rafmacalaba/datause-encoder-data huggingface dataset:
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE telecommunications incumbent retains monopoly control of the international gateway. As a result, internet access charges in 2008 at $47 per month were still about four times as high as those elsewhere in the developing world, even if they are typical for African countries in the benchmark group. Experience from across Africa indicates that customers only receive the full benefit of cost savings associated with submarine cable access when multiple landing stations compete (table 16). Imminent connection to a number of new submarine cable projects in West Africa that are expected to be complete in the period 2010 to 2012 should intensify competition and further reduce prices.

**Table 16 High international call charges driven both by technology and market power**

|$|**Percent of**<br>**cases**|**Call within**<br>**region**|**Call to U.S.**|**Internet**<br>**dial-up**|**Internet**<br>**ADSL**|
|---|---|---|---|---|---|
|Without submarine cable|67|1.34|0.86|68|283|
|With submarine cable|33|0.57|0.48|47|111|
|monopoly on international gateway|16|0.70|0.72|37|120|
|competitive international gateway|16|0.48|0.23|37|98|

Source: Minges and others 2009.

# **Challenges**

During the last five years the percentage of the population in Côte d’Ivoire living within reach of a GSM signal has remained at around 55 percent. This is well below the benchmarks of 62 percent for fragile states and 95 percent for middle-income countries. Simulations conducted for the AICD suggest that close to 100 percent of Côte d’Ivoire’s population could be reached with a GSM signal on a commercially viable basis, making it one of the most attractive markets in Africa (figure 13). This result is based on the assumption that 4 percent of local income in each area could be captured as revenues for voice telephony services. Even if this assumption were relaxed to only 1 percent of local income, 97 percent of the population could be served on a commercially viable basis. The disruption caused by armed conflict may have contributed to slow network rollout.

28
To compute the routine and non-routine income shares we define routine and non-routine workers as suggested by Acemoglu and Autor (2011). That is, we consider workers employed in “management, business, and financial operations occupations”, “professional and related occupations”, and “service occupations” as non-routine; and we define routine workers as ones employed in “sales and related occupations”, “office and administrative support occupations”, “production occupations”, “transportation and material moving occupations”, “construction and extraction occupations”, and “installation, maintenance, and repair occupations”. Note that this classification emerges out of an extensive literature, surveyed in Acemoglu and Autor (2011), that originated from the seminal work by Autor et al. (2003). They and many other contributions in this line of research use detailed information on the task content of at least 300 detailed occupations (depending on the study) obtained from the Dictionary of Occupational Titles (DOT) and its successor O*Net. The classification used here is the “consensus aggregation” suggested by Acemoglu and Autor (2011) that captures the key insights from the more detailed micro analyses. We drop farm workers for all our analyses for comparability with the BLS measure of the labor income share. To compute the income shares corresponding to each occupation group, we simply compute the aggregate annual wage bill within each occupation group and divide it by nominal GDP.

Finally, we proportionately rescale both group specific income shares (which originally add up to the series labeled “CPS(R+NR)” in panel A of Figure 2) so that they match the share of (non-farm business) labor income in GDP, as estimated by the BLS (top line in panel A of Figure 2). The resulting routine and non-routine income shares using the MARCH earnings measure are displayed in panel A of Figure 1 and Figure B.13 in Appendix B illustrates that income shares based on the MORG earnings measure reveal a virtually identical picture.

This decomposition highlights a striking feature: in the U.S., the decline in the aggregate labor share is entirely accounted for by routine occupations, while the income share of non-routine labor has been rising.

