rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9304

# **Abstract**

This paper discusses and illustrates the analytical foundations of international comparisons (or benchmarking) for assessing a country’s potential for improvement along various dimensions of social and economic development. By providing a methodology for international benchmarking, discussing various alternatives and choices, and presenting a cross-country illustration, the paper can help practitioners be less arbitrary and more systematic in their approach to international comparisons, as well as more realistic in their expectations for a country’s improvement. The paper presents the stochastic frontier approach and applies it to estimate feasible frontiers or benchmarks for each variable, country, and year. It then interprets a country’s (one-sided) departure from the benchmark as inefficiency or potential for improvement. This contrasts with the literature that compares countries by looking at raw variables or indicators, without considering that countries differ in structural endowments that constrain the maximum performance that a country could achieve in a policy-relevant horizon. The Stochastic Frontier approach also improves upon the literature that uses regression residuals to measure performance. Regression residuals are hard to interpret as inefficiency, because they are mixed with noise and take positive and negative values. As an illustration, the paper uses a panel of 142 countries with yearly data for 2005–14 and considers a set of 10 development indicators. It finds that the potential for improvement does not follow a simple relationship with economic development, with some lower-income countries being closer to their own feasible frontier than more advanced countries are.

This paper is a product of the Development Research Group, Development Economics. 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 nloayza@worldbank.org, vnorambuena@worldbank.org, and kkar@binghamton.edu.

_The Policy Research Working Paper Series disseminates the findings
# _4. - QUALITYREGULA TION IN THE AIRPORT INDUSTRY_

# **4.1.- The need for regulation**

The main reason for regulating quality is market failure. Consumers are imperfectly informed about the quality of products at the time of purchase, and are therefore unable to distinguish a bad quality provider from a good quality one. In general, regulation is needed in order to overcome such informational asymmetry. Nevertheless, the quality outcome may differ with the type of market and the temporal dimension. In competitive markets, firms that produce low quality products and sell them at high quality prices will acquire a bad reputation and will be excluded from the market (Klein and Leffler, 1981). In monopolistic situations, the quality of the product is always lower than under a perfect information setting. Imperfect information causes quality deterioration (Shapiro, 1982). Regulators, however, face similar asymmetric information problems regarding product quality.

As we have seen in section 3, privatized airports are usually subject to some sort of pricing regulatory mechanism. Less common is quality regulation, in spite of the likely exploitation of monopoly power in some airport operations. For instance, the British Airports Authority (BAA), although subject to price-capping,28 does not have to comply with a level of quality specified by the regulator. Rather, BAA itself keeps track of their quality records by periodically carrying out quality survey monitoring. It seems that being subject to Civil Aviation Authority (CAA) scrutiny acts as a sufficient incentive to keep high quality standards without any specific regulatory provision. Nevertheless, BAA and the airlines actually agree upon the level of service to be provided. Areas such as check-in, security queues, jetty availability, stand availability and cleaning, project development, and departure and transfer baggage, are usually the main matters under discussion. Performance measures, services standards and compensation payments in case of non-fulfillment, are included in the final service level agreement.

# **4.2.- Monitoring quality: the case of BAA self-regulation
****|
||(-5.620)|(-7.394)|
|**Effects after first 9 months**|||
|Policy Implement.|-1.999|-2.243*|
||(-1.440)|(-1.738)|
|**Policy Implement.*Palm**|**6.769**|**12.49*****|
||(1.113)|(2.734)|
|**Policy*Palm*World Price**|**-0.0332**|**-0.0654**|
||(-0.439)|(-1.142)|
|World Price*palm|0.0748|0.00812|
||(0.866)|(0.0988)|
|World Price|-0.445***|-0.361***|
||(-5.670)|(-4.805)|
|Diesel Price|25.26***|27.31***|
||(6.214)|(7.569)|
|Dummy if World Price was rising (DW)|-0.0719|0.0362|
||(-1.132)|(0.654)|
|DW*palm|0.462|0.279|
||(0.321)|(0.203)|
|Palm|-1.835|-1.234|
||(-0.872)|(-0.666)|
|Relative world prices of palm-soy/wheat-rice|-0.860|-3.606**|
||(-0.522)|(-2.200)|
|Relative world prices
of 2004. To complete the GQ upgrades, 128 separate contracts were awarded. In total, 23% of the work was completed by the end of 2002, 80% by the end of 2004, 95% by the end of 2006, and 98% by the end of 2010. Differences in completion points were due to initial delays in awarding contracts, land acquisition and zoning challenges, funding delays,<sup>6</sup> and related contractual problems. Some have also observed that India’s construction sector was not fully prepared for a project of this scope. As of August 2011, the cost of the GQ upgrades was about US$6 billion (1999 prices), about half of the initial estimates.

