06**|**8.80**|**9.81**|**10.00**||
|DerivationofWeights<br>**1.**Principal(O<br>Merchand<br> <br>|fflcial)Ra<br>iseTrade<br>|teforPu<br><br>|blicSect<br>|or<br>||
|Exports**%**Total**GNFS**<br>**20.9**<br>|**21.8**|20.4|**16.6**|**193**||
|Imports**%**Total**GNFS**<br>48.9|493|44.9|**36.7**|**33.0**||
|**%**ShareinTotal**GNFS**<br>**69.8**|**71.1**|**653**|**53.3**|**52.3**||
|2.Secondary(Parall<br>TradeinGoo<br> <br>|el)Ratef<br>dsand**N**<br>|orPrivat<br>**FS**<br>|eSector<br>|||
|Exports**GNFS% **Total**GNFS**<br>7.4|6.4|**6.8**|**13.1**|14.2||
|Imports**GNFS% **Total**GNFS**<br>14.9|**13.7**|**18.6**|**23.9**|24.8||
|**%**ShareinTotal**GNFS**<br>**22.3**|20.1|**25.3**|**37.0**|**38.9**||
|**3.**TertiaryRatefor<br> <br>|Tourism<br>|||||
|**%**ShareinTotal**GNFS**<br>**7.90**|**8.88**|**9.37**|9.64|**8.82**||
|SummaryWeights<br>||||||
|**1.1**Principal<br>**0.70**|**0.71**|**0.65**|**0.53**|**0.52**|**1.00**|
|2.1Secondary<br>0.22<br>|0.20|**0.25**|**0.37**|**0.39**||
|**3.1**Tertiary<br>**0.08**|**0.09**|**0.09**|**0.10**|**0.09**||
|***0**<br>"COMPOSITE"Rate**0*****<br>4.4225|4.5269|4.7844|**5.0701**|**5.0676**|**11.225**|
|Computedas:**[(1*1.1)+ **(2*2.1)**+(3*3.1)]**||||||
|MemoItems||||||
|**GNP**percapita (Atlas..principalrate)<br>**1919**|1849|1946|**2053**|**2155**|1954|
|**GNP**percapita(Atlas..compositerate)<br>**1703**|**1629**|**1670**|**1688**|**1701**|**1711**|
|Ratio:CompositeratetoOfficialrate<br>**1.13**|**1.15**|1.22|**1.29**|**1.29**|**1.00**|
|"Composite"percap/"Atlas"per<br>**0.89**|**0.88**|**0.86**|**0.82**|**0.79**|**0.88**|

> Source: IMF, "Recent Economic Development" (Unpublished); March **6, 1987;** and March **9,1988.**
- H2 (military spending): More military spending in oil rich countries is associated with a lower risk of violent conflict onset.

- H3 (social spending): More social spending (education, health and social security) in oil rich countries is associated with a lower risk of violent conflict onset.

Our dependent variable is the dichotomous indicator for conflict onset and empirical estimations are logit models with robust standard errors, that include the duration of peace years (or years since independence) and cubic splines to control for time dependence (Beck et al., 1998). To test our conditional hypotheses, we use interaction terms between the measure of oil value and measures of broad public spending and the specific spending items described above. Similar to previous work, all time varying variables are lagged one year.

# **_Dependent Variable_**

Several data sources have been used for conflicts, explaining part of the different results found in the literature. There are four major datasets: (1) Armed Conflict Dataset (ACD) from the Uppsala Conflict Data Program PRIO (Gleditsch et al., 2002); (2) Fearon and Laitin (2003); (3) Sambanis (2004); and (4) the Correlates of War, or COW (Sarkees and Wayman, 2010). All four use a threshold of 1000 battle deaths to define a civil war. The UCDP/PRIO dataset also codes smaller civil conflicts of at least 25 battle deaths per year, leading to three common UCDP/PRIO conflict constructions: low intensity (25 deaths) conflict years, cumulatively high-intensity (1000 death) years, and high-intensity years only. These competing measures differ in relation to when to code the start, what counts as a war, and how to treat breaks in violence.

To measure the onset of violent conflict, we use in this paper the Armed Conflict Dataset (ACD) for years 1960-2009 (Gleditch et al. 2002, Version 4.1.) to generate a dichotomous variable that takes the value of one in years with a new conflict onset and zero otherwise. This dataset has become the standard reference for cross-country analyses of conflict determinants. The majority of the recent studies have tended to use this source. Furthermore, along with major civil conflicts, PRIO also codes the onset of minor conflicts as those above 25 battle deaths per year. These smaller conflicts provide a relevant complement to the rarer full blown civil conflicts. Adding these smaller conflicts to define conflict onset should assist in estimating the determinants of rare events such as conflict onset.

