rafmacalaba/datause-encoder-data huggingface dataset:
quality/higher price models, the application in the form of a specific duty, in terms of its ad valorem equivalent based on those average unit values, was less onerous for the EU exporters (5-7 percent), relative to China (15-21 percent), Indonesia (10-14 percent), or Vietnam (9-13 percent) than it would have been had Turkey applied a safeguard as an ad valorem duty that was common to all sources of imports.

Furthermore, this example points out that the application of a safeguard as a specific duty may also have important differential effects even within the PTA partner economy. Figure 6 shows that the average unit value on footwear imports from Italy in 2006 was $60 per pair, whereas from all other EU member countries (mainly Spain, Portugal, and Romania) the average unit value on footwear imports was only $26 per pair. Thus the applied safeguard of $2-$3 per pair was likely to have the least impact on the high-end Italian footwear (ad valorem equivalent of an additional 3-5 percent tariff) and the greatest impact on EU-produced footwear imported from Spain, Portugal, and Romania (additional 7-10 percent).

> _IV. Dominican Republic – Safeguard on Polypropylene Bags and Tubular Fabric in 2009_

A final example of how countries apply safeguards so as to impact PTA partners is taken from the other extreme and involves a policy-imposing country that has arguably used the safeguard so as to _restore_ MFN treatment between PTA partners and non-partners that had been disrupted because of the PTA.

Consider the Dominican Republic’s 2009 safeguard on imports of polypropylene bags and tubular fabric. This safeguard was at least partially structured to address an increase in imports from PTA partners arising around the time of implementation of the Central America Free Trade Agreement – Dominican Republic (CAFTA-DR), which entered into force for the United States, El Salvador, Guatemala, Honduras, and Nicaragua in 2006 and for the Dominican Republic in 2007.<sup>22</sup> Before the CAFTA-DR was implemented in 2007, the Dominican Republic applied a MFN tariff of 14 percent on all foreign imports of tubular fabric and 20 percent on all imports of polypropylene bags.

> 22 This section draws heavily on the analysis in Bown and Wu (2014), which provides a legal-economic analysis of the formal WTO dispute in which CAFTA-DR partners Costa Rica, Guatemala, Honduras, and El Salvador challenged how the Dominican Republic applied this safeguard.

22
In the third phase and continuing in the same class, we tried to separate individuals who import their own cars from the resellers. We assume that, in view of the average personal income, an individual would not import more than one car a year for his or her own use. Thus importing more than one secondhand car per year should be deemed a traded activity.

Therefore, we assessed the share of importers for each category of car and the share of importers who had more than one operation a year for each category. This approach offers the lowest ceiling for evaluating the share of informal traders in view of the fact that some informal traders may use numerous tax identification numbers at the same time (the importers classified as “importers for personal use” can be also traders). The aim is to evaluate the secondhand car informal trade in a rank order (see table 3).

**Table 3. Main Characteristics of Importers and Imports by Car Value, Douala port, Cameroon**

|Category of (declared) value|Percentage of imported<br>cars of total number of cars|Percentage of<br>importers processing<br>more than one<br>operation (as share<br>of all categories)|Percentage of<br>importers processing<br>more than one<br>operation (as share<br>of own value<br>category)|
|---|---|---|---|
|≤1 million CFAF(€1,500)|48.0|15.00|30|
|>1 million and≤2 million<br>CFAF(€3,000)|29.0|8.00|28|
|>2 million and≤3 million<br>CFAF(€4,500)|12.0|2.00|17|
|>3 million and≤4 million<br>CFAF(€6,000)|4.0|0.80|22|
|>4 million and≤5 million<br>CFAF(€7,500)|2.0|0.30|14|
|>5 million and≤10 million<br>CFAF(€15,000)|3.0|0.40||
|>10 million and≤15 million<br>CFAF(€22,500)|0.8|0.10||
|>15 million and≤20 million<br>CFAF(€30,000)|0.4|0.08||
|>20 million CFAF|0.5|0.10||

_Source:_ ASYCUDA system.