The NS-EW network, with an aggregate span of 7,300 km, is also shown in Figure 1. This network connects Srinagar in the north to Kanyakumari in the south, and Silchar in the east to Porbandar in the west. The NS-EW upgrades were initially planned to begin in Phase I of NHDP along with the GQ upgrades. The scope of the first phase of upgrades was smaller at 981 km, or 13% of the total network, with the remainder originally planned to be completed by 2007. However, work on the NS-EW corridor was pushed into Phase II and later, due to issues with land acquisition, zoning permits, and similar. In total, 2% of the work was completed by the end of 2002, 4% by the end of 2004, and 10% by the end of 2006. These figures include the overlapping portions with the GQ network that represent about 40% of the NS-EW progress by 2006. Since then, the planned upgrades for the NS-EW have expanded substantially. As of January 2012, 5,945 of the 7,300 kilometers in the project have been completed, at an estimated cost of US$12 billion.

# **Section 3: Data Preparation**

We employ repeated cross-sectional surveys of manufacturing establishments carried
# **Figure 5.9: Hanushek, Ruhose, and Woessman (2017)’s approach versus LAYS**

<!-- Start of picture text --> Human capital derived using Hanushek, Ruhose, and Woessman’s approach applied to TIMSS<br>and Barro‐Lee data versus LAYS<br>0 5 10 15<br>LAYS using TIMSS<br>Source: Authors’ analysis of TIMSS 2015, and Barro and Lee data (2013).<br>Notes: For ln(h), see text for details. For LAYS, the numeraire country is Singapore. Illustration for case where<br>learning starts at Grade 0 for TIMSS. Correlation coefficient is 0.99. Spearman rank correlation is 0.99.<br>2.2<br>2.0<br>1.8<br>1.6<br>1.4<br>1.2<br>ln(h) based on Hanushek, Ruhose and Woessmann approach<br>1.0<br><!-- End of picture text -->

# **_5.5 Quality adjustments using different approaches:  How do they compare?_**

We have illustrated how each of the approaches described above compares to the _LAYS_ approach by graphing one versus the other. But this doesn’t provide a sense of how the different approaches would characterize international gaps in quality-adjusted years of schooling. To provide a sense of this, for each potential approach to measuring schooling, we calculate the average value for the four top-performing countries and the four bottom performers on that measure, and then take the ratio of these two values. The results are reported in Table 5.2. To understand how to interpret it, take the example of Row (1), which examines the simple quantitybased years-of-schooling measure. Row (1) shows that the average years of schooling of 25- to 29-year-olds in the four lowest-performing
extractive mechanism or a tool to reassert traditional gender identities. Specifically, of the 14 studies analyzed, 11 find that cash transfers are associated with a reduction in the risk of IPV.

However, not all interventions supporting women’s economic empowerment find positive results. Notably, a randomized control trial of employment in the flower industry in Ethiopia found that receiving a job offer had zero impact on the exposure to violence (Kotsadam and Villanger 2020). In Vietnam, women entrepreneurs who participated in gender and entrepreneurship training that increased their profits faced a greater likelihood of victimization, probably because of more frequent conflicts over the gains in income (Bulte and Lensink 2019).