Following Ross (2012), we regard a conflict as a new conflict onset if it occurs two years after the previous conflict. The ACD defines violent conflict as “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths” (UCDP/PRIO 2012). As mentioned above, in addition to the low intensity violence indicator, we also use the 1000 battle-related deaths threshold to measure the onset of major civil conflict.

10
(2011) and Ag ́enor (2014). Data sources used to match key ratios include the IMF’s World Economic Outlook (WEO) database, the oil and revenue management policy framework provided by the Uganda’s Ministry of Finance and various research papers such as Pritchett (2000), Goldberg (2011), Ang ́enor and Aizenman (1999), Kopoin et al. (2013) and Ag ́enor (2014).

In the representative household’s utility function, the weight on leisure _ψ_ is set to 3, which leads to the steady-state value of household work effort to be 40 percent of available time. The household’s discount factor, _β_ , is set 0.93, implying a long-run real interest rate of 9.68 percent annually. This assumption is fairly reasonable and consistent regarding banks’ interest rates in most of low-income countries (see African Development Indicators (ADI)). In addition, the share of capital in the production function for intermediate goods in the non-traded goods sector, _α_<sup>_N_</sup> , is set to 0.45, while it is set to 0 _._ 3 in the traded goods sector. These values indicate that the informal (non-tradable goods sector) sector is more intensive in labor than the tradable goods sector. The depreciation rates of capital are fixed to 0.1 in the informal sector and 0.075 in the tradable goods sector. All these values are consistent with studies on Sub-Saharan African countries and reported in Jihad et al. (2012), Cherif and Fuad (2012) and Ag ́enor (2014).

In our paper, we use the empirical evidence based on Mexican data from 1980 to 1994 by Arestoff and Hurlin (2006) to pin-down the parameter that governs the efficiency of public investment _ε_<sup>_G_</sup> . This paper shows that the coefficient of regressing public capital produced (effective investment in our model) on investment expenditures falls between 0.4 and 0.65. This range of investment efficiency is in line with the estimates in Pritchett (2000) for Sub-Saharan African countries with a linear specification between effective investment and investment expenditures. Based on these empirical papers and the macroeconomic efforts undertaken by the government to sustain public investment, we set _ε_<sup>_G_</sup> to 0.6.

The nominal price rigidity parameter in the non-traded goods sector, as well as the nominal wage-setting parameter are set following Calvo’s model of staggered price and wage adjustment. As in Christiano et al. (2005), the probability of not reoptimizing for price and wage setters in the domestic country, _φp_ and _φw_ , are fixed to 0.75 and 0.64, respectively. The degree of home bias in private consumption, _φ_ , and the elasticity of substitution between domestic labor types, _ξ_ , are set to 0.4 and 1, respectively. These values are estimated in Christiano et al. (2010) for 22
5) Other Agency includes Nafinsa, Productive Chains, Financing, PNAA, State Government Support, Municipal Government Support, and Others.

Out of 30,199 year-firm observations, 18,435 are for the control group that reported never having participated in any SME programs, and 11,764 are for firms in the treatment group (Table 16). The last two columns refer to the treatment group with year-of-first-participation information, which was used to define a post-program indicator variable with a value of 0 for all years prior to the first-year-of-participation and a value of 1 for the first year of participation and all subsequent years.

**Table 16. Distribution of Treatment and Control Groups**

||**Total**|**Group**||**Treatment Group with**<br>**Dates**|**Program Start**|
|---|---|---|---|---|---|
|**Year **|**Sample**|**Control**|**Treatment**|**Pre-program **|**Post-program **|
|1994|2,440|1,488|952|874|78|
|1995|2,444|1,490|954|851|103|
|1996|2,465|1,505|960|837|123|
|1997|2,495|1,524|971|819|152|
|1998|2,511|1,532|979|763|216|
|1999|2,522|1,539|983|642|341|
|2000|2,528|1,544|984|427|557|
|2001|2,534|1,548|986|331|655|
|2002|2,541|1,552|989|266|723|
|2003|2,573|1,571|1,002|185|817|
|2004|2,573|1,571|1,002|9|993|
|2005|2,573|1,571|1,002|0|1,002|
|**Total**|30,199|18,435|11,764|6,004|5,760|

_Source_ : Linked ENESTYC-EIA panel data.

The treatment group and the control group increase with the size of the firm (Table 17). Firm size is defined as “micro” with 15 or fewer workers, “small” with 16 to 100 workers, “medium” with 101 to 250 workers, and “large” with over 250 workers.

