rafmacalaba/datause-encoder-data huggingface dataset:
WPS3718

**Services in a Development Round: Three Goals and Three Proposals**

# **Aaditya Mattoo***

## World Bank Policy Research Working Paper 3718, September 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*World Bank, Washington, D.C. 20433, USA. The paper has benefited greatly from the comments of Rudolf Adlung, Jens Arnold, Bernard Hoekman, Hamid Mamdouh, Julia Nielson and Richard Self.
||IMPOR<br>|T-RELATED BARRIERS<br>|Ai t Ril<sup>Ll</sup>|
|---|---|---|---|
|Barriers|Possible Corrective Measures|Progress with Current Measures|Recommended ctons a egona<sup>eve</sup>|
|Cargo Handling<br>Charges|- Elimination of fictitious charges<sup>for services</sup><br>not actually performed (improve<sup>productivity</sup><br>through incentive bonuses, restructuring<sup>of</sup><br>shipboard and dock handling gangs).<br>- Placement of handling operations<sup>on lump-sum</sup><br>basis.<br>- Introduction of uniform rate schedules.<br>- Creation of competition among<sup>handling</sup><br>companies, both public and private.<br>- Introduction of uniform charges<sup>for cargo held</sup><br>on docks.<br>- Modemization of handling equipment.|- Refer to "Export-related Barriers:<sup>Cargo</sup><br>Handling Charges.'||
|Domestic<br>Transport<br>Regulations|In both the road and rail sectors:<br>- Revision by the competent authorities<sup>of</sup><br>licensing requirements throughout<sup>the two</sup><br>sectors, with view to greater flexibility,<br>clarification, and more rational<sup>investment.</sup><br>- Definition of network responsibilities.<br>- Setting of standards re guaranteed<sup>short</sup><br>journey times.<br>- Client to have choice of method<sup>of payment,</sup><br>client to be able to deal 'at single<sup>window."</sup><br>- Liberalization of international<sup>and national</sup><br>backhaul freight rates.<br>- Training for TIR truckers.<br>- Elimination of abusive internal<sup>road traffic</sup><br>controls.|Morocco: Study under way on<br>reformulation of ONT's mission<sup>and</sup><br>development strategy.<br>Tunisia: Liberalization under<sup>way in</sup><br>conjunction with process to privatize<sup>public</sup><br>enterprises.<br>Algeria: Discussion under way<sup>on national</sup><br>transport plan.<br>- Temporary suspension of measures<sup>to</sup><br>facilitate intra-Maghreb rail transit.<br>- Agreement on road taxes ratified<sup>but not</sup><br>put into effect.|_PA_- Reduction in border crossing<sup>times on</sup><br>rail system (currently 2448 hours).<br>_NM_- Introduction of common intra-<br>Maghreb rail tariff (competitive<sup>prices,</sup><br>uniform tariff structure) giving<sup>economic</sup><br>operators substantial advantages.<br>IM - Elimination of intra-Maghreb<sup>road</sup><br>taxes.<br>IM - Standardization of traction<sup>and rail</sup><br>rate schedules throughout the Maghreb.<br>IM - Reactivation of the intra-Maghreb<br>railway clearing house.<br>IM - Standardization of rail system<br>maintenance techniques.|
|Quantitative<br>Restrictions|- Gradual elimination of the priorities<sup>system</sup><br>(liberalization of imports, complete<sup>or partial</sup><br>removal of prohibitions).<br>- Contraband control measures.|- Liberalization of imports well advanced<sup>in</sup><br>Morocco and Tunisia.<br>- Signature of an intra-Maghreb<sup>agreement</sup><br>on mutual administrative assistance<sup>in</sup><br>campaign against customs fraud.||

IM = Intra-Maghreb EM = Europe/Maghreb
# **Can the Introduction of a Minimum Wage in FYR Macedonia Decrease the Gender Wage Gap?**

Diego F. Angel-Urdinola<sup>*</sup> December 2008 JEL classification: J38, J23, J71, I32 Key words: Minimum wages, Gender Gap, Wage Differentials, Macedonia.