The figures in table 3 reveal that the share of traders who may also be resellers is non-negligible when the value of the car is less than € 7,500. One assumption is that some informal trade may 11
**Table 4.2**

**Ranking of Schols by School Level Residualcs English** and Kathesatics*

|||ENGLISH|||MATH||
|---|---|---|---|---|---|---|
|2nnk|ScheolIR|School<br>-XPL|Residual|_School_<br>12|School<br>_Tv23_|Reuidgal|
|1|15|HF|.87|21|M|1.93|
|2|2|A|.81|1S|HP|1.28|
|3|1|A|.69|19|M|1.16|
|4|24|M|.48|2|A|.91|
|5|21|M|.27|25|DC|.59|
|6|19|M|.21|24|M|.52|
|7|25|DC|.17|38|DC|.45|
|8|10|8(r)|.16|10|8(r)|.33|
|9|27|DC|.16|28|DC|.26|
|10|4S|DC|.11|40|DC|.26|
|11|46|DC|.02|27|DC|.19|
|12|39|DC|.01|39|DC|.14|
|13|36|DC|.00|41|DC|.01|
|14|33|DC|-.06|36|DC|.00|
|15|40|DC|-.07|45|DC|-.02|
|16|48|DC|-.07|48|DC|-.04|
|17|28|DC|-.10|20|M|-.06|
|18|38|DC|-.15|30|DC|-.19|
|19|41|DC|-.15|23|M|-.19|
|20|42|DC|-.16|33|DC|-.26|
|21|13|B(r)|-.19|42|DC|-.26|
|22|14|B(r)|-.20|13|8(r)|-.37|
|23|20|M|-.22|14|B(r)|-.38|
|24|32|DC|-.28|46|DC|-.38|
|25|30|DC|-.35|32|DC|-.43|
|26|22|M|-.37|1|A|-.46|
|27|23|K|-.45|22|_M_|-. 66|
|28|16|HF|-.63|16|HF|-.98|

* The four Group B (urban) schools and one Group A (urban) school are not included in those rankings due to tho limitations of ML3. Schools having >255 students were excluded from the residuals calculations and there was insufficLent time to compose a sub-sample from these schools Ln order to have them included.

48
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third assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.

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23
Policy Research Working Paper 5303

# **Abstract**

A large number of studies have shown that contribution of exporters to economic growth and development is much higher than non-exporting firms. This evidence has lead governments to improve their trade policies in order to increase foreign exposure of firms. However, improvements in trade policies can only be fully effective when they are complemented with other regulatory reforms that improve the investment climate for firms. This study focuses on a particular aspect of investment climate, namely employment protection legislation, and shows how these regulations discourage firms from exporting. Using a rich set of firm level data from 26 countries in the Eastern Europe and Central Asia region, the author shows that firms that cannot create new jobs due to restrictive labor regulations are less likely to export. Evidence shows that firms that plan to export expand their size before they start to export. However the rigidities in labor markets make this adjustment costly. Higher costs of labor decrease operating profits and lead to a higher threshold value of productivity required for entering export markets. As a result, a smaller fraction of firms chooses to export.

This paper—a product of the Enterprise Analysis Unit, Financial and Private Sector Vice Presidency—is part of a larger effort in the department to understand the functioning of the private sector. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mseker@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
# **1. Introduction**

The last decade was a good one for growth in many African countries, as a reduction in conflict, improved political and macroeconomic stability, booming commodity prices, and a number of microeconomic reforms led to a growth surge (Radelet, 2010). The McKinsey Global Institute (2010) calculates that labor productivity grew annually at 2.7 percent in Africa during the 2000s, compared to negative labor productivity growth in the 1980s and 1990s. Yet despite this reversal, productivity remains low in many firms. Van Dijk (2003) finds that labor productivity in South Africa, the most advanced economy in sub-Saharan Africa, was only 20 percent of U.S. levels in 1999. Likewise, Harrison et al. (2010) find labor productivity in sub-Saharan African countries during the mid-2000s to be 36 percent lower than performance in the top half of the set of non-African countries with income levels below $3000 in PPP terms. Increasing this productivity is vital for long-run growth prospects and generating the jobs needed to employ Africa’s young and rapidly growing labor force.

Private sector development policy agendas of most governments in the region pursue two interrelated approaches to spurring private sector growth and increasing firm productivity. The first is to provide a better enabling-environment for businesses through maintaining macroeconomic stability, reducing administrative and regulatory barriers, and investing in infrastructure. The second, and more activist, policy approach is to enact programs which work directly with micro, small, medium and large firms to enhance their growth and create employment. Examples of these programs include matching grants, business trainings, partial credit guarantees, and wage subsidies. Despite, the millions of dollars invested in such programs, to date there is little rigorous evidence as to their effectiveness. The purpose of this paper is to discuss the challenges and potential for experimental and structural methods to help policymakers in understanding whether or not these policies are working and why.

