rafmacalaba/datause-encoder-data huggingface dataset:
- Cargo owners (exporting and importing firms) are currently subsidizing vessel owners and tenants. This division of revenue among the three main players is the main source of the high port charges in South Africa.

- Commodity exporters are favored over manufacturing firms within cargo due pricing. The current pricing structure therefore lowers the competitiveness of manufacturers in direct contrast to domestic policies of industrialization.

- Cargo dues for bulk and break‐bulk products, which have a similar cost bases, vary substantially from one product to another. On the container side, cargo dues on a 12 meter container are double those levied on a 6 meter container. Standard global practice tends to make the cost differential between cargo dues for 6m and 12m containers markedly smaller to encourage shippers to use larger containers, thereby increasing crane efficiency.<sup>12</sup>

- Cargo dues pricing discriminates between imports and exports, despite the cost base being the same. The distinction is made as an attempt to discourage imports; however it also penalizes local firms who use imported inputs and can lead to policy inconsistencies.<sup>13</sup> There is also evidence to suggest that exporting firms which use import inputs are better integrated into the global markets compared to firms with domestic inputs only. These firms export a wider range of products (on average 6.9 unique products compared to 5.3 products) and export to almost twice the number of markets (on average 2.1 unique markets compared to 1.2 markets).

**Table 2:  Cargo dues by product type**

|**Product**<br>**Import**|**Export**|
|---|---|
|**Breakbulk (Rand per tonne)**||
|Iron ore<br>22.07|22.07|
|Stainless steel<br>95.63|22.46|
|Cotton<br>125.03|54.42|
|Textiles<br>102.98|44.12|
|Fishmeal<br>25.02|25.02|
|Loose fish<br>125.03|44.12|
|**Bulk (Rand per tonne)**||
|Coal<br>8.52|2.94|
|Cement<br>22.07|4.41|
|Sunflower oil<br>51.46|44.12|
|Liquid molasses<br>5.76|2.94|
|Salt<br>5.16|4.11|
|Sugar<br>26.48|26.48|
|**Containers (Rand per container)**||
|6m/ 20"<br>1,866.23|614.35|
|12m/40"<br>3,732.43|1,228.70|
|Empty<br>72.10<br>_Source: National Ports Regulator (2013)_|72.10|

This outdated and unbalanced port pricing system has a direct impact on the competitiveness of South African exporters, which are already disadvantaged by global geography. However, many of these disparities have been flagged by The Ports Regulator, through its tariff strategy for the next 10 years,

> 12 Efficiency gains through the use of larger containers are limited by the ability of the market (i.e. exporting firms) to fill and manage larger containers.

> 13 For example, the cargo due on cotton imports is R125.03 whilst the cargo due on textile exports (which may have cotton as an input) is R44.12.

_11_
# **Table 1: Summary data on citations to economics papers for selected universities and World Bank**

||_N_|Median<br>citations|Mean<br>Citations|_h_-<br>index|Normalized<br>_h_-index|_qi_-<br>index|
|---|---|---|---|---|---|---|
|Harvard University|2191|27|96.16|240|41.0|66.6|
|London School of Economics|1634|13|43.59|124|30.2|22.8|
|Oxford University|1117|11|33.45|94|28.2|12.1|
|Princeton University|1299|26|78.09|165|40.5|32.6|
|Univ. California, Berkeley|2612|22|78.13|206|37.0|62.5|
|World Bank|2448|21|67.50|189|37.7|52.2|
|Yale University|1470|17|65.33|140|34.4|29.8|

<u>Notes: Only articles indexed in SCOPUS and in ‘Economics, Econometrics and Finance’ were included. Multi-</u> disciplinary articles were excluded. Bibliographic metadata were collected from SCOPUS in May-June 2010, and Google Scholar citation data were collected in June 2010. The _qi_ -index uses _c_<sup>_*_</sup> =6,063, the highest citation count for any paper.