8
Table 6: The role of bank branches from the trade-partner country: IV

|Financial|6.627***|6.674***|5.951***|6.674***|6.628***|6.674***|5.861***|
|---|---|---|---|---|---|---|---|
|development ( <sup>_Loan_</sup><br>_GV A_<sup>)</sup>|(1.040)|(1.039)|(1.074)|(1.039)|(1.042)|(1.039)|(1.079)|
|<br>TP bank? (Yes=1)||0.186**<br>(0.084)||||0.186**<br>(0.084)||
|Share(TP branches)|||0.061***<br>(0.006)||||0.065***<br>(0.006)|
|Regional TP bank?||||0.186**||||
|(Yes=1)||||(0.084)||||
|Share(Regional TP|||||-0.004***||-0.008***|
|branches)|||||(0.001)||(0.001)|
|Log(Sub-regional|0.305*|0.307*|0.209|0.307*|0.300|0.307*|0.192|
|GVA)|(0.185)|(0.185)|(0.186)|(0.185)|(0.185)|(0.185)|(0.186)|
|Log(Importer GVA)|0.832***|0.830***|0.968***|0.830***|0.847***|0.830***|1.004***|
||(0.093)|(0.093)|(0.098)|(0.093)|(0.094)|(0.093)|(0.100)|
|Log(Distance)|-1.348***|-1.348***|-1.327***|-1.348***|-1.347***|-1.348***|-1.323***|
||(0.164)|(0.164)|(0.163)|(0.164)|(0.164)|(0.164)|(0.163)|
|Regional<br>(exporter)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|FE||||||||
|Country (importer) FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|1.02|1.02|1.02|1.02|1.02|1.02|1.02|
|F stat|49|41|57|41|42|41|48|
|Kleibergen-Paap<br>LM|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|test p-value||||||||
|Hansen J stat p-value|0.000|0.000|0.577|0.000|0.000|0.000|0.487|
|R<sup>2</sup>|0.554|0.554|0.557|0.554|0.554|0.554|0.557|
|N|37440|37440|37440|37440|37440|37440|37440|

The dependent variable is the _Log_ ( _Exportit_ ) expressed in 100,000 constant USD. Robust standard errors clustered at the pair level are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level.

15
and their children need to be considered. Widow pensions are a policy that a number of developing countries have adopted, including India (Dutta et al., 2010). The receipt of such transfers conditional on the loss of a husband irrespective of age, could well improve the status as well as the well-being of widows in Malian society. The amounts might be predicated on the number of dependents.

Finally, the results from this study demonstrate once again the importance of examining the allocation of resources and the treatment of individual members within the household. They also hold an important implication for data collection as well as for the study of marriage and socio-economic status. Surveys typically record current marital status but rarely collect information on marital history. The findings here underline the value of collecting the details on marital history and taking it into account in countries such as Mali where marital status can be a critical determinant of living standards.

27
|**B.Argentina'**|**OldSystem**|**NewSystem**|
|---|---|---|
|Structure|PAYGDB|PillarII:fundedindividual<br>accounts(IA's)_or_publicDB<br>PillarI:flatorreduced flat<br>benefit|
|Contributionrate|27%(lowerbefore1994)|11%toPillarII<br>6-16% toPillarI(varies by<br>regionandtime)|
|Benefits|JO:70%ofbasesalary+<br>1%foreveryyearover30;<br>JEA:50%ofbase+1%for<br>everyyearover10|PillarII:AnnuityfromIA;_or_<br>publicDB(.85%*yearsof<br>service*salary)<br>PillarI:flat=about30%of<br>maleaveragewage+1%extra<br>for yrs>30;_or_reducedflatat<br>age70(21%ofaveragewage)|
|Basesalary|Averageof3highestannual<br>salarieswithinlast10yrs|ForpublicPillarII:averageof<br>last10yearswage;not<br>relevantforprivatePillarII|
|Pensionableage|JO:Men-age60;women-55<br>JEA: age65|Men-age_65;_women-60;<br>Reducedflat:age70|
|Years foreligibility|JO:20yearscontributions<br>(15before1991);<br>JEA:10yearsservice|30years forflat benefit<br>10yearsfor reducedflat<br>nominimumforIA|
|Pensionifworkedfeweryrs|0|Annuityfrom IAaccumulation|
|Indexationprovisions|Adhoc|Public benefitadhoc(has been<br>constant);<br>Privateannuitynotindexed|
|Minimum pension|$150after10years'service|$140after10years'service|
|Widows|75%ofhusband'spension<br>_and/or_ownpension|70%ofhusband'spension<br>(jointannuity)+ownannuity|

