rafmacalaba/datause-encoder-data huggingface dataset:
- 34 -

**Table 13. Apparel Trade Under AGOA, 2000 and 2001**

|**v**<br>_L_|_esser Devel_|_opedBdneficiary_||_O_|_ther_|**_All_**|
|---|---|---|---|---|---|---|
|_Swaciland_|_Lesotho_|_Madagascar_|_Kenya_|_Mauritius _|_SouthAfrica_|_Countries_|
|Exports(inUS$million)|||||||
|2000<br>31.9|140.2|109.6|44|245|163.3|776|
|2001<br>48.0|214.8|178|64.5|238.3|194.9|975|
|Growth(in%)<br>50|53|62|47|-3|19|26|
|Of2001exports:|||||||
|Grantedpreference<br>8.2|129.2|92|51.6|38.8|30.4|350.4|
|(in%oftotal2001exports)<br>17|60|52|80|16|16|36|
|ofwhich (in%oftoalgrantedpreference):|||||||
|sourcedfrom regionalfabric<br>0|0|0|0|85|97|18|
|sourced**from**U.S.fabric<br>0|0|0|0|5|1|<br>1|
|sourcedfrom otherforeignfabric<br>100|84|79|.<br>100|**0**|**0**|82|

Source: OTEXA and authors calculations Table **14. AGOA's Overall Impact**

|_Category_|_Tariffs_<br>_(inV.)_|_CurrentExports_<br>_(inUS$mnn)_|_Exports UnderAGOA_<br>_(inUSSmn.)_<br>_(inUSS_<br>_n)_|_Increase_<br>_(in_/)|_Exportsunder_<br>_Jullyunrestricted_<br>_access_<br>_(inUS5mn.)_|_Increase_<br>_(in%)_|
|---|---|---|---|---|---|---|
|||**Categoriescovere**|**dunderAGOA**||||
|Energy|1.5|15569|16029|3.0|16029|3.0|
|Apparel|12.8 1/|776|840|8.3|1234|59.0|
|Non-energynon-apparel|||||||
|A.Restrictive rulesoforigin|9.4|424|459|8.3|497|17.2|
|B.Liberalrulesoforigin|9.4|424|497|17.2|497|17.2|
|Sub-total|||||||
|A.Restrictiverulesoforigin||16769|17328|3.3|17760|5.9|
|B.Liberalrulesoforigin||16769|17366|3.6|17760|5.9|
|||**Categoriesexclud**|**edunderAGOA**||||
|Duty-free|0|2386|2386|0|2386|0.0|
|Dutylessthan5percent|2.5|14.5|14.5|4.9|15|4.9|
|Dutygreaterthan5percent|10.9|25.4|25.4|21.4|30.836|21.4|
|Sub-total||2426|2426|0.0|2432|0.3|
|Total2/|||||||
|A.Restrictiverulesoforigin||16809|17368|3.3|17806|5.9|
|B.Liberalrulesoforigin||16809|17406|3.6|17806|5.9|
|Total(non-energy)2/|||||||
|A.Restrictive rulesoforigin||**1240**|**1339**|**8.0**|**1777**|**43.3**|
|B.Liberal rulesoforigin||1240|1377|11.1|1777|<br>43.3|
|Source:Authors'calculations|||||||

1/ Note that the actual protection is much higher because of the quotas under the MFA 2/ Excluding the duty-free category
growth. In this case, a 1% increase in the area of a country experiencing high rainfall coincides with a 1.8% reduction in GDP growth. These results support the hypothesis of the economic importance of precipitation extremes.

**Table 2: GDP per Capita Growth with Country and Year FE’s**

Dependent variable: GDP per Capita Growth

||(Precip)|(Temp)|(P, T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)<br>|(T,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|
|Precip|0.01<br>(0.01)||0.01<br>(0.01)*|||||||
|Temp||0.41|0.44||0.28||0.28|0.08||
|||(0.21)*|* (0.21)**||(0.25)||(0.27)|(0.32)||
|WASP_minus1||||||-2.02<br>(0.51)*|-2.08<br>**(0.52)***|-2.67<br>(0.98)**|-2.69<br>*(0.98)***|
|WASP_plus1||||0.25<br>(0.52)|0.29<br>(0.52)|||-1.83<br>(1.08)*|-1.82<br>(1.08)*|
|Observations|4898|4898|4898|2790|2790|2962|2962|1686|1686|
|Countries|181|181|181|178|178|180|180|133|133|
|R-squared|0.047|0.048|0.048|0.057|0.057|0.050|0.051|0.053|0.053|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1).