**Figure 1: Citation curves and the continuous** **_h_ -index**

No. citations ( _c_ ) <!-- Start of picture text --> c = x<br>h<br><!-- End of picture text --> No. papers ranked by decreasing citations ( _x_ ) 15
Figure 9: Channels of impact and interaction in the CGE model of a trade liberalization policy <!-- Start of picture text --> Economy-wide effects of<br>AG TRADE LIBERALIZATION<br>(NAFTA)<br>Rural Urban<br>∆domestic prices Pushes resources<br>Small negative<br>↓ non competitive Out of non-competitive<br>effects on income<br>↑ competitive In of competitive<br>↑ Factor prices<br>↑ Factor prices<br>↑ Water shadow prices Wages<br>Wages<br>↑ charges of water Land rents Land and capital rents<br>↑ WUA savings<br>↑ Household income<br><!-- End of picture text --> Source: Yunez-Naude (Interim reports submitted to the World Bank during 2005-2006) Figure 10: Channels of impact and interaction in the CGE model of a water supply reduction to irrigation, charging by shadow value and investment of proceeds by WUA <!-- Start of picture text --> CGE effects of reductions in water supply<br>for irrigation<br>Rural/Agriculture Urban/Manufactures<br>Pushes resources ↓ Food processing<br>↑ Water shadow out of irrigated Ag. ↑Other manufactures<br>Value<br>Factor demand varies<br>according to ↓ Wages Small ↓ in<br>↑ WUA savings Crop & region • Real GDP<br>• Household incomes<br> (rural rich, the biggest<br>Investment for efficient ↓ Wages &<br> losers)<br>use of water Land rents<br>↑ Exports & Imports<br><!-- End of picture text --> Source: Yunez-Naude (Interim reports submitted to the World Bank during 2005-2006) A more detailed impact description by region and explanation of the linkages is provided in Tables 2-5 for selected macro policy intervention and external shocks.

33
of this paper. Among the few parents who are dissatisfied by student and school performance, most pointed out specifics about the incapacity of the child to read and write properly, and the mismanagement of the school. It may be that those parents are more educated and better able to assess the progress of the children and the performance of the school. These findings confirm that the WSD intervention may be more appropriate where local capacity is sufficiently high.

In addition to increasing the capacity of communities to hold schools accountable, districtlevel literacy could be a proxy for some other effect. One possibility is that the literacy effect, demonstrated in Table 19 and Table 20, is actually a proxy for some other, correlated characteristic of households or districts. We test this by interacting the WSD treatment with socio-economic status at the household and the district level in Table 21 and Table 22; those interactions are not statistically significant. Another possibility is that adult literacy is a proxy for some baseline level of ability among the students. We test this by interacting WSD with baseline test scores (Table 23) and again find no significant relationship. A third possibility is that, rather than having additional management capacity, more literate districts are wealthier and are contributing more to schools. In Table 24 we present evidence that higher economic status does not associate with higher likelihood of financial or in-kind contributions to the school. Taken together, these tests further suggest that the human capital effect is in effect a proxy for the capacity of parents within the district.

## **6.2.4. Improvement in the control schools?**

The lack of impact on average test scores could also be due to the fact that control schools have improved as well, through mechanisms other than increased participation. Since the school management manuals were made available to all the schools, it is possible that the control group would implement at least part of the practices, although it seems unlikely that they would have adopted a similar set of practices to the WSD schools without any support. Qualitatively, we found no evidence that they used the manual. In addition, our test score data from 2008 and 2010 were collected at the same grade level. This allows us to conduct a before and after analysis in each group, including the control group (25). We find no evidence of a positive time trend in the control group between the baseline and the 2010 test scores.

# **8. Conclusion and future research**

In this research, we evaluated a school management training program in The Gambia called Whole School Development (WSD). Intermediate results one year post-intervention showed some basic changes in school organization in the WSD schools but no effect on test scores or on student and teacher absenteeism. These results served mostly as evidence of project implementation. Two years post-intervention, we found no effect on test scores but modest positive effects on student and teacher participation measured by the prevalence of absenteeism.

20
Policy Research Working Paper 9242

# **Abstract**

The paper uses Google mobility data to identify the determinants of social distancing during the 2020 COVID-19 outbreak. The findings for the United States indicate that much of the decrease in mobility is voluntary, driven by the number of COVID-19 cases and proxying for greater awareness of risk. Non-pharmaceutical interventions such as closing nonessential businesses, sheltering in place, and school closings are also effective, although with a total contribution dwarfed by the voluntary actions. This suggests that much social distancing will happen regardless of the presence of non-pharmaceutical interventions and that restrictions may often function more like a coordinating device among increasingly predisposed individuals than repressive measures per se. These results are consistent across country income groups, with only the poorest countries showing limited effect of non-pharmaceutical interventions and no voluntary component, consistent with resistance to abandon sources of livelihood. The paper also confirms the direct impact of the voluntary component on economic activity, by showing that the majority of the fall in restaurant reservations in the United States and movie spending in Sweden occurred before the imposition of any non-pharmaceutical interventions. Widespread voluntary de-mobilization implies that releasing constraints may not yield a V-shaped recovery if the reduction in COVID risk is not credible.

This paper is a product of the Office of the Chief Economist, Equitable Growth, Finance and Institutions Practice Group. 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 wmaloney@worldbank.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
# **_4.2._** _Short-Run Sectoral Growth_

The co-integration analysis of the previous subsection allows us to take the long-run information of the series into account in analyzing short-run sectoral growth, as the restricted co-integration relations determined previously may be included as explanatory variables. The resulting model is a short-run ECM.