# Notes:

1. Argentina had special provisions for the self-employed and many special regimes. We focus here on the main schemes for employees: JO= Jubilacion Ordinaria; JEA = Jubilacion por Edad Avanzada. The JO applied to those with 20 years of contributions, while the smaller JEA applied to those with 10-20 years of contributions. However, the old scheme changed frequently and wasn't always implemented as written, especially in the absence of records. Widow's benefit was treated unevenly. The new system also has an ordinary flat benefit for 30+ years of service and a reduced flat for 10-30 years of service. However, the government is considering tightening conditions for the reduced flat.

42
**1 Introduction**

As the world economy struggled to climb out of the Great Recession during 2009-2011, emerging markets were the source of much of the relatively meager economic growth taking place. The IMF (2011) estimates that emerging markets contributed more than 75 percent of total global GDP growth in 2011 even though they accounted for less than half of world GDP. For countries seeking an export-led growth strategy, one implication is the need to _reorient_ trade to take advantage of emerging market growth.<sup>1</sup> However, as the global economy rebalances after the Great Recession, policymakers in emerging markets have been placed under increased pressure to impose new import restrictions, perhaps partially _because of_ the adjustment pressure associated with the reorientation of exports. While many emerging economies withstood pressure to raise generally applied import tariffs early in the crisis (Kee et al, forthcoming), a growing number implemented significant new bilateral import restrictions through antidumping, safeguards, and countervailing duty policies – referred to jointly as temporary trade barriers (TTBs). Bown (2011) used data from the World Bank’s _Temporary Trade Barriers Database_ to show that, in comparison with pre-Great Recession levels of 2007, seven major emerging economy G20 members collectively increased by roughly 40 percent the share of nonoil import product lines that they subjected to these import restrictions by 2009.<sup>2</sup> Newly available data for these economies indicates coverage was an additional 13 percent higher in 2011 than 2009, and thus stands 67 percent higher than 2007. This 67 percent increase is particularly large relative to only a 13 percent increase for this same period by _high-income_ G20 member economies, countries that have gone through a much weaker period of economic growth.

This paper provides a threefold examination of issues concerning emerging economies and these newly evolving forms of import protection. First, we characterize the heterogeneous economic

> 1 This reorientation is not limited to _emerging_ economies’ export-led growth strategies. According to the U.S. President’s Council of Economic Advisers (CEA 2011, Figure 4-12, p. 102) more than 70 percent of nominal United States export growth between 2009 and 2014 associated with the potential doubling of US exports was expected to derive from exports to emerging markets.

> 2 Throughout this paper, we use “imports” and “nonoil imports” interchangeably and “exports” and “nonoil exports” interchangeably. I.e, our analysis always strips away oil products from total imports, total exports and TTB policy actions.

2
(1) by including the underlying elements of speci...cations (2) and (3): We now proceed to report the estimates of the ...scal multipliers for both OECD and developing countries using the four empirical speci...cations just outlined.

# 3 Estimates of spending multipliers

We ...rst present estimates for the linear (single) multiplier, then for multipliers in recession and expansion, then for multipliers that di¤erentiate between increases and decreases in government spending, and ...nally for multipliers that take into account both expansion/recession and government spending going up or down.

## 3.1 Linear multiplier

As a natural ...rst step –and following speci...cation (1) –we compute the cumulative ...scal multiplier for OECD countries, which is illustrated in Figure 1, panel A. The multiplier is 0.31 on impact (and signi...cantly di¤erent from zero), reaches a peak of 0.41 after one semester and remains signi...cantly di¤erent from zero after four semesters with a value of 0.38.