The results for the country fixed effects are reported in Table 3. They are similar to the results with country and year fixed effects. With country fixed effects only, events that affect many countries in a given year, say a currency crisis that affects a region, are not accounted for. However, drought can affect multiple countries in a given year due to its often regional or larger span. For example, droughts caused by ENSO have a global pattern that affects different parts of most continents. Controlling for year fixed effects may cause an underestimation of the drought effect. Therefore, we generate these results with only country fixed effects.

Again, the WASP variables were significant with signs that were consistent with expectations. The WASP(-1) variable was again significant at the 99% confidence level. Similarly, WASP(+1) was significant at the 90% level. Each had negative regression coefficients and the WASP(-1) coefficient is 14
# [insert Table 2]

Figure 2 shows the lasting negative effects of the low aid quality on the post-tsunami labor market developments. It divides villages into two groups: 49 villages with a minor quality problem (i.e., less than 50% of aid boats were in bad quality; left panel) and 31 villages with a major quality problem (right panel). In the former type of villages, the fishing sector recovered to above the pre-tsunami level in 2007, but declined in 2009. On the other hand, in the latter type of villages, fishing did not recover to the pre-tsunami level in 2007 and continued to decline in 2009.

# [insert Figure 2]

_2.2 Survey_ The data are drawn from a large field survey on fishermen and coastal villages in Aceh, Indonesia (“Population and Economic Recovery in Coastal Aceh: Aid and Village Institutions” principally investigated by J. Vernon Henderson (Brown University/NBER) which has been financially supported by the National Science Foundation (SES 0416840) and the National Institutes of Health (R01 HD057188)). The data were gathered at three levels (household, village head, and local heads of fishermen’s association ( _Panglima Laot_ )) in 2005, 2007, and 2009. In 2005, information on pre-tsunami household characteristics was also retrospectively collected.

The initial survey was conducted in early 2005 and covered 544 pre-tsunami fishermen in 111 fishing villages. The survey covers the universe of fishing villages in the tsunami affected kabupatens (Banda Aceh and Aceh Besar).<sup>v</sup> In the fishermen’s survey, the targeted population is fishing households living in these coastal villages which were most heavily damaged by the tsunami.<sup>vi</sup> The survey on fishing households represents 25% of pre-tsunami boat owners and 45% of surviving boat owners in these villages. The sampling frame was drawn from the list of former fishing boat owners assembled by _Panglima Laot_ , and the fishing households were proportionally sampled from the list in each village.

Among 544 households surveyed in 2005, I use 348 pre-tsunami boat owners or captains 5
though Jamaica’s Norman Manley International Airport could possibly serve as a hub for four routes, they do not appear to be realistic, because of the distances involved.<sup>17</sup>

**Table 17: Possible airport pairs served in the Caribbean with The Bahamas, Dominican Republic, or Jamaica as**

||**the transfer poi**<br>**Pibl i**|**nt**|
|---|---|---|
|**Non-hub Airport**|**osse pars**<br>**served (2013)**|**Possible Route**|
|Jamaica Kingston|4|Barbados – Jamaica – The Bahamas<br>Barbados – Jamaica – Haiti<br>Antigua – Jamaica – Haiti<br>Guyana – Jamaica – The Bahamas|
|Jamaica Montego Bay|1|Bahamas – Jamaica – Dominican Republic|
|Bahamas, The|0||
|Dominican Republic|0||

_Source: Authors’ calculation based on data by Diio (2014)._

# <u>Continental Caribbean</u>

Belize, Guyana, and Suriname have different profiles in their connections to the world outside the Caribbean. Belize has the highest number of countries it interacts with, although traffic with the U.S. makes up nearly 87% (Table 18). Guyana only shows traffic with the U.S. and Canada, while Suriname’s connectivity is with the Netherlands and Curaçao. The differentiation between Guyana and Suriname’s extra-regional connections, as with their intra-regional connections, are language based, with Suriname being Dutch speaking, and Guyana English speaking.

|**Table 18: Traf**<br>**Country**|**fic between Beli**<br>**Est. Seats**<br>**2013**|**ze and the**<br>**Share**|**Rest of the**<br>**Annual**<br>**Growth**<br>**01-13**|**World**<br>**Annual**<br>**Growth**<br>**08-13**|
|---|---|---|---|---|
|US|614,315|86.5%|4.3%|1.7%|
|El Salvador|71,136|10.0%|10.7%|16.4%|
|Honduras|7,618|1.1%|||
|Guatemala|7,280|1.0%|-11.8%|-3.6%|
|Cuba|4,758|0.7%|||
|Mexico|4,680|0.7%|4.6%||
|**Total**|**709,787**|**100.0%**|**4.3%**|**3.2%**|