<u>Corresponding Author:</u> Diego F. Angel-Urdinola 1818 H. Street, NW Washington, DC 20433 Mail Stop H11-1101 E-mail: dangelurdinola@worldbank.org

* The author acknowledges support from grant TF094614 from the Gender Action Plan at the World Bank, as well as comments and suggestions from Victor Macias, Arvo Kuddo, Jan J. Rutkowski, Jane Armitage, and Gordon Betcherman. The views expressed here are those of the author and need not reflect those of the World Bank, its Executive Directors or the countries they represent.
# Table IV.3

# Woven Textile Sample: Usefulness of Collective Marketing Support

|l<br>________________________________|**Early**<br>**Expor**|**ters**|**Late**<br>**Expor**|**ters**|**Large**<br>**Expo**|**rters**|
|---|---|---|---|---|---|---|
|**1.**Number of Firms|_5_||14||10||
|2. Average Relative Importance of Support by Public<br>or Non Profit Agency for your Firm to Penetrate<br>-Export Markets.<br>_|||||||
|a. At First Export|1.2||1.5||1.4||
|b. In 1991|1.2||1.3||1.4||
|3. Proportion of Firms Receiving Service Provided by<br>such an Agency and its Relative Usefulness.|Ever<br>Used'|Usf|Ever<br>Used|Usfl|Ever<br>Used|Usfl|
|Information on Export Opportunities|.40|3.5|.23|4.0|.40|3.5|
|Introduction of Buyers|.20|5.0|.07|4.0|.10|2.0|
|Participation in Domestic Trade Fair|0||0||0|-|
|Participation in Overseas Trade Fair|.20|_5.0_|0|-|.10|_5.0_|
|Support of Exploratory Marketing Trips|.40|4.5|0|-|.20|3.0|
|Export-ImportAgency|0|-|0|-|0|-|
|Obtaining Foreign Standards|0|-|0|-|0|-|
|Overseas Distribution of Promotional Material|.40|4.0|0|-|.40|2.8|
|Joint Venture Technology Ties withForeignFirm|0|-|0||0||

Ever used = Average probabiity of ever having used. Usfl = Average usefalness score on a scale of 1 (low) to 5 (high).

Note: Early exporters began exporting before 1975. Late exporters began exporting after 1985.

60
officials at least once) and bribery depth (percentage of public transactions for which a gift or informal payment was requested). Regulation is measured with two indicators: percent of management time spent on complying with regulatory processes and number of visits made to the tax office to meet with tax officials. Productivity refers to labor productivity computed as sales of firms per worker.

We estimate the model using sample weighted OLS (Ordinary Least Squares) regression analysis. To mitigate the endogeneity problem, we control for country, industry, sector, region and year fixed effects, and extensive numbers of productivity determinants cited in the literature. To further mitigate the endogeneity between productivity and corruption and regulation, we use the averages of the latter two over all other firms in the cell, where cell is defined as the group of firms in the same size category, industry and city within a country.

We find that the negative relationship between corruption and productivity is amplified at high levels of regulation. In fact, at low levels of regulation, the relationship between corruption and productivity is insignificant. To fix ideas, we find that a 1 percent increase in bribes that firms pay to get things done, expressed as the share of annual sales, is significantly associated with about a 0.9 percent decrease in productivity of firms at the 75<sup>th</sup> percentile value of regulation (high regulation). In contrast, at the 25<sup>th</sup> percentile value of regulation (low regulation), the corresponding change is very small and statistically insignificant, though it is still negative.

This paper is the first using global firm-level data to investigate the interlinkages between corruption, regulation and productivity and it is among the handful of firm-level studies on the subject. To the best of our knowledge, De Rosa et al. (2010) is the only other study using international firm-level data to analyze the corruption-regulation-productivity nexus. However, their data cover only 28 countries in Central and Eastern Europe and the Commonwealth of 3
# Underutilized potential: The business costs of unreliable infrastructure in developing countries

**Jun Rentschler**<sup>**1**</sup> **, Martin Kornejew**<sup>**1,2**</sup> **, Stéphane Hallegatte**<sup>**1**</sup> **, Johannes Braese**<sup>**1**</sup> **, Marguerite Obolensky**<sup>**1**</sup>

_1Global Facility for Disaster Reduction and Recovery, World Bank, Washington DC, USA_ _2Bonn Graduate School of Economics, University of Bonn, Germany_

**Acknowledgements:** This working paper contributes to the World Bank’s 2019 flagship report _Lifelines_ on resilient infrastructure. It has benefited from helpful comments, feedback, and inputs by Asif Islam, Marie Hyland, Claire Nicolas, Jason Russ, and Joshua Wimpey.