What happens in developing countries? Figure 1, panel B shows the multiplier for this 12
Panel B: Distribution of number of overseas banking subsidiaries of EMDE groups

||||Home r<br>EAP|egion<br>ECA|LAC|MENA|SAR|SSA|Total host<br>EMDEs|Overs<br>subs<br>AEs|eas<br>from|
|---|---|---|---|---|---|---|---|---|---|---|---|
||EAP||47|2|0|1|3|1|54|64||
||ECA||2|51|1|10|0|0|64|113||
|n|LAC||1|1|66|1|0|0|69|90||
|gio|MENA||0|0|0|26|0|0|26|17||
|st re|SAR||1|0|0|3|2|0|6|8||
|Ho|SSA||1|0|0|12|7|80|100|48||
||Total<br>EMDEs|home|52|54|67|53|12|81|319|340||

Source: Claessens and van Horen (2015); Bankscope; team calculations.

Panel C: Cross-border claims of EMDE groups on overseas EMDEs (US$ billions)

||Claims vis|-à-vis ov|erseas EM|DEs||||Claims vis-à-<br>vis AEs*|Claims<br>countr|vis-à-vis all<br>ies<br>Intra-|
|---|---|---|---|---|---|---|---|---|---|---|
||EAP|ECA|LAC|MENA|SAR|SSA|Total|Total|Total|group|
|**All Sectors**|||||||||||
|Brazil|0.2|..|2.9|..|..|0.0|3.1|77.4|80.5|..|
|Chile|1.2|0.0|2.7|..|0.0|0.0|3.9|11.2|15.1|..|
|Korea, Rep.|69.0|7.3|13.2|1.7|5.1|1.6|97.8|121.7|219.5|..|
|Mexico|0.1|0.0|2.2|0.0|0.0|0.0|2.3|16.3|18.7|..|
|Panama|..|..|..|..|..|..|..|..|55.4|..|
|Turkey|..|..|..|..|..|..|..|..|35.1|..|
|South Africa|1.1|0.1|0.2|0.1|0.4|8.6|10.5|31.4|41.9|..|
|Taiwan, China|91.2|1.2|8.3|0.1|3.1|4.9|108.9|271.9|380.8|..|
|**Total**|**162.8**|**8.6**|**29.5**|**1.9**|**8.6**|**15.2**|**226.5**|**620.4**|**846.9**|..|
|||||**Ban**|**king Sect**|**or**<br>|||||
|Brazil|0.0|..|2.9|..|..|0.0|2.9|75.2|78.2|55.3|
|Chile|1.1|0.0|1.9|..|..|0.0|3.1|10.8|13.8|..|
|Korea, Rep.|38.3|1.6|1.6|0.3|3.0|0.1|44.8|76.2|121.0|41.1|
|Mexico|0.1|0.0|0.5|..|0.0|0.0|0.6|10.4|11.0|..|
|Panama|..|..|..|..|..|..|..|..|33.4|14.4|
|Turkey|..|..|..|..|..|..|..|..|28.7|17.9|
|South Africa|0.7|0.0|0.0|0.1|0.3|2.1|3.1|24.3|27.4|12.4|
|Taiwan, China|65.9|0.7|1.1|0.1|1.2|2.8|71.8|129.4|201.1|92.4|
|**Total**|**106.1**|**2.3**|**8.0**|**0.5**|**4.4**|**5.0**|**126.3**|**388.4**|**514.8**|**233.4**|

Source: Locational Banking Statistics, Bank for International Settlements; team calculations. .. = Data not available.

* Including claims to international organizations for Mexico and Taiwan.

9
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44
statistically significant and positive impact of t <mark>he geographical clustering o</mark> f formal and informal firms in India on the productivity of formal firms, but no significant effect for informal businesses.