_Source: Authors’ calculation based on data by Diio (2014)._ Belize’s traffic has grown annually in the 4 percent range, with a recent slowdown. The number of airport pairs served follows the actual capacities very closely (Figure 22). Tourism is now becoming a more important part of the economy, which will increase the demand for access. Beyond the US, the next strongest connection is with El Salvador, which has been growing rapidly at an annual growth rate of over 16% between 2008 and 2013 (Table 18).

> 17 Only airport pairs that are not already served with direct flights are included in the calculations, under the assumption that there are no direct connections for the routes shown in Table 17. However, it is possible that these routes are served by other transfer operations, such as Guadeloupe with Haiti ().

51
**page** 14

Table 2. Estimation of Life-Days Saved Per Year in a Population of 50,000 in Rural Java When an ImmunizatLon Program is Added to an Existing Health Center.

||With|out Immu|nization|||With Immu|nization||
|---|---|---|---|---|---|---|---|---|
|Age Group<br>(1)|Pop.<br>in<br>Thous<br>(2)|Life<br>Expect.<br>(3)|Death<br>Rate<br>(4)|Disa-<br>bility<br>Rate<br>(5)|Death<br>Rate<br>(6)|Disa-<br>bility<br>Rate<br>(7)|Life-<br>Y ears<br>Saved<br>;8)|Thousands<br>Days<br>Saved<br>(9)|
|0-1 Years Old|1.5|48|104.0|*|85.9|*|1301|*|
|1-4 Years|7.0|52|28.3|21.0|27.4|19.9|298|22.5|
|5-14 Years|13.0|50|2.7|*|2.4|*|176|*|
|15-44 Years|21.5|35|5.6|3.6|5.6|3.6|15|0.0|
|45 Years Old<br>**and**Older|7.0|15|*|5.5|*|5.5|*|0.0|
|TOTALS|50.0||11.0|11.4|10.23|10.9|1790|22.5|

- SOURCE: Unless otherwise indicated, all references to pages, tables or appendices in the following notes are to R.N. Grouse, J.L.deVries, R.L.Tilden, A.Dievler,S.R.Day, "A aealth Development Model Applicatdon to Rural Java," Final Report of Grant No. AID/otr-G-1651, Department of Health Planiing and Administration, School of Public Health, University of Michigan, October, 1979.

- NOTES: Immunization program consists of 27,000 shots per year against tuberculosis (6600 doses BC G vaccine), diptheria, pertussis, and both neonatal and postnatal tetanos (18300 doses DPT vaccine plus 2100 doses tetanos toxoid), but excludes measles. See pages 30, 34 and pages 2 and 3 of Appendix A in Grosse et a.

- Column (2): From page 27 and page 20 of Appendix T.

- Column (3): Interpolated by author from Ghana Health Assessment Team (1981, Table A). Column (4): Deaths per thousand population from base run with a health center but no additional health programs. See alternative 1, PV I in the first line of Table 7 on page 47 or of any table in Appendix F. Since these tables do not provide the mortality rates of for the two groups over 15, an overall rate for both groups is interpolated.

- Column (5): Days of disability per person per year. Source is same as column (4) except interpolation is required for those under 15.

- Column (6): From Table 7, alternative 1, PV 3 with interpolation as for column (4). Column (7): Same as (6) with interpolation as in (5). Column (8): Using C4 to represent column (4), etc. the formula for this column is: C2 x C3 x (C4 - C6).

The fifth row uses a population of 28.5 and a life expectancy of 30. Column (9): Thousands of days of disability saved per year computed by: C2 x (C5 - C7).
The number of foreign trucks operating in Zambia is high because Zambia is a net exporter in terms of freight volumes and consequently this makes it economically viable for South African companies to run round trips. The South African fleet is the most important in the sub-region benefiting from economies of scale. The Gauteng truck fleet of heavy trucks is about eight times larger than the Zambian fleet<sup>25</sup> .

Thus, some large South African trucking companies have taken over control of several large Zambian companies, which is also a specificity of Zambian trucking industry. FDI in the trucking industry has been the main solution South African companies found to bypass market entry barriers. However, it is worth noting that although some large companies benefit from South African capital, they are run by Zambian management.