Causal papers that examine the effects of macroeconomic changes on IPV are few. This research is important because many more people are impacted than those participating in interventions. Furthermore, interventions are limited by funding; so, their impact may occur over shorter periods. Using the private responses from the British Crime Survey, Anderberg et al. (2016) find that unemployment among men is associated with decreases in self-reported IPV, while unemployment among women leads to a rise in reported spousal abuse. Aizer (2010) has most broadly explored how labor market changes impact IPV. She focuses on low-skilled workers (who face higher rates of IPV) and finds that an increase in wages among women relative to the wages of men reduces hospitalizations among women in California. On a smaller scale, Munyo and Rossi (2015) also find that IPV rises if the wage ratio increases in favor of men. They use plausibly exogenous changes in the Uruguayan exchange rate that generate opposite effects on the average wages of men and women in 24 police jurisdictions and find impacts on violence concentrated in poorer jurisdictions.

# **Background: The Brazilian Context**

A middle-income economy, Brazil is the fifth most populous country in the world, and, similar to many middle-income countries, women are more well educated than men. Brazil has a relatively high female labor force participation rate, which is closer
Other data were processed from the Cameroon’s 2001 Supply and Use Tables (SUT) of Cameroon, and from the underlying Integrated Economic Account Tables (IEAT). The general

> 6 Other products that have been among the top five exports for at least one year during the last decade are: crude wood, raw aluminum, coffee and bananas.

> 7 The EU is considered here as the 15 member-countries of 2003, before its enlargement to 25 members in 2004.

7
When comparing aggregate spending on economic competencies, we know that the US-Brazil gap is overstated because of the omission of Brazilian expenditures on labor training in the services sector. Figure 9, in which this spending is disaggregated by industry, suggests that a considerable gap would remain even after adjusting for the omission. Since labor training data are included in the case of manufacturing and extractive industries, estimates for these sectors are comparable to US estimates. In Brazil, manufacturing industries spend about 2 percent of sales on brand equity, firm-specific human capital and organizational improvement. In comparison, the US spends as much as 7 percent of GDP on these intangibles.<sup>20</sup> Another source of concern identified in Figure 9 is the apparent decline in the manufacturing sector‘s spending on economic competencies.

Figure 9 also suggests that compared to its manufacturing sector, the Brazilian service sector spends very little on economic competencies. To make them more comparable, suppose that we make a crude adjustment for missing labor training expenditures in services by assuming that the ratio of labor training expenditures to those on brand equity and organizational improvement is the same as that in the US (about one-fifth). Then, our estimates suggests that non-ICT service sector spending on economic competencies is about 0.6 percent of sales, in contrast to about 2 percent in manufacturing. Another interesting pattern in Figure 9 is that low tech manufacturing industries spend slightly more on economic competencies than medium/high tech industries.

<!-- Start of picture text --> Figure 9: Expenditures on Economic Competencies by industry (% of<br>industry sales)<br>2.5%<br>2000 2008<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Agriculture Services ICT Services Low tech Medium/High Extractive<br>and related (except ICT) manufacturing tech<br>manufacturing
|3.9|
|Average unit tariff, passenger, US<br>cents/passenger-km|0.6|—|1.0|1.6|1.1|2.3|0.8|

Source: Bullock 2009, derived from AICD railways database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available.