The rest of the paper is structured as follows. Section 2 describes the data, provides a descriptive analysis of the informal sector in Zambia, and presents the empirical strategy. Section 3 discusses the baseline results and a series of robustness checks. Section 4 shows the heterogeneous effects of geographic proximity across firms. Section 5 analyzes potential spillover channels and, finally, Section 6 concludes.

# **2 Data and Empirical Strategy**

## **a. Data**

The analysis employs the World Bank Enterprise Survey (WBES), the World Bank Micro Enterprise Survey (WBMES), and the World Bank Informal Sector Enterprise Survey (WBISES) for Zambia conducted in 2019. Even though the surveys were conducted in parallel to each other by the same implementing agency, some differences exist. While all three surveys gather information on the performances of firms and the business environment in which they operate, they differ with respect to the assumptions of formality and the sampling approach.

The WBES differentiates informal from formal firms by using the information on the registration status of the entity. In the context of Zambia, informal businesses are defined as those that are either: (1) registered with Zambia’s Patents and Companies Registration Agency (PACRA) but do not hold a council permit and are not registered with Zambia Revenue Authority (ZRA); (2) are not registered with PACRA but hold a council permit; and (3) not registered with PACRA and do not have a council permit. The WBISES captures informal businesses, while the WBES and WBMES survey formal firms.<sup>7</sup> The WBES is a nationally representative survey where a stratified random sampling approach is applied to formally registered firms that have five or more employees, have at least 1 percent of private ownership, and do not have legal status as cooperatives. The sample size in Zambia is 601 firms. Formal micro-enterprises, which are defined as employing fewer than five workers,

> 7 See Appendix Table A.1 for details on the registration status by survey types.

5
# 4. **Empirical Findings**

Section 4.1 present the results of a cointegration analysis over the whole sample (1965:Q1 to 1998:Q4) using quarterly real GDP data provided by the Banco de Ecuador. To test the robustness of our findings, we additionally investigate two different subsamples. Section 4.2 concentrates on the period from 1965:Q1 to 1989:Q4, section 4.3 focuses on the period from 1990:Q1 to 1998:Q4. Section 4.4 estimates a dynamic shortrun sector growth model based on the results of section 4.3. Section 4.5 disaggregates the industrial, agricultural and service sectors into their components and presents the evidence of bivariate cointegration tests between different intrasector components. Appendix 2 repeats parts of the analysis of section 4 for an alternative data set, using annual real GDP data from the World Bank Latin American and Caribbean Regional Database.

## 4.1 Evidence of Cointegration

Our sectoral growth VAR model includes a constant in the cointegration space and 4 lags of each of the variables industrial, agricultural and service sector GDP. This is sufficient to produce random errors.<sup>4</sup> '<sup>5</sup>

> 4 The deterministi **c** omponents of the VAR were defined according to the rank test based on the so-called Pantula principle (see Johansen and Juselius (1992). According to the Pantula principle three different model specifications (no linear trends in the levels of the data (Model 2), linear trends in the levels of the data (Model 3) and time-trend in the cointegration space (Model 4)) are estimated and the results are presented from the most restrictive alternative (i.e. r = 0, and Model 2) through to the least restrictive alternative (, i.e. r = n- 1, and Model 4). The critical values for Model 2, Model 3 and Model 4 correspond to Table 1*, Table I and Table 2* in Osterwald-Lenum (1992). The test procedure according to the Pantula principle is then to move through Table Al from the most restrictive model and at each stage to compare the trace test statistics to its critical value and only stop the first time the null hypothesis is not rejected. In our case, this is for r = I and Model 2, The rank test thus suggests the inclusion of a constant in the cointegration space.

5 The model specification is presented in the appendix in Table A3. The diagnostics on the residuals of the system show the absence of autocorrelation but indicate some non-normality. Since Cheung and Lai (1993) have shown that the trace-test is robust to both skewness and excess kurtosis, we decided to estimate the model with this specification.

8