rafmacalaba/datause-encoder-data huggingface dataset:
However it is also interesting to note that 42% of the freight forwarders in our sample report that the efficiency and quality of border agencies improved over the 2010-2013 period, while 40% indicate it did not change, as seen in Table 9.

**<u>Table 9. Perceptions of Performance of Border Agencies</u>**

||Number of<br>freight<br>forwarders|Improved<br>Share of firm<br>efficiency of|Stayed the<br>same<br>s indicating th<br>border agencie|Worsened<br>is regarding the<br>s over the perio|Does not<br>know<br>quality and<br>d 2010‐2013|
|---|---|---|---|---|---|
|Small|141|43%|40%|16%|1%|
|Medium/Large|12|42%|50%|8%|0%|
|Total|153|42%|41%|16%|1%|

Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by all 153 freight forwarders.

# **_The majority of freight forwarders absorb the losses when a shipment is unexpectedly delayed_**

The response to delays in clearance at the border post or terminal for more than half of the freight forwarders (55%) is to absorb the related liability and losses and the shipment continues as normal to the initial destination, whereas for 21% it is to redirect shipments to another route/border post or terminal, and for 15% it is through a driver response in terms of driving faster, as seen in Table 10. Only 3% of freight forwarders report experiencing no costs from delays or no delays. We also find that 84% of freight forwarders communicate with drivers about shipments when experiencing clearance delays. The largest difference in the response to delays across freight forwarder size in Table 10 concerns the redirection of shipments: chosen by 23% of the small freight forwarders but only by 8% of the medium/large freight forwarders. Another meaningful difference is seen in the proportion of freight forwarders that contact customs officials to expedite the process, which is 8% for medium/large freight forwarders but only 1% for small freight forwarders. This finding suggests that larger freight forwarders have more leverage with government officials and are likely to get more attention and assistance when they face unexpected delays.

# **Table 10. Response to Clearance Delays by Freight Forwarders**

21
# Acknowledgments

We thank the Government of Sindh’s Education and Literacy Department and the Reform Support Unit, in particular, Dr. Fazlullah Pechoho, Saba Mushtaq, Faisal Ahmed Uqaili, Dr. Hamzo Khan Tagar, Muhammad Nasim Qureshi and Sania Khursheed, for extensive collaboration and assistance; the World Bank’s intervention design team, in particular Priyanka Pandey, Lorenza De Icaza and, Mariam Nusrat Adil; the Weitek Group for field implementation; Amar Fateen and Saindad Joyo for implementation support; RCons for school and household surveys; Ravinder Casley Gera for extensive support to the drafting process; Dhushyanth Raju, Nazmul Chaudhury, and Syeda Shahbano Ijaz for substantive technical discussions and edits; and Ilagno Patchamutho, Rachid Benmessaoud, Amit Dar, Keiko Miwa, Mario Cristian Aedo, Tazeen Fasih, Umbreen Arif and Margo Hoftijzer for their encouragement and support for this project. Zunaira Mughal, Robbie Dean, Joshua Gill, Cameron Friday, Ali Abbas, Amn Nasir, Tooba Akhtar, Xu Wang and Abhijit Banerjee provided outstanding research assistance at various stages of the project. The authors thank Alaka Holla, Amer Hassan, Juliea M. Trias, Deon Filmer, Owen Ozier, Achim Schmillen, Juan Baron, Koen Martijn Geven and David Evans for helpful discussions and insightful comments. We thank discussants and participants at the 2016 RISE Annual Conference at the Blavatnik School of Government, University of Oxford, 2018 PacDev Conference at University of California, Davis, 2019 AAPAM Fall Research Conference, Denver. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors or the countries they represent.

# Funding

We gratefully acknowledge the financial support provided for this project by the Strategic Impact Evaluation Fund (SIEF) of the World Bank and the Education Sector Development Trust.

# Appendices

Appendices are available online at http://bit.ly/asimriazappendices.

# Dedication

The study is dedicated to the living memory of Mr. Muhammad Nouman Bashir, Managing Partner of Weitek Group, Pakistan, the implementation support firm. Mr. Bashir met an untimely demise during the implementation of the intervention.