Through a combination of track rehabilitation and improved operational performance it should be possible to boost traffic volumes to 5–10 million tons per year, which should be enough to accommodate 8
dnkjpn itafin fra israutbelcani r l nld swedeu gbrcheusa .3.4.5.6.7.8 r = 0.84N = 125 R2 = 0.70 ml yemi bfatgosdnginlbypngtjk azemngdzacodcivmdgirqmmr cogmozcmruzbbwalaongakazbolvengabecubgdhndmarzwepak sen benkhmnamlkarwadomtzaomnzmbgha vnm arm ethmdaugaperalbtun blrbihkwtukrmkdbgrqat egy jor idnicri nd kenarekgzurypgtmrirny saujamchnrus pan mys ph l ltusrbcoltha svk rougeolbnchlhrvgrcarg zaf prt po svnturl b rtwnkor a mexauscze hkg nzl sgp hun nor esp dnk jpnfinitafisr ra autibelcan r l nld swedeu gbrche usa<br>.2 .2<br>afg afg<br>.1 .1<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Economic Complexity Index [Trade-Research PCA] (OEC, 2021-22) Economic Complexity Index [Trade-Research PCA] (OEC, 2021-22)<br>AIPI≥0.7 AIPI≥0.7<br>Other Countries Other Countries<br>Overperformers Overperformers<br>Linear Fit Line Linear Fit Line [weighted pop. size]<br>AI Prepardness Index (IMF, 2023) AI Prepardness Index (IMF, 2023)<br><!-- End of picture text --> <u>Source: The AIPI score for 2023 is obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI] and the ECI on</u> trade and research for 2021-22 are obtained from the OEC website [https://oec.world/en/rankings/eci/hs6/hs96?tab=ranking]. Notes: The first principal component (PCA) of ECI trade and ECI research, with an eigenvalue exceeding 50 percent, is used to capture the multidimensional aspects of national economic complexity. Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020), with data for Taiwan Province of China obtained from the Statista website. All countries with an observed AIPI score exceeding
1992 and 2005 imply that some malnourished children who would previously have died instead survived and are measured in the 2005 survey. To the degree that selective mortality affects overall malnutrition levels or rates, the reductions in child mortality mask some of the improvement in nutrition. However, the results of this study suggest that progress on the fourth Millennium Development Goal to reduce child mortality only lightly obscures the results for the target for the first Millennium Development Goal to reduce malnutrition and hunger.<sup>16</sup> Similarly, the findings imply that differences in mortality are unlikely to explain gender differences in anthropometrics—or their absence. While the NFHS data do not

> **15** This rate corresponds to the high mortality rate of 14.2 percent observed among boys in Cote d‘Ivoire in 1998.  The rate for Cote D‘Ivoire was the highest mortality rate from 30 DHS of African countries reviewed.

> 16 This target is measured in terms of underweight rather than height for age. Underweight is somewhat easier to measure in a survey covering young children, though height for age is a clearer indicator of cumulative health. While underweight rates and stunting rates often differ, trends in the two tend to move together.

13
). Defned at the inspector<br>level. Source: authors’ survey.|
|Corruption colleagues|Inspector response to the question "To what extent do you agree with the statement<br>"My colleagues act with integrity"?", (1="Strongly disagree", 2="Disagree",<br>3="Neither agree nor disagree", 4="Agree", 5="Strongly agree"). Defned at the<br>inspector level. Source: authors’ survey.|
|Corruption supervisor|Inspector response to the question "To what extent do you agree with the state-<br>ment "My supervisor acts with integrity", (1="Strongly disagree", 2="Disagree",<br>3="Neither agree nor disagree", 4="Agree", 5="Strongly agree"). Defned at the<br>inspector level. Source: authors’ survey.|
|Unethical behaviour is sanctioned|Inspector response to the question "To what extent do you agree with the statement<br>"Non-ethical behavior is sanctioned", (1="Strongly disagree", 2="Disagree",<br>3="Neither agree nor disagree", 4="Agree", 5="Strongly agree"). Defned at the<br>inspector level. Source: authors’ survey.|
|Promotions are fair|Inspector response to the question "To what extent do you agree with the state-<br>ment "Promotions in customs are fair", (1="Strongly disagree", 2="Disagree",|
||3="Neither agree nor disagree", 4="Agree", 5="Strongly agree"). Defned at the<br>inspector level. Source: authors’ survey.|

60
Policy Research Working Paper 5839

# **Abstract**

This paper investigates the impact of remittances on financial inclusion. This is an important issue given recent studies showing that financial inclusion can have significant beneficial effects on households. Using household-level survey data for El Salvador, the authors examine the impact of remittances on households’ use of savings and credit instruments from formal financial institutions. They find that although remittances have a positive impact on financial inclusion by promoting the use of deposit accounts, they do not have a significant and robust effect on the demand for and use of credit from formal institutions. If anything, by relaxing credit constraints, remittances might reduce the need for external financing from financial institutions, while at the same time increasing the demand for savings instruments.