The aim is to obtain a parsimonious model of short-run sectoral growth that is able to pass the specification tests. In the previous analysis, two lags (and a deterministic trend) were found to be appropriate for Ghana and Zimbabwe. A specification with one lag would be adequate because two lags in levels correspond to one lag in first-order differences. The same reasoning leads to an initial specification with four lags in the case of Cote d'Ivoire. However, when moving to first-order differences, the system for Cote d'Ivoire becomes severely afflicted by singularity problems, which persist at four, three, and two lags. Moving to one lag yields a specification with no singularity problems, which further passes the diagnostic tests (table 11). Because all three of the models already are relatively parsimonious and further reduction of individual variables affects the results of the diagnostic tests, we choose these specifications as our final, dynamic, short-run models. Tables 11, 12, and 13 present the models and the findings of the diagnostic tests.

Table 11. **Dynamic Short-Run Sector Growth Model for C6te d'Ivoire**

|Explanatory variable|Equation||Explanatory var|iable|Equation||
|---|---|---|---|---|---|---|
||Industry|Agriculture|||Industry|Agriculture|
|Industryt-1|0.214|0.040|Servicest-I||_0.415_|0.408|
||(1.320)|(0.224)|||(2.042)|(1.815)|
|Agriculture,,|0.0199|-0.076|**CIt**l||-0.351|0.194|
||(0.093)|(-0.321)|||(1.912)|(0.956)|
|Services,|-0.329<br>(-1.856)|-0.202<br>(-1.030)|Constant||-2.548<br>(-1.899)|1.447<br>(0.975)|
|_Diagnostics_|||||||
|Single equation analysis|a||F,,,(l, 24)|%**norn**|**aity(2)**F.c|h(l, 23)|
|Industry|0.078||0.970|2.517|0.1|35|
|Agriculture|0.086<br>||2.685<br>|2.615|0.0|32|
|Vector analysis|Fr(4, 44)=|2.159,X<sup>2</sup>.,,,,i|nu(4) = 5.073||||

_Note:_ Values in parentheses are t-statistics. Subscripts denotes the time period, i.e. t: current period, t- 1: lagged one period, CI denotes the restricted co-integrating relation. _Source:_ Authors' calculations.

17
rates widened and yields became substantially positive in real terms. The payment of commissions to banks also helped.

The first Greek government bond with a foreign currency clause, linking it to the Ecu (the basket of European currencies that was a predecessor of the euro), was issued in November 1986. This was the first Greek government bond issue since 1972. It had a maturity of 3 years and a coupon of 8 percent. It was indexed to the Ecu, but redemption was made in Greek drachma, exposing investors to a residual foreign exchange risk. After a slow start, that reflected lack of investor familiarity and a relatively low coupon, the program was later expanded to include issues with 1, 2 and 4 year maturities as well as indexation to the US dollar and German mark. The program met with greater success when its coupon was raised to between 10 and 12 percent and when settlement in foreign exchange was allowed for subscriptions that were effected by free foreign exchange. At their peak, foreign-currency-linked instruments represented 8 percent of total debt.

Drachma denominated fixed-rate bonds with maturities of 2 and 3 years were also issued between 1987 and 1990. Despite paying a relatively high coupon of between 20 and 22 percent, when inflation was below 16 percent and falling, these bonds did not meet with strong demand and the program was suspended in 1990.

In addition to treasury bills and foreign-currency-indexed bonds, retail investors were also offered bonds with floating rates. These were launched in 1992 with maturities of 2 years, later expanded to 5 and 7 years. They were issued at a spread over the 12-month T-bill rate that varied with maturity.

After some early difficulties, the new strategy was highly successful in aggregate. Sales to the non-bank public grew very fast from 3 percent of the total new borrowing requirement in 1986 to 43 percent in 1990. Over the five-year period, non-bank investors accounted for 27 percent of new debt, domestic banks contributed 51 percent and foreign investors provided 12 percent (Table 3.1).

|**Table 3.1: Sources of Financing**<br>|**the Pub**<br>|**lic Sector B**<br>|**orrowing**<br>|**Require**<br>|**ment, 1986-90**<br>|
|---|---|---|---|---|---|
|1986|1987|1988|1989|1990|1986-90|
|Bank of Greece<br>9.3|6.6|-1.4|10.0|15.0|8.8|
|Banks: Treasury Bills<br>28.8|36.1|47.3|42.2|16.1|32.7|
|Banks: Other Loans<br>30.7|28.3|19.4|15.8|13.7|19.3|
|Non-banks<br>2.7|20.1|31.2|18.9|43.1|26.8|
|Foreign Investors<br>28.4|8.9|3.5|13.1|12.1|12.3|
|Total<br>100.0|100.0|100.0|100.0|100.0|100.0|
|Total in billion GRD<br>776.1|843.9|1201.0|1594.7|1957.4|6373.1|
|Total % of GDP<br>14.1|13.5|16.0|18.2|18.7||
|Source: Bank of|Greece,|Annual Rep|ort for 199|1:110||

The policies that were initiated in 1985 were intensified between 1991 and 1995. During this five-year period, non-banks (which were mainly represented by retail investors) absorbed 67 percent of total new debt, while the share of commercial banks was only 17 percent and foreign 27
outcome variables on export practices, and assumed a Gaussian prior with mean equal to this product, and standard deviation derived from the standard error in the cross-sectional estimation and the assumed standard deviation on the impact on export practices. Full details and the registered priors are contained in the study’s registration in the AEA registry.