**Table 17. Distribution of Treatment and Control Groups by Firm Size and Sector**

|**Sector**|**M**<br>**Treat **|**icro**<br>**Control **|**S**<br>**Treat **|**mall**<br>**Control **|**M**<br>**Treat **|**edium**<br>**Control **|**L**<br>**Treat **|**arge**<br>**Control **|
|---|---|---|---|---|---|---|---|---|
|Food, beverages and tobacco|1|3|22|44|81|101|154|171|
|Textile industry, clothing and|||||||||
|leather industry|0|1|8|31|56|107|62|82|
|Wood products|0|0|1|7|11|22|12|15|
|Paper products|0|0|1|9|31|71|25|55|
|Chemical|0|1|27|56|84|138|93|139|
|Mineral products|0|2|10|21|24|38|21|40|
|Basic metalic industry|0|0|1|8|8|21|19|22|
|Metalic products, machinery<br>and equipment|0|4|18|61|93|135|116|137|

20
Policy Research Working Paper 10840

# **Abstract**

Submarine cables enable international connectivity and are essential for high-speed internet access. This paper tests their potential to improve the affordability of internet access by supporting a price drop through cost savings or increased competition intensity. The empirical framework relies on a dataset that combines the capacity of submarine cables with price data on fixed and mobile internet across 150 countries over a decade. Using a two-way fixed effects estimator, the analysis finds that the expansion of submarine cables is associated with a statistically significant drop in the price of internet access, up to 14–21 percent, depending on the technology, for every doubling of the capacity of submarine cables, and with large regional disparities. These effects stem from cost savings in the short run and tend to decline over time, concomitant with a rise in domestic telecom market concentration. The analysis also finds that these effects can be enhanced by telecom regulations, especially de-jure independence of the regulator, and the regulation of network interconnection and access, shared telecom infrastructure, and competition from international players across the broadband value chain. The main findings are robust to alternative estimation strategies, including an instrumental variable and a staggered difference in differences.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at joel.cariolle@ferdi.fr, ghoungbonon@ifc.org, tsilue@ifc.org, and dstrusani@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_SECUPJ TIES CLEARANCE AND SETTLEMENT SYSTEMS_ Box 1 presents the typical procedures that are undertaken in a securities clearance and settlement system. The lifecycle of a securities transaction involves three phases: trade execution, trade clearance, and trade settlement.<sup>9</sup> These procedures should be designed and developed to work within a specific legal and operating environment and thus take account of local regulatory and oversight arrangements. An understanding of these latter factors is essential as they influence the way in which the procedures are used and thus contribute to any inherent risks embedded in the system. The nature of the operational environment is assuming increasing importance in most countries as technological innovation is changing the way that information is processed and managed and thus has an impact on all other factors. The way the authorities adapt to the new operational conditions is critical to maintaining efficient and safe systems. It is thus clear, that both the legal and regulatory oversight arrangements need to evolve in line with technology driven changes in operational procedures.

<!-- Start of picture text --> inttBuying t i Buying tEx Chon A Selling ini r Selling<br>Books", Cusering both payments and securities clerance and s eBroker f Ln Customer a<br>Emerging Market Economies World Bank Discussion Pap, N. 3<br>Pay;0 ing00 7 ' A gent'6<br>9 Vid. tehm, , Apri 1996,Clearace andSettlement SsesfrScrte:Ciia einCocsi<br>2.iei te client's<:t.* ffW tae dtail of trnation ex aeted. f<br>fww.iphe-whpf.org) seords nato d by C hearn Agetrad BrnkanCEML,wtrstiiaino bodrneo<br>intitten thradoeeugitIneationcubonal Advisrety between theC isnerlabratieng asaitiaeorts, b Yelleen<br>Bok" coveringse bothrog payentscupoc seuiiscommranicatnd ands se5ttement foais comerocn tatd<br><!-- End of picture text --> _Caribbean Region._

- 9 _Vid. Stehm, J., April 1996,_ Clearance and Settlement Systems for Securities: Critical Design Choices in Emerging Market Economies, _World Bank Discussion Papers, No. 321._

_6_
# **Economic freedom and level of per capita income seem to be related. As**

Figure 5 indicates countries that enjoy high levels of economic freedom are those that are associated with higher levels of economic development. The positive relationship seems to be even stronger when resource-rich countries are not included in the sample (Figure 6.

**Figure 5.  Economic Freedom vs. per Capita Income, 2007.**

<!-- Start of picture text --> 13<br>12<br>11<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>4 5 6 7 8 9<br>Economic Freedom<br>ln GDP per capita (PPP)<br><!-- End of picture text --> Source: Freedom House and World Bank.

**Figure 6.  Economic Freedom vs. per Capita Income (excluding resource-rich countries), 2007.**

<!-- Start of picture text --> 12<br>11<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>4 5 6 7 8 9<br>Economic Freedom<br>Ln GDP per capita (PPP)<br><!-- End of picture text --> Source: Freedom House and World Bank.