2
column of Table 4. Across Mexico, the business owners reported that regulation costs averaged 3.45 percent of sales, with the state level averages ranging from 1.32 percent to 4.71 percent. The EGDE survey also provides our measure of generalized trust. Managers were asked whether they agreed or disagreed with the statement: “The majority of people are trustworthy.” Responses were scaled from 1 (strongly disagree) to 7 (strongly agree). The average responses ranged from 2.7 in the state of Chiapas to 4.1 in several states, with a national average of 3.77. We use the state level average response to this question as our measure of generalized trust.

The final column of Table 4 gives an indication of the level of financial development by state in Mexico as proxied by the ratio of private credit to GDP. These data are the best available indication on access to finance for firms. However, the data on private credit have a couple of potential problems. First, a substantial part of bank lending taking place outside Mexico City is attributed to the Federal District, due to internal reporting procedures at Mexican banks. As a result, credit figures from banks overstate the level of bank activity in the state of the Federal District and understate the level of bank activity elsewhere in the country. In the regressions where we include private credit to GDP we therefore exclude the observations from the state of the Federal District. Second, as mentioned before, previous research has shown financial market development itself is a function of the efficiency of the legal system (Levine 1998). To address this concern, we use the residual of a regression of private credit to GDP on our measure of judicial efficiency as financial development variable in our regressions.<sup>16</sup>

> 16 From the 1998 census we only have data on interest expense of firms, not on debt. Interest payments are an imperfect measure of access to finance, and may be jointly determined with the measures of firm size which are our primary focus. Furthermore, interest rates paid by firms are likely to vary across states,

25
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# **Appendix A.3 Consumption parameters of model households**

## **Frisch parameters of households**

|hhd-soci hhd-reti hhd-farm hhd-enter|hhd-work hhd-remit|ngo|
|---|---|---|
|-2.09<br>-4.73<br>-3.15<br>-4.43|-2.05<br>-3.81|-3|

Source: own estimation from Moldovan household survey data. For notation, see Table 3

## **Expenditure elasticities of the households’ LES demand system**

||hhd-soci h<br>|hd-reti h<br>|hd-farm hh<br>|d-enter hh<br>|d-work <br>|hhd-remit<br>|ngo|
|---|---|---|---|---|---|---|---|
|c-agr|1.148|0.582|1.041|0.908|0.855|0.739||
|c-mining|0.765|0.875|1.212|0.867|0.964|0.787||
|c-food|1.148|0.582|1.041|0.908|0.855|0.739||
|c-bevetob|1.129|0.657|1.256|0.889|0.951|0.861||
|c-garment|0.678|1.216|1.363|0.896|1.007|0.708||
|c-woodpap|0.717|1.169|1.466|1.613|0.803|0.977||
|c-fuel|0.765|0.875|1.212|0.867|0.964|0.787||
|c-metplast|0.765|0.875|1.212|0.867|0.964|0.787||
|c-machequip|0.509|3.146|0.585|0.664|0.916|2.423||
|c-enewat|0.646|1.152|0.766|0.778|0.792|0.522||
|c-constr|0.988|4.700|0.432|0.717|1.834|3.733||
|c-trade|0.988|2.086|1.053|0.982|0.962|1.224||
|c-hotrest|0.988|0.350|2.319|2.176|1.112|0.785||
|c-transp|1.217|1.326|1.803|0.891|1.069|0.807||
|c-posttele|0.502|0.657|0.992|1.020|0.813|0.584||
|c-finance|0.000|3.339|0.013|2.121|1.516|0.448||
|c-realest|0.663|1.774|0.590|0.763|1.103|2.035||
|c-itserv|1.217|1.326|1.803|0.891|1.069|0.807||
|c-othbusiserv|1.807|2.086|0.316|1.508|0.640|0.152||
|c-pubadm|0.000|0.569|0.227|0.843|1.540|1.224||
|c-educpriv|0.000|0.097|1.023|1.102|0.798|0.441|1|
|c-educpub|0.000|0.097|1.023|1.102|0.798|0.441|1|
|c-healthpriv|1.079|1.011|0.880|0.602|0.937|0.996|1|
|c-organiz|||||||1|
|c-recrea|1.926|1.076|1.680|0.898|1.550|0.708|1|
|c-othperserv|2.069|0.525|2.252|0.549|1.087|0.811||

Source: own estimation based on Moldovan household survey data. For more details on the estimation procedure, please contact the authors. For notation, see Table 3.