# **3.2 Comparison of Transport Costs**<sup>**26**</sup> **in Zambia and South Africa**

Transport tariffs in Zambia can be considered to be low<sup>27</sup> for road transport especially for a landlocked country in Africa. In June 2006, road transport tariffs were based on between 9 ZAR and 11 ZAR per km, depending on back haul rate and on competition extent. This translates into a transport tariff between 26 ZAR cents per tonne per kilometer (tkm), and 39 ZAR cents per tkm, or 3.7 USD cents to 5.6 USD cents per tkm<sup>28</sup> .

One of the important inputs into setting tariffs are the transport costs. The South Africa Road Freight Association reports total operating costs for a seven axle interlink, typically used on the Zambia – South Africa route, to be 9.80 ZAR per

> 25 The Gauteng – Durban freight corridor study, (TMT March 2005), indicated that there were 81,000 registered heavy goods vehicles in Gauteng, (representing 38% of the vehicles in South Africa), of which an estimated 50,000 were rigid trucks, 20,000 were articulated combinations, and 10,000 were longer 48t to 56t combinations of the type used for regional freight transport and of the type operated by the Zambian trucking companies. Gauteng represents 38% of South Africa’s vehicles, but more likely about 50% of the registered heavy trucks in South Africa.

26 This section is based on original data. Despite several cross-checks of these data collected from freight trucking operators, these data should be used with some caution due to the fact that vehicle operating costs differ between companies.

> 27 That is also why, railway services have great difficulty in competing on price and even greater difficulty in competing on service levels (transit time).

> 28 A recent pre-feasibility on the construction of the North West railway extension from Chingola to Solwezi used a railway tariff of USD 15 cents per net tkm as a competitive tariff with road, clearly off the mark by a large margin, indicating that the railway project cannot compete with road transport. Railway tariffs in the SADC region, for general freight on lines carrying 500,000 to 1 million tonnes, are typically of the order of 3.3 USD cents per tkm, but sometimes up to 10 USD cents, when there is competitive interference or government intervention (like in Zimbabwe).

15
group, as indicated in Chart 1<sup>9</sup> . Under these circumstances the public sectors in these economies could service their existing debt through new borrowing, without the need to generate their own fiscal resources for the purpose. The absence of solvency constraints on fiscal policy in these countries was manifest in large fiscal deficits in many of them during this period (Table 2). This suggests that, for a large subset of the HICs, the origin of the crisis is to be found in the public sector. Based on Table 2, obvious exceptions are Chile and Venezuela, though Colombia also experienced relatively small fiscal deficits, at least through the end of the seventies.

At the beginning of the new decade, tight monetary policies in many industrial countries, designed to combat the inflationary consequences of the second oil shock, combined with expansionary fiscal policies in the United States, reversed the relationship between the real interest rate and the rate of growth of the HICs. As shown in Chart 1, real long-term interest rates began to rise substantially above the trend real rates of growth registered by these countries. Under these circumstances, servicing the existing debt through new borrowing would have become a Ponzi scheme, in which the debt/GDP ratio would have prospectively risen without bound. Creditors could not be expected to acquiesce in such a scheme, since it would imply that the present value of the net resource transfers that they would receive in return for their new loans would fall short of the face value of such loans. Thus the public sector debtors in the HICs found it necessary to begin to service debt with their own resources. This meant that the previous fiscal performance could not be sustained. To maintain the solvency of the public-sector borrowers in these countries, a credible fiscal adjustment was called for that offered the prospect of generating sufficient resources via primary surpluses and seignorage revenue to service the large stock of debt that had been accumulated during the previous decade.

The severity of the adjustment problem confronting the public sector in the HICs was magnified because in many of these countries the public sector had also acquired a substantial amount of domestic debt. Table 3 indicates that in several of the highlyindebted countries, including the major external debtors Argentina, Brazil, Mexico, and the Philippines, domestic debt contributed more than 10 percentage points of GDP to the total debt burden of the public sector by 1982. The relationship between the magnitude of the adjustment burden and the size of the total outstanding gross 9 The real interest rate depicted in Chart 1 is the annual average of the monthly series of real ex post annual yields on 30year US government bonds (IFS line 61), deflated by the US wholesale price index. The average growth rates for the HICs are from the IMF World Economic Outlook.