16
**Table 1: Summary statistics**

|**Label**|**Short description**|**Source**|**Mean**|**SD**|**#Obs**|
|---|---|---|---|---|---|
|_Panel identifie_|_rs_|||||
|geo|Country identifier|||||
|period|Period identifier|||||
|_Dependent var_|_iable_|||||
|lrgdpna_pc|Log of GDP p.c. (in constant 2017 US$)|PWT10.0|8.94|1.25|1,556|
|_Small model_||||||
|linflation_na|Log of (inflation+1)|PWT10.0|0.09|0.14|1,556|
|lrer|Log of real exchange rate|PWT10.0|-2.87|4.16|1,556|
|ltraderesid|Log trade openness (corrected for population and global GDP)|PWT10.0|0.34|0.54|1,556|
|infra_index|Infrastructure index|WDI|0.18|0.85|1,525|
|dum_fincrisis|Dummy for financial crisis|Laeven & Valencia (2020)|0.22|0.42|1,556|
|sd_temperature|Standard deviation of monthly temperature levels|WBCCKP|4.04|3.16|1,537|
|dltot|Log changes in the terms of trade|WDI|0.01|0.12|1,556|
|lkg|Log of (government consumption / GDP)|PWT10.0|-1.76|0.51|1,556|
|sd_growth|Standard deviation of growth rate|PWT10.0|0.04|0.04|1,556|
|durbanpop|Change in the share of urban population|WDI|1.95|2.07|1,556|

[continued on next page]

-9-
<!-- Start of picture text --> 250<br>200<br>150<br>100<br><!-- End of picture text --> <!-- Start of picture text --> ; in oll<br>High Income Latin America andthe Sub-Saharan Africa<br>Caribbean<br>TM 1950-59 1960-69 TM 1970-79 TM1980-89 TM1990-99 m 2000-09<br><!-- End of picture text -->
to an increase in the volume of imports.”<sup>48</sup> Figure 2 shows two variables: one represents the behavior of the number of initiations and the other, the margins of over/undervaluation of the Mexican peso.

**Figure 2 - Investigations Initiated and Over/Undervaluation Margins of the Exchange Rate in Mexico,**

**1987-1997**

<!-- Start of picture text --> 70 50%<br>40%<br>60<br>30%<br>50<br>20%<br>40 10%<br>30 0%<br>-10%<br>20<br>-20%<br>10<br>-30%<br>0 -40%<br>Investigations initiated Over/undervaluation margins<br>Investigations initiated<br> Over/undervaluation margins<br>1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: Estimated over/undervaluation margins, based on Banco de México’s information for 1989-1990 and on the Purchasing Power Parity (PPP); investigations initiated in 1987-1997, UPCI.

Even though Figure 2 above seems to indicate that the antidumping activity increase in Mexico in the 1991-1997 period could be largely explained by the behavior of the real exchange rate, other political and economic events or circumstances taking place in the country contributed to the expansion of this activity as well.

> 48 SECOFI, (1997). _Informe de Labores de la UPCI_ , p. 19.

19
28 TABLE 3 (CONT.)

|Author, Year of Data, Country|Primary|Secondary|Higher<br>Education|Total|
|---|---|---|---|---|
|Rural|||||
|Selowsky, 1794, Colwnbia (expenditure)|+||NA|+|
|lJaUade, 1970, Columbia (expenditure)|NA|NA|NA|+|
|Petrei (contribution to Gini Coefficient)|||||
|Costa Rica|+|_|_|+|
|Uruguay|+|_|+|+|
|Meesook, 1978, Indonesia (Share of recurrent<br>expenditure compared to share of school-age children)|+|?|_||
|van de WaUe, 1987, Indonesia (expenditure)|+|_|_|+|
|Dasgupta and TIlak, 1977, Andha Pradesh India<br>(expenditure)|+|+|_|+|
|Riboud, 1986, Costa Rica (expenditue)|NA|NA|NA|?|
|Selden and Wasylenko, 1985, Pem (expenditure)<br>_|s.|+-|+|_NA_|
|<br>*+- pro-poor;-<br>pro-rich, 0<br>neutral; +-<br>eqpntue<br>and decrases tdwreafer ? - no dear patnte NA = not av|s<br>per hou<br>ailab1e.|sehold incas|for first few|deciles|

"Expenditure indicates that the equality conclusions are based on educational expenditure for households acmss income groups; expenditueAncome indicates that the equaliy conclusions are based on educational expenditures as a percentage of income across income groups.

SOURCES: Meerman (1979), p. 112; Selowsky (1979), pp. 6667; Jallade (1974), pp. 36-40; Foxley et aL (1979), Castenada (1989), pp. 30 and 60; Petre (1987), pp. 84-88; Dasgupta and Tilak (1983); Meesook (1984); Riboud (1990), Table 111.4; van de Walle (1992), Table 14; Selden and Wasylenko (1992), Table 3.