Recently there has been considerable debate in the broader development literature about whether the profession is overemphasizing randomization (Rodrik 2009; Deaton 2010; Ravallion 2009; Imbens 2010). I agree with Imbens that, given the question which one is interested in answering is possible to answer with randomization, there is little to gain and much to lose by not randomizing. I also agree that there are many important policy questions we wish to answer that it may not be possible to randomize for, and that economics still has something to offer in 2
the network (using average daily traffic on each link). We aggregated the performance of the least-cost routes at the national level using weights that reflect the importance of each OD pair. Indeed, a simple sum of the road user cost over all the routes in the network would be misleading because some routes are used much more frequently than others.

We thus assigned weights to the different OD pairs based on the importance of the origins and destinations, which we measured in terms of traffic. We did this by:

- Summing the traffic getting in and out of each origin and destination node.

- Calculating for each route – each Origin-Destination pair – a weight _w_ based on the gravity model:

where _TO_ is the total traffic getting in and out of the origin, _TD_ is the total traffic getting in and out of the destination, and _kmOD_ is the distance between the origin and destination. We also use these weights to distribute traffic (measured at each link) between the OD pairs in the calculation of the total daily cost of the network.

The impact of a disruption at the national level is then the difference in network performance between the baseline conditions and the new network configuration with second-best routes when a link is removed. The criticality of each link is thus expressed along the three same dimensions: first, by aggregating over the network the average increase in road user cost (dollars per ton-kilometer) when the link is removed, compared with a baseline condition with no disruptions. Second, by aggregating over the network the average increase in kilometers that users have to drive when a disruption occurs. And third, by calculating the total direct economic cost linked to the disruption.

The candidate critical links are ranked according to these impacts on the network when they are disrupted: the higher these three impacts, the higher the criticality of the link.

# 2.2. Exposure of critical links

Once we have identified the critical links, we overlay the network with hazard layers to identify which ones are also exposed to natural disasters.

<mark>We used a database of flood scenarios produced by Global Flood Risk using the IMAGE Scenarios model—GLOFRIS (Ward et al., 2013; Winsemius et al., 2013). This model simulates daily discharge and runoff at a horizontal resolution of 0.5° x 0.5°. Deltares simulated eight return periods – RP5, RP10, RP25, RP50, RP100, RP250, RP500, and RP100. The model was forced using daily meteorological fields of precipitation, temperature, and radiation for four time periods: 1960‐99, which represents the baseline climate; 2010‐49 representing 2030; 2030‐69 representing 2050; and 2060‐99 representing 2080. The meteorological data for the baseline climate are WATCH Forcing data (Weedon et al., 2011). The future meteorological data are provided by the ISI‐MIP project, and consist of bias‐corrected data (Hempel et</mark> 6
**Table 2: Impact of African Diaspora on African Exports**

||**_All g_**<br>|**_oods_**<br>|**_Differentia_**<br>|**_ted Goods_**<br>|
|---|---|---|---|---|
||OLS|IV-OLS|OLS|IV-OLS|
|VARIABLES|(1)<br>Ln(Exports)|(2)<br>Ln(Exports)|(1)<br>Ln(Exports)|(3)<br>Ln(Exports)|
|**Migrants_ij (log)**|**0.0993*****<br>**(5.184)**|**0.178****<br>**(2.344)**|**0.114*****<br>**(6.852)**|**0.202*****<br>**(3.187)**|
|GDP_i (log)|0.927***|0.932***|0.787***|0.808***|
||(9.241)|(9.403)|(7.352)|(7.661)|
|GDP_j (log)|0.0776|0.0247|0.223**|0.146|
||(0.708)|(0.208)|(2.054)|(1.200)|
|FTA|0.386***|0.338***|-0.0490|-0.121|
||(3.532)|(2.909)|(-0.432)|(-0.992)|
|Distance (log)|-1.327***|-1.175***|-1.463***|-1.301***|
||(-14.35)|(-7.096)|(-17.75)|(-9.234)|
|Contiguity|1.148***|0.966***|0.881***|0.653***|
||(5.905)|(3.889)|(4.906)|(2.729)|
|Common colony|0.252*|0.243*|0.633***|0.654***|
||(1.792)|(1.750)|(5.141)|(5.339)|
|Colony 1945|1.203***|0.990***|0.988***|0.765***|
||(5.044)|(3.383)|(4.599)|(2.844)|
|Common language|0.517***|0.422***|0.478***|0.368***|
||(4.214)|(2.752)|(4.450)|(2.774)|
|Constant|-11.96***|-12.51***|2.597***|-4.614***|
||(-11.15)|(-11.73)|(3.176)|(-4.655)|
|MR terms|Yes|Yes|Yes|Yes|
|Year FE|yes|yes|yes|yes|
|Country FE|yes|yes|yes|yes|
|Sargan (p.-val.)||0.491||0.433|
|Underidentification<br>test||0.000||0.000|
|(Kleibergen-Paap p-val.)|||||
|Observations|6,151|6,151|5,601|5,601|
|R-squared|0.583|0.581|0.617|0.615|

_Notes_ : Robust t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Observations are clustered by country-pair.