7
<!-- Start of picture text --> Sea and Air export and import survival under external and domestic lockdowns<br>= a: External lockdown-Exports = b: Domestic lockdown-Exports<br>33<br>a<br>Sy 75% —Sea aS 75% »—Sea<br>= 50% 5S 50%<br>a a<br>oa 25% oa 25%<br>22<br>E0% £ 0%<br>iri} 0 2 4 6 8 10 12 14 16 18 20 iri} 0 2 4 6 8 10 12 14 16 18 20<br>Survival time (months) Survival time (months)<br>= c: External lockdowns-Imports = d: Domestic lockdowns-Imports<br>3gs 100% Air 3sg 100% Air<br>2 °, — —Sea 2 ° — + —Sea<br>S 75% & 75%<br>ss<br>S50% e 50%<br>3E 25%0% 3E 25%0% .<br>iri] 0 2 4 6 8 10 12 14 16 18 20 Ww Oo 2 4 6 8 10 12 14 16 18 20<br>Survival time (months) Survival time (months)<br><!-- End of picture text -->
nonetheless, the empirical evidence on the existence of this effect is mixed (Javorcik and Spatareanu, 2005). We expect competition effects to be dominant within the same four-digit sector classification, while potential technology and knowledge transfers might come from foreign presence in the same two-digit sector.

**Table 6: Competition and Spillovers Within and Between Four Digit Sectors**

|Dependent Variable: Firm Produc<br>Sample: Domestic Firm|tivity<br>A|ll Firms|Con|tinuingFirms|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Spillover Competition_|-0.077***|-0.015***|-0.057***|-0.018***|
||(0.005)|(0.004)|(0.005)|(0.005)|
|_Spillover Knowledge_|0.346***|0.225***|0.329***|0.119**|
||(0.032)|(0.006)|(0.008)|(0.009)|
|Observations|36,638|33,354|16,192|16,492|
|Firm Fixed Effects|yes|yes|Yes|yes|
|Sector2dig-Year Fixed Effects|NO|YES|NO|YES|

_Source:_ authors’ calculations based in the Orbis dataset for Turkey. _Notes:_ Estimation performed by Generalized Least Squares (GLS) where weights are the square root of the firm mean squared predicted residuals. Standard errors clustered at the corresponding level specified in the table are reported in parentheses. Results are obtained based on the sample of firms with no foreign ownership (i.e., firms that were never acquired (in any percentage) by a foreign-owned investor over the period of analysis). The dependent variable is the log of total factor productivity which is computed following Wooldridge-Levinsohn-Petrin methodology (WLP). See the text for the information on the construction of spillover variables.

Once we focus on effects within the thinner four-digit sector classification, where multinationals operate in the same two-digit but different four-digit sectors, we find negative competition effects and positive and significant knowledge spillovers. The point estimates suggest that, if the foreign presence in your same four digit sector doubles, you, as a domestic firm, have 1.5 percent lower productivity due to competition and business stealing effects. But if you are a domestic firm in the same two-digit sector but different four-digit sector than the foreigners, your productivity would be boosted by up to 20 percent.<sup>7</sup> Columns (3) and (4) show similar results when we use a sample of continuing firms and do not allow for entry and exit.

> 7 It is worth noting that the spillover variables are average foreign ownership shares, i.e., they are between 0 and 1. Thus, productivity would be boosted by 20 percent only when average FO rises from 0 to 100%--not when it doubles from, say, 5% to 10%. In that case, productivity gains could be smaller.

12
<!-- Start of picture text --> Period -5 Period -4 Period -3<br>104 150 56<br>145 54<br>102<br>140<br>52<br>100<br>135<br>50<br>130<br>98<br>48<br>-20 -10 0 10 20 -20 -10 0 10 20 -20 -10 0 10 20<br>Normalized annual Normalized annual Normalized annual<br>accumulated consumption accumulated consumption accumulated consumption<br>Period -2 Period -1 Period 0<br>48 90<br>95<br>46<br>90<br>85<br>44 85<br>42 80<br>80<br>40 75<br>38 75 70<br>-20 -10 0 10 20 -20 -10 0 10 20 -20 -10 0 10 20<br>Normalized annual Normalized annual Normalized annual<br>accumulated consumption accumulated consumption accumulated consumption<br>Average bill Average bill<br>Average bill Average bill<br><!-- End of picture text --> Figure 4 Average Bill by Annual Accumulated Consumption _Notes:_ Scatter points correspond to local averages computed by bins with a bandwidth of 1 unit of normalized AAC. Solid lines correspond to parametric fits generated from regressions that include a constant, a treatment dummy and a linear term for normalized AAC. Dashed lines are the 95% confidence interval.

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