This paper is a product of the Finance and Private Sector Development Team, Development Research 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://econ.worldbank.org. The author may be contacted at mmartinezperia@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
<!-- Start of picture text --> Density ‐ based on Administrative boundaries<br>250<br>200<br>150<br>100<br>50<br>0<br>Kigali Nairobi Addis Dakar Dar es Paris Barcelona Kuala<br>Ababa Salaam Lumpur<br>Source: Population statistics and land area numbers from http://citypopulation.de/, calculations<br>made by author.<br>people per hectare<br><!-- End of picture text --> While this measurement provides a helpful indication of overall density, it relies on administrative boundaries rather than the ‘true’ physical boundary of the city, and does not provide insight into variation within the city’s landscape. To overcome this limitation, alternative more in depth approaches have been developed which ignore political lines and examine the varying levels of density throughout a city. Using data sets derived from satellite imagery and disaggregated population estimates, modeled population grids such as Landscan, can be used to measure how a city’s population density changes across space. Referred to as an urban density profile, this graph can shed light on how a city’s population changes as one moves away from the center to the periphery. Previous studies by Bertaud (2001, 2003),<sup>1516</sup> Muth (1967)<sup>17</sup> and many others have demonstrated the value of density profiles when studying urban form.

A variation in a city’s population density is arguably more influential and indicative of its performance than its average density. Following a monocentric model that assumes most jobs are located in the city center and that residents will tend to cluster around those jobs, a population gradient will show a higher concentration of people living near the city center than the periphery. In this simplified model, residents will choose to live near the center because they are likely to benefit from shorter commutes to work, school and
The dependent variable represents a fiscal variable of interest – including the revenues from own sources, the different components of realized expenditure and budget surplus/deficit – for district _m_ at time _t_ (2001 to 2006). By construction, the binary variable takes the value 1 if district _m_ is in the treatment group (i.e. direct elections were held in the district between June and December 2005) and equal to 0 otherwise. The binary variable takes the value 1 for post _Pilkada_ years (2006) and 0 otherwise (2001-2005), while denotes the district fixed-effect summarizing the role of all observable and unobservable variables at the district level that do not vary over time. The disturbance term summarizes the influence of all other unobserved variables that vary across districts and over time, assumed to be uncorrelated with the variables in the regression, though it is allowed to be correlated over time. In this framework, the parameter then identifies the effect of any systemic difference between the treatment and control groups of districts on the dependent variable in the pre- _Pilkada_ reference year (i.e. 2001). The parameters identify the ―year effect‖ on the dependent variable, namely the effect of any systemic changes that affected all districts in the pre- _Pilkada_ years. The parameter is the parameter of our interest since this identifies the _difference-in-differences_ (or DID) of the impact of direct election on the dependent variable.<sup>12</sup> Specifically, the DID estimate of the impact of _Pilkada_ provides an estimate of the average change in the outcome variable in the treated group from pre- _Pilkada_ (denoted by the subscript _preE_ ) to post- _Pilkada_ years (denoted by the subscript _postE_ ) relative to changes in outcome variable in the control group over the same period of time, i.e.,

# _Data_

To conduct the analysis, we assemble a large dataset, compiled from multiple sources and linked at the district level. These are regional electoral information from the government (Ministry of Home Affairs
|VII.|Plastics|147|10.5|102|13.1|
|VIII.|Leather.|8|0.6|1|0.1|
|IX.|Wood|16|1.2|.8|1.0|
|X.|Pulp and paper|65|4.6|.<br>47|6.0|
|XI.|<br>Textiles|95|6.8|_5S_|7.1|
|XII.|Footwear|24|1.7|**9.7**|1.2|
|XIII.|Glass|59|4.2|_15-_|1.9|
|XV.|Base metals|374|26.7|178|22.9|
|XVI.|Machinery and electrical equipment|212|15.1|. 83|10.7|
|XVII.|<br>Vehicles|27|1.9|7|0.9|
|XVIII.|Instruments|19|1.4|21|2.7|
|XIX.|Arms|3|0.2|0|0.0|
|X.X.|Other Manufactures|27|1.9|27|3.5|
|||_<br>1402|100|778|100|
|Explanati|on:|||||

*Includes Poland (25 investigations) and Israel (I5 investigations).

_Source: WTO, Antidumping database._ 57