Rauch (1999) established a classification of internationally traded goods in three groups: those traded on organized exchanges, those not traded on organized exchanges but possessing reference prices, and all the others. For disaggregated values of exports, we rely on BACI, the CEPII international trade database at the product level, constructed by Gaulier and Zignago (2010). By aggregating exports according to Rauch’s classification, we obtain for each country the value of its exports of differentiated products (those not traded on organized 15 exchanges) to each of its commercial partners for the years 1980, 1990 and 2000.

> 15 In this section we cannot refer to the year 2010, because the version of BACI that we use does not contain trade flows for that year.

15
Charmes, J. 1998. _Women Working in the Informal Sector in Africa: New Methods and New Data._ Paris: Scientific Research Institute for Development and Co‐operation.

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Dickson, M, and S. Smith. 2011. “What Determines the return to education: an extra year or hurdle cleared?” Centre for Market and Public Organisation, Working Paper No. 11/256, University of Bristol Dixon‐Mueller, R., and R. Anker. 1988. _Assessing Women’s Economic Contributions to Development._ Training in Population, Human Resources and Development Planning Paper number 6, Geneva: International Labour Office.

Dohmen T., D. Jaeger, A. Falk, D. Huffman, U. Sunde, H. Bonin, (2010). Direct Evidence on Risk Attitudes and Migration, _Review of Economics and Statistics_ , 92(3) 684–689.

Duflo E., 2001, Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment. American Economic Review Vol 91, No 4 (Sep 2001) Esposito, J.L., P.C. Campanelli, J. Rothgeb, and A.E. Polivka. 1991. “Determining which Questions Are Best: Methodologies for Evaluating Survey Questions.” _Proceedings of the American Statistical Association_ (Survey Research Methods Section).

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23
<!-- Start of picture text --> 8<br>$<br>8<br>x<br>&<br>=<br>o<br><!-- End of picture text --> <!-- Start of picture text --> High income Low income<br>8<br>2<br>&<br>32 o<br>Lower middle income Upper middle income<br>8<br>2<br>:<br>No EL.<br>HEE Sub-unit of a ministry MEE (Semi-)autonomous agency<br>MEE Joint public-private entity ME Private<br>MO other<br>Graphs by inc_group<br><!-- End of picture text -->
Consequently, given the limited resources available for this study, it was decided to calibrate against an existing runoff dataset. Hence, for the calibration, simulated runoff data obtained from the CD of runoff fields were used. These were developed by the University of New Hampshire (UNH) and the Global Runoff Data Center (GRDC) (Fekete et al. 2000). The data on this CD have been endorsed by WMO, UNESCO and the IGBP (International GeosphereBiosphere Program) and they are believed to be the best global runoff data currently available. There are three datasets available on the CD:

- i. Simulated runoff data derived from a climate driven Water Balance Model (WBM) which is very similar to WatBal.

- ii. Observed river discharge data from approximately 130 gauging stations, located on mainland Africa, predominantly in West Africa.

- iii. A composite dataset derived by estimating inter-station runoff (i.e. difference in flow between two gauging stations on the same river) and applying correction coefficients (based on the ratio of observed and simulated runoff) to the simulated runoff.

The composite data (iii. above) underestimates runoff from some grid cells. This is because in areas where flow is reduced by evaporation from wetlands, lakes or reservoirs, inter-station flow may be significantly diminished or even negative. Since the UNH correction was applied to all cells located ‘upstream’ of a gauging station (i.e. not just those for which evaporation is high) the ‘corrected’ runoff tends to be significantly less than occurs in reality. For this reason it was decided to calibrate the WatBal model against the simulated UNH runoff (i. above). Thus the model was calibrated against existing model output rather than observed data. Clearly this is not ideal, but was the best that could be achieved given the constraints of limited time and resources.

The three parameters (i.e. alpha, epsilon and AWCmult) were calibrated for each cell by simulating runoff for 30 years (1961–1990) and minimizing the root mean square error of the WatBal simulated average monthly runoff and the UNH simulated runoff data. This approach tends to maximize the ‘goodness of fit’ of the total volume of runoff, but will not necessarily produce a good fit in periods of extreme high and/or low runoff events. However, since this study is primarily concerned with water resources, it was felt that total volume was the most 14