40
To measure the quality of a paper, we use variables from the RePEc database. The database assesses both journal publications and the gray literature (working papers). In our baseline meta-regression, we use the simple impact factor. Figure 8 (right panel) displays the histogram of the simple impact factor. The figure shows that the pass-through coefficients in our sample are most often collected from studies with a simple impact factor of between five and six.

# [Figure 8 about here]

Table 2 shows the expanded descriptive statistics for author and study quality. While both _h_ -indices from ResearchGate, with and without self-citations, differ only slightly, the _h_ -index from the Scopus database shows substantially lower values. Nevertheless, the variation of the measures is comparable and the correlation coefficient between the _h_ -index from the ResearchGate and Scopus databases is 0.94. This high correlation confirms a similar assessment of author quality across the databases. Looking at the ratings of paper quality, one may notice greater differences among the various ratings, owing also to the different construction and scaling of the indices. Interestingly, for all measures of study quality the median value lies bellow the mean value, indicating similar skewness of the ratings’ distribution.

# [Table 2 about here]

To control for the difference between journal publications and the gray literature, we create a dummy variable ( _work_paper_ ) taking a value of one if the estimated coefficients are collected from working, discussion, or research papers, and zero otherwise. Figure 9 shows the distribution of the estimated coefficients between those collected from gray literature and those collected from journal publications. It reveals that our sample contains slightly more coefficients from journal publications. Using estimates from both the gray literature and journals helps us control for a possible publication bias. The problem of publication bias lies in the review process. Reviewers, especially if they are somehow involved in the topic of study, might force authors to re-estimate their models until the results match the generally known and desired ‘truth’ for a given area of research. Hence, some original research results may stay unpublished.

[Figure 9 about here] 12
**Table 3.2: Characteristics Included in Bias Adjustment**

|**Characteristics**|**Ethiopia**|**Malawi**|**Nigeria**|**Uganda**|
|---|---|---|---|---|
|Residence(Urban/Rural)|X||X||
|Age of the household Head(age and age squared)|X|||X|
|Gender of the household head|X|X|X||
|Household Size|X|X|X|X|
|Marital Status(Married=Yes)|X||||
|Literacyof the household head||X|X||
|Language spoken byhousehold head||X|||
|Education of the household head|X||X|X|
|Highestyears of education amonghousehold members||X|||
|Consumptionquintile(Q1-Q5)|X||X|X|
|Wealthquintile(Q1-Q5)||X|||
|Food consumption score||X|||
|Household received remittance(domestic or international)|X||||
|Household received cash transfer fromgovernment||X|||
|Anyhousehold member has a financial account||X|||
|Anyhousehold member had access to credit||||X|
|Household owns dwelling|X||||
|Number ofpeopleper room|X||||
|Employment status of the household head|X||||
|Household head engaged in agriculture|X||||
|Household head engaged in casual labor|X||||
|Sector of household head employment||||X|
|Share of household members engaged in wage employment||X|||
|Share of household members engaged in informal (ganyu)<br>employment||X|||
|Copingstrategies index||X|||
|Mobilephone ownership|||X||
|Number of months household head was away from the<br>household||||X|

Following the bias adjustments, the weights were trimmed at the 2<sup>nd</sup> and 98<sup>th</sup> percentiles to prevent outlier weights that would result in a substantial increase in the variance of the weights. Lastly, the weights were post-stratified to match population totals in each country (following the same poststratification approach utilized in the LSMS-ISA weight calculation). For full details on the sampling and weight calculation for Ethiopia and Nigeria, see Ambel et al (2020) and McGee et al (2020), respectively. Full documentation as well as the microdata for these four surveys can be found on the World Bank’s Microdata Catalogue<sup>10</sup> or on the <u>LSMS website.</u>