## **3.6 Weakly Informative "Default" priors**

For comparison purposes we also generate Bayesian results using highly diffuse or "weakly informative" priors, which are now the default standard for Bayesian analysts in cases where specific outside information is lacking (e.g. Meager (2019), Lemoine (2019), Thorlund et al. (2013), Chung et al. (2012)). These priors perform mild regularization on the estimation procedure, and we follow the literature above in using diffuse Normal priors centered around zero for coefficient parameters, and using diffuse half-Normal or halfCauchy priors on variance parameters. In theory, the impact on the estimation from these priors should be quite minimal. This enables us to see how much of any difference in results is coming from the Bayesian impact evaluation techniques alone, versus through the specific additional information contained in our elicited priors.

# **4 Data and Methods for Estimating Treatment Effects**

## **4.1 Data and Main Outcomes**

Our primary hypothesis is that the program will lead more firms to export, diversity the range of products exported and destinations exported to, and improve the export performance of participating firms. Our primary outcomes of interest are therefore export outcomes. We use annual data on exports from 2010 to 2020 provided by the National Directorate of Taxes and Customs (DIAN) and supplied to us by the Colombian National Planning Department (DNP). 135 of the 200 firms exported at least once during these 11 years. These data provide export values at the 6-digit product and destination country level for each firm. For example, perfumes and cosmetics exported by a firm to Ecuador in 2018, or leather products exported by a firm to Chile in 2020. Using these data, we first measure the extensive margin of whether a firm is exporting at all, and then construct measures of export variety (number of countries, number of products, number of countryproduct combinations). We define export innovation as exporting a new country-product combination, and sum up the total value of exports. We merge these export data with data on formal employment provided by the Ministry of Health (the PILA), and use this 14
# **Greece**

42. Greece also experienced a real estate bubble and a sharp increase in household debt with a similar genesis to that of Spain and Ireland. However, in contrast to Iceland, Ireland and Spain, Greece pursued sharply expansionary fiscal policies, largely driven by social transfers and increases in civil service wages, and had not been observing the Maastricht criteria (Table 6 and Figure 3).<sup>17</sup> Loose fiscal policy could be sustained for a long time because of low interest rates on euro-denominated bonds and buoyant revenue reflecting strong, largely consumption-driven economic activity. In the absence of structural measures to improve the competitiveness of the economy, growth weakened and debt sustainability indicators worsened laying the ground for the crisis of confidence of the financial markets in the solvency of Greece and the ongoing sovereign debt crisis in the Euro Zone.

43. While the real estate bubble was important in Greece, fiscal profligacy along with failure to implement structural reforms aimed at improving competitiveness was the critical factor in Greece.

44. Since 2009, Greece has been unwinding imbalances, but through deeper recession and slow improvements in competitiveness. Real GDP has declined by more than 13 percent since 2009. Private investment led the downturn in 2009, while public retrenchment started only in 2010. With falling incomes and employment, private consumption took over as the main driver of the recession in 2011. Current and leading indicators suggest that domestic demand continues to contract sharply: retail trade volume shrank by around 11 percent year-on-year in the fourth quarter; industrial production declined by 11 percent year-on-year in the fourth quarter; the manufacturing PMI has fallen to close to historical lows; and industrial new orders declined by 8 percent in the fourth quarter of 2011. Competitiveness gains are not yet evident on an economywide basis. The recession and losses from government debt exposures have taken a deep toll on the banking system, undermining financial stability.

# **Portugal**

45. The genesis of Portugal‘s crisis was different from that of the other countries in that it did not reflect the burst of a real estate bubble or fiscal profligacy, but mainly a broad deterioration of the business environment and competitiveness, seemingly accelerated by the country‘s accession to the Euro Zone. In the context of the recession brought about by the global financial crisis, this poor competitiveness resulted in unsustainable debt indicators and triggered the crisis of confidence.

46. Prior to the crisis, Portugal had begun consolidating its finances; in fact, between 2005– 07 the government succeeded in reducing the structural balance by over 3 percentage points of

> 17 Recent corrections of government statistics on the fiscal position showed that Greece had misreported the fiscal deficit prior to the adoption of the euro.

24