**JEL classification:** D22, O18, Q54

**Keywords:** infrastructure; firms; resilience
**Table 2. Pros and Cons of Pricing Options for Natural Gas**

|_Price based on_|_Pros_|_Cons_|
|---|---|---|
|Costs|*Reflects costs.<br>* Prices are related to costs at the wellhead in most<br>countries with a competitive natural gas market.|_*_No marginal cost of extracting<br>Mexican natural gas because it is a<br>byproduct of oil.<br>* Does not reflect the opportunity cost<br>of selling Mexican natural gas in the<br>North American market.|
|Comparisons<br>with other fuel<br>prices|*Reflects prices in international markets.<br>*Prices of substitutes are economically related.<br>* There are price series data.|*Potential prices of substitutes<br>subsidized in non-explicit ways.<br>*International markets of substitutes<br>have different dynamics to the natural<br>gas market.<br>* Accounts for opportunity cost of other<br>markets, not the natural gas marlcet.|
|A benchmark|*Considers the opportunity cost of Mexican<br>natural gas in the North American market.<br>-The relevant benchmark, the Houston Ship<br>Channel, is a liquid market, it has an associated<br>hedging market, it is close to the physical<br>connection to the Pemex pipeline system.<br>* Methodology has some similarities with prior<br>Pemex methodology.<br>* Marginal costs of imported gas and domestic gas<br>are the same at the arbitrage point.|*Brings disturbances from U.S.<br>weather into the Mexican market.|

The economics of transportation is key to understanding the market for natural gas and the way the CRE's netback formula works. The Mexican pipeline system looks like a "Y," with Ciudad Pemex-where 80 percent of total domestic natural gas is produced (associated gas)-at the bottom. Reynosa-Burgos is in the northeast arm and produces non-associated gas (17.3 percent of total production). Ciudad Juarez, an import point, is in the northwest arm. Los Ramones marks the junction of the three branches.

The netback formula is based on the benchmark price in southeast Texas, the arbitrage point, and net transport costs. The arbitrage point is where northern and southern flows meet, and where prices from both sources are equal (Los Ramones). This point moves north as northern flows decrease and south as northern flows increase. The price cap for Mexican natural gas is the price at Ciudad Pemex, which in turn is equal to the price in Southeast Texas plus transport costs from Texas to the arbitrage point minus transport costs from the arbitrage point to Ciudad Pemex.

One attractive feature of this method is that the marginal costs of imported gas and domestically produced gas are the same at the arbitrage point. But even though linking 13
quality, higher per capita income, higher government spending, and more open economies along with better financial conditions are likely to contribute to countries’ macroeconomic performance after natural disasters.

On a similar line, several studies have documented that economic development plays an important role in mitigating a countries’ vulnerability to catastrophic incidences. Skidmore and Toya (2007) investigated the effects of the level of development on disaster impacts, using a dataset of natural disasters incurred in 151 countries over the period from 1960 to 2003. The analysis included two patterns of dataset obtained from the EM-DAT. One used the number of killed to assess the disaster impacts, while the other considered economic damages. The OLS regression analysis demonstrated that human and economic damages from natural disasters are generally reduced along with economic development. In particular, the results showed that deaths and damages were lower in countries with higher level of educational attainment, greater degree of openness, more developed financial sector, and smaller governments. The authors suggest that policymakers could consider further efforts in developing economic and social infrastructures, which can contribute to decreasing natural hazards.

Taking a different approach in exploring the impacts of capital and labor losses on short-term growth, Caselli and Malhotra (2004) tested the empirical validity of the predictions of the Solow growth theory. The theory suggests that a decline in the capitallabor ratio resulting from a natural disaster would lead to an increase in the country’s growth rate, while an increase in the capital-labor ratio would curtail it. In their empirical analysis, the total number of people killed, injured, and affected by disasters were used to calculate the percentage loss in the labor force, while the immediate damage as a percentage of GDP was used as a proxy for the loss in capital stock. The data were compiled from the EM-DAT database for a sample of 172 countries for the period between 1975 and 1996. Using the real per capita GDP growth rate in the disaster year to estimate the Solow model, their empirical analysis found that sudden losses of capital and labor did not bring about a change in the economic growth as expected by the Solow 8
# **Table 3**

**Testing for differences in firm financing features between banks operating in developed versus developing countries** This table reports values for small, medium-sized, and large enterprises and t-tests for differences in firm financing features betweens banks in developed and developing countries. SE stands for small enterprises. ME stands for medium-sized enterprises. LE refers to large enterprises. *,**,*** denote significance at 10, 5, and 1 percent, respectively.

|Variable|Firm Type|Mean for Developed Countries|Mean for Developing Countries|t-test|
|---|---|---|---|---|
|Share of total loans (%)|SEs|12.01|2.48|-3.98***|
||MEs|10.08|13.69|-0.86|
||LEs|27.88|32.80|-0.61|
|Share of applications approved (%)|SEs|66.63|77.42|-0.51|
||MEs|80.01|78.58|0.12|
||LEs|86.94|80.76|0.47|
|Share of loans for investment (%)|SEs|72.07|42.13|2.67**|
||MEs|68.47|44.41|2.37**|
||LEs|72.93|49.82|4.26***|
|Share of secured loans (%)|SEs|55.83|79.97|-1.67|
||MEs|59.17|82.33|-1.97|
||LEs|42.80|74.85|-2.92**|
|Loan fees (% of size of loan)|SEs|0.41|1.18|-4.27***|
||MEs|0.38|1.04|-3.48***|
||LEs|0.22|0.83|-3.23***|
|Interest rates on best clients (%)|SEs|5.08|11.99|-4.79***|
||MEs|4.65|10.85|-6.09***|
||LEs|3.99|10.22|-5.32***|
|Interest rates on worst clients (%)|SEs|8.97|16.57|-5.22***|
||MEs|8.68|14.88|-4.38***|
||LEs|7.29|14.09|-4.21***|
|Share of non-performing loans|SEs|11.02|7.01|0.66|
|(% of total loans)|MEs|8.44|5.47|0.64|
||LEs|2.54|4.13|-1.19|

22
||agri-<br>culture|service<br>sales|profess-<br>ional|manual<br>labor|not<br>working|earns<br>cash|works<br>all year|
|---|---|---|---|---|---|---|---|
|PANEL A: Women||||||||
|active*mine|-0.025|0.024|-0.017*|0.012|0.006|0.054**|-0.013|
||(0.039)|(0.031)|(0.009)|(0.021)|(0.023)|(0.026)|(0.033)|
|mine|-0.025|0.056*|-0.001|-0.012|-0.018|-0.069***|-0.012|
||(0.031)|(0.029)|(0.008)|(0.018)|(0.020)|(0.022)|(0.024)|
|active|0.014|-0.000|-0.006|0.009|-0.016|-0.037**|-0.007|
||(0.015)|(0.016)|(0.006)|(0.011)|(0.012)|(0.015)|(0.016)|
|Observations|12,176|12,176|12,176|12,176|12,176|9,262|7,085|
|R-squared|0.350|0.103|0.124|0.024|0.234|0.095|0.042|
|Mean of dep var.|0.237|0.358|0.045|0.117|0.739|0.891|0.877|
|PANEL B: Men||||||||
|active*mine|0.050|0.020|0.027|-0.069*|0.006|-0.013|-0.015|
||(0.051)|(0.020)|(0.026)|(0.036)|(0.023)|(0.028)|(0.051)|
|mine|-0.060|0.002|0.000|0.041|-0.018|-0.009|0.066*|
||(0.042)|(0.016)|(0.020)|(0.030)|(0.020)|(0.028)|(0.039)|
|active|0.000|0.002|-0.001|-0.029|-0.016|-0.107***|-0.025|
||(0.021)|(0.014)|(0.015)|(0.020)|(0.012)|(0.039)|(0.028)|
|Observations|7,157|7,157|7,157|7,157|7,157|4,374|2,794|
|R-squared|0.290|0.415|0.084|0.183|0.076|0.107|0.104|
|Mean of dep var.|0.328|0.111|0.137|0.214|0.209|0.928|0.841|

_Note:_ Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and district fixed effects, urban dummy, age, and years of education. Active is active status of mine in the survey year. *** p<0.01, **p<0.05, *p<0.1. Results for women’s partners also available upon request. OLS = ordinary least squares. The women and men samples have different sampling frames and therefore we are not analyzing these results jointly.

Two further caveats should be noted: (i) with many variables tested, such evidence should not be given too much weight, (ii) number of sampled men in the treatment category is quite small. Out of 7,157 sampled med, only 484 men live close to active mines. A bigger treated sample would provide more reliable results.<sup>10</sup>

# **5.2 Child health**

We explore effects on child health, such as size at birth, infant mortality, anthropometrics, and incidence of cough, diarrhea, and fever. Panel A and Panel B use different variable definitions

> 10 Results for sampled women’s partners are similar.

18
investment promotion is a cost-efficient way of attracting FDI to developing countries. Based on data on investment promotion efforts in 124 countries and figures on inflows of US FDI, Harding and Javorcik find that sectors designated as a priority for investment promotion purposes receive more than twice as much FDI as do non-priority sectors. Although the magnitude of the effect may seem large, it is not implausible. If one considers only positive flows of US FDI to developing countries, the median sector-level flow was equal to US$11 million in 2004. Therefore, the estimated effect of investment promotion translates into an additional annual inflow of US$17 million for the median sector-country combination. With regard to the costs of investment promotion, on average, an investment promotion agency spent US$90,000 per sector targeted in 2004. Hence, a dollar spent on investment promotion corresponds to US$189 of FDI inflows. Alternatively, the results indicate that priority sectors experience a 68 percent increase in affiliate employment compared with non-targeted sectors. This implies an additional 1,159 jobs for the average sector, or US$78 per job created (in 2004 dollars).

These back-of-the-envelope cost-benefit calculations should be treated with caution. On the one hand, Harding and Javorcik’s analysis considers only flows of FDI from the United States. As investment promotion is likely to have a similar impact on investors from other source countries, and their analysis underestimates the benefits of investment promotion activities. On the other hand, there may be other factors that contribute to the success of investment promotion and whose costs 28
||is...|green<br>occupati<br>ons|are<br>green by<br>GTI|green<br>occupati<br>ons|are non-<br>green by<br>GTI|green<br>occupati<br>ons|are<br>green by<br>GTI|green<br>occupati<br>ons|are non-<br>green by<br>GTI|
|---|---|---|---|---|---|---|---|---|---|
||GSI = 0|723|178|50|46|106|19|53|51|
||GSI < 0.1|512|167|261|246|89|17|70|66|
||GSI < 0.25|396|148|377|343|61|16|98|93|
|x|GSI < 0.5|284|124|489|431|42|13|117|109|
|nde|GSI < 0.75|207|101|566|485|31|11|128|118|
|GSI i|GSI < 1<br>GSI > 1|155|84|618|520|24|11|135|125|

Although the skills database may not be the most appropriate tool for identifying green occupations, it can still be very useful in understanding the skills (both green and non-green) that are required in green jobs identified through the task database. This skills profile can then be used to inform training programs. This is discussed further in application #2 in the next section.

# **4. Application of Green Task Intensity to Indonesia**

The GTI index at the 4-digit occupational level can then be applied to datasets from countries collecting occupational data with ISCO-8. As an example, this section uses the case of a developing country, Indonesia, to calculate levels of green employment. It shows how different the results are when using the proposed methodology or O*NET data, either the O*NET GEP or the GTI calculated with O*NET task statements. Differences arise from the level of data collection in the country (4-digit)—which is the usual level collected in developing countries—and from the assumptions made when using crosswalks between taxonomies. Next, it describes the skills in demand by green jobs. Lastly, it applies the GTI (binary and continuous) to explore other green employment characteristics: gender and wage premium.

Green employment can be measured using the GTI index and Indonesia's labor force survey (Sakernas). In 2016, Sakernas started collecting occupational data using the national standard occupational classification system, _Klasifikasi Baku Jenis Pekerjaan Indonesia_ (KBJI) version 2014.<sup>57</sup> The KBJI 2014 corresponds to the adoption of ISCO-08 by Indonesia. However, two important warnings should be kept in mind. First, the illustration is done for Sakernas data for 2017, which is the latest year for which we have access to 4-digit occupational data.<sup>58</sup> Unfortunately, the shared micro data contains KBJI 2002 (based on ISCO-88) instead of KBJI 2014, for which the GTI was computed. Thus, the analysis needs to rely on a crosswalk for employment from KBJI 2002 to KBJI 2014. Such a crosswalk contains several many-to-many matches, which had to be split equally across matches. As a result, employment estimates could be inaccurate for these occupations. Nine out of the 35 green occupations are subject

> 57 BPS (2014).

> 58 The team is currently working with BPS to have more recent data including KBJI 2014 classification, and hopefully available by the time of the publication of this note.

27
Figure 4 South Asia The Movement of MCY over time <!-- Start of picture text --> 0.8<br>0.64<br>04-<br>6.2~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>1977 1978 1979 1980 t981 1982 1983 1984 1985 1986 1987 1988 1989 1990<br>year<br>-8-- India + Indonesia *K<br>Malaysia<br>-H-- Pakistan -X- Thailand<br><!-- End of picture text -->