> 10 Ethiopia HFPS: <u>https://microdata.worldbank.org/index.php/catalog/3716</u> Malawi HFPS: <u>https://microdata.worldbank.org/index.php/catalog/3766</u> Nigeria HFPS: <u>https://microdata.worldbank.org/index.php/catalog/3712</u> Uganda HFPS: <u>https://microdata.worldbank.org/index.php/catalog/3765</u>

9
# **7. Recent International Initiatives toward Introducing Output-based (Performance-oriented) Grants to Sustain National Minimum Standards for Merit Goods and Achieve Expenditure Needs Equalization**<sup>**2**</sup>

Results based finance has recently been used to sustain national minimum standards for merit goods and achieve expenditure need equalization in a few countries. Most of these practices are in the fields of education and health only but in a limited number of cases such finance has been used in other public goods such as infrastructure. The following paragraphs highlight these examples.

# **(a) Results Based Financing of Education**

Several countries have launched innovative programs to create results based accountability in financing sub-national education programs. These are reviewed in the following paragraphs: _Australia: National Schools Specific Purpose Payments, 2009_ The National Education Agreement specifies the following objectives for this grant program: _ensuring that all children are engaged in and befitting from schooling, with a goal of lifting the Year 12 attainment rate to 90 percent by 2015_ ; and _ensuring children meet basic literacy and numeracy standards and continuing to improve overall literacy and numeracy achievements_ .

These are equal per capita payments for both government and non-government schooling to all States. Out of this the government schools component for each state is based upon each State‘s share of full time equivalent student enrollments in government schools. The growth factor for government school component is the product of the growth in average government schools recurrent costs and the growth in full time equivalent in government schools. The grant funds are required to be spent on schooling but States have full budget flexibility to allocate funds as they see fit to achieve mutually agreed objectives. The program has not set any benchmarks for performance but as part of an overall reporting and accountability framework, states must provide performance data to the Commonwealth and to the general public. The COAG Reform Council will collate data for all states and will publish such statistics and analytical reviews of performance for government and general public use (The Australian Treasury, 2009).

_United States: Race to the Top (RTTT) Competitive Grant Program, 2009_ This $4.35 billion program was launched by President Obama on July 24, 2009 to mark a new federal partnership in education reform with states, districts and unions to accelerate change and boost improvements. The program invites states to apply for financing by undertaking to implement four core interconnected reforms as follows:

> 2 This section is based upon Shah (2009).

27
**<u>Table 12. Average difference in GNI per capita between origin and destination countries (current US$)</u>**

||||**De**<br>|**stination C**|**ountries/**|**Regions**||||
|---|---|---|---|---|---|---|---|---|---|
|**nrn**|**+**|**+**|**r**|||||||
|**oueseos**||||||||||
|<br>**of Origin**|**Canada**|**EFTA**|<br>**income**|**LAC**|**ECA**|**MENA**|**AFR**|**EAP**|**SAS**|
|**USA + Canada**|6,918|-8,026|-10,907|-27,428|-29,265|-32,099|-31,287|-30,939|-29,570|
|**EU15+EFTA**|8,694|3,334|-4,332|-12,129|-20,061|-20,297|-20,041|-21,571|-21,367|
|**Other high income**|5,419|2,743|1,152|-29,321|-17,856|-14,407|-19,881|-18,316|-19,147|
|**LAC**|29,523|17,560|22,511|2,290|-2,944|-2,626|-3,890|510|-3,933|
|**ECA**|29,018|20,755|15,123|2,586|269|487|-839|10,862|-1,268|
|**MENA**|29,244|21,298|9,853|1,577|-107|168|-1,220|9,209|-1,207|
|**AFR**|31,290|21,395|12,700|4,667|819|1,825|199|2,226|-50|
|**EAP**|27,820|19,959|16,827|-977|-1,432|465|-2,843|11,320|-3,191|
|**SAS**|30,879|24,034|13,046|5,681|989|4,036|-39|9,204|62|

Note: "EU15 + EFTA" includes Switzerland. "Other high income" includes Australia, New Zealand, Japan and high income MENA countries. Figures are weighted by the number of migrants. Note also that the calculations are based on 180 countries excluding those for which we were not able to obtain data on GNI per capita for 2001 Source: World Development Indicators.

31
discretion in meeting regulatory capital requirements and are often allowed to use the internal ratings-based (IRB) approach to compute capital requirements. As the failure of larger banks can have systemic consequences and pose public externalities, our results have important policy implications.

We further investigate whether countries are influenced by neighbors while adopting capital regulation. Several studies have found strong peer country effects in financial regulation. Demirgüç-Kunt, Kane and Laeven (2008), for instance, show that adoption of explicit deposit insurance increases with the proportion of neighboring countries that introduced explicit deposit insurance at the same time. We examine peer country effects in three new capital regulations that have been implemented in the aftermath of the global financial crisis. Specifically, we analyze whether countries have adopted Basel III, countercyclical capital buffers or capital conservation buffers. Using data from the latest round of the BRSS, we create three dummy variables corresponding to each of the three regulations and set them to one if a country has adopted these new capital rules. We use the regional adoption rate to capture peer effects.<sup>24</sup> This variable reflects the extent to which these new capital regulations are believed to be best practice by countries in the region. As more countries adopt a regulation in a given region, we expect other neighboring countries to follow suit and adopt the new regulations. We use the following model to test these conjectures: I(Regulation Adopted= 1)i In the logit regression, we use the same set of controls as described in Equation (1a) above. The dependent variable is an indicator variable set to one if a given country has adopted a new capital regulation. The Region Average is the proportion of countries in each region (excluding the given country) that have adopted these new capital regulations. The results from the logit regressions are reported in Table 5. We find strong evidence of a positive association between region peer effects and the adoption of the new Basel III capital regulations. These reforms have been adopted regardless of whether the countries have

> 24 The regional peer variables are calculated as the average of the regulation dummy variables in each regional group described in section 2 of the paper.

34
**Table 1 : Present Value of Debt to GDP and Growth: three-year average panel**

||1|2|3|4|5|6|
|---|---|---|---|---|---|---|
||[OLS]|[OLS]|[FE]|[FE]|[GMM system]|[GMM system]|
|Net Present Value of Debt [PVY]/GDP (avg)|-0.606||-0.19||-0.397||
||[2.66]***||[0.63]||[0.47]||
|Net Present Value of Debt [PVY]/GDP (initial)||-0.484||0.217||-0.496|
|||[2.42]**||[0.88]||[0.65]|
|Initial GDP per Capita|-0.674|-0.598|-6.981|-6.691|-1.933|-1.913|
||[3.01]***|[2.68]***|[8.46]***|[8.22]***|[2.04]**|[2.11]**|
|Population Growth|-21.165|-21.329|1.449|0.965|20.304|25.419|
||[1.33]|[1.31]|[0.08]|[0.05]|[0.58]|[0.63]|
|Secondary Schooling|1.312|1.277|0.021|-0.115|4.93|4.996|
||[4.94]***|[4.79]***|[0.03]|[0.16]|[3.07]***|[3.21]***|
|Terms of Trade Growth|0.017|0.016|0.011|0.01|0.038|0.058|
||[1.28]|[1.23]|[0.99]|[0.84]|[1.07]|[1.21]|
|Trade Openness|1.013|0.935|3.643|3.47|1.21|0.987|
||[2.24]**|[2.07]**|[4.43]***|[4.23]***|[1.14]|[1.11]|
|Observations|604|604|604|604|604|604|
|Sargan P-value|||||0.62|0.41|
|Serial Correlation(second order)P-value|||||0.97|0.82|

* significant at 10%; ** significant at 5% ***significant at 1% Absolute value of t statistics in brackets Note: [GMM System]: Two System Estimator with Small Sample Windmejer  (2005) Robust Correction [PVE]= PV data from B.Easterly; [PVY]: PV data from Yuri Dikhanov All regressions